{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-05-16T00:20:21.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-05-16T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":43330,"title":"Solve expression III","description":"Solve expression for given vector x.\r\nExpression = (tan(2*x^2+7*x-30.25)+log(x^3-2.25))/(nthroot(sin(x^3)^2+1/5*log(x^4-2.5),3))","description_html":"\u003cp\u003eSolve expression for given vector x.\r\nExpression = (tan(2*x^2+7*x-30.25)+log(x^3-2.25))/(nthroot(sin(x^3)^2+1/5*log(x^4-2.5),3))\u003c/p\u003e","function_template":"function y = your_fcn_name(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 2:4;\r\ny_correct =(tan(2.*x.^2+7.*x-30.25)+log(x.^3-2.25))./(nthroot(sin(x.^3).^2+1/5*log(x.^4-2.5),3));\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n%%\r\nx = 6;\r\ny_correct =(tan(2.*x.^2+7.*x-30.25)+log(x.^3-2.25))./(nthroot(sin(x.^3).^2+1/5*log(x.^4-2.5),3));\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n%%\r\nx = 30:2:40;\r\ny_correct =(tan(2.*x.^2+7.*x-30.25)+log(x.^3-2.25))./(nthroot(sin(x.^3).^2+1/5*log(x.^4-2.5),3));\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":3,"created_by":90467,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":91,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-10-11T09:42:37.000Z","updated_at":"2026-04-07T19:08:21.000Z","published_at":"2016-10-11T09:42:37.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolve expression for given vector x. Expression = (tan(2*x^2+7*x-30.25)+log(x^3-2.25))/(nthroot(sin(x^3)^2+1/5*log(x^4-2.5),3))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":49850,"title":"Simple Circuit of Resistors","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 345px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 172.5px; transform-origin: 407px 172.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe configuration of a group of resistors is described in a matrix with two rows. The first row provides the information regarding the number of resistors at each junction and the second row provides the resistance of each detector at each junction. 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data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe circuit_def (i.e., the matrix defining the problem) for this configuration is [1 3 2; R1 R2 R3]. Find the resultant resistance for any given configuration.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = tot_res(circuit_def)\r\n  y = circuit_def(1,:)./circuit_def(2,:);\r\nend","test_suite":"%%\r\ncircuit_def=[3 3 3 3;1 2 3 4];\r\ntot_corr=3.3333;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n%%\r\ncircuit_def=[1 2 3 4;1 2 3 4];\r\ntot_corr=4;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n%%\r\ncircuit_def=[11 12 3 7;4 5 3 8];\r\ntot_corr=2.92316;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n%%\r\ncircuit_def=[3 3 4 4 6 7 8;3 1 1 5 9 10 11];\r\ntot_corr=7.136905;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n%%\r\ncircuit_def=[2 5 3;11 2 20];\r\ntot_corr=12.56667;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":"2021-01-17T21:52:11.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-17T21:37:24.000Z","updated_at":"2026-02-26T11:54:37.000Z","published_at":"2021-01-17T21:52:11.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe configuration of a group of resistors is described in a matrix with two rows. The first row provides the information regarding the number of resistors at each junction and the second row provides the resistance of each detector at each junction. Consider the following configuration:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe circuit_def (i.e., the matrix defining the problem) for this configuration is [1 3 2; R1 R2 R3]. Find the resultant resistance for any given 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of nine?","description":"Given a positive number n, return true if n is a multiple of 9 and false if not. Do not make the division and do not use functions like mod or rem.","description_html":"\u003cp\u003eGiven a positive number n, return true if n is a multiple of 9 and false if not. Do not make the division and do not use functions like mod or rem.\u003c/p\u003e","function_template":"function [ y ] = multipleof9( x )\r\n  y = false;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = false;\r\nassert(isequal(multipleof9(x),y_correct))\r\n\r\n%%\r\nx = 9;\r\ny_correct = true;\r\nassert(isequal(multipleof9(x),y_correct))\r\n\r\n%%\r\nx = 10;\r\ny_correct = false;\r\nassert(isequal(multipleof9(x),y_correct))\r\n\r\n%%\r\nx = 111222;\r\ny_correct = true;\r\nassert(isequal(multipleof9(x),y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":61785,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":112,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-01-04T12:46:33.000Z","updated_at":"2026-02-18T14:18:36.000Z","published_at":"2016-01-04T12:46:33.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a positive number n, return true if n is a multiple of 9 and false if not. Do not make the division and do not use functions like mod or rem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60809,"title":"Inductor Energy Storage Calculation","description":"The energy (EEE) stored in an inductor is given by the formula:\r\n\r\nWhere:\r\nE is the energy in joules (J)\r\nL is the inductance in henrys (H)\r\nI is the current in amperes (A)\r\nWrite a function that takes inductance LLL and current III as inputs and returns the stored energy EEE.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 228.181px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 114.083px; transform-origin: 407px 114.09px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe energy (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) stored in an inductor is given by the formula:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 55.8889px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 27.9444px; text-align: left; transform-origin: 384px 27.9444px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.6389px; transform-origin: 391px 30.6458px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the energy in joules (J)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the inductance in henrys (H)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the current in amperes (A)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes inductance \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and current \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as inputs and returns the stored energy \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function E = inductor_energy(L, I)\r\n% inductor_energy calculates the energy stored in an inductor.\r\n% Inputs: L (Inductance in Henrys), I (Current in Amperes)\r\n% Output: E (Energy in Joules)\r\n\r\n% Your code here\r\n\r\nend\r\n","test_suite":"%% Basic Test Case\r\nassert(isequal(inductor_energy(2, 3), 9))\r\n\r\n%% Edge Case: Zero Inductance\r\nassert(isequal(inductor_energy(0, 5), 0))\r\n\r\n%% Edge Case: Zero Current\r\nassert(isequal(inductor_energy(5, 0), 0))\r\n\r\n%% Larger Values\r\nassert(isequal(inductor_energy(10, 4), 80))\r\n\r\n%% Fractional Inputs\r\nassert(isequal(inductor_energy(0.5, 2.5), 1.5625))\r\n","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":383919,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":408,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-02-17T16:08:16.000Z","updated_at":"2026-05-17T20:44:47.000Z","published_at":"2025-02-17T16:08:16.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe energy (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) stored in an inductor is given by the formula:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"50\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"113\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhere:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the energy in joules (J)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the inductance in henrys (H)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the current in amperes (A)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that takes inductance \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and current \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as inputs and returns the stored energy \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60810,"title":"LED Current Calculation","description":"In an electrical circuit, the current (III) flowing through an LED is determined using Ohm’s Law:\r\n​​\r\nWhere:\r\nI is the current in amperes (A)\r\nV is the supply voltage in volts (V)\r\nVf​ is the forward voltage of the LED in volts (V)\r\nR is the series resistor in ohms (Ω)\r\nWrite a function that takes supply voltage V, LED forward voltage Vf​, and resistor R as inputs and returns the current III.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 275.611px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 137.806px; transform-origin: 407px 137.806px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn an electrical circuit, the current (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) flowing through an LED is determined using Ohm’s Law:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 82.8889px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 41.4444px; text-align: left; transform-origin: 384px 41.4444px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAIkAAABNCAYAAACSXUdrAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAAAZlSURBVHhe7d3PaxNNGAfwb967yBr/gm6FioiCWw8aBXtwFw+eCq3ngpjoSVAhd4UNiuDBpJ56EFJBjylNBT3sVvxRJEHESzYH8Zil4D8wXjL7Zh+bTvZXTJvnA4HtPmsN5pvZmdnZNSeEEGBsH//RHYxRHBKmxCFhShwSpsQhYUocEqbEIWFKHBKmxCFhShwSpsQhYUocEqbEIWFKHBKmlJv2pQKu6+LDhw+hfRcuXEChUAh+Xl9fx8+fP0PHLC4uYmZmJrQvjlF+d6VSCdUB4P79+3RXdsSUcxxH6LouAAgAwjAM4ThO6JharSY0TQuOWVpaEp7nhY6Ja5TfXSwWgzoAUS6XQ/WsTX1IhBCi0WgEHwANiGSaZvAhpq1cLgd//zCyXqvVaClzw9/VlFGFRNd1oWma6PV6tJSY4zj7hsTzPAFAmKZJS2PBHdcRrK+vw/M8vHz5Evl8npYz9/jxY2iahufPn9PSeNDUTCvZL6AtSa/XE5qmZfotbrVaQ1sS2crYtk1LY/P3u5pSss9BQyL7C7QzmbZhITEMQ+i6TnePFZ9uiF+/fgXb7XYbjx49gm3bqQx3R9HtdoPt1dVV7OzsYG1tLXSMiuu6KJVKsCwLlmWh3W7TQyKZ+nkSybIsNJtN2LYdzEFYloVOp4NOp0MPD3S7Xbx+/Zru3hedhwGAXC4HAHAcB4VCAb7v48SJE1heXo7UF6lUKnjw4AFqtRqOHj2KGzduwPO8ZCGnTcu0sm07dO6v1+t7nn6owZHJqK+9+hf0dFcsFiOPpuR7KRaLoX1JcUvSJ7+Btm1jZWVl5G9xWi2JbMkcxwEAXLp0CfV6HcvLy6Hj9lMqlVCtVoPWKDU0NdNqsCUpl8uRv8VJDbYkhmFEGk15nheakDNNU5imKer1Oj00Fg5Jn2yq5Yc17plNGVL597daLXrIUJ7nBe9fXlZwHCe1ERmHpG+wbxHlW5wWGZJhfRYVOdcy2B9JCw+B92DbNt01NrquY2Vlhe5W+vjxIwDgzJkztJRY7JC4rotcLvfXy7IseuiBUCgUYNs2Go1GJv/QKouLi7BtG2/evIk19S/nQk6dOkVLydGmJYrB6WTZ1I2zs8f+ZxiGSPhxDpV4CDw/P4+dnR0ASD5pw2LxfR/Hjx+HYRj48uULLScW+3QjyYAYhsEB+Ud+/PgBALh69SotpSJRSAavM2T1BpmaXH55+vRpWkpFopB8/vw52L548WKoxsbn69evAICTJ0/SUioSheTbt2/B9tzcXKjGxmdrawu6rmc2KksUkq2tLaA/tuf+yL9RqVSwu7uLe/fu0VJ66HAnCjn0Tbo4OM6V1GGvOLOVB1Gj0QiGvVmvno/dkriuG2xfuXIlVGPZm5ubw9OnT9Hr9fDw4UNaThdNzagGrzWksWaBTa7YLYnsUaM/pX0Q0UsKh/mVROyQyE6raZq0xA6ZWCHpdrvY3d0FAJw7d46WD4z+UompeCURKyRpT6INu6Ic57XXzdUsmVgh4Um06RLrKrC88qvr+r63G7DDIXJL4vt+cOV3dnaWltkhFDkkb9++DbYXFhZCNXY4RQpJt9vFkydPgp9fvHgB3/dDx7DDJ1KfJDdkUibCr2AHUKSQsOh83w9WjlFHjhzJ7PJ+mjgkGXNdF9evXw8mHw3DCFbDN5tNaJqGYrGIu3fvxlolPxbkWg7LwOBSiMG7CXq9nlhaWhLI6KaqtETquLJkBlsRAMjn87hz5w4AoFqtDhw5WTgkYyAXKs/Pz9PSgcAhGYN3794BAC5fvkxL+P79O9BvZSYWPf+w9MmH9tEnBbRaraEP9JskPLrJWLvdxtmzZ4H+o64A4Pfv39je3ka1WsWxY8ewtrY20Qu3OCQZW11dxa1bt6DrenCtq9lsAgAajQauXbtG/sTk4T5JxuTd/jdv3sTm5iY2NzdRq9UAAM+ePSNHTyZuSTI2OzsLz/NCzzGTN3gDQKvVmvhZV25JMuT7PjzPA8hi8Xw+H6wNlg+fmWQckgx9+vQJGDK8lWuD379/T0sTh0OSoe3tbaDfclDyCQCvXr2a+OUWHJKMbGxsBFPtnU4HGxsbofr58+eD7du3b4fuiJw03HHNCH123MzMzF8PDi6VSsEzXhYWFsb7X6ZFwCFhSny6YUocEqbEIWFKHBKmxCFhShwSpsQhYUocEqbEIWFKfwB96ZJ7tHRfMQAAAABJRU5ErkJggg==\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e​\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e​\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWhere:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 81.7222px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 40.8611px; transform-origin: 391px 40.8611px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the current in amperes (A)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the supply voltage in volts (V)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ef​\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the forward voltage of the LED in volts (V)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eR\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the series resistor in ohms (Ω)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes supply voltage \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, LED forward voltage \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ef​\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and resistor \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eR\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as inputs and returns the current \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function I = led_current(V, V_f, R)\r\n% led_current calculates the current flowing through an LED.\r\n% Inputs: V (Supply Voltage in Volts), V_f (LED Forward Voltage in Volts), R (Resistance in Ohms)\r\n% Output: I (Current in Amperes)\r\n\r\n% Your code here\r\n\r\nend\r\n","test_suite":"%% Basic Test Case\r\nassert(isequal(led_current(9, 2, 1000), 0.007))\r\n\r\n%% Edge Case: Zero Supply Voltage\r\nassert(isequal(led_current(0, 2, 500), -0.004)) % Negative means insufficient voltage\r\n\r\n%% Edge Case: Zero Resistance (Short Circuit)\r\nassert(isequal(led_current(5, 2, 0), Inf)) % Infinite current (theoretically)\r\n\r\n%% Larger Values\r\nassert(isequal(led_current(12, 3, 1500), 0.006))\r\n\r\n%% Fractional Inputs\r\nassert(isequal(led_current(5.5, 2.2, 330), 0.01))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":383919,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":406,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-02-17T16:26:01.000Z","updated_at":"2026-05-17T20:46:47.000Z","published_at":"2025-02-17T16:26:01.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn an electrical circuit, the current (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) flowing through an LED is determined using Ohm’s Law:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"77\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"137\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e​\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e​\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhere:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the current in amperes (A)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the supply voltage in volts (V)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ef​\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the forward voltage of the LED in volts (V)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the series resistor in ohms (Ω)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that takes supply voltage \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, LED forward voltage \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ef​\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and resistor \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as inputs and returns the current 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co-occurrence matrix","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1287.17px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 643.578px; transform-origin: 407px 643.586px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eConsider following transaction dataset;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 391px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 195.5px; text-align: left; transform-origin: 384px 195.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"911\" height=\"385\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWe can transform this dataset to a binary format and then create co-occurence matrix as follow;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 695.172px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 347.578px; text-align: left; transform-origin: 384px 347.586px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCo-occurence matrix shows how many times product pairs are bought together in market baskets (transactions). For example apple and tea bought together in two market baskets, milk and lemon never bought together and so on. It is a  symmetric matrix.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = cooccurrence(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [1\t1\t1\t0\t0\t0\t0\r\n0\t1\t1\t1\t0\t0\t0\r\n0\t1\t1\t1\t1\t0\t0\r\n0\t1\t0\t0\t1\t1\t1\r\n1\t1\t0\t1\t1\t1\t0];\r\ny_correct = [2\t2\t1\t1\t1\t1\t0\r\n2\t5\t3\t3\t3\t2\t1\r\n1\t3\t3\t2\t1\t0\t0\r\n1\t3\t2\t3\t2\t1\t0\r\n1\t3\t1\t2\t3\t2\t1\r\n1\t2\t0\t1\t2\t2\t1\r\n0\t1\t0\t0\t1\t1\t1];\r\nassert(isequal(cooccurrence(x),y_correct))\r\n\r\n%%\r\nx = [1\t1\t1\t0\t0\t0\t0\t0\r\n0\t0\t1\t1\t1\t1\t0\t0\r\n1\t0\t1\t1\t0\t1\t1\t0\r\n0\t1\t1\t1\t0\t1\t0\t0\r\n1\t0\t0\t1\t0\t1\t0\t1];\r\ny_correct = [3\t1\t2\t2\t0\t2\t1\t1\r\n1\t2\t2\t1\t0\t1\t0\t0\r\n2\t2\t4\t3\t1\t3\t1\t0\r\n2\t1\t3\t4\t1\t4\t1\t1\r\n0\t0\t1\t1\t1\t1\t0\t0\r\n2\t1\t3\t4\t1\t4\t1\t1\r\n1\t0\t1\t1\t0\t1\t1\t0\r\n1\t0\t0\t1\t0\t1\t0\t1];\r\nassert(isequal(cooccurrence(x),y_correct))\r\n\r\n%%\r\nx = randi([0 1],150,9);\r\ny = cooccurrence(x);\r\nassert(isequal(y(logical(eye(size(y)))), sum(x)'))\r\nassert(issymmetric(y))\r\n\r\n\r\n%%\r\nx = [1 0 1 0 0\r\n    0 0 1 0 0\r\n    1 1 1 1 1\r\n    0 0 0 0 0\r\n    1 0 0 1 1\r\n    0 1 1 0 1\r\n    0 0 1 1 0];\r\ny_correct = [3 1 2 2 2\r\n    1 2 2 1 2\r\n    2 2 5 2 2 \r\n    2 1 2 3 2\r\n    2 2 2 2 3];\r\nassert(isequal(cooccurrence(x),y_correct))\r\n\r\n%%\r\nx = [0 1 1 0\r\n    0 1 0 1\r\n    0 0 1 1\r\n    1 1 0 0 \r\n    1 0 1 0\r\n    0 1 0 1\r\n    0 1 0 1\r\n    0 1 0 1];\r\ny_correct = [2 1 1 0\r\n    1 6 1 4\r\n    1 1 3 1\r\n    0 4 1 5];\r\nassert(isequal(cooccurrence(x),y_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":"2020-12-10T08:46:02.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-12-10T07:43:57.000Z","updated_at":"2025-09-17T22:29:41.000Z","published_at":"2020-12-10T08:42:18.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider following transaction dataset;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"385\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"911\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe can transform this dataset to a binary format and then create co-occurence matrix as follow;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCo-occurence matrix shows how many times product pairs are bought together in market baskets (transactions). For example apple and tea bought together in two market baskets, milk and lemon never bought together and so on. It is a  symmetric matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc 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\",\"relationship\":null},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44132,"title":"Find my secret function III","description":"only write a function gives you an outputs as\r\nexpls:\r\n\r\ninput: x=2 -------\u003e\u003e\u003e Output: Y=1\r\n\r\ninput: x=100 -------\u003e\u003e\u003e Output: Y=[ 1 2 4 6 10 12 16 18 22 28 30 36 40 42 46     52 58 60 66 70 72 78 82 88 96]\r\n\r\nHint: here we use the same function used in the series of \"Find my secret function\".","description_html":"\u003cp\u003eonly write a function gives you an outputs as\r\nexpls:\u003c/p\u003e\u003cp\u003einput: x=2 -------\u0026gt;\u0026gt;\u0026gt; Output: Y=1\u003c/p\u003e\u003cp\u003einput: x=100 -------\u0026gt;\u0026gt;\u0026gt; Output: Y=[ 1 2 4 6 10 12 16 18 22 28 30 36 40 42 46     52 58 60 66 70 72 78 82 88 96]\u003c/p\u003e\u003cp\u003eHint: here we use the same function used in the series of \"Find my secret function\".\u003c/p\u003e","function_template":"function y = your_fcn_name(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 2;\r\ny_correct = 1;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n%%\r\nx = 23;\r\ny_correct =[1 2 4 6 10 12 16 18 22];\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n%%\r\nx = 100;\r\ny_correct =[1 2 4 6 10 12 16 18 22 28 30 36 40 42 46 52 58 60 66 70 72 78 82 88 96];\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n%%\r\nx = 1;\r\ny_correct =[];\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":37163,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":"2017-04-26T16:02:09.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-04-25T14:50:07.000Z","updated_at":"2025-04-25T12:27:08.000Z","published_at":"2017-04-25T14:50:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eonly write a function gives you an outputs as expls:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003einput: x=2 -------\u0026gt;\u0026gt;\u0026gt; Output: Y=1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003einput: x=100 -------\u0026gt;\u0026gt;\u0026gt; Output: Y=[ 1 2 4 6 10 12 16 18 22 28 30 36 40 42 46 52 58 60 66 70 72 78 82 88 96]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint: here we use the same function used in the series of \\\"Find my secret function\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42643,"title":"MATLAB Basic: rounding III","description":"Do rounding towards minus infinity.\r\n\r\nExample: -8.8, answer -9\r\n\r\n+8.1 answer 8\r\n\r\n+8.50 answer 8","description_html":"\u003cp\u003eDo rounding towards minus infinity.\u003c/p\u003e\u003cp\u003eExample: -8.8, answer -9\u003c/p\u003e\u003cp\u003e+8.1 answer 8\u003c/p\u003e\u003cp\u003e+8.50 answer 8\u003c/p\u003e","function_template":"function y = round_x(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = -8.8;\r\ny_correct = -9;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx = -8.4;\r\ny_correct = -9;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx =  8.8;\r\ny_correct =  8;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx =  8.4;\r\ny_correct =  8;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx =  8.49;\r\ny_correct =  8;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx =  128.52;\r\ny_correct =  128;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n\r\n%%\r\nx =  pi;\r\ny_correct =  3;\r\nassert(isequal(round_x(x),y_correct))","published":true,"deleted":false,"likes_count":18,"comments_count":2,"created_by":27760,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6259,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-10-01T05:28:46.000Z","updated_at":"2026-05-22T10:59:01.000Z","published_at":"2015-10-01T05:28:46.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDo rounding towards minus infinity.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample: -8.8, answer -9\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e+8.1 answer 8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e+8.50 answer 8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":223,"title":"Which quadrant?","description":"Given a complex number, output quadrant 'I' 'II' 'III' or 'IV'\r\n\r\n        |              \r\n   II   |   I          \r\n        |              \r\n -------+------- real\r\n        |              \r\n   III  |   IV          \r\n        |\r\n      imag\r\n              \r\n","description_html":"\u003cp\u003eGiven a complex number, output quadrant 'I' 'II' 'III' or 'IV'\u003c/p\u003e\u003cpre\u003e        |              \r\n   II   |   I          \r\n        |              \r\n -------+------- real\r\n        |              \r\n   III  |   IV          \r\n        |\r\n      imag\u003c/pre\u003e","function_template":"function y = your_fcn_name(x)\r\n y='IV';\r\nend\r\nend","test_suite":"%%\r\nx = 1+1i;\r\ny_correct = 'I';\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = -1-1i;\r\ny_correct = 'III';\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 1-1i;\r\ny_correct = 'IV';\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = -1+1i;\r\ny_correct = 'II';\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":166,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":369,"test_suite_updated_at":"2012-02-02T03:13:00.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-02-02T03:13:00.000Z","updated_at":"2026-05-13T14:28:28.000Z","published_at":"2012-02-02T03:13:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a complex number, output quadrant 'I' 'II' 'III' or 'IV'\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[        |              \\n   II   |   I          \\n        |              \\n -------+------- real\\n        |              \\n   III  |   IV          \\n        |\\n      imag]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":54350,"title":"String Manipulator","description":"Write a script that takes a string as an input and returns a cell array containing  –\r\nI. the count of vowels.\r\nII. Find the index of ‘o’\r\nIII. The string removing all the vowels.\r\nIV. The string removing all the letters from a to j.\r\nV. The string removing all the consonants.\r\nVI. The string replacing all the vowels with an asterisk (*)\r\nVII. The string removing all the digits.\r\nHint: Use Regular Expression\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 291px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.5px 145.5px; transform-origin: 406.5px 145.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eWrite a script that takes a string as an input and returns a cell array containing\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e  –\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eI. the count of vowels.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eII. Find the index of ‘o’\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIII. The string removing all the vowels.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIV. The string removing all the letters from a to j.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eV. The string removing all the consonants.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eVI. The string replacing all the vowels with an asterisk (*)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eVII. The string removing all the digits.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; text-decoration: underline; text-decoration-line: underline; \"\u003eHint: Use Regular Expression\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function k=strman(x)\r\n  k = regexprep(x);\r\nend","test_suite":"%%\r\nx = 'david attenborough';\r\ny_correct = {[7] [13 15] 'dvd ttnbrgh' 'v ttnorou' 'ai aeoou' 'd*v*d *tt*nb*r**gh' 'david attenborough'};\r\nassert(isequal(strman(x),y_correct))\r\n%%\r\nx = 'ilove bayernmunichhhn';\r\ny_correct = {[7] [3] 'lv byrnmnchhhn' 'lov yrnmunn' 'ioe aeui' '*l*v* b*y*rnm*n*chhhn' 'ilove bayernmunichhhn'};\r\nassert(isequal(strman(x),y_correct))\r\n%%\r\nx = 'n';\r\ny_correct = {[0] [] 'n' 'n' '' 'n' 'n'};\r\nassert(isequal(strman(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":5,"created_by":1274403,"edited_by":1274403,"edited_at":"2022-04-26T04:59:21.000Z","deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":"2022-04-26T04:59:22.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-04-23T20:36:19.000Z","updated_at":"2026-04-07T08:26:15.000Z","published_at":"2022-04-23T20:37:11.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eWrite a script that takes a string as an input and returns a cell array containing\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e  –\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI. the count of vowels.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eII. Find the index of ‘o’\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIII. The string removing all the vowels.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIV. The string removing all the letters from a to j.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eV. The string removing all the consonants.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVI. The string replacing all the vowels with an asterisk (*)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVII. The string removing all the digits.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHint: Use Regular Expression\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":46783,"title":"PIN code III","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 40.7273px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 20.3636px; transform-origin: 407px 20.3636px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.3636px; text-align: left; transform-origin: 384px 20.3636px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA cell phones requires a PIN code to open which only includes numbers; how long (in terms of days) does it take to enter all possible inputs with X input digits. (entering each digit takes 1 sec- Round up the output)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = your_fcn_name(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 4;\r\ny_correct = 1;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 5;\r\ny_correct = 6;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 6;\r\ny_correct = 70;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":430136,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":48,"test_suite_updated_at":"2020-10-16T19:08:35.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-16T19:06:20.000Z","updated_at":"2026-03-16T11:52:29.000Z","published_at":"2020-10-16T19:08:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA cell phones requires a PIN code to open which only includes numbers; how long (in terms of days) does it take to enter all possible inputs with X input digits. (entering each digit takes 1 sec- Round up the output)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":46973,"title":"How many bytes an audio requires from RAM?","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 670.4px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 335.2px; transform-origin: 407px 335.2px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 62.4px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.2px; text-align: left; transform-origin: 384px 31.2px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eAudio files, as many other files are stored compressed in our computers: MP3, OGG, or M4A, are all different types of compressed audio files. Audio must be first decompressed to its total size before it can be played: hearing compressed audio is utterly incomprehensible to humans. Your task is to compute its full length.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 104px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52px; text-align: left; transform-origin: 384px 52px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWe may calculate an audio size in bits with the following formula: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"97.5\" height=\"17.5\" style=\"width: 97.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, in which \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003et\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is its duration in minutes/seconds, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003es\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is its sample rate in hertz (Hz), \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is its number of channels, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eδ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is its bit depth, the number of bits used to store audio intensity. Usually, an audio file's duration is easily obtained from any software, but the other parameters are not always available. Sometimes the bitrate is presented instead, which is defined as \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"78.5\" height=\"17.5\" style=\"width: 78.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and is given as kbps \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e(kilobits per second)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. Bit rate is used as a measure of quality for digital streamming:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 100px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 50px; transform-origin: 404px 50px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eDigital \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eWalkie-talkies have a bitrate of 16kbps.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eOn-line AM \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eradio has a bitrate of 32kbps.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eOn-line FM \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eradio has a bitrate of 96kbps.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eCD \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eaudio has a bitrate of 1\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e,411 kbps.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eHD \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eaudio may have a bitrate up to 9\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e,216 kbps. (over 24 bit-depth, and 44 kHz sample rate)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 104px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 52px; text-align: left; transform-origin: 384px 52px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eCompute how many bytes an image requires from RAM, given its duration (d) and bitrate (b) or sample rate (s), number of channels (c) and  bit depth \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eδ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eMoreover, make the results more human-readable by showing them with units: bytes (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"15.5\" height=\"18.5\" style=\"width: 15.5px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e), KB (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"20.5\" height=\"18.5\" style=\"width: 20.5px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e), MB (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"20.5\" height=\"18.5\" style=\"width: 20.5px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e), GB (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"20.5\" height=\"18.5\" style=\"width: 20.5px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e) rounded to 2 decimal places. There must be at least a non-zero integer value to display it in some units, you should always use the greatest unit possible, and data cannot be stored in less than 1 byte (8 bits).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.8px; text-align: left; transform-origin: 384px 20.8px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote-1\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e: The range of \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Hearing_range\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehuman hearing\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is 20 to 20,000 Hz, and unsurprisingly, audio sampling rate is usally greater than 40,000 Hz (\u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Nyquist_rate\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eNyquist rate\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 62.4px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.2px; text-align: left; transform-origin: 384px 31.2px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote-2\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e: Audio files do not need to be necessarily wholly loaded into the RAM; they only require that a certain amount of bytes per second be always available in it (their bitrate). However, loading full audio files into RAM for playing is the best option to guarantee a smooth sound (without lag). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; font-weight: 700; \"\u003eNext:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/46913-how-many-bytes-an-image-requires-from-ram\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eHow many bytes an image requires from RAM? (Problem 46913)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eHow many bytes a video requires from RAM?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = audio_size(t,s,c,d,b)\r\n  y = t/s/c/d; % or t/b?\r\nend","test_suite":"%% anti-hacking\r\n\r\nfiletext = fileread('audio_size.m');\r\nassert(~contains(filetext, 'eval'),       'eval is forbidden.');\r\nassert(~contains(filetext, 'fopen'),      'fopen is forbidden.');\r\nassert(~contains(filetext, 'regexp'),     'regexp is forbidden.');\r\nassert(~contains(filetext, '!'),          'Shell commands are forbidden.');\r\nassert(~contains(filetext, 'mlock'),      'mlock is forbidden.');\r\nassert(~contains(filetext, 'munlock'),    'munlock is forbidden.');\r\n\r\n%%case 1. The Song of the White Wolf - Sonya Belousova - The Witcher (55)\r\nt = '3m45s'; \r\ns = 44100;\r\nc = 5;\r\nd = 32;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '189.26 MB'));\r\n\r\n%%case 2. Overture - Gerard Marino - God of War III Original V(001)\r\nt = '3m36s';\r\nb = 9216;\r\nassert(strcmp(audio_size(t,[],[],[],b), '237.30 MB'));\r\n\r\n%%case 3. Rip \u0026 Tear - Mick Gordon - Doom (Original Game Soundtra(002)\r\nt = '4m17s'; \r\ns = 384000;\r\nc = 7;\r\nd = 24;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '1.93 GB'));\r\n\r\n%%case 4. Live Free or Die - Volturnus - Audiomachine\r\nt = '3m13s';\r\nb = 1411;\r\nassert(strcmp(audio_size(t,[],[],[],b), '32.46 MB'));\r\n\r\n%%case 5. Bury the Light - Vergil's battle theme from Devil May Cry 5 Special Edition\r\nt = '9m42s';\r\nb = 132;\r\nassert(strcmp(audio_size(t,[],[],[],b), '9.16 MB'));\r\n\r\n%%case 6. Rob The Frontier - UVERworld - Nanatsu no Taizai Season 3 Opening V2\r\nt = '1m30s'; \r\ns = 22050;\r\nc = 2;\r\nd = 16;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '7.57 MB'));\r\n\r\n%%case 7. Ich Will - Rammstein\r\nt = '3m37s'; \r\ns = 384000;\r\nc = 2;\r\nd = 16;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '317.87 MB'));\r\n\r\n%%case 8. Ratamahatta - Sepultura \r\nt = '4m30s'; \r\ns = 92000;\r\nc = 2;\r\nd = 24;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '142.14 MB'));\r\n\r\n%%case 9. Dark Crow - Man with a Mission\r\nt = '4m38s';\r\nb = 9216;\r\nassert(strcmp(audio_size(t,[],[],[],b), '305.42 MB'));\r\n\r\n%%case 10. On Our Own - Bobby Brown\r\nt = '4m54s';\r\ns = 22050;\r\nc = 1;\r\nd = 8;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '6.18 MB'));\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":443343,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":"2020-10-26T22:59:29.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-20T20:01:52.000Z","updated_at":"2026-04-14T13:03:30.000Z","published_at":"2020-10-26T20:08:12.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAudio files, as many other files are stored compressed in our computers: MP3, OGG, or M4A, are all different types of compressed audio files. Audio must be first decompressed to its total size before it can be played: hearing compressed audio is utterly incomprehensible to humans. Your task is to compute its full length.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe may calculate an audio size in bits with the following formula: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey = t*s*c*\\\\delta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, in which \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003et\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is its duration in minutes/seconds, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is its sample rate in hertz (Hz), \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is its number of channels, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\delta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is its bit depth, the number of bits used to store audio intensity. Usually, an audio file's duration is easily obtained from any software, but the other parameters are not always available. Sometimes the bitrate is presented instead, which is defined as \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eb = s*c*\\\\delta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and is given as kbps \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(kilobits per second)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Bit rate is used as a measure of quality for digital streamming:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Digital Walkie-talkies have a bitrate of 16kbps.\\nOn-line AM radio has a bitrate of 32kbps.\\nOn-line FM radio has a bitrate of 96kbps.\\nCD audio has a bitrate of 1,411 kbps.\\nHD audio may have a bitrate up to 9,216 kbps. (over 24 bit-depth, and 44 kHz sample rate)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCompute how many bytes an image requires from RAM, given its duration (d) and bitrate (b) or sample rate (s), number of channels (c) and  bit depth \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\delta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eMoreover, make the results more human-readable by showing them with units: bytes (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2^{0}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e), KB (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2^{10}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e), MB (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2^{20}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e), GB (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2^{30}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) rounded to 2 decimal places. There must be at least a non-zero integer value to display it in some units, you should always use the greatest unit possible, and data cannot be stored in less than 1 byte (8 bits).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote-1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: The range of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Hearing_range\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehuman hearing\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is 20 to 20,000 Hz, and unsurprisingly, audio sampling rate is usally greater than 40,000 Hz (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Nyquist_rate\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eNyquist rate\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote-2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Audio files do not need to be necessarily wholly loaded into the RAM; they only require that a certain amount of bytes per second be always available in it (their bitrate). However, loading full audio files into RAM for playing is the best option to guarantee a smooth sound (without lag). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNext:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/46913-how-many-bytes-an-image-requires-from-ram\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eHow many bytes an image requires from RAM? (Problem 46913)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHow many bytes a video requires from RAM?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2577,"title":"Sum of series III","description":"What is the sum of the following sequence:\r\n\r\n Σ(2k-1)^3 for k=1...n\r\n\r\nfor different n?","description_html":"\u003cp\u003eWhat is the sum of the following sequence:\u003c/p\u003e\u003cpre\u003e Σ(2k-1)^3 for k=1...n\u003c/pre\u003e\u003cp\u003efor different n?\u003c/p\u003e","function_template":"function s = sumOfSeriesIII(n)\r\ns = n;\r\nend","test_suite":"%%\r\nn = 1;\r\ns_correct = 1;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 2;\r\ns_correct = 28;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 5;\r\ns_correct = 1225;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 10;\r\ns_correct = 19900;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 42;\r\ns_correct = 6221628;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 99;\r\ns_correct = 192109401;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 124;\r\ns_correct = 472827376;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 222;\r\ns_correct = 4857776028;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))","published":true,"deleted":false,"likes_count":4,"comments_count":2,"created_by":3062,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1848,"test_suite_updated_at":"2017-06-13T18:03:10.000Z","rescore_all_solutions":false,"group_id":29,"created_at":"2014-09-10T09:51:33.000Z","updated_at":"2026-05-22T14:02:20.000Z","published_at":"2014-09-10T09:52:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhat is the sum of the following sequence:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Σ(2k-1)^3 for k=1...n]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efor different n?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":43078,"title":"How many bottles can you drink?","description":"Sometimes if you buy a drink in a glass bottle you can return that bottle and get some money back.\r\n\r\nLet's assume we have \"x\" amount of money, the drink cost \"p\" per bottle, and we get \"c\" back money for each bottle.\r\n\r\nIf we have enough time, how much can we buy drinks for \"x\" money?\r\n\r\nExample\r\n\r\nWe have $10, drink costs $1, for each bottle we return we get $0.35\r\n\r\n Step I: \r\n We buy 10 drinks\r\n We get $3.50 back for bottles\r\n \r\n Step II: \r\n We buy 3 drinks and we are left with $0.50\r\n We get back $1.05 back so we have $1.55\r\n\r\n Step III:\r\n We buy 1 drink and we have $0.55\r\n We get $0.35 for bottle, so we have $0.90 and we can't buy another drink for that.\r\n\r\nSummary for $10 we were able to drink 14 bottles.","description_html":"\u003cp\u003eSometimes if you buy a drink in a glass bottle you can return that bottle and get some money back.\u003c/p\u003e\u003cp\u003eLet's assume we have \"x\" amount of money, the drink cost \"p\" per bottle, and we get \"c\" back money for each bottle.\u003c/p\u003e\u003cp\u003eIf we have enough time, how much can we buy drinks for \"x\" money?\u003c/p\u003e\u003cp\u003eExample\u003c/p\u003e\u003cp\u003eWe have $10, drink costs $1, for each bottle we return we get $0.35\u003c/p\u003e\u003cpre\u003e Step I: \r\n We buy 10 drinks\r\n We get $3.50 back for bottles\u003c/pre\u003e\u003cpre\u003e Step II: \r\n We buy 3 drinks and we are left with $0.50\r\n We get back $1.05 back so we have $1.55\u003c/pre\u003e\u003cpre\u003e Step III:\r\n We buy 1 drink and we have $0.55\r\n We get $0.35 for bottle, so we have $0.90 and we can't buy another drink for that.\u003c/pre\u003e\u003cp\u003eSummary for $10 we were able to drink 14 bottles.\u003c/p\u003e","function_template":"function y = bottle(x,p,c)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 10;\r\np=1;\r\nc=0.35;\r\ny_correct = 14;\r\nassert(isequal(bottle(x,p,c),y_correct))\r\n\r\n%%\r\nx = 20;\r\np=1.5;\r\nc=0.4;\r\ny_correct = 17;\r\nassert(isequal(bottle(x,p,c),y_correct))","published":true,"deleted":false,"likes_count":13,"comments_count":1,"created_by":90955,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":80,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-10-05T21:45:06.000Z","updated_at":"2026-02-06T20:05:06.000Z","published_at":"2016-10-05T21:45:06.000Z","restored_at":"2017-09-28T06:15:07.000Z","restored_by":null,"spam":false,"simulink":false,"admin_reviewed":true,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSometimes if you buy a drink in a glass bottle you can return that bottle and get some money back.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eLet's assume we have \\\"x\\\" amount of money, the drink cost \\\"p\\\" per bottle, and we get \\\"c\\\" back money for each bottle.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf we have enough time, how much can we buy drinks for \\\"x\\\" money?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe have $10, drink costs $1, for each bottle we return we get $0.35\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Step I: \\n We buy 10 drinks\\n We get $3.50 back for bottles\\n\\n Step II: \\n We buy 3 drinks and we are left with $0.50\\n We get back $1.05 back so we have $1.55\\n\\n Step III:\\n We buy 1 drink and we have $0.55\\n We get $0.35 for bottle, so we have $0.90 and we can't buy another drink for that.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSummary for $10 we were able to drink 14 bottles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":467,"title":"Mr. Pyramidad \u0026 Ms. Whissy Bolower","description":"Businessman Mr. B. M. Pyramidad was drinking with his new girlfriend Ms. Whissy Bolower. He disclosed to her that he has an illegal business plan:\r\n \r\n# He, as Boss-I, will recruit x individuals as Operator-II, \r\n# Each of these operators will recruit x individuals at Helper-III, \r\n# Every helper must recruit x individuals as Peddler-IV. \r\n# All of them will be promoted to next higher level as soon as each of those peddlers deposit $1,000 cash to their acting Boss-I.\r\n# Then, whoever was the acting Boss-I, he or she must retire, but the business will continue to expand as usual. \r\n\r\nMs. Whissy Bolower has decided to blow the whistle. She is calculating that Mr. Pyramidad may come back at the Peddler-IV level after retiring. What could be the minimum x so he collects minimum y dollars net after retiring z times? Please try a general solution, the test suite may expand. For more information, see \u003chttp://en.wikipedia.org/wiki/Pyramid_scheme Pyramid Scheme\u003e.   ","description_html":"\u003cp\u003eBusinessman Mr. B. M. Pyramidad was drinking with his new girlfriend Ms. Whissy Bolower. He disclosed to her that he has an illegal business plan:\u003c/p\u003e\u003col\u003e\u003cli\u003eHe, as Boss-I, will recruit x individuals as Operator-II,\u003c/li\u003e\u003cli\u003eEach of these operators will recruit x individuals at Helper-III,\u003c/li\u003e\u003cli\u003eEvery helper must recruit x individuals as Peddler-IV.\u003c/li\u003e\u003cli\u003eAll of them will be promoted to next higher level as soon as each of those peddlers deposit $1,000 cash to their acting Boss-I.\u003c/li\u003e\u003cli\u003eThen, whoever was the acting Boss-I, he or she must retire, but the business will continue to expand as usual.\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eMs. Whissy Bolower has decided to blow the whistle. She is calculating that Mr. Pyramidad may come back at the Peddler-IV level after retiring. What could be the minimum x so he collects minimum y dollars net after retiring z times? Please try a general solution, the test suite may expand. For more information, see \u003ca href=\"http://en.wikipedia.org/wiki/Pyramid_scheme\"\u003ePyramid Scheme\u003c/a\u003e.\u003c/p\u003e","function_template":"function x = Pyramidad(y,z)\r\n   % mimimum y dollars net for Boss-I\r\n   % retire z times\r\n   x=12; % peddlers-IV under every helper-III\r\nend","test_suite":"%%\r\nx = 10; % peddlers-IV under every helper-III\r\ny = 1000000; % net dollars minimum\r\nz = 1; % retire counter minimum\r\nassert(10==Pyramidad(y,z))\r\n%%\r\nx = 10; % peddlers-IV under every helper-III\r\ny = 1999000; % net dollars minimum\r\nz = 2; % retire counter minimum\r\nassert(10==Pyramidad(y,z))\r\n%%\r\nx = 5; % peddlers-IV under every helper-III\r\ny = 373000; % net dollars minimum\r\nz = 3; % retire counter minimum\r\nassert(5==Pyramidad(y,z))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":166,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":"2012-03-10T05:26:55.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-07T19:11:12.000Z","updated_at":"2026-04-17T11:00:57.000Z","published_at":"2012-03-13T19:35:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBusinessman Mr. B. M. Pyramidad was drinking with his new girlfriend Ms. Whissy Bolower. He disclosed to her that he has an illegal business plan:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHe, as Boss-I, will recruit x individuals as Operator-II,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach of these operators will recruit x individuals at Helper-III,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEvery helper must recruit x individuals as Peddler-IV.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll of them will be promoted to next higher level as soon as each of those peddlers deposit $1,000 cash to their acting Boss-I.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThen, whoever was the acting Boss-I, he or she must retire, but the business will continue to expand as usual.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMs. Whissy Bolower has decided to blow the whistle. She is calculating that Mr. Pyramidad may come back at the Peddler-IV level after retiring. What could be the minimum x so he collects minimum y dollars net after retiring z times? Please try a general solution, the test suite may expand. For more information, see\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Pyramid_scheme\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePyramid Scheme\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2867,"title":"Matlab Basics - Rounding III","description":"Write a script to round a large number to the nearest 10,000\r\n\r\ne.g. x = 12,358,466,243 --\u003e y = 12,358,470,000","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 51px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 25.5px; transform-origin: 407px 25.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 190px 8px; transform-origin: 190px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a script to round a large number to the nearest 10,000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 149.5px 8px; transform-origin: 149.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ee.g. x = 12,358,466,243 --\u0026gt; y = 12,358,470,000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = round_ten_thou(x)\r\n  y = ;\r\nend","test_suite":"%%\r\nx = 12358466243;\r\ny_correct = 12358470000;\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = -1235846.325;\r\ny_correct = -1240000;\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = 1266243.325;\r\ny_correct = 1270000;\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = 987654321;\r\ny_correct = 987650000;\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = [1:4999];\r\ny_correct = zeros(1,4999);\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = [];\r\nassert(isempty(round_ten_thou(x)))\r\n%%\r\nx = [-5001:-1:-6000];\r\ny_correct = -1e4*ones(1,1e3);\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = -54321.67890;\r\ny_correct = -5e4;\r\nassert(isequal(round_ten_thou(x),y_correct))","published":true,"deleted":false,"likes_count":29,"comments_count":7,"created_by":34237,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5374,"test_suite_updated_at":"2021-05-09T15:34:34.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-01-21T17:19:47.000Z","updated_at":"2026-05-22T21:40:31.000Z","published_at":"2015-01-22T19:27:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a script to round a large number to the nearest 10,000\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ee.g. x = 12,358,466,243 --\u0026gt; y = 12,358,470,000\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":52188,"title":"Roman numbers: how old is that building?","description":"The front of old buildings sometimes show the year when they were built in roman numbers. These number are rather confusing and not easy to understand, Can you make a program that can translate any string with any roman number between 0 to 3999 back a decimal number? (an empty string should return zero).\r\nI found this even more challenging than the reverse problem (problem 63 Encode Roman numbers) which I liked a lot and was obviously the inspiration of this problem.\r\nExamples:\r\n'XXIII' should return 23 (XX=20 III=3)\r\n'MMXXI' should return 2021 (MM=2000 XX=20 I=1)\r\n'MDCLXVI' should return 1666 (MDC =1600 LX=60 VI=6)\r\n'CMXCIX' should return 999 (CM=900,XC=90,IX=9)\r\n'' (empty string) should return 0\r\nOnly integer numbers between 0 and 3999 can be handled.\r\n\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 355px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 177.5px; transform-origin: 407px 177.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe front of old buildings sometimes show the year when they were built in roman numbers. These number are rather confusing and not easy to understand, Can you make a program that can translate any string with any roman number between 0 to 3999 back a decimal number? (an empty string should return zero).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eI found this even more challenging than the reverse problem (\u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/63-encode-roman-numerals?s_tid=ta_cody_results\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eproblem 63 Encode Roman numbers\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e) which I liked a lot and was obviously the inspiration of this problem.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eExamples:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 100px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 50px; transform-origin: 391px 50px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'XXIII' should return 23 (XX=20 III=3)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'MMXXI' should return 2021 (MM=2000 XX=20 I=1)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'MDCLXVI' should return 1666 (MDC =1600 LX=60 VI=6)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'CMXCIX' should return 999 (CM=900,XC=90,IX=9)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'' (empty string) should return 0\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eOnly integer numbers between 0 and 3999 can be handled.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = rom2dec(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 'XLIX';\r\ny_correct = 49;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n%%\r\nx =  'MCMXCIX';\r\ny_correct = 1999;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n%%\r\nx = 'MDCCLXXVI';\r\ny_correct = 1776;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n%%\r\nx = '';\r\ny_correct = 0;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n%%\r\nx = 'MMMDCCCLXXXVIII';\r\ny_correct = 3888;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":1260358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-07-04T07:36:30.000Z","updated_at":"2026-04-14T14:01:58.000Z","published_at":"2021-07-04T08:42:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe front of old buildings sometimes show the year when they were built in roman numbers. These number are rather confusing and not easy to understand, Can you make a program that can translate any string with any roman number between 0 to 3999 back a decimal number? (an empty string should return zero).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI found this even more challenging than the reverse problem (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/63-encode-roman-numerals?s_tid=ta_cody_results\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eproblem 63 Encode Roman numbers\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) which I liked a lot and was obviously the inspiration of this problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'XXIII' should return 23 (XX=20 III=3)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'MMXXI' should return 2021 (MM=2000 XX=20 I=1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'MDCLXVI' should return 1666 (MDC =1600 LX=60 VI=6)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'CMXCIX' should return 999 (CM=900,XC=90,IX=9)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'' (empty string) should return 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOnly integer numbers between 0 and 3999 can be handled.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":43664,"title":"Use a timetable to analyze a train timetable (Part 1)","description":"Oh no, you missed your train to Boston! Find the departure time of the next available train by analyzing the train timetable. The trip should not take longer than 5 hours.\r\nExample:\r\nDepTime = datetime(2016,11,1,[6 8 9],[22 51 05],0)';\r\nArrTime = datetime(2016,11,1,[9 10 12],[17 32 09],0)';\r\nDestination = categorical({'BOS';'NYC';'BOS'});\r\ntt = timetable(DepTime,Destination,ArrTime)\r\ntt = \r\n         DepTime          Destination          ArrTime       \r\n  ____________________    ___________    ____________________\r\n  01-Nov-2016 06:22:00    BOS            01-Nov-2016 09:17:00\r\n  01-Nov-2016 08:51:00    NYC            01-Nov-2016 10:32:00\r\n  01-Nov-2016 09:05:00    BOS            01-Nov-2016 12:09:00\r\nFeature Tip: R2016b introduces timetables with related functions which may be helpful. To learn more see MATLAB Timetables.\r\nRelated Problems:\r\nUse a timetable to analyze a train timetable (Part 1)\r\nUse a timetable to analyze a train timetable (Part 2)\r\nUse a timetable to analyze a train timetable (Part 3)\r\nUse a timetable to analyze a train timetable (Part 4)\r\nUse a timetable to analyze a train timetable (Part 5)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 518.333px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 259.167px; transform-origin: 407px 259.167px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOh no, you missed your train to Boston! Find the departure time of the next available train by analyzing the train timetable. The trip should not take longer than 5 hours.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.5px 8px; transform-origin: 31.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 204.333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 102.167px; transform-origin: 404px 102.167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 208px 8.5px; tab-size: 4; transform-origin: 208px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eDepTime = datetime(2016,11,1,[6 8 9],[22 51 05],0)';\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 216px 8.5px; tab-size: 4; transform-origin: 216px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eArrTime = datetime(2016,11,1,[9 10 12],[17 32 09],0)';\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 188px 8.5px; tab-size: 4; transform-origin: 188px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 108px 8.5px; transform-origin: 108px 8.5px; \"\u003eDestination = categorical({\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'BOS'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'NYC'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'BOS'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003e});\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 172px 8.5px; tab-size: 4; transform-origin: 172px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ett = timetable(DepTime,Destination,ArrTime)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ett = \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 104px 8.5px; transform-origin: 104px 8.5px; \"\u003e         DepTime          \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 44px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 44px 8.5px; \"\u003eDestination\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 40px 8.5px; transform-origin: 40px 8.5px; \"\u003e          \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003eArrTime\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 28px 8.5px; transform-origin: 28px 8.5px; \"\u003e       \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(255, 0, 0); border-block-start-color: rgb(255, 0, 0); border-bottom-color: rgb(255, 0, 0); border-inline-end-color: rgb(255, 0, 0); border-inline-start-color: rgb(255, 0, 0); border-left-color: rgb(255, 0, 0); border-right-color: rgb(255, 0, 0); border-top-color: rgb(255, 0, 0); caret-color: rgb(255, 0, 0); color: rgb(255, 0, 0); column-rule-color: rgb(255, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(255, 0, 0); perspective-origin: 80px 8.5px; text-decoration-color: rgb(255, 0, 0); text-emphasis-color: rgb(255, 0, 0); transform-origin: 80px 8.5px; \"\u003e____________________\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 16px 8.5px; transform-origin: 16px 8.5px; \"\u003e    \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(255, 0, 0); border-block-start-color: rgb(255, 0, 0); border-bottom-color: rgb(255, 0, 0); border-inline-end-color: rgb(255, 0, 0); border-inline-start-color: rgb(255, 0, 0); border-left-color: rgb(255, 0, 0); border-right-color: rgb(255, 0, 0); border-top-color: rgb(255, 0, 0); caret-color: rgb(255, 0, 0); color: rgb(255, 0, 0); column-rule-color: rgb(255, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(255, 0, 0); perspective-origin: 44px 8.5px; text-decoration-color: rgb(255, 0, 0); text-emphasis-color: rgb(255, 0, 0); transform-origin: 44px 8.5px; \"\u003e___________\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 16px 8.5px; transform-origin: 16px 8.5px; \"\u003e    \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(255, 0, 0); border-block-start-color: rgb(255, 0, 0); border-bottom-color: rgb(255, 0, 0); border-inline-end-color: rgb(255, 0, 0); border-inline-start-color: rgb(255, 0, 0); border-left-color: rgb(255, 0, 0); border-right-color: rgb(255, 0, 0); border-top-color: rgb(255, 0, 0); caret-color: rgb(255, 0, 0); color: rgb(255, 0, 0); column-rule-color: rgb(255, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(255, 0, 0); perspective-origin: 80px 8.5px; text-decoration-color: rgb(255, 0, 0); text-emphasis-color: rgb(255, 0, 0); transform-origin: 80px 8.5px; \"\u003e____________________\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 164px 8.5px; transform-origin: 164px 8.5px; \"\u003e  01-Nov-2016 06:22:00    BOS            \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 80px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 80px 8.5px; \"\u003e01-Nov-2016 09:17:00\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 164px 8.5px; transform-origin: 164px 8.5px; \"\u003e  01-Nov-2016 08:51:00    NYC            \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 80px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 80px 8.5px; \"\u003e01-Nov-2016 10:32:00\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 164px 8.5px; transform-origin: 164px 8.5px; \"\u003e  01-Nov-2016 09:05:00    BOS            \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 80px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 80px 8.5px; \"\u003e01-Nov-2016 12:09:00\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 42px 8px; transform-origin: 42px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eFeature Tip:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/help/matlab/release-notes.html?rntext=\u0026amp;startrelease=R2016b\u0026amp;endrelease=R2016b\u0026amp;groupby=release\u0026amp;sortby=descending\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eR2016b\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 266px 8px; transform-origin: 266px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e introduces timetables with related functions which may be helpful. To learn more see\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMATLAB Timetables\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 64px 8px; transform-origin: 64px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Problems:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 161px 8px; transform-origin: 161px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 1)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 2)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 3)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 4)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 5)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function nextTime = myFun(tt)\r\n  nextTime = tt;\r\nend","test_suite":"%%\r\nDepTime = datetime(2016,11,[1;1;1;1;1;1;2;2;2;2;3;3;3;3;3],...\r\n[6;7;7;8;8;9;4;6;8;3;10;5;8;4;2],[22;27;39;43;46;17;41;40;10;8;30;58;21;36;14],0);\r\nArrTime = datetime(2016,11,[1;1;1;1;1;1;2;2;2;2;3;3;3;3;3],...\r\n[9;10;10;13;11;12;7;9;11;6;13;8;11;7;5],[17;32;09;03;26;46;13;20;19;28;40;38;27;32;24],0);\r\nDestination = categorical([1;2;1;1;1;1;2;1;3;2;1;3;3;1;2],1:3,{'BOS';'NYC';'DC'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,1,6,22,0);\r\nassert(isequal(myFun(tt),nextTime))\r\n\r\n%%\r\nDepTime = datetime(2016,11,[1;1;1;1;1;1], [6;6;7;7;8;8],[0;10;0;20;0;30],0);\r\nArrTime = datetime(2016,11,[1;1;1;1;1;1], [13;13;12;12;11;11],[0;30;0;20;0;10],0);\r\nDestination = categorical([1;1;1;1;1;1],1,{'BOS'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,1,8,0,0);\r\nassert(isequal(myFun(tt),nextTime))\r\n\r\n%%\r\nDepTime = datetime(2016,11,[1;2;3;4], [3;5;7;9],0,0);\r\nArrTime = datetime(2016,11,[1;2;3;4], [13;12;11;10],0,0);\r\nDestination = categorical([1;2;1;2],1:2,{'BOS';'NYC'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,3,7,0,0);\r\nassert(isequal(myFun(tt),nextTime))\r\n\r\n%%\r\nDepTime = datetime(2016,11,[1;1;1;1], [7;7;8;8],0,0);\r\nArrTime = datetime(2016,11,[1;1;1;1], [12;11;10;9],0,0);\r\nDestination = categorical([1;2;1;2],1:2,{'BOS';'NYC'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,1,8,0,0);\r\nassert(isequal(myFun(tt),nextTime))\r\n\r\n%%\r\nDepTime = datetime(2016,11,[1;1;1;1], [8;8;7;8],10,30);\r\nArrTime = datetime(2016,11,[1;1;1;1], [12;13;10;9],0,0);\r\nDestination = categorical([1;2;1;2],1:2,{'BOS';'DC'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,1,7,10,30);\r\nassert(isequal(myFun(tt),nextTime))","published":true,"deleted":false,"likes_count":2,"comments_count":4,"created_by":102743,"edited_by":223089,"edited_at":"2022-04-27T08:40:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":38,"test_suite_updated_at":"2022-04-27T08:40:00.000Z","rescore_all_solutions":false,"group_id":16,"created_at":"2016-11-18T20:20:09.000Z","updated_at":"2026-04-24T15:06:39.000Z","published_at":"2016-11-18T20:26:18.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOh no, you missed your train to Boston! Find the departure time of the next available train by analyzing the train timetable. The trip should not take longer than 5 hours.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[DepTime = datetime(2016,11,1,[6 8 9],[22 51 05],0)';\\nArrTime = datetime(2016,11,1,[9 10 12],[17 32 09],0)';\\nDestination = categorical({'BOS';'NYC';'BOS'});\\ntt = timetable(DepTime,Destination,ArrTime)\\ntt = \\n         DepTime          Destination          ArrTime       \\n  ____________________    ___________    ____________________\\n  01-Nov-2016 06:22:00    BOS            01-Nov-2016 09:17:00\\n  01-Nov-2016 08:51:00    NYC            01-Nov-2016 10:32:00\\n  01-Nov-2016 09:05:00    BOS            01-Nov-2016 12:09:00]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFeature Tip:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/help/matlab/release-notes.html?rntext=\u0026amp;startrelease=R2016b\u0026amp;endrelease=R2016b\u0026amp;groupby=release\u0026amp;sortby=descending\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eR2016b\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e introduces timetables with related functions which may be helpful. To learn more see\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMATLAB Timetables\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Problems:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 3)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 4)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 5)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42841,"title":"Return the sequence element III","description":"This problem is related to \u003chttp://www.mathworks.com/matlabcentral/cody/problems/42832-segmented-number-sequence Problem 42832\u003e.\r\n\r\nGiven positive integers x1, x2 and n, return a positive integer, y, which is the nth term in the \u003chttps://en.wikipedia.org/wiki/Ulam_number Ulam sequence\u003e beginning with [x1 x2].\r\n\r\nAn Ulam sequence is an increasing sequence of positive integers, beginning with two arbitrary integers, x1 and x2. Every subsequent element is the smallest integer that can be expressed uniquely by the sum of any two distinct preceding elements. In other words, integers that can be expressed as sums of two distinct preceding elements in more than one way are excluded.\r\n\r\nAssume n \u003e 2.\r\n\r\nExample:\r\n\r\nx1 = 1\r\n\r\nx2 = 2\r\n\r\nn = 5\r\n\r\ny = 6","description_html":"\u003cp\u003eThis problem is related to \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/42832-segmented-number-sequence\"\u003eProblem 42832\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eGiven positive integers x1, x2 and n, return a positive integer, y, which is the nth term in the \u003ca href = \"https://en.wikipedia.org/wiki/Ulam_number\"\u003eUlam sequence\u003c/a\u003e beginning with [x1 x2].\u003c/p\u003e\u003cp\u003eAn Ulam sequence is an increasing sequence of positive integers, beginning with two arbitrary integers, x1 and x2. Every subsequent element is the smallest integer that can be expressed uniquely by the sum of any two distinct preceding elements. In other words, integers that can be expressed as sums of two distinct preceding elements in more than one way are excluded.\u003c/p\u003e\u003cp\u003eAssume n \u0026gt; 2.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cp\u003ex1 = 1\u003c/p\u003e\u003cp\u003ex2 = 2\u003c/p\u003e\u003cp\u003en = 5\u003c/p\u003e\u003cp\u003ey = 6\u003c/p\u003e","function_template":"function y = ulam(x1,x2,n)\r\n  y = -1;\r\nend","test_suite":"%%\r\nx1 = 1;\r\nx2 = 2;\r\nn = 5;\r\ny_correct = 6;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 1;\r\nx2 = 2;\r\nn = 22;\r\ny_correct = 77;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 1;\r\nx2 = 3;\r\nn = 49;\r\ny_correct = 212;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 1;\r\nx2 = 5;\r\nn = 10;\r\ny_correct = 22;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 2;\r\nx2 = 9;\r\nn = 8;\r\ny_correct = 20;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 2;\r\nx2 = 9;\r\nn = 26;\r\ny_correct = 77;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 4;\r\nx2 = 5;\r\nn = 29;\r\ny_correct = 123;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 6;\r\nx2 = 7;\r\nn = 29;\r\ny_correct = 123;\r\nassert(isequal(ulam(x1,x2,n),y_correct))","published":true,"deleted":false,"likes_count":5,"comments_count":1,"created_by":15521,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-04-29T20:39:06.000Z","updated_at":"2026-01-19T17:42:01.000Z","published_at":"2016-04-29T20:39:06.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is related to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/42832-segmented-number-sequence\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 42832\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven positive integers x1, x2 and n, return a positive integer, y, which is the nth term in the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Ulam_number\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUlam sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e beginning with [x1 x2].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn Ulam sequence is an increasing sequence of positive integers, beginning with two arbitrary integers, x1 and x2. Every subsequent element is the smallest integer that can be expressed uniquely by the sum of any two distinct preceding elements. In other words, integers that can be expressed as sums of two distinct preceding elements in more than one way are excluded.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAssume n \u0026gt; 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex1 = 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex2 = 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en = 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ey = 6\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44338,"title":"Recaman Sequence - I","description":"Recaman Sequence (A005132 - \u003chttp://oeis.org/A005132 - OEIS Link\u003e) is defined as follow;\r\n\r\n  seq(0) = 0; \r\n  for n \u003e 0, seq(n) = seq(n-1) - n if positive and not already in the sequence, \r\n  otherwise seq(n) = seq(n-1) + n. \r\n\r\n  seq = 0, 1, 3, 6, 2, 7, 13, 20, 12, 21, 11, 22, 10, 23, 9 ...\r\nindex = 1, 2, 3 ,...\r\n\r\nTo avoid zero index, start indexing from 1.\r\nreturn the first n elements in Recaman Sequence\r\n\r\n*Related Challenges :*\r\n\r\n# Recaman Sequence - I\r\n# \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44339 Recaman Sequence - II\u003e\r\n# \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44340 Recaman Sequence - III\u003e\r\n","description_html":"\u003cp\u003eRecaman Sequence (A005132 - \u003ca href = \"http://oeis.org/A005132\"\u003e- OEIS Link\u003c/a\u003e) is defined as follow;\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eseq(0) = 0; \r\nfor n \u0026gt; 0, seq(n) = seq(n-1) - n if positive and not already in the sequence, \r\notherwise seq(n) = seq(n-1) + n. \r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eseq = 0, 1, 3, 6, 2, 7, 13, 20, 12, 21, 11, 22, 10, 23, 9 ...\r\nindex = 1, 2, 3 ,...\r\n\u003c/pre\u003e\u003cp\u003eTo avoid zero index, start indexing from 1.\r\nreturn the first n elements in Recaman Sequence\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelated Challenges :\u003c/b\u003e\u003c/p\u003e\u003col\u003e\u003cli\u003eRecaman Sequence - I\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44339\"\u003eRecaman Sequence - II\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44340\"\u003eRecaman Sequence - III\u003c/a\u003e\u003c/li\u003e\u003c/ol\u003e","function_template":"function y = Recaman(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 0;\r\nassert(isequal(Recaman(x),y_correct))\r\n\r\n%%\r\nx = 5;\r\ny_correct = [0 1 3 6 2];\r\nassert(isequal(Recaman(x),y_correct))\r\n\r\n%%\r\nx = 8;\r\ny_correct = [0 1 3 6 2 7 13 20];\r\nassert(isequal(Recaman(x),y_correct))\r\n\r\n%%\r\nx = 10;\r\ny_correct = [0 1 3 6 2 7 13 20 12 21];\r\nassert(isequal(Recaman(x),y_correct))\r\n\r\n%%\r\nx = 5e4;\r\ny = Recaman(x);\r\nassert(isequal(length(Recaman(x)),x))\r\nassert(isequal(y(954),739))\r\nassert(isequal(y(7589),17654))\r\nassert(isequal(y(12345),18554))\r\n\r\n%%\r\nx = 1e5;\r\ny = Recaman(x);\r\nassert(isequal(length(Recaman(x)),x))\r\nassert(isequal(y(1e4),8658))\r\nassert(isequal(y(2e4),34358))\r\nassert(isequal(y(3e4),92474))\r\nassert(isequal(y(4e4),102344))","published":true,"deleted":false,"likes_count":3,"comments_count":8,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":321,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":34,"created_at":"2017-09-19T06:55:43.000Z","updated_at":"2026-03-22T11:16:16.000Z","published_at":"2017-10-16T01:45:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence (A005132 -\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://oeis.org/A005132\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e- OEIS Link\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) is defined as follow;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq(0) = 0; \\nfor n \u003e 0, seq(n) = seq(n-1) - n if positive and not already in the sequence, \\notherwise seq(n) = seq(n-1) + n. \\n\\nseq = 0, 1, 3, 6, 2, 7, 13, 20, 12, 21, 11, 22, 10, 23, 9 ...\\nindex = 1, 2, 3 ,...]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo avoid zero index, start indexing from 1. return the first n elements in Recaman Sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44339\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44340\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - III\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":56503,"title":"Cricket - Career Bowling Statistics","description":"Given a vector s of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \"O-M-R-W\", such as \"31.4-8-123-4\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\r\nNote that overs can be given as integers (\"31\" meaning 31 complete overs) or as pseudo decimals (\"31.4\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\r\nCareer statistics are defined as:\r\nAverage = (total runs conceded) / (total wickets taken)\r\nSR = (total balls delivered) / (total wickets taken)\r\nEcon = (total runs conceded) / (total overs delivered)\r\n(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\r\nFor example:\r\ns = [\"23.1-8-55-5\";\"24-5-84-0\"];\r\nstats = bowlingstats(s)\r\nstats =\r\n   27.8000   56.6000    2.9470\r\nHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 472.533px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 236.267px; transform-origin: 407px 236.267px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48px 8px; transform-origin: 48px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003es\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 330px 8px; transform-origin: 330px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \"O-M-R-W\", such as \"31.4-8-123-4\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374px 8px; transform-origin: 374px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNote that overs can be given as integers (\"31\" meaning 31 complete overs) or as pseudo decimals (\"31.4\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 100.5px 8px; transform-origin: 100.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCareer statistics are defined as:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 170.5px 8px; transform-origin: 170.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAverage = (total runs conceded) / (total wickets taken)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 152px 8px; transform-origin: 152px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSR = (total balls delivered) / (total wickets taken)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 166.5px 8px; transform-origin: 166.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEcon = (total runs conceded) / (total overs delivered)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 355.5px 8px; transform-origin: 355.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 41px 8px; transform-origin: 41px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 128px 8.5px; tab-size: 4; transform-origin: 128px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es = [\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 52px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 52px 8.5px; \"\u003e\"23.1-8-55-5\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 44px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 44px 8.5px; \"\u003e\"24-5-84-0\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats = bowlingstats(s)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 120px 8.5px; tab-size: 4; transform-origin: 120px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e   27.8000   56.6000    2.9470\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function s = bowlingstats(bf)\r\n\r\ns = mean(bf);\r\n\r\nend","test_suite":"%% Random figures (of various levels of realism) part I\r\ns = [\"47-5-6-9\";\"4.4-43-167-1\";\"35-3-73-1\";\"37-8-158-1\";\"43-11-176-4\";\"28.3-39-136-2\";\"43-18-110-10\"];\r\nassert( max(abs(bowlingstats(s) - [29.5 51.0357142857143 3.46815955213436])) \u003c 0.001 )\r\ns = [\"38-8-148-4\";\"35-42-147-6\";\"39-45-164-1\";\"46.3-4-107-8\";\"16.4-24-113-9\"];\r\nassert( max(abs(bowlingstats(s) - [24.25 37.5357142857143 3.87630827783064])) \u003c 0.001 )\r\ns = [\"2-41-199-5\";\"46.1-48-41-4\";\"5-50-76-1\";\"43-22-142-6\";\"27-22-33-3\";\"1.3-17-65-6\";\"7.5-23-162-1\";\"30-39-44-7\";\"37-38-66-5\"];\r\nassert( max(abs(bowlingstats(s) - [21.7894736842105 31.5 4.15037593984962])) \u003c 0.001 )\r\ns = [\"16-4-85-5\";\"15.1-46-74-4\";\"22-33-171-2\";\"28-48-197-6\";\"7.3-28-11-2\";\"7.3-16-72-4\";\"12.5-5-200-10\"];\r\nassert( max(abs(bowlingstats(s) - [24.5454545454545 19.8181818181818 7.43119266055046])) \u003c 0.001 )\r\ns = [\"13-9-186-6\";\"38-1-177-3\";\"17.5-44-124-8\";\"19.5-43-179-7\";\"25-17-42-2\";\"11.5-5-52-3\";\"15-32-1-2\";\"15.5-21-19-6\"];\r\nassert( max(abs(bowlingstats(s) - [21.0810810810811 25.3513513513514 4.98933901918977])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part II\r\ns = [\"43.5-36-199-6\";\"2-10-104-3\"];\r\nassert( max(abs(bowlingstats(s) - [33.6666666666667 30.5555555555556 6.61090909090909])) \u003c 0.001 )\r\ns = [\"26.2-48-119-8\";\"26-31-180-7\";\"24.4-39-102-5\";\"8-4-107-6\"];\r\nassert( max(abs(bowlingstats(s) - [19.5384615384615 19.6153846153846 5.97647058823529])) \u003c 0.001 )\r\ns = [\"26-32-62-4\";\"7.3-49-89-2\";\"14-37-134-9\";\"47-49-165-7\";\"30-39-64-9\";\"27-27-132-10\";\"47.3-23-102-7\";\"13-32-32-6\"];\r\nassert( max(abs(bowlingstats(s) - [14.4444444444444 23.5555555555556 3.67924528301887])) \u003c 0.001 )\r\ns = [\"50-45-186-2\";\"32.1-27-22-8\";\"43.3-14-167-8\";\"25.1-9-31-1\";\"1.2-3-172-2\";\"13-9-120-7\"];\r\nassert( max(abs(bowlingstats(s) - [24.9285714285714 35.3928571428571 4.22603430877901])) \u003c 0.001 )\r\ns = [\"14-13-8-7\";\"40-10-88-7\";\"31-44-14-4\";\"35.4-2-196-5\"];\r\nassert( max(abs(bowlingstats(s) - [13.304347826087 31.4782608695652 2.53591160220994])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part III\r\ns = \"11-16-166-7\";\r\nassert( max(abs(bowlingstats(s) - [23.7142857142857 9.42857142857143 15.0909090909091])) \u003c 0.001 )\r\ns = [\"45-43-122-9\";\"13.3-6-153-3\";\"48.1-36-33-1\";\"23-28-99-7\";\"45-27-153-6\";\"24.2-16-61-3\";\"30-9-168-10\";\"1.4-16-136-10\";\"37.2-34-66-5\";\"21.3-27-108-7\"];\r\nassert( max(abs(bowlingstats(s) - [18.016393442623 28.4754098360656 3.79620034542314])) \u003c 0.001 )\r\ns = [\"15-37-129-8\";\"1-20-101-10\";\"15-35-107-2\";\"34-4-148-4\";\"8.2-38-55-1\";\"12-3-48-10\"];\r\nassert( max(abs(bowlingstats(s) - [16.8 14.6285714285714 6.890625])) \u003c 0.001 )\r\ns = [\"37-7-85-7\";\"40-33-108-10\";\"29.2-2-93-10\";\"19.1-12-126-8\";\"9-49-101-2\";\"45-48-159-9\";\"46.1-50-143-2\";\"38-4-114-8\";\"19-10-32-1\"];\r\nassert( max(abs(bowlingstats(s) - [16.859649122807 29.7543859649123 3.3997641509434])) \u003c 0.001 )\r\ns = [\"3-22-47-3\";\"46-33-148-4\";\"9-4-176-9\";\"5.4-20-155-4\";\"16.3-22-8-5\";\"34.1-21-111-8\";\"11.4-14-118-5\"];\r\nassert( max(abs(bowlingstats(s) - [20.0789473684211 19.8947368421053 6.05555555555556])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part IV\r\ns = [\"34-25-192-3\";\"5-32-164-3\";\"50.4-38-85-1\";\"29-35-164-6\";\"11-7-89-10\";\"5-46-77-8\";\"4-19-142-3\";\"2.2-27-48-7\";\"17-50-36-1\"];\r\nassert( max(abs(bowlingstats(s) - [23.7380952380952 22.5714285714286 6.31012658227848])) \u003c 0.001 )\r\ns = [\"17-27-112-5\";\"17-1-158-3\";\"39-14-83-4\";\"20-50-17-7\";\"11.1-36-118-6\";\"29-11-128-5\";\"12.2-33-26-4\";\"9.4-7-174-6\"];\r\nassert( max(abs(bowlingstats(s) - [20.4 23.275 5.25886143931257])) \u003c 0.001 )\r\ns = \"28-17-161-2\";\r\nassert( max(abs(bowlingstats(s) - [80.5 84 5.75])) \u003c 0.001 )\r\ns = \"23.3-35-107-3\";\r\nassert( max(abs(bowlingstats(s) - [35.6666666666667 47 4.5531914893617])) \u003c 0.001 )\r\ns = [\"21-29-169-6\";\"22.5-28-158-1\"];\r\nassert( max(abs(bowlingstats(s) - [46.7142857142857 37.5714285714286 7.46007604562738])) \u003c 0.001 )\r\n%% Tim Southee's stats\r\nts = [\"23.1-8-55-5\";\"24-5-84-0\";\"16-2-59-0\";\"18-3-63-4\";\"16.2-5-62-1\";\"27-1-100-0\";\"18-1-94-2\";\"12-2-58-0\";\"17-4-62-1\";\"31-8-64-2\";\"16-2-62-1\";\"11-4-41-3\";\"19-4-68-0\";\"19-3-61-4\";\"23-4-89-2\";\"33-6-119-3\";\"4-0-11-0\";\"29-5-94-1\";\"32-10-82-2\";\"1.4-0-10-0\";\"28-7-102-2\";\"15-2-49-2\";\"28.2-5-103-2\";\"1-0-11-0\";\"12-2-32-1\";\"19-3-77-2\";\"3.5-0-8-2\";\"8-2-20-0\";\"10-1-40-0\";\"26-4-100-0\";\"19-5-70-2\";\"14-4-30-1\";\"24-6-64-7\";\"18-3-68-1\";\"18-4-46-4\";\"22-4-62-5\";\"20-5-58-3\";\"15-3-45-1\";\"36-8-94-0\";\"32-9-77-0\";\"23.2-9-44-3\";\"30-6-77-2\";\"28.2-8-58-4\";\"19-4-50-6\";\"26-6-76-2\";\"15-4-51-0\";\"16-1-52-4\";\"29.1-4-101-2\";\"15.5-2-58-2\";\"11-2-24-3\";\"28-3-79-4\";\"8.5-5-12-3\";\"19-6-38-3\";\"23-4-81-3\";\"20-0-93-3\";\"16-3-50-2\";\"16.2-9-19-4\";\"9-2-32-2\";\"30-9-69-1\";\"4-1-21-0\";\"21-8-63-1\";\"16-4-28-3\";\"23-5-62-0\";\"9-0-33-0\";\"30-5-67-3\";\"11-3-21-1\";\"24-4-54-1\";\"11-3-20-0\";\"12-4-17-2\";\"37-8-91-4\";\"26-3-87-1\";\"17.4-6-41-1\";\"24-1-104-1\";\"34-4-162-2\";\"30-5-83-4\";\"18-7-43-1\";\"24-8-70-1\";\"29-6-88-0\";\"25-4-97-4\";\"17-1-50-1\";\"16-1-58-0\";\"27-4-71-3\";\"21-6-52-3\";\"21-5-63-3\";\"12.3-2-26-4\";\"31-5-87-2\";\"25-4-85-0\";\"7-2-30-1\";\"17-8-28-2\";\"15-3-68-2\";\"28-14-73-1\";\"14-7-35-1\";\"23-3-80-1\";\"35-5-114-1\";\"16-6-46-3\";\"19-11-20-2\";\"23.4-10-53-3\";\"21-4-80-6\";\"24-6-60-2\";\"34-5-158-2\";\"13-5-34-1\";\"28.3-7-94-5\";\"12.5-2-48-3\";\"27-7-98-2\";\"6-2-17-1\";\"19-9-34-2\";\"19-3-71-2\";\"10-3-25-4\";\"26-4-86-1\";\"26-7-62-6\";\"19-4-65-1\";\"25-5-56-1\";\"12-3-42-3\";\"27-7-68-6\";\"25-8-52-2\";\"15-5-35-3\";\"27-13-61-2\";\"14-2-76-3\";\"24-4-98-3\";\"15-2-52-1\";\"12-1-57-0\";\"7-3-17-0\";\"12-2-33-1\";\"29-7-63-4\";\"12-6-15-2\";\"32-7-88-4\";\"20-4-60-1\";\"37-4-90-2\";\"30.2-7-93-4\";\"21.1-6-69-5\";\"33.1-6-103-3\";\"15-3-44-0\";\"20.1-5-49-4\";\"21-6-61-5\";\"13-5-38-2\";\"11-2-36-3\";\"19-7-35-4\";\"15-2-62-1\";\"17.4-6-32-5\";\"22-4-96-2\";\"26-7-69-2\";\"23-8-33-2\";\"23-7-61-2\";\"20-8-45-0\";\"25.1-8-43-6\";\"17-1-37-1\";\"22-6-64-1\";\"19-4-48-4\";\"27.4-6-69-5\";\"22-2-75-3\";\"22-6-43-0\";\"13-2-31-0\";\"38-4-114-2\";\"5-2-21-1\";\"12-4-28-3\";\"17-6-54-1\";\"12-2-33-1\";\"17.4-6-35-5\";\"32-11-75-1\";\"26-5-90-2\";\"14-3-55-4\";\"23.5-5-87-0\";\"32-1-154-0\";\"11-0-67-1\";\"23-2-100-3\";\"19-5-68-1\"];\r\nassert( max(abs(bowlingstats(ts) - [28.9942363112392 57.8933717579251 3.00492807008811])) \u003c 0.001 )\r\n%% No wickets\r\nfor k = 1:4\r\n    n = randi(10);\r\n    o = randi([1 50],n,1);\r\n    m = randi(50,n,1);\r\n    r = randi([1 200],n,1);\r\n    w = zeros(n,1);\r\n    bf = o + \"-\" + m + \"-\" + r + \"-\" + w;\r\n    s = bowlingstats(bf);\r\n    assert( isinf(s(1)) )\r\n    assert( isinf(s(2)) )\r\nend\r\n%% No runs or wickets\r\nfor k = 1:4\r\n    n = randi(10);\r\n    o = randi([1 50],n,1);\r\n    m = randi(50,n,1);\r\n    r = zeros(n,1);\r\n    w = zeros(n,1);\r\n    bf = o + \"-\" + m + \"-\" + r + \"-\" + w;\r\n    s = bowlingstats(bf);\r\n    assert( isnan(s(1)) )\r\n    assert( isinf(s(2)) )\r\nend","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-11-08T15:13:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":29,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:38:20.000Z","updated_at":"2026-05-14T20:49:15.000Z","published_at":"2022-11-08T15:13:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \\\"O-M-R-W\\\", such as \\\"31.4-8-123-4\\\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that overs can be given as integers (\\\"31\\\" meaning 31 complete overs) or as pseudo decimals (\\\"31.4\\\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCareer statistics are defined as:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAverage = (total runs conceded) / (total wickets taken)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSR = (total balls delivered) / (total wickets taken)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEcon = (total runs conceded) / (total overs delivered)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s = [\\\"23.1-8-55-5\\\";\\\"24-5-84-0\\\"];\\nstats = bowlingstats(s)\\nstats =\\n   27.8000   56.6000    2.9470]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1642,"title":"BULLSEYE Part 2:   Reference Problem 18 BULLSEYE ","description":"Given n (always odd), return output a that has concentric rings of the 1s and 0s  around the center point. Examples:\r\n\r\n Input  n = 3\r\n Output a is [ 1 1 1 \r\n               1 0 1\r\n               1 1 1 ]\r\n\r\n Input  n = 5\r\n Output a is [    1     1     1     1     1\r\n                  1     0     0     0     1\r\n                  1     0     1     0     1\r\n                  1     0     0     0     1\r\n                  1     1     1     1     1]\r\n\r\n","description_html":"\u003cp\u003eGiven n (always odd), return output a that has concentric rings of the 1s and 0s  around the center point. Examples:\u003c/p\u003e\u003cpre\u003e Input  n = 3\r\n Output a is [ 1 1 1 \r\n               1 0 1\r\n               1 1 1 ]\u003c/pre\u003e\u003cpre\u003e Input  n = 5\r\n Output a is [    1     1     1     1     1\r\n                  1     0     0     0     1\r\n                  1     0     1     0     1\r\n                  1     0     0     0     1\r\n                  1     1     1     1     1]\u003c/pre\u003e","function_template":"function y = bullseye2(n)\r\n  y = n;\r\nend","test_suite":"%%\r\nn = 3;\r\na = [1     1     1;\r\n     1     0     1;\r\n     1     1     1;]\r\nassert(isequal(bullseye2(n),a));\r\n\r\n%%\r\nn = 5;\r\na = [1     1     1     1     1;\r\n     1     0     0     0     1;\r\n     1     0     1     0     1;\r\n     1     0     0     0     1;\r\n     1     1     1     1     1;]\r\nassert(isequal(bullseye2(n),a));\r\n\r\n%%\r\nn = 7;\r\na = [1     1     1     1     1     1     1;\r\n     1     0     0     0     0     0     1;\r\n     1     0     1     1     1     0     1;\r\n     1     0     1     0     1     0     1;\r\n     1     0     1     1     1     0     1;\r\n     1     0     0     0     0     0     1;\r\n     1     1     1     1     1     1     1];\r\nassert(isequal(bullseye2(n),a));\r\n\r\n%%\r\nn = 9;\r\na = [1     1     1     1     1     1     1     1     1;\r\n     1     0     0     0     0     0     0     0     1;\r\n     1     0     1     1     1     1     1     0     1;\r\n     1     0     1     0     0     0     1     0     1;\r\n     1     0     1     0     1     0     1     0     1;\r\n     1     0     1     0     0     0     1     0     1;\r\n     1     0     1     1     1     1     1     0     1;\r\n     1     0     0     0     0     0     0     0     1;\r\n     1     1     1     1     1     1     1     1     1];\r\nassert(isequal(bullseye2(n),a));","published":true,"deleted":false,"likes_count":2,"comments_count":2,"created_by":13514,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":161,"test_suite_updated_at":"2017-07-12T16:03:39.000Z","rescore_all_solutions":false,"group_id":31,"created_at":"2013-06-11T06:57:35.000Z","updated_at":"2026-05-07T19:38:11.000Z","published_at":"2013-06-11T06:57:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven n (always odd), return output a that has concentric rings of the 1s and 0s around the center point. Examples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input  n = 3\\n Output a is [ 1 1 1 \\n               1 0 1\\n               1 1 1 ]\\n\\n Input  n = 5\\n Output a is [    1     1     1     1     1\\n                  1     0     0     0     1\\n                  1     0     1     0     1\\n                  1     0     0     0     1\\n                  1     1     1     1     1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44339,"title":"Recaman Sequence - II","description":"Take an arbitrary starting point as input and create Recaman Sequence. Then find the 1, return its index.\r\n\r\nFor example: if n = 0 (default Recaman sequence)\r\n  \r\n  seq = [0 1 3 6 2];\r\n\r\n1 is in the second place. \r\n\r\nif n = 10;\r\n\r\n  seq = [10 9 7 4 8 3 9 2 10 1 11 22 34 21];\r\n\r\n1 is in the 10th place\r\n\r\n*Related Challenges :*\r\n\r\n# \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44338 Recaman Sequence - I\u003e\r\n# Recaman Sequence - II\r\n# \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44340 Recaman Sequence - III\u003e","description_html":"\u003cp\u003eTake an arbitrary starting point as input and create Recaman Sequence. Then find the 1, return its index.\u003c/p\u003e\u003cp\u003eFor example: if n = 0 (default Recaman sequence)\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eseq = [0 1 3 6 2];\r\n\u003c/pre\u003e\u003cp\u003e1 is in the second place.\u003c/p\u003e\u003cp\u003eif n = 10;\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eseq = [10 9 7 4 8 3 9 2 10 1 11 22 34 21];\r\n\u003c/pre\u003e\u003cp\u003e1 is in the 10th place\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelated Challenges :\u003c/b\u003e\u003c/p\u003e\u003col\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44338\"\u003eRecaman Sequence - I\u003c/a\u003e\u003c/li\u003e\u003cli\u003eRecaman Sequence - II\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44340\"\u003eRecaman Sequence - III\u003c/a\u003e\u003c/li\u003e\u003c/ol\u003e","function_template":"function y = RecamanII(startPoint)\r\n\r\nend","test_suite":"%%\r\nx = 0;\r\ny_correct = 2;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 1;\r\ny_correct = 1;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 3;\r\ny_correct = 4;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 90;\r\ny_correct = 35;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 123456;\r\ny_correct = 895;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 123456789;\r\ny_correct = 46633;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":281,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":34,"created_at":"2017-09-19T07:08:59.000Z","updated_at":"2026-04-07T13:57:31.000Z","published_at":"2017-10-16T01:45:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTake an arbitrary starting point as input and create Recaman Sequence. Then find the 1, return its index.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example: if n = 0 (default Recaman sequence)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq = [0 1 3 6 2];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 is in the second place.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eif n = 10;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq = [10 9 7 4 8 3 9 2 10 1 11 22 34 21];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 is in the 10th place\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44338\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44340\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - III\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44819,"title":"Relative pose in 2D: problem 1","description":"We consider a world reference frame denoted by {0} which has its x-axis pointing east and its y-axis pointing north.  There is a robot with an attached body-fixed coordinate frame {B} whose origin is in the centre of the robot, and whose x-axis points in the robot's forward direction.\r\n\r\n*With respect to the world frame origin* , the robot's centre is at a distance of 123m in the x-direction and -74.6m in the y-direction.  The car's heading angle is exactly SSW.\r\n\r\nWrite a 3x3 matrix homogeneous transformation matrix that expresses the pose of the robot frame {B} with respect to the world frame {O}.*","description_html":"\u003cp\u003eWe consider a world reference frame denoted by {0} which has its x-axis pointing east and its y-axis pointing north.  There is a robot with an attached body-fixed coordinate frame {B} whose origin is in the centre of the robot, and whose x-axis points in the robot's forward direction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWith respect to the world frame origin\u003c/b\u003e , the robot's centre is at a distance of 123m in the x-direction and -74.6m in the y-direction.  The car's heading angle is exactly SSW.\u003c/p\u003e\u003cp\u003eWrite a 3x3 matrix homogeneous transformation matrix that expresses the pose of the robot frame {B} with respect to the world frame {O}.*\u003c/p\u003e","function_template":"function T = user_function()\r\n  % return a 3x3 matrix describing the pose\r\n  T = [];\r\nend","test_suite":"%%\r\nT = user_function\r\n\r\nassert(all(size(T)==3), 'The matrix must be 3x3');\r\nassert(isreal(T), 'The matrix must be real, not complex');\r\n\r\n%% bottom row\r\nT = user_function\r\nassert(isequal(T(3,:), [0 0 1]), 'The bottom row of the homogeneous transformation matrix is not correct')\r\n\r\n%% x coordinate\r\nT = user_function\r\nassert(isequal(T(1,3),123), 'The representation of the x-coordinate is not correct')\r\n\r\n%% y coordinate\r\nT = user_function\r\nassert(isequal(T(2,3),-74.6), 'The representation of the y-coordinate is not correct')\r\n\r\n%% valid rotation matrix\r\nT = user_function\r\nR = T(1:2,1:2);\r\nassert( abs(det(R)-1) \u003c 0.01, 'The determinant of the rotation submatrix is not correct')\r\n\r\n%% correct rotation matrix\r\nT = user_function\r\nR = T(1:2,1:2);\r\nassert( abs(atan2d(R(2,1), R(1,1)) + 112.5) \u003c 0.1, 'The rotation matrix is not correct, check your calculation of the heading SSW and whether you are using radians or degrees')","published":true,"deleted":false,"likes_count":3,"comments_count":7,"created_by":13332,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":175,"test_suite_updated_at":"2019-01-20T07:53:15.000Z","rescore_all_solutions":false,"group_id":77,"created_at":"2019-01-06T07:56:12.000Z","updated_at":"2026-05-17T21:46:18.000Z","published_at":"2019-01-06T08:55:49.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe consider a world reference frame denoted by {0} which has its x-axis pointing east and its y-axis pointing north. There is a robot with an attached body-fixed coordinate frame {B} whose origin is in the centre of the robot, and whose x-axis points in the robot's forward direction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eWith respect to the world frame origin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e , the robot's centre is at a distance of 123m in the x-direction and -74.6m in the y-direction. The car's heading angle is exactly SSW.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a 3x3 matrix homogeneous transformation matrix that expresses the pose of the robot frame {B} with respect to the world frame {O}.*\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44340,"title":"Recaman Sequence - III","description":"I want to create a Recaman sequence where there is a \"1\" in the n-th position. So from which integer should I start the Recaman sequence? If there are more than one starting integer that generates a sequence with a 1 in the n-th position, return the lowest one.\r\nFor example if I want to place the digit 1 in the 7th place in the sequence then I should start the sequence from six as follow;\r\nseq = [6 5 3 6 2 7 1 8 16]\r\nYou can also start the sequence with 12 and obtain a series where there is a 1 in 7th position;\r\nseq = [12 11 9 6 2 7 1]\r\nRelated Challenges :\r\nRecaman Sequence - I\r\nRecaman Sequence - II\r\nRecaman Sequence - III","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 308.167px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 154.083px; transform-origin: 407px 154.083px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371.5px 8px; transform-origin: 371.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI want to create a Recaman sequence where there is a \"1\" in the n-th position. So from which integer should I start the Recaman sequence? If there are more than one starting integer that generates a sequence with a 1 in the n-th position, return the lowest one.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.5px 8px; transform-origin: 368.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example if I want to place the digit 1 in the 7th place in the sequence then I should start the sequence from six as follow;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 104px 8.5px; tab-size: 4; transform-origin: 104px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eseq = [6 5 3 6 2 7 1 8 16]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 294.5px 8px; transform-origin: 294.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can also start the sequence with 12 and obtain a series where there is a 1 in 7th position;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eseq = [12 11 9 6 2 7 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 72.5px 8px; transform-origin: 72.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges :\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44338\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRecaman Sequence - I\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44339\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRecaman Sequence - II\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 75px 8px; transform-origin: 75px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRecaman Sequence - III\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function startPoint = RecamanIII(onesplace)\r\n\r\nend","test_suite":"%%\r\nfiletext = fileread('RecamanIII.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'str2num'); \r\nassert(~illegal)\r\n\r\n%%\r\nx = 1;\r\ny_correct = 1;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 2;\r\ny_correct = 0;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 3;\r\ny_correct = 4;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 9;\r\ny_correct = 13;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 13;\r\ny_correct = 15;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 15;\r\ny_correct = 26;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 26;\r\ny_correct = 54;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 54;\r\ny_correct = 208;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 208;\r\ny_correct = 2485;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":12,"created_by":8703,"edited_by":223089,"edited_at":"2022-10-11T07:22:46.000Z","deleted_by":null,"deleted_at":null,"solvers_count":104,"test_suite_updated_at":"2022-10-11T07:22:46.000Z","rescore_all_solutions":false,"group_id":35,"created_at":"2017-09-19T07:36:19.000Z","updated_at":"2026-05-15T02:34:25.000Z","published_at":"2017-10-16T01:50:59.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI want to create a Recaman sequence where there is a \\\"1\\\" in the n-th position. So from which integer should I start the Recaman sequence? If there are more than one starting integer that generates a sequence with a 1 in the n-th position, return the lowest one.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example if I want to place the digit 1 in the 7th place in the sequence then I should start the sequence from six as follow;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq = [6 5 3 6 2 7 1 8 16]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can also start the sequence with 12 and obtain a series where there is a 1 in 7th position;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq = [12 11 9 6 2 7 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44338\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44339\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - III\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44238,"title":"Mastermind III: Solve in 1","description":"\u003chttps://en.wikipedia.org/wiki/Mastermind_(board_game) Mastermind\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\r\n\r\n  Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\r\n[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v) \r\n\r\nwhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\r\n\r\nChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\r\n\r\nTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\r\n\r\nFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Mastermind_(board_game)\"\u003eMastermind\u003c/a\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eAnswer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\u003c/pre\u003e\u003cp\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/p\u003e\u003cp\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\u003c/p\u003e\u003cp\u003eChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\u003c/p\u003e\u003cp\u003eTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\u003c/p\u003e\u003cp\u003eFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]\u003c/p\u003e","function_template":"function [guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\r\n% mguess and mpegs are kx4 and kx2 and will not be empty\r\n% The player gets only one guess\r\n guess=[1 1 1 2];\r\nend","test_suite":"%%\r\nglobal m mpc mc mpc5c v\r\ntic\r\nv=1111:6666;\r\nvL=length(v);\r\nm=zeros(vL,4);\r\nfor i=1:vL\r\n  vp=v(i);\r\n  for k=4:-1:1\r\n   m(i,k)=mod(vp,10);\r\n   vp=floor(vp/10);\r\n  end\r\nend\r\nmdel=sum((m==0)+(m\u003e6),2)\u003e0;\r\nm(mdel,:)=[];\r\nv=m*[1000;100;10;1];\r\nmL=size(m,1);\r\n\r\nmpc=zeros(mL); % 0.030\r\nfor j=1:mL\r\nmpc(:,j)=sum(m==repmat(m(j,:),mL,1),2);\r\nend\r\n\r\nmch=zeros(mL,6); % 0.038\r\nfor i=1:mL\r\n  for k=1:6\r\n   mch(i,k)=nnz(m(i,:)==k);\r\n  end\r\nend\r\n\r\nmc=zeros(mL); % 0.06\r\nfor j=1:mL\r\n  mc(:,j)=sum(min(mch,repmat(mch(j,:),mL,1)),2);\r\nend\r\nmc=mc-mpc; % remove mpc part\r\nmpc5c=5*mpc+mc;\r\nfprintf('Initialization %.3f\\n',toc)\r\n% finished initilaiztion calculation in less than 0.2 sec\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  1 0\r\n3 6 5 6  2 1\r\n6 6 5 5  4 0]; % case 940\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  2 0\r\n3 6 3 6  0 1\r\n6 4 4 5  3 0\r\n6 5 4 5  4 0]; % case 900\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 2\r\n4 4 1 5  3 0\r\n1 4 5 6  1 3\r\n6 4 1 5  4 0]; % case 850\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 2\r\n2 3 4 3  1 1\r\n2 1 3 5  0 3\r\n6 3 2 1  4 0]; % case 816\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 2\r\n2 3 4 3  1 1\r\n2 1 3 5  1 2\r\n2 4 5 1  0 2\r\n6 1 2 3  4 0]; % case 750\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 1\r\n4 2 5 6  0 2\r\n6 5 1 5  1 1\r\n5 5 3 2  4 0]; % case 700\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  2 0\r\n3 6 3 6  0 1\r\n6 4 4 5  1 0\r\n5 3 5 5  4 0]; % case 650\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 0\r\n1 1 3 4  0 2\r\n3 2 1 5  2 2\r\n3 5 1 2  1 3\r\n5 2 1 3  4 0]; % case 600\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  1 2\r\n4 3 2 5  2 1\r\n4 4 2 3  3 0\r\n4 6 2 3  4 0]; % case 550\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  1 3\r\n3 4 5 3  2 2\r\n4 3 5 3  4 0]; % case 500\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  0 1\r\n4 1 5 6  2 1\r\n4 5 5 1  1 1\r\n4 1 6 4  4 0]; % case 450\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 3\r\n1 4 4 5  0 2\r\n3 6 1 4  4 0]; % case 400\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 1\r\n1 3 1 4  1 3\r\n1 1 4 3  0 4\r\n3 4 1 1  4 0]; % case 350\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 2\r\n1 1 2 3  2 1\r\n1 4 1 5  0 1\r\n3 1 2 2  4 0]; % case 300\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  2 0\r\n1 3 5 5  1 1\r\n2 6 5 3  1 1\r\n2 5 4 5  4 0]; % case 250\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 1\r\n1 3 1 4  1 0\r\n1 5 2 6  0 1\r\n2 2 4 4  1 1\r\n2 3 3 2  4 0]; % case 200\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 2\r\n1 1 2 3  1 2\r\n1 1 1 4  3 0\r\n2 1 1 4  4 0]; % case 150\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  1 1\r\n3 2 5 3  0 2\r\n1 5 3 6  3 0\r\n1 5 3 5  4 0]; % case 100\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 1\r\n1 1 3 2  2 1\r\n4 1 1 5  0 1\r\n1 3 2 2  4 0]; % case 50\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 0\r\n1 1 3 4  2 1\r\n1 4 2 5  1 0\r\n1 1 1 3  4 0]; % case 1\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-06-18T19:30:49.000Z","updated_at":"2025-12-12T14:19:44.000Z","published_at":"2017-06-18T19:58:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Mastermind_(board_game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47073,"title":"Find the nth Fibbinary number","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 308.75px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 154.375px; transform-origin: 407px 154.375px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63.25px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.625px; text-align: left; transform-origin: 384px 31.625px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 255.117px 7.91667px; transform-origin: 255.117px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe numbers 0, 1, 2, 4, 5, 8, 9, and 10 form the first eight elements (i.e., elements \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABOUlEQVRYhe2VXbGDMBBGj4c4wEANREEV1AEO4qAW0ICEeLgWqgEL3Idkh21uEmiB2+lMzkweYMl+2T8CjUaj0Wh8iAvQAw6w8Z2Nz5czRT3wiOIWGIAfYI6rO0P4Fp0/AJPYZmX7V2Etfj9a+Kqc24y9W7G/jQGm6HgsfCNZmfibFUPoCVmeFxrSsURV2uSj3RcOPqpD9Su+npCoU8eCVYdziW0gX4qJMB1Vao4FTz4zRr1PSyF7qv2hU37N2HuWzKT1loPnIhS/1cmoid8IYzfyXJYh7pO9NfFSKYGQxll9aOJyLPMukUs3y0RsEV+tu0Sml+7eKXm/RWCzOISUO0KN0/926TKRkdot/g67G24vUo4UGbXcBB1G7SdTuqAOQ6ZBj5T0wqlRCx3LhXInTMShN1+j0fgefgFsKIIPBf1eagAAAABJRU5ErkJggg==\" alt=\"a_0\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.71667px 7.91667px; transform-origin: 9.71667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABJ0lEQVRYhe2WUY3DMAyGPw5lMAIlEARFMAZhUAajEAyFEA6lUAylsHuYc3Ozutmm5aaT8kl+aGz5d+y0KTQajUaj8SV6wAMj4GTNyXNfUzQCi4g7IAAzcBU71RA+S/IF6DLfVfn+VFiLXz4tPKjkbsd/KvjfpgNWSTwZMakrK9uuBFXUnhULHVWwdYqj+GNW9CKFucxm7PFtSLuOht+p4ka17kXYyhlKwlZiTaTcGU06P0MpULd8L9hz70w+b4vwbOyR+Jnb3Ca2YwnYXUKErYO7oVfiUartJHk6MGnnQewoccpnnYUH0s60TdzbtmbrR1wk7pnx/DJw263n8bv9ymWyYL81VUlfQf8NcU/FG6/ELPbSvD9BavnRK1iNdPFU+8NpNBr/gx+twmvaJTM4bAAAAABJRU5ErkJggg==\" alt=\"a_7\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 102.683px 7.91667px; transform-origin: 102.683px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) of the Fibbinary sequence. The name is a portmanteau that arose because of connections to Fibonacci numbers and binary numbers. The connection to Fibonacci numbers \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEEAAAAmCAYAAACbBvanAAAClklEQVRoge2YbZGEMAyGXw84wAAGVgEKcIADHGABDUjAAxbQgIW9H23Y0KVtUrr3MddnZudm99oS8vEmABQKhULh11EBmOzfu9QAxkxnHTwADImfVnB+BWAB0Hj+3zAbJgCb/R6zeUVmR1TWiCf7rHi/6dH+Tmt6wbkrwjfVOtfeIbu5zp6dlQfOTqgDa8noWMRmGMdJ2OyZi3A9YDJHer4IHo0tspYcFopYB3lUK8izy923Ix4MMTMzJOZdqskQG4xjJbTs2j7t8DEiHjQxvBQkghcytkM8UzgjXnqgpYbc5iBcD3wp3CGsE5wVutomsZ0Ve9z9qXsPuB5cGV9DXt8UGWkp0HquBzVMYNyPD8qkW/C25xpPbU7qaSoFqVjRet6Rarx0YkdcLLXXfIMr8xOm7dDgslgjnvZCEiirpAI34boj9ZAPRFTOms5ygivzjvNwNCOsE1cs0KUmzQcT+62DrsYb6ErwDYqET5hm6ERO4wQynmfagLSI3hHWIxK+dFqURmmc0LNrNzA3MQV3+NFOmwdcmX2j8uz53YfGCVRuG17inPo8kOwEHolcU5fGCVyLeIfSqry2LZ/go3JqGrpQz44JKR/QOpz1QVvbdFaSE6j9ZRk7LZRdsWjyAY0ctiBcmj6ow6nnBO55zZwvPTcmppT+vI754EQPcTSshewjh6rvgdL2jhj52BAuLz6guSnsasN0scZlRcI9PHAuBckrLQ0jwk+EfEBzr9s4tsX0gRwqnWgBuzj03jAHpNY+w/h7Td/+IbCf00M30X4rIxL7tpINN54ZPk0FY6AqTZUM+MDL1tw0MI7QtDvN2fuHzs5OC1MWOWuW2qb2feSP0iCfIyqYjvGnHEDkyoQq41mFQqHwf/kCrd8DzeCsuWcAAAAASUVORK5CYII=\" alt=\"F(k)\" style=\"width: 32.5px; height: 19px;\" width=\"32.5\" height=\"19\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 37.7167px 7.91667px; transform-origin: 37.7167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis that if the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/47058-determine-the-zeckendorf-expansion-of-a-number\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eZeckendorf expansion\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.71667px 7.91667px; transform-origin: 9.71667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.94167px 7.91667px; transform-origin: 8.94167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.25px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.625px; text-align: left; transform-origin: 384px 10.625px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.70833px 7.91667px; transform-origin: 9.70833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e     \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"n = F(k_1) + F(k_2) + \\ldots + F(k_m)\" style=\"width: 199px; height: 20px;\" width=\"199\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 7.91667px; transform-origin: 1.94167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 13.6167px 7.91667px; transform-origin: 13.6167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ethen\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 26px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 13px; text-align: left; transform-origin: 384px 13px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.70833px 7.91667px; transform-origin: 9.70833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e     \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"a_n = 2^{k_1-2}+2^{k_2-2}+\\ldots+2^{k_m-2}\" style=\"width: 192px; height: 26px;\" width=\"192\" height=\"26\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 7.91667px; transform-origin: 1.94167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 357.083px 7.91667px; transform-origin: 357.083px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe connection to binary numbers is that the binary representations of the Fibbinary numbers have no adjacent 1s. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.25px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.625px; text-align: left; transform-origin: 384px 10.625px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 42.7833px 7.91667px; transform-origin: 42.7833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABJ0lEQVRYhe2WUY3DMAyGPw5lMAIlEARFMAZhUAajEAyFEA6lUAylsHuYc3Ozutmm5aaT8kl+aGz5d+y0KTQajUaj8SV6wAMj4GTNyXNfUzQCi4g7IAAzcBU71RA+S/IF6DLfVfn+VFiLXz4tPKjkbsd/KvjfpgNWSTwZMakrK9uuBFXUnhULHVWwdYqj+GNW9CKFucxm7PFtSLuOht+p4ka17kXYyhlKwlZiTaTcGU06P0MpULd8L9hz70w+b4vwbOyR+Jnb3Ca2YwnYXUKErYO7oVfiUartJHk6MGnnQewoccpnnYUH0s60TdzbtmbrR1wk7pnx/DJw263n8bv9ymWyYL81VUlfQf8NcU/FG6/ELPbSvD9BavnRK1iNdPFU+8NpNBr/gx+twmvaJTM4bAAAAABJRU5ErkJggg==\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 148.975px 7.91667px; transform-origin: 148.975px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 10. The Zeckendorf expansion of 7 is 2+5, or \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"F(3)+F(5)\" style=\"width: 79.5px; height: 19px;\" width=\"79.5\" height=\"19\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 17.5px 7.91667px; transform-origin: 17.5px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.70833px 7.91667px; transform-origin: 9.70833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e     \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"10=2^{3-2}+2^{5-2} = 2+8\" style=\"width: 155px; height: 19.5px;\" width=\"155\" height=\"19.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 210.442px 7.91667px; transform-origin: 210.442px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAlso, the binary expansion of 10 is 1010, which has no adjacent 1s. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 83.1px 7.91667px; transform-origin: 83.1px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to find the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 65.7333px 7.91667px; transform-origin: 65.7333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth Fibbinary number. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Fibbinary(n)\r\n  y = f(n);\r\nend","test_suite":"%%\r\nn = 0;\r\ny_correct = 0;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 4;\r\ny_correct = 5;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 7;\r\ny_correct = 10;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 77;\r\ny_correct = 321;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 777;\r\ny_correct = 9282;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 5000;\r\ny_correct = 140562;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 7777;\r\ny_correct = 278597;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 77777;\r\ny_correct = 8455236;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 100000;\r\ny_correct = 9704021;\r\nassert(isequal(Fibbinary(n),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":2,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-25T02:19:28.000Z","updated_at":"2020-10-27T04:39:32.000Z","published_at":"2020-10-25T03:48:13.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe numbers 0, 1, 2, 4, 5, 8, 9, and 10 form the first eight elements (i.e., elements \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"a_0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"a_7\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_7\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) of the Fibbinary sequence. The name is a portmanteau that arose because of connections to Fibonacci numbers and binary numbers. The connection to Fibonacci numbers \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"F(k)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eF(k)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis that if the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/47058-determine-the-zeckendorf-expansion-of-a-number\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eZeckendorf expansion\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e     \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n = F(k_1) + F(k_2) + \\\\ldots + F(k_m)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en = F(k_1) + F(k_2) + \\\\ldots + F(k_m)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethen\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e     \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"a_n = 2^{k_1-2}+2^{k_2-2}+\\\\ldots+2^{k_m-2}\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_n = 2^{k_1-2}+2^{k_2-2}+\\\\ldots+2^{k_m-2}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe connection to binary numbers is that the binary representations of the Fibbinary numbers have no adjacent 1s. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_7\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e = 10. The Zeckendorf expansion of 7 is 2+5, or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"F(3)+F(5)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eF(3)+F(5)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e     \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"10=2^{3-2}+2^{5-2} = 2+8\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e10=2^{3-2}+2^{5-2} = 2+8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlso, the binary expansion of 10 is 1010, which has no adjacent 1s. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to find the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eth Fibbinary number. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":50629,"title":"Solve the snake puzzle","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 752px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 376px; transform-origin: 407px 376px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eMy son received this puzzle toy from grandma for his 7th birthday, but he's having a bit of trouble solving it. Can you help?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe toy is a \"twisting snake\" puzzle made of 27 blocks connected by hidden strings into sixteen segments, each either two or three blocks long. The snake's path must make a 90-degree turn at the end of each segment.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe object is to arrange the puzzle's blocks into a 3-by-3-by-3 cube.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThese pictures show the puzzle's configuration when laid out two-dimensionally and its final state when solved.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 518px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 259px; text-align: left; transform-origin: 384px 259px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eYour code should return a 27x3 array containing the path traversed by the snake when the puzzle is solved. Each row corresponds to one position, with [1 1 1] representing the back left lower corner of the cube and [3 3 3] representing the front right upper corner. For example, if the path begins in the front left lower corner and the first segment proceeds to the right, the first three rows of the array would be [3 1 1; 3 2 1; 3 3 1].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function moves = solve_snake\r\n    moves = ones(27,3);\r\nend","test_suite":"%%\r\n    result = solve_snake;\r\n    assert(isequal(sortrows(result), ...\r\n        [1 1 1;1 1 2;1 1 3;1 2 1;1 2 2;1 2 3;1 3 1;1 3 2;1 3 3; ...\r\n         2 1 1;2 1 2;2 1 3;2 2 1;2 2 2;2 2 3;2 3 1;2 3 2;2 3 3; ...\r\n         3 1 1;3 1 2;3 1 3;3 2 1;3 2 2;3 2 3;3 3 1;3 3 2;3 3 3]));\r\n    d = diff(result);\r\n    dd = diff(d);\r\n    assert(all(abs(sum(d,2))==1));\r\n    assert(~any(dd([1 5 8 12 14 19 21 23 25],:),'all') || ...\r\n        ~any(dd([1 3 5 7 12 14 18 21 25],:),'all'));\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":877713,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-03-01T15:12:16.000Z","updated_at":"2021-03-01T15:12:16.000Z","published_at":"2021-03-01T15:12:16.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMy son received this puzzle toy from grandma for his 7th birthday, but he's having a bit of trouble solving it. Can you help?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe toy is a \\\"twisting snake\\\" puzzle made of 27 blocks connected by hidden strings into sixteen segments, each either two or three blocks long. The snake's path must make a 90-degree turn at the end of each segment.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe object is to arrange the puzzle's blocks into a 3-by-3-by-3 cube.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThese pictures show the puzzle's configuration when laid out two-dimensionally and its final state when solved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"540\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"810\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour code should return a 27x3 array containing the path traversed by the snake when the puzzle is solved. Each row corresponds to one position, with [1 1 1] representing the back left lower corner of the cube and [3 3 3] representing the front right upper corner. For example, if the path begins in the front left lower corner and the first segment proceeds to the right, the first three rows of the array would be [3 1 1; 3 2 1; 3 3 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AUz1J5HtjGPbB6EEbepajF8PbGSeSCS/FyVWNlcRkMM8NnPGCeR6AYHWvGvEGsS+J/Fctylr5U12y7UEm4DChQMkDn5f1oSuNe77x6Z4r1R9f8ADDaVbwH7QyRCSRsgMwIJPTHUZ4Jz2rMs7fyLKBWYzFIwC7qOOMcZPXIP6d6FsSTFc4dPlJPAJ4xn074/pzxik85UYCWdYyw43vggkcn88n6Y96pDNS5w6MSMg8gkE9u3r09K888Qrc2FxJLZyukdwm18cj6GtFERhOH+zLMyEb1zz35x/StG08KX1/pbX8ajzGIZIR3X/E1HNYt6or6VbTw6oIJY3Rh1VgQQccV1+v6FrGleH7bU5rcR213J5Q3H950yOPQgHn29waiSvM3p1FGl5lC3ZCI3SyMdpEVSXyyRuPJ+ZuQCcHH074OfSIfFWo+ILlWuY/KgVgI4UzgE9M568cH6jAGatS10M6kVZNnQLt3qMrvXOTuGfcfqv51i+IPDh1Im/wBOzBqKrh88CUdMN6HGAD+HSqRiyrJG8c7yLsHAwnG5WPHHPXr+mc1nvbwTOY5hguMhzkA8Y688c9M5496yKC3hUNiGTz8Muz5ySM89T3x+nfvTop5LS9jkUjzo9hVMffYYIPQk8n2/XFIDyrxH4t1zWvLOoagZSrZRdiqBnjICgVueH9Ct4LWz1eaQvcyA7I9uQD68d8Dv+XQ1aXu3Ce/KjpVmlY4lYZTIVVHPTj9Afamh0BAAUluW+T77ccDj3GfwpIkczobZ5OhjX+HGTxgdB/nHavMdRu5pryTfI2AxwM9OaqIM9guXjkTDvnbxnGSDx36df51ianY+cpj8pmB4+7kemffGK1AyoLC2Fv8AYbhQ6ITsYdgeSAf1/wD1Vp6Dq6aTcLEB5kEOIpYu+B0I/L8awki0z2PS9N0eeKO+WzilJAZHYZDZGQcVFr1jpvill0e8uJMwSJO6xNgrwwUE4xzycdenrR1sZ63uYniax06z0210LRrVITHMLkjqehGWY5OT2z2UjPQViW3kWpWFQPMVA8jMBu253HHX1HGM4+nFFXb3N+0YvMwZSgKqdrDAJOcjHpgc/wD6q0UkVs7gnmE5G3+6Mdfpnp/9eqQHJSFj8jouQPlypB9c9e4zz/PBqEmFlOEVP4AN656dc+3X6fTNZDFEZYyIQ4AC7Y3yQOCc9AB/IZPpXnevaLqmo6xcPaWF3dWyBY1MUTOq/KDjIGOpP50mXCyep6Lcaho0vwyt9KW7t57h9Jjh8hRuaOQRgYYdFYMOh5yK4fw1p9za6R5d4rgMWYRg8qPf056/WmtjI3zZ/eZXZsMQcY3c4JBXJ9BzxnpTABdS+avEZXcAcjqM5HfOB0/lQMiugEtJuVO2MkAOdvfOOnrXlkpzIzDuSa0iJnuDrKMFuSMqW29Tk9D9B6VTMm4khXYqOecAscjHpx0/oeasDGvbcXDMnlhUxyp/Xjt/+r1rkruO40+6FwpbaxPLZOeehqWrjW52uifFG907Q002KyjLIDiZnJxn2/l1/Gn+FtT1Zbq41P7RL+9cBmIDbjnpz9cf5FSrFOKSfmbcF1Itwy3MxMjBi3mdTgZzyfx69qnnMvmeZFHJI0i+YrpheqMDyeARgDJ9T70El1SpWSK380zecoB2lsDaoI5x3B/I9q3Yv+PcP5W+ZW+4kgY8OejcdsjH/wCs0hHKSv8AaC4RiVwWDYxg5JAJycnnOSf60jKrQ7PMLl8K3mg53dAe/H4nOPrWRYkdvNdsILeIPI65cxsWznqTgH19efxq4fFmlaBZvp+qSva3MDEiLyHy6nkEHGOpI5x0981LBK7sjx2DWrVfHN3qYhlSO5mZ0BPILHOSB15rvp5vNCCPIj6oRgnk5I6/5yTVy6E2toMZZix2ZJyoCnkEHHUenv3/AAFIu/Zwx+VScHrjHOR2H+e1IChrLrHpd26k42MFHTuBn9K8zckk89e5q4Es91RTKQI5SwaRizBSCCSSevXAYD/JFMLbFJMQG0jYFJxnnrg5Hp9TVjM64InfYAu0A8JIDtJ5YcDv+X6Vm/2XFflMjKyoz7jjoNvIHHqeBzx+NFwKtv4Vt4p1LCY7iwMe0np05xxz65yK2wqWdp5VuAN6/M2zIUMAMHHHUE9D9Kkd7lvSm+13UkqjYq7VUO2TnqQPb73tyPStdLv7GxZ/OeSKFyq8hCB164ycnOff6YALFs0MTvElxIZXUMJXXG0kkbucfNkY5z0XnrnQt7iez2ABGGcu+CF3DAPuDkMSc8Y98VSQjjbPU4J9NS6I/donnBkOewJzyRx1P+FVRr1u13bWNmy6jqE7qvlRAuzkbueuAPcnGOenFYWL8zoNL8Vw+Elu4tftbmzkkRXiDR5Mm3PyjB9+vTjqOleb/EPxvpnibVra4t7S6iWODy8yquSdxPQEjue9Plb0HF8r5nsZ+leGW1CCHV5JEMGRti7sAT1PQdD+vIxXclzgpIF6425wSehPPJ4OeffgUPawm7u4GJUVGZlYuduFQ/IR3/z7Ujs8qtI2ATnaudpJ9Dxz+XGPwoEYfiO6jj0iaLzNxJwBjA5xwPfjrzXnfBznmtIkM9niljhmikkPnhNqK4bdu4yeemMtgZz2qxJ5f2gEyx+YUClvmypJBC/iGxn6HtVXGV7lYjK9tbsLdo24CjkDcQGGfUgcDHUetX4NJntUZUDFlI2tJgmJWPPoQMNwSRngDrikgKM0cnnyLLHJC+G/dlTkkfKNxI9QR7bMU2eMT3OZGMURwrKqkOyYzjB54z79R68oZFpzf6cIw2bfeMsFyGDDOe/qcegJrYthBP8ALiN5I4yTBKuxtxTgEnAwe+McYznPDQGzCnm2sM283CIgMcjcbnyc7ee5UHnnk0+5YLL5hI3BzgIfnUEhiB3zkZwDyN3XFUI8A0/V5/7NbSpZW8pHDJzjI9D6jODg9xXe/Cu5h07xnI9+DDFJZSRJK6kIGLI2CcYHCHrWD+I6HpTcTf8Ais48RLZW+nASizLmSUkKq7gODnHoPz/PzFPBd9eXCLNJFFGr7G2ks2TgDgeuRTTs7mX2bHd2+mpptqkEMO1IThiMlumMHoPU9s5+lWIkVhIImG142wG5zg8HpzwMfr05pCFd2feWZVYHLHcTjrkn07DHtis+8uxBprzwsksiKSmzIzkYz6ccgGmB5pfXt5csftDufRTxjmqXce9aJEHrDvMkkUrXLBnl81ULDttGASezHOenB6d9G3hnXzJFkIkaWPyjJuAfAXOeM42hx0PTgA0IZIrouEUp5KgbolXBTAz15GccEE5H0AqWXUb0QSWr3r+TctHG8SgMMqAF4wcfdxkkfcXuOC4WMu91K+vbi1a4nklmQHAA43nhmwvBJwcnqccgN0msLY3cSvcyF4VKYVnA3BioBbknBzjHA9D2pbsew7SyL+6Ro7WH93sMbBhlg7KHcnA6p0yOBjjpjWjuZoLa7vCwlu5oXlhwOMhQVIBz1+T8WA71QGjcLFJqVorXTxl3Z4h6KCrEH6FRj2LDrRYXTx27yfZ5AYtgdoyfnYr2UZI4x83sfpR1EfP1uIoNYt5dhWIuD16A+/sa9jBEg813GG2kHBYOB8x6DC85Ocduaxex01lao7DLpnaUFRHO74BbHEnTK8AY7fn+ddwpk3MjB0wMgHa2WGfxBPPb8s0jJlh7Z23Eblk4w2Dwdx7Hg5x09sZz1YI08kqkgVAfkUOPunORk8g/N0AoELcopQqvO8hiGB+Xv6njH8we5qudqt9njYvuAYE5wc8E98DJ5Pf8aYjjvGsUEKwmKBI2LEMEUgDge/8A+rPtXG8ZNax2Ie56tZzIHhWRwLgrt80khvvYbGTjGc4wOeTnirUNx5lrNHLI6rGyjAX5lTG4BlIzjODx7/SkMuxSsyIjOkSx/KpiA2sw2+nB+YluvYduKqXLW7RtbbZVVvkb95jGeeBnB7Y/rnljKCONjXNuZIohJtUs2MnIbAHbkj8Mds1twGOAo4i8ktKGWRyQVB+UA5POCcHt8pOAQBQA+y+027zG9LA26KTIJQUKFgcc56kKccd+9X7ZZWuI4s58wB5Ztpyy9FBypwCxP0CnmmIsafeW9vEZly0AX5XIzmNRwc9z827PHXHWnwQzo0ls0YdSYy0rkBVYBA5z1GRu7ZySe9ID55uQBbR4GODXsGksVtF2kjbGm3B6cjpWUtjrr/EvQfbswtLZwSGKyEtnn74H8uKsXYDMNwzgMRn18ykjFCI7FLJixLSW+9yT95sHk+p96jbi0wOmxjj34pkjQq/Z87RkKuDjpllz/M1VuGKas+0kfIOn1b/AflQI898Tu7XcQZmIUYAJ6DArA/z/ADrVbEM9aVE/stZdi+ZhfnxzyDnn3yfzrFVmaSRSxK7jwT7NSYzZsSZYrh5CXcW8Shm5OCvI+hwPyFadtBE+jO7xIzC1JDFQT/q//rn86fUCmztLq3lSMXje+t9yMchsxgHI+nFXbhF82Fdo2iSLAx63TZ/OmBVviR4TvJB999QdXbuwBJAJ7jIH5V0kKr5mmjaMMIyeOpwR/Lil1GSan+7sCkfyKttMoVeAAIwQPzq/KzAbgTuxcDOeeHOP5D8hTYj/2f/tACxQaG90b3Nob3AgMy4wADhCSU0EJQAAAAAAENQdjNmPALIE6YAJmOz4Qn7/2wBDAAIBAQIBAQICAgICAgICAwUDAwMDAwYEBAMFBwYHBwcGBwcICQsJCAgKCAcHCg0KCgsMDAwMBwkODw0MDgsMDAz/2wBDAQICAgMDAwYDAwYMCAcIDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAARCAIcAyoDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwC7qEdx4gnuJ7xpHa1AK7Wy4A67uo9qztD0tvGNzHo8cktnfWckl0smUXzkAJOA2Bx1yD9M1asvHGpX0aWVu0a6TDEi3rQugn8l2/hJXJJOOM5qjdeJrOz8WwwzbbfdMtvFJLwsJJwSzY5bpnGAee1fLuPU+kMvSk1C9j1eSOaO1MLMjlj8wIPLMPQ7SePUVc1jxPp+uaZFcRxzLrhiUK6KFBVQfmyMZByTnr29K3tZit7jXL6K4hkkaHEJvVy0UhKbgX6bl2YHHTpXL6XoM2geD7y9m02ZrexsPNW2S5WKcqGPGSAVBHG0ckDFUmUlYydF0az8RaDqly+pQ2usQuJfOuDshAJw+T22nGc/0qLVPCEfhTXvLmvo9SvWiQG5i5SNSCWj3Ec4IU5HHT1rrfh9D/b32i8tbaOws44GuQGURlvMPESjBO5ccnJJ3ckVV1fSbfW3uluvs8VxalQIin+qUqSSSSBnkDPOc9PSdijAgsbqP7O8MUdvN5peMkY45xg9Du6YArV8P2N2LG3a6gvJLtjM4uJU2wrGSNqjofvDnjkGtTRNIfTIRfyXDapDebI1slhJ2opDKxfJO8MAdoUdFHStPwhLFrEF3a3Vx9olVpWtYnI81QzZAbuoXt6VcSJHMbbjTobhoWZtWiZjdeannRyKTlQpHHOM89OmO9ZGsRtdQR6bY3l5dXV9OA8Em4RaZEF8x2Yk4zxnb1pNVOrav4jezsYZLU6a32hRJnbKp4G5f4uamvpnu/D0N9ZpHd3TIHEi5Gxv7o/ixVxlYRqaN4gt449QtLaGxjX7LHHO3kszu/VXwT1P3sDpVVRDaapp9lJdbby+kN1HeKrShkUAbPTaCOR7GsjSrjXtd1S7uLizaK7nyRbxwFppJQuEZcAZJPGOSRW74V0qZvDDzX8d1a3KtmW3KbV+8dyDtgnkjv6VXtAMDxZ4obStP1S5jWzutWt7j7PbxiQrDI7ADkjJ4BPp+HWu58NaTNrq3V9eWMrXklsJUjhUOs/Yx/L0OduWPXAz0rn9VtrW4llvprOzhWB1uwjD91EuQvPHBz39x6V1WofEGK18AxaqY4LGC+ENqLbTzvnTYzbipHzMzZ546Adc1ondXA43VdTu1EdpqAt5pGUqC0bBdxbdtYEkZC5GcDge/JqUVw/ihr2HVLrTWnkhWLT1mQW8QDZHlgruGTy2GIIxxTfE9jeXetX2oXDC1sZGj+wC5PDDac+ZhvlPGf8AOKk0G0s/ENxdah9juFWQLFFcR5kFrgEsy5/hYYGcnFTy63AmsfDreLbd447qazub24aUxL8wllbaDzn5clRzjvVWzjbTb2TSdSuFgsYbpIpYY5AY7h8E7SvU8LycY/lWkNMt/wDhFrvU9FabzY1eCN2JjWVjtC7WByCCc5HTtWLd/DO48MXllp99FHqWsXBaXzVI8u7fli4Y5yoXuTk4JqgNzUtMWCKbS4rj95q0AvYD8vKRtkjPOGKZwMjOe9bGnW1tqv2e8hAt9B1LUvtbRyhfMR8DK54+YeveuZ0/xDaHTGtZrxY9JtWMklnIzSR71B27VVhuYkBecgAnsTXQXWrX1rpOn3MQW3W88u/aHKMrRkKwRl7MAce3agC9bG8sdcuUELNHbyFYUWUIzqQcBiTjk88nvXM654Yt9SsbG4jYLPfXbefA0LM9js5D4UbTlumCT1OMYz1ttr2i/ExL6zvL1NNuLIoV+Zk81z8ysrHhscr7bcVgeNda+x6RbKmoNcvK4AZE8xmHACnr824E59zx1yXYGTqN9rNj400vw3DeXl5Hh5JZrgqDJEvRARghTzkbRnH5bFnocrXbxhmsWjSMRhN3lDBO4lW6DJAz1/OsXVdVh8LWi3V4WbUNUnSG3mih3SW0i52hOCyli/zngfKtVNcuNYhUSAzNqkxaK+WYEmWNWyoH8Kg+g5OM+tAEuraRqEurFP7Ut7xpArvcQI2xXzjZ9enQnrRZaXbaHerb30duZriIiHEyFvOPUlfvL82OTxmtAa7nS4mutGgs44btJA0UDM0TxjlFOcbWyOB0544rnY/GF83iG8uoLWa3luXZGjMf3Y2PPPAXjn2oAu6J4Rt/Ds0F1Isck7xFXSWMGSL5vulGH3e+e45pPGmps9tmCa1vrYSK6Tq8kaxspz93cDg9OSetWYIZIdEuriC7sWjeTchKt9ofjacleoHoa5bVLu8TV4RHC1rMzhn+b5NoB4dSDu+poArn7LqUbXF00ckU8u+R2BXc7bi+OoOSTya19SvptBubeHTZILe3ukLSmVVkCKAOnI5JA71LcaC2saYu14bW6uIDN5pKNG5DbcBSMqQeMDrzXM6jfyJ4LtoJrWxkv0uLi1MSOJfPY8o3lgDAChhx6H3oFy63Lltb2+qXlzqVxcQ7o5AN6srMrkDpGRn5gQMjI/KsnxZp9z4kt0G4wRWJkkcOD5iKu3oB0OfWui8OambXS4Y7i2eaKbaJUhlEJDfdUJJjK4xkjHPSprfw/aQx30Mdv9pa4mcTpNMWMgfOSScgAc8+9AzmfDd5eWvg+yuHuIGm3hIgsClVXJyxLZ46Z98elaWpbbWyjhvrrzg8skbEjDAZyAuDjbnB6dDj1qpFLp1jodro+nvcRxoxhjMw3RNjJK8nJ57j0qeeG21C8jtYYY4po1/fgLkSdBuDehPagDakiXVdGjs7W32w2JaRId2TMVwQc987cY965/U7c63KLqG3uIrBW3SoMKsR79a1NbvfsSaXBF5NjGz7ZJn4XG4blwBnGM5Izj0PSo7V2hvLvTra52xZIhSSQFJDnO7b0+mRmgCFLcSaS8M03763l3rIPutHjoT9Tmp7LSLfX9Pk+1tDGrTrboWl2oisCCTjOeAMduKnXX7SbQpI2Fr51xO8bC1Ecs8kYwN/qAw6nvnvV7QNMXTLBl09oYbC5iHnxyRhWlx0AX+8Nxzxx70CSscdH4EGj6o1vH9luLFswTXILRyK3JUqfunAGM8deela+qWE2kRRzNHcbbdVUqr7945OOuM8n3q4dPj0Z7W4t7WZ45pxuikGVIGOE689eo45rQ1+ZhJeW9tHCsPmbrT518uMt97lV4PPTHPSgZy+uTN4rjlvLzyoLd3WOJCd0ioFGT8vQH0PJxTtOluNX0pomt5Gs9PAhVlbZMEHJJX+L8OasalZwz2lm1l/pEkMbubWCzEOdpJZmCjJwADuPY9qk0jXhqltBPa24tVjQXFyrHbIzZwVGfXgjFBNyxrOm2+oQQpZyLLbzqRHsjZUVcckj14PXPauf0y3t7c3cFuqveSFlglX5kKgkOCOoxxycgjOM810c9p/bWlC8sbe1khbMLCVd6wZG0hVBxk7hyB1o1C5t9PuPs8Mc0d0kQEUigw+T8oBwB6EnnOMcigkyNSikkMdrY+cs2mo1y1wUEaw5YZDMOGOfmGRn8OK5fVfGd2bWQG3WS6upRKksgAEgBPzdc5OOmK6q7tptF0uS9aSRLWOTzSX3KQ2DhmH8j1q54m0iG3e1tdNgi+0AAmC4zD9mJXnBwW4Xt6nHegiUW9jy/Q/iBNrSXfm2Mv2z5UhiQfLIOck/wCyPeuw0dZLjzt8zMzP+8n+XahxjaFCjIAwv4d6sX/gGz8PaW9xElx9qyV86PAWVAcY2gHb0PzZyc1paVql7p/huK8gtb7c3Cl4WaIKGOfmK/MCcg4PXNBZTuNN/wCEVuvtF03yXMIgaVTkx/3XXPA5yOBnkc1raVrUEk8O25MclvEArMNzSNg43H9axNa1ADasCwxTvGJpIc/udufungdwaZplqILKa/WS3MaSb5FSMQrCWYgBXzlvu0AfWv8AwSr8Yf8ACH/G3xpq3mTXF7D4JuQQ8gOxX1jSEAxj/azX3H4X8W5025vJtxmLbs5/Ovzx/wCCZWlrafF/x/JcTL5dx4Gdzt4x/wAT3RcEr6n196+7NUsV0/4fTSW87ecQdoDYye1fQ5XL918z4zPv96XovzZ6qfF0HiLw4LfcpkkiwAGHHFeaeEfix/whHiy40nUJlW3jO5XcY5rmfhjr1xpGh6hqV9O7fZ8rGm4kn8f0ry34geNJvHfixZ2Tyo4znCt9OtevHEcrTR5XKnue9eKvi+3izUzDDJtts4U4++P8Olangjx00Un2frGvBKjpXz7D45sfC2nNdXlxGrKojjQtnb9K5bX/ANuW18IytZ6XpeoajqEu5VdNyR+uc/jW1DF3qqUmVChKWx9h698VdP0S3aS6u0iVV7nr7Cudtv2hdDuP+Pa481v4gR0718G6j8fvFHiC+uL++sb2SRm/dQlmMcY9ADx681j658evGVvo839k6atnPKpTzZRwnvjHPevpIZhgqdH35XZUcDVctj9AvF37b/g34VaBLd65rFrpqQ53LI4DEgZHHfp0r5D+Pv8AwWRu/i9q/wDYHwz3fZ5f3dxqe0gKM4yinBzx196/Oz4t+HPF/jPXnutbvtS1J2bLCV3Kg+ynj/8AXXpX7MPgKTSriK4uVMexfl3dTxXyOYZopNqmj2sHgmmk2foH8CPFUlnoMZe7kupslnllO53c8kn8TXuVr4XsfHfhaS11aDzrO6UBh/F06j0r5M+H/ilJIre20/bbupBZifvHP9a+k/hF8QI9Vjj0+8Zo5FwAw+6fQ18nUqe9dbn0FGnaJ8x/tJ/syyfCDxb9r0WBm8OupZyBtVCMZz69TXG+FPEdrcanDJCytGpHzDgY/Gv0K8ReA9O8a6JeaTdeXdwXCFcnDYPrXwj8aPgv/wAM/wDj+6tfLlOkyNujndcLuYnj26VpGpzLXcUI8ux2V3r9rNZRyLMu4c7Qeetb2h+JYxp+/ccqeMn2ryHwzqUEu7dNG3JAUtnjJ7V1lpqyzXEawsskcI3MgP3u1cstzsgelaf/AMTqKR0kG5SBwOn+cVq3WvQaLYqkLZvM855IPvXFeFPFUOjXzKzKWbBdR2644/rVy61pXuzLk7pCSuRyfaueW5Z3/hXxZNahPNb5Wx8w6HgjNaeuas120YVg3mH73YivM7nxh5el/ZVj8pm4bC4z+NdFofiKO80JYmkkW4H3Q2cjH+NSB11lebZI2xu2jn3q1NpcZ1aa8jUL5q4b8OlcPc+LJtCvbXKySRO3zg56fWuutb97+BZoZi0JOSPMPX+ooA2NP1aZHaNcLuJzx2rQtbi1mQr83nL85ye/biufstUjttQ3Y3r3z1B5q1cz+bqSXFsVxkblBxngdRQAeKtHS8ubPUYyyXUJAJXrXYTytLYW/mbTwAGHU/WufE63Uuw7fMIztP8AFUt/qkiQxrIWXkqqg9PwrSMrIUtjT1q3heONmf5lO4EHoadMjLYfaY/nONwA6n/PNZtwP9Gbc3+s55OcHp/KtDT2+x6YsbfvQVGQTyOOxpqXQgjh1lLi23YO8jkHqKdeTrDGoJzv547Vm60kj+VNabS2ecL98DsR6+9PtZ1u0U3G6Fl4JB4qwFgmU3Kpt2tGevqDVyWX+ytQVlXzF7gehqjBF9vvm3sytyUfOO/cVrQwrFP++btnPWg0KoYXAWOP92vdW/h5zUlrqMejO3nMNuTh+xqnf3atesq5yDgFe9SNp/2zRCtx8wycHPK+4PtWfJqATaxGL+Xn5mT5Tng5psqXmlJDceYu3dvAHQ+xqjHBBp1o0c7boiw2sW+ZT6571FqviGbRbq1t2ha6hmYEOORt9Kh6MDqbjy9Qs2bdHukTJHoev+NY8Df2fYRoxO9CcSD0/wA/pimxn/iYSXJbZbOpxH6HHP8A+qjUbpbYwyeWJLWb5WweU44P+Nap3QDtUmW4gwjb93O4VmI66QI4zuaSRmMTD7oPX/D8qs3SxeGLxBJMy29w+ArdVz0qvfny9V+zyFVhlw8DOf4v7uan2YFvT5Gu7dluE8uRyWHq/wDnNUvEMEYurZGUOvX/AHTWfrur3V8un3dr5sE2ny+VcxE/LIoyMjse1aHi5mFtb3UcYG4fOF6YPNZgSSyg6ZtZdy5xheOMVg6jZyRan9nkVZrOSIrtx94eh96tDXZEubaFYd0OdrsT8w9KfrEcfh/V7V5nkkguMgg/ez2I+npQ9Ro5SfwhK+nzWku77C0mYPNG5kPbn0rB/s6eHxNDHHL5M8ClHY42kc9f5V3/AIgivNcgeKNhCFT92c9T2yK5zxXp8Ojf6UVZLoW+JFz8svHUHvUOOg+Yr6KIZdOh3HybrT7g/PHx8ueg9q6DUtmlma4ldZYpnG0r0bPPI61ivYKNMj1KPy47efb5kPQseB8o7H6VNa6iv9qm3K7rFxlhKOn/ANesvZsk0NVs/wCxNQa63G6tGj4J+8gbHWquqNDeN5LbZROm5Bn/AFmeMg1c1nbdazIturPFJbYBDZUfUVl6forahozW4urXzre4/cThfmh4yUJ6gZziqjGw0zlfE/giEHy7qEyKq4Vzz17H2rzG6+Clu91I3735nJ4x617kFlurAx3Ufk3AyrCRsq5XuCfUc1zstt+8b94/U9zVCPlJYZH0e8MLxxzfNM4iVR8mcjJ+tZ19dfbNCvdb1SS4t7a4t0A+yQo85Q8CWPcCpZuf++vpW54H8L2/ii0ez1ORdLmhuZszTDZC0QUEDA6knI6+n419R8ELH4Tt9Kkjjs7OG4imV0XcwiXkwJ279weT6U1dmkkkXPDfia6i0pGuIFhYyqEhYruMSpkdeC2ME5HrWg1/L8RIopFVhdNb7pVcoqzeXnCHA3HO7dnIXsBkHNefU9P0hLe309JLq+jiK3Uig+SFbJYKOxwQM9DzVnwlbWviHw7qy22qNpM00KyAIfmZQWySB83BxjGBnOc00iXIvaQ8+t6NFNI1rZ6bp8Mv+rRnWYLgEHGWBBGPmODn2rEurtrNVupoEhOrQM8MLqrssY+RnOQQCcnGefm4rc+H159n8I3WiXkeE06M3CzX1w0iai+SQ8ke7c7ljk5O3p8o6VDPBPceGo5b6G1vDNfC/SbzPmutiMijYMFUUtwuQPlX3zRN2R6fHKdVhazT7Fb+e0cxJ3fZ49m4c8/NuC89f5VT8f8AhZrv7BeQ31zbm1RrlrgqVMqIRkNtxknrx1x17Va8KTWumSXlxqz6lafbJ9yhEDIQwwhbn+9gk8gjoAabrXiCPxbqbW13MNQt7NYWjlhTYI9h+fdkg7cf3eevXigDHttbubV01i3ZxN8trb28qCSRlLfe59G5wSeDW/qW3WtSsV02GDzNL8m3uYzGIlkEnIXd7joQetVLG+HifXLFpbeRLdZzbEwwkl4ugKA45OMbjkVHH4Y1HQ9YvFFv5sN7eCExwv5hG1R5ePmHHTBx601FvYCPXdM1HTL21u7a3snkmd44YpgA9rISQ/zgbpDzgZOARmqMF/ZroV8thGsklvGYi8s0ieWdx+YHkH+tanxG8M6hY2N9JcPmCMRrDG5+dDkhgo9Tjr71zfgmea41FtH+2CG4u/LuTYrEfsyHg7SOW75xnrWui3Al8b6HY6j4QudCuLh7XU5rtYzPGrx74BtYKdwX+IHnjrWrplrDpF8tnq0NvCtrG8NwsJzMkm0YlU5OCck4BIrk9W8Qr4k1O7ktv9N8uVo5LqSORgsqn5gNxJ6Ec1T0/wCIrah4il0ueOa71DyRFaPEd6FFU+dJKxPDKu0jpk54OBUykraAbyaWfE6zabdM01npLefDDeDc05YYwDj7xA3DccYDdKsvd2N54RutWsYZooLWU26IiAeXwFIO0lWxk9M9PrVuLQLLxAlxptrqV639oFXJeceZdIqnKkEdASBxg9Kh0bVIPDpOixabHZWFmRCqxMIkgUdXA6sxJOfqeKqOwFGHU5vC/hdbixKzWsmIRCIAy3D44XcSMEscDGB3PFT6gJdNvrZo7WObUNPtzELoviGKSQfMf7v3OBxgDOO5OraeELHxTo0ccOpTW628yXs6CNo2WJRkFNylclh16jPI9Ib3xdpkdhqE00KrDcOEkkmkPl2wQjDdf4vQDnPYVQGLoHhKy1Xw5oxME1leXDMt5IrjyAfMwsgZz93ZwRj3re1Hw/OJLiCO4ltPswIZgfNSSNeFCN0KnHUDp09KxrrQ7jXls7fT7q0uNLjkHnXCBoF2P0URtycE4zjGfQV7D8KfhdefEu6bwzoun63q3iWOAu0Gn6cZd8A+4WkbES/N3YgcdKpRb2FKSW55rqvhm88L6HZ6hMzXF1qBkdILf5hbAS4EbcHB24J9CxrE1Vby98KX00S/Yplk+z+fGF4HBYkMeoUnPHQg5r1/xN+wX8Vfgx4fuNS8YeD/ABVZaVYwOqXVu1tdbkcqT5rQO207icFwFUVz+g/sjePPj94b07VvCPw71zWI7Ii1v9RtGic/akwZIsllBAV1YHb1OMnBAfs59iPbQte5xvjNI5dSsjYt5kVurKxCiWa0Rh8oIyG+fAbPJIUZ9Km1jy9f0n7HcyzafDa5mn+bLmUJtRTgcfK8nc816Nff8E9PjXq2oasIvhn4+tbUXCCBcwLJcIoPzFhJwcH6c+lcH8U/BuvfCn4s2PheXQ77TdYuTFp8+jPGJJknmMbRqwUkGSQMhB44ak4yW6Eq0HszkYYY38E28MdneJ5d0ys7XBYGLO4BueGJ646A9ccVdl12x8b6aLSwuZIp7dPMkVgD0IUlMf3cZ565FdH8Tvgz4g+FGuDw54q0m68K3l4IrtNPu4As90HfYHGGK9R1BAyp4rS+Fn7LPjT4l3WqXHgfwr4i1xNIZRcyaayZtpTnbkMRuJAPyjg4NDi07B7aNrnES2kiX9jatcQzXsj5hjjG1GQDcUdl4UkA/wC1zwaxviJ4ht9caaSztk05rVwZp0ZpfMQN/DuJAbjoK921z/gnr8YLG0uL2b4c+N57WO5SWS1+yrI9wmwbziMk57YA6mvMfCf7NPxA+L2o6tZ+AfCuveI20lPMubWKzVWiWVjwQxXb88bjBXPymq9nJu1hOtC10zjdUmk2QRvbSW8kMbG3kuYVE0iyEncSDgZznHO3OO1R+FNPutP0uaPUJGLM29I/s6maRiM435ztIxjjsB617Tp//BPj43m90e91H4P+NMxOsklnFFFLFExYkkDfgA5BKnIHI4FcZ42+GXiT4WfES78M+MvBOueDdTvHNxp8mpWyRrq0aBfMaH94zeWHJG47eRgDBFDpySu0EKkW9GY11rZsdPs0sURIYYgrpdqDcIxOSY0xg5zjk9zgU8T/AG/W9P0y3hfbcOY5XIwsWQNxYZGc9s1m+bFc+LG2SRyRmIjyGG4wsvUttHf29K6nUbmPTdOtbhglnI84iMRJUQhto83BHzBs5xnjAz1rMtHKeKfDNvpmvyXesKslrDuhj2FY1dthxjaeCGAOBjIBBzzTtL0RreK2kWRpvs8WXhjxsRSpI3ueSRnOBnpW14b1S38Oa3NLqkMt8sRJtrZo/wBxLwRlnA4ZssV6AANkEkVhxQxa5cLd2d0kKKTAS8hVYpC3U5+8DkLz65oGHxA0K3j8DwxLqV1Hqt5F5sE0UKMIpNvylA2QNp5O8YbHbrXPeGFsfEPiOO41ESNFFGqPLbgvIjFcbmwDwRz8o79K62FEuNKubK/mhjjteBcRn52Izlldjt4+lcxZ6fFd2EUdm32lWuPmuHJaViC3QrxwMd6CjQ0m1uNHup5rdYfsjRCK1LIhfYeqhcdeBnA4wecVXvpH0zS0uGmjWa4meSU42EOrf8s8ZIzjPT+ddD4H02Sz8VTosxj84PBbRxn54SxGSM8cpnr0qCOyuLeW8SEQzwC4fYk8yqOAeoYkkY5yuPrQBleNriS/04tp91cCK3t2MDRsFdHODuORz1OSBms5dej0bSNKguJJZpLnMYkEX7p5PfJ/UAdMdRS3fhm+0u7hS0m4Yk71m3wkMxLDPpk4/CptZ1O613SkuPs8J/s9kVkK7fs7ZPABOCp4OfUmgDptBudLaK3vLWSSx1AztDPKYwpk2xnonIKkgKM4zu71V0kf21aRtbxwzK105+YgSSMFwSPRV5GM/h6ZVrdTXlxDeI0cn2iMiSF48BFGAMkEZxliMYxx1zWtFqkOjz2Ni0cWmzM7u1wvySyAK2QOdoyehIyf5guhQtdWazvbhfmysIgmhjbg5Od7D15HIweKj8RWElqY7p22QeYRHiNpZJAOTgDgZx1PX1qK5vLfxHol5LpJXz1Ky3LNE0j4yM5PToO5+lJpviyPSdUuri1hjh0h7eNELKeJVzlkIPOSce+aBW0G6t4rfUPBM1rNcNbrJKrszTht4ycbkI27iMHlh1q14f1O+k26hfPPc6osrhrmUv8AveTnfngEj0FYfiPw0t1Ooe0h1S1Y7o4b0ExyP2b5ewIrS1fWfsGpyQqZGifECEKUTPfKZLY+rUEmgby3luvLk3puj88u5G1QCfVj+gAq3a3nm2JkheKG3mzHE06kojE5JAP8JJJ+XGCfrWfq95/aPhbT9T2rG1jcG3Y7QgnQHHlleW28eoye4q/BZ2KW0d7NdXNxbrmSGEH5oSMqoIAJx06+/QcUAc7fXFvcN5c0sd807NHGEJUg4+Y7ccr7H8q5vw7b6vo15Z28Ww2wUxBCpbIBAUbSMdT356+9dVdB5vFHll1haSAlkjBEbPzjJPbn1Fc/bD7X4tsoZPs6tppZzHKxaMElcOPLI3FccDJxz1oA+mP2BJobXxF8R3ma1hvofCGx4ww3bTrui4Prjsc9yK+hvEnx1tNB0fyZJBcXAXCKjcKffFfNH7IOnN4j1/4pWbXRkabwhG3nxbVbYNf0TPI5z9c17fp3gPSdKicpaxPJIgVpWG58emTx/ntXoYXGRpw5TxMdg1Wr8z7D734vXWreFTb6RDJdSzMWPHyisbRPhn4m1m2uJtTvFskk4RIgNx689O39a9A8HaXa6fYTSLaqvljcvHsK0oryRy0jAquCoVs7R/8AXpzxknsKnllOKOZ8MfACxt7WKa9+03swbYWuHOGOOoUHHv071P4b+DOnjxJNcPZxqsY2KQpGPbg16DoVot3FH5zOfJXI+bg9OtW7Szjhlmbdti3bj0xWUcVJbs7o4NLZHEa98K7NGjW3hj7EjJ5PPvVHxJ8LYRpO6S3jUspXJHUf5716RY2Eep3f2hWLRqfl5Hana/ZreBIiCQc4pPEtm0aNkfMvjH9nvTzbsy2+XkG7ntxXmGvfDxvC98Ps4Cxq2MH19q+u/EPh/wAwvt3DapU5xj+VeV/EPwit1Ds2/NnJKkZrlqVm2aRpxXQ8t8J3cml3Ucka7WyMc56Y7GvZPAHxL1K2kjklhWO34XJUBsCvGdZsH0W/2x/eUjAf7p6emK7DwJeSarcRnUbq3WFQEEathcfjk1jKTZofYvgvxhHBY281rG9xDKAVbr1/wwaf8e/gfp/7Qngb7FcKkd0F/dtkqQeeOD715v8AC34i2vhueHT2dfsLE7WPLMT3z0/SvX/Bl9PLrDNDua0blXPatYz7Ezhpofml8Vfh1qXwD+I0ml3Vw6tExCHgqV75JzzzTtJ8bTabrFvMXkkj3AEKeo6197ftefst6L8Y/CtxqAiWPVIYWk8wAfO2OOgyfzr8z/EGi6l4E8QTaXeRslxG+1Ax6gdx9cfpW2kl7pnGXLKzPpKy1HT9ZnTVLeUpMqHdET1BHenr4z8zUI40UeWnJyoJHavFPDHjOfSUU3BCHbtVjxjNesaBo0b6FDeCRZGkTLbfw/KsJxtubxlc2PEPiNrp1Vfk6HOByK1k8TfYJYZC26TAw2AMY74riTrNtbiUMyzKwyhAwykU608TfbdPczDdHGw2sRyorAs9WTxYdQtEO7dt5HA64+ldVZzTacV2lktyOA2Dn/D8K8b0nW1vlhRJPLDLkgnnNdrH4/tbKLy5XVZG4UOfu1PMNK5373sf2d7i1Ys2OYznk/4Gjwpr0kF7tKMrNwd36CvP4fHUNtHIDLtLEkN/d9xXWeG9VivlVlkyHABI7HHJ/HGadwsd5MkU80d1uaNoRkrn7vqK05oo72GNmbzHX3wT6VzUMsenaf5MjebFICGZu4PH+NamjLJDo/zM0nlkhWJzuHarjuI0b50QqsjYf+72NS6ZqcdxqK2p2dMYY9Rjr61nzxx+JdCwzOs0Z3oyEAqR06g1DpduJdWSWZBHdRrggZ2vgcH605fEFjobFvskslr5b+XI+5XPbPv1qzqWlrDcwhgGVjhhVJNVdbmJpAHQsAxHUAYrT1i7W5LPHlugwe3vWpnsZWpWkdnKu75G3ZHJ6jFMuNQa7hVhIGzwQAKdrCCNo5ZGPluxBYn7tVrGxja7bJY7h+H+TQFxjLiRZFXeDxg8c+oqqnitbiZrAzLDPkqI5RtEh9jjv/Srk5huL2O1U7cn5ueRxWX8RtJtzaWokVZG3giX+OPAx1Hr9KDQk1xN+gFZ49txG+E3HAz+H9ah8OandWukRw6hb7pFG1ZAMK/+H1rTdDqGg+XdfNgbQSc7gcY/L+tROjm2aDzP3akSIoH4c/nWcotsCxcR+bo7SfcjIJIB7e1WPDFpa3Wk7G3NJ06nkY9M4rldR1z7JbGBW5jwrpg8AnGf1rptNube00jzEZg0YyOfvVUVZAQ+KLf7RMg8tZ/KTgA/MuM4/wAmqo+x6/o6ebDJ5sRJUliMH86FvLi2u1vmXdCh+aPuymruoPp+p3H+iybV/wCebcHPrVAYGm6gs+lTR3StFPGxjlzn51z97+vFWhePpljNaCX7YyoZItwAJHXH4Ve1DQme0m+6rFDjvu781zvgqVr642XKrH9nJ8vt7Y+lFkRqy9ZaadQ0lLpv9FmY/PGx4bHf2/D0qnbW114zbybtGhutNcyRsQAjqPoehH8q6C8twYT3jXJ4459KzfBerR6lbO8YkT7wUPwfcVnya3Hqiv4j1pdKtUuUi83a22RAeoIrN18f8LB8OxqP9CnfKoz5ZRz0P4Vvax4RPiXRLyGOTyWki3xFumcZ5/H9K5/whdS6td29m0LQyWcXlzpjjcvDEHuMj8iKme41sPttBj8q2hlUiOArghyVJBz+Wf5VT8Y3lv4dnxcQ+bbytzInHljt0xXUpcR6QZFlIVGYw7j/ABEj1rD123jutMkt7pQ8dwrRYOeR2IqLAUbmC50bWotQty0ljMoRoiOUAHP86inj/swfboJ4U+2TCRlBzvAHIwe/PpVLw9rbXmnx283E2m5glB/iXPBFVdeKeDfEgZVkl0+/cSjed2x+cgccZqBmrqesW+sSR28cg2wkztGwxhe/P4dCfWsia+svObbNDjccfMK2dRubXS7lpL61H9n3EIZ5IxwAQPT04NcpP4D0e4meSDxEogkYtGGzkKemfwqeZFaHzb8SdEk1bRNPMU0tqzTmA24iImDBtxLNnb02/lUw1ma212OGTUJdRWxjeWW3lRUV967V5XqwPIfvjpzW8vnXfhuDTm0e1ZdJD2/2pZmXz++9txHOCc47VyOm+EL3w5qEk1vYNJ9sfdLvMkqrGSTiPccsAp6nAGOK3vpYJRtqdJqlzpE81pb2Mi6c15tiaQRb9hC8knODyT34pvhT4U3OpeOT4b0u+0/ThNYvPJqeosLe3eIFnIaQk7CSuBycnjAzWjY6zHq3h+3W6h0aaxs3GnTfYncXEZI3xuQwxgD5WIbdngCq2pWqXniC4tWkt7MPE+2aRjHHEo6AvyTychcc5PfircdLoko213pvi7WbltS82G8jhFuLZw3kwjcQJWPIdG2gjGPunNTXui20ehahH9uunn8pmtxudoG4KjAxzjPH4881jeGvDTaDeLcJqFq0ak2p8x/3csnVTypOBn+MDqetX/Hemy6xq1vbWdx9ulsIVXyrRgwlyedvTOKzKW+oXPiLSNWvbfTZHh1CLTbeE2b28Ii807RG3mDBCKCMKP72CDVVNaHgvw5rj3C200ek2oU3ITabVGJH3yfmbkEAHovSp7HwvH4YgnW3jSxmhYSeTOqqTk4/hzuPIOBjGCaX4dNq2keDJLW+0/TXt726nuGvrhirSFeFVQcjjnblcZxnIzWijpqErdB0nhzV9XsdFuo2vtYtxt+03se2ZXU/OqIc7FA4+TGR61oeG9Qj1vQf7Y1Dy7bUGZD5LsZpLZgcBfM43Ee44z0710mhW1vp2tx/abq4hkuoAq20V1tYvj76xquNoAG5uuT0PWuF8YeD7y01a9sNNvp5re3BdpXtyX80DG3CKSw4zVXitiS1onjSPxFrrJdfZRNbqzXE7RlWZgSEjcfxHjJYAd8561i+AfC8urW19dXFsum304+0wyPOqvuSRgGwTuGRjj09as6zZJoWmxyWsdx5d5F9rnaWyXzUbbhhIQx2gknpuwe5q5pWlW+qaclxHNDbwtHyhn3ybh1woJPPXkDqKybbAwEsJNQv/sFvD9qjkuSuIZxHHvxuYseCT1JPPYZNZN54Obwh4xtVm00rZ4ea5uIY2ZYozxkE/MW4OB3wemK6TS3m0vTrFrXzyLpvKFu0RZrcGTquDhuCTknOCfpWn4l0u+e/vJVurOS1glRbhppQkl228hEtwecqoYkYx8y+tWoprUDCe60/S/DsMllDC32mVpofNUxyxgAjdgjjOfug5zg9qPC7SL4iv9UvAjRXUeyGxWzCxQ9t245Y5POfl/HoU8Q5bQbWO5t9Riv9PuXl8pgqlRhlXcueuDz71u6bdwW+kxpIsyPLB58ckqqZOMFieT3woHPLDPHI020Ay9V8MXk8X9l3V1Jby3UbbY2IZgBz+7wfmxkE4Oe1O0zwrquoaFbq2jra31vPHcWlxNAZv7QABXywhGwZBLZYn7p9RVxH/wCEqS1ijf7LJNp5ubZ5gFkViCxGFDAMcAfKQc45NWvBXh230ia4N1It5eSMsok+2SiVwFwqH5UUcY6DtjPWgL23Mv4Z+A1gnhvtSW1/ti6YwmKG4QvImNwWKI8L35Gep5r7X/Yp8PT/ABC/Zd+IHhvwdqC6H4y1DxAJJbdrxbTUNQ06NVRo0O5XRdwdSQQMhuRur5AW/wBI1XUbX7DJbLqTgQxE5luIN5O7jbgLtBGeWHp3r27wh+zjc+Nf2Tbjxj4Cm1HVfFXhnV2h1PSbOMTzWdmdzrJAi4kaQgqT83Y4GRitqF+Z2OXFW5Vdkvwd1D49fsJfFvWNW8ReB/GOpeC/3yXVjqF87aZfRMhGGdEnjj2sVIfAyFKkfMSPCofiFr+i6drB8L+PPE/hvQZtSuLmDSNK1i4t5Ii/RQqlFkI2ohdV5VAe20fVn/BPf4i/G+7+Nvh/RIk8YXPgOOaRNdh12GaS0gtTliwkuV3h93ZWPLMOg4+VPjXY6Bo3xp8Qat4X2XOi6TreojTY0cSW9rE80ikHruUjlRzwwPBNaVE1FamFPWTjbc+g/wDgot8ZPG/hvwr+zN/Z/jrxZoN9rnw+E2omw1S4hN5MY7LdNKI3USNlm5bn5jg9ax/+CeHw9h1z4xX3xL8eXtxrFj4CsLrxLq+pavL58k91Ayx2aGZ3JB2fOud3+pxjnI3f+Cj1tp/ib4U/s36rc2dvcXUngaIwwiRolhRo7MucD+EfL78dOtT6F8VtL/Y1/Ye8K3eo+F9D8WX3xt1tp7+z1a5mt7eLRoEdIpmCKW5l8gquNrLMxJyNprmi53fQlQap2W7Zh/ti3d/+1/8AsG+Bfitq8lrdeJvCGqTeHfFE9tEFI8xxLAwfd8ihniA4OfPHPPM37Aem+IrH/gnz8fm8Lr4quNfvI9NksIbB5Xv1LGQZiEIMgbG4kLknb6mvSv2fvih4R/a28PfED4Iw+BvCvg2DxpYzG2/sq+lcXN/bos8Mjhvu42bgVBP7nkYArzf9i3U/EXwy/YZ/aQu9OuNQ8P65px01oJrOdormylSSaNlVuNrAowyDyG61N7yUvITTUXC3U5P4NeC/2pbnxvYJpM3xqt9WsmW6hk1RNQj02U7gvlSvO6oy4YMQwxhDwc8Q/wDBTP4myWn7bfjDxB4B8YalpehNBa2t/wD8I9rE1idQu40/eEPA6h1VyAR0Lh+c5rB+Dn7dnxo8CeO/7W1T4h+KNQtLG9hc2t3eyX1vdxlgHjZX+VQFPXru29iSOx/4Kh/C2y8D/tjeKF0nTbPT4Ltbe4to9ipbJ5kCNPLsH+0GOduN2eOppSqXpto05JOolNI0fFfxr8bH/gjv4U8SXnjDxlY69dfEGZGv4NXuFvmi/fskTS7/ADNnA+UsffNfI/xI8Q+MviDd2us6t4y1jVdS0uHcl1NePeybDlhFulZiikMAQD1XPavsWLRLWX/gkX4ZsY2j1HzvHtzHbtM3mDcwm64wMjnsMegr5MTRJ/h7DcafMtvfMLhYZn8sLwfm2g5x8oPUZ6euaVWTsjTDxSbuc3LoV14c1eyj3Nb3yuZmlQtu+fjvww69M9Kt6x4rt9VkkhuI5rmxVRv3pukDgn5TkfKuQvHfH0q94g1D/hPfGtnJe7bN3K2cMqMQABkK4BJ457cdeOtULD4c7fnkS/t5YbqSG4adhuuduCrLtyCDk9R/EOBXOdWg3+09QvhDptjp/wBsW6ZHeVZWWRgu9tmOQPmKqOvT2wdfS7LRbuAWdpeSC786W4dbidY1Z/LyU2EZyiqWznJx0FV57C8tvh4sNqsdrq3ntdRzHyzJAgkTJPOchUfjbj5hznils9Otb+S7uFWz1GNURsjd5ceWwW3AAjI4xgHJxigNSkHmudZ8y4t9ohfagkVEWaJ+C4RuBkHORnrVxNEt/tYsx9j026tfllNwV8uQnGGUAH5sEZ5HQ/hk6iiajpkyz6g9wlnK0YY7k8pPRd2TwMgZB6Vo6Fq9t4g0OHT7q2jEuohhcXRleP7JCp4wi5y2ACTxkjqKCroxNJ1aS0vZDBHNbvZvJ9jIc+WiYI24cbm+ueR2HWt3WJDf2iw26xRxTwxzT3iISryuvzg91GckLkgbulavi7VbF7m0mtV863vHFvNKwzNGo5G0AnPJJJGDzWJ4msJNNvP7KjuhJ/aBNvbl5vLDE5C9BycevPWgmLuU/Ddr9k8SxxSWMd9p8Enk7VJjjtt3zBnfb9Bxjg+5qCTRFtfFqqqR28czmKeKzcySS+rbSQCu30J6GtZPDIt7e38tVkmjgPmCRGCO6ZUInXdjAznHIPaqGjX2o6Pqu1jcRteOJdnlq00bqCG2E9AQSMGgor6dqKzalf29nHcM9r+4mkuLVizqckKCfuHjnGegz2xNp+mWV9brJcSNctDIQVRirQjjghuuT0IOMGodNl+x38dvHbTiN828xYASA87mPzAbuvUHp1FW7q8ku9Hs5I/PXUbFxAVRWSLUIxuIXbyS59QR90/g15kvscpr2lR+HPE8bOtzY2slwZJJLS9YNdRFD8jcY9exz046jel8Iw3WjSXEVxcCzuz9ptVKFjY4BKxMPVsZ3DGBzhu9o6NDqSSafcW7LNNkRiD5lDeYMbzz74wSeB9Kszxr4Nt2t7q3XzbrKK/mMRIVBGAARznAJYD73enK19A6amBqmrTXUMMYtljuUXa0pJbdGBn5U4I9CO9VWNtqGlw3UBuJLSeCOd7ieEwZ38hcsAcj0I/CtTUNOsfDesia+O1LWwQRhbjdJ5rfxYGOCD3z9Kq6xq81nYQskczLbgxm1bOEIJGSPu9/0qSSPSbmbWNGaG1aS3tfLDSSyHa0rZwOvckdjx70acsw0aSK2imXcGSKaNw7ZDHcHGc8HPPpjilsdVnb7ct1GscMNmJEUfeC7slmHdwCPu5Ge561lbLrw0q3EUKtc6kcQ7pyzRoRnc2OOQAT1xnFAHQeKLmWHTZIbiRopI7bMeWOHLdyejEYrFvYNPtPDsMsf7y98nY8hASNhwWwx+5j6nOfatC7vLjX7+zLwWb/AGkhk3scRleOFz7nPtirF7pDS3lxp8wtpLM97cbfJC4yRnqT26DPek7W1A7f9hzxVdaPffE290nS7rXLpfBEaJFEcMWbxDoSkc8cD5s+i19S6F9o/s+M3W2O4cbpI1fzAp9N3evBv+CfkcWj+N/id5P2h2t/BQQzSbef+J5ouBhSRnGM89q9ti1iOW7/AH77I1x14zSlJJJIxdLmldnoVjcRw2yKxzvj54xkEf8A1qsPD9ujVG4XOenSuRttRN6U5xD2weSK2JdcjnvYWRm+VcHHejmRpyJHVR4eLyFxGCMlvSpmtVv4o7YsfLzgkDORyax9NkLXDOzN8wyPatRdSS1jMm7JC8D15o5kVY3tDsYdNjZVXoMAE9Kmu7bYEbcuWOBx0rP8OXMmp2rM4Vfm/OtSWRZZ1jUHLZHPQUcyHZmHrGh/boJyJNoUEZK5z+teb6/4Z+zxSM3zMxODtx/WvVtbH72WMfdjODzXPalZQTQN8vuDWc9yuW587eO/CIjtJnK/MVJyR04+tebtZyC88z7Q0axnGAa+gPF9lC7Msm5t3XAyMV4x4k8Ora6i0UIJ3Es2fXJ6VnEfKejfCTV5NYkjVlXyLcEKw64r6l+E/jM3+jfOWVYxhlPfAyPzr5F+GOqm0/0S3jEfI+b1+n/169n8CXV5pzR/6QTGx2lQeQKuMmtgPd/C/ir/AISJprdl+WNsZ+9x6EfQYr5D/wCCjf7Imoapr48WeHoGuJCAZVjX7ow3QA/Svp7wpEkc8nlSBcqG5ODz6/nXV29tDr2grHMscy5PLc810UXZ3ZlOmrXR+Qcmg+IbHUo47zTLyZU4JCkH34r0PwV4mktJ1hWdo0UAiJ+Cce39K+5/EXwr0+x8SyMLOHD5wrICGx+FeU+OP2WtD1vWpJoALXLb1CH7vt+tFS1whoeBXN6sZfbh9xPz52/pzTV1/wA+DyZDHHnghfSu4+JXwFvPCGnyyWj+fbxkjdySAO3SvFtZSK3BkkZo2Bw8Y+8P8/WsHG+xqemeGryJynkupkjPAzyeK27rXNP1G2xdbYWUhmeQgRuR0yeMfnXi9l8QovC2lXMkkn7vbujViFaU+nr261h33jW+8XLh7hVVssbOQkKo9sAlqxlZHdhcHKrqezap8TdFt3a3mvvtBhBCpaxmVhz06Dj3zWh4T/aMsdFszC1vqM23O0goMDsSd3GOPyr55j1ebTpWWf0yMA8jtVyO7WdhIu5fMXHDYpcyPYhllFr3j6q0X9r/AEW60drO/wDt0MytgP5QkV1H0Pv+leleA/2mvA+s2EdnH4gt4JMYIvFNvg9h83HPrmvgkTL9oVd23bgkj0rQnup7i3ZYZIXDY2hj8zj8j096qNTUmplVJr3T9FdK1WCxaFfMWaG4bEUsThkfPIwRkH6ZqbxD4mj0S/jEjbeNwJPQGvzw8F/FjxF8N7iObStQurVo23eUjCSFyOeY3G38sV9HfBn9tPQ/ife2ml+MrVdF1ZcLHfDaLG6P90nOY3P90gjPAar5rs8utl1SGsdUfTOnTrqlrEySLtlwyMB8p9Knu1uNJuVjlYeXIuUf15PFN8JyWYtFtoT5kbZVWU5Ufj/hU13F5tytvK5kjXgN3HXAraN+p5stx7WKzaWI5mZ1Y5GR0IqpLdrY3KrkN367e1O1edtM0p5lUyBeoXsK4bxPrCTx/aoJipIKru9fTFO9gib19eLJqf2q3Vlkg+8r8b1q3dxLrOmSRtlWmUhWJ65XsfbPT2rl/hz4lTXbK6j1Ftv2P93M54KDGcn2xUcc9/ay3H2GQXUMOJYRI3yvg8YIz1plFjX9QutH8NxWfmSGS3KgS9NwyOD+nc10Wia/a3oigkkRbmRA6HvIPSmeE76z+L3hJrhVH2lSY5YxgYx16d+KxtZ8ILHLLe6fJ89upMaN1B9qAZT8c3FpY3Y85JIfMbaJUO1gx7H1Hb2zXRaVew3Ghm13MkiqOWX5sHgEetUdHu4/FOibdWt3UqQHBQZDAj5voSK09aWGHRVurdVuEteN0Z+YKOuaCY7Gvomp28unm1LLLMpAcN/COx/nWL4q0mKGaNo/l+Y/dGO1Q6fdxpOlwo8t5EALDuuM9Pxp2uySf2jbvGGZmB3ZHHt/Os5S7FD28Q/abe1ZXZWR/KkUc9OAfpVrU7USxo0ccazx4b0DCs+ziaa4XYoWROHQ+/en6lfyRW8cUY3XULgGNu6HrTi3vIn0LTXDS2ytG2Fb5WVuv1+vPX6UTaI0V/C0LLiE7+DjJI71j6brE8mpX1rJt8mFs4I+dcgdR6e9aOjX8UyySiVirAqpHr+dCbb8gexo2lyuy88+TEZ/1YAwfcVj6VbvDKv2eTdJbk7uMeahB4xnqB/Kq/jnUmv9H8q2zDdK4GWGOPXjNN0uKSx8PrtmR98ilpATlWI5zxUT3KRVs7iHUbCaGSb/AI+3aWNWG9YzyMZq1rkCWfh+3mj3JJa4BDfOcHHPbNYOvWsun3lvp9xJHAQ+/wA5O/fJ4966HUNEa88MRRtcLLfxI0bMx2rIp6A9+OecVm7gc0mgf2Pqf27O4Qglht4uOnv/AI9ah+LrR2/hizbbuadkmi3fwk44z7DIx71e8Q6jJpVp9j3KyxxAgn39DWdDar4rSPTdSdnW2cFGHqeq5H4/lU2YFaPXrj+xIbeZRJbXAMJ43dOoA+lalt+z/pktvGyXMioygqpA4HYdawW0W38JawtnbXlxffvjLPbTc7VP3Sh9j1rp4fiPdRxKoVQFAADIMj61PKirI8D1zwnfeHFt2uxaRi8cwmExMCCVwHQchsH0z+VM8Z+I49T8KXeitH9q1DyIpTJ5SqyFPujIAxGcc5IPNQeIL2bT/ihYf2JcSXWkW0EbStexz/uZHOGjCvzuGev0qtpuhx+GPHHivUdXmhmXUVEFtJtMMenKMn5wxwwJ2hfYe9aJDk2R+LLLSPC2jNa+Gv7Puru4u0WCC6kbzIgyDc4zgH5t3KhgO/NVviP4U1DxO8kGg3EMdvcW8Unlt+/uLcKSGbJVc85IHXuPa3pWsaT4g8T6o00GkrNBawi1uJVWTzpFk6QnOBuBzx1znnrVyRb7UbpLWG6srBmZpnnwjgZwSpPylFO3OCO/XmtHJtWJuZPh7wQuqX+tXas82lw6bbjdLEZJhcRBwxYdgxGeR35xWpZeHrHSbex164u9NsLELvikw8mbhge3BDKofg+vToRh6Npt94ktDFpn2q1MDzXd3LCztHPErDcrYBGT1Ac8Ak1oapd30+nQXEdwtnpl5uH2FgpaNwSN7Ach245x0xVRjpcRleJNT0WLwpNf2txqNzcRXLMhKP8ANmRVMmGC8AHAUc+vHNRafd6X4r8NojXkd9Hax/ad9zGYpIWDcKBjcXJI+7kAnPrXTWXgzytEWTWLkarDCm6zikXfNEzMMxqVG5cHJOQTgnmuTMWn6brDWP2qe01q7k2sJIG8qJnIVVIyQQoySeCevHWqUroDa8ZanZvd6N4n0/T4r2WyBW1+0zFfJcgqSgDA5wWB3Djng1y9tdyx+IYp/wB82oMVinO8eait8o2BQAGJ5ycjrkCuke2jk0+O1lmEX2RwIIPKLiEc5ZRuzyST071TuZrzVLCZI7SaezuBHFeTxzgzWrg72AGcuCFxlckZ6ACsdSpKweJb1VtFs7WC4htYnBdmbdcXLFhuD7cIcYzwMHdxRd2Md1d2401d32y5Vo87G3Y+UgliOrA9cY/WqPjC8mtbK6u7Xbb3F1mK1WSQHLDgE4+6o4OSOnOKydO0jVru9tbe1jsYxbQNmazykLFSxMgLdTnPOa05USW7bS/Kvfs9o9laXMJWAW9x5m5HZyrONqkYUAHOe/FUGS41y+s9Hs/sy3DTedI8kiKrPEch0PBGegGcnoQSK6C+1+Sz0aG7m0UXVykZxIjf6PdBvnJZj/Eu7rgjGBxUJt9Im8L+G9a1Pw3HYvqOoXMs0MT7PJ8wqcFVAKKCmVz03nHBq1orAWtdht/+EWNmzTR30F95snmbWmlUrzjaAeS3Tr+RqLUdPj8Ia1JpuoKswVVnEyjKmM8fKe/0XPINXdKa31Jna5jeS3jhSJzKf3gQnIeIsCxHAXdux82O4zg6r4KvvCekRapZrFeWkEklpEiILjegbBbHG1cnOc8DJpgbOna5Ya5o9xodr5Nj9lbbBMEfdGrByAoYgMzHavJ2qTk4GadpHnaBoUNpqtnHEluCz3yr50xTGQzfPtDDj7o5FV9c8JSaD4LtIzpM080qrLAbNisR3DcrM575B25+/j5c1sRaPpfxFu4IbzVNQsbNkV5LYPFNNlVO8eVkHoMZPGRQBwmj6h/wj3jTTWuWVrxLeZrKZpAsxQ5CGQAHC4LAbVLZ68V2Hwr+Jl58O9Z0i90PxNfaL4gkM9iv9mTvELZg5c5OwBk2MDhwcgdAcivNbh7zXfGsMlvYfaru0cxC4lm4hjBxks3bnpx1rr7/AE+++HEiatNZm8s7iFJZdrbRAxTG5EIJPce/rTjJp3RMop7nrfxR/au+Jup+doOsfEHVtd3bJjb/AG2OSCWLYGIcQqi87gdrEnqDzXkOn6RBrlrI8Vwz2l1A14ZTkiIhimTGuCGyANmDhSCeDVfRdettc1C+hjspNFaPZdS3FuxlYgKAN3ACFuO/f14qTRdQ0nw7ZTi4t5/tV7clLbExlVgUA2p05JLE9egPanOTluRCjGLuiTUvE/iH4m2VrZ6zrF1dQ+GdN/s/Sop7prmK0tyoTagPzJ/q1Gz0HsDXC+INZ8feNH0ltW17XvEH9hqlnp0N9cea8cAIKRxZbasYxwD0xXY6l8SNK8EeZJJNb6TIyGA227mdQpAIOAPM+YgA8ktUVxrFjrGnR/ZLloorphCJjGVW3wu7Ddw2MDqOpqJX3NeVXKeq+Lb34L+O4fFFlqV/pPiCzuC9utnL9nmgI3K43q24ttlc5HDKWU5zivRPC3xW1rWPDV4za9rHl+LGe71qNr50t9QQM8gaZnIHncsyluARyDXmOkeG9Jm1u4uLnTGk1O3tklWW5uDttyrHcfnGMFOcDPWn+ONJv7XR2bUIWsUXe8qM5AmcAFCVIHUHgeh4ojJ2uJxT3CKPT768vpGlkuPD80jW7rNuMLq7LmYnHLgDhlGMZ61d+Kvxf8SfE7xCt74q17xF4ls/KaHTbu5m8+5Cgk4LsM7eSecf0FG/07VNS+Hem27rCtusphcm9EkvIyVRCoyo45J4xxWH8PPBP9s6rDqGqRRyWdrKRZxwv50fOc+YoBBIOQB1bINKUn0NYwv73Y9E0jxf4k1X4c+H/Dml6pql9pbuL620ZJ/3NvcBCxlw2F3KCcnvnis/V7iR5rf+0rf/AE64VxeQSSLgAZCNlQdvykHGT1rN0y+uPCVze3wjlW3s7zYimFoo1PzDeN3P0xnbmstfHdx4s8ZajdXUc0dvMIoprwx+cJ0CDDH7oVlAVOCcgc85Fae0TVmc8aaWpl6jb2mi38fktOZREZI55CfLTcdvBXscY4Haus8I3NxbwyWJ0vdJdJFIySe5Y+dubnaNvAGDycjpVPxCun3trIIbFLX7K5lgxf8A3o88RrDyMAgtu65Y+laWgeISnifTWtWhk5ht726vr1Y5IU8xd4jQ4ZshiOnYdKkvlNPWYbTV9FbTbyJfOuA04uI0f9252hkJU5YYVzjpyc44rH0vxMPC9jdxtCbqG+jMTFGzkghkJwTjnHB5B745pfiDBb+JNYkns5I7q1tXJmkibKiMfxK6MFJOSCuePTNYus+HZ9cubko32CG6IW3UuqhTjJwu7cvTHPrQDuXINDk1S/trRpFtXuPmkVmUqCuCA45B6EYyOoBI7VfE/hGHSp7yz+x3X2ySHekHnqu0twTu2n5hjO08cMMg1WtJrjT9Vhn1hdWuJljKKYIQoi6A7gRzlc8cDqa6WzubfXfEV5fJPc2V9Gg+zSPOtupwASZNx+Zzxj1Bzz1oFYwfDOm3E1tcRW7XEzOBEzP8piKrlm3AAZyRgDnGKTxd8PLTxpfQiG8bzYguVijPnu6D5mwQQOnX5TS+PZW1qyjuCzyWrt8xO6EoxI5KE5BJIGTxjFV0sr7UF02aeGNrpYfOUS3e7zVXHzqucEdM89TQFkR65q1rbXDSz3Vnvhi8lGdSsjkDapVl6sAABnsKy57238S6PaiMx29xbstoZd7qY0dirTHAJynLEkc9s9B0mmW8uq2t4dTjjvJFQQWiFBGJM5JfaDuG3kBj7EZGCct9ItdLuvsF7HcaaLUGO4SdmEhwudx3Bfl+ZfXJz60FLY0b3TrxLq3ktodvlpJFDOblI/tMWEHmKr43A5yNxB+uawvF1xeeGbNmsZNrZBt2cqXRguWGVAIyRnnjjrVqxv77UPFtjHL5y20aGSOSGFWclF/d5djhFx2yM1WuvEt34q1W6tVWC6vJbUxSQmZFVSQWL8kBemODjnvQBfudUj8XaZGzMbERwKQ4hCJ5mRy7dQeT8xwCSB7U7SmtYrptGgdrm4Vnu1uw/mRrxt5Y4GSSCEJAO3rWToTQy2EYsw6yNGIwkkizqwQhvmOQCO3r+OAeuvvD0Qsr5Y2t44rx4jmOMLsKnJDHOAFJHINBne5Vuoryxs5mjvGlMwMsjKFdr4I3ELBhlOg5X061kteLa6FezQwo0V3IXj3tkISc5989geAak1SNtb0WFfMjjmtc7oRENzKDgsR+IOemCPWqGoSyaXpk7w/2dNHtEUcV86qkBbJJBwAcc9MmgCCOODT4ZLx1aPcxduQzbcnIBGQM88H0rJstSkgldreNfJmYvFMSqnrwnqB2zjHvVi7VZobqR5v3kkymCGPeygZ5O1sbyPpkVD4gMM1vHcWMtrPHbg+eGIZSv8XCkleSeD9aCuUtalF9ouov3U1x+7KRqrbC+SMMN4B/vZ7Ee9S24aaGaZYZUgCYd5ZFEjqpAK45PX2rOsdX/s/5mvlkmuHRYgCxNkuR1HVR1POBj8cSSeJTNqOqaZbJCrW52yTupKCNuQwCnaOh+b3oepJ7V+x5ItnpvxSvoHTEfhSOJo1kXhm17Rj688DqOO2ea7a4m/tSSHzptm4jCbs4/LNeU/svSWunaL8ULxbic2k3gyEPIwKxI48Q6EMAn+9nIx6Gu28N3sMN7HcsxdY2BUnocGs5q1hx1v8A10R6l4X1eUwrAwbaowmT1Fdd4ZRfOVZV3ZOAT1JrgYtbW8lVoSnmMoCKDjtnNdFpOvH7JCGmVbjdkruGaxcuxfLc7PXb9rJVW3HzMMfnU1uy3lzErLmR8fKR+dZ1nqENysTNJGW2g8kVvaVBCTHcedGzHOArD6UJgdPY6nHaRCCFFbauCcEZqxBMUm3MwV1APA6VhR6gtuTIo/ec81e0RsmR2cndg5JrSKuwLckIvZZOTtbljjrVDU9J/wBGljT6A+ladrIXm2x4Cr1HbNPmKsrdC2eg5pS0YHk3ifwTJYI93c5CqcAZDZ/KvOPHuk2rxR/LskYE5AOep717549she6b5e3vnAFeY654U8/edoYJ6r0qOUrmPNbbUZdMuf8AR1BUHmQcD/PWvRvB/iee38uRXEzSgZRzgY7Vx15o3lTMkS7EU5AI4rT8N28LsqjfvhPzN2qiT3Twn4guWvt0g2rKgAavUvBtysFqq7uOWGfU14n8O9VY3It5dzRqvyMTgt1r0fRb6RUf5tqqBtz2reOwpHX63YQa/bPGzLvXBDD1ryP4nCPwrM025iqjawwfl46/p1r0zw7dR6lp7Sb9rbttUvif4DXxf4en2BRMsRBJGd/FUo30I2PAJ/i7Zm1e3ZopY3fDo3IJ79vevBf2yV8KeCdEs7q3maPUtQBKQLn8/bn1qr8aLzVPhH4mu4biKVbdSf4Cqbfqe/Tmvj/47fHNvGXiq4vr273CMBYlaXmNFAGB+X61CXKrI6aUVJ6mxrPjMajd3D3Uw/eNtBzwoz1AxXSHUIb63YLJ5mAFU7SCf61846j8Qra5dttxF68t/wDXr0bwF8U7fU7GFZlCyIu3KuDux3HpXHU1ep9HhGoR5Uemw69IbfybgNNGhwJMHevrkdwPzq2uoQkb32+TxgxhtoPoRyfeuc03XIrm9VYZodrruKE5YnmrMN+Le93KdqjO/aflxn0rNo9DXqdDHqkNqMp+8/3eTT7a9jmiV7fC7TyAawY57eaVvJkSNwfvdN/tVhEa2ZvlcN1Y461IG3/aHmjP+r56imOWZslUk3ZwTjODWWLzyAoYrIki5BFTW2rRxyR/aLlLaEnaZDj5Rz2rSLaHJaH0b+wj+11N4d8U23w18QXH7i8BbQ7yQlirnINs7c9SPlJwBuxnivtJJjHcQhj+8b371+MHjTxA1x4ni1CxuJoG08qkVzHleQ+Q4PqPUV+n37Hvxkb9oH4I6P4iuHU6jYBbLU41YEm4RVBf6SDDj1ycZrpoyZ8zmOHipc8T2hLovI1ufvNgEelcn4r+H9vNexTbmi8tizBPuyemR1rqNPvY7zV5Cv3lUHPY1BrFv9uRju2t6Gtzytjgr24t7yDU9PgjSG5vIwu4KcsQDj/D8a4zwN4y1DwTeyeHb4eTt4iJHDhuMAntnt15Nbvje1ubbW42h3QvGd6yoep46n6Vg6Pqlj8SPGiWWseZHeWqgwy7SM5OPTseamMm2Ud58EvEFn4T8dT+H23Ri8Q3BfB2vnJOD0/Wuz8T28dlN9nh3RyMcqw/jPpnpXmPiu/NnqS6Z9mmhubeBltr5E+90GCRzXcfDgT6v4CFveT/AGq5jckyBsn8/wAxWl9LEMPt7Q6xJa3EIuLOaPJcHBU+/r+Aqppcg0G0eOFm+zs2HD5y+elat1HHcQrHCV85Ox5I9RWV9m8wSwybnhYYyvWNsfpWcpNaBcsadqK6lqcduuE8tcIzD5W9B7fjUWr3/wDZFzbrqW6Pc2xWBztJxgHGeKx7O/j0W/kt5po8MPkd327h+Pf6Vt6xbL4g06ZJvJlZE3Ju/ve1ZximDdyHXJTbI+S9vcRMDHNHyki/5/GqaXlxbzRyyfvzkFiCMuvOOM+naqvhbWP7T8PSWN+rCaGQ7Q3932P5Uuq6LHqsUckM7Wl1at+5ccow46iqltYs09BaHXtd/tSE+XJCrRyg5VpOe+eowariP/hGZPLmjCxzXDGEsMxvnqMjoe4zjpVbT9Ze98T2NnNGYbyGQ+Y0a/LKmMHJrXfVI9b8mKRfMt0vPKkU/eQjoy/jj8KIyewC+HtSfU76WCaEeXasUDY6jsRWFYTvY+LNU0qG8ZFumEtuG7sfmYZ6dM/nXS6hpcnh2wkV3ZdzhVbozZPX3rmrxI9R8bWapCWhUH96gz5TDnt6nilL4gG3IN3rFzd6hDIs1im7YvzAhVwemeeBUPia8vpfGGnNatPHp+oQeWxDbT5nO1jn645rpb6+TTPEbQywszsm8Hbu3n+vFO8Q2dxr7W8kFnIsUK/OpQ5Rl79PQ5/GlKNnoBk+LrRbO30/S70LG9xbtIGXuUI9M9an06BLHS9QmEa+X5CsFPDSZGa2/F1nazQadNdsn2izQ+S0hGGJXhTnrnA/KsLU/COpatBZakuLKFozDLazAqxPJ4HcelSBxuoa7aR6/bX2nx+ZJ5WzMvGw9MjOOnNIzanOxk3RtvO7O9Oc/jS+EIl1+71bTfJUshaO3JAVuh3fk2PwrlYvhP4mt4lj/tkLsAXG08Y/Gs2nc0OTt9S1DWvANjBG0a2NxGLqTMQWWTGcknrncGA+lZOs2sd5c2uuXE1lcWenypevZ3EolO8fMkbRjJLHgHPHX2rWtdPvrHw1a2qyafe2KhPNllIREdgAEJXLMoHcYz3Nc3e+GI5LTWN62qoZUtVRIZEitiWYb0OMbfQ55461oZiaJ5fxF8RW81hp9npeG82SQybo0PRRjBPJHTA612mkX2n6BpUS6l/o2sbgl3GqLJ5srsQnlrleSuM5GRgHGCM8Bqvg+58LeG72Tw/q0K6OrqLghdskrMo3qWxvIOSc9uBnim6trc3jzVNFsNSaWxsdNu4r62u55PnuXjw2GcHcWO1VVe5YDJzgacttQO+/4RmbSPF1xdySXllDJCLKazMflx7XDKrybunUkY7g57VjX506w8V2ei3U/wBht1kWLzJiNswK/MwIPPJUY/Wur1uC4Opf2nCslxZ65C9xqNp9kdi83ysoO3ncTnOem33rz3x98NbG28P3niDUobee+uGgmtoY/u2zZ4VF6h9oyS3Hy1cXpcDstDltNEsdQvNHja6mHlxuT+7WMybhhdwyTlf4c5Ge2ccD4ttFl8ZXy3DG48nZL5zpHAIxIm0jAwzkEYOD2r0SQafFcKskVrealfzJBJNHMHuJI1QnAIzgEpyR2BHc1yPiq/vAjGHS4JLmHNqJbiPettCOAy9OQO/bis57gRzWzeIdNu7OwjuJItoUTIQJogBwVLHABxjkjrXBeNJ77T/G9xNYTCG1ZEC3Lny3DkYLKoBG0jjJr1WPwhNr/hu0jW80nSbm4011Mkso8m8ug5VFHO0FkYnJG7J64zXl8Ph26ttTt9L11jNYW84+0FXAjlZFyIw4zkZx0OcGlKVxozNR8RRafZCO/WO4vLqECa4SVI1Mg4kw5xlSc9OSMcV0vh+LVtA8Jrp8l1Y2cKxbXtxF5tzE7ZxIocjCYPPBznODms3X9PjutE2yWmmYiCuLO+g+0QmLcSirGQS2ccbccEVq+LrC8v7V7mFre3Ecx3QxBIrkfMcqynlVHQfL0C9epUXYcpX0OrNhHDo+k/aLu3aGExtfXMRB82Da0e5MAMMso4Py5Heuf1XQb7xhd+dJJNJp15K0ak7JJI1iwdy8AKMZ5BySPauR8C+OL7W/t+n2KHbYyqbbfO0zIgIChTnCtnccdufUV69pLW+s6lqWl2twLq4uIjNLPFG6wjG9WGCAMk856nv0rXdEmZaTW0ENtbWduLnT5bVUiuxtyVibDFizFcqeuO/HSovBeq/2pbabpbR6herZxg3cZmEURVi/mA7Tzu3beoxx1qr4a0y5vYYbLVbrZBDPHp91bX/KfZ0DPH5QUALkcfMTw7cdxR1jToY9LvtUmaPS/sUq2kkEcnlyOAARgYxjGOx4FUAav4mPiXRNNtbWa1Wx0+4MEKP+7kifc32eIMvLMpyAckAjnArP1e3sfBviiO/1RvtV/cIbiLzJGZ7dgODEcn5uvpj0NYviLxPqGqQ+HZrTRY5FZ2dned5HtwpGJgNqgAqfujnOK3hdNrN1faneW9zH/ajSJcNO6sQh6qFIyp4GDkmgDJRl03SoWj1G0ur6b/SGwp8x1B5U5HK5/iJzxVm7+IFxDos2nJe28c10W86KAsVn37juOF7ZxxxUXhl9J0zxvPdf2fuhWEQxhNkhs41/jVGz82evPT8ya9o/9o/EJZoWl1SPXI9zmCP54VB+UrgfKx3DOeCc46UA9DF0uGOPS9SWaGawjmTMU5lwJXQcp1yRjB57CptD0rUDYNfanDqB0u7Yw6dqVnCXj3R5DbHOfQ8j0re0/R5tS8RXtnJb29rZw2rT+T9nLSsgwokkUZXJKn65BPUil/4THV7+W8sbzTbGTzAIYzJMIxbw7QqyhE+QSYYLxnhBkCgCp4W8H29vextqvkTW9p5l1ayPGfMTd1kywG52zzn+6Kq/B6XT9S0zVLfT9PuVtbF5Tb3kdmqW95KXVtmSckrk57cDHWtjxCJI59LtdPaOWxhh2S4AeRxtMeJCM7QzhiRwWC5GOtcvZaimjRaa1rJp+pW7zGOCzVmFqHOQdrA8N8pHvzQSlrc0PH9pcRudQzHHNelIzIh8sIpHO1ecgg4Ppn1rn/FWq6lplktv/aE11DbWxjxcOZhO+3mToMZ7Y6etWNa0iPUNM1KO1h8hpbuKQRq6yeSoKgn3Y/OOnHHpzNp/hKPV7LUZmvraZ1ZPIilc+c42/OwGcYHygAc0FHGzatqckqr9o8uaZWWJQ5ynsPRq77w38Sft3gWWNpNPgvre7/1dhGI3jPYbehHPArzfVrq7F0+n2sayLbbmZjlfMY/xPjliO30+lcjp2u6joNkl21rBdKs5kMXKQoBx94fXrnAoJkz6C07VG8VaTc2t5MPt1gzQmQIWWVSMfMpOFfAySOhJxUen+HNB8OWE0cFmxmkAVzJI0kcv8QdGOSzAZBGAMjHauZ8Ea/B4i0C2/tKzh0+6uY2nCKTIySMchSx4JBOSy8VXuZ2+1Jp+oNcfuZvMkAbJuWIx8uMrgDA9ePWs/aFGpF4+0u6vrpprdbOG4l8p5VKLNCo5D7W5HYYGR+tSX+rWep6lu0WGa/mkkWO7uZDHZ+dEv8al2AZhzkLzz71Rk8GyaDPDq0kNvJd3+5bCN3SSGRTu+8FbcpwCAW9qk/4S63sdLisNQ0+S6WYEtBHcbVsyjHZEhGdqck/7xbt00TugNRrO8n8i4t45PLmcpDFEv7q7QEqTlRyg79ecVv6xcaloHh23kbUPtFncQsun3coTzC3SRQinOzGQGbnr0rz6w1i20NrXcrTNcK87QWatM0KcZLbcHb0BPqfeug0nU11PT4Vk02C3hhZEt53PksA7hmLAj5OOpYHjj3ABc0/Um8WQ2YuL5mtISsd3CtoIir4ypBxu5pttp8eoW+ntHcLdSXVy8cccLLI5RFJclWwMAA9fSqOoW3/CSzWljuu20yOaR5ZklURnnu4UZUDqfTNT3Ea2vjbRZ5I7WMRusinzBIxVSMMdjhsHA9DR1J5SeMahfXGtW93eNNocj+ZbW0kKq8UwOCxcZJyvylfy5rjpoLXTZdiSWk32p90VzbbSqqSQw3dd2euRwRXU6T4rbxB4gmt4dL8xZoivnW0RDIxIJOThWBI6YJq1c+G5vFOj3U17AtpdWNsbhFDBmYBika4HIOAMr0HSgkyfCunahrEjyeRB9n09GWSSWZTIVGcNjpg5AyMfietHXNQXxDcWtndwTeW05lvJ5iXjbIBUDA3Y4AHPrVrTtP8A3qxlVOqNIZBK6YRSfule7HGAc8Zz6Ve8UaEuk6lYX8lvDcbZi80PmgRzOBgI3OdmdrEDHOeQOAFcxY8OwQ6lp1vFHc2kE03mAxm3ETXL7iEUMd7KFUjOMHJHWuRsfDFrfWd1ujgtrhVNvcKw8yK4UPk7H25B3Lg9D+BNdH4lsJtT1iE6tJYxwrC7wQ2kQWOKRhnLdSfn6bfeqE0N14ZtJtEuYFkm8lbrCjy1jVhy6ozZDAbMcev0oJZHptr9pDfa9PNrdeafKtET5TGFB38gZICnqf4R1xg60b2kEaalJN8rXDEW0JCqsjnOcA5XnqeMAcCqXiSVbzVpLpNQWTSY0hhaBmNv5wXaQzOD1yc555xV+W7hsLO78u/k1KOGctbrMwmaSbcCHyFxtDZwT2PWgpIydTtJL+DyreCGK0vGKrI8h8tSx+5IM5Kkj6ce1ec/GX4tX+j6xY6fJ/ZMSLNI891LbrG06YY4wCpLlyNvUDp0r0i101m0ab7J5PmXxDCKOESBX2sMkZGBz09a56/8NWniLRrBtQkWa3vGDQRyRRguWT77yZYtluwAxjv1qoy5R2PNPh3431b4geJWvLWN4IbVgqSPAxW4U8lj/Cue3fmuxvLsxygvHDJbzABolZQ0LMBlgcccnNdCdB87zE/eLJYozQwRMFMaqMBRnB6AA5546A5NZOnWU3mRLcxqsikzhNxUNGFOcE4BJyRyeDmlJ3dxovaPJpumyNdHapmjFuyoyqSCfmJ3dcqO3Wub1a8jub5bXSmjmSTDXK5Hl2wGMs3OGAyR3wcDnJq3feDdP8WRT3k9rDaraqGg82QYKckPtVj8xKuMH0HtULyobmGNbcR26w53PKGWZj90nnjHYe5pAd7+zpBbeK7T4vaJHNcLb2/gu3kVypULjxJoBKg9D2r0zQFjuNNjjbMaxrjIGTj1rzz9lDQ28OaX8Vry91K4vL6TwXGiQecGSxhbxJoRVEULwCeTk16L4biW10hmkkVmIJCg8nrWNZ6r0Jj1/rojvvCGl293YLdRyOywocEjGa1rC2Wa8humdhGxIT0zWL4AgVdNEcjDcTvH4iuk03T47nU43kyqxHdnPU1zylYtOxvSP9kMZxjgAium0JlZbdlZtyAlvbrXL6c0kl/KrrmOMZyejVsWGqrbqyENu4HsKIyuUnc6G41JYrhY+8nStO1nkt448ZEePm459q5nyhe6hGyyfeQ5wOlaFyf7X09rX7qtgEn+LB71pzC5TrNOiFs0j7mYSHP0oe7aW9ZeNq5PHesdZPs1tbRkAqvGBVu7k+23MccPytjq3pijmDlKOtXrzFl+70yfSuS1KRYxInytuzkj1rupIotMtpIl5/HqT1rGk+Hd54r1+1tdOt5Lu6vCVSOPr+PYD3PFO5J5TrsKxyHbhB3P4Vu/Cz4QeJvHrK2h6Tdalbs203Crtt0PfMhwOK+tPhV+xdoPhC4TUtcjj1rVQAyxyk/ZbYjoAoxvb3PHtXtEXyQKi/Ki4Coo2ogHAAA4Ax2qecD5n8F/sa+Iv9De9vNNsVUDzEDmV19cbRj3616BH+zjhE3akzZGAyw7VI6dzn15r1jZ8rejU4QK4Le2MVHOwPLYPgLJp9j5drqdu8m7OHUjOfpVc/CrVtNDtdMssKg/6s5B/wAivWlh2leenWo3OIj24/rWirSQH5o/8FhrDS/hf+zBrWuPbrDf3E8GnQZUb90pzkD/AHVY5r8WNW8xptskzs6++c1/UT+0T+zb4J/ao+H114W8eaHBrmi3DCRELtDPaSjpLDMpDRuMnkZHqCOK/Ej/AIKef8EhfFf7D8U3iLw/NfeLfhY0ij+1Ni/bNHdjhYryNMAAk4EoARuM7TnG0Kino9zahK2jPhae4CN27D8KsaXr9xoU+IpWjSQgY9snpTtWtnguNzK0nOQVPBFZN3B5Msi5U9ORWco6WZ306jTuj0rwx4ykvoi0M8ySLwGB6Cuu0zxzqFlZxsboybhjayhtq5xkH6mvDtH1eeyljYv+7UKAPw/+tXc2viGHUbRW3YkwMg9a55RtsenTxHNoz0xPiReRsjZjZSMkYxWrb/FLUrdgqm3OAGV3X5l9hXnOiXyy2rKOec57V1HhqA6jdR2/kTeYWCoq87uc/wAh+WaIwb1NPas6nTNb1jWomkuLzbATjbGg+Wum0fTLCw0r7ZrV0tnCqMTJPkllHTaBknP0x71wXjn4nQ/DXWY/DfhvyvEHiXUgls0NnG00sFzMcfZkAGPPRsBtuQCcZPJH6H/8E+/+CGsXibSLXxr+0HcX2ratqapcxeFbe5C29mmMqLuRfmkkIwSgbAJxWkqNleRzVsyjCLjHVnxJ8JPgx4l/as8SyaT8K/DOveKFhkEUtwkaxW9uTjmSXPlpk84LZwRX6V/8E4f+CWvxU/Z40rXP+E417wza2viBInXTrCaS5lglTd8zNtEYOH2nax+6Oa+7fAvgfTPAXhi00fRdPtNJ0uxULb2doiwwQD/ZVR0/n1PJNbKncMenBqo1uVWSPExGKlU3PLLT9lOOwdWbVGEi8AxwfL+p5pNV/Zoa/Qtb6oofkYeH/A16oG3n7uKHbHbNHt59TkufOviP9kTW7ctdWbafqT4OEEhjZ+Mfxe38q+efiD8NfEHgf4q2b3mk3OksxwztGfLfBHAPev0O8xcgcbv5VDqmlW+s2bW11DFPbycNHIu5SOnTHuaPbPZiPhtNKaae6vr6NisYBjYclT04+v8AhWh8K3mttX1Qq2+zukSQHOACC2cfka9z+NH7Ms1z4TvH8G/u7ojP2GRgVkB6qnv7e1eIWF1eeEdMSNreS1ZQwmilXDxSAcjHbpXRTqKWqAd4yuGeHzbcCO4gXduX+f41V8IeMI9XtY5/l82RB5q/3xyMj8q0vDekSapZ/wBo3fyRuWAUD75wetcHHp83he+uLzT8fZfNLsh6LnrTlsBteMbSO6kiuNqrNAvyEN0H0qvPrMOntHcCeb7P5aiTJ4U8/wA/6Vbk8T2ureHTtkYLnADH7p9VrnfD0TaTcagss3mps2lcZ3qc4z9P8awA37LV3SZo5P3gnci3k27jH3xn6Hoau2jsuiXEyMmFygznG7/69cVpcEdpbXlkLCSSQESAhvlYH+IfWneCJ7rT9RuIdsktveElYm/5Zn1xWkNyuY6f4YeJIvEVnHA22O/t2bBI+Y4Pr+OK6Dw/oENtrUk0bLsuGWaUE58tsn/GuM0LUbGy1bbIFkug3mpcBTwvQhvoR/Ku0fUfIS6uIpoo/myCOjE44B6HNaPRFFq/1c6r4sfTWXzoE2kg9UzXK+E/Mg13XIbdhut5HdT97jfjv7VqWd0LqG5mbc97fReXFsGMHI/wra0Dw1jVWvLgLBcKgEwJ5dKx5ryuBNeW9vrGrW8iLiaNMh8ZAIBHP1x+tO8J3lxPt0vUGVXkR8yL9SB/n0qrqHiGLTPLW12PZ3C+YD7lulTNItxqzFd0ixBGRx97Oaqe4EviPSJNbjs2v4/9EjIZwDjlRgHirPiZ31Xwzb3VnJu8tkZUz1VQQQPfpVvxrqix+DJJGbbuJRDn14/rXLfEHxAnhLRfD1pEf3szfOAMsfkIqnKyAy20218OanBqAjS3upJ28hguRIHALBvQjBrabQEmYu0i7mO48HvVDxpp97qC2LWckf2UEC63j7y8Yx6etdjZ6To62cI8yT7g6Lx0rBxuwPi26+Hsfw1uU05plukupkNtbAuHjQclVLklQMdDwT+VYOuXkfixZ4Jr7UNLsbVQ7WkkR23jKSU5IxgEAY6c1uah4zv3uL66vEtbxtWjR7e5jkbdpu3IKEEfMGwDnr2zjiodF8Na3PY6XZWrrLY6oyw+aEiBhVvn2889Dnnmn0uOxn6h8QbHQ9JYrY3BW3QQrvkCpNI/PRuOBj1rL8K+E9PvL68vrrVLoyXEZuvJuXbbHKAQIEJyDnaDxgcn61keNbrVvhdreoWFtbtLEsj8yQi5tZIgwJfDFjGwJxkcH8a6DwPeT3GnS3FqGunvLY+RbXaL5O5zs2hgAwbkEHPAI5HNAjv/AA3Lf21va2EbX2l2cS75pLjKNOXAYqnc8lhx6jscVi3mtaj8QvEdrYTWU1vp7TLE6JAvmFUyPNCyZ5AOOCOCa0dOuo5fDZs5prVLjS4PIPlFZPtMwU5CfOCChBByTkms43v2OdJPtk0moSWzR262w8wuHA2n5AcHnB7g45GTTi7MCj4a0vQ/DfxE1yaXTbRoY5VjS9eIXE0DoGVtpXAUMGAIH1OcCtqz8vXvHDJcXkdq2loHtoYJRJFe5XcBISSuOMbTng4NN1fwHZ6b4LPkefH9nXdNGkZZ7xhjOSzZJz1IOeK5Xwlos/jUXlranUF+xstxPaWzrH9niLFVjJUbsucg5OcAkc1pzodtLk3jvxdH4jiYaareHv7Huf8ASrszqI1ctgGKBVC8A9ugFV/Hmo6bM2i2n2S3s2uppIbiSAeXDBJ8rxuECgOSoxjFaz6PJY+E5v7PSNZpp4phYonm3U7k7WO6TkAj0IAz0BqtY6VHF4ha7sZtH1G+s7vyIIJZDJIQijzH2kdQuVTB5yDjPNWIpat4NXUvD1qLy4uLq8tYvJ7QqoQ5PyqMkAdSTxis3StMt7v4h28kNvPPdarOsccwIe2hYp96QM25uVO4HuT612vj6SO2t721ZJfMnlERWWR4iqSEqG5O456HH/16peN9Vs/A9jaQyanaQppYQ6fCLAM3mumGwwOGLli2Soxnkkgk5ezYHMarqtnNaXl4baztE08l7oW0KwrdTDIRI1HPG4E44JyMc839NS1sriwuJrZYJowHEKN9nuNLAb5leMklmkXrxxtHpVTRL6TXfCdxBqGn3V1qVtLbwLhlj/cfM8pZ1A3SDPDdcZyeBUPiTVJPEusWMMyGPTrhsfbpJQzRBiNvmOAGboQN2epqlJLQDX13W7WykkstPU37X8hu5EbDM2QoV2yM/JjjuN3BrlvGXi248Qm8hOp2c1vPcI0ICkyzuQBvJ5IweAeTxjOK0db0Oa61K21rT5rGU6fYsS9s+xmCPtdNgGMeuSSdue1ZfhWOx8Qah/aOpw/2TbxpthtIV2tcIp8xWLYDDc3Hy4PUE4zUSldga6XMfhaJkuFkuNNt7YTO7KI3Mg+XCofvAkYGD6e1b0mraXb6eL7SfPv9Wjs0k/s5oysaFmBC8ruH8WSen0Nc74w0XVdVurW7tZIZrTSnSa5jaTzP3IYOUXGSe/Ocgjin6Dri2L2s1uzXGo6tZyK0kbqrWrM4AGHBXgLjDDHQnkAi6YEt54j0+wu49Ue2tobrfJuW3ckpE2AYk3AkepcHIwegqjc+NdQl+KMiQ3SLYW1qZDJbQ8XSlvlJc4yw7HtXFeOtf0lI42vFaG9hCtHeQxNP8jDlThguRzyQa3NRmg8P+KtJs1t5YIbKw8i4M21dsoUKc4YhmIAO04I7ijnQHUXl/Yadq2oaw9xqEmoNaNBcQz3p2/Z+HRVReHJbHBAGSOp5qSfxfbz6HpvkR3DLq1sIrnExikgm5LBty72K5X5hwQoHVTWL4atNWm+Il7NYzmHbb/avIUYk2qmPMGR0CHJIPG7tWf4c8Uyan48ZlinjtYdypcbQ8m/nLKCfmG0jrnuetHOgNjRfDlroviPU7S0ma3s5IWlWeEmG9kZRzIxAJOQWC9ODVfwDosl9BcWK6HNDoMDSTaY1vgQWjohLxxgKoEv3myOevUZNR3TaxD4/vLFbPzIbeJQ1z9nB2ox5Lk/d5z349sCrGvWcNqbjSdNvGt7G0j2SOrGMqWyT93jBBc56kYq07gVm8P29jpVu2l3+mzTagPImK3gmNszZY8Y6gZH1PWsSGxXSSkNvN9rdyVhlM++VcEEocEkc9qqaNYWvhrxFqU2hw3X2bEbwXNp8kkT4+6rNxjOCSO276VS8Oa61xr99balNDC+luzXAlXZ5Ug5BR1+dvm54IJ/GgCLVtDXTbIXRNx9v87YsaKPLlQ8ncTz26VmQ+G9K1IalNcQ319DbpEI7YRr5BQKN6O+M/OQW4+7xiuivvtN/Da6PGpZxH5quh3FuTguG59PXrWhb+DZNN0SG81CbTZ2jtyDbwZ3DnarOE2jscHnpUe0RMo3K9lZaV4mtYr6fdp9rCvKROdpnbny1yoxtUDJ64qG20q1v9Zhhup0j06NWKXClnaYFceWNqlmbc3c9gfarngLT5vF1nN/bFxc2+j6dcvLHAxzLOhQnzEGSAeAoBwcYzznNfRdYFj4Vumsbq7t1tkR49iqVWYudzEnou0qBgZLA1EotalHV6nY6cIbzSLqzhsrpf3tvPar5f2dxjMZX+LgAjoQWzjmue8QeHrTXTpqvDIq2AJuZoU2O5fG0vgbmIAYnPcisrRbmwTVl1KWO5vJlc+aCGWV5CMEnb97jHJ5/IVd0jU5PFGq6hbwWPlmwWSWa5ncjjcAAPVhzw3GD9aqMlaxOpb8aWNv4U1OSXSby2mtZLd7aOSxbdFkFQC2eC/HPHBHtWloEFgLWxtVnjvhD5KB5lM824t8wYKc5LZUKBnpWZqHhH+y9fhktRb3i3jxo5jnDJFnbu5H8Y6dMjB+tZ7eGv+ER8YzahbzRpJZysLdJ5iFLkZwPmO4gNkFs8++KrmRWvUseJNYXTNO1HGoLY21mjqLS4gf7RcHaSTGFPysgHPfJAPIqXwpoa65rT3EP2aGG8tWjUu4a4jccAsuCduOhY+nanQ3K32tXCyWK3GqXv7+1RCokSTG0EM3OCzAkj+7Wn4P8PpJdXSt51neW4BuJyAQp44Z8YAfPQjtx70LmJvDNle+IPEl5cM15Z2umZWQR58lSwOBGrDB56gNgdaktUiHxCu5J5FFrF5kUKGDbcAMTjzJAR0bsR3rYsbW40gagn9nspkLwxK0qoUc5Ibcy5OI8naFBOB0Jrl/FegX/AIfg0NoLlJL3WbcXNyRuUNbqEZ3ZOqK28bdxLfMMkkZoIL2rPHLP5lt9ht5rckSRo23zlz1Hqw7jPcn2rmrzS7y2la8tofs8lspldJpl8yfJAwoA3LjOSc5x7V0F7I1/Zx6hDFY2ulWLNNCklwQ0YOVOCvUls/Lzn65qtF4it5r3Xpr430Ot2qbAYk7fwgqCPmzuOTzgj0GArlZjW/jqHxTYTNNMtjJpICpGCQzzP8xdW4ywKjk85x0Oc42kwx+C/Era3M0Ot399EYbiC6DKLNGwPMMgfLkbRjcDjPGKk8VeD7zwjDGzafKt1KiTu7KG25+YFvmJzzzzjkfhbvYItS0bTZJNStLnWdYHlGK2tv3doF3AAjks3Cn/AOtk0BystPrOl69qEVu4DW8yCJruJWbcg4UKzHGfqMVB4w8LwrcRWbT3lmrSmFngDABIzywKgpuxj5hwCRXH/Ej4l6LpHiOaHU/E2g6K9uI4JIru+ghkc5AO2MnP12gcV12jeK18c6Hb2un65Z61B+8UzWcsd4sQfHmOpThQAQeeOPwoCI7UdKt9SDSaLYRRTXEa7HiT5JI8chmyGDcDJPXk1X+IXi+S/a1ksbiZdoRZEuFaV3IbLfKeSSM81j+G7q5sEuJbyBmjkjZVuIoo2kkK5AUYIKgnGSfauoXw8rT2Nwvmlp5yZLJ/m8oMMiMuQpfAz6HPfFBRyNherCl/NDb3kt/bIJs3JQqu/JwD1QL02nJHQms3WfEDW1yk0k4mjjcLI+WlO4KWCjjI/wCBcenGK6vWr+JLDUfMaHS1mlMElqV2lgMjn0wQTwe/U1xF/YTXtoq/aILX7szywxD5gmU3HI+bjoMgdKBSNjUr281TRluGaS3hvpA+yRl3E7VUkhep+UjJ5wB2xUfiHSbeawWO1gmku9PkBZ8CGLacYOR34PJ9Ka19bW3hSGOzvJLq1gmJiyqBZMgEtxyCRj5egOcVoWuoprmk2EkKt9skkKyxmRl88A/IGCn5QD36nnPoQg7H4AXl5ZeEfjFHcNCqyeEbaULDyqk+IdDUfN/Fx37V2XhFPtLQb/mjVMHPqa474Ftav4Z+LtnDGitD4OgMhjclVP8Awkmh/KMkk49a6jwFq6z2MO5lVtwBB7nt+dY1+noaU9n6/oj1zQordNNXy1UMMAllwwrpvD6+dY7ZNvmdd3TdXMeFI1bS2kKt8vIPtgVvQXJMcRX5VYHNcIG7Z6p+6+z7fnZtufWrqRRiLa23zAOfrWVYRoJVdm+YHOcnrVm3uEkuJWZvmU8f7X0poqKu7G1pt39juMBQ3yE5PPX0rT0icPM+5vmbkKa5+2vBAfMY/MBjHc1Jc6tHZ6zb7ctGwO7B6f5yKez1C9joL/U1mijSN2Z0YbgDV22u1N4x8zaVGM1yuoXkek6latEgImIDDJOT+NaMxdlzuC7vmx3x1raLvqUaV7frqCmONmEvXGOtfSn7OHgKHRvBVvqLRo19qSKxcjlY+y57d/zr5jkMZ1Gzdflk24OPSvrf4JeIYtW+HuneWoV4IliYDtgf/XrNy1sEoO1ztSpk+b7zE8E9celGKaJ8jhuB+lN89R33H0oMWmPI4p6yKsfJ6k1CXMg+U/epphKLy3A56VXKK/cmMnm+tQyyqvzfw+lRS3nlYKn71NLGQc/N7UrFXI7iWJgML9flpbDwjY+O7LUNG1bT7PVNJ1C1eC7s7yBZre5jcFWR0YFWVgSCCMYqOeJREcL909O59q7PwH4ak0y2knuhtkmP3Omxen/1/wAauEJOWhlWqqCdz+db/gtB/wAEqbr/AIJ6fFFfEHheG4vPhT4uu5P7NkMef7CuXYt9gkYdAMjymON6jHVDXwbfTxiQtjHrX9bH7an7Pvhf9p34CeKPA/i2187QvEVhLa3DqcSWzEfup4z2ljc71II5BHQkH+UL4/fCrVvgD8YPEngnXpI/7X8MXr2NyygBJiMMkq/7MkZSQY4w49DXZ7sly9TowtZ8l2jlXumWVWjkdQpyBk4/Gug8MarHNG1u0h8xsEMTyT6ZrjJNTUHGxuvUHrU1nqccMoaP5WYgcnkVi6bvY9SnWjuesaBqCWUu2Rt3qc8dPX8OtdJ41+NTfDPwK1rp4kj8Sa1GoSfYfM062IADp0zJLu2qeoByDmvJZPFf9l6V5kjBdzZJI4OBxX0H/wAEqv2ev+F/fH+bxhrUTXWh+D5E8tZizJdXhRvKGc/MsSjdt5AynHStItUoucjHEYhtqEOp9x/8EXf+Cctp8JL/AEjx/wCNtPjuPGuqJHc2ttdwrJ/YELYKqgblbgjG5xgryB1NfrtYxbYkSPCrHjAHAH0r4++Gmvtpc8DxyMjBlPIz0PHX8a+o/A/jO28QadG0LAOqgSIT8ynHWvJ9u5T5pHLUj7uh10cioo5+bHNDz8HbnmqsUiyw7m65plxO8SLs3Y78V0HO09y4ZSCTyPpUkb7gvU7vWqtsXK/vM7vcVOrbQvVuOlBJLtG7PvUq8rn0qFVb7zHavXFKXVf4hz70PYUpWJQVKt05wK83/aj/AGdYfiF4Im1/TYWj1exheaWO3TH21FUkggfebHT16V6ZpWkzapcKq42/z612sVhHZacI1Hyop3cn0rXDQbfMcuIrLRI/MP4d/Fp9ZsZtGlaRTayGGNz95sKcBu+eMc1lahJqOhG7jaVTaXI/eqw3beB/jWn/AMFBvCVn8F/jXLqGisLax8SSfawkS5EUy8SDngDJJ+hFZ/h/xtZ3fg/Grc33oTtOCOOBj27V17o3pbalFtc0vTtIjtY7gSNjPsOent9K29Phs7W9ljaQyR7d5JA2j/PrXnF7YWv2m8k8sKinduLnaM9+tb+kX4i0gW9wwzIpgMueGPasXGx0HU32nPotsLprr/RJxtiYHhN3Ix6fhXOeMNUv/D+naXqul5mmjnBnTG7cuQMkfQ5P0NaWlTtceBpNHk23UkZ8yNmJCr+I9MmoD5MfhW3mtW8y7tX/AHqHqME8Y6dPWtFNE8po6f4qk1PUXms7O2j3Qu0/Iyj4BOPY9a0fBxl1axIibzrGSVJJCWyUz/gR+tefW96dQ8WyW9n5dtJOm9dx43fxAn8a9Enl/s9LHS7GFraPVIysrrz5MigE+tZ6tlGxquvWfhPW7aOJs/Y2Ri3ZsnHNbhkN/frqLySG3lX5442O1lPTjpivOYLe6mlutNvGRWV+JSBnaAcHp0rtLa9m0bwjbrbbZxZsqrj+NSf/AK9VH3XqBTs9FfTfDccNzIskdo4wytnCeYSOT7V0Vlqkej6TLqHl5j3bXjAzwTwaydesms7M2cUit9shEqE/8swedn8/eumXTF1LwvDZ2oVmZAZjj7vbHP0z+NHxaoC1rNkviLSl0z93JDOfNO7pGRgjHp17Vg+LvCDa5rmnpc7kl06T5ZFGI5YirLyT35H5VatNRaWSGCGDzW6SsrceWpKl89e3t/Wuk160bVZlghIkjjgQq+PvD/JFaAcN4D1W7mn1DS7w7bjT4ykKOciSPsefbFa66ZJtH75vzqG40pY9P1eOOSL+07ZSVfOZE+XcBjPqB1/lXM23im4nt43MLMXUMT5ijORWbko7lcp82aPJ4d1cWViNQs1knYtG9wzQwxzYz5MnHPruzjkVWsJY5ryx0nUbiGxhu7hGilhQcyhAF2naSyuANrD1yTzW74l0XSdN0tZry2gj063yFjJEc4L9Gk6gEdscnHarmn+B7eDwxpusafcrdzXEcV3YCaIiSJjyj7jxjOOBjG2pjy7MWpg3HhKez+JdpdXmpT/2PbObVY2QTHUGdFOCcb02bJCAMKSRnPGZbnSofCpOt2raprOni+Eem+cyQrasuG2gj5mGWU7cd+1WviB8R7eLWJrOHS9QbS41hSVVu5OZFCs7qSPnDNv+U8DIFXtMuP8AhcENzr+m2B0+LwvcxwWk0yCQxORkt5QwOBxycnJ7Cly66D6HM6Daz2mt3DXUccr3pknmbbuaHe2XCLj5QOPfHXPai/iuPWr2S10GZxZ6bmDzNgRUk3dmIVlPy4+me2K6TVYtMF9p9qyzXuratceVc3xgkWKSQ5yzIpbYOvyg/MT14qHxT4Xbwzrs0d5LZ3Wm30qeSkm6KONtpyfJ3E9QpG4nn3xSkuhJk6X4w8UeMrG6a7vVgaxtmkKybcxKpH3Se5wcEZyfTmp/DEF54l+GMlvb2l21/eOEvpXKxST264IkZsgl1Y8dyf0v3d/HLq+nyeS+oLcI1t9nVQJY4IyGPlDOCM4yGwSrEdSM6/joJ4mittSsi8emrGHuYXtNrQGPGYnwQWPYjOcHvSjfqXzIwPiPqEmmJIum2mmyWd4FWS4WHM0YVehcH5cbce5I9RWf8NXm8O+OrzRY4dOs7WawWbzJiskl3PGskhbcuCitlRyc5Uc10a+HtL+JEV5df2bdW15o8B1C3gYn7LIVwoJA3bskjAYY+tcxp+sDxL8S9Us7tl024uLXfb+S5nWI4yAFAydwGPr2xitZNog6Tw5EupxR3l1cTMslxGqq7Zs0jQnc6JICfNLAfNuK4PCjrXC+OdQ0Lw7ohYyX14rXxaVfMRLobM7RHn5VjI254BznkV2Gp31o2m6VZ2OuSWu60Szu5d7Rm4k2gNmNujbtwAzleK8r1XwNe3t+umRrdWFpHLM10Uj8ye6OT93OSSRzjJqOdgbemeNrTR/L1K6s5bW/1CKWJES6cb7cgAoEIOCeT1zwOcVi6Zbvq1rZ32qw3kkk0otdPSVV2WsB3GVyeSxJ2AdOmee2Tq7w6drXhW6afX1Wzty9zvTHno3mKmd+QHXHJHJAA4wKn+KXiiHTfDH2XT7jVLibVI1NpbLYtcTT5OCVKjKqM9TgfXtN0B200F9oF1b6fcWtrf29jMH0m0kuit15SkljIFGFyXBBIwfm5HFaPiyfTWn8u8b7dJp8aXSwpIBhvMxhRnLbeT2xjNVLWzj0zwTptw2oXFv4k0r5BeiLKxxhWCxvu5f5eSCRg4qO41SazNrNcaXb6nHFAsgvp5dvmvyGB4KtGXxxkD69KLoDlV19vBEof7RNEutF1FvEAfLLMdq8jGTnpnGav6TpMfhN11C+kN091F5t3EjgRrOUB3Z6fl0Pek8XXC+LmvLmOGONtNMcsqRbVjiY8gRg4HGOMVFpOgah4wsJIlj+0RxoGlEcjlrchgQZUHB4OBz71e2iApSzeCdcsrqxjh1BWlV49Qa1QRPBGF3BxvPrkcKM9Ko+HbzTtb8NJPaJeSaTBeiVYzKkk2/A81wGzl9o5z1wOepO3beHzqPw41DXNGXTftGoI8dutxAPMlVXCnyzgF9hBPPHoaf8Ffhl52ntatfT20LXzRzXcdtvfEmQFSEEc/w8MfUVMtNwJtQ8UaHPcaXdabbTXFzKkrSTTSsGUMNqoDnauUVflUYyDkE4rktbsNU05FUrBYzR7ZYpGYDAcsqsArbgW5GDjkZPB57u20CyXw9Pf6XHOsNhdzW0enqf3Y8k8yyEgtvDMBnjA9a559A1DXdFl8Q3F3bvHJK8Nnb2e6+uCyKNwOFXOFfd7c8d6Tv0GjvfF2kNKky6i2rJquoWcFrf29qAVhVAzAgghGbliGZiSOpOOaXieH/hGvEGnR6fefaNVuoY2QE5/dJGuPOcEBgCOQv93OeBUWoeMLzRPC+kX2pXErWMlusRaQCBnVRhVlj5wRluo6fWubt5rjWpbi6j1K8uPs5WJIrabZMkLgho959SQMAZwc1tpayEiaTWJoPA2q2MGoK10xKmM/NFEqEvJIoxliDkjnNYsXheZ57mHUJE0q81K2iuoROrstygUImQSSrMcE8gAEnAFdhZ6RpkfgK4haEZt7hi091FuezRlHyk5J3sQecgkDGOaydL0m31LSbXXL6+WfUpQP7MiuTJJIIQOMLnCgjGAuSaiUmtGBysPhPUNBEl8JLqaGeYQJNwoViASo4z8vIzjHAruvGXga3trWxa+uBeW9nC++GzdcTZVcYZslos85PJz2qzofhK81HxPJNcTCO105GltkvpkVLwOu07YiTgKTnBPU5ArnE19raWQ3FoupQ29lIqyCXarZH8JxggZzx/d60Rt1A506pDqFvb22m2t5pNukuVkDbRKofDhcLzkcY9/SoPijp1rd6pHPpkdy8l5KJYoZQoZowB852nG7cB8mAcEnvx1/hPwatveQ2987WUkxa+SXzdqQAS5C9SXZlIORt/iyeCaz/H/go+JGuI9H8vTbO2aKK3vpZCJJSANxJ3bcBsjLEcCtJK60Aw7LwNN4hsrq++WFk2nah/1R4OCcA5HXHbPvTpPCtj4e1C+s5mQ2NxlkkAOHfOdnA5Zjnk8VsaM9x8NPGHiCOxmOtaNan7KZ5U2W1zIwBBJUsAwG3PzHjFcv4k1SaaWxjtpI7yK6voVupQRD9nSR9phVzkDB4yx6YOO1Z8rFc0tWhVNGsZoYyii3fYQ+w28pb5c4UZ4H+eKp3guDZ3EMMNu0iNG+nNKp3biv77PXsM85zj3rvtR0m10/VL5b63s4bXR4ftEkUc5xdReU2EI2g4LAZIUjIFct4Yn/tWQNJZrqNhqEMdxYS52pAXG0OCzAsu1sdOw69jZ6jMNtbkl8V6fJNZtd3ELpJNKkRj/drhjyMDcMDn17V6J4YluDo15HH9hhs9RmF1Pb2xYKxijIMh3E/NsPOTz2A6VyAt1+2tb2NtFcXjyvbmeZ12vuwq7crlAAASScD8DWnbXF7od+VWztr6zuEAurZZC9sOCOXxnP8AtbSOehrVST2A0IltPF+p6RJrZ8uO482SaeIlvJEYIgU4+f5zzwMHPUZpdWsrqG6bUNMhnum1CyMKSuVDPEV3bCM5X5QMHufSq0IvINdjmbUQbe0j869skUgI7LlId5YMwU4JYDBx2p8uqRmNr4bbzUNWHk2EDHdDbqWJ4wQSyr1OOcc4pcyA8i8G6T4+0bXdZutct7u+sWiWS0iivwwtHI+XOeAMr05IBPfLV6H4W8CN4n07UA19DcRK5eW9WXdbyhRhlJ4LvuB53cVufDvw3ZeG7jXpNSvLi6OqqkcrTTGGGyKuxWRIt2M7W+8WPftxWTrVjdaPbf8ACO294t1pN5dvMlmuZI2kI5myuFXOAxPfBOOuRSTB7D7nwh4kF2/kyjQdNnjaJ3mtHTzbYxlAyljgrwST/CApOQa+Hv2n/wBpDWrVLjQvCN5eaLoao1rPc207x3WrFSVaRpBtIibsoA+7yW4NfYfxt+Lz+E/2ffETwXVxc601mukR3P2gv5UsrKjOoQfIoiDDDHBzkHg1+f3i2yjm1CG3k2lbcHIJ4z/+rNY1KrjKyO7CYVSi5vc8baySJG/d9fvkry31P41Z8G+LNU8Aa9HqGh393pN9CwdZrZjGxwQcNjG9cgZVsg8g16b8RP2c4dK8OR65purRXlxIA8+lSReVJBuYKDG4JDggklSFIAJ+avML/SJrSaQOHVl/h9B6da6dTKcLOyP0c/4J1eOr/wDbnuLnw/5Oj6V4os1E+reTuiW6Qnb9rjUk4zgb1XAVs4CrgV9DeK/BUcetXMU1vbyyabcNGfNPzqw43Ecdfyr42/4N8La6t/8AgofpuFwt3oGqRygoGzGsIcHH+8Bx75r9e/2nP2LL/wCJatrWgmyt9a8kGeKdNsN6R383Pyt25DZ4qKjvojm2Z8RXvhy3mEyzaSytIWPnwPhsc5IHI569PSvLPHXhGHwJJcaneSWkOlxgBb+ZGDRDq6uoO3BycEDJx65Fe8+K/hH408D6lLDqXhfWtPbcQs1tF9pt5ACRkMo79e2c9q8X/a5W6i/Z58WW+pMGhmtQYo5bUxyK6EOCCTx0b9axjOVylFS0PA/H/wC3d4X0C4On6Ha3euRiGOKaRAbWEyI7ExqfvMmNp3bedx9KxbT/AIKI2Goaurax4burWybCeXazfaliA6ZDbdwGAe+eelfMl3pkjyySoP3ch3Djrx6/hVFf3TbcfexWkZO52Sw9OMT9Zv2JfiXpHxZ+FHxWvPD2pWOpWcPhS3V44sxz2z/8JHohCyRPhlyMnIBXpzXc6fZRwssit5ci9I/WviD/AIIzSbPiL8dm82OBv+FXqfMZsY/4qvw1jJ7+g+tfdPg3OtWg8xIxKGPz5B/HFZ4q91bt+rOGOknFd/0R6doOvfYNGto+v2lQrY5xxWhpF+63kKNjaK5vwvMklmqn92Vztxzg+ta0c0FostxcTRWsUKGRpZWCRxKOpZjwB7nFcfU25UdpZ3bxvJlPlTODUh1G3stP+2X1zBYWysfMmnlWKNef7zEAfn3r5N+NP/BRc6C8mm+C7S21C5jyraxetutlI4xFFj9725ZgP9k8Gvl34gfFrxN8V9WF54k1zVNYZW3KlzOWij9kj+4oxgDao6VvTw8nrJlRh2P0I8Z/tzfC3wTcyRzeJW1K4jYr5Om2r3hY+m5cJ1/2q891r/gqd4T/ALTVrPwn4mvI4sgSTyW9sCcem5iK+HvO2blVcpngDoKZf3bCcpt3cZyT0zXSqEOpXsm+h90WP/BVrQXEP2rwPrMbKSVK6hBIy8jsdtdl4Y/4KZfDfxVrNul9ca14b2NiRtQsy0YOD/HFvGPfFfnlea3YPounx2tleR38O83lxJerJFdAkGMJEIVMWwbgcySbiQfkxgxQzvcLJM0YVd27ap460exhsg9mz9V/D/x/8O6prX2qDWrG9s7ghYZIpg6OOOh//VX0Z8BPjpptlJItlqEN1G42vFv/ANWcYyPevws0TxJcaFMslrcTW0iYJVH+Vz7j/CvVPhd+1TqnhDVLdnvP9HcbHdXZWUjpnn9a5/qtneLCS5V7x/QV4e8aL4piZ4Sq7eq+nrW3H1BxjPqK/NL9j7/gogbG7s9P1i4kurWT5RcNJhl/3vl5/Ov0V+H3xEtvG2kQ3VvcQXVrMAyyKwOQQMDjP86nladmcvmbZmZG2jbx1zSPK0q/MVArXs9Cs74Ftr/N33ZzWtYeBtPnj3SRux3Y5brW0aMm7I5alZRVzi5Y1nI55xwBWlp/hq81Z1SKF1xyWfhcV3Vp4WsLFgY7WMH35q6ZEtUZjhVUZJJ4AH/6q7KeB+1Uehxyxj+yYPh7wPb6PKss0jXEy8jcBtX6DFVPij8X/Dvwf0CbUvEGrWun2sSk7pWG5voO9fNf7ev/AAVd8L/ssWNxp+lzQ6tr+CixQzBgjZ5zwenPHHSvx2/am/bp8aftPX8lxrWt3hspCB5ImKqw+g6D29KKkoL3KS07mtDCzrS56p+h/wC2P/wW7+HekQXmn6TNqGrNGxVYbO3LNKR/ecsEUfma/H39t34l6Z+158Z28YXGjrod19kjspI7e58xrtY8hGlYr95VOwbeiqvLHmsXXNZWSVlxuB6ndXL30zOfLHWPOw5+Zlrmp0VCXPd3PXtaPJHYx7XwDotlJmOzikaVSqmQbs+vX6UHw9psLRrHY2uM4Y+WOP8APNaFzJiAbO3MRPf1qCLh92flY7WBGME8fzrtUo9TON+hZi+GehaxbRpdWOzbl/MgkaKQDpwRx+h+lfRn7HH7Vmkfsn+C7fww3h17zSYZHna6trhTeSO5y0kobakjYCj5dvCjjNeDqRbQ7Cdz24wcHqOv9f0qnNe4cD+FuRnt9KxlR9quV7GluXV7n62/Aj9sP4f/ABL8ttN8QQtIuNsEqGGdPZ0bkfUZFfUfww8ZR3CedZ3O7zOVMZyGB7iv5/ND1B9LvFuLeaSGeM7keM7SD/ntX1b+yL+334g+Hd9DYzXMt6tvybaWQqJ48dUODtPqQCBycYriqZW46xJlUvoz9vPDHi6S+TyJ/wB4PUdq6qys472x3LKxbHOeMdq+d/2Yf2hdD+M/hOG902ZWmOVliaT95E4HRhgfn3GDXvnh7UPIiVSu7zFzkNwKmFGy0OapJI210O6kB+QfhUtv4cu5Tt8pmrS0e7Z2yv3JFBU5+tdFoblovrz1/CrhRcna5xSrO2hzEHg68n+Zl2Z7ZrQtfA2wfvpPlycj1H/166Vn2jcfrXif7WH7aHhn9mfwfeajqeoW1ubcElnkG0EAnH1wOnetqtGFL4tzOnUq1fdieleLPGui/C3RWvNQu4bO2jGfnPUdP6jmviP9r3/gsX4V8A2ElrYyS33nIXtoLAHzJwB1ZyQEU5HOOe1fnx+09/wVG8R/tJ+Jbq/kvJ4dBhcrY6d5/wDx/HqHl+XlFwG2YwcYJ5r5c8XfEy+17VZri+aa6uLpt0sjzHcxwevGMYOAAAAABU8spqz0R30sKoay1Z9EftOf8FNPGv7Qdnb2MWm+HdD0uwumuLZkie6v9xBX55XfZgjssfXvXh+uftL+OtbuFurjxVrizwJsjeK5MJiXpgBMD9K4S+8QqZVbdtReqjt+P/1qrXd3Hf28bx7fLckMxHK81pGKirROvlT3O0H7RPjYtlfFfiGXzOG82/lYH8C1dVof7ZnxC8MQeSNcS+t16wX1pFNG2M4GQA/GT/F3rx0w+YnDKv8AwH/69OlulkjMZ+oOM02k9ylFH118Mv8AgpbHY6Sth4l8M/JIwJvNIcloRwGIgkbkZycK5I47V7d8N/2kvCfj3w5dLo+t2d9cSMR5e1oLlByPmjcBt2AfUcV+bEN4kf3nZFbp8vBP09qtWWtGxv0mjaUSQndHNFKYpE9wQM1EoX2DkR+mOjeH5xq+n3i7vsxmaJ5/RsAjP1yK9K8DfEe60vxV9nuLOK4WPAZ2GVB6ZGCOtfCf7Of7eV3ouit4Y8WTSHSLiVXXVxvklteikTLg74yM5YYZc5wR0+zPgXc6f8Qg8lrcLdWViAVeJxIs24kqyuOGXjI6/hUaxZnytHa6j4emt/iBfeIo7mO40ZrWOWS3YdQeWAHXgg1dt/Fljq+gIsD+VbzyiOM+oBwMe/t9ak8R+MNP0++ht5LSSPNq65/5ZyDvnjqOP/rV5z8HfDd5e/Ea6TdJdeGdPLzxFThBMwJCd8EEnJ9ulKWoj1fxrcnSLS41ZUZljCWwZect0B/OtDTomvLK1vILuSNIox9oVSAN3U/0rC+I/imbS/h9DZRxozQ6imXXgbS2Rx7H+VP8W2F1H4EWPT8edqkylGUbApOM8+lF2thG34Wuf+EQ8c6pcTKv2XUogLMse5zv49yRVrwpr15b+K5NAhVWZmM0kxOFiQkdz2HArz3xu954lvrLTftsJ1bTdhmUcfISOgz7V3mp6lanwTc30kn2aO4P2We66tC646dMg/hWkX3ApfEzwf8A2H8UtM1DT7qZrbVIXtL8K2EmK/d47elMHwp0m0HlCCX938n3/Tj0q1c6uuo2kb/aI3srZkeWQ8rtxkHOepxmuD1Dx3rlzfzyW8cjQSSM0ZBPKk8dvSneD3Hdnz78RfAV9qVzHo+pQTXMBuEuJkt5NhkUP5i5kKk7TgjjnGeRXd6p8QrJbm3XT9BuRHb3MFuLGedmhKsdvl5HII5yFBxjjNYV9qtxZ2GqTR32o6hc25+zQXGpQ4kvjKw3MyR4VQucAAdB1rBvNA8Qx6/JJHc276bDPHbW0scRXEw3KZgB84RWVly2N2Mjg5OUopfCB0UlveaZFcf2bY2uqSaxeEtbyod1ioyjDLYOwFeuAfbvU3gB9I8G+Pb62stRWxma1E+p2sW9xcM5ZNyj+4o7nB3N0xyNLwb44k8OS3nhKK1ia7swbm6u5p38zZKxkYjPLBi+RycDjjFc7bW0HgPxhdeLJNY/tKOxX97NKu5o3O0rDjJDrhR1AxnPpm9lcRoXWijVLvVLVodburWNBdpE02zBT5lIIbdgsT0ycA9OKy/BN9b/ABEsNS8TTx3EOvaNA7W9krs9nBIDtwisuGY/McsMgk81zXxK+IF/4q1uy/s2ORYdUQ3QW1cBcSFSSOhA9c8DHbNdr4U1yXUvCmoWvmQ6VdaDPHFLJCfOW5Z1ZyF4GNoOWJOMsByajmu7sroYFx4pv7LwzY2d7Ha6dq0Mj3DTLyivK2cISucjgkHIBHGcVe1TxXJpGk6bp+yPUtQuInazt7GRFn5yssk27AbcCCDjnHarlz40TV9c06O8vLW8l0W8dmsvsxgEm6IqMswKkqxDd+lcpr+szeE4dV0/TbiG8stSmPlzybjcLNJwsUchUMFDN0x2HPNFyTur65muvDVjrOl2trcx2rtYTWt7d+XMttt4GFO3cGwcbu3BNcv4jS0+H+tSWbMiX1rZ+V5xmMS2pBOxhvPmM5zgkgf1rI8D2N1ZaJdw6vDp9xdXTB0UZkuLJ4htXPPO4dwTgGtVtN0vX9W0+O78ma7tYJpL+eFAyQAKwjVpipXd07556U3K4E1tNcab4K0IX1xY6gsl6sputmTAr8mVpDgO2OcY4PXNZ3iXTdQ1Dxfq1q0di0kcQudPu4JixkGTuKYAGTkcc85o1PSrq0fQI5tIkhs5GWa3eblZ4jyJF5ywI68AkHIGCK19K8Ny6F4t0++n1CbS7drgNM9ou2WNWGQ0fVmHzIcAEjPPQ4iSHc8/l+HcHi/xHex395cLaQ26mW4kdFW1Oc+Wozzkk8++O1RReJlvPFN/Z291Z3tvprQrDd2x3PFAwcbDwApAUggHByfeug+I1rD4Z01VjknMFvcecs90PMaTzTs85gB0PHzH+6MdK5bWLbULPWdQ8Oz2+l3Wh2ex7q4iUxpvIJQ78ZKgqehPB981Ijaj1zQlt10++nsbhkuBcR2s91skmb7ilmU7j94Hacg49hnE1f4nahqerSeXbxmx0mJbG3lkiMKSbD82FHVgpHJ9Ae9Zlr4N/tC+0C4sLFdFgt9SjmvdTuN0tv5i/MigBd2GGOvHc4xXST+L4vEfjNtQuGTVltTjyrkZWY8gbOCA27u2M4H1o6lWM3xba6knhPSYL+4jmjyr3bW8mDdRjB8pnx8rEZGeMZzmoNUuNS+G962nadcNctesLm8tbdhPHKi4xEj5+ZyvuB1FdvBpOk3c07alZXyrdTYPlHCQrkP5gVerIQ2OMEL3yKo3PhzRvHdt9n0a8ka/sd7rJLci2tmReHO0gOpGRtJwNx9Axq7okw9N8YbPAlq0O2S8aEpJcG03KWLM2VBPyHkDKgHIPbr0XgnWLjS/E1vcQ7U06+sxDNZNEPlkOWLhypMYU8k9sYGRiuV/sjStC8G6lYJJAGsboQMkcZcjaFZXBBIOWyPlY+vQgncsbOy+JlnHeaxHb6C1qxhSyhdwlwHQmN+p5Ugj+fXm+a4F66+Hl3cWFrqNxNPvuJTJcx2kvlRuzj5Y9gwDlduSDknJ6k1gL4m1LwhbQr5cenw6cJrOxURGE24k2h8hS3JUgZLE/L7mrh0xNNLNMsUN1b3lv9njK+Z9s3JuDYUkKVCjIbGSTwOgZfTQazp8z2mn2s0eoSvcC4jZjPAN+3Eh24B3q5AJ6MPQUrMCvrmkXa3ksVvJa6osjiS4d1DkKfvOdu487c4x/WuZ8cWdrDcvd6f4kRra3uwZoLXMb/IFOwbhneTjuOAeRwD0WraBrB0SKZ5pdHhBdmmGN0sYIDnKtyMFfz471yvirw/Gq28cEDT8efcOMdSNuRnncxxznHFTLRAekzatY67JcWdwjeH7G7tTeXEqxkyJIygKqRszeZn5VySCMkjPbm4baGGbw3Y2E8N01ooaJZRKBvRlAkmB5TJydvTGenSsrVdUg8P3NnL4k1OeSONFmS2gj+0ouVYgzMvGQVTAz97BFdlojR3tzLeXiRyPqohu084Kk8kYzgKOo3Z5yOOM9KgaRZ1LWoY0u7u6jkESz5ySN7uVBAVEJO0Njpn8Oh4bSfDEqeKby1sbrUbxpLNbi6SKIiOEl9rqgx36YHqelenaS2l/DLwRqF8zSNbtdC52BTMquOGd9qhtoAA2jO7t0zWN4O8Et4q8Z3+qWuoSXMEtoUSyeYJ5QHzfxFcHdzk+natbN7CuJ4W0LSz8QPEDbbyTTbSKK8uxcoYfs8Lo4ihYtld5YOTtJJ5OTTvFnhiPSvDENrdWs0dr5yzR6cp/fi3dtzYLYG5s5wecNTvhLpdv43Hi/Up5Ld59LnFra2t9K22CVNyqdvG+POQCM9c8ZJo8e+N7vwwdH1K8s7XVLjTXl+2MJGZZXkwSVwCN6I6YAJ4ANbXAzfDGn6hLonii3kksJIdP1AQWSqchI3AGOVBZVGzLEZBLDtXPeN/AEPiOc293ElxbGaMBY5P3ErDqSVx8vHrk5HSu1hvLXQviHaiHzzayQG4v5Yo5HihiEZfeAoJPmbVBbJ5JG31gvfiV5rafa7JmuPKlxHb2yu5Vl425X5R1JLex4p3RHUxrA2Phzwo00ml6o11AxEF7cXW+aeMZHk+XuyI0JOGYDP5Zxdc8PzX+kXGux3FjJuItpIbVm863Rduc5UKzfMBhDj1IxVrRvDVv4i8M6lLqE8UapcLc6jJjzQIv4duzB+VY8FRu57Hg1Dqnj23g8L6nbwuW0612JZK8OxiWdQQigDgr3ODnJ7VjLctDtXZfBWm28NrPK10CiyTzMT8ijO0YwRgH0696jshbx6JFqFjPvV5/LkBMjRyNzkkAn5Ru6DH0rP8ADniWLVNNjurm4s5JrS5mLCaYb93yhUcA9AcciktNHsL/AMEQas1qtrosE80eyMuskhzku7K24ruPGPX8QLmWwrkT3U0N1q10sjfY7pV3gr94cAYwPXAqaw1bS7Z7O7kWSPUfJMEMVsrTeZIO5A5HPXHvWhYWEM/heNr5pIJvEEnnWqNIJXjs8Kys4UsI2LFvkfa2ADtxyeRvr+Dwrr7JpS2t5cWMzRuZ0B6nGGydo6HHp7UrMZ6EYrrxXonnxq9sN5EtvcHYUlUAcBhll7kH16cisfV7uXXLOSDTLyaXUIVe6uSkZS1ttzAbkUDdyAAFGec84qvBod9ZqbpmjvLG9iPnxWU+6OIsc7sIcttXAIAHPGcisqLWNRn1CbT9JureTWLhTawF4ZN1vGoJ80lflGFB+8d3A4yauG4Hjf7ZWsJonhG3Wxa+bTxcKJDPGULyKpZWkJbBZyZMBcgbSM8jPkH7Kf7IXjz9tL4ijR/BtnbtJGBNfalqEjQ6fpqHaA0jqrEsQ3EaqWPJwACR9KftFfCCHx38Jb6G3uktZGdr1VgdjHLcpu+YllLAFmOAcEBmyOOP0F/4Jd/CnRfg5+xR4Nt7O2+y3eq2MWo6gFUeZcXEgBkdmx1zleeyDB5rDERs1I7cPiuSnyrc80+Av/BDL4P+BdIhbxw2s/E3VQMsb+8lstNj4+6lvCVLKDyC7k8D6V6d4j/4JN/s9+I9GazX4T+FbRSpxLbwurqcYHO7Jx9cmvob+04bcswPmbcY3E/NngZ/So28RSrjiNWcbj5anamPz61jKs92znlJt3ufBH7Pn7HPw7/4J4/tovq1nJc6LP4gs5vDumQXF00lqbid4pQsZcblLRxsAN2Ocd69p+Ln/BRJtJ1C40nwuV1BoR5RupSGtwf9gD7316V4T/wWvubu6+BT65bPJb6h4f1uyvoZU++gG5AwPYglff8ADmvHfhP4xh+I/hzTtagVVivolkMSLtWJ+jAD2ORir5p25jHqe/67+094w1y38y61GKRJDygt1VeffrXyv/wUP8cK/wAIpr64ws+oE2CADjewx/L05r2O9n2xIvK9OnPHb+dfCf8AwUt+Oq+K/iHYeDtMkjmt/B5ae+dD969lVf3eehEcZGewZmGcggFJtsuNr6nH6JL4I0DwPfyardK1xMg8i2WMvKxz0wOn5j8a8V8TXVjd38rafaXEUOSMHAx9Bk/zqqztdybpFPmEZYk/1ppBRQFDKvoTXdypm06zvofV3/BIzwqvi/xV8eNP3TK1x8LkfMTbWUr4s8MsOfw5r6+8K+FPEngaa1m07VFvFQAyW9zld4PYcEfiSK+Xf+CKFo118VfjWscm2Rvhhnd9PFfho/0r7Sn1K603xSsnkrKqqA277p6YxWGKk1KK8v1ZjTTk5S8/0Rx/iTW/H0Fzdah9p0vSNLsomlm3TrtijXLM7HHAHPIyfrXh/wAb/wBozXvizZ/2dJdyQ6TG+8W6/L9qI43vj3/h6fjXS/tqfGgXvinTPAumttVoE1LW8ZByDmCD9S7DuNo68V4w8ZkOCu1guB7iro0/d5mdFGN3ZjbqVlnbcd2WJPOabJN58WF3BkOScdaa5Knbjcy4HPbimqcjpjd2rQ6YrsPnRUBbay8Y/SmMPPddm5j0x3NBdi23Hb1FOxtTIXcx657UDGjdhd393tToLqQEoxOz0+lObA4bn2pVOSFHNAakqzrGVGMlvSmjU4YrhdysNx24Cj/H6U26swttHIjfMDwAapwRvNfLHt3bfmbLAYqXJIOXW9jtvCfxLvvhnrEM0M0n2FnIZDhtnQ9zX6K/8E+/2/x4HvrS3lufO0W4I8yLALITj5uSB+tfmV4hWKOyKtIsm75uecj1rJ+GfxXu/h74gVVkaO3lPGSTg9vzpOPMtDjxVDklpsz+p34ceOLDxTpFve2Nytxb3Q3KykfLnnnmvRdPv45LZVXd83+fWvxz/wCCX/8AwUObQri28OaxcLJaXxX7PKxb5X+YBc4+lfpt4Y+OlrLbrvaI9fXmlTrJOz3PMr4aTV0ezJOpbr94YAr4b/4Kuf8ABR2H9nrwreeH9Du/L1KSCTz5lCkx8HgZbr/hXt3xu/au034a/DrUtWaaKOW3gPlMxJIY4HHHvX8+P7a37QVx8f8A403JlnLWrXLTynDfMMjb19ia3linUjyIxoYXllzTMX4l/FXUviM02s6pNJc3WvXBSLzDloIAdxwPViAOp4LVzOuXQtbaNtvRRgLgjp71zHxB8dbLqzMbYt7EhcFDyCMH/H8K149Rj1LS0YkSL5e5fy/zx1pOKSSR7OHimmY98wkkbbu45XI6fWq9xHGEPDb+2R+lWNQdc/L8vy9qrpIyW7Bo1mZl2jcfu+/UVJ0eyiZbr6q3BOB6Gn2Mypc79u7cQjh/Q55qd4/lClfL7cc1BbhY76PPrhgf4l9RR0MY07O5p3Ya2ZflZQBkHHVf85qqMIuSu7JJBPYVoXJZLVVVg+3dtwCN464Oax5meOXbubC9P8K0o1GgrR00LiyqUUjjPY1ueCdYm0zUFvoGjWe1YbN/RvUfQjiuXjdsHLHb1J9/rW54WgkvbYyQ7ZI97KeR2+tbzqPlMaVNOSufWHwH/apv/wBnv4h2OoabK5s53BMBPySDoUbn723p17c+n7B/s1/tGaP8bfAVjrGl3G+C6hjYofvxttG5WAJwQTiv59pNa+32EkJYPJkMDnoVHH8q+o/+CYf7Wtx4A8eR6RcXDLZ6x8o3FvlmUDpxj5sY/CuKWi5iK9FSdkfuz4a1SOW32rIrYK7eeBwa6zw9cKbYfMM7SOPavkHw18f40jVluFxjcOoyPpjNdxp37R/2K2Lbt27G4AH5QB14rGnWje5wywcnsegftNftA2fwV8B3upXUnlRwxOxJwNxXsMkV/Pn/AMFAv24Na/ar+Kt5atfzf8I/p9xuWJcbJGGewPOM9a+lP+C2/wDwULbxXqC+F7G6WGO1TyfLyyKXIUtkkDn39QK/NfSn8zTPMjbzBk7mVt2Sef5muinFVJ+0kdNGnyRsdEnikpcWsf8Ayz3YyetT3zFwrduvNcVftJJcooZhIpz/ALprYtPEQvUWKRm8xRjJ6H/PWtpRilc6I2ZdnmjYMnAJ7mo1s8R43L+B4pJYEJG/5jjPHPFNeRYzhe3IFYl2J4J1iJBm6jBAPP4Cp4tkbZ5b6VmvdurcErnk4OKXzB/eZfoCaBl+88uQL820Mc8HoaJSzWu1XjLBe5PP5Vni8WF1Xa0jZ7YUfrU0GsyBZEfzFTsu8cD8KAGRapNFL15U8AbjX1H+wT+13c/Ae6vPDWpQRzeGfEskcIk5DaVKeFcE4/dknawGcZBwcGvl/fFIm4HaWPZq2NCY3N2kLTb43UgqWPTv0qZarUqMHJ2P2C1OW6tNEsbydY7yxW3c+Zj5s46H0yOfyrN/Z18RyR3viLwvG8ayG4GowTs3yeU6/dJGcNk8jBryz9lD413HxH/Zfjsbydri40sR6ddZ5kZSo8t+nVlBP4V3fgFrPwV4/uLmZvIsr21SKNwwG1+gJ/PuBWMbXszGdNxlZnYW2q3H9nai0qo3+lKstvMuVIzy6d8dD+fFdfFrf2ny4pI2+y2rgLHDzJCpA+bPcGuQ03UptVvVj1q3UfYSUlMJ5eNhkNjJJGGzxXL+Gfik1h8adP0a3kaaS5laAnBO5BwAfTA9fWlrcnQq/EQSab8XbXUrVmZo5fJvFzyqDOGx+P8AKvQPGPiGTxf8GtWvY4i1nbTmKNIxl22kDcR6f/Xrz743eF4bPxzdX9neSCO9RoJ7b+GKVNo4OOufwrtvhpZL4X+HNla2v2rU5NcglS7uHPy2+CMqR6gHOe+afLfQr3UaehaEus/Ay123SQ2t5ia7YnDAqeADjocfrWtp0+iDT7fZImzy12/OemK88+PelXD/AAs+y+G9SWKzWzjhWKPJZpPMCk/Q/wDs3sa9B8KaL4X0fwvpto1u1w1raxQtKUf94VQDd074zWkYKxmfMuoeHIdY8YR6vf6tp5dLz7AltauFgjGD++cNyBz1P1qTwc//AAhWha5ewal/aDXU3lW7WZ3WO6N3CRMQWUnGOe5Y8V12u6Xp3i3UIrS3a8sZTi7vZFtQsXAYeUiHq54/OsL4iwX3w78CDw7otslnBM0MkkcUkX76IxhpUJXvyxyBye/esWVGNyp4G0BdM8Raiuq/ZL7VNWhivri4MwVLWNMt5ICjhSzcZI3Fe/NO+OHhfSvFvhixvmLaXY6hcxfaDIWVG+b5gVByzn5ffAArptT0+Xw74LvLW7uobWeWxVgkKeWhiACCM555IyT6luorlYfAl9r2sWrXk01v4dSPJtp5jb7gFf8AfKzfLtDBVJzu9Ke4bGPZtceEtPk1CNFt7cQPpv2T7GZPOi4AkJYnYDk4X2I7ccDF8WW8C/FW68NzNdWum61B9pt5ItwVLnaF2v8A7BXcCCQMhTngV7FcWaaS/nSX+3SfCpBSe2gN19qlIDAAglZFAxk/7Q5rl/iZ8CPDPjXxDoGoP5cdteDLLbXEiywYAaUfOdw4HQnHJ74pS2J8zFtPina+MNA0x9MiWZrcMbxElM63TochnOMe4A6nHpXUXhUalofi+x+xra6Jtu5rOzgG4TDBQtu7gqM8Doec/MK//CodK+B+hNZ+GxY3klxfG4AEnmStGwX5Zg3XAJwRzkD0NS+M9f8AD6arPpFra291C1oIxb7yHRshi2V5bHoe1K2hXMS3GlnxXcW91JHqltbPPumu4WSOa+GN2I0X7wwfvdOlHhK+Xw2usW9jY2knh28kIS3nT967dC7gjKMcDknJOe9YXw71i+8Q2UsM7W2n29tZNBD9nJjuCiNwu/PykqDz1wK6XxSNJ8EWEmkxyXUd5dwpbwyKhaLcDkMUxlmJBBOKuK0uSZ+ia3p8HiG4iuNTjs7G6ZCtnNPtt0KgpkEnjrk7etR6Bc2fjDX5pG87UrzSQ0Vl5J2RAmQ72hLALLIcZx25+tV/EMVr4mvILGzmt1tEjOye2hWPy1j+9nIBJBB5PJ785rm/FL3Wv/EeO3uLm8jXT1QxXbMYzNtC4fA43AZBB9PTmpchxOg0OwksNCns9TvJJkv557m7gumBntLVWCpAVXrGuA2ONrS44GK5fRte0fXvDdrBp8d5NYLeSRwrasVmnhCjeWVemfk+ZsjjqM89Bd2vka5LfRXDT68yG1jVEDzCN8MWCk4A46jNTaXZW8Nna3FjDYX9+oaGe4gnMbx7znasSj55DgjkcfL0B5oGYpRV067j8u5vtFsXS7MlzMsf2R1GF2pnMjjOCMYIya4vT/HE3h/WNQtY911c6oqbpFizHAQSQXAGEP1xztFdr4n1eTw7PKsdvcLe27EyxTxjkLjjB4xzjnAH14NbXdEtPEWijULKNoVvsQJFaxpjYCC3nFeWJbeQSMjC+gqZRvqNStoYtn4mk8NRzy2OrTNfS2v2W5DP5y3LnAJjwOGPOMcmpfDhtfCSalNBfahf6tfxILm08hPKRiflDsfmGQfbkVPruqtNp0V40Mcz2MAkyY0iEmxcgjAClsKODyTx34v+E3s7HXbiRbG8ms5dBee+voHWTbcFVl2qGHChQ3IGcg9qlR5Q5jrfFN8mvaTJaQ6WtnD8qNcTt5gJwFBWNlB3E4+fJyWA61x3g/S3i8LzaTfXFpJfq00K3t0kplsyigKCchUO1Tw2OvpUE3jCPW/GWm6Hp7Wep6erGXVLyNDHZ2+1M7d67yPmC4UHk1a+J1tcWusalFZXEwuZLsF4+i2gZT8wJGMg4IXvz8uKuxJBFq2kHQLG41RlW5W9aztpkKGSSEH5pkVTzhwyjJ6Ec1r292sUl5ZQ2919nuJ7a2bT/s8kMzxr5hFyUK/u1HmOrOxAxzzWTqngyKdnt9NtbiNFhjlVd7Z37ASzBsLliCxIGDx65rlPDvi641DVdU+1at5slv8AK7zzOt5LtPMceBhl3bu4OMn0rT2jSJ3O31G+urbxDqH9h39xZ6eofygJFuIwckNGG+6eCvQDGOc54ydBvtU8MS3jCOS8j1j/AJCfmYj3+WNv3sEDavfHUmqtlrcdu91BbRwmPUZ4wLTeHaEHJ3bhnBZj3PO01Y1HSYbm/wBR00XEml6l9na5ktpZDtVRhdhXBJkdnBXAweecYzlLXUojk0F7PwHcXFvocjaddXaXE08Fwky3aIykLt5K/Mnf34GDindfarzxda6lqF1GsETZtZFZZMBRnyYzxzxyBnpVD4d2+qG2vLS3urhdHdXmngklO9lYgAyfKAh39kOScDocV2I8JQXvhxYbayGk6da208KSyIfMldx8ygsC24ng5xjcMGpAsaAzSWum6vcXE19oN1DK8mlPIEXUCGOCXUnGGBKjackY9asBnWyguW0u2hv5k8154IWaCYDHDDqDk8cAnFYnga6uNU0SxWGTTYbNA1qlrcSGNUWMsMmQgKpYjPU4zWnZMuueFCEku0FrK8ylAQk20lAUcnoWzjIHK571qm1sTIm8JaNfW0TeLvsM0NxqVy1ptiMlsmmOq58+R8DYFJyc4wDyeKzrbwB/bWhf8I+3k6rdafMbi2SNzGs8hwZHYhsuo3p8+cEED2rSmutQbUZ2upG02TxBuhe3NwWXyxwynrl8lB2A6Diufu9Uu9A8Q6fNpMnmSwmQXKQxGbz7d87xkH5CGVTzt57+qFE6/wCI2r2OuS26ySWdxfabAslyPNNuVO7aMErkxqBGAuCpIK9c1UutNubz4Tf25Y26tJNcvbJJJCEclGYcBANqZzt3EZ7D5TWV8Nb/AFLxVqfiTVo4LWbw99nTTLKV3/emYSCR23Y4VVK5643Dk84k8Uaz4gbSbfS7KdtHsdSkKTvMEaOTY5CyKf8AZMhJKnPzD2oHynNxWcnhrT7rT7i0863vrqK4mgIZYwUBAC/KR5hJcduGPXFaX9lqutwaxIv9mnTibWG3uLPzJZi7YZucbMIwG/BJII7ms3xhLfWtjfWd9qFp4gso7SKVdQlSSO7WYAKCGUhGVVONuM5cE++P4T8LXth4h+y6lqGqnR47fzvnlH7ljkoq54+9yc9unXNaRirXA1vFKTSxT6XZRx6Xp8zTPMkIB3yNjEmD/tHJPtXlP7Sn7RPhz9ny0t47aK61yaS2k8nQVuihVtoXzbiTaxjXcp/dqMtuVhxzXpmufaLXVtHupr2S1sfti/2jdTM7NLGvLKTzwB+HyjtzXwn+1nrreKviRrGpQ7vs02q3KQBv4IQSqAH/AHVBxR7QulG7ZzPjD9uz4keJGX7B4hh8O2e4qsOiWy2uVwOWmO6ZycfxPWXon7a3xS8Om6iHiq81m1v38y5t9VRbxJznOSWG8H3DCvOPE/h648I3VvFdRrCLuMyQbjgSp0yvr+HSqSmQjbux3+taS13NJWR+l37E37alj+0Hcrof9n/2X4mhs1s4dPgbOQWz58PAaT59oIbBTPcYJ+3/AIWfsaR6Nol0viq+mup78l57SyYwru55aYEuX5IbBA4A7V+E37PHje6+Hnxy8L6xZzTRyafqduxZDjcvmpuGfp2r+lzXbFY9duY0U4Vy+P7qnkVx1pWehlueUR/sh+A5tDk08aHNb2cieWypfzhnAOeW3ZOcd+vNdz8K4LT4Q6LY6Pbx3EmiaZD5MbPKZHjXcSMk8tjcR64x6Vrtcgfuxs3t0APWq0unnLKrFd2chfzrDmctxaI6zWfix8Ofh5pS3viXUrXTrZlDRmXUorfzR2IDAs3TPygnj8R86+Pv+CuXwD8OapcWMXxA8P2rI2wKk5PHbJbDH6nFfmJ/wWZvNc8D/tpa5pEOtap/Yd7pdjqtrZ/aGMVv5wYMi8/KnmQswHYk18YTHzi3HLHnk8mnyx+0d9HCupFTeh+xv7b37QXgv9qL9lrxhD4Y8VeG/FUMtn5pj06/inmjKOrBmRWLLgjuBX5/fsXft36L4G8HSeH9Q8P+LtY1I3LT2kOk2IvGZWAJQjcG4IJyAa+d7O9uNFdpraaS1nxgSRMUbnrz6e3SvQv2RrD/AIvrax2MkdhLeQykSZKqrIpc4AIxwuPxrsoVINcplisHKHvRZ7l8Wv2y/ih8WdDvbP4b+Cdc8KxOrwtqepQut8QcqfIXAVGxnJBdl5+tfNWqfsn+OvB+hR6o9qutRzAy3EmnTm5eF9xyJRjcT64zzX6Kad4UtdE8Hy6pNNJsuoDssvlEYkbjdnDE4z3INKfAFzqlja282naSt02I0EVq5wjKT5jHcF4UZ6d+5yTumkuVI5I3TuflvM3lyPHJuhaE7ZFZdpU+/pRDp95qcpS0t5bqQMERIozIzEkgYA5PTtmv0ev/AIFaLbatctqlvpt2ruYBGlqpmaRArqc7MBTuBBJyecjgZ6KD4V6BoesGG30eyiuYrWK4lU26hreWQ7cg4AbDZ/yacZJO6CUrnjv/AASO+HOteBPEXxq1i8s57OE/DhbYCZGjkLnxT4bb7rDphWr7Gvr62tNBuNYkkjazsbdppS2Puxjc35AE5rE+DFuJ/AHxYhWzsUuB4Pt45ZbeLaSy+IdDBBPv94j1ry79qbXtS8G/s0+LPs6sv9oWKafGEPzBp5EiOPQkMR+NcmKfNUjft+rNcNJwi15/oj47X4qTeLfjFrHijUGJk8Q38t1IWbmKN3JVP9nau1cdtuO1ehNdxyvuX5FA4JPWvA2WazkVbiKSFm6BuAR9a7zwP8QPLgW11Dc0cKbY5QMlR2DD+tdfLZHTh6ivqd1GQ90Wb+I8nNRuhaY/NtU8YFV4pvNRZFdWX1XlcfWpoWLtv69PxqT0vZIcVEcvDLgcc96RZXdfu+vUdBQswmJyuGBxSypvU/eX1btj0NA/YoY0/nPuG0NjGf8A61P3KO25mGOO+PSo4YWkbEavK2OEQZZq09K8C3uqfvLrFnbhsYzmQY65weBQR7OSeiMuKSTUG8u3HmSL/CDwvvVu98jS9LjtyfOvt3mFz8vXjH6dKveNdU0vwbYlbdXt5DypOC8i/wB4HkYPWus/Y2/Yr+IP/BQT4jzab4MtYbPRNPnVdY8RX7FbPTeASgUfNLLjkRrz0z1rlqSXxMqpOMV5nl39pefdrC2ZJJGCpGpy7MTgADqc9h3r6L+CP/BGf45ftIC1vm8Nj4f6RcFSl94rSWycrgHetrtM7Kc8EhAecHHNfqz+yB/wTM+F37HmnW11puix654yVT9o8SasgmvnJzkQr9y3XpxFjOBljivoERLuBPzZwBx9ea4545p+4jjqfvFaR8Jfs6/8EKtB+Fkdq/iT4meJPEV5bssm3TdMj0u3SRTnKszSyMvTBIU8Z78fZuh/BrR9B0yO3jk1Sby0Ch5bvzJG+rYGfyrpI1Z88bQvX2qRNy8Fia451pTlzS3OW3L7qPK/jX+yl4a+NvhSTSNUvPElrEzDD6fqAjf15DIynp3FfC/xh/4N+7i3v7zVPAPxLW6uZiWFl4k00JuP90XFu7Y+pi+tfp2RtflVIY96oO29f4aqnipw+EcYpvU/nk/aq/Yd+LH7KNzM/jzwXqdrpMknlxa5ZRtfaNM3I2i7jBjUnsshV84G0Hp5ZoGstb2KGORZoG5AzwAPQ9Pbiv6Xr6zW6863mt7e6tb+Py7iGZRJFInpIjAh1Pocj1r4Z/bN/wCCH/gX4s2Ooa98KYYPAHiXBkOnxlv7Hv3wP4OfIJPH7vC5525r0KeYJrlktTeNNRPyfj1yzulw8ixt6N1obcF+hIq18X/gx4i+AHxDvPCvi7S5NJ1yx5eJzuWSMsVEkb9HjYqcMOOvcEVj/wBhyBCY7iRVU/KVb5W+grs59Lo1s3sWpR5hXr16iq9zGkVyzZwyjgk8dKqtDfrGP9IVQTkbwOat2+h3N7JCst0n73O5kGduPb36fiKvVrQfLI0TI7QqW3KoBwSMDpWVMwvLvbCskzfd2INzA/QZr0HSfhhamKOWaOWaNhgqzAMoGMnGasW+h2tm6+XaxxsXIXHysx7AMe59KunFkzSS1PM08O6pqEh3RtYwddr/AMWPbtWhpPiu18NeEvs0aMt407goeCoGf5nNV/jN8WY/DofT7aZluo/mklfBEPHPJ7+1fQf/AATo/wCCZ99+0Nd6f4k+IEckfh+6IlstGLlJb/IH7y4K4KR55CE5OcHiprSVOHNI5oySdkcL+x/+yd8TP2wfFscPgvQZ5tP80CfVr1mg0y3HQkykfNjuEyRjnFfqD+yt/wAEPPBfwiv7XWfGniPVvGGu27LcR21g50/TbaQdgVJllAPdmUH+7jmvrj4H/BrR/hN4Lt9H0eyt9OtoFwIYIxGqdBgAcADFdrHZLbHqprgeKlJWWhhJa6mL4b+HWjeGbcQ6do1hZxqMDbACcfUjNdCbaGONfLSPI4IVRRZxm5kbduVc4B9atLDHAeM+nNZWAy77w9Z6zE0V5p9heQyfeS4tI5lPth1Ix+FeT/GP/gnF8CvjrZFdd+Gvhm3vGBVdQ0a2Gk3keQckSW+zOcnIYMDxxxXtkm6UnavLdD2qS2sjCvzN945A9aFdbMD8t/2hv+DcKG+Wa7+FHxAkjdfu6P4pj3IR2CXkIyuOf9ZER7ivz1/aV/Yx+KX7Herta/ELwX4g0O1Y7I9Va1aXSrg9cRXiboHOMnG5Wx/CO39LDnyyPLRVPX6Vl+I/CemeLfDd3pGt6bY6vo+oxtBd2V9Atxb3SEHKujAhh165weRg4rRY6UHyy2J6n8vWi65JaoFC+YFOQpPzYPv6cg//AK61Z7pXIZcLuA+8Olfo9/wUt/4IWQ6BBqPxA+A9jM1jalp9T8FozyyQjq0lgWy20ZY+Sx6fc4IFfmraa7Hq+lxH93NHInySKcA1206kZq8TaGpP5+6T+E8euKWWUpIu1wy9+c1Da6Mt3Ky/ahBnAX+JWPua2LLwLeXNx5Nn/pMjcHcMAeuKs0jTZkzo2RIcMOv1FOE6wHOY+nNdcnwa1pLZWk+ywR+Xv3yzYHsP8+9XLP4B3U1ysc1/b7Nu9ikR5HfBPHWizexp9Xl1ONsLaa8ceXtY5yABmuq0LwrcCdWCtHLIpcOy4CgZzj69K7Dwv8L9F0SZmvrowRQoWDSJkzNkYRE6OT0x+eBk07x743t9Ru4YrGxjtYYQclpGeadzjLO3T+EYVRtGT1zXPXqOPu2PQoYWKipHun/BO3x3Fo3j/XNJv4ZvsuuacYIyx/1UkTKUlQdOE3g467vavffimNT0nTJreKzPl3syPbXbqdky7hu2nHzArnpmviX9nzXZrP4h2gjkKsyPGp7jcu39M1972niK41fwZ4d0G/hjn/sItcO5yz+UVPGccYz0zWcdzzsdFKd0N8J+K5Ib6Hal0gnhMWXY8MMLsIrS8NeGLuXx8fF66bNbx6TKqTBozlycZ28delcN4hmvvEHxI13T/Dsa3VtIwuEZeNqbVY7T9Ca9D+FfxGuNWg1TwvcNLHb21tHeC725ZDuwwPcjit4xvqcBd+N91DfwNN9nYLqQNzbtECrbuASfc+n1rW/Zp03xBoPgrV5tSXOk3N0JEjdT5jA5UjnnoBxVH4r+HdR8Q+IvDNjpUnm2c0bRzu3/ACzaNhgfUjd+Vei67q8mjKq2VmZrdGVnQDCoRxz+BFaxjrcHqrHNz/Ce1sfh7qV1ouqR/abC6F39lID+WucmI9/U4xWvoFpqjaFZF4GRzAhZfsZ+U7RxWRYabeaX4g1KGVZJbXV9RW4ZAdoiQDJ+qnJHHpXZP8Q57JzCmnsUhOxSB1A4FAHzj4W1K80i8tLyS3v761ADPMwMYVMfKNo53bsHaOx54qPx58VLjRn0211G4W4RYBavcXqBvssUrAEqq/d+UKNuMjGO1XU0bV/Dd5psdncLNoNxGH1NIFdJPtLrheo7nb0xjFUvC1ro+p3U1nrlhff2zDcGJYroFi0O3apGThjxk7ucknvXDGVzS5l6xrcQNrrGoW0iTTTS2FjDDEVghjIPMecsd3Xlvl6dqZ4gdvDV9pNmbNfOuoFshbyTGVg3J85j2IxnjbgHtWJrOoXvhvxnb2sUP9t3MQbyVlkJh03BOGixwGKgZZuecdK4/TvHlx4g8ReLvF15eTW15ZommwW1wu+NOA7yjJOGYtsI4OAfWtoyS1J5WewePorrVr+xEdjEoZzYm0tB8rTGMsuJMnj92x6596w9S8QNpOjfYNR0e4fUtQjMFokpbMLtjGAvHRSQT61n+Gv+Eg8Y/wBm6leW8Edrbw7ozazi3/fbSI5SBkZQE8gA4cjvWhaeJNNvPEMNnq2k2tzdW8u6LULWaZwkoB2hwOATnG7px+FNyvqTa2hoeCYYfDOgx2VzbwTWuoal851JtsulkoFMrbCPMXcq/NkHJAHTB5O8+H1tq/xmWG3g1LVI7ixlL6x9rWBYTHhokRXYEqWYnpzjrzWt8PZJvC2neJrmeG1u7u1vHntLeZZJoZYGwzRs3KswIwqEDOemauX8MvjbRtS1K5aZZIoVFvFN+7NoSrMAQ3UdAPYY6VCiByvh9L/w7daxb3KzajY288V7c2ZOyS4ZTlQVXgYbGcHmqN9r+s/ECbVJrjT5I5bi4F2byW6RpvJwMxop+ULk+5B5zxXV/DOOzi1SG81z7VdSagfLvi5aVLbcAdxyR0xjr0qv8XtBsdb0hrW1hvrHVWs8xRRR/Z/sqK4w5JBYllzyQM1QHA/2vH4D8TTRWLNYx3kzTW+8vNId75UyYGGAIAJ7nmu7hstedNWvL2SSw1jU4wIVtsNHNsYJg8cD6HJNZEXgx/GOsLHCs115OmW4inADzEqNihQACWyC2RzuJPWvQ7Tw5q2q6THvWaCxt4DaqJpWSW7lWRhvMX3lGE6cdO+RWY4nksOq3nhL4ea5ca41rfeKbeRxeTwn70ezEabh02/McepNXNb0hfgmbVtU0+aPVNYtVeyv4ZMNCqqjOUxjGWdGO7k/LjGDmLxto9w763ayWsckd0qhkjGEuiHHOG6sSSCec9Oea0T4V1PQ/D0japZwfaY7eCGS2F48yWCRuxAiTcFDOvyn5TtEajjnN9A6nI/DrUb/AFK6uf7Q1e+1C11ZJLG5kW4AJV383k7cbhtGck9OnNbHh/V9N8K+DY9MvvDt80dvI1lazWjjddyvMokaQngjy3Y4AwCBjkVPpFw/ju2bUo9FuNJ0xZ5LO3to9vmfaim7ziAoXy3jDDrnI9Dxf0j4M674g8NaHrFxb3n2O3mkvntIjujWI74i7ZBCgNhvYgGltoO1yXxx4M0nwNbaYkt5b/aL2aFbiaceXHtUZ8srywfAIBIwT61m+INU1pNL0/UtL01dNvr6RtN07TbqHYoh3f66Z8gMx5OR/COlUviSNH0TxPJp2jz2d5qWvWojYyzfaZYwIzgrKSSuMngfrWJoXjzxWNTsNMudedb+xHNhIBeCTGVLfMODyB6nPSjmE1Yu/EL4hap4duk0u3+yrqEJWS/1e2hT5kKgGNQo2DIyTnJAI7812vhL4u6ZrfwNW1hvrizk0+UWEsFwP+QhGSAIYSwyibFCkpyR1J4rli2ljwjqGlQl9S1TdNfaqL6AlQgK7GTccIMbVIAAIUVy+reHrv4hTTabHcW+k2+lxGdCg2YaMAD5d2NxyuCMYOSelShGpYeLb2/8QXDQzXMEzW/2UwiUx/Z4lO0R5yN6AcjjOAK52z1m407UbVbpLWWx0dnlj3kZvn3Od2zJz9SQTxxwM3tfu5kawj0mGOx1TT2aSSBRhXTB3sbnqS2CSwJbLetMuNJmnNrZ3yw+ZfF57OATZjgIXcGC527yQc5/hK4PSgHsasGkf2de27eHLGG613UGOqbZsGJANuQVGCpCsVJGDyMZxWXrNrqGp+Ivt7XVva6ZzaRLEZZvPkGFZg5H+rBDKec5Xpg1LpWrXy+G5Lc2M11bzFZVu7RQwhm5CxMg+4GMjcA8BOam0zw2niL4OxrrAtY44TIIkAb7ZPIzuWMmM7FB4BJwQB61a2M2UNA8OzW2paddNNp/2PTSY0dw6Nd7V2lcHumWfPGSMDGa629gh07+34JNXulgYRTWEboRHqcoLKFjfcTG+CBjA3E98Cs74eaZa6T4PtbifyS1m32mS0mlZnmd9q56/MRndnHr71v2uo6XrHiq8+yzW80cIGqm4uY921oG3mNVxkZQEjP1qZFRMDSPDt1FZb7PR/7RmtbxbiKKa43LcO2WMT9yCQScEdBXNfDvR9a1Tx95NxdW97cXNyNbe8Vo7drdUKDCb2AKnG3CgnBJ4Ar0K88QaZ4wuJtU0u1utHtbEIJomhb97IXKhkwVLjvnnA571r+KPFOqaH4WkOntq2l6Vq94Yb+w03UyJtVYJz5oQfMgJ+6cAgEYxxSCRl6rYTeNPE/iCOy1K6jaF838rNtjiIJKwxn+Jjgr8pOMHmnWFzDbaWvjrwzAskmqW86N5kUkCqiyYZN5OQ4ZflyTniq994svLHwuml6TcTarp8G1rmKBNzoX+aRVIGdoywztwBjmqXjBde0jTZorDTP7P0/W7aNLcNeFmhREHy5IJlxtGAMbfujpVcwugfDhofE9s0N9eNe6lHJJPdW8kUcHkkne4z3XPIBJ61uraxa+l1cycQ3T7YFkiCptAUN5YT1LAfNgnaMZ5xg2ENxa+D9MgsvD0pW6mjt5ZjMgvllDBmIf7370bgVfIUDPer8XiCIaFe214P8AT5JxHaWSANGsYOYw7j7zbiTn8eck1Qh+veDrFLK3j8+G00rVis0cU67I4WRH3ZUkuHZgdo+5ycda5W78Y2VlZh7ezDNYkCC5DYe4YqVOWzjjOBgDqaittU1SwXULHXZJL2+t2ZraN2JbT4uoUE9Rk5wDjk8cg0/4eDWtebT9L1DStGhWHMQaa8MryNtaQuIQgEeV5BDEcZ6jBUnYCSRI7TV49afVo9aurO0LTSxk+WZZAxYbdqhmRWIyeM9BzX5w/Ei/SKOOTd+7Sdpl9uv5cn9a+/PiNqt9pEUENrNZ2ccd0LaVOfNlEjYEkjEDCrzyckZJ9q+FY/g3qPxz/aO0f4b2141jNqWoSRXs6LuNlbR73mkxgDhFJHbLL607dTai0nqanwK1H4m/H3QbvwP4G8N33izTJpBPqEO1RYwyhSFkkeY+XGwUYyGDHpjoK6m2/wCCNnxg8RuZpbPwRpLMS4ifVzluSf4UK/kcemRiv1//AGSPgF4W8F/B9tK8KaDp+h6bpcrwQW9rEvLbAd7tjMjt3c5Ynkkms3VbGRruQzR7ZoyyyZHIbJ3Z985rN41y92JcpXdz8d9T/wCCW/xo+DnibTbzUPC1vq2kvqNqstxol/HffZlMq5Z4xtkVRzztx+GDX9EHiTQLXxHo8bR3CW+qW8CRruztmCqFG4jpxwD0GBmvlvXLMLpfmJt3R5YHHSvBv2uf2kPF/wAPdH0WzfxBqFj4Vv5WsZfJnaN4JWyYg753GI8qASACM1jUrOckmjI+359OjsrporqWx8xOCouY2Ge6jn8Kz/FviS08J6Y95fTRxWsIyxBBZsDoo/8Ar1+cGn6xJLLG/mvIy9XZyxz65PXPrWzqfxY1Lwn4O1OS41GePT7e1kmlWWdjHGBznk8dP1+lNaaAfMH/AAUF8Q3X7U/7Yniq+hVWvo7a0sraEyrGoWNJCEDOQudpzgkZJx3r5i1jwk2gX7WdxHNbzR8NHINrL+f4eorTb4pXWseLdW1xpI1bUrw3DB/3nyjATI6nAAx/9al8Z/FvUvHVxHNqN1Nqc0EYhjmuSXaONeERS2SqKOAowAOlXUpxex7eErKMVGRz76BH/DvPXODVjwx4nm+HnjjR9UtZsXlncCRT2U7SpB+oYioZdbmYYQJGO+09ayJFZ7lWYszdQT1Jp0aclLUjHYyPs7RPvr4S/Eu3+L3h2z+yeTaXkjhZmllMm0455PQDBxxwMV6Z4isJNG0C3Gg3lxdTxxbEkDvMykksxjUYBG4t265r5Y/4J+RXGs654g8y1iezsLeOTzx80cPUfOD0zgA4r7A061s7yxtrvc9mrQtHDOQdu8HGM8/KT0Bx+Pfp9meJGRn+E9ZXUYYVX99b2JRfPS1+ztJJjDAqxwGJySWGcknOMVualFqHha/+y26mCOWaOSa3BEkhVPu/OrBMEO2cccVT1HwvFo17cKtxeQ31ypeaWQvIxcE7tu3OQRz8w5JbtVrw5qLXfiax07VGWbSWtxaJO67GLlvlM7HBZjnk5z8qg8CpjpKw7nSfAnUmfwt8W4pIw89r4UhWSYlWLZ8RaKwCsCSVwR17jHavOf2o9On174CeIljjP7m3juSMfdEcyyE/kpr1r4UaRHo3hj4w29pHG1tJ4Ytna7aNQ7uuv6OAoOclSCx44JA9q4t7htchm02ZftFlKjrcZGfkIIx+prHEStUT8v1Z1Yb+HL1/RH583VpHfRyRXAWeNuSp7Vy1/pMnhmfzofntZAVGOsZ967/xn4Tk8D+MtV0mZSsljdSR8rjcoY7T75XBzWHLZmeB1dVcSDlSc7q7Fqrk052Zj6PrbW14pEjxd96ferrIPGN4qts1CbbMuGVkDggdjx6+tcPqVg+j3G3bmEkYOPu+1XtI1H7HMGPzN1Zc8N9amUraHrYeMXqjuF1G8vZm/wBKjX5d2BbqOABnFdJ4f0q2v4FS486d5FOP3u0cAluD7DPHXpXA2Xi4NdrIy+W0OCV9VzzXbaJqccmpWMKFiuTgn5QGPYgdunsKqPvHfGKiro73Q9Ht9O01DbwzeTLtZBECyyZ6AjruJ4rjPjZ8SF+GLXOn2W1NUdtlyuP+PMjqCP72Rj2rf8WfGC3+FPg251C18mbxBrsU0FplRJHp8HKG5jYt8swIZUOCVBZhyAa+f/hx4D1r9ob4taL4T0V/+Jv4huTBHNJmRbdeWkmbk5VFVmPPbrWkqCjre5wYrGcseVfI9/8A+CdP7Cev/wDBQL4vbtUbULHwDptyn9sagh2S30uA32W3Y8F2BG5gDsUk9eK/ob+BvwT0L4C/C3RfCfhzTLbSdE0e1S3t7W3XasagDOM8kseSzZZiSSSTmvAP2D/gH4d+A3hPw34U0G0js9M0pdiAD5p5CdzzSHqzu+WLHJJPWvraG28m2Ukkrkhfb2rwMRWdWVlpFf1dnDzWWpSuNKWI7l49RVaa3EvTj2rWkwcZG72qGS0Uj5VXp0xXNyGJlvCyLtzuVe1QyPvhfjG3Jq1cRNH+HXFQ3GAy9lwQfc0nGwEIJRcrgbqp3k6tj7uc8jPPSrVzFJ5e1fkZhwewrjbu7vrTU4x5ZlZmGQWx3FYTqcrOqnqtDoHkZVY8Y7CsbXYW2ySo23jjH3gcVFaeLpjqJs72xaF5ZSkLQHzAU7FuBtPfHOK2rvTlmRlZVbPAzz+tVzcy0DW9zx79r79h3wn+2T8JbfTvEcM63cKO1lqMAX7VpsxAzJESD97jcjAq4ABFfiZ+0f8As9eKf2T/AIoX3g/xdaiOa2IktbuHJtdTgbBWaE8/Lk7Sp5VwynoK/o2sNLb/AIRvyVjRWA3R89DgdfSvmf8A4KYfsS6L+138EZNMnnh0nxFpwa60bVvLUvY3GBuBJIJhl2qJEBwSEY52CujC4hrSWxUZNaM/DG3ljjtP9Yu1m3AE8H2NdFojM1zHIrr5aqV+bu2O3cjt07V5brEuq+FNd1DSdZtJdO1LSriSyvbOQFfss6Eh4yD/AHSPxBB71d0f4sNYSCPcFCjbuXhtp9/UdPxr6ShZm0qiasj6J0ZLaPRbzz5ItrKGRYyPOBBwSBg7QMqM8HnjGK8l+LvxDbwlpElvb3zySXj9JANrp2bGDnqevSuo0P8Aax0PR/BF1Y3GmozXUu95Vi3SKgjbCqxJUBnOGGN2DwwrxbwF4Uvf2mPjna6KXeCx3edftESFtbZRnYvPBb7o9znrmurE8kIaP1OOcnL3T3P/AIJzfsZy/HHxvY+N9fs/+KesLsS6bBIny300bhjKwJzsVgMdMkrjgV+z3wJ8LW+h3Wnrh9rEdTlskV84/s6eBLHwfoFlYWlra2sFnFGsccESosEa8BFx0AzjA4xX0R4I8TFNXtCgCiNg2fpXydes6k7vpsONOy8z6Ys9PKxKsKny2HGakERtJecHjtTdH1631DTVmgYqki5Az0PQ1NbbZt275mzxkV0UneJySVmN+2M35VetoGmjVt21cc9eeBVdlhhT7qtg84XpViBywXaW2sOBmtBc1ti0JlijCr0Xp7VGGJRmIqu0yQS7mY47jHFNfUVC/gRnFAct9SV75YflI9/pWVLKxZdy/KDTpJQ7M3zHnoap3V87wsG+UJ82e9cNSq5aG8aelzS0e5a31RFXPzSZBDbSD7Ec/lX5Tf8ABdz/AIJsQ/C/V3+N3giw+z6PqkyW3i+yhwY7W7kcCO+RMfIkrMqSAfKHKtxvOP1c+HmiNrfiWGRldoI/mYnoMA4ro/i/8JtF+LXw517wrr1lDfaD4isZbC/t5VDI8UilW4IIyM7hxwwBHOMehls3FNdzkrS5Zq5/LBZ3X+mxx7VVnTCl+hJ7fh716P8AD+PfrUNu58td6n2kbHJx1wMHt296oftZ/s8ap+yj+0R40+HurGSa48I6lLDbzsu37da7i1vOB6PCUb6k1Q+HmuC81ixlnmaxWNxFHMI9/knIw7cgnIyCO5Oe1fR0aceflZ1xqX1ifU+gfC6TxF4eunRbfz41Vpd8B2ndgYDNkAkc5xkgYGM10/xe+Hmi/BH4VjxXf3UEkMyRwWFnDMonv7yUAyqEzuCRna24jGM5JJGb2sap4RbwG2qSatDoreXC9ykoCSAIqkIq5JbGWEZf5W5Yc5r5S+Ofxqvfjt47uNbu1WC2jPk2NsjfLbRcLwBgbmCruIHXgcAV6OMrUcLSatq1oPCqrXqWvoYmreI7jWtVkvLg/vpBltpBC99o+n59azb29bymdv42Cqfc+3em/afKjJb5u/PJqrHOLm+V9rMqMCBjPPQYHrXxq1lc9+paMLI+hf2D/hQvi3xDq9xcZVLOyHlMowBKSHBH/AUP/fQr63+Hnjaxt/F0cWsXC29ukBilLDAVgPlP0xXkfwg8F3X7Pfwc0LW4wJGvJUbXQp+a2YgjYR/dVWAIPAre0XU7W78Wfatchiaz1CJoldXyqxnIDZ7jGK7oKyPm8RPmm2jqP2fdB1bwN8UfEK3KTw6Tp8R1C3m258xBncFI9snHvXa/s8+O7e68V+IIp5lki8VRNJZTE/u2jUuACex5wQe9Hw6e60a2vND1S683TY41S21EsfMUMB827OSuD8wzz3rzTw8l18MvFUmnbVCQSzCwmhi+S4icvkDHcHn8aoxPZPh8Lvw/8dbi0v5PLs9P+eBJmG5mnxhuD2249Oa9P8beIJPBtzqWltI0019Et3azKm5FwdrRMfTG79K8n0/XbPxVBY3eoNt1pbJrVCB+8eSMqypu68DJH0rq77xfcL8fNAs76HzrO8sGguo3TPkltxRiDnDEAjPfmgTVzs9Da61WfwnfQxqlxoge2uoPMBZoWUAFeewXP0I9DXBa747tYtcvFW6h2rO4G7OfvHrU+oSajD4wks7Oe4W4sVEumXcAJ+2nORDIPX+EnsPasGf4kaLdTPLe6PJb3kjF54jZE+XIeWXOOxyKCDxrWviN4p0WG3ZPsa315dIGMKD95sb7pU91A6Dmt6/11rnUzqWpJdG3n2s11KnlTwzqckR44KDPIPbHXrXL6de6PpvjzTm0W6mgs47qOeJLWIJIs205fOCApPU8n3rodR1m+N5pdrfL9stYbqRLcl32iSXG7135IBB9qxVFIr2yl8JhWfjO18JXurXjSTTX0zG5tzKENrLGuASRnIK52+nFcH4U0XVdZ0G+hvriOFtcmfUo2kt2i86FiANvIJXhsH9eMDpl8Aa1pusXlnfSS3Vv9rO5ZrUJHBFjJIdhnHOSFPX8arz+O7DUry5NrNNb6Hp8gtczs7TRY/iTt5ZBUcYGd3FZyjY0juTWvxHHgkR6TpUs9heSxpbpI2GRjjBOW34HqSCQOmOh7608AwaJpj3Ama80m9iE80iuR9smAI2hgQQMgncMY2DjBIrmG+H83xV03xNpfhcvGukiASatJhWckO22P7udwBB4wDt55roNN8Vw+DvBOieE5G1OOZplhimntwwmBDMwdgAB+82jIxxn6gTCS10NPw/peteANJkskayvDqcv2y5tSwWJNqja4cjoMdB1PUd6Txb4kfVLy60WwZtU1Odle5Ukq0ZjXOxif4MdCAD0qPx/8dbbw9YeKNQvrHT/ALUbPbFZxhpEgXykjSMMM7d8xwDnq9cx4M+KDaVfrrWqaS1nJqtqLcKtws8MbEqNx53BQN+SSSRjAHSqQnodnFocNne2tnHeWl1b3Fyz3Vs1urNIdnMjNuyF3A4AIBA6VwHiiy8QXd+17dTszyyKkElxDHumVAN+XCglDghQRmuonMOkeAL7Ulsvs91NdC0nWR97Qs6ll3tkPggE8H5RgVl+BtG+3nRb7Vrm6iWxmkngHms02poAwKcnBIydvqQM8GgRxPiLWtWstehm8Hi40S6uEFvdmWESi32ZOI8kYAcNhjyQOSea7PxF8XpPDCWsO7TYdQvPK+z3trbPKLaVVHmSShV2kOQ+d5I3SY6AYfqmkX3irxNH4fubbSNF0e8uHvU1GQiK4mQEyRoY9zbZACFIJKg8c9+X8K6bZ6BZ6zaalrmpt/bgSWS12gKBGxwcjJGeAcL0I4ByazLsdx4wv7TxV4Ttbb7Za65DNAkaQQwmGGwn37txIHzOOvJICkdAKyNd1TWPDFtrGmNJayrqGmoGilnFxNEyBj5iSAcLIGAK5+XaO9Y/hfxjp1pcre6L/aul6PGmYLTzf3hYgpIdu0Hbj+P09s10eq/EG5XwrqEeow6W1xrjx3do9qjme0QNIMSSZ2szZBOzaML2PNVfQhxd7mX4Z82PTLjUkLRrdabb201lAjq5+XmQPnIYbcYHBEnOeMcro66jp1/d6DpX9uS2rQrItlBMzSXUO8M6En7gKnqSDyMcmrHhbxU3jK4h0+yvGs5r66kN5NPFuWzt4QCJMkDJ3EjAznIx0Jr1STSbfwRqaalZwJdPdQSCWeCV2ki3H7uCR8vTjJx1qStjynSdCjvNW1Sx0nQbyx0yYx3e+WHzprWSLO5Fk3blUfLnb/hXJ+CvjD/wn/iLTEs9M1DRJ47qSASXLMPNRDmSRVZt4AwcknGcY7V6lrfh6SVJ9Wzd2uljyoQZSqTQq/DsAvVTuXGcnGaxPFPw+t/D/i6PUZJGuNsfkWepfewsiY2bTw3OeQMZA/F3JlJHCeIvBFnovxIk1i5mbUtH2b0EbhWuYyoGcDOQD82Pauk8EeM9J8OaP4ge4jmuLC68m5tyQqbNu5Sp8xiWRgQcY42jpnmxrOtaL4ltbOG3tTHBaWR/tZrT7sc+AQcMmQpPUgYwfXp5P458VNFNJpsOnNcrNqUZhijBx9lG0NhyflwMgnIyefaocrAeyaAljfSPYajd+ZqCXSzQ2csK/a1jA2Aq3EfOQcLgAepFcR488NjW/GEbXk11Z2E0H/EqtriRA04O47WkUfu22oGy24twPStP4hwN4k8GN/Y99Na318hdECCe6Rv4V3YHRRnPIHHcYq9Fod1p/wAKYdVkmGo6pqTRwQxSKbiSACMM7LJkKu4EBlO44VTkcYOdAU9N0vXNQ8AK2nTxz6J9riif7QRE8yR8uNincGUlwrE4YOemDWL4k8fGO7vLfw/rVvdWtxOkcccsYjSLAJLMeRwwxg9c1rW+jaho0tmtvMNLgkbzrg+Zt83K53lX+XqB9Qxrmdb1PQZb1IVh1rVbjULoS6u0lskSsFBzLDsRQFVfUsSTkY6A9oiWhui315qnj28OuTXEwmhFxFOheMgAZATyyOcgZwRw3PTFdppvhpfHHh28tbeDzLO+hZbucuxmR3JX5VQgk8H5WI+6B3rjYL+P4e29vqdvNJqugX0bRQf6WouIgWyC4ZTwfQjdjv0xj6Xp2r6R4pW2s7qGG11IxtcTaY5iW5Dje0bscksVG3IGeeOoo50FmeveKjcaZL/YiJb3Gmw2vmz+crR3FwwwkcQSM7h03ZBB65PrD4a8KL8LbW4uLqHUNQV7JxfXFzP595ZyLgJlc7YtzFV4A6/Wn/CeW21i71QX0H2PUNJQtpl27pFHkg8spUyTENjgnFc5458IXXxM+GtlbzO2oHXAFu5op2Zljik3ou9SVXa2flbJwfoaalcOUvaN4S8RSy3WpateWfhPU74P9ktw3nefAf3jNMFAjUDHRcnOQTwKm0vV/t11eX3nSy6XYRHzrrYxgeaMBQqKWJ27QD0z34FN03wNZz6PoeoXlxdXccVzcW7Wa3eXhIf5UjzuCoT25yePanXk9/caZdaVa3UNtbNDO6m508locoxjiXkEPuYMcYyAM9qZKMfx/wDFVfDXhjw9JGs1vq1wsN3cRzRkQJOCwWTAk/hjYDJ6k8qcA1g+OPGx8ea1aPYtJarPaK4MaqQg2qFUHnk4yCcE7uelWtfisfHN9p1vNatqGn6Wgl8l02ys5VW3SSADkBVJQ9396bpl01qdeuorW4vvLlVbwvGVjtYJPMKiMj75yrDGf4Rz0o5rbgP8NveeFNDNv4nktbjUMyMIxIF5JHlh2IxwuflHHrnmovBRV/idod9Z3WnW+raisqo98ubewhMTh5XCjsM4IXIbGO9O+FtrqPxt+OcPhfT1uL66axlUm7ct9jiEkZ87O3+Fflwc8ye1fXGg/s5+G/BxU30L61eQgq0twcxqT1UIAOP97P16YwlVSV2UoNnyd4quYdf0bU9SmWyaaW2+zTNFcNdRysA+MFgDuYjOMcZ61T/Z4+GNjrfxG1D4hyW8S65Fpa6IyLbtCkaNKDv65dikSgseefrn7VTwfoaQNDDoujwwMc+ULOPbkd9pGPxIrmfFHwP0fUorhtHhj8P6lMQ5uLILHGxHQyRY2OB9N3oRUfWuZNI0pU7bnun7EOjNe/Dgbv3bXWpuQzD/AGVA/Lmuu+O/7Mv9s601zo/lyX0kZebyg7wzMOSTtztbk9ABX5fftP8A/BZHxh+xn4Vj+G3hnw/ZaT4901A2oalcA3VtErFtstsu4fMef9Zkrgdetfnl8ZP25fjJ+0Brs+oeL/ih461aa4dmMR1y5it48nOFiRwgHPAxgDgY6UqdHTXQ6qeHlUd1sfux4h+HGt6IjRX2n3ELcgKy4VvTBPWvgn/grn8RtO0f4VNpYfPlzW8ty+3HlMJHCrx6Ekn2Ir4S8A/ta/FD4TTeZ4e+IfjPTIx9+BdXnmt5R3DxSOyMp5yCvPNa/wAf/wBsHVP2lfhoum65aWcXiJ7uAzTWcbxwXEKEtuCuxKue4yR6AdB00aa51cjEYaUPe6HoX7O37X+m6VoJ0vWptQvZbNFWz+x23nSSoN3yY46YGMnnJyeBVP8AaZ8cfEb9ofw1Npfh7wxfaB4PjKTXk94yi41AY+VCw+VVOQ3lqxZtueinHA/sieHY9T+Lul3DXk2ltp84KuCEVG5wWJ4wPqOtfoRrHww0/wARTabBHqul6jb/AGZReCztntntTjarKXGx2OOSm4ZPsa7VGEW5JHmOpJPQ/KNbXZIyyKIWzsdT8mwjGAR27enXvU0dnuG4MrrjIw2c1+iHj79ljw/4iuY7TXtF09rW3EktpeOhiv54Xx96WJlLbduQDkAg8GvONT/4J26JrmsXzeF5vE88MZkkdluVmjii5wNvlBsqOW+fnBxtzWDpu90d0MZFL3lqfHPkKqbmxGP9o4qfQPCV34x1mK3sIVk3MqL1yWPAbOcAfXjFfWUH7GPhfw54ovNHjtdX8StZ+Tcz3s0cq+WGD5hRtqpJjGWAAZfxr3Lwn+z3p/gOK5Xw/okksM0KzTXEY3SWafMu117BvX2yOK0jFRRnXxKn7qOT/ZR/Zk/4UP4HurzxFJfbrjazS28sPlCVA28BsfdU4wfxFeneH9Sk1vTF1DRriOTQZW+0QGJo50u1fKqd4z0I5IwQeDzmpNXlsdH1y0t4o7y4jj8kS26MixRjgnOQOCeTkHFdRrGr315ZzxaTBZR29lB5d4LkD94Dh3C8hSBu44AwBRKStoc8L3HWOh318NUtblbFYrMhpXVgZXOxScSYxtwy8ZGDn8OZ1/SJrm8ltY90EcqeaGCgbgCOAxJJIyeSOta/h7xn/ZWui1sodP8AsYsVt7hpoBJIYi4JEZUYyGBPc4Yjpij4m3snifw9p5tYdGXS7EMWCs4nvcZA3AY24PABJJz7c1HYdnzXOl+H17b6v8J/iVdWMLaesfhO3gdZJGke7/4qHRts3zE8AKy/LgciuAu5v7Fh8mFmVrggs465PavS/h1otzbfBj4n3UitDC/hizhtLBZAwgQa5o+7cOW3Zx3AGTxyK8p19bq9Vm8vawGVA68CuXFfEvT9WduHmlFrz/RHgP7Y/hIC+sNejjHmCMWt02cbufkZu+f4c/SvD2GwivszVPAVr4k02aK8YypdQmF0IyAD3HHUdc18j+MvCdz4K8UXWl3X37VsJJjiZDgq4+oI/EH6Vph5uS5WJnN6jb/abeZZV/d8HPf8K5m3LWV55chXdnMYY43CuwuV3QN/tEflWNq1it1B9xt8QyCOCR3H5VtKJ2YSuoSSZW3rIqmNv3jna49RntXTaZ4thZGXPkLlSTuJf5TnjnjnB4rk7WBPL3KzEHjnt7GrksbJZTS/N+7UBR/DzXPztOx7s4+5zdDF8d+Im1XUriXcf3xy2QMtjgE/hX2N/wAEKvhFHrfxP8VePb6NWh0a3Gj2bN/DNKRJIwHqEUDP+0fU18L+Ir2QXLLtH5V+qX/BHjQo/Bf7I+jTSKsdx4gurrUnz1kUzMiY6dFUcHPArTFTdPDPzPm5y9pXt2P0i+Ges/Y723uo5Nqq67T6c819KaRq8GtaZDNGwKsB0PfFfGngnxUlp5cZ5UkcH617X8LPiNHp9ysZZmjcchjwPpXzyZcqbep7LLFgD5T+dLCMEg/eziobfUYdVt1lhk3Lu4A4Iq40QO3HWtkznqFO6tFkXO2s+6ss7lZGPUitSKVpp5Iiv3WwT606S33fTHelJXREZa6nPNb4wDlsD8qgNokkwZoY2b1xzW5cWCoNx3A1Sawknm/druIGeKyavozaMn0KkmkRMW2xp83PTkVNY+Fpb+YeWrMuQSBkn3rb0jwlc3xRpFEa8klhhcYrsbKCz8MaXIzSRxrH8zuWAx3qOZfCHM1sc/b6fHp2nMZQYobdd7k9enSvDfjZ8QIbx57O1w0GMIwI56fj2ryX9uj/AIKyeEfhhrM3hvRbi48Rarukg+xaavmZdTt/ePkKg9+a+B/F/wC3p438SRSRyPpel+YzeXBYoZFgU9A8jMS7e6gCsZRnbljt3PSwuFnN83U8n/4LXfs+nw78UdJ+IWj2rfZfEiLp2qxRqzM18m5o5wMfxwgIcY5g55Oa+NtK+DXizxQCbXSbqEqu5mupEt0KngffOevoM19a+LPEupeL9R+16rfXF/cMSoeaQt5WT/DnoPpWSZVE+5f3+08PJzuxx+OMfpXqYfMHGCg9Wj2FkrlLmbPnuH9mrxQ0KGW80LLqdsZuzIc++1MDH1r3X9h20039niz1ObxBHcTapqV0JJrizjE8aQIPkQZYHgknpzwOQBi094Q+5irFSWBJ+9x/n8qisoobeGOCINJIrGR1LDqRxjgY71pUx3OuSWxcsnindM+3PBH7XXgOG2Vm1yOHefmEsEsZQe+4Dp7ele9fDX4naN4hsI5NL1KyvVkUFGimDZJHBx1/A+lfle0sc0Gy4V2UcbDk4/8ArVreF5brQnibTb+601STJuWQqqHnqBiuflg1c5K2Wzv7h+2Hw08eSaPeRBy72shKMvXAzjP55r1vSNbg1GEyWzrtGAxJ+6fSvyS+Af7fviL4dajY2PipmvrCGXakypmV1YAEljxxkkdDnvX3t+z98d9H+Iemrqui6gske5TJHJncCRnBH41pGEo7bHiYii6btNWPoqG1Lt8w+8B14/lU0jrbRrt4K/KKo6V4pXVrWPzFWNsYA/GtG20v7TIpWUKzDdhjxW9zgMy4lMhbd949BTQFOd3qe9b0Pgia/mQpInJ61esvhRPPcsvmx7W7tzz/AJxSlGUo+6glUSWpx9w6wxMy/M3QJ2Oe5q14W8J3XiW7EfkP8xw7OcBBkAEAda9G0r4TafprL5/mXEh6jK7Rj/gNdRbabDbIvlrsAGMipjg5SfvGcswUY2juYegeCofCtntRvMkbAYgEZ7cVceDcpOxuKm13XLbRD+9kLSkZVBz+dczf+LLmYF1ZU77QOP516FOgobHGpyl70j8gP+Dmn4H2vhb40fDn4gW8Kwt4m0u50S/25Pmz2jxtCTnP/LKfb7+WM5r8wNCugixj598Mu/jnIGMHHTtyMenvX7U/8HH+knX/ANjXwfqbKrz6T4uQbu4Wa2mDAf8AfCn/AID9a/E7TdPk1IxKfk3Eh1XjjNelKvdKSPUwfvaHoHiH4x6/470G2tdU1C4ngtS3koqRxI3/AAGNVHAJ5xz3zgYo2Z3KuOmz5j71TS0V9qLuCjhQD2FXIY/Jj2g8YwfUZrya1eVR3bPpaEYwVokl1LGAVJXOPWu2/Zf8H22veP4tW1SNptH0Bhc3Cqf9a5yIhx2DYcjuExz0rjtI8P3PiLWbe1s0E1zdSCOONjjJOOp7D19q+0PA3wGtfAGh6LZ6fIuof2YzS6u7EMZXkABkAGMqGAxkcbe9KjFtnJmFZRjZM7GTUJ/CF3FbapLBcaL4qt5HuHP3ZWxkEem5Sw4xyB35q1q/gPTNGsdFa1njazjjeDYv7wIFB28nPXAqT4+appPj74Ix6LtghvtBnEtuyfKAp6Y74HI69DXj+k+J9U1PXpfDqmb7ReKHgI+RQrJkt6HBA54rtPBZ6F4y+KDeJ/GEcRlePTLWErshwCMAnJ+gA69hW98H9c1Txz4G1C3t0aa+8Mzy31ruUHzigDyQ7sHhoyvuD0IqfT/hrp/w41bSNE1iCP7VqljvjlVlzPKF27c+hPGDWxq0lt8CPDFrHFarHeWlwbuNCNxukwd65GOdpHXpjFAGl8M/Dq2vxftNUvrrztFNmbvazAMJWUlTgcHAYj8K6X45ePbjwv4Nj8UrHLHcWGnxQTFfm81RKXjbv0DH8M15vF4p0/V7zQ47e4W0t75P9H3nmBlJG0+x3Nn8K9O1DVLW0vYvDeqmGSG+sH047huhuEPzxsO2cqR60EyOs+GPj231+fwzrcRa3N/bxySsoDETAHn02kZyeoIrlfEXi3xFH4gvlm8MzzSrcSB5E27Xbcckex61T/Zk8J3XhD4W6vIytq81hqDx2lpE3+qhjJ3YyM7gGLdeQR9a2p/jKTO/lrMke47VaQBlHYGq5Xa5J4bpnwyk07RF1NbwXTw3DC2sIVZpY41UNlpBxwTnlQOOK7SwtrPUdd09v7L1SS60W3a5e6jHmRtM+3YzRqpwAd/U4GevHOXr3iHUvhvaRaldw6TN/bFmyrHfStD5IYbW3bDwV459fWi51hvBXwM8mZrq4lNlElzBFdlbq446q5wTkHgHGfWub2jKVJboxfih4khg8DaiuoWMuqXE128VhaBfLhvAy4ZxAP3kgLZO8PtAHTnNNvbHwxZeHLi+0m4e6ks9Piit9PltdlveEk8DcMblzk4JwMdetaFr8M7VPEGmR+KNQ+zSLpSSaDZ3bsLqxjXmaNznHmNngHjHfvUnjfwRpcM2j3kcg0/Q45gL7zSQYYQv305wCSAMZ5wfYVk5mmhzfw4svEWlXOj6layR6hqGsS/ZdRtIFdYbONCWWPd90/KW6DjP5+iLcWfiHQtWeG5aa7hnZZbWWxMqWOVVtiSsRuzxlgABjGDmobyKHxFqun/2O1zb6SjSS29yjCJCFUFm6nKlcc88c8Vg3uqy6n4ss7LT76a0s76RpJRISsDxxBXkO/jqoIHqfbkBRk+MtB022ne3k09dSt9QRLy7tAm5C0ZYxbcg4wy7sAHJXjBGR0fhHwTa6z8MptN1C2+w2bxf2lDqeQzWqFRiIoOSUZd2wnkdqu/H7w6ulaxcXU2nyQw6e8b2ltZT7vKtW5DPuYEtIMgAcAHIYHNbs+vWtxp1u1ky2Njqy7rbLiSOHbGcq5G7cWwRgAkk471USZGN8RfDV94j0uGVtT/tfUb6eMXAaRIEvIgu5nhZWJj2qpX5gSMnp0qHxTa22iaRqkOpNYaXH9oMOmy2ty7q5IA81d3zJjGCOQTnpXQeCLpNL0O3T7G1xNpMcbStbxENHu3ZfcwVQASOeGwRwa5/Ttc0Hw3pen/ZNSW4/tCyS4YCGK5ZJizMQ4f/AFSMW6jJHp2qiTF8dXslx4o0vT47nRtWgCwXVzdiMExKkilncKdxwAcknvz0yc3wlq0HjjV/EXiC4sbeS1tZpIbXzHKQXWSA+GblRuXq3Q5A7Ctf4n+DptA0a71CweBp7Tzr6W+dd0TQyNvCggfMWzgYBU5zxms258OaP4n8H6ZeGz1yS3gYtPa2l6FguGAOdyjBKq5V9vBOTWZb2Maw0KM+EtcbT76z0u4+xQ2UBmuUimthJkSErx8o6E5J4GR2Dr/RLebwbo6y30dxqWn2a6fKJoo4BcBM7pFfoFkxxjJynPJrQvNQ0/xLqVreXMNhdQw3D26yhTGsOBkbwOTsyc5GSTjHArM1e0uviH8Sr/SNBuHXw5ZpG2rXNqBCt2zlirLks2wbXGBgnzF444BavUpa34YtW02Gx01Ly4bVJUmtNQjHlJbQlY9yvli0exj6kNnI29K6L/hPpotEht5382z0GZYb7VoGM9wQzqiOy5y4BIBHcEnoMGjLFb6VrUjQ3AgihUW9rOjlGYrjbExCk7GILEEEZjHtXmP7Tv7QviD4S2kOkwtbrZ+JnTTPtEFrHDN8zqwZnTA5YYGQOuOd3ASeja58QdWbSPEF9df8TVbK9K6SsyYks7VSADFGowc43bcFuij1OT8P9FvvFeqot14gk1rQfDmnhY5FtlS1W6kceUgi3LgBCQc55X34+b7f4w6p8O4rKPSrqfWvEFxdi0dLvDPIWO1Y05GSflHOBk9a9u0b4i3VnpVvI1jpjXWkxRR36LI9v5atgjeqnO4dMdcdjyQBubuuaNH4DvV1CGO3ZNa1NdJvo7KfdDHk8kZKhgp28cj3B5ORq3hdfEnhvXl3Xlnq1vqbWFnYRxrdNdgcxiPy+NvAUnBHBPTitLX9GvNV0HUNQ1h7iKwt5xcDzkLQzKzKpY7Rjd6Dp3zVO/vI7nSdPs7G4ma1WJ9t0ge1lsJYwpjA2DBUrkK2eTgnA5pNJ7k8yEt9LiuzHZ6ZPd6nqFjbiS5i3/8AIPbdzH5iqPlVe+BnB4Bp+j+LJvh5o1ykk0mpRWtufIjWYSbn6OY0PsMe+B6Vd8JeD76y8Y61NZMtvpd5Z2sIWeTbLK5RjvXIGRlTznJGOOtY1xpeqeGJL1rl9DuIoytlqUNtG89xbsCG3xRlQyrg4I6ck5pcqGy9q15pz+BY9WiutasX1aEmK0vI/OuIm6bVycJg8bccgqaxbvxY0Hw00uO+vIfONuIrhlRd8MYPMbEAAsWVM9ODj3p3i3TrjQtAmuNMtt8+oKssVxeSPGkK5b5/LPG7a3A4BI5xwadL8Ho/FWkwaHbs39o6hA+rQX0s5jE7RqPkaNT3OeMdvvdinFWFzdyT4jeEl8R+H7C+OhwaXpOrXCNLN5ojjiC7dv2dSWXYxwpJ6bsjkCuf1OebSvGHh6309bq30ua8WRFiiYTx+XIgYgEYBwcBscZz2xWxpkGrTfYb3UNPka10AJJKlx81tIwONqjOcEZ59focOs9Cl8deIo/E1xMJI2uZraGIjYsSE4YmJT8xA+6d+OKjlY+ZPY6TOm+HPitGNNsdW1SZ/KjxcA3IWWQqoYHbjncBj2Fc1498TWdl4+1rw3YalNBa+I9rXkFtKI4Y9h4KsOFZtqgquCdoq6ugf2hoUkktxdRwq2IbeRyWjXqPl/h5GfvdSMGq+u6UunzatpNxb29jqnhpIbpb1AGDCTIWMEqAzkhgQQQMg81bstiOa25l+Abu6t/iPcaTo+iiaaWBri0g1O8EWnHa2CPmDFXZiCDywOeCTkdh4o8bNqesx2qX/k2It5t9pE7JDPcbYw2VU8BWBwRjOOd3Wub+JmknUJ57jTdPWW9hgjRJNUZoYbQsBmFSjEZ+VgDzzjtyPRofhtqE/ju702zs2hW6sUWO0sw7SFCqZZWbLLyOuMZz2xSTfUiN76nB2GhLZwSG4kuHxcpHIsfzzSlCG3BQRheQCDycckACm/EPw9fX3hu4msNHRbGR5X+0NKz5kAG4OqsMhFkLBT0OK+gG/Yt8RXeLy81nSdKtZE3mJo5J7yNzj5jt2IuF4C7mGc5q1rv7D+pS+HriPT/EljdtdYlEN4s1tDIe6kxkgZ/3falJpo01PJf+CcfhSPw1+1jbLFfQ3v2zQpbiRNqlvuAoS2S2NocYPfntivqjxbC+m69e28iuskUpHIwW5PP0rwv9lv4RyfA79szT7O+0u402W40i7ZZJNrR3DYUYik6OoyeOoGTjrX154r0bQvGsSzXgaK8WMCK7t2UOUyPlZT8rDPfIPNcGI+OxvS2uzxuRsycnP17UeamPvD8q0PFfhhfD/nSSXE8iR5/eGBkDDt3I/L9K878Y/Ej/AIR/T5JLVJFZjtWSVcKD2I5ORWS5l0NJWR+a/wDwWr0e3u/2qba9sEVmk0KKGcL3dJHzn3+b9K+KXHlr1zt4INfZX7dus2vjL4x2K3FxHn+zUXcX+ZiXcnn196+fPH/w78MaVo1rdWHirT0vbiNjcafdoyyQlRnKuoYPn6LivTpwk46HoUaqjBHnjuGXGP8Ax7+lQyr5cYbdkhsKuOv+etXnuLUKyxzRyMo/hI9qw9S1JrzUEWNdsasWXPrjFbUVNMjFVoqm2j3z9lPxroug+I5Ir6Nprm+jWBC7qERjk/MDxzgdx6d6+59I0yT4oMlxb6s2kabHeKz3OEQ2wiAxhQ23IL8IG3Hrgc1+VWiy3lgYpgy7Y2BKqxDOM+vHp71+lH7K/wASZD8I/DdreWkjaTJBPNM5BKoWdQxAb5d2VX5uvJHTNb1LnhxUWrnpHiixsJEaKzWTUI2JFteXcLtPtX+MIcbQcnjtnvWXa+P4fD39mTW+pTWc+lTQR3VrAgj86AEbtuDhiwBDKc5J5IpJLLTdc1GCS1e/jgvGeC5uw3mQQHZkIAOFJZVwe5btWDp39mSaxeSabf2kj2mYUjuWwGc8Dd3OW61hqXY9dg8Vw6jeG4jm/tOzn1eO5SyZ0BtpJDsxuCjLN8/Hbd1OOafinXV8EaXa3V9ZXVrfLC0M0MszQS5UHYZAykZAOMcnjtnjlvAXh9X8Mz+dqUy6bIGvZLiAo9ubgOFAPORjjpk9eKzrPVLr4gSStDbySSHa8sl5LukVg2MBS5dweP4SeD2HOkbsSilsX/GkMOj+CNL1S1mge+vAlxMYZQH+YArGVUEYXOCccegqx43sf7O8ORzW929xeRu0l7FanaqBlQBFLfeDY5G0c561hStD4cvBNND9qha7lspLcTeUzSIxVj5fDr82TghemAa674h6PZeFPCOVe81bUbtU5dAyiRslI4xnAXayje5LZz04ArlYKKWxyOnaqYdJiuDaRx3koDxskfzhemMEHoBjpWx4UubXSNIvJNQtZLjyZI5LcG42nID79qEHneY8N1BIwOTWxqPhmbwAljZzXMc1xHahy4QO0Ekg6ZJDDBX7vzAepyQOa8VadqEZjjvpftTqGa2MEarJnILZ+UHj5Txn+VP3kWoqx237P3im61nwh8WLqa1+yxReEreMhcYlJ8Q6NtYfxEgbgx6Z2+1ZOpQebezK20MsWzOM4BFa37L5bVfDHxKRlW30+DweiA/NuDHxDoZOc9zg0eI7C3V5L1f9UWwMHnj9K5q7u16f5mtLZ+v6I4mC1WzvEjDFsKST90Aev8/yrzj9of4Lf8LJ8JG5023j/tzS9z26pF811Hn5oyc8HpjrkjHevZtQtLeKxEhx+8Afr264rnbfVlsYpGI+aRzs5rOnJxlzGp8KkknaQyMrEEHqpHUEdj7VTvF2TMzNu9RXtn7Rvwfawv7jxJYxqsUz77y3TrGzZzIOOmevPBPft45InnAj+90ruhNTV0RsYX2UW955aquybLZI4BrTvrkTaYIxH0HJBqHU7JkT/aB3CoRfBovlb7yj8K561PVOJ7+FxHPS5Zas8+8ToyS9xzwfSv0q/ZK1l9H/AGXPhxJaMq3djpMEgI4IDopPP4sPxzX5v+NfmmfjIUc191/sI+L/APhJf2afDcbZ3aXFLpzDA5McrAZ5/ugH8RVYuKlSV+54ck415WPq3wt8Y9Us7nzlnZxwSGbI4/CvcPhJ+0Tp+qFbXUJvsd4SNhc5VvbPFfJsFyYl+Q4OeB2PPpV3TNTkjmmkkkdlY5RRzs6dOnevOlQj0K9pI/Tfwf4tuLeKHyZd3mDduzxj8OK9L8PePvtkAW4Xcy992Og9MV+d/wAAf2q73wZeQ6bq0sl5pRb5WOA1vz9Rxz619mfDvxjaeLrKO7sriG4t5icFTyOM4I/GsvZ8pDsz1+z1OzvVYg7Wboc81p22lR3Ea/vmG7/Z/wDr1yOnWwO1lZRuGa14GuLKMHduOc57AVr7PTQwZ0UWh24/1zbsdOMf1q5aWNnbbvLQbvUmubtvFxjnZZfT0HvVPxx8aLLwbpUk38UYJbKZ4A+v86iVNRXNIqPM9Im5478YWPg3Rpr/AFC6t4bCzQtK0kgjEQA6nPb3r8of+CjX/BXvWPip4k1H4e/DCaays/NEVzqsd0PKdcDJUKBgc9c+hrg/+CrP/BTrVvidrl58P/B95c26vL5Goz7FAkXglVYEnbng5A4r5D8NWEejWMkcOXkbPnTAfeJ5x9Oa5Y4e79o0erg8K6k7I2LZ10U3EcMklxcXDH7ddyDE12xyWBPJC5J6Hv1qEy29u21pPm/hbGVX0GfWqqXK7edx75PfNVby5WzlWFvnZjx6e5/UU3ufa4bDxpR0WoX95IH3bt+1sBE6NnP+c0x42lSFlk+bB+Qcnj1Hak2rbX0wdlVjhlGelFtbSTyGVeMgjrgFa5OZp6HQl2Jry28iRFK7vl649acDHbXiwxtGQcPJx868dz/Sn2t5HDEvnI0mRsCryxP40DSVm1NpJGYMwG2OMZYemc4Hf1NP2r7j9nfcfpEMkl8lyN2znaGH3se1aul3lvcQvBJKuWYLtPJz1H15rPghms4iz/wgLjPb/ZqF2W4IuPLZF8z5FYYYnt+pqo1JN2D2aidFJI0pkW7j8wSZVRu+529OK7n9n345a18AfF9veWeoTrbzEGTdISoA7MOhX3NecwX4imVed2Nx7896tQh7kSSOALdWG3P65r0cPW5fdPMzDBRrp6H7Ifsm/tOaX+0F4YivLeWNNQtV23Nr5m50bJwegypxwcV9CaLqC3TLxu7KCf8A61fhv+zP+0Bq37OnxRs7u3mk+xqV4A+SaMkZQ8jkdvr2r9k/gL8S9N+KvgSw8QaXJutryMMQCPlYjkHk8joea9ijFTR+fYynKi7M9z0GCNreNhg7x0xW7pWFvMcKDXNeCLhZLJZHb5VwEAHNdlotp5kHmYHUnmu6OHPJqYhNWLS2vmyFvQ+lZ3iLWV0e3+Xa0zcKuelaOo30el2kkkmcdBjua4G8vmvZ5ppm3SO3AHYf41tUpwirmFK7fkU7q4a/u3mlJaRhgH+7ULBZEbnCt/F6fhTZ5tx29M5/rWbcymKRcuoXd1J4H1rjqNbo9CFN7I/P/wD4OQfFkOmfsleBdFWb/SNZ8WNMMHa2y3tJQ5+mblPpn3r8h9O0FY9LkuBM29ht+5wOT3zX3t/wcLfFdvHf7TPg/wAG2bmS18C6I888ZbiK7vpQ7g9csLeG2HPRi31PwuYTDEyhdo4PseKxn8Nj3cvhGEbvcq2yM9zNtyqxtsXAyD64/Kr9rB/o57tIcHjkAdse9Q2ESw2u522ruxnPf/69a+hXlu21lC3ILBCV6Rt1JzXLGm5vQ9CtioUoc7Z3HwUtrDwz4sRr+BZNQh2zQo52rsyA2fcg9PSvqD4JeKNNTx5NHZ3UdmbzcxhuW847SpDJk4DocjqOK+UNA1Wzk8S20bFbhohtlk7gEY/rXX+BJb6fxXZ/2fdRpNpblf3rbfLjJ5PQ8cd67o03FHgVK/tZczPRv2hfBWrHVZpvDc8zWsl19naEEAQHJ4xnkZwB07dK9G1TVrrwwfC27w/DJqjaQtvLNuVWgcKM5IGeMdMc+ted/FPxJJqPieO1a+tftGrbEEittgjlPQvxnJ4HAOa9e8EQDxaLTRdft7mPUbNFa0uyNtrOTxsL5yAewx1oM3J3NHxjo0/xY+G9rq8zS2154ecb5S5Yyx8bgP7pXnn1H41neDfFVr8T/EE1jNcPcWtvEDb+cxeQSKOGGcAA9CCecdak0iWWwgksFXzBDfmG7G/jawyYpB1Gc4DAHtWX4j17TfgB4rSW10uOTTdfgdRICHKFeSuT6E9aA1PO/Fupxy/EH/RrhoZrW4DW9uVxuZ9u5G545HXHevpax8Lrc+EtOl1xvIW4uSxCne1iyYPytnPynd7EN2r5x8BaJpfxC+O2sahcSSNZxxrqP3tqhTwxP6cV9IfGDSYdJ8A+Hb60vprzSrqWEvDEufLjcbD8w7jI61pTim9QkejfCvQbi11vUtF0XVlmgZhqlpdquBKCeUOD1wM4zyMjjrXdXHh3w3NcSNcaPYm4ZiZSIQBv78Y9a8X8G/E6b4UeONWs7qWGSFbaK60qQkRyYVTvU9jxtOPftXSp+2nb3CLI91ou+Qbm3RnOT1zWnu2sSeD/ABS8L6fqGgWvmyebctKkc0IZm3qrAmNlzkDC44pNf8ISeMdCXVJobgW+oSmEp5vlRhk5RsFd7AYBHQGobXTtJ1PWPD9vcatdWena0TBJfbCjWlwqlt0rHgFsBfmI/Lmuk1G/m8K6Br0eltp94rW5tJhJl5VGWw684RtoyG7gjtXk77nYtFYr/D3wrNe+JLXX9QaTxBeQ2bpLDG8aqgz+6DZJTcVUH1+hNTDSY/HuiLeXlra3P2N3hlsllQCABvlkIKnO7dtGOhXgcEnN8FeGv+FZ21ppum311LJqUyz3MVnMs0rwMN7NuDcIuQCT71a8ZeIL74Z+CtVvtAsLy8spr0Xs72VuHFtKyRgRq3Rm/d5IByAxOOSaXIjNxG+INanjtPDdxqEs2gyQxQ2wiimeS1WQ/LIAwwSoTC5xzlueayrf4Y+HdY+Kuq29nfLa2NxKoLLK6rK7NuDKAoVcjIOSCQencY3hLxVq3xzuku7nQYbyHzmfT7dY/wB4rhcSSbcYABCfMMjnqOM63jDxDZ/DXwverp9ks0MqRG7t7qY3EsEvP75X46YK4HAMgHerWgHpXiO9uLvV5PC0Fvaz6ZFCk9xI0H792DqYkQ8mQDaPmOABkdevD6l4jjn8vQ4/Jhe11NHggt3MbOysHWNl53AsOg55PqBU3hfxlqDXOha4Fm1fSdaZbaF4kEck0bnZGGyTlhMV56AkZx2b8SPCX9jeJbzV7C1XUNUt3bK+WqyiOPOTuiG0ucH/AFbbuNw5FVzE21KF78TE8FaF4g/4SLUodNXxPcc2dn5zTvAnllQkiqzKSFwVAyQx96Z4itbH4haq76bZW3nWsv2d5Ciqm3qdxIB6joM8jp3rL8P/ABB8M3njzQ9UuLWOOa108tdxiSJnt2JO1izZ+bHGWORWtq3j6fw/4Zu2v0WPT75ovsj+QkMnneeArsoJ3byQmAePejmCSsV/DX2nVNQXSfEEk9xp0BFsCsRt/s8QJWEFxwygADrkY5ArqNa8Pzp4e16OHT7W5WS1CadqEeoxRyGZcAnYuThMBSzYzgEZ61i2Hia60/XbDT5odV1DUtQlS0nMkYkgskhPLjBJyxyTkbcY5zmptQ8Y3msra6dbWtjDdx6/9naUFWuJ4nUSEDaDtG0t83QDb9alkrzOVtPD2n6F8KBZrqk11q1zud3iiGxp2Yb1XIBU8Akt60zwt4Pvr6Y3Ed5Jp9rpImEt3CNryuScxZIBfJTHPy9CKo6Ppt1pnxq1TQWX/RdZgb+z0ukaQQy72aWRwo/557BuPGQa1LpU8N+Ik0+4S7+w2O5bi6lIjtVP7tU2gggksSC3qfpWbnqUpNaFDwl4e1Hxj4xmn1qQva6btCGNhLasxXIGBjnaee3br08z+JPh6Lxd8Tra3tbq3km0+GZrxjEEjdhkqFHI44Oc/wAPHOK98bwnD48tItQa7XT9PvoJA1jbyG3S2SHALyngqzhdwLAcEnoCa42/8PWOp6adQ1bQZLF2uPsWmSmBhNqELkYnyF3MF79SQc9DQpu4rng2jfs0QeIPiTN4ks7WTVvDOkWkkdjqEreUZLhSDM4QthcDncRncR07d94f8G3Fz4B12STy4YvFGn+faWzxfvCFygcuzZbPyngA8HpVkeKofh7Da6ezLJaxSTOJYZJljmZ9vz4PyBQQCe2ME4HSxF4vbTLiziuIv7Xa201FDTbGkUschAVB3bgTkHpwT0rS4jmfil4is7jRza6LdazYae8MTJBui23LtgtgR/NwwwQRjkEcc103hfUG+JVlpEMl9eaPZQ2EDXCPHgiaONDJtZT8xZgSN2Ocdelc7p/gHXPFF4fEWoR21rHb3Qm8qFRh8oCIwMjCjIyfXtXaaHeadfR2p1xpLO6YKyxsytZmJm/d4APzYHGADnAIPas27uwVLW0NP4kXOpW3ivQ7yCyS5sEmg8m5acn51DhGEW7dnDSAtjI5+tZxt9dv7m91Wazjs4QJpIbsbXilWNcmN03eYxHHzEYO/AOQcFuUXxK93oc9rdM8YESIwUGBWAYjklSMEDjpx3qfTvENvqunLDpUdxca1qVy8lzEwESafDv+d/3vytuWPgDnL4Haq5mZmD4d1y4ur6w1a60e3vvOlEd3iU7UPTIzu2qD6/iTjNdZ8QdCs9L1u8mijt9QuNHtESV7acCOHzgJEMb52szAEHGVUFs4PFZWl+N7HT9L1TTIIvKjtb6SK53OszRxsN24r1fc4YfKcAA8ciuRTVbHRtQm0axmuIdS1CFZLGC1iCSkRkjYnOF++zbiMEAAnpU8zM5NvQ6jw/qlrqXw8h0KYN9stXAim81y9+zOT8+0bMKC2AeMjjmsFLTVLrxI1iLiGwk0HY9tbBSst2z8Opw23B9TxWt4ctIdJ0AXga2sbi8EkFnLJctFGjKreaoBAVmXO7r0UdMmutufh9b69d+Ro1zp9xdLaKolkdDLfRqq5dG5LMNpJGeCccVLk2TFtHFabFe+JpLz+0bhodO06B53wNx892C/MwPJjClgBnPvXRanp94+qSWMd9cNZXiRS2HlRh0kZUDuZSemCu0AgZ3cVL4BsdP0m/vdDmhmuGurZ5o5EhVkickHkNjIIJxng4PqaXxRfxaT4OvNWjtjdQ28A3EFnEo7NvUENk+h7VUYvqEm2ear4ej8HW2sW94urtZ6xqUhLGbzIQWkYN8mdyDJOMgAA8ZHNffn7MHgPT9A+Ftvrtnawre6nmzmnCBWPkkoq5yT91ATzjOa/P8A+H+rN8Wra1vBY+X9gnEggiyv2uH76ysCCwBCk4YZOfrX6PfsKQr8UvgDcq0K2ckGoSXEEQJxFuUHHQdct25zRJ6WNkb+oWH27TpoWHzTdec5H+RTbC1kh02FT/rIRtLA4zjpWrcabNZuyXEb28kZwySKY2H4GqZHl7lXO084rJaaFdDwv/gox4UvfEX7F3jy+0e8uLHxJ4R0yXxJol7EQstld2Y89WUn1VHUj+IMQetfkn8Vv+C4/wAUvit4O02LRbhfCM0luouxp7FfMm/icSfeVSP4B05GcV+vn7cPjSHw5+z/AOJdFUlrzxBYTaaV7RxTROrsT24OB/vZ7c/zY6TZfZPCljJ9wLks2OnO3H6124ejCcG5LVBG/NY6f4ifFbxL8WdWa68Ra9rWsXGc5urx5MD0HOMfXNReFviP4k8AX/neH/EGtaPJjG61vHUH6rnaR+Fc68shb5V+fGCDz0pzyqD8sn+9x0P1ro0taxpdm98RPjvrnj7W/tWsSQNcPEsSzwxeUCoz/Dk4OT9KwEu95aRW+8QzY/T34rG8SQ+bH3/c/wCcVW8N68DttZsKc/K5NbRopxujOVdxaTOj3tMchj1456VG8bY+VvmGPwzxVhbNv3flrJIrHAKoSCeeh6VueH/h7qmsTqEs7rcWIRDCyluBzkj/ADisrWZnOWg3wxpN5qWoW9rHAXkLbGXI5P0zg9OucDNfoR+y1oeneHvD1np/iDR2vdQvrPy7SHDP9jQAtJIdmVzuwN2dozjkkY8O+E37KWteDbyLVfFGnXi3iqr2lkg2vIzAFSdw5IO3hQfvc4yDX138L57HwkYdSvpoJLyG1NsRE25Y4zhnRgo3DJA5OBnHtUVNURCXQzvB2hXMdnqASaW4+yzfuEV8ouePu9T9cdu3WtvRdN03XPBVrezRS6hqFrILieN8LCilgQHU4wD3OeBnOAKy5Ge6um1HUJo7htTMsMFpCsluYl24EgZcb88/L1HXNWPHGjXkQt7X7Lb6asVqJUjS92ttVS22UsPvHgFSevFc73NjpfGGnw3FitzFcWNi25YrjSIEeaSBMf60BflK5A5UnGRn1rirVJPA8D6o/wDpNvI+z7WRIWnZuCoOPlYdcjjnHFdB4Itr3QYW1rT9Qkj1ZVWBil2siSLIATE8W3nON3GcY6enM3/i261aK6bUFmnhXECog2q7nkttU4JyM9P51Qi94emGt+Kby40+xZJPsPkRM4WSUgDBkJYj5gOfqTXQ+AvBGt28ViupWtvq0E0hSJ7i4EU/lFXIZBKfvKylc4PQAelcxoGrRvq1g0t1Jp/2fb9njhi+eKZTlXY5G0FgM5BxzkcGuq8e6pHbMrXVnp7SqVljvrq2LySjAk2LKykBjlsheuCD0rZysrlJFbUPFOoW2oyaXFaSz3AuzqBu5Y1kuLeLaoKl+jRkpwqjhnY9Dml8L6+NAS+1bUrfSZLqME2lrIm24YDcz71UhdrZjGeAOeeeNEeJZ38Rajq1u0bXl9CEuEtj5EMiquR90Bcdsgc8Dtgcd/wkN4/if7Ut19mtdTzDc+VnaI+Plf2GT+tZuVyj0n4AyyzWfxau72aCOObwfbstqrqTGDr+i/MQOfoenNU/E1x5FlHZqvmM53ccjn36VP8AAPQtPk8O/FhontfMfwjEPOhdRFsXxDohwTxzkD9ao+JtehSy/cx+XGVwJWP3uOxrnrbr0/Uqm9H6/ojmtW1T7RcxRFmZY8Aj0HpXNWPiK21Ke6jcsqqw8tWU55znnFaUti11dNeeZtjVtoX1FZOvaZFLqklxp67QyghT6Dpx7/0rGw+Zmz4b0qz1nVLWH7O1z5kgiZH6Nk4wc4GOa8h/bO/Ypvfgzqdx4h8Nw3F34VkHmXUWQ8mluTjPHJiYk4IyUxzgYr074aeJTpniyzmkbAjuM4I6NzjP419IeF/HFn43ZtNv0jSWYlByCswOcgZ69/lrSE5QdzTc/JS5V3csvzBRk8+2f6g1zupxf2fdNGuVGcj8a/Qf9pr/AIJo2eoXFzrPhC6j0aWQl5NOmiP2RzySUI5jJz0xt6nvXyz8RP2NPHXhy4jmu9CvpoWUkSWcBnVgO/HP44rujKM1oy6NV05XPBPFVussYYL823aT3J9K+kP+CXPjuOd/FHhKZszRlNVtQCQ2z5YpcduGCfXdxkV49rnw0vLaJvOtbxNn96Bxg/lWR8KvFGofAf4vaX4mhWbyrOVku40BV57dxtkXHc7eQPVR3rdUeam4meIrQlNTR+lRgk8w7WVtp7U2W6ngvVUoGhK5LZG4HJ/+tTo9TtdU0y31CzuIZre9jWWKaN1aKZWHylSueox+NLGyzNmQ8+zV5Di1oyrroXBqKyBSrNuz1Ir139nD9pS7+E3iKGOaSSbSZnBmjZm+Q8DcAOuBXi4g4Aj3Nj0PSpbW4khmwS0bKchgePoakzlpsfrV4J8b2/irRbLUNPvI7i2uIxKhQj7v06/h1rqV8UxiIBzu4z0P+FfnV+x1+0vJ8PvEMeh6hM7abfvtjO75Ldzk59ACT0r7LuvEU13IWikVlK8Bfp/+qgzldanUeIvHCWQdtyR56EA8V8B/8FR/292+Ffw5vNJ0mb/ica0JLeF0Yr5abSrNjHXJGK+gPjp8V18H6FePOOI4zgFgM9P/AK1fiD+058ZG+OHx71TUBNI+n6e/lW4d92ApA4Pbp/L0rnpxdSeuyN6bSXmZ+ialJa/aNQumaTUrk73kc7mJPJGfrXfeDdQVPB8HmSFWuXLsdpbkMRyfwrxXXPFizDy1zhTjjrXofwg8RR6x4aNnvzcWrn922PuE5zW+Ij7uh9Dk9RKtZnWzmS6DeRuxHHufkcD1x1xVa4uZr5/3kMcb5xkMMN79aknljhlaSMsski7ZMHr/APWzUF1HITuhKtzjB4x6/WvKlKx9YEOnxNJ+8DBccEHv2qxHDsuokjaQRsMMx/gP+c1Ys7dET9822TAIxzzUd/HJYG3IOXkyTx1/CuKUr7G0KbWrLNzaiGVVt2WSZfb7pPv0qLVp5fsa7T5d0zqCMHcVHH61NBdN9kZpPmSEg4Jxg1Wvr7zoH+yxyNdSfxsu5gPalHXc05SWDU/KvWUyeZtVV2gHMfPI9/wzUOvNJD5U9xhbNidqqp3r/n0qhBb3tveBPL81Xi8zzP7jjt/n0qW819ZdMX7VEd0J+RTzljwSfzNddOm90c9adkXLC/aO58oxGQ4G1sfdUgdfetiwu1W3YtJ8y5DKOcfhXGqk0beYjSOssmXTcQVHbFdZ4HlURSs1vHH9oJj3yDhR/e9T1P5VpKXL7xhQ5qjsbVxENf0GGSGST7VbKHQ4/Pnp2r7p/wCCNv7SznVr7wReTr9nvC19ahg3yyAKJEH4AN+fevia0uIdHljhRUaNuoX+E+lbXwF+Jc3wZ+O+naxb+Ykdndi6CKcfIc7h+Wfyr3cvrXR8ln2DW5+/HhnVT9rRVXy4duchuAef1P8AWvQ/CWsSX6NztjjPI9sV4x8OPEMeu+FNP1C3ZJI7y3SdCMYKsAw/DBr07Tr/APsvw/I8ZUSXJEYzzxjk/wCfSvejKyuz4N0+hH4z1ybVdS8uP/UxfdXOOvf9KwWvCiHt7+tWTOsH+s3b/rWRd3jAN2wOOK46lRuTNqNNJhcXwjbMjYXqOCa53xj42sPA3hDVtc1aZl03RbWS9umA6RopYgDHJOMDHUkVoGyu9UK4aNY2OSxH3R9Pwri/j6kOpaUnh2FfOibbNdtjd5nOVjPqMgEisbNu3Q717ux+OfxA0nXf2ifi74k8ValpV1dX3iXU5tRdFjLCJZXLLGCM8IpCccfLXRaB/wAEt/FXxOFu+mxNoNvMAZZ9WxtXJwdiq29jgdMYr9KtJ+GVvbRZihjRiSxKrt5Jz2rudC8ER2zIyJuZhnO3APv+dTNI3p4uSVkjxL9gT/gkb8LfhGs83iDS08ea9fWksMl1qkCNDaxyR7JFgh5RSVLAO2WBbIIGa/MH/goP+yeP+Cf37S2teCQtxNoEp/tDQ5SMebYyH5QSOrR8IehG3JGCCf6GvgX4QaxhkvpFxwY4wRgAcV8uf8Fz/wBju3/aM/Z/03VrS0tzr/h+SSG3uDHl/Lk2MUz1xmPOO3NdeBg2ve2PJxWKlOZ+E3hGbT9S1K73STbJY/3Gw7SceufxrvNMvIbG+tbqzlnsr3aI5A77o7gdDkYPtXkXi3QdQ+FviGfTb6GW3ubOQxsCpA6/yrp1kmudOs7rTJLhmhdXkMn+rbkdG/PrWtajYujVuexaU0N3qVzHdqt+wjE9q448kqQQrjuQQDkenvXsng/9pvTdc0OzsNTS8bVoVLqq27CGcgcqxHIPU7hxkdu3zb4s8W2M2rQ6lLdSWc8iqqtAOM/3CBxX0VoXiGLxQlhe2Oite3FnZCWOLycLMNgD5PXJyeV/KuE64yutSv8AGvQtQj8T2viKO+k0/T9aiEcgGcxED5SSOCegz/KvRoND8OeN/hlpEy38F7HaqtwyTOfKmnX70bNxs3Y74B9a8x8I+Ljd641j4gvobG1SFmSzuJQyFTxj5j1HtzXT/DzVNO8IX0ljLZ2L6dql7Equih0ZGY7kfqvGc/jQaRMqK28G+CvHtwlt/aGnabqdpLakw8uQxVgrZAPByORjivWl1tvB+jaPo15f3M2heIod2n3bHMVw3OyNj/ASFK5wMNj1ri/jH8JdD174gafZPFe2VrCQ05tn4UY4Zd2M4/unrzjmvQ/hH8OLb4gfCK80HXNNuLnRtF1QXthNvLFgACrx99pbOcZGeOoNaU9GSVrDwlY+I70aDqVxqs1vZ4lsIgFMscZx5gdv9h9p5xkH2zTbv9kfR3upGHiKZQXJAx05rLt54fiV4+t7rS520ttHuDFdok5SQru2sVPUnqSvPBbtWxqXj/w/Z6jcQtpt/M0UjIZFlYK5BIyOO/WtOVXAw/EvijVPAXxKtbXVIrfT7e3DTRsyh8B02bpCpxyTzj2rsfh/4GuH+HmsXGqRW9xaa5LLLGYpiwiCDZtbPG4A9+c96g8S6vJ4lisLLWprNLUXWLm/urYQy793y/vOXbcO3YVJpPjSfxj4l1DwXoupyzGyERslWMyCWRt2XR3ULjg8ZBOOM8V5DstzrTMU+B9Q0rwFDP4fuLiK+mbEP2bi5lXdseNSpBGcHIbjG3HvHoPjttQ+H3iK8t7eSHwpputJbNBKhla1mRUDx5yWH3gep5f0NTmaa90e40VdS/s/W1uPsr6zHbsVtwymTjbj58HBB44HpV3VNNt9Hn8B+H/tUd74dnuhFfyalIUR1CSMZgBjDOUAJ9SalSIcuhzeqeDdR8ESJ4m0R1Szvrdp9Mlix+/iYKWQkjGegzjGevUVX1ia11rxz4T0w2s0mtaleQyXBs2juLeBApyzZAzIN2duCOp7V1uuz/Y/GMIbU7zSdK1eSTTdAhCq1vLbRAMwiJB8wFz8jZXAOR144rU9Cl0zXrLVLG8SGW4uAyhz8skkQ3nKDknjOMjJUfjTnYnmG32i/wBsz+IdFh86Cz0dzLLdvcMkhhR1ZESNT8rFstuAB68DrTNL1/S7nwBr/wBmmbUtQurL7TcQyW482AiMh0+b50Zlx8w5yOOa0dE0HVvGF1cTrHDd69qUouY0JDHyxkF5Qpwq7RnBIPy/SsnwNbr4I8NiaHztaks5C17Cqb2E+csw2k7uSCFHPQDBpxd0OJV0H4ev4T/Z/wBPn19byO6meW8g0u7lRrdYCz+XGuw7m3Lg/MehxVnVtW03X/Hem3Wq2P2TSbmeK3FjcweSzeaNxk8rLBVj6Nk43EYz1robjVPsfhy81trZUt9Lu1fztSQC7uY3X94IUcbgFIK8cEgZqPxz4x8N6nqF9ctaf2w2tRRwwXcUbscZJUPuAAzntj39aIxsQ9TK0W/0Xw3pV5b6ILfTbHVL7+zdKNpAsLRyKdo34x0O4nbwTnOc1f8ADvgWST40eJNNg1J5Lua2WeIxokUkkabd7ORnC72VT+B/ixVS80SHV/AMl1tt7WSaeSOLSo4kKqI0EceX24A+8cDnB6k81buNet/hprusXFtHBby6xaW8NzBGjyAKoBPlhgMYJLEHucjilU2AsaJ4fmm+PMbX0QfXNItWUxCXzLWa3kjZRLvAwCHJUoPmxtOOa5H4keN9QkfRdNWTT/7N0u6mJEdt9pk3upUDDEfM27coyfmQH0NdJF48hMv2Fbu7vPt6uNUnSAQNcxlmZUQZ6A5O7kkNjtXNaZ4+W+8DSeGvD+n31re2pu5zLcyJKuJXJjTLD5ONgzkZ2+wNZAc4ILxvD3h0XepXsy6hcSpHtWVri4JDqJJUXIzhXXA6DbW54y8RXs3ge98zWZ5tWt5IILe5tpk22sZ8sZPy/VD15OK3vDmpWmlS2/iyW1u5tUht4NNt4rM5mW6R9rbhwrBhk7vukACuG1LxHbx6q9v4y0PULyLT2lu4Ee3YR291gqpI3BSAcA/NxkH0BadgOctPG+neH9YmuryMaxJoturJbs25Zsq3mmQ54A4xx3yelN+H2mSeJ9HtdS1W3SPS765E9s9vKRII1OPLOADzjtmtrwpLDrXw41bVpb6Sz1vUpZUbThbM0NlFFuWKYnH7wtGc4UnnjBBrR0bW9K+IPgKy0/Q9PvYdO0d83+oXJWG3mnUBBCi8SMzSEsNowoU5NIDGk1C1v9GvrGyae2b5r2ycMztCVCH5gTgA+h9K6WS5tNR0dftcNncT3SzAiKQI9vt4eVlDYBJOQBkZOO1ZNzo0lj4f06+vL7T5L7xLbK9xcxygW4UnCoc4Bye3rVLSvAK6PZ3kM0iyW0jNbyiFl/ds2CsIC53qB0A9MVtExlKxj+C9ZjCXP9l6gt1fWTPb2rhNw8ncS/y4PAJxyc5HpXbaRptrb6jqm1ZIZtSRLKzkz50M5bzHdSMcEbS2DgAEnPasibxG3hX4hWWn+HdDj0uz8hLhfOtFjM8YABkKoPl3SBwBg5xyM5NdGt5d21ousa1a6lZ3Wm2t08krlZVl818oWXB2IWVUCnncc4wQaHG5EpXR59rB1HwLfPJBokmqWbRIs0QZI2MxDBzGGYb40deWz0IPerNpoEdpp2r6pNDdfar6JFs7raha2YAKyxjDFl3EAkc5I9TnoPiTrupL4D1r/RtOh1u802yXSpZ5MrbW5G9lijX5Xc7l3Z4Xyxx1rJ8D6NoMvi1b7w7oAh22EtzJLeXi+bK5ZI2fBOSSzMcAAfMOD1EbOw4y6B4ZS+h0mHTZ7qRZ40BjO9pEsyzYcpkYVjgnisG5sfE2n/Drw9p3hXVLF5L7VLtLpb3zXvZYRNlZVb5d4ddx2s20ggdDXTfFLVo9W0CQafMtj4gmKIbUwhpLsZxLkk5AZN5z/eUZJGaueHr/AEnx7c27XN1HaweGrSOOHT1kcMWbKklgMRh9hXcejMvXBpmh13gOwk8TXa/2nq3/ABMr60naZFtkitLa2TIiRI1LFR5YTjPBJwah8ZS/2Hdx2uiyQ7bxfJnWzYqrIApMhyfugk8Y61j6T4s0Pw1rniS4t/D2p3i3FtGEWOVZrqaR1HzAYxGqqduRyT1rrfh/4jjg8U6XeMsOlW2nxSyWUX2czXl4Wi2qHKnapMm5tvcDHXrOxMY2KXgm+tPBGjf2LbWtvq2vXa3NxZ6kW8l0Crwrp0cKC3IPPPB6V9Zf8EztUuNJ8Eanpd3bqs0EyYWAk7m+fLDOOv3uvGT2FfMX9r2/i6w1C8866t59HcwraS2HkMqsgIdMgMxkYOoZflRVGO1fP/7eP7VWueEfgdq/hLw/e32iN4smUX0sDjzLmyit4t0AfHG9iocjk7MdzT3KirtI+1f25P8Ag4B+Cn7LWrXvhezt774oeKLNtr22lxxrY2z5KlHvJOSRt52KRzwScgfCfjn/AIOcfHF9qsx0P4T+A9I09mOyOXUryadkHXLr5a5x1wO/bv8AmVqjy3kshb92yOSfQ46flwPoBVKG5ZAc856Yq+VJansU8vhy3lufoB8QP+C23/C79BmtfE/g5tFmcbjc6VctdW5JG0bxJh1HPv0+mfz+8Ka5CbOKP5D8zE7l3IFLEnPtj2pv2jbI38Kjkgd8/wBa5zxZMdC1dWh+VZlyc/xc12YWV/3aMcRhYU37Tod1r+i2f2Vr3T2ljV48SwyuG8tgDnYwGSvseR61yc0Mgf5Vbk465BqrBr8+o7VaT5QBkDv7VpWMIkRm6bTx+Rq5RadmYStLWJRuR/pKx9UjBbB7nBFVPC3hmW+1SNtqsXOQMcGujn0QLbySp95vkRR95j1OPpXXfCP4b33ivV7JYVjkYHeEbAPA4Pc9fat41Go6HnV43aPqT4Mfs1aX4w+EOl6hfWtmqGHessjjaGycIqqd7DnksMAmvVPh54eXwRbQra6XaQrcHyr4yWyOzbuAF3D5eFX5hzyea6j4f/Cyx0DQtJ0drq3mt57OOD7SzeV5bLDukRWZQMEEhge4GK3LyePxnJDqF08d/DFtWUW6CJQf74bIBYlj1/GuSpJ3uEYp6Mz/ABP4eurRVktdLuNRkaNp3mEk03kI3DGQ/cKoMDdwRkY70LpFufClvqCxwssg2zNOoDNIpOQo67f546enQ6z4ik0q1a3kkuI9Fkzb272UzQMq/cQuQRuGACVJI/XON4d1m60WS11C8tZLjQrkt+5e6K/bCvAGwD5cdc5zxx1JrJy11NlHohupac1zDZ31vNcWtxbvG7NtSOe3CPlim7IBBUfUE/Wugl1zWPFWoX0mnrpRk1QNFKupg7ZmZ/vLtUgZdux71zetJqXiaaw22+oSQ6ozo8O1pnLZ+6pA+ZguD1yAKZqfgzVLO8t5LBhpeV8lFuv38u08FlweGH3hnuOnakncb0O013wzpmk6jb2jLHClxPFcXLRj9yV3b8bOyj7vHOPrWNqfgmTxHZ6neRQ7bGzuC7eXclN+F5whbaQCM8DP5VR1mVNJ8UafYwXP2FfsgJulwr3ybeEYsrLuyMdMDPrirWu+IbjU9XmtooZYLiciadrm8Qxv8v39qtsVsD0yfSmOJN4V8Kw2199sv5I72zhs1meGCVWki3BCm88FPvD5QDjpnjJu654jmbxjp9xDcW9xa+GZRcXEdzJLDNbO6tsjVcYJIYkN29eax9K1FpPCWsJFHcXVvGwFxNBaSW4Qg/OhYHa4OByODtHSqUOrx+IbOSGHT2tGhIitZZbd0m6EkNuAyCcc8cDinGNyjJ1Dxa3inxjOlvM0fkuFkmBG+HPPz5z1yO/v1JJsSaGvhPbfSSFJYGLZhUMrIcbuGyvGBnPrx3qa98FaV4evINStTJJqBhDShgoVjk7mEacsMZAY5PXk8AO8c+GJ59amTyTIzWjxyASsyIZERzyem7jgehz0FX7MD0z9nzxxY/FTwl8RtJsbi4+0R+CoZJs2XkxKq+ItEDYfGWJyOM49uK4/xgF1jUP7J0xlZbdcSYPEQx3r1P8AZi8XN4w+HXj7TZLe2tbrTfCEEQjtwvlxxrreijgqfp+Oaw7jwfY2cWoXlvGbeKRSJGX7zse+KmpHYKct/X9EcFZQzR2iRW1uuFyhmk6E9DWF4g0C8tEVoWLmZipb+HA6Yru/Eej2uk6NDuY+THGqqueWP+PSuVnu7jUENxNHtVSQgPXHSsTQ5XS9MurG5+9Gz53Zzz6/0r1PRtV+32FreRSLFuHDKeVcccfiDXm2tizjcSSTY8wfvefveg/l+VdB8K/E9vYRNZySBVuW/dDqARnp/wDXqJ7BzWPqr4D+Obf4lWLaXfxLJqlsOVYACZeBuyT19hTviN4JhTUGs5FhaWbCPL0S3jzntyM9/pXhuk6xdaBrFrqFlII7u1cSxN6Nk8H1B7j0NfWngXxFp/xw8BpqFtJDDdQny7q1YjfG/GeP7p5xUQvfQOe589ePfgVZXGnsscK+VCDI42li4xxXyh8e/wBmjR9SlurqG1W383G1Au057n29a+7fito66PpUkOW8nO6WQj5v+A+n86+T/j3qYHmbgV+0HKYOQn/1/wDGvWjiLQsYuinqjyf9lbx/LoKyeCNQkaQxktpknZFxloD345ZfQcdq9tguNsm35TnnPcV8d/F/XItAga6DMt5bsDbOpBYSfeVh2+U4/KvZv2Zv2mLb42aB9g1KWJfFWlxqLyA4H2xMAfakGectnI6gjnAIrhrU21zo0pSWx7El7I/K4U5PQ0+O9WWQbz8p4FZQl8tt37xVz0GcD6U9rhYyDuVVfgMe59K5TY3QzW8SzQSMrKdwIPKEHqfyr7U/ZU+Pf/Cf/D+3W5Vn1GxzBJn+ILwGNfB4maEEBufUdq9K/Za+Kf8AwgfxGhinlU2+qAW7buxYjB/TFBMtj2L/AIKk+IZPCv7LXizWrfMdxZ26FWBxt3youfwzmvxCk1trPQ/tDHdcXZaYtnt71+zn/BT/AFaHX/2FPiBFHu86W0jWJcZDsk8cvb/ZRq/ELV5AZfu9AFGew4rswcFK9zn5mnoW4vF+yTEy+vzetdb4C8b/ANg6tHcQyNJ/CUPAkB6qTXmhb5OlJBeNbSBo3ClSCARxW1ehFrlOvDYqVOSl1PsPwxt8V6Wt9bTRw9A3mj7hwDg9u9bttYWt5nbC5bGH5718u/Dv4q3GjXkUkLRxzwkYDDKy47Y6V7v4f+NFj4gjje5/cXC8bc/L6da+VxuHqUpabH6HlmYUsRHXc6eSOISFYULSCTGO/FVfEWnG81eBrhTH5akAZ4Xjr/Krmr3llCymOb7RM0Y2xqO556+1M0zRreR7eW6w0zEvHEOVXGTg/gK4j1t9DOn1OSafyUV44duN5UfvO2ada+VpkiyIG+X15q74ullkmZpWV+M7D0x7Vjtpcd9DuYeZJG3yx54Xvkj0raEVuzGUn0Ita1p9Svlhhb95j6YFR3cUmGikMe+MD92ec9On861dF8OWcl5DJHHJ5kRDg9Wk5HB9OfWu28LeDZPEWryXGtQfYYWT5cYyf9n2A7Yrd1EloZRpObs+pzngzwdN4nnW4O2K2jUSTMBkKPT6ntXWat4We1shcRweXbqMAEY9/wCtdFdaxY+F4THCWWPA8tU7k9yfrXPan4tvNa1Zp5Csa7duW+ZYwO4HrXDKpJs9Olho04+ZmGdoHVZFZTGc7D1XPrUWtySRajZ3QUjzkMX6E/41q6NcQ2c+6H5hjBPTv2pvjq53+GbW4XEYkuwvHQ5Vv8K9jLaz5+Vnzme4RPDyn2P2U/4J3eN38d/sweCruWZpGbTo7Zie+xnjB/JBX0xqc/2Tybf/AJ4qB9eM/wBa+Rf+CSyNF+y94T8zh5Inm5H8Imm/+tX09qWs/ambZn7/AOVfVyraJH5Q4Wkx+q6q07BgM1Ss7WS5kbzD8pFSbRGAxOXbqWNAZpV2luDisPiZT0QXd8uk23nEZVRiNe8jcj8q4WXQGvrl7i4YySud7N7+letaNpVrqIVZoFkXGQWPeuk0n4Z6TK3m+UxLd91X7FvQy9slqzxDTfCpnkVI4wzHoq9SK9K8H/Ca4nkjmuwVjydwc8gfSvRNN8M2Okofs8CRd8jqTVm9vo7NCWyT2A71ccC780mZ1cZzK0QtbOHS7NVQLHGoGAB7V5L+2K8eq/BC+gkhZlaeEKSOAcnP6Zr0V5WvZdzjK53DjlRzXg37XfxF+0+INP0O3bfDZq0lxtOf3hxgH6D+dd0tI2ic1PWSPzL/AGuP+CfVn8cNQuNc00QWV5axZTgATsOCD+GK+D/ip4T8QfBbU5tB1iKO0a3fCMh+Vwf8j86/Zy/u1i0RL6RiIJp/KZT/AAgkgN+Bz+leG/tm/sd6d+0F4Thjt42h1G1VpopB0ZgAdv44qItPSZ3Shy+8j8wdH8YSeF7a6t9YtbGbSrxQGdVG9Bnqp65+les/A3xWU+Lvh+30/VmTRb4GCRLqR0MSMO319fevDfFkk/wp+Il5pmqafJasrNE8MqFVU9jWn4X8WCC6W5BjWeF/MtZVYboG7EZrmqUWpXKp1E3Y+t/2hfh5pvgf4o2a5tTp+oQ4M7ncH7ZQ9Q4x6dRUvwL1G18NadfRaxqx1Tw+2oLHvT5po4lA+X1z7jtW78EviXZfHvw1cWHiUSa5qVjEksKtEGZmXnfkDJx7151r/wAVPD+keMNSjjs10u4t7j94qK2zcp67D/njrUyjfU7D3zxHd2vxV+KujW9xdXNppurQNb2NzanazSqOFl9fb6mus+AvixdGubrwxdfa7O+8OM9tcN99XOSQ2OrA9x1HXtXzf8M/FqyfFqx1iSS3axmUXMMin92ZFbGwjsfvDtnNe0fGXw/eeKvHWm+OPB+m6hHeeSlveQXCiHzghLD73B+UgBgeQq+9OMrgcr4u8PzH4t61deGXa11K2H9oXVjIGKTxEHc8Z5GCNzYOMHIrmD8W5h93UJAvYEDgV6NoPxTs9F+MjagttJaT6vF9j1KJl3+VIvzDbjvksSR615l4o8DG68TajJHc3HlyXUrLwOhckVzy3K5j2u60Hw18WfBsdwZvsereH502xXt8wF9MuSSEXAdRwAoXhietUvh34t0+0+HPiDxAdHlvrmHWpoZIbGPZJHdRNgebtO7B5KjgAnGOopula3avrmoXOu6fpenabYlGeBVJbTWByJEJIDl2wG3hgFJIGQBXX6L4g0VLfxFqVreafbtCXgTT0dNnGZDIMcnk+mCXJrz+bmOiMkznfCHiGx1L4nWniCz0+6ij09ZdR1aKVCAh8kpErRsBgE9GXJwc8ZxS6/od98UtYj0aF2021vIJ9XtWdmjSKBOWd2bp/d2gdAOxrU0LQV8e+DNW163uLXwzJdzpB5kxwJFES4Kx8LtzkksTkkYHOBX8caZHeQT6pqS6xbzw3AWxt4JBC6Wxj2+ZIQcKXcyAqwxtCnGMGo0FzIo6BqQ8a6JY6VqRuH1zwbHKtprkuxysA2gi3B5DtGsYYqc/uwe9GtfC2TRvB9nrmoqxe41BhFLeIk1wylSyMwIzHkKefpnqK0/hhqlld3t9cXugw65eaXcRabFp6eVNDZt82Hib7s7BVGXYD+LbyCag+Kdjo1y8kYuBJfSFoI7WNZP3XyMTIxY4OCAPm7EYHppHYW4/SfFEejeD7vWf+Ef0mG41r900tiwgkiYvjymaMZA3EZC/eDAEcVy3w7+Ii6HpeuX+l2zXxuLZ4btLpFRrGZZc74VkGN52kb1G8e+SDHB8RvDuj6D4dh+y3GkLayNPeyW0jySX900cipL3ZTvEbbVG35cYFaUejWWn3Gj3325biP7T52pWEiCJ7yENnyQ5HU4IU5BOfmOM1QtEVfEaXeq6VNDqdvZpNLEslrPays2FPzbpGzlSGIJC9ST3qjpt5oll8NrO81Tzm1CxIP2N8D7UVHyMDjDckDBzyK19c0wDTdRvbfztHVZmsvsl18rhe6gkd2XJwT1HNQanpy2ehWFvNZ6XNYra7r2DzZVW5LAAMsmVY4wDwRj8aASsc9ouqaj4J/svSI9FWa8klOrXMF0rwtp5kJJhLDrtXb2wWJHrWL4+ttV8e+NJr220m8W1aEebFCZJxJwC0jtjIxjjsARXa/8ACW3XjlpfPtoYde08wPDcOxRngXGyLHRhtXqeWJ5JrlvjD4r1zQvCE3ijTrr7LeWmotp+pjylW3hDojxbAP4WBJPy/eBHSs47hqmdRLe2el+H1g8P6dCLhpxJa3V0gE6wFQ247hnzCQVAxjATnqKTxXpur6rNoq6bIunLeXI8+/kZY7m8f7vzcjAjZfvHJXfxjJrL07xlq3hBk8P21na32o67HFIsd1h5LSTAIIlzuSPIXqcYyO5zc8XfEfUNK16GDSFit9R06KaXUYpcS2MjsCVKsd3y+fhty7TtVxyMAEpLYgXxq2qW+rW/hWTVIZZJpBeXCsBOs0VunQMoyWwWOSeRu+o5PR7vVvEckbxrEl3eGWOayEQmiEWTnyySRl1VeSAV3OO5zjfEvUtWM9vr2pazC/iZYIXurW3uDJayO67JEjYBW2yHjoNoOAeK7H4b2+saVoMniRWi06+voxbRWobzUuotx3RxrjKsCrDcQc7gCcHiYq7A5bVdO1aeaztVubqxs3iNmyXES/6MrgqgO0HgZwG6KF5wK3B4bT4S/D3RfDN55OoahO9xLHcT2xeKNfv7mRhggZwuCM1zv/CQaXawag2oSpbrbsJLiaW4YSCcSKfs8O0fMACd4G0AY962PiH438P+JJbfT9Q1TUP7QuLFbKC7u5VXZCqFwpQZRWYKI1Kgk5BJBJNbEybNzxfpuhfEn4V3mn2drEum2sccdraxogmDgB1kYAbvLYrjjAyXz1OZ/ht4u1OzudC8JqLGxs7PTbe5ivHnUFzFGBMoygG18J95jznHesqT4r6F4N8fapomoaHPrN01uRdSyhW8knlEzHhQuw/dyRlgetcv4etdOvI7jWLjT7qS1tTO0lqWORDIW2R71OeBhioJzjqeaz+Hc5SrqupSeIrC/wBWsTNcWKzJZWDRj/iYXEr/ADhUC4L7dpCk4UbsYIyT0vxe8U3E8+h65o/h/UNQ01k+0Svq0oKwRjhPORcgSMxJKle0e3FVNE+H2raT4d0/xDoNrY3mmLfwS20EM7K08Mc23aGP3DuT73X5SM4JzpeLfD/iDUvF0PiSbUo/DOirM8b2lusd8Li6QsC2JF24IEQAZSATuJ7VXNccXZnD/EzVr6DU/A+hrqXmSGV3eDTrYxyxKVUsHJBZVBk24OMbmXua3/FngODT/GEc1hPcWen20EdpKkjkm4ZQC7uOkWNuBt5PU9MVzmraz4c8FazcwyWV1/a14jpZeZFJP9qU5aQyAnOeF+bIAJGAAa1PgX4sutU1LXPEd1awNYRjytOW55eJmAG91wQWG1+u7ijlZstdR1zrtjG41azh0uXWtRjSGzs5CiyrB5r5KydMbVbdnBK8egrqvC2m6j4S0iHUdLbRVbXLPypbb7I8kcZVGzIIxjdtdmy2cgckMDiud07U7G91f/TFhjuLdXlUNc7UgQqVKhVZScnI7jmtLwtJPrF1M2j6gEluLhbbDovk2iygjPJJ6joDk4xipGN0T4gQ+HdUgt9HZo2vwY9YlWNplt8AgyNtHyjd0/A84q54zuI9Y8NSXFjNNo9jBPmO4uUD3LFT1jBwwGc4KgfePFZ/xDu4fh9cQ21heWv2e+CWHzQfZxqVwwCmRgATkDd1HI55OK0PGPj2P4l/EmO4vrGGTR47pC9tboD9oUA/OisDtUkL8qnA2nnrkd+pMZJjPgpFrvjBby+W6trq3t5Nrm4INwy4Jjfbx83QknoSa+bP+CgVrJcat4fjkjit5mhu/tKfeZpB5S5LZIYdMAYwMDtXvE3xC0nQdX1A6f4ofwrD4gn/AH1qXjSW22DDxtGzE/MwPOCBz0HFfNf7XeoLqltoM9vrUOsRo92skkZHy52leO2doycDJyehojqb0rqSZ8h6/wCArrUILjUbCzkmtYEzMYYN3kgDlmCj5V56niuNaNVUMYyMjunWvaPgX8e5vg5rEOoQ3z6bfQSM4u45NhhG1AACOeqfiODxxXbT/s5eP/24PEU2veBfhfrEkd9IZ5tUSxTTbK9kk5MhywDMzBmLKvJPUjFbKilC7ep6P1xRnqtD5ha3ilXzBt24wSRjmuP8cXH9q3c2zP8AoqqB75IH9a+1vEv/AARI/aOttKef/hFdJmydwgt9XRpAcE427R1x69a+U/ip8D/E/wAD9an07xZoepeH77A/d3kW3cA4GQ3IIz6GunBxSne5x5hjFNckdjz/AEOC7e5XYsjZYLgN719CfB79kvxd8TtFa+021s5II9ud0ylmLHbhV6sQeuOgry/wRp7NrNvEsBn8x0OxeS3I6d8ke9fo3+zT8PL7wB4B8O6mfstnC1tOmIZFmmjLHaTKmDsOM4xz3rpxErs82jKUUfNXhD9iXUodX36/eNDBbhoo0t0Erbx1H/6q+nv2b/h9oPgPTF0qOHRdH8yGW4k1fULYedAUXjYw53HBKgcqcY6V2WkaZZ+HPDcMzXlxdTfaZGW0DxspXAy7Z2yKScnP4DFXtF0yGW1kurSxZf7Wi8+NguAshGCRvzle3TB646GuGVV7G8o8w28a30i3s5ZL65vriZCGMsUkLAgfu5AOCqlMHn72evPMlhoP2CzW8njbTbVogyHymRbknOCEztIBBOSM9fQVj+Kddg1vVJY765bUtSt5UthCSf3nG0DnlewGODj0oKXE2tWuiXU9zHcWL7HguZ/lt0GTgDkKvUcdDms9WXGNjS1DUdStfA0hh0S3u1hh85Le4kNnJeFnXoWO35xuO9wQO33jksdSt9QuNraVdaamoGNYF+0IU03YGLgqgxI/uOoB45rc8aaRLJfXH+j7YNNeKNIpZ/MhihOQDuUc5APGeCO3fCsYW129mmuLqCz0RLbbDGWO95+pccHoAR6fNUlF2S01jwXrWn3sOp/Z9PtpT9mjabgIx5fpkZ44x3Parj6vZ+KWurz7OUvyyyj5ftEZbdlnJxhWYgY9yM1ydy+n6zpd9JdW7XGoRBbKxaSSR3QJtdig6MMErlvQ+ldR8LVGlSzWazfZ4b3a0jPKVBcDtxzyfu5AHseapWE0Wda0ez8ZSWf9rWN9ZNoaLC+JPMDeYx+eFdgEYHynByx654FZGpeD76zij0241OGKzUlwJXFw7shIAKqScsynuRiuu8QaqsusybluNUjs7M3Un2S3wsgBCbnJboCQM/TFcXpcT+JdVm8RW8S6hpehlUmS5hhRYEK5CDHzyOp6nkEdQa0i0twSsa3hvxwuvWO4yafbyWcL/abJxLHGis23flsKuOvGQB9KvXd7JrV4unrMt1dWu8O0YJDJk5ZXJIfHAAznGP7tQweEEPia5ubyOCWz1BHubSKK5kZQsn7whjkEAAgAcg9/WsDQrqLQfHVjaWUV1rV1fJIWtFcQiLcx27nB3bEUAnJ5yOoxWikmM67Vrmw1RLrSYbFpdeawCxojCNozHwHEn8ODgnaO/rXN6dHqtuPs+sabcR30CE+ZGxzNJ3b/AGs46n0HrV7QLa81LUpLrT9JuGvFkmgWYI5gYYAZSzcMACPXtyTjG3400WzvNEtY4pmt7xZolkjELFrlWGSAowV6nGO2ORzVCex2H7MKXOn6D8TIby1NjdHwgLh1MYVlD6/ouxSw68AYBPANTWsd1rgZZoVtbHaAzsu3HABwO9dJ+yd4Xsm0n4hWv76ZZvC8Uru1yZhxrekFVHO4dOhPrU3ji/8AM1CaF4RDa26EthR0xUzjexMOvr+iPJb/AEu31e7uIVjX7Lp5KrJJyZWJNY+pRxXNjs/giTgAY5r0LxKkd1bRW1vakLJh3lxwwPrXH+L7Mx6TIttHtmIYJgdx2/UVjKDRqpHj/i+FTdFFj3NIAEAHeoYY721kRo5hHPHyvzYwfSul1TTma8sZriNYVaIOwJxggE1la7q1tcW6zBAskmTwfyqeUOY9J8Oai2qaTGXcNMq4cbvavQfgR8U5Phb41hmky2m3Z8q6jJJTBBw+OmQe9fM/hP4gzaH4m+zDdiMr5qtyHQ9ef8K9Zt7+PVrFZ7eRZoZCeVwR24pctioyR9e/EPTbbxJoUk8H76Rk3qoAZWGD39a+Ev2nbebSdZuIWyskOQwYfdBPT647dq+kfgv8T5tR8MzaPIfNNqhMW99pK9OCOSeema+f/wBsedVY3TR7uSnLfMp9W9T7nPetFqyN3Y+DfjFqB1LUW+8qQsFQZJye5rgbXWr7wlr1nrGk3V1YajZtvhuIJCjR88/UHoR0Iru/iBaNqEs0kaM2xiSR9OtcOmyWJkb5guSfp611xmkrFezZ9efs3/teWvxftV03Uvs+m+KohiW26Q3gH/LSIE56EZQZK5446evW1/ujY5VPM+8hbcCPbNfmxqGh/YJIp4JnjER3wSxsQ8TexHKkc9K9o+Df7bt54duYdN8Zebe2P3V1OJV85AMAeaoADAAfeHzHqQxJI46mHvrEtXW59hR3f2cq0W7avRCcg1d0nWNt5DK0MYeOQFSDjBHpXHeGPGGn+LNIhvtPvLe8s7gBkmhYOpHPcd+O+OlbNrNJLIUhVnYjcBj+ftXHK63NuZSjZH1P8cNNX4m/sqeJtNbyW+2aOzQKZA+5hGfYYr8O9QYpJ82fujvX7CaF8SbfSdDgtbi8km08wmKZdyny1KnOBjjn0696/Iv4oaP/AMIn491jTWIZbO8mihPqgf5PqNvc812YGWrRwyVmc/I22PqajVs8/hTPtDSAAqQPWl7d69KUbgpWHLOyHK7gVORg9K7Lwl4z89PIuG2yKM7s/K3NcUKEuPLdTu6c5rnnh+ZanXh8Q6UuZHvvhL4hXemSlhOcqmMuc8egz+Fdpovj+41JF8tTG7dZkbBTr3H8/evDvA+pr4gtFWaRfMU4IPG7H0xXpXh+6XTo9pWQrHxle/8Aj1715csHBytY+qw+ZTsnc9d8L2H9owyNdbptqbvkO5hzjJzxjkVsad8P/Mv1UzL9quGCkIMh8/dHH61zfgzxELnTfNtpGXaNkq45c4zXoHgOLUPEtxH9kDNcWiNfysiBnCrgjYCME9cg46110Mpg5LQ3lmUrXZ6H4Q+Bi+FLGaZdPlmjtYRLcSlQFdsjOD3xjoK5X4heJY73UJLfTFj22rANJJ8qvjg/X6V0fxc+KkmsyNapeyrJdLtnmQ7FkTAABQfKp4zx2zXmt/5Ub+Wv7wqOQpOM4rzs4lRpT+r0V6nt5TGpKPtq3yRT1LVGvL9sO0m0846VHPNLbWIj25Vjvbnn6fpVe6vWiT92SInbc4ABJH16/kaWfWFuYjH5eCThFzyw9yeMfrXgxierVqczNyxuY7y2MSxN5iqRnosdQeIkvPE9toWkWS/arq+vlhgWNdxZsEAADk8sPyNVbLy5UKbiZMGRxnGAPTHpXcfAD9pj4T/BPx/cah4w1iGz8QaI32a1gMMsq2rEBmfCKy7jkgdcexr08tjeqj5rPq3JhWu5+tn7Ffhf/hWHgbw/4ZgkVv7M0tbctwCXC/P/AOPFzXuSNHBHgYkx/dXlv/r1+VOkf8F1PhL4FMksN14o1i4QAothped2MnG6Z4wM+/H8jwvxg/4OQPG3i2wksvhz4L0jwy75A1PWNmpXSjGAy2y/uFPPRzIODlSDX0MrvVH5xTwtST2P1i+M/wAffB/wC8JDXPF2r2+k2BbyreNWEtzqEv8Azygi6yOM5OOF71xf7Lf7V0H7Tmm6tdxWJ0efTb0p9jNwZm+zvkwyFsDlgrZHQEccV+HmrfHfxh8cfHsPizxv4h1TxH4o+419elM7D1CIiqkSAHGxEVfavvP/AIJOfFu3tPjlp1k8xEevWrabcRKcrK+QY/f7y7QRz+8rTCy5pWZpj8G6dJs/VzwRHJJIPM3MoAzlsivSNOhSC2Vdqlu+BXB+HStuI4lURrnIUCu2j1FYbHzdvQfma9iMbLU+blK5NqN/Fp8WX9MAAdaxJLh7iTdJ97uB2pl5cveTtJJyBwucfKKr31+mn2sk00yQxwgu7P0VR1P+fWs5SuZ6t2Rl/FD4nWvwv8G3WrTyLugwI0z99zwAfxPX2r5F8Ra1NrGtyXEsss91dSC5kaRi5yxyeSegB49gK1P2oPi5J4316wMeV0VbjyIk4xNncQx788GubgtPMtZrrzfmZNqD255/z61Dlbc9jD4fk1YstnaNFqFveRLNbW/3gVBUbhkHHfqfyqp4dca7ZzeZuVrWVrd0T+EqMfyNMi1P+0rW1EX7yed/JulPSROR/nGK0f7EfwprF1brIoa+IuI5M/fOAD164AGayvqb8utz5h/bK/4J/eH/ANoGwj8R/Yo11aNWNxsjwZc+uPSvy7+OXwpufgn8UbjRbqOSPS7e42ecFIOAeo+nX8K/e2ygJ0mTc6Kzthu6sDx/9f6V8+ftL/seeHPi9YXGk31pG2pXik205DLtPUtkEZ79a19oiJU+qPzV8KfEaT9nTxrpviDwl4na8uGUJKJmLxlTjIfnJxnv0r1H4p+Kbn47G38Qabp2myatbhGvhbwqrMrDJYjILcknPNeJfFf9njx98FPHA03XNNiurOM8+WiqqR9AWPXtnHvU8PxAjtLea1jkmt7jaFQ2suzZj/dIz0PWspx6ihUXU+h/2ePCejzeMpDqGqRm20XZqFlHEoaOVx1BQnA9CD1z9K961L4833jfRVvH8P3rrM43TWe5YLDB6gAkpw2cDFfL/wAFPglq+seH9N17S/HVm+qrKTOuppsWHk/K6gcqcDBOSea9G1TU/F3wg+KNidS1bTbjRdShC3Vvaf6mQt/d3DngY56VjKLa0Nozu7Hufi/xDFplha6xb2ul30luVZ5fIVvODADcxAyGABHPWuCn8bWt/O8/9ht++YyfKAV5549q6bx14juPDXhddP0mwtdAtb6MbJw3mYJ5HUEDr0weWrwK7sfHyXUip4q0llVyFYSBcjPXGOK5xn0T8eI4fi14k0XT9Y0lk0WaSNb57BGtobRyq5aXcSTtJAMZGeetaPw9+F2i2Ph7xZf6hZ2elrplmbO1v2Dia6hROIVVuGQ8N5gUZ4UEEHNseOZNO+Guh6LdaFZx2dqJJvtFzslmeRmJMzP95l+bO0nHI545vz6UniHxpczWl9J4i0+GGC1ihmcCGAAZPPVlPy/L0+XrzXFHlOp6bHGfBL4v3moaFp+n67p91canPqINrc37o8ENpGFEKoMjGMbsHJ449tT4yeKrrX/FFwml3Auby+kaXV4Y7cqghjZirgkjOT/DgnA565roLRovC3jObTd9nD4XtkCTO+XXTy5LKY8tlAXZux/iPQ5rm/2kL/QPDNzd2trcebqgVfsTWimcmJ2wzZUZbGG+TILYPTvn0M/QzvhX4iubn4y6fJHarfQaXGlvNq//AB6xw3MysB5cbZMhRFkLkDAJUDO4iuG/aL+KGk6Z4ok07Wr+KzhsbmK4m+yRGScLuyC2GIfIbAO3o+O9UfE+p3HhP4Y6l4q0+SSGbRYf7QC3mLN5V2sy4RzwWxjB3YIPcYPzhc65deIPFGk+Ixbx+I5fEzk3cNwXRLMmF9pI2/fXkA8AFB3IwrvYOtmfTvj/AFaHXb3TbjSY4LGTU3E9ldJPHC08aMHUEENs2bCdpGehOADXVeJ71vjLr0c1rZ22l6bLbC0gtokMl5aw5xIccJ90kggFiMEV8s+FtN8vQrP+29SuY/C0VzJI0sgUT2EUwEY2yD5uGIG8kZ4HGMV9WaDp2s+INEk8X3V/bzNHpzR77W2MdxqSKuwIsYdjuZAqA55+p40jfqU7dDY074gaT4s0+00eHT7mRrVZNt1eW261kmRQwZXbAZW7gHORjOea5H41/F7VPDsF/pviA2Mk1xP5D3FvEYAFK9BuYllwvXdjnpWlqHiO88I6roGlalDbQyXCbpbCK2EclonVYwp+VmwyktuGCTmuF8Ht4d+MPjHWPFF0y2uheHLONYrTU7iSOKWUs4DjPVnwV4yQUHTIxUpJB6nffDhtFvfC8nirXJ5LjUtauFh0WB4JIPtUSkYKl0IZ23AghsEDvncT4qa1HrllqMdnZ6T/AGTG7W8kWrIW1CRwoRWMabVOMsquM425I52i/wDDn4hal4p8DWGpf2VeXH9nNt06GQ7mtT8y24hOOWEarycEn0qfxR4Yt9aF3qjNHDb6hh5BcDzbxZMFXlxuwoJUjaCR0YnJIGcpLoZnkepePbLQ/DPiC3hX+zdU8oSW89tc/NeLxEU2sMBxhjnPGBgDBzHd3g0n4S2f2ddUurqYJLdX8dyPs8NuUC+VgjzDINxO7OD1x2pvjfSrPU/HNp4e8J29snl2zXTb2Ek1iYckltvLMwZPmO0AjJyDx6lpfhzQ9Wvrbwuslx5Kq2oTRxl3uLifAz5xUFdmcYJwoJIxzUxXMx7bnnWpeBJtI0zR7i1uLfVJb2SOG1807QCYywy4Tnbt9udx7Cs/45eOr7wb4Hh0i1XyJPtSreanCwEcEEkyMGw6q2QMjOD7dq9B8eR3llomn32h2On6fDrCynT0hUSvtJUecu0gLk8FcBhv74Oadz8LLHxhoX2O+igvNS1RD/xLp7pZJ4AoVWMg9VZgwJwQFPFOzQ7nyfoumapc6zNp8ywwrDcNd/aZW3C4jR8/uGPBd9wJVR2BOcbT9Bfs76Zpfgn4dx6x4guN3iK8lZJVu4PNhW2acLGqpgsp2AHcDwc9uBzmseFYb7wDoen6jY6PJr2jzfY5oPKEn2dYn3KysF2+Y6qCHCk5OccEHS+JnxgsPh14Qaz12+t9Uvltx9kmjKxrbw5yD5ZAyFwRkHk88dKcZdwvfY9Q8SfDvwyst1qUckcMn2gSwjertOB93zQuMqemQBXBeI5tQuPEN7rE1rb6f4e1yKdJJXXYfPi6LAvVchmALAgkL7g838EvFPhvXfDmqTWd/c/avtK2ttPqHloVYKGcghmwPm7Z5zwK6bxj5mpa34Y8K6lcTaj4P/tFGu76K0UTxxuxd1QMctlwPn4OGJ28bactdjllG2iMrwjp1n4d0NdS2MbrVHDaU1zA1wYJgFVsJjau47umD1JOa6rR471rzSfE90jRtDM8P2dW82NVaJwpaMEljukByuOgxyDmH4h2smtS3WnR3R0aHRUIjkspVVwykpJ5ZVVx+9yMgE1LpHiTUh4R0CebwpaXvh2+ngkv9V/eB7FVK7EMu3AZs9Djq3J6GY6MnlZU8TeD7rxh4o0DwnJ4XHhvUpTNAbu7jWdXhkRdjKsbl1fCMNj44PXOce4/sj/8EmtH+GOgQ3nj64ufElwB5MOmtO32No94kWSVCfmYFSAAQACQcnDU39kXxJ/wmX7Zmk6LqEUhfT9Jv5YJWfcly+wOJFQ91CleMDnOT0H3J4m0+Sx1LyfuKoBAC7QBj0z+tOU+iHaRynhrwLpPhWzSHS9N02zhjQIqRWUUewDOAMLnv3JrM+IfwH8LfFq0VNc0PTLiSNxLFcpEIbq2kU5WWOZAHSRSAysCCCAe1dhHF5abt2c/hUkkLRBW3Haw5FRdi1R+MP8AwVctte/Yc+Lek6RqF03izw34lS41DRNR1SMSXkTRlWaG4kj2h2TdjOASv518E/EL41+LPidN5mseIdUMJAaO2ikaFIweowMetfqN/wAHPGkxXfww+Ed1HCJbq08QXUTDusctozYPsTFnHevyR1MxyMzNcYZXIK7cn8806kmknE9fL6EZR5+pm3Gi2sjNIttD5hxl3UMT789/c/8A16jfw8k12sltiyvY84lgPl4PuRyRg461JJKr/Lv/AAxVhLZYV3rcBZMHI2daFUmldM9SVODVpH1x/wAEHP2PfDv7Tv7VWuTeLoZNS/4Q/TlvNOsLmRJIfOMqqZHUg79oYFcnALc54x+5Ueg23h21FrYxrDFCoi4+8QvQE8cDsMcZNfj7/wAG0uomP/go1rmnR/LFqfg673qD99o57V+n0wfyr9s/iF4AuNDvGmWFmt5yXEiR5C9Pvc8H+fPpWeIm5zVz56pDlk43OIdQJyzRqzY6jOexrxj9tj9kvw9+1R8GtX0/WrGK5vIYWuLOVwGYMuG27iMqPlP44r3Ga0IPmBvbG3Hb6+1cz8VNbg8N+Eb6S4ZcvbkKDxnPy/1rCU+R+7uZuPMfzX/FH4fv+zj+0Jf6TGsvl2c4mg807j5OdwGQB0wfyHfmvrr9lD9o/R/FuorpuoxQaeb6NIt7ymO1iYEc7vmIz6Z/Tp4r/wAFS9Lj0z46afqcf+u1CxlD8dNkwwBz6Ofyrw/4c+J9Q0HxFZz2s5WYHfHswXUjodpBBFe/FuVPmZy2s7M/RnxzodzpmsaZqCQtqVvIQqoziRJgxwQxOCeoIPGD7cDsrbTrrS5Le1t7QWd0tzFFdSxKWaJVBBBHI+XaD8hxnkEjFZ3w78calq1hpN1qFpPqC29osbQtGLeNW5Iy2Txt2ngHr+J6e71aa01SzuIdXh0231ST7LeSRW5uobFHfJcyZVshuCQBxx2GfPnFrY7NDiPHXw4/tWHUrvR9Qhmhtbk/aXWYi4nbIkyi435HryPTHFdRCLqw+Gn9i6h9o03XLmNbljKEKvLkAsWXcSNigbW+brkDitDw5oGn2+q29vMbWOSMyO07KzJqLjOCN3bgDBJzgHPYaUF3o6aPepqFvNqpvE2RsYpFhti5B5Of3YGG4OQSx5xVRv1KOT0/TriOxWCG8ktYUi8xvMR1N45xuhUsAq7iqkkjp0xnIr+LvDP9jeGFvPLuLcPGHdVKlypOCN2MDnvjPGO9ejXnii0j0SztblZpLOaRAp8ozW9kmxY/MdgDt3BScNyQoP05DVNKgtrK10dZbWa0LtKt75crbjglYxGeBuyT5hOAR0Oab3Ap/DS1BVLVfssUNust4JLuPzJr1sECMMm3d6kY6A8npWzdX194vhgs/s9hNeXhAFumUitFJPzgn7qjuSWHXAPSqvxT8ITeHBpMWmyWs2i6h5TRrczpNdQHcPmBGBtJPoPTFGvaveTaxNJdLbyW9ra5QRR+VDbyYwGK46KQGzkqMEgYqogGra3deGfC9voUMMd5q0hktGuGLfZ4Iyd+NyNl+VHI6+nQ0s3gmzt/Eum6HYS20c147yZyUa4iHO1MAKxPHqOan1KxVZvtkENjqU1vbvK5tlUDYQCDheX29M5FRaH4dh8VeItK17+0bjVo4IG+yRXIwxkLcxrtb5BjJLYOcdMdGGvUxtL8M3HgX+1DN9ssbJXR4BPIGkV5HbdmMEkZ7gj6YrWg8H3kNvJp8ktvNHHL5sV5D5kfmxnqFzyr9sbuoJ6cVqatPp+jeJdSk1GOwulvETcwB/0S4Q5KJ1DEk8MRkDFUrHxb5N7MzeII76zWMGKyk3FzM/zEDPy5XkZ4PPbFaX/lCxoXXhW9TxBa2elapJZaB5Bjk8i5CXM207jG0ZHA5xuGMlT7GtDSvEFromureWPhy3ls443tY7hLhldAMLI8gZwQwBXbjJyTn0PFa+dWv7e+nk1B7G1tWJ5LF7pX6pgN8oxtyS3p0PNdZo3jTT7DQ7OFtKvrfS44DFbwh3a3hTcPNYnljIwEeff0xyR5r6iex6X+zHrfiYad8SLe8ksWs5PDkb2X2eJQ7Fdd0ePc7g8sQecDb3zV2+tbe4nmtVma9m+/dyHpHjgKOB6e9ZP7JviD+3Na+KV5bW8iyf8ACIZSAsdy413R9hyR2Hb9K2vDGgi10B5Lefy5pJpLi7Ei/OSSeM5rZ9CY6XT/AK2Od1ie71rVotIhVYoWYRq+OSP/ANQrm9Us1+23kfksFtovkkJ+8x6/zrrrLRp9St21JJNvnNIsTEfMACBu6+tOsfDq6zYT3H+s00HYspHErDIPPt0qGk9xxPK/FOhwnw/ZyM/75Y19MsOcmvNvFXhpShmVm8vIKj1H+RXr+q+BZL3xteSSZ+yLbhIIlPyge/6Vg65osMml/YcRzXMh2RBOiVEo9ijxXVUjsUurqFtrTpgv/dwO30xXlvwd/atm+GvxDurHVpGuNB1S4JaTlms8ZAdRnpk8ivUPiLb/APCO2M9nLt84MY1jxglmBr5S8U6Gt1cSQqpW4hcqQB3yaqjTUrphJ9j9IvB/jFdG1Kzv7OaOaOUhhIDujljPUjHP61mftVeHv+Ei8N3Fxaqi5USKkPzJ2POcnP418M/BD9rjWvgVMuk6rbzaxoe4EQG48ua2AP8AyzJB477ele2XH7bvhfxxYSLDqs2nTMvMNydrfTPT1HHFL2MoS12KUkeE6tb+TPJGxxufn/61cx4g8Kx3v7y3+Wbn5BgCTNdH4t1631fVTJbTQXCtx+7YHArHlucP90fMPWrOynKLRydrP5MJik+ZujRsM7abJ4SGs7mtWUyKD8rVseJtJ8xjdxJiQD94o/j6c/Xr+dUNIuGCMysQ+cqqnk/jQ3bcqVO+xlaHZ+JvhxqhutHvNS0q4kPzNb3GxXx03J0b/gQNehaB+3H8SfC6+XLa6HrDL8pe7s5Fm98NFIi9u6nkmuXmu2u3Z5PNd+29sbT0pkHlyI0jr5hwVKA49Oc//WqopSdpIz9lJao7rWv+Cg/jOfS5rceE9Ng85dsskd1KpI/LI/8Ar14d4q+LF14x1tWvbGC1aWQncrMxyeMEnHFehXnh+GS0ja2Vf3hAJYDn2FcP8VPhg2j6dHqQkijSSQx7WB3Iw5xg9fqMgevSuynh4pc0UefUbUrIpx3TtJtwMdOlBnb7SF4xkVX0PVEv7Rfm/eR/Kw7/AFPpVgw/vA24duKnW4E/mUg+tNzx2/OnY8w9h9O9VysLl7w/q50TUY5AdqM2GA9K9Z0jXFdISCWB5z6/WvGAqoQWbgd8dK7jwJrC3uniHfiSHj1yD3rhrU2nc9bA1l8DZ7F4F8TNp9wkZnaBQ/mgjoTjA/pXsHgrx79qtpBB5aGUqwkZvLbgfMM46E/SvnDT7ptiuzq3lnvXtXwu06afTReSA+VJ80a9Uk/p/OuXE450aTknqfRZfhfb1Uui1O31u5W+toWhErStnzHfGwn/AGQMfnWP/aTQfaI5mXOCMjI9vWp31mSVJI2VlC/Kgz0qvY6D9ql8+RWZCSSpHT/PpXysqnNLmk9Wfbcr5bLYqPbzJfxs21VUCQqPugDtVwR/bFXPy+WRjYMZx69aGjQRTNcTM2FJVVATPoM5P/1hUuqxxz6dGYfLj8mP5+m1TjJLNxkL+XHQZNVGLexMtFdmH8UPiD/wrPwhe3UyRvLGpSFOzSnIVQc/iTnGAa+T3uLrUbySaZmkmnfzJpScs7kklvxNen/FvVpvilrsMNqwTSbAbLcZJ8+To8uO2cYGc8AHvWx8O/2Zp/EfkyTXCRo65O8bQeh5OQB14FfSYPCyjH3VqfDZnjI15Wvojy/S9JaW5hVI5Li4ZhgE5xng4xjtXtHw2+EVxp9pHe3EbSXNw2yCFAASeCOvTnHJwK+gPhr+wovhPw0dYkgQDyTK9wI/MKgcDABwclhk54Bzg9K4XX/F0nh7XpLfTZm820l+W4yMIRwQBj+tdGKo16cU5qxhgadOXvKzL3jL4Ut4D06zml1DS/t0nEmnpNuuF56/LkDtwTXafshfFO6+Fn7Qng/UGXbaR6xaLOQfuqZ0BJ56DNePrqUr3b3E0011cyHMkkshZ2P1P512Pwe0S48X/EzwvpNqjPcaxrFnYxKBnLy3MaD6/ezjjpUYT3ZpozzCNOdOSfY/o78F3X9oXaxKMxnkEdhXWXs3mBl3bVTIA7H3Ncr4MjXRrWSSNhJk+WuOBjpnNa7X621i1xcPHHDGGeSSRwqoo5yScDFe/Kpqfm/s5XsiykiujuWKqvQk4zXyx+1t+0iPHOoN4O0Wf/iT8/2jdxErK7qVIjXnG3k9j9a5b9p//gopY6vd6ro/g9nn03S99vcaxFJhLiYcGOL5enHUE5IrwzxWl9o9reaxMWtzawNeShedzYXAz/XFc1Ssk9D0KeDlF+8eh+LPFMkGkw6XDGs91GVWFcbmOAcN16fyrWjkm0zwB5N0yzXTxssJjOfvcH8s15z8HtXi8eavDqUcm7VYYpFeIn5YgQpA/HcO1d419DpFjpkNxJ+8vFG6MN80bZzkH0/Ko9qnuegZ/wADbO7vzq014uW02fyYwwI8xQqkN9eTnoOBwO9z4i+IZbnwUurW+5ptFdXx6xs6rIp/A5HTpUY8TnS/DWpSW+0TxTFTHtyzlSobP1BxWzo3hE6fIulySG4t9SVneKVflVSMgE559OewqJTVtAK3gxpLi4mvLqTbDIu+KDPyoV/i78EGtPX9DbVdZ03VPPP3PKEfqp5DfX+lYkmr2/hvX44bjzILC+K6dHj5kt5D8qknv6Y46V1FtKJvDFm9x5Ud1bYV0Xnbjjg9x1rLmlsB478X/wBm+2/aQ8JeJbHWEuLe4u3a3tnjbZswuAffmvyr/af/AGRvFn7GniS3j1C3+02N03mRXW7duHXkcd/6V+4FnboLVriRg0dvPmVSOdrAYOfQ9PauH/aE+APh348eHmGsaet6Npt0ikTcu0n7wPGP/wBVdFGrZWkc9anfWJ+OHwo8d6o7SXljcaTaTTOrSy3DOdpBPIUNk49D619En4ix/FeD/hHPEmty6zCsAmhl0+2QOh4zyQTgdOnevGf25/2Urz9kH4z3X9lWd83hu6w9lceWfLk+VSwPJAIOR196rfBb4tWerC4N7YxyX2wlGik8u4ixgAqwGemTtx171rKNtUTRq62kfVq6xrHgrwPaw28K3Gj6tGtuU1JxLHIiHAKnjDd/rj0rl20HRY2K/wBkap8px8spK/gcdK4m2+Ilo/w5bS9t7dT7mkhd5vMZGLZ+aM45Psffk1DY/Hf4gWtlDFH4VuJI40VVfpuAGAcbe9cUk7nVZs+zfCPiOz0fwHNZ3i3esazb6lJp9voRSNbpkZkKxhMgmQ78HsOeTineLbLVPD3h9tUhsk8P/vQlhZWh86RVyN/mxKWy6ZC5JPIP1rhrUaX4Z/aE0uGa3m1XzL99VuNSJ2TR3KqSFVmxuxsHfBx1NbmseMdN/wCEufybfVrTQ9WWGeZIVVmeJiXbAziMc8DsK86Tt8J0Slcd4s8Gt8TNLtb6bT4LNnw9x5sjus6Egfe5ZWyoOCMDkDoM9FdeFdGm+FMdxpMdnca1axBLe8Mp2xxxoAFcYC7gwc8gkh159Knix5NE8N20FvCq6UpWOEXDZbyyw5dFIbOd3P0PcZpeGvGlwvjyfwr4SkujZ32mm6muI7OSOWSd+HjjLgYVVCklgVIBwx7Z6k67nmXxxt9UvfB095HpsfiS7m0xJpr7fFGunXROdkaEHev3sBgDjnA6V8qeFviXqUHis2+q2cke0FCHgEYMu0qAASCOCWzn+H7ucY++Pie9jY+FrPTGtbaxjubVINWuoSftFtOMFp/QSZUgAjAD5AxyM/wx8KfDPiu63MlrJpEt4JI51bzI5JUTI3OOvoQTjn2p31F11PlOL4Ja98Q9JsfE/idL628Nww/Z5IF/d2ztExKh0ADvhmQDBxnBP3ct6z48+JWt+DfD2l6nbzXjIg8yKSzkCrZwR4wqgg5YjOMgY4zjnHq3iXwvBDaXlhc20djp+oh7WzvvKysJVPMbYA5C49WGPmx1IrgvGOqCL4dppl/a6fNDYxC2tLZGAbVyyFY2LrnBGzJJ4G4Uc7LjseTfFDxnrXxhcXkK399o2q+ZbpfXtobdhISHORgEMMEc8ZAxkHJ9b+Hnwst5fCWgXWtpZQ2dzFsudjsqYXjz2YAbWHp6npXH/D3xWupeGv7JTT4bW3k1ArZQiYSrbO2FTeoOWYtnOARyBkGvTtU1Dw3D4O1nTfsd5p62uNMurzeVaEFVdkaNshsk87c9fYilKTZG4Q+DbrQ4F1TQoJFksJGaHcylljwXXaSMHCk4JGc469ab4m0fQ55be7bVLiOzm2Jb29zL5cm6T5iXkB4QEgYVW57813k95deLvhDe3VppcqTS6Zaw6W8MfM8PkAi5dgQELgo5Rvmyx7cDxPxjpOteKvBGm+H9Whh1DUrdks5Z44SnkrkyBgM/eARfmyOd2Miny62QWOw8K/Caz0PwxqniZWhbUpL4Ge8i/wBZegrGqoWOCFGCMAYO7nmtI6TGh02+bVo9Og+3/ZriSB1luJbmZMpbIOCyB2XcSTjIIyay11Kaz8KWWnyfbIfsimWDzJo1sbmUKQJGU4JOEXO4jOFqn4AvtQ+JVvcW8Vrbr/Zy/abS4tGjWJ0XczzByDIr9TgLjKDrxQtEHqaXi7xpJ8Il1XTVurp9d19hbtI1xG1va20Z2hUiC56n5yoO4lBkVyth4SsvGaW154ah1jT9cuYorm5voZ3aZZtjh/mkOEU8gKowR6cV1cVx4Z1bUNaXTZo5dW0HbHcXt2p3Sh0UuMnhkU4yV4Jxwe3mXgTVz8QPEviS+lCwWsF0ttb3FgrRl/LADSbtx3FiQB04yMehzsRz/wATNZ1S/wDGmkz2d1DpMOmAvPZ7HnnuzHksu4AnnDYJzjd0HSvn39qC1174mfFWCbTfDyzRR2FvCvkyNKskoHPmKWyPmbHQdBgV9UeDtQ0/4a6hrC64zLeLcbtG1CR5EkcSAho3VgxbduBB+XHPIPTP0Tw/Y67qsLrJDa3X2nfM8r8XDHJywHUDCjt146cSCbR5D+zwurQfBbSpNc0NLGPQdUluLZWj2zapvcDaSAQdvIDOVPbkfNXt0svhXx94i1dvO8RTJaxJst7qBYGKFmMkgxlPMXacgsFA5JHIqvAZvBStbrfXF7o8jiXVLeFC4ukIwZUIAdQMnKnOMZxk5r1b4V/szeLvinqdovhNltfD+tIv2pnkAiazbPLHqxII+Ucnvgc0BY4/SfhVY634Jurj7RcXEdhujXy5GvLSOMuxYySAAyDcMlVPXHpWp4XvJvhzpEkumXGnzeBVafTrqwluG3SXcgjdAgw2yMbc4z24HJr60+GX/BPPwf4D8Jx6PrGp+IvEdvCrp5Ml4be1Cli20Rx4yBnAy3atTUv2DvhLf6N9gXwfb2sbEfNDcOr5AABOS2TgAZ64FHMRY+ef+CeOrDxR+0H4R168voVdrm/0/TbKRds0Nv5Eo2E5LHBCgEgEgLnngfpNf+C7PxjZKs001tOoJSYAdcdGGee59eK/Nn4+/seeI/2Rl0/4ifDW7bULfwneDUxaXsaTPpxXoxVShnhIZw6qVYAA59Oe8Kf8HJ/huXwbrWnahoMlj8SvDsrQXeiATSafNtwPtMNxtx5bEpiORgwy2CwGTSi5aoSjKTsj9CfFHw91LwzdOJLqxuE4+ZLh8Hn0YDH61zut358O2Mkk2+VlTIVDuXpxgnFfib+0X/wX9/aC+L+qTL4f8VL4J01iQqaNZpFMw9pHDMvHHXPbvx84ax+3F8aNXvpLqf4tfEyeZ87nm8RXDbh3+Xdt/DGPwzUnUsDN6s+8f+C3fipvFnwrsbi4bdJY+JbSTYeymKeEj/yIPyr8zdQ+HDano81/Z3VgrwyBZrGeUxXTEjIkjDALJGenytuBH3cfNU3xt/aG+I3xM8KtY6t4t1HXrUXMdzi/VZZd6cg7uG6143L8XbsL5K7bcHjbguMDtz6fSuiOHlNXia0ayw65amh2VxZCzmZWUKynknt/X86r6hcx6eCzMkjdMLzmuNPjO4vZA/ms0jEZIyPr2rqfD08F7pokuERm3Zbd6DP9BVfVZLc1ePjJWR90/wDBt3qs1v8A8FO9BdW+SbQ9YWUk9B9kz+Xyj8R7V/RE3iOF0WOSRPmPQ4+bPt3x/Wv5zf8AghHr9j4M/wCCgcF5bzRq1n4a1B5AM7VMgjiHPfIZuBk4GenNfqd8bf2uNa8T3k2n6Dcf2dp8TGOeRWzNMwx0YcKoIPA5OfauHExaqJLsebKV3c+nPit4t0vSVbadNtZiQctIq5OP8mvlX9o3xiNY0e4jjkWbyU2jy23Z+YHqOPWvHdWmn1S+Z5na4Yrkl23HP41kvFNHfFYdy+YSNi/xZGOg9awlTbd2ET82f+Co8i3nxG0CNceZCl1uyeuWjxj8xXgvgHQ7h9RiVdjSTEJEByWJ4A/p+Net/t5+KrX4j/tC3Vnokn261s55LOKWL50ed5AGVfXGAM8gkcV6D+yT+ynLq8dzqGvhdMvrUp9jt/KdpXHLeZjGOCo+XO7vivdpytBI56lrn0Vo8GqeGLAJJeS3UMUawzEwlVIGAGjBGSMDqcH2rpNA8Ew3/wAPdauNEtdUu7dXjS4lvfkX95k/KjNg5IJ+XIGe1dJ4C0/TfEEF7Z6tHqx1jaWkuYrhBawxNkAhMZLZ/hzkegrOs/AVp4X1+4vL6G7juRtFnZQsr/7zyNyCQMY24GWJ5zXNK7d2aRv1Leh26+Hby6XUNHutUuLW3xB5WGAywBDDIJG0ZGF4z78x/YPtc0lot3EkN8xd2iTbNHhSex2lcbR97GQateGb7VtRvYRJcLZ2d5K0VjEJUeRDjDh3LDqVJHYDAz2q1vs9Js9QuLi+t1nWUxR2qEMq8ggYzyeQMf3R7g04miY74f6Zq2nJNFLqFvb2GoskCRyL5jyHkA7dvXaRx6/hVbxtfNp3xMt7jdJqkkwaKG2nRo455iAN3ynjADAA4HPqKfZaVput3VjZXUiLqUHkQxQvKkcK3BbgckZXABAGWIB9Kks7S38K+LNWt761huo4iGilsopVkB3pu2lhwqnjkY96T3KHJaaekjX67Usd7xRxxr56WifKBEwcsUxnOMn69qzzoVrpErzXlvY3lvq2ZyLe5ZzCj4GFHC8ZyRXUazrVxa/DnXNcsY1k8JSXf2WWOxtnklkuR5cYIjIOVV2G7cRkAsOBXnIez0rVbeT+0rWOO4VbZhtMUtuGbkEAbdpB568Z5qrisXdH1zWPAOq31l9qgtrW6H2i3laAMGZgV2l224jUEZ4PPasjwN4js73R1h1ia6XQfMM0bwyCCZWDZK57ZYYyMnnHrXYXnhm912zkuv7Utbjw3bSuiPGhnumAJ/eoign5SdoGN3Q4Nc/4l8MvaeE5rPSJJoVkKI9uwMa8vvJG/HO5jwvJ469KelrlpXOutfDWk6z8EdS1/UNR1azsdFivNQYRRReZP5amQbmPcnjI+96iuV+GHiGPxH4a03WNWtY2hiZZYIocSTW8jHccq2FPynkLnk8jvWl8cZW8Bfs66T4XgkS4m8SX8dk7TJIcwRhWuGYA5wDtGOp3YHeszwn4kl8N+EF0/wA177TWHnWgEZigjbdy6qx6kjkY65rHB1HO8nsaVoqNkjYuV1PUodQuriG3kj0+9EbErGSuVD/d6n5WDdj7VbtNSY+HLjT/AO29OaaNQ1vFtEaTISSqxFM/NtHzbgDngA9a5HwZa/8ACQ/Fm6hvpLpLGS1do9l2LX7RIeQSOrAHOAOwPbmu6XxB/Y1nprOtp9nVZLeO5hRUk+Xbj7ygjBzy2OvBruMTof2QbG4tm+JrMZIVvPC0GGI+VMa9owK5/Gu7h1Jb3XdVsEkWE2tsJNnViGGAT+P6VzX7KU1946vfi1PayqLOPwgsIifh/tDa1o7+YM9tqHJ9cVftIopLi4mkaOHVhZLK3+6eChPsc96uzdjPqyPSNYUeHIrO1VWuI3FuOc/X9cmsXX9baXS7Xw/oLSKsNyfNE3AVQcscjPHP156CnWd3HA9vewx+XD5zideeZOoPrg5/GrNhLa6Z4bmaJfPvZnK3DAHdjkk/ToPwqqkLbAZEUE93ez3l7m2sLdgkWD89wAOSfYk+tZtxcRDU5I4bXyLeOMMshH3iSSBmrniGW91zRrjUFjb+y7NA8vzgc9QAPvHPHQVmavrU1j4TOrXX35ogltCR1LHHbPQY/Kos0UpHk9p8LF8U63rOtahuaOObdADkdAMnHevl748+GotC8aTT20TxwzNtY9t/t9a+3vFF1GlnCruY4TAoZMbQD1OD1JPT05rwH9oTwEPEGjSNGvk7j+63HDNk9fyA64qqN1LQObQ+SvFelf2tbZVV8xeSSfauA1iFrZWjfhlO0Z+vau+1O/a0uXt5m2yISpGDk/WsPxDYLqVuCqbpEweeAa7VG+5NzldJ1qbQtWhnDjy1bDj/AGe9enW96lxbRyJ8ySKGUjnIPNeW6ja7JNoRQe4HQdK6D4c+IPLL6dO27nMLHoc/w+34+tYVoo3oyadmdsJtw/i/KsbVtPawuzdRnEcm3OOx5rZ2qAeP0qO7i+1WZj3Y3cA46VySjfc9GLRlXU6tFv8ANj3MM+9ZUmtfZrhtpZlB29Opp2oL9ilaN+JF6Vj3LJsO5sdulZ05O5tUjHlujWv/ABzLbxLt2qsfK49e1cF4j1m/8bazBDia5uJGEUEMfzctwAo7fh6VPr12scO1W+6Du6jH49K+iv2PP2ZZY7K38Satast9qEYa2Riv+jQnGGxnO9uDzyBwQDXXPFOlS5pP0PJnDnnyROL0H9mOTw/8P5rm43tqkgDeapbYjHH7sA9h0zjPFcK9lJp8vkzRlJFbDK3Y1+nD/ASOfwBZ2Mip5zKXO3+IsOM18p/tEfsy3mm3Nxc2dvI1xGCWVSu2bA6A5xnt17Vy4bGub94zqQ5dj51+zhv4Vpqx4H8NXbuyNlM0cytFJHkMrDBBFRNanjau3Fel7TsZlcx5XtV3wndtaaw34KMVXaML1Wn2I8i8WRRt+bORWNX4TSjO1RHrHgZpPEGrwWcMe64nYImex4wTX0s6Wuh2lvpduS1wqhAqfcwOpLdjn0rwf9mnRY9Y8ZQzbBN9ljaTk7dp49cete3HTWe+S4jZVbdtfnjbjnj1r5HMZc0+Toj9OyOmo0Pady9ax2tqmCokk77j8q49D3/GpLy7aSONrVsGFT5jPkKc9s4/xNVprOZHGMruO1vmGUAPp1561YhsGvojlgkdt+8lkkb92oPX2yOT+FccaN9T23WSRS1K9S+05fJjy4bax2YMrHkhR6dB2zjtXjnxs+L3nPL4csZm8mPK3TjG0HJzEp5JHJ3HHXgZFe9ftifsufFP4G/sw6P8QLzQpNH8N+JNQFgWlfF9aROn+jzyxdY1uG3Iu7DAhCVXem74ykst0GFx5kajr1r3MvwaiueR8lm+dJv2VB37nUeAvL1TU4reZmjjOCsvTy26q30yMHvx3r60/ZH1O1l8q1vI7e4k1aRoIlVAJYgpQs2WOPupg9fvPwF5r5D8E6k1tqW5egUpKrDOQxyxH0x0969L0nx3dafa+XY3DM/lKjTIXUwspOHB9WyDxyMdulfU4arTo/vGfJzpSqLkh1Prf9r79tyxh8Jf8K68CNM0cJK6zrz+Sr3Z3NtghEYwqjc+7jggAEgbq+VbO/EpZm4yT1PXuSfesq2ElzcMwLSE/M8jN8xJ+8xz3NXYYFgi/eA92yDzj3ryMwzCeLqc7enT0PWweHjh4ezj8zUsLhSMybhz0NfWX/BLDwPY3/7QKeM9YuLK00vwLEl7E13KI0N05IhJz/d+aT22r17fJOmoJ4vM3bhwAMfe/Ou60/4hzWvhQ6Fp6stvdN5lyxHM7Y25b2A4rlpVFD3pdDathHWpuK6n7rav/wAFHvgz8LPCkM2rePNGup44hmzsZVuLmQ9wEUkZ+pAHcjrXwZ+2P/wVZ8TftPQ3Gg+GYrzw34PYFfswYfbL5cH/AFjKTtBweF4wcZ618KRXBsolwyFvToK6/wCDGsNp/jBbi8iHkvay+ST0LHK5/Amq+vVKz5Y7dTjhk2GwVN1J6vofU3wA8Ttp37LWtalcQ+dA2o280UJQb1B+Uj35JPX0rtfHXj641bwRpuqWdnJdW5Rre+tgMl4gR8xwT7/lXifwg1ybU/AFx4QimQyXd0rRyZwowcjJ9vavevhJ4Z/4Qyws9Du7kz6neia2uB02x/e3Y9sj867Fax4NSTnJzkdJ8PLS1+HWtQtbhVh1JpXWU/eZDt2qPcZGOexrsdX1GPTNMhlkhWbVppDGivwI0V9ufwAB/GvP4Z18YSafYSSyfZ/DkgaRl+Uo6ExFGPfgZ4yK73xD4Va4Ed48rSXVtJkY4VkchXX6gKP160GbsdF4Q8PpLY3wdPMeTJlkAyRk5yP89q6DxHbxpDa6la3Cq1oBDc72+8uev9M1F8L7r+y/DH2i4b5ryMq6N2wOP1rnvhGH8SahqcmtrtsbqX7NCN2Y2HPXHocCtOVWJOV+L0t1Z6JIkK/apoNUh1PaB96EON2PcDn0969C8OefcadBaXXly2102beZQclip+UnAx+NR+KvBqf8LAtbZWk+yrBJalABwWGAD7EZ/StzT1/4Rn4d+TebfNto1J4zuIAHbP1qOVgY/h25k8R2VxZrAzKrfZnOf9cAeMfTOPwrrdb0X7TpJgsYVW4sLZYgGPyhupJ6+9YMWqQ+H9b1iFY1hjs0E8TkEiUOu4MMd92RVzwL4zXVNJ1SYTN5mrQhkhYcwttxgVpTtbUDjvjn8FtN+KWunTdXtbS+00W+7yJV343ABiOODX5Q/tq/sQap+zv8ary80W3vG8HSHzUkhz5kQJY4UY6DGOT6V+wmtaXNpmvWupXDl2tbAQz8/KOeCPwrL8efDzTvG2z7dZi7tprYx5dQcgnpz7GuunK2jOWtR6x3Pw78K+JrXw/qq3DSzXSwyCRWlfa4Uc4OPrXpyftM+FGRSbjV4yRkqt2cL7Ct79u3/gnRqHwF8aaj4o06P7R4QuLkkwhgZIlYBt20dssQAPavnePU/AaIq7JhgYwUOacqd9gjOVtT9dviR8FrHSvCml6pfPbrqWq26XF5cSSA+TKyhtsKsMpkkAA/Mc+4rxXxnrLf8Iz/AGTazSJJdybp5Jlbfbjsu4YAOevTA7CvYvAnxH0a48SahDrlxNH4f1C1aHTzdqGUz7gNqluANg4JweOoFedzW2n6X411TT7O5+0yW1w1uqPAfJkhC7lIYAgttwcivnFJLY9KMTqYfi5L4vtNN8L+J7E2d9psa2L6ytwklrM3ljyzjPykrg5JOWGe/Evh7QfEnhH4hWvh1tYvJrbS7ZL59Qv4Fa9kYEq0XQBvkVUOM8c1yPjPQnHh3SRp95Z29w8o1CZ2TdbhlYhUyPmZiABjBxmup8PeIPEHifxDeWOp6fHpPiy1TzzaXheWSziZhggyfdVlVyPQ/UVSld2BS1sUHuLLVfFF9Ne3X2j+2NRjiuGUA/Md27OCCWCkcHsF9OcHwNoMvh258RWOiahZ3Wl6PKbq7gvFMq6nHLlUSONGLRspdfmHZGB6mvU9Y8TWHjPVdd0eHSfsc1zpplt5SqJtkXKyZTnJbKYIyR81ed+DNQay0ddEFr/wjurfJbwGUxf6SwJKuwPO3aeTgcDrT5BNX1ETTr/xDpF6+nyTTX2h2senWt2I1SO2t2BaRGAYqWLkjPJOVHUYFazMvxj8EXLW+i2UNxJc7otVECKLCYlCJEHDfKwD/JwO+Kg8RapdaBqen+H49amh07UVuLy81V4gjR5jJ8iNVJXc5UINw+UtnORXD+JPipZ/D6W20vTJGj0W6K2doLd8+XOwTAc8jJPBbpyTjioasLVaI2l8Ct4E8fR3Wq6XceIJpLX7T5tjILeCC7iKiGZzGGYZ3HgYBZj1Irrvip8OZPFevad4miu7O+XVlErWkGwZyPlyqk78AAknGAM5rI0X4t+H/El9p+hSaxYafr11bS2V5dTKwtrpRho8MpAX5guGI7k+lYnjH44SfA3RU1Cy+yTLo6tJALgxy2cabSNmMck54J6HGPWjTqVGL3Ot07xjpvgrwfeaDq2pavoc+rEPM+szfZ9PlkiJUm2J7gjG1cgBQO1cx8TvFureE/iWH0+7sVit9OVlZbpZ9xXaS5UAbVyWJPIO4HjPLLP4lXHxU0ux1HxR4f0qfRfEuiJLpcUjiV7Izsk0e1ADh3y2c4KlB3yBo3V5oPjHx9fZtbe3j03TvIhtYmeVrqXcGcz7yOQMjIByAKXoKMrMwvgn8RNTkuZLnxTZx2MN8JolvntWaFpCAUC4GFzjB56Y4555y88Gz+K/E+jtol9e+G7TQ9SmhlurC48q6kgeJndoVON6MzKeeFxx3rvvEcdjd6bBp80kdrd6fL/asWntMzRzKyqjKEbCgqEBwOcfSpvEF/Z+GJrOPw7Z/wBoXVxeR30Nt5KMJQFLSoxwevTJx90c9aQt2cXrl/rWsS6bot/HfR+H7WD7LZEKWmmuuCjSkDLFiWGewYntWxrT6b4V0W18OqsOlT2N4rvcBsSL8y5HbJzkFmYEhsc8A7ninxPZ/EjXJNL03TbyHWMG5lniiH2UB0ARGY4PB649GHIIxT+M+j2XhnwVrCzJaalcJCBqE43bI5P4Rkcj7gKgfxbQeKroS7pnK/FLTZ/iV4nmtdLhvrmJRuMtzCd0gGAysrfeUlhgHjGa1PDeh29vc20MLLa3umOZ4JlXY8q4x5D5z3PBGcAYxVf4UeKL7QdEZdQVbFLiyS0uDcRCUrvOFd+DgYBzyCOTXQaVonhnSvGOqaTo+tabrD+bHGyWUpaNLklUBaR8Jg5XgEnnkdakaRwuj6Jqk1/DdSXF1ZzyXywveLGYbeCPhXSSTkbdp5/wr9MP2VfBFt8Pv2eNFms7X7PcSW2ZySXZ5y7eZJzz87A49Bx2r84dbtdQ8KvfeGPs9pBaWxkabTknlWC3kkCKZU3NuLtsAIYEHPGMZr9Cv+CefiiP4k/svyWltiFtLvTaRxtI7k4hicHJ7Es+AMjn8azqPQGjvHumYHdJHGxJwGPzH8KabiSNPmk49qfJpRjZvMZty5GT9abLaL5P3qxjduwigs8d/FJbzSF7e5iaKeNhkSowwVPsQTn2Nfzk/tzfCyH4Q/tkfEzStPjZbRrtJImx/CpaI/l5ePzr+iTxt4ks/h74ZvNZv222djC0hxjdIQOFH1r8F/28b+z8XfH241iTf5usLcPMCRu5mDqBzzje35VtSm43R1YWK9omfNUsbtOyqvXkcdajeMr9/K5GBngV1dx4E1LTtJutUhtZrzR7PYsl3Eu8W+5goEi9UyxUZPyknAJPFcnqV7vdFLKy7gNwOc1d9bHsbjceUN3y7s8HFeL+ONLjtPEkgUNGjuWCnsM9K9b1rW7fSbdSCWkH3VPQ15h4nBYws/zsWI9/xr0sB7rZ4eZU1KFmU9A01fOdzJu+YrGM7QecV9v/ALEfwO8M/EL4cXn2jR1v5NPkEs1ywJl5JABGD8uRt/h+tfFXh4lrsxMF2YIB/ij75r7p/wCCcWuyeFbDW5po7hrORIQpjbHlhXYl+oPPIz6104iTa0PHjHzPpb4e/A3wvpT3djZRSeEb+4SW7i1aK4MLWzqECru67QsYAXpz710MPxT8TeF9UudPvLfS9c8i3jeKXzDbT3xw2WBwwcnAycDGR1zTdd8ExWerzX+pi6uNHm5azaVi8kWcAhsZDBiANp7/AErptQ+HM5ntYxpaLcLbz3cVx9oN0Y7cbdvzjkuB1C5HI5NeRKTb1OqMnscHrf7Tes6bK0cXg9IyqmaUz6izMi47KsO5iSQAO/6Vwfi3Tfil+0Z4Qu2/4SHT/C3h2+U4t9HtWiuLiMFQVkuGYvyTghAuRkYwa9u8cfCFri90a7hmvL63s54VjvV+/gsA6huGbG5sKeRgelal7qukaP4Nk1LRpjHLHO7S2d0pdriNlxuVcfKeuQDjp70oyCT1sfNvwz/Yv8O/Dzwzpeqaa1nqUlldIb0XhCS28q4fgsMcnqwGR1Oe/oeqa7p2oW6alDHcK9nKIrm2gEfnjn5WiBA88nkYUcllHcV0Z1bS9J1a1uNQtGuLG/3wXtkY2jiuSVO0FgAAeMHHNbXxC8I2vivSP+Egjj1WO1Qw/wBiu6RssKRjI3A/eCsABnritvaMPZml4w8AQH4makulq9i9nABOJNPNjDG/yqGlByUyzgAHJLcVxOraDqE2t2S31xfMwaXGo+SJLXaWO4O6sRuwCQo5Y5x7O+FmtD4gXevzeJ59RhuLeJDdrYK8drbYbI3uPmVPlV1yMZFdZJb6bJpdjb6ZqlzfQ6vKJFJZpI3QsSjqWAVhzlXHJBB71XMaGdq+mnwd4dmGn63HM0kw3zrZBowp2Y2E43EqegIPzAVb8Swwf2TZXmix2djqF5p/2l7m0dvOKtkbGA+Utj73O4dDgAVzNx4s0PV/FE2hzw3LPYiW7YIr7pX3D5ZNrDgksxbrlfc1pWOlx+IPGOk2NpfRpDBC4haSUiFY13k9TjnB4PTr3FSWjin0S+0WCC4mt5LwXUYkRpwFZGzklRj365P1rqfDM9h/YCWN/Lq1xeSB9gmm5UE5JznLYbA9iR3xTNOj0sXV9JrV1arYWZ8qVoJBPMCxZQNrAD5tp2nPbtVay8Fap4f1h9WtUjk026k2xNONgjDR4UtjIX1x64oGaUb3fwgsf7K1C+caTesbiK3tn89IpP7x7Kx7g8niofDWmLL400K3VIbq4mhmvGeB/tSrHkbROm35BjjHT5scd9TwJruh/F/4eLPD9n065triRPsN1OQ9wwwm5GJJ5KkgEDpXO6FpF54J1q6mY2NrLeFYftCz7Ggj3cqwJBLA4JA64ApyKSN/wXM6+MNXMnltaW8qS299ZRHNnNuysMv8KZ2knI5AHSn22iXOsvFcWX2vWtQt5kkvUcrFbtACjAMzHqzA4IHBFZerj7Lol1JdR79JZQjTW87rNcs+75gh+993rg7c1L/wmd/4P8BXmp2txeQ3EYa2MEyeZHdRsAy5XaBnk4JIwRS5bhF21NvxLDY+Pfjxod5DHdL4V8MaZbxtBLbDzvPnRpnBG4D93sRWIyCQRzmsjX/F2h6hY6k0d1NNql5MZolgthHbW8G0bXyTjcCMFR6E96xte+LWl6hZ6fcXmiBtX1CJBq1z9oEjxocnMacbSAQCB9K8v+IGv2ml6ZJcQtDeWrPtRM48kc7d/XOB3+lXSppLlQSld3Z6J4p8TL8RtPsWvtdkvILWVj5ccHlxM4VcqeQu/wB89CKbo/xg1j4eXZGm3dhJZsjrGk4F0se48tk58tiACemcZ7Gvml/jjL4c0G4tBMYbNZ2lWBPnQlxtJzxzwMZ9BzXJ6d+0zJa217Eq2t1HcLsIuFy4UZyV9Dg/rXSlZWM2z9MP2afFx07w74+kj103u3wrBL5BRVS1Y69o+6PdtDPkyYyenTmnXPiH7R4kvL++sLm3+1aaII18ohXbzuxxjoT+FfMv/BLT4jTfFrxT8WbHdH5kfw9M0bKmCNviTw/tyOp7819MeHIb3U/hxNZ3skN6GjZwyTAtDICcLjqDmuyjG8bmcd2WdW1RNa0/UrW3sfKkCqrKh+ZoxnDAe3r9Knj8CLoWjJqEyyQ3lwWMiMcA46Y9sVSsLu1OuaPDLIq3txpvlyxkZbc2NoP5VV+NGtXt5aK1nPLIdPVhcRDG1VOBnOe3NbSinuUZo1NtS0LV4iq29nGY0ZRxuAcA4HfgfhXM/wDCXDxjrOo3k1u0Gg6JD5VoGTCzvyuV4wxwvUZ65rf1LSdMm8GeXd3Ejf2isTKobb3BxXKeMRDqlzp+h2m6GysLWQzIOFB3c8+vX86zjG5N9bGXb6fF4kgt9Y1SeS10+N90cL8faFHYE+vTv0NcL8ZHj1m/vrmGNktbWNIIsn5WZj+XA/nXa+NtNm1bwnD9nlWO1tlymDyFUHH9a5K00WbxdHYyLIP7Jt5i0rDnzXXGc/kO/etIwSG1c+Tvj98K5LF1urGJZDa7luZQpHmEck49sV5H9r807R19uor7H8c2tv421bW7eGHfZ2KmLMfq2c/jgV89/Fb4ZLaxR3Om2/lyQnbMvdh6/WqI0TPJNf0okeZGp7djT/hf4Bk8e/EbR9JTzFa9u0jLLncFzzyOatyu8m5WX2Oeorrv2ZnXTPjz4bkxlftWz8SMD+v5VhWdotnTT11O3+N/wX1D4SeI9rRzS6XeEm0uChwemUJ/vDPSuMWNnj6HP0r7i8a+GLTxx4PuNM1KL7RaXSkBmHzRN2ZT2NfH/wASvh5d/DLxS+nzN50LLvt5+NsyZOPxHevLo1Lq0tzupnA+KbFiu5Vy2MEgZxXJ6jCyqwxzyelei6hb+fbN8oGVyfeuJ8QWpRm2+mMVrGN3dDqS9wn/AGefhT/wuP4y6dptwnmabau15fDHDRoMhD/vNhfxPvX6P+AvBP2dliSNFVhg542emPYelfNX/BM7wNYrofiDxBdSQqbq+Wwi3/eCRIHb82kX8hX2lYWkElij2rK24ZyBjpXj5lWbqcvRBh9I37nommaHHrHguwmjWMskYjyij7wwDn3xmuL+IPwp0/xPZSMlr5gZTwfm28ctntXcfC66OjFVm3NYXS4lCDcYT3bHHFdhcfDwWd00cbRvHeRCSFwcq6ng4x+PFc9HEWIrU2z88f2hP2G21iOS+0+OSG8Yfu5vL+WT1DDuPevkvxf4A1LwFqzWeqWdxbyR8bih2P7qcYNftzqfw7jnhkjkgWZURliP+105H1rxT4yfsdaTrVw1vd6fHPYyIWZHUff7kHqPwNe1hsYtmccqdlofksIQRwGPNS2dl5twvysRn0r7G8f/APBNeCz23Gn3l1YzXBJS1nXzoggyeG6+mBz3rkdL/YZ1zTLmV44ba6jiIzIzGNT2+UEc8n9DXe6kWjCMbO5yv7L8iab4yaNus1u6ruPG75cDNfQFhoV1rupboofJWM7HGcqp+tcr4W/ZG8S+G9atZpLWztZJJF8pkuQxXPT5cd+nNfUXwX/Zm8QePNUtNIaT+0JpF2wJZxjfNnr7ZOO3pXz+MwvPU5on3GU59Ro0PY1dzz34e/CbVviNr9v4f8P6Xfanql1OIoLa1t3uJXYtgcKCcdTk8AAk4AJr9Nv2F/8AgkZpfwnt7XxN8QobPWtc+We10Zo0msdPYY2vKDkTyjrnBjU/d3Y3D3D9hj9h7Sf2YPCMZmhs5PEl2o+2XEfJhU4zEG69QM59K+izpsaR7jtVFGM9q0w+HUF7x4uccSzrvkoaR/Fnhn7UX7Nmi/tKfBDxN4J8RRrLpnivT5NPupGXc8e75klXP8aSBXU9QygjFfy+/Ff4Yah8IPifr3hXWYJIda8OardaRqKsjLmeCZ4nZd38LFd6noVdCCQQT/W54hSyu9Kk/fbW6BiPlP41/PL/AMF//hLF8N/+Ci+ralb26Qr400ax12QIMeZMfMt5XI6BmeBicdya7cP8XKeHRrM+HNKtJzqcX2dT+8kKDK5LZ4OR6V6Pb6JHpVmkILMygb2/vnHbtxj8KyfhPpK3uoSahMu6O1PlxrjOWPJ/L+tdfNYNPu3Iy855FTiJP4T6bBxSjzGZaoo+RVbceTg9a0bWBrmT5hyxOQeP0pttZNAPMcrHbx/MzN/CB1JPoP5V+iv/AAS1/wCCQzfFi1s/iL8VtPaHwrcRGTRdCmZo7jUwwAW6nUcpCPnKJnLkqxAA55TolWhBXZ+e+lWNx4m1Xy4QosrX5JHjGQD/AHOOM+v4e1dJIYdN092MghkK42sQpI+h+ld58WPgdJ+zN8TvEXgmc/vdB1CWLgHBQtuRvqVK/kKh0v8AZ2l+JPgG51qS+axht5ggQR7mkQq3PfuuKzVOdSVkeg8VToUeeT1Z5noaXfivWobe0DSSTSeWiqpYyNnGAB7+leteE7WTU/Akdrb27f2leXLx2YMe55WVvmiXHO7gnH1PArpf2bfg/o+ieDNQn1iH/ib6XHJNY4JVwYsHdg4JP+NaPwz0y48Q/Fa4/s3ZcQaZPFqkYj4ZvNUpvT8QTgd816VOioKx8ri8ZOvO8tjpP2cfBlx4QGg32qW9zDNLqj28kNwMKAxwrZyeOnPuPUV9Q+HCvi79rMXUNrMLP+wuZQhaOKdWA2lugYgdM5OPavLLEafdfC2a61S6a4tWKyFt37y3lDEOPbov512/7M0muaJ8QvEtxdXTXGl6pJDeafLxkwsiqyfhIf1rY8/cl+EOp28X7QGraBcRXEkWuWS3ZVPnw5kYO2P94fhXvMumKfDN9bNIqTB1MYJDMg3fe9/cfWvnD4OaiulftIyT3MbLeXKXmkQ5XiJxdFwfptNe5/EGSa6tiLWQrNZzxxag6nGUIzu+lHkZlbwv8QZdW1vXtAkZFn0UBs9PMVskMPYgjnpmr3w+s7fS/hlb3WoTH7Ks8guonfaIm3rtPPcZzn3rBjtLW78ZeGdSsdyR3l02n30gH3lIYqrf98jFaXxOspLH4A6oI5Eh1SQrCnlnc0riZApx3ODiqjKzuPod5etLfsl5bk+ZYlS8hG7zlHf8v0rn/iNrFwfiHpFmZVj0+8fzVuCcoXxnyz2Ocgf0retbibR9N0kMVkuLjypJxj5ZU+6y/r9a4H4u+X4I+Knh2x1fcdH1W7Z4GXt8pIGeox/Q1bqN6CO40yyk8R6rNaysredE6QTYBRSM/uyemQc8daz9F8PSRz+GdSkZrL7OrpNGAVBYZHOfpXWaZo82h+I47faMW9xFMsw58yOTqw9MNmovEKXGs+G9Wjt1zqFgZRGfuo+GOBn/AGv5kUoRvqBF4h12LVre7vBzZQRusyb/AL0QH+sHt79Oak0zVbbxP4e0+bT2DWtmwIdHDLKu3g5HWvO/hF8Q2+I2grJbx7bjTZGsdSjm+UBVJXawHquPxBr074W+B7fwrouoaAs3+jT7rm2Z/vRK/IXPoBxXQByfxO8D2fxDK2N8kdzY3HyNBjO8H1r5h1r/AIJReC7nWLuSPSVSOSZ2Vcn5QWJA6V9dafYpY+JnhWTzPswCAk8/d3E1rvpMEzlvNY7jnOetVGTRMopnxlplhp8XhO4tbvSptQmhu0SS6gbEcMKHJXYBtzuJPU5zjHeqVv4PvLXVde8WaL591o6n/Q5IZgpmdCytGVb73/LMcDHzcHHJ7PwD4MbxT4Oax1BtLjtdD2aeFtzsm1Z9ocGUjPHzAluWOOoxXF+Ib0fCT4l6hokNtqE2h2Nrd6mLWyukg8zyl8xkAYhUZ/Kxg8ZUZ3ACvBudkZXM3w5oU/xw1XVGkkbQrOwtFlu7eC1S7mZjwCcnbn5SB6Z9q0/HOpP4mutJutLkudUaxlha8gkfy7iRUO197lsAgAnbnP5jPgGpftO+IJfFmo6f4BSHTNR8QKJHhu4Uv4/LUgxtLIXVIgc5QqoLjk5zk+zeDQ2g+GIdbXxF4bvrszNJqdssJtri5d+TGYmPzbcHLjII4zwRVRKvY7zV9WuPC+sanc6PNeTawLVdtnJbrMqo4wCsgO5tpxlc8574ryfxHr0mieIoZrWRJLdcvP524/vG6hQvI+ZjxyOMZ7H0rUobXxdqWk6po+oQx6ldFrr7JHhVKRkME6Afwn1AxnByKxfiL8VdC8SeG7eFrNZfEFwC13YvDsWzILhZi5GFj6HOSPnHHQiZS6GW7sebeKvM8XfESyXVIprhtLhL2877yqIw8sCWNAA+CF6j5Tz2qo/w90nVtadbzULdxHI8okceRGixkBTEmCQcY4I5IHFdJ8OfBkviLUNQEzxLFKqsrqcR3MA+fB5+YNwM9DkVsaTrq69ouo6baeCVvtSuLmbTY5mjjw6ogTZH18zb24yTmsxNWZ5l8WNdi8Ow3Ul7c3Sw3kiQLcWx2vfx5DPt3YVBlVGf6Zrx3WPHv2vxZrWsNDdWun3w+3iBWd7ezCsEA6HaAy8buTj6GvpnxR8G5fBXjjT7zWLrQfEWh20aCKC5RdoduAMHK5XA5IyOenNXr74PN4P0j7VqOi6LoehaiXMcSRJ/pJDMYieQP4lGCvIXGSOAFc54b8E/ipqlh8IPGUkWjqmk31/JcaTf+YFdnCmORfu7eViR1GQxMhOMEU3wHqSeEZ2vtQX+0VsZQ00qQyuHy25Q8bE7sAjGOMDPFeleNob7Xfgp4ts5ov7Lu7i6iuNNNnCILO5uN+3zHJPVljRSVH3VUA7QAPM/hxrWoaXb3Gianpyf2sskkK3GfMihO84bcq88kY55yRntQTurHr3xR8YWPirxwlxa26Wkm0xwGAlod5B3glckAblB9Nw5rYttYtdN0TwzDZi1t5tNItdQvYoygl+YFuwZlUF8OchuuOlc14PeXQ9et9P2STWLFrm8LKWaN5SAflPCqfLBOQeFGegq3b6tLpc+tLLp2mato6Xfl2F0GJaFdzZOVBDAZUAH5SODkCgrlsjbvb7T/CB1GWdl+wiRLm8Jj86YRFcEoB83PynPTjB7Vi+CtZl+Iaa14dsbcTSa5IyaPdSN9lt0tBG2Wdsksybd3IJ5AFdn8O4ZvDviLVrDxJf6fqdheRo63scg+1XCBABbZI/1fqVIyQOoBrzefwRZXXjfVL7Q5jZw2IEOnWKXjxoFOBKo5BVD3GcEjPNAaHVeH/C8vhbw60lr5OpWupWzR+agYx3JcBSAMdcHrnGab4Ft/DnhfSbqKSGHUo7P5fszWywzXSSMWLoqggKkp24PLD0PI19E+KV74j8P6Zpdn4dtdFnsdkXkWY3R+WQecEeoOCOFPvXAWnhlfFMHiZrO+vHaEJIk8Kl109dp3Fh0+VlZ+T1weDzQB2Hgvw7qlhr+qWFlq6aNDcqzrZXFsLgxlQF8nLkbsqMLzkc4r6Y/4Ji+Kn0e98TaN9pkaG8trHUbAldhljXzImdWPVcshAPOGXOc18zt4DuPiZomj3VvrzrqNxBNNdT3AguMQRho2lbaNsTAYw7g/N2Bwa9I+C9jo/7PfxUsPFE11qlrazaG9peC/vRL5B3K4feuMFjHkgKOg9hWdRNrQGfoV4w8T+G9O0Ce/wBel0/S0tUJkv5bhbZVxwPMdiE6cfMa+Kv2h/8AgtZ+zf8AAm7lsY/iJDrmoLw0Oj2NxqHl9RyyxiPt/e6Y9Qa/JD/go5/wUz8Vftf/ABD1CKz1LV7HwvDOW0nT/tjm3tIgNokEfC+c+C5c5I3kZFfKSrI6vuLSMxLMWcszHHU56k1Kire8d1HL7x5pM/U/9oL/AILa/Cn4uRMlvqPiaNfmEct1pJVEB5wArEjp29q/Pv45/HvRPif8QVk0nUVu4pn3KShj3MQc9fpXkc0Ud3Au9cr/ABKfvCuL8ZWjaXGk0J8ps5Vxw0eM9CK6sPRhN8vUmrTeGftI6pHs9/8AEqHwxdRsZtsy52iL7/IIzkdOv4iuVvPHI12+EmxWaSTLPjGSTXlEXi+4muWNwzzs3BkY5kP1PU10WiazHdMvlsdysPlP3uvYV1SwMqSs9SVmMKrutDrtRtf7SXav3W4OTXN+LdHkhjhKtysgXkeuM12OgxjUIESNfOmkUlY0TzJGxgHCjLHr2Fddpn7MPiz4hz2d1Jpt5pekyb/Ka5gbzX2DLtswW4HtxRRlZnPjpRasmeV+EdAu7+/js7OMSFSsSMmTkjoMdc/41+lf7F3whh+GXw/M2sSWN82p2cs0sE2x/s6JMcMrdmyGODz6GvMvhD+ynZfs8aJo/iaG8t9ek1y2YrAsLRy6fIQNp34yGIPIxlSCODmvpz4Q6JpS/AjxA8119uvLK8FmsF1dAqiugfayFCJWIOdzY+8RzRiKmup5pV8FadZ6d4lub/UpLLVL3UmkvtNtopTK2nhMiKVwpykzIXYjptdPQiu3bxp/ZttDpOoW9rfn7FbyWbwuqxrgfKkwx8527AoUjHz5B7ct8QPCtnZeBbO406R21BE+V4owu4ZztCgZJ2qOhHbjpWZH4t0Gy8Avp32qxF1Z3hFzhyJlkkCSETqfl2DcVXGdqowOMLXHuzSMLq5210F8SWN3qTWtvNcXNwY9NtJHMYicAhpVTIC5yQN3r0PBGT43tLXW/Bl99qhGi6oyx+XaQxuivGpQhi54PKt90kZ4rhPB+p+JPEeoyxx3WpawrEPK1tIZZLDABDOcnoFAI+g9BUguZvGPi+ynW6uNStbeQwpJLOyqcqV2MrHcoDNkKeh57UcpcVZHVfCzStQ1X4dX0sktrayaS/2nzb2NlmkITOyHA2liRnkYHGa6248eP8QtPsNTvmmh8O2cyQzSTbVVUBIcAnJDbuuOPXmuItY9YfWrq11I2seg2cjJFc+aN6N8oKBueMg/l0rHto4PDMOoWk0N/Db38yvErXmYTjG5hHjYdx5yR3/GqGa//CHT6d4q1GHQ9cl0+3vbeU3lksq+TcBBkZXBBxhcAelRad8W9U8JXCQbbGHUbEfZIIbuMstxF8uzJ4C7QgAI4HpSeG/Emk+IPEdjp8uk2CXV150mo37QAswWEtvhVlG1nwigFuDk+laHi34fRvqGrTR6fqyzaTD5ETSRYmWJAR+8xzuPQ9Qec9a0uBsahq+jr4Y01v8Aj+1LVwdQuQZVjjt5yfmMQXllUAKA2TwK5vWLbQ9B15bqPUs7lSRoktlkjSMkrtdiNqvwXyw649RTtG8NL438D3mrf2hqVjLoMyW0sAtAwZXj8zqcGMHqSvJIJPWq1npDQa5JJcX8lvZo0d06xCK7a5j3dAM5V1A6YJwAMYxQtyoljw18KYNF+IcmsWl1Y6xdaxpy+UkcsaCJLdpXknkUggyus6gbCBlDnPGL9vF4g8V+Fm01tYhuG066WNrSOFI7yJHbZG5cld8bZUcbgCeQOMa3i5tP8XeP7S+0XSJLzT9Jh3zX9whivo4SykO4GGIYrs24OULA/KSDyvj+6/4T34i6feaXb2WqSxyidrYILaJI48cDooVeBgjGeoxmqbuNKxoeNPD2h+BZLvTWulstXsXEtmDIrTBVBLFmGQSGGcAnj0NZHiC8uL3TrfVLcWjXE0TBtkby+exHJcY53H09a6Xxz4Lj07WbjVLyGG8sVkV590qybIzjcVYDBUOyg7eDwKzV0xIdWjuE/wBB0mW5Qs0cqGRYXwYyqFs/kpxzwehznEZk6XJD4v8AhPaQ6xJJHqkMj2shnDxmFd7Y2gAFU2jocmsfx740/tr/AI9beK1juWw9nJMuwBWIVnGc5A6Hpmu48deCVntW16wtP7QRoy10by6Dy3bN91lAIJYew4NeU/FS60nTNQhWxv5rqRgDdxTW/wC+gKgFc5PT6e9aQjzFJ2PG/jD4xuNNMC3ksHn6W8gt5Uf5hg4+Y9xx14z1rxPxj8UXS/a4Ty47qZsu6DDOcfXH5VJ8dfEMx8Rzx3EjNb3E7j91xHKgY8Y9RXmeslL5GmV2G1jtH93HA/TFdVOF/dMZSNrUfF2Ga7uMNNnBXHUevXryaxx4gS8KbI2XzGwzMP5msWOZr12VyreWMgMev1p8MXzxt8se37wHy8/5FdUVZGcpXVj7t/4Ic6/NF8b/AIwSR/vGt/hdM6iP7rEeJfDn+T+NfbXw80v+0vAvij7G3k3VxeNcxlDjyyFBP+8dwNfCH/BDmAap8dPjTbSSSKo+F8mSjHOP+En8Nngj6V+iPhe1Ed/qmk2rxw2+k6Wbm7mHysruDgcd+a2jK3uip6prz/RGR4HsotR8Z2+pJb3WoarDokV68AAIEsgYHPoOKyvHGnWNz4thlukUW+sw+QsMbfeaM5LZ+v8AKt74ParD4C8NzTSXEmsatf2qWk9wUCKoQkKgx1ArFaHTdR8Y2sMkyhvD2nK2D1M0rsx/IEj9KqMbFxjY4nX/AIdXuqWmmW9qywyLK6GSRSfKCOSv4leBVeZY3lF3a3RuFt4zDdArtJYttxXRXev3/inxbeafbeZDGt6s7XBJVVVQCVHviub/ALHj028vxJO0/wDwkWsSizhQHEcS/MWY/UgY6ZqiauxyugeFptdt/EGnxwyrpihlgyOEzycH8T+Zpuk6Zp93b3Nva7o5oImtrcs3AVU5fHf0rp1l8QeGdVgsms2ms7s+YTGf9WF+9n8K5jxppa6B4r02SFmja4Vo9pOCu4Yxjv1qeXW4Ri9zynw3Zx+EtBvtNVZXvJmLSP8A89CR97+n41za/Da71q5hjmXybe3nDuhAwB/CD2z0r07UvBssni3TbNjJCLqdUuZ1G1ljHJ/UY/CqfinXY4tSmtbS1dIY4zcAMuXk6BSamUtSZ7nyn+0F8OP+Ef1s6hblnmaT94qLw3Nee2WrzaJrdnebWSa3mWRQw7qc/wBK+l7vwRJ8S9RvQZPLtbNfMmc/Ng9cDP8ASuG8afCvT3vGaGBcEbQSgYrjqQccdaiTuEKji7o+qPB+tx+MfCWn30Mqyx3UIlRh79f1qh448B6b8QdCbT9QjYoDlJFOHgbpuQ9jjjHTBrz/APYy8TmLSrrwvdthrANLbBzltjEkqM+nHHvXsV3YeXKeFPOM14UouE2d8Jcx8qfE/wDZo8ReAj5kNu+qaZPkxXNsm4j2dByD+leNeIPD91bNIs1vNGyk5BjYY/MV+jWjuqx7JhmGQYI9Oe1ed/Fz4YqZppFiDLIMIdg5Byef1reniGtzacrx5Tgv2KoxF8F/sn/LS31S5WVT2YlD0+hFe06J4mvfDUoktJfLKjlf4X+vtXlXwWtV8L63qGmrtjS6xcIAu1d6gg/iQR9Qo9K9FaVUj/i9uK5K2ruRG6PoL9nz4qaf4u1H7LI0dlqEYzJE3yhye6eo/XFfR/hGBprRPlEyQyb3t35VxnnOen1FfmtNqdxpepx3VnNNbzRNuWSNirA/UHP4V9k/sfftOQ/EnTBomqSQ2uuJkBmb/j8TjoeucdQc1wey5ZXR1SleN2fTY+G1rqsMMlnLJGWBaOFzyuR0B6Y/Gub8TfCC6sLBWmheNvMLyM+FTnjr9APyr0HwRqH2nT7cKqyDZgbxn2r0Dw3At81xBJJIishATOVJ7cdK66dO5wymonzPefAqLW7rzprfzIYl2qFAGSMnr6Vz0P7LD3cgjS1ndZpC+EHPzZHYehr7g8KeHrXULWPzLLTZmjAQ+baqc+54HNdh4a8JW2my/u7XTbdiflMFsibfx/Gu6jQm1Y5J4qO1j408I/8ABPHVPGeq2crWcOladAQqz3TYLdOiHBJ/nX2N+z5+zLonwR0xW02CS4vnQJNdTf6xh32+i16F4a0iHzI1X/SPLO5t4Dbc46CneNfFUemAw2oXzsbXZTjZxXTLCqmvaSZySxTl7sR2s65b+HrddjLLI2Qq5+6feuU1fXZtUmBnbeAcj0H0qheX3nqp+ZmHcjqartNulVju6/WvPdVNlRjZak1zLujfaozgn68V+JP/AAcX6vH4k/bd8P2Nr88mk+C7OKX3aS7vJgPrtkU/lX7ZO6yRt95c56Cvif8Aar/4JdeDf2sPjrrnjbxBrniSC+1Ro4kt7UQ+XbxQoIkVWZScYXPH941vRlHnvI6KPc/GvwBo7W3hK1jCl9zP0GMDca67wh8Kdc+IPiS10fR9JvtW1O9cLb2drEXmlPsvYDrk8V+u3wo/4IlfCTTzb/bItY1KONgfLeYRIRnOCI1XNfZHwH/ZB+Hv7O9oIfCXhPRtHdh+8nisoxPLkY+aTG49O5rR023dHpSzVRgklqfB/wDwTk/4Im2fw3udJ8cfFy1j1PXrV0udL8OEh7TTWGSJLjHEsgO0hT8qnsSK/Re70tuu1st8xx2z3H+euT3wNvUobXS7V5Zm2jrsH8f0rm9Z8YvfMfIX7PHH0bOOnep9iefUxkqj5pH5c/8ABTv9nHR9W/bL1DVNRDbtR020u1gQ43ybXRmI9BsXI9xXgfw+8W/8JZ8PfEVhb+VbzC48uykbgkhtgU+zZJ/Cvpr/AIKHeIY/GX7ZeomO6n+2aBoNvYOVbdGJA7SSD/eAPOO1fF7XUfwz1+6a3f7RpYBu5iAT5S568dxk8ntW8Y8qsjo9rKcU5f8ADHpHgzTF03UZo9cuI9Pv4lC2cygNHFebNrAezNkHJ5DGuw/Y/wDDN5oXg+SO80mXTdctZpLuS1kB8ySMMSVQ98gA4HUcVheH/CuhfE6G4t5NR1ARvNBIJopP3nmTJvhnUdRg/KcYOcV7Tpn9oaL4EmmvYV/tbw+XC3GMNeRLyZM9SSvqTzx2piuYfws+HUOha58Ro7pludE1Sdp7e3x9+2bcRMP+AuB7bK9B+HVppkfhbUrPdus0QG2lAyrJnZj67gOvasHQLtRFpmpfbl2+H5p7aVyn7q8spCGEbHp8qsOuQM9sVn/CTVE8OeM/GHhW9LSQ6cyTB2OTNDK3mKVP90ZHA4+lBEifTvCjfErWbfVotXh87znVQEKiGYDacn1+XnmvUPh5Dda94KF0DBcapGz2t6iHkSRsyru54OAR+NcD8Kvh23w6v9Q0+bVFv7LVLt7qz2r88cjFmKHnj698V3XwaePWPCnjptJhW3kuNjEq2JPtKqwYnHRjhcnqSPagOUxfEfiS3+GNh9qt5pF06+lEmHG7yJzLHmM+25WbHoa7DwxeafdeIdUkab7RHp5Wb5vVwSfpjnp2zXHfFHQdL+LNtexyQybliQXHlSbBb3cGCkpX3zgnv61H+zTqMPiKfUlvZttxq1qB5Y58tV3qGPYk8/TFAWtqd7rmpQ+IfiIuktJ5ml3CJIY0b94rgBgR7EZI9xVzxr8PLH43eGdO0nUpN8ljcO9k4bByqNjPuKueC9BSz1yBbi3tY76GwAW5KjdIEXBw3U8fpntVfwPrEfh/Q7Jr6OOS6vtRnuoSqYZfmbp7FcmgfNfQ7H4RBfEvgy086FoZGhAIY/MSPlLVif2xDrWp3mjxOZhpz7Js/LIV5xz09aj1LxrJ4GFq1ivnQasANMx9wnOGjB6ZyCcehzXL2/xBs9J8S6lq01u8SajaNFeOEBFvOm4Et75wOaBcp0kegaXoOk3ElpHCsepTkXYC4aXfnOfpW5rFxJGNPhuHUXlu2dw+YTxBtrfz+tV/gAjeMvgpZaheLCuqSRtkuAynEjYbB68d62baS1m8T3l1IokhsozLA65ZYCo+ZR256+nHtXRTi1qK9znodKfVfHd9ptxP5Mmm3EMkUijH2qJgWVW+gGK6aTxBZpIyi2wFOAPSszSdct9chuL9l3RylLqFgR5jKgz1H5j61PD4w0+/iWdS22YBx8nY81oI+QPB+ow6FpUOtaT/AMgmwE9/fKrCRWYjahQnnKuucZwRwcgYrZtvF8Wv+B7DUNL8QQaxL4hjSfVWvLeJktFCiQxfKi7ZBuKlckbge1cZ8LNHuvh5aWtlYtbW2ka4Nkr3U5WaO3Z2ZWZf+WgG4naMdRiruoab/Y0FxHpchbSrfUZEuYYpxFcXMj9CSTs8sEEhcA4yM55HhnZY5/xR4M/4STU9U8e2trp+mancRJDse1iWSWOJVVGKgYf5YkA9Bj8eysZLPxToTa54q0d1uvEUKx2iFFkktkAYybVX/VqxC4yA2PQHnR0bwIvxG8IaIsWqwjUJvPa6iljCJbwrK6RkDuNgRsZ6tnNRXOi6nqa6Rb2t7ot3pvh+ZdPmS3Ty2twCd5bBG9sNknB4I/A5ieZXscF4W1CfxH4psdNkvjbrJZrp9sVYqdNSMHEajHzKx+XLHPUZx07q58R3F/4lks49OtZNNkMcMziLcsaIuGJjYMHRzt6nqa43TPgxpniTx1cw+GNb1CC91LU5pWhh3uEwpI2j+FNqElRxkmtLRrnUPCmt6lFdTrZw37RxBiNrwBRlpFbPGQGGD0JHocrQOZJmT4hsob3XrCK31DMWiylrr7DIttIUZJNqnbnaFXAIIwQMdeayviP4wvvh7q1vq2k2Vjp2mxwxpBEoVpmkdwFlBwWVsAksCuec55qx4isPCthq6XEK3Gmy2IZpI7e783zYiRhX+U73LEH5s49uo53W5dQ8VWGseLL5Jrmx0rUEt7eGWSS2eztIA7XDIhUpJI7gIgPB3DoASZsGjPUdc+GCeJfDMMLzQQ6RJaC5OotaRmNGJHmMzHJON3BUZ6HrXJah42axs4/D80M2sto8kUVvcvGwi8kYVYm3HPy4+U43NnkmrXhXxtN4lLf2Ous3GnWU0aWcd5AyXDqB86S8hVXfgAhD8vbJreXVJL69h1zTbCTSJdJlEkdhd7ZF3SRmMyP/AAlV3MwAYkNg05tN6Ect9jF+Ivw/1D4gQWeo6bcWeqabZxpc3FvOEt4LOMEtFHISxxKMHKEjI5HSvPfEMkekWdj4ks9IG2+kPmW6Q/NMu4oX2lsqobIBxzgHpg16LdeNbybVZLi6/sfS5Na1kTyT28srxXZViIYfLyVaMICMc8Zyx61S8ReLLrxj8W44fCFva3CX9s91fmSPyB5UPEirngLwSFC5PFUrNWQomT4Ql0bxdbW7wtfy3DT+fNaYLPKwjUbWJyoHbAOAUJxzztQPDaaXb26aWqrqVybaOB5wi3pDAbMRn7oJIYjB5XrWTb6VH4VvZpvDV5NYQ6fcRyoQo8ycPjJIKg7SDgnAxgdc8dZqky/8Kx0u4utsSJZPciaAkvFmQ/MxHyhj1zwOvB4xDVtA6lHQfD+sW9uv9rTaDH/ZM032CKKJmZCFUxx7yBlQpk4JJ6fhR0zTNU0y/wBebVNH0nULL9wbefTruGRUmd/Kc7ZF7K5+nJHODVDwP4d8T6r4rt9FuZ7qHQ9Yu47+W4ntikmnwsrGOSNiAS5UA7TnOc4wDXZ+G/CGl2vji805tQguLpP9KuJQw+1XKRtGqg7gV3nBY46gHABp8o2ef/Fq4uvhezX3hvUJJPOFvErwWv2iQeW5LBXOQCcnsPfjitTQ7rVtV8QaTpelaDbTyWMKtqtq6XCr9k+U7pGLnOc/U5we+KnjfU9S0HxxbWupQ2d54Xj1MT2KW8O26dOflPVTjOcFcHjoKi1D4wjU9Mk8QfD+8bVPFHk/Z5fDk223g2rIPklL4JYoc8MvsKi4yvJo+paB8RdekupNN1OyvdOSDztPnlhtrWFgxMG6JApmyFdgFU7SoIHSofFnj7UNR0ObRrq6hv7iBrhYgDEkd1Gi7VlyBuLcq2MAY7A5FdZoHj7/AIVX8K7rVr/TbWdtavTGLO1cPHZSOI9zsCQzNtGSxJJHTHJryf4qW2m6/wDFWwh0G6hsrjTbme9NzOVaGWRIzISsfdXZiAv3sbQPU0lcD8t/EljJZa7dQyK37mXYvGD8vGD9OlZN7PLauirJy3PQc+lerfHfwC2jfGfxNoN1NHY6xa6tNCIpNscO9n3KGPRN4dTnOBuzwDxw+ueDrjw1qMtjq9tcafexHbLDOm2SL0PTBB55GQRyDXNK8HeWx9RStKCcTl4w2xt2ef1rL8fQRyaOqlVbbKBxkYyD+PTPeumezUO23cIwN3mMQFI/KuK8e6p9rukt4cCFSG+UHk88/rXVg7yqKUThzCpCNNpnCLYlbxlZSdrHI/z9a2NEtVhu4pmXYq4OSW+Y/nTjbyWeoTTZGxWGM9Oa0PDdkdavArs8fzZ3KcY54A4r6Tmuj4qO5+jH/BK74RDUdMtfEGlMxurySWxuCrPuFvjChRH8ykMynIwx7mvqG/8ABEOm6mLxLeO1vbeP7NcahGu+G5ifoFZgSdx9cEcepNeRf8E5vCUnhH4T6b4Yd7vS9S1aUXxMjCB4JWU+WAV2uNyhWGSQefYD2KP4wzeF/CN5pn2awa8t2K7mBk+373JUfeGGUBj16YxjBz5FaT2R3QiupR8PeLrv4c6lcRyJo8mnypNcw3sSZvdwYymPY/y7XLYBH6jiuR0bRNPXQ7y8tbIXdleXcheFn8qUO7MSzsAchScKAcAYXoKv/HH4nWuv6Xplxpst9N4ghsLeyktBbLjzQ+BFwThQ2QHyeBzXJfENZ/hH8QNHkunhkgvlmiYRXDPbpKSpDOMqQcDI5AyenTHNzX0NeXsQr431Dwn4huJJZB9lafZB5O2cxjZgqY2X5eFI6nrnIJ4m8VaBNq3hrU5Eks9usFQtrc25891x8xWQHAZlPAIIG0896ydC1O38a/D7xTeQyb9Ut5bj7Kblzs2+YDsBHIcKQMHozHjFbXwks7s+F7q4vvt0MsdvGYFO13kb5iQ2NuABjPIPzdeKnlDYf8EYNS0rQrnWre6axN9K1nqI6k4YYjY4IBwo5GCMEA4JB6vwheW/h6XS3+z2jXVmXuFm+zeaknBwWUAbuuMnoSD2rM8FeBW0yzumnurx5dUI1B7BMPFEy/ddhjLErvzg4HA4PWVfFQ+Ft0sltdSXlxeyG3h0ueErutQQ4dSCCD944LYHcdKBs7qLU9P8c3sOralDHaaa1zDbysJI5w0rNtD+WJBJ5fGeFzngdcVxPiTw7qSu2pvpokspoAywOxGSrbT83QBzhsDkZxWzrHifTvETDVLPTZLO6hhdEEgjfcMZZMp8wYEYBbjn8a5H4f8AjCfxN4jXSb9ry70m2z5RWEuUweVJOBxxgYoELoWjw+EPC+i6nIg2X0r27TvjEB4+Zu545GM8A++ew+I9rd+GvDN1qia1HeMrxy3Qn3yLOG35CFcAA8gqelcb4s8R6kkEmn3dx9q0+xkLwQ7irwkpgEnIGDwM46n04q1471bT9Sh02SfT7pbEPFm1jZVkdOCyZyRuJbqRj6VehUSx4U+IMvhPWLq6N9btZ3cwuksZLQSLK6JgIoxkk7uODxz2439HglnW41mbTUuobr/RFfTh5c1rLuLsWjJG7G8KxwTgjAXbk8t4iaTy/wDhItN1GLQNQ0y6SOB8b3tJET5BtBONykruHGSTxmm+EPigviHUmsdWmitZbjddSyzS7R5igblBIPYrgdffoKqwNlrSLy9inbUpdQuJLVJXspi7LFHBGG5HmY3FlPIXnJHGB1q/EzTY9fubF7HUlvNJ0W8V5ZmVdjOUcDJOW2ncQRggECr+oyq3h+z/ALMk+1C/jZ5LR5A0UJ3tmQYwWPAZtxJORtxzWBeXK+IPE2hxrcreLbu1u5aILtkwzEKAWUHJ6sCRgDikNbGzoqLrXhC8ka0026toA0ZMc8mGHDAhlGEAxjaCMnn1z0og/siLw3NqumWNl9lYlvs9z5rDbFvj3hhtfBwSpJz901ylrq2p+BvCV9o8Oh2otr+93XLTkNHKW4YP/DnbyMAcha37S+1TWbXT7PT7W3mtbaJb21hviFkOxcbAQu3HBGCOe5qojGrcZ1B9Y1ACHSOPscbIXMzAlQ7KnReGHHXuK+f/ANou5i0W4vLua4jjvEjWPbEQwcdN27vk5xx2r0LU/G0njLSbiP8As+zuI4biaKG1bf5tuct8ofhcE8gAdxXifxV8XLd6FJYyWawXUwLtdXD73VcZCr8xCqO3BOT1ramTI+X/AIkatcXWqPBKJoPszNt8xRubk/qa5y5l22OfKZdw6n+I/nW749vGvb796srBSyLIvO7071yd1FdSSIu5YwjZZXH31/oa6qUWndnPJPqOt7aNA7Kq/NyTk8Go4bb7Srb9+MADnFTbo4gyqWG4ZAps9ysJjXhc5zn9K2JPs7/gh/epoPxw+Ml0xNvGnwvOXIzyfFPhsd8+or9BtR0WXw74d8VJG7SajqMO+a43FU8sLkA8dck18Af8ELtFTxj8dvjBp93Huik+GDOQp2livinw246+6ivv19Jvtc1ZvtF9b25ukuLgow+faoCBAfQYNUou6l/W5dPZ+v6IzfBOnv8A8Ibbhd8hezScSv8A8smZQ3I4zyT1rB8VPa2l3/bCo0f2plW5BOHULgA46YJ/kPU1q6NYXgtrsSajHHaxsbHA+YDYMc46HFZuhn7dLfeC9YMUt7Z25e3ueR5ynpySSedv61sanY6lolmbiMXDLb2Kje00eAGZlwMn8DXF/wBsr4W8Uwxj7PNp/wC/FtJIvy5wJCATzzjFM8VfEC1u9J07Sbt40uzbFfIII+ZSBuHPrxnvmovidb24u9NtbXcs1hcQulu3KjcVDH8cE/jQBh+E7a8k8KDUNQluv7c16aZ4o0OVt1L4xjp91c9PSsf4kXFnq9/bySKuNBkEssgJ3EKcEY+tegalbNbeIbl42W3+zmS5t2wAjEALj8yf0rz/AF+7dPBlzq39nyRy3V6gLsPlnWT5HA9eo/H1oAxvH17DeaPJdxuWup4WliVDwpIJCfXNcdaaPNHoq65qW3+0NQtBDBADt7jaSBx/jXoV58Lbzwnps1jqDRTLcW4aCSN9ywyfwjPv3rkPiJp+7wZ4ejcbLprxIpNp5wDjH0/rUysTK2xgeItIbw74fEMMKrCoCBm486Ruv6nFcv4j8GNY6BNO6lGSMh2wTvcDJAz6cV6d470CbVPiHoVrhItNs5DNKj87gnzY+px+tZvxDM3j7xrJpdjHHbWNramWZ17bien4Y6g1k1bcUY2Wp4+mmzeDrnS9Vsf+PiGITvIP4m4+X06eor3bwv4rt/HXh63vrYj96Nsij/lm+MkV5nfaINQ0m4trWRVjtD5SKecA9fwFY/gXXZvhTqZljLTabeTMs2SScjowHr+B615lanzo1jJrY9wtZxCGXcueeK2reC38ReGZbecK1xb9HJPzLiuVtdUi1GzjvLeVZI5TlD7e/vViz1Tyrpdx2lvlyp7HFcLjZ2Z083U8x+IOiv4S12C+jz/o7Z2juCcH+ddNb6kupWMU8TBobgb0Pt2FYfxn/cCZd5Zc8Z+orhPhN8T49O8S/wDCN3jhWvHzY5zxIesf49fqOtVGm2rhc9J1SHMbHb9DnrTdG1i60HUra8s55be4tXV45Y/vRkf575qxdEuNvy5HH1qiRkyDptFYyszpp6x1P0o/Yb/aUs/jR4Pa3uGEOv2Cql4Fx+8OABIo6YbpwMAg9K+pPDkmLrzFP7tjktjPT61+Lvwg+KGofBv4j6Z4g0uTy7y1YAqc7ZUycoQCOv1r9cPgd8WLP4t/D7S9c09kkttSjZunzRkMQyn6MDXTh9XY4cVTlHXoev8AhG9+zXs0LfNh94Psea77T54/MTAwD15rzbRZ1TXoztULcW6sfqOP6CvTPCkUcqrcTL+7hwx9/QfnivTp1HHc8mUZN3OludVj8OaPtgP+kXEZ5Bzt4461xVxPvZgx+8ctu7mrmq6u17cTSbtwbOAfoaxZpTE/XO71rz8VWcpWKpU9dSWZtjL3U8CkfmSML93OGFV/M3Rq3y5zwM+1F7rtnoFjNqGqXkOn6bZRtPdXUn3beNeWY/TB/lXPGKbudHs2Q+N/EUfhTw0GH/HzeMI4e5B6E4Pp1rD8LiG8iDso3scck57f418//CD9sHTv2wLnVNa02KaxtdOv3s7S3mP7wQgjZIwzwXX5+33sY9fdPCU4Kwg9+T+VbRWtjTk5FY9a8HGODYRtXaoJ9jXYa/4jttB07zpCrM4+SMHk8GvPPD935FlJNK3AAwF7/SqOpazNr+pSTTM2xRgD+6O3416EZWVkefKDlK5Prni2bVrnzJ89cRoOgrlfiN8U7P4Y+CtS1/UGWPT9Lt3lcNj5yASEHuSMd62bu1XG5pNvGcf3R3J/Dmvz0/4KR/tPP8U9XXwf4WmW80nSrlVv3jVmMkxzjOMDaOR061J0UY6nifg/xRqXjzxv4k8fa1dwzab4i1Wa3tpc4MVxKCFDgAfLtIH/ANfmuT0rwDqfw3uVbXoLeS3uDLps8aL5qTq+0x4Y9AykjI5ya9C+A/g+2uPAui+E7ho57fUbpNYmJ4UxpyR9QcV6z8SPBmm6p8PNc0WWMgpbtcabMF6SorPGPoShXHXp0oO2Ox5f8PdITwa2n+KLe3jXT7jTRo2p26jiGaCUGKUZ9duMjHJ9hXsviea3tvEepWd80kem61pQa3m3kbWOVbHPb5T9CPeuB+FviWT4j6ffWtmsLWusaWgmgmX5YJXUbnHoQwPXPWr3xN0+8034daLby3Ed5DbzJp8pkB3Rq/ysQc8fWgZqfCjX9K+IXw0uvDV9GLDUJLfyPI3FWfkxGaPJG5Tszjnr0xiuf8Gmz1XxTqcclv8AZb7SbU6PfrvbzIWVUMUg5+ZWHQ/X6V5r4lWPwn4fsJ4VuJpvDF99p/0ly32mHfkqG9MAnAxyTX054zbQfHukaJ4u02S3tdSuNlrJdbQkV4rqdsUvfthSTwTyT3mMrgcz4A1K5uPDljNLuXVDa/uCsnyzYLL+bbWOTk8CrXwT8dWnhiz1rXFj+z2TXhg1K3LHcpaTBlIPAClsnpx27Vzni7X7TwnqOja1HHJ9jUMs0J+VYGb5SMDH3WH5k9q6XRvBUeh+NvFq/ao7iHxRZR31qjAMjNKrCVSuBwW2nnnPemwND4Y38t78avGHhm+khPkzNexyKwAu7adUKFR3C7T+Oap/svaDfeFT4nvHiW4vNBuDZRRbt3yBZGzj0yy9q17rUH1HwHp+sWSaXD4isUt7Nt0YR4GRtpD458tkJB56+nStf4DSTah8XvGn2e3jTTZvJMrc70kZSSq8/d4U+uCeau13ZClsdtf6xcePNDfUtPmQz3CrdaYqRbGjIXDxHsQw45qNrSTxZpi3tjG1u2jpL5duX3Mku0o6H82Iz6dqzfAU9z4V0KXSp4Sl7o900ckafMrRjJWQH0K4z/vexxofCzXFtfiZeS3UP/Ei1qLdYzKdsNxMSN8bY5Dgr1z3P1qnTa1IMn4P3MvxW+BVrbq8Ml9YXrXFkXBQ2dwrlvLbGCNpGDnOQe9cb498YzLoHiCG3tZTqi3Tpf26RcJMc7wdo4DYJBrsNCux8OPi7AZLOT/hHvEzvG19GQvlzhP3ZkAxhmIwW4H8zrXum2fgv4z6gyozatq0Uc0MhceXdINyhW28E5P15FZoqJpfA/xjbQfA+3sU029i1KxkFvLHtIYZO4AAnoVYGt/Xr8eHPEF54fMix2eqWstzbynhlYtgx9OSMnr61w3xUXU/C3w7tdeT7RZm1mWWeLzt0hj3Elc99p4U/wB3g5xmvVPDVxo/xetrXWmt5JYbURukso2tbXBUbwT64xk9CDxiuyMdCTlPhtd2k0U2mytDay2NubWOFT1UZRjg85zg1a0vwgLXTLeNXXbHEqjPXgAVF4p8Jtovxmg1zTpI7hEEhvYM/wCsjaMHIHGG3AH0rPuPGmjSXEjDUpIwzEhSwyvt0rnmpJ6lKx8q+LfEeq+KI/DcklnD/osDDUHXEUUm2NhHx1UFyp7gEdeeK+geGr7T9Rnt9S09n02C4W5tk+0r8iNyzufvFU4K5HzD0rttJvWsvAtppOoab9qhu5P9L3LuDx4GGV/vAggZz2z3NYNpoyfE7wf4s1a1uNQj0vTbp9FtpbZ1bztgZCG3JvdU2rwuO45GM+b7ptzD/E3gbUfAnj7Qrqa8t7yOG5jks9M8yRBfqSv7mfA27SDnCls55A5Fcx8SPF11D458VWFla3H9q655cUcHCyKisd0mMAbTuwDncBGBggA12GgaRpdj4X0C4nj1K81KwtE/tSS7UxTNKSyK8DhwBwB1VQAehxmuf+OmlL4R+KNlqlqseqXi3ENmXtYv9eyEsxBbJfaCxyQoI6etTYvlRF4Sl0v4d+JNPaS+kS8tmUwJHIsEd07KIyJnZvlByeGPzHI96zJPFF7cy6kdVnVi7S289meI0iLJiUAsRuORgDtu561d+KMP/CZeEJbcPbiO5ujuVgI5gmN7bwM8Acgj+6enfivD+m2MWkQzNIq6lbbpEiVdnmKmRvdmyHb7uPu8HHrUS3FypljTIPDfw+1e6Y20d5HJK1ylu37gyl/lY7gGJIUZBJHSq/jv4kXutTWMcFxfaXo9xeIY44iJNw4XbhsK7YI4OeOSO1V/EWk6h4muNa8VWl1atY6ZaRoqpIWE9w7HBGBjA2gEg9+h5rW8Gaba6B4+t9P15pLzQdU0pL5SgEi/bnjyQpyGSNE4YjBLFeDyQitil4A16bwTaasL6x1CaLVL+4t49Qu3kfzWJyrCUEqrbSTtYgHHUYxW3rnhDW4bCTVb66vI7jxAPtlthvJLKfkxgHDIDkjIGT/CMVNqOh+GbLwss17p9xqFnqEqpHafagqREvgAQmRd7cZy2ODVFtdtdD8WWOmzXjX17byRvdwQOWisxMhMkhRumG5ADHOTwDxQTzWehN8MfDKXE9xbapqEepaimlpDp+nvCHxKWZnwobGVOBtIyeecVR+JPg6bWvHfhv8AsPR/MbwuZNf1S5068WzmEUcI+TIyA0sshGw5+6fvE1ta78P38GePYfEHhe6S3bZ9okgaVpDGdhKMcj5dy4+UHgk1xc+oSaB4i1HxDcSa5p9xfxQw3Mmlv5jPCwG6EwkYAYbSzH5iD2HJrbYUrX907iw+IEY8Ptptxp813Dqkh1C5tki3X1wWhTJaTd0DDoASQgHpWV4h+Id7f/D3wra22pR6bYXE0tuN2nNKmmRKcP5i7g5+VV+Uldx44GTWz4b8X3k+qr4g+y/2PJsaK0UR70vI84aTcwBEZ4KkDOd2cYxVrwh4oute8RakuvmxuNJvrG4tdLEbnzkIKyMsi43LIN4UsAw3cZxg1PmRruRaT+0Ho+t+HLWTyri+1O8f7PDcW8xhklReDIUxmJQvy7SSv3RuJKis3XNWm8FTSxrb6Tdatqk4vFmilPmQx7tygOyrlwB0ZQDnjtWdd/BRY7O18WaRpsQsWl+wadHI52xT+aF5IHTcxJY56DGaw/id4O1Lwl4hu4/EF/YXmtQqg+02TPJBFEzKiwhtilpAWJ5UADOCTgEuC8zV1y31v4qPceNNMgXTdN8MpLbSWjXAYXMhjLOGwOdqxsyqMA85zmvMPiEPDPwr0W48San4w0zw2l6ipFZ3V/HDfzuVP7yOLJf5SSMEYO7qMc8X+2J+1drX7PnhSLwzoN59hvNUtWlvEaMMYuHSOQEn/WMCQCBkAg9eK+BPEHiCTX9WkvLp5JLics7yO5ZnJ65JOTRK3Q7sPhZVdT7ltv25fgvoehaG1xq3iK41gRmTW7aK1nure9uGYodszFcIsYUjaoJPfjmvp/7efwJsPEkNnHquq2WnteKzy6jpjRzGJ5N7KZiXIOABu6854xz8Itu+07t3yLwQfSm+JvCVrrVgrhdt0vO4Dh/Y/wCe9KlXgpe+dWIyqTheD2O0/az+KOheNP2lvEmueHbtLjS71bdo2V+GKW8UJbJ/veVkk929sntfgJ+yN8Xv2s9Phm8HeEfEN9o8oEaancn7HpcOAPkW5uCFYDI+WLd14BJNT/8ABFP9hjSf2y/27rHSfE1rJd+HfDek3GvX1ixKx3ZhkhijicjnYZJwxHGQmM84r+jjTvCtj4BW3tba3jhaCIJDHEu1LaPGAiL0VQc4A9a6cVKjB9zy44ytFezSs0fh74d/4N7vjV4lsl/tjW/DuhwyAARwpJfN743tbg446Ejnt38p/ad/4IlfFD9nzS5NSM9xq9vHxvbRjbRMME8Os8ozx0OO/PGD/QjMvn3jSEHnrUF1Zw3E8kM0VvPbzx7ZYpV3JIp6gjvXLSx6p6RjoZz5qivNn8kPirwzrHhnVpLPVLWW12sQUfGPb9cflXQfCXxJa+EvF2najPa2199hk8wQXHMe7BVSw6YGc4PHFfob/wAF8f2EtH+BGtQ+LvDNm1v4e1mVZRFGWK6fOWCSR8/wsXRhyTyeBX5r6aHs2iWQRu2R9zncM++K+gw1aNSnzHBKioysfqJ+yT8afDXx+W60+88T/wBn3UNst8jyWTT3fmx7VS3DLyyjJIIPHTbXp2oXd1/Zt80Wk/25PJuR5HfO5VRsgF0OWwevGK/K74VeN5PA2v29xBNNAzybH2n5l7561+nXgj4sXWrXum6poml29lpOtRXGbi4If7OowFkVfVumT6jg159bfQ1pi+LfhPp97oOg33h9bDUo4XsfKjVWi+2cKXjYD5yvIDn2JIHSsr44aLb+I/EOk3ukxWFlLHMm7S54nfadmXbyyQ5U4BU56gDkCl8O+NdW13xitneeXeaLC01xbXFrsi+yyuzgAjGApyQFzx0wccWvigx0/RLWO486115VENw943mebEZG4UqAdoUk54wwxk81z6I2iYN5qmn/AA/8b3VxtM1isiX8ErRCKU3R3pLBI5DIUddh3FFwcAcqSdzVNcsbvUdQa1tN0eyeaS5miSN5YVxtyFLAN84xjGTn0Bqt4K8P3t18NrW214WNm2riSVWMghaVTypPzEfcUDqeQfrUvhzTrfTbi3vl07VtSsLiRo3W5cG3gVMFVG5trZXdgEDPFEth2QeBtV1CHWLPXL6S+0vRruF9PS7Ft5ok3r8vy/wfdz5gDY2gdTkXfFmpP4Ju7fVrdYvFNrfBYGnuLbzZLEZAaX5Dlj6jIwOtX9JaO5sPM0qx1D7BDcNAlnNKJlgdnXbyr/Knz/eGVTJHHfX8BwaXeaTrRtZP+EdutLPka3aXFwrG+ndwvmptZhsG5cOME+hBOIiJrsRweK47XUG0OG3vZl1KCSaed4DGZItoAKqx2tw33A7HqDWHNpcug6fb2ljcRS21zbgpIwUywybjuB2kE545PT361TsNBuviT43vdD+2WUdjZJ9r+1+djc0ZRFQDA655YZPcAmtLwz8GtStL9Fjks4bqztpWf7ZdssXlZyqq2wsSOoyPqaNXsEUZvhDw6mpa5f2d9bXH9pEkNCvy/uGXKv8Af745OeCCBk1e8O+HpdR8NzNqEd5qtwkrRWspl3bY0GEG08jAAHOcbe3ZPFurDxV4eNtqUL501IwAv7uaVVbODxlVOF5BB4arngHxlDqgvFuNUubWGNy0cxG6fZ08sqx2DbgDC5+tWri1RgWfhWbTppry7ht76HfkRrABJG67VzKDwp4UjoCMd+TUsLFdPvtQ1K98PwyW95bIwtTKs0gDMQ06sF5I9sE7R6Vv+FNQvI/DPiTRWvr6zj1GQSQiaFQlyuCpO7Iwcc9hz+NcT4p0LUIdShltLq4FnbwuJgW+Q4LZ2kE/Luzj/IolJ2HZG9qOjabb614fs7e3W3S8ukS1hu2KRyxvwyu+ATycjOSOfU51o/hTLeeIoI9JhfS76zSW7t2tlbb5yHbvK4GRlh83Y4HNR+GtD0/4heErq4ul1B7y0tXihEcq7gdnEi45XHGCvPNcr8MvCOp6Brr31hr2vfb4bbbZw6jfO8PLBjywJ5KL1OevBqYpvVlHo3iMateeHJtSuriG7aZ2M8shXh1ULyOik/MBnr2HesPTyfDOv+Fms1EOn3M/2a5jEpkXEgG5m3E4bBPHT0xnNaGu/EDR7uS6s9QtLq2uZI1+1NbuZvIdsfvAc4Ks+BxgndjFUb/+1Fhs1+w+TY3TKTl0aRFIAxtH3e5G75s4zWgEfiPQdFjv3vfNt9Kt5A8cyxnzJJPmIBZSFByMDI5FfLvxtgjtLzUo2uJ9UGnxkKjx7d4P8W7HH0wa+kbDSrqfxBdW6q1j5cbyXIB8yExD5QzBlHPI4x1HcV8vfGG5mSS8ubczeWyfJ90FlzjkZreiTI+d/FNzHO9vDbv+9ZNzqf4FPPT6ViXix3F3+7lVjHyc9W9q19ctl1DWrhfLa12DBXGC3XocmsO6iht7VFVQeTk4wSM16KMZtBuZpdqsoUrkPt3Z/DPt+tV3SXWU5RU2HbgHk46MR/Sp5LuEqq7WX5cDApfLW3hdudjjLEn72P8A9dBmfbH/AAQoLS/tBfGJVYRS/wDCrJNjYz8//CTeHMfrgV90aHotvrut3Wu/20rXeilrK4gkhyilhg/Nu4ySDjH86+D/APgiLeWg+OfxakdvJi/4Vom4rJtZf+Ks8Mgc/X86+1tN8J2dt4B1zT7G4vIbjXIWv7iK6JMqFyUXBH8Xyg9x6ZquZq39dSqPX1/RE/wy8F3UOhyPM37zxFdSTSO8fmJC4+UHHTnGeo61gfE3xFu8YafI0ax6ppOYbi5iUkNESMH0+UgDBJ613GjSSr4F0zTrtms9S0y0MMzgfKhIG2QnJJ5xk4HfrVXwld6fJeXBvju/tSwVpYvldnuIzhxuPBBwDweh5APFVGXc2OF+LngWfxB4r8M+JltN1jYkJPLEQdylsru9u+eh4rorqJtR+LVuybfsq6e10sh/idTnb7cV0Wi+H1h+ImoaQzeXperaF51vCxynnZLcH8Rx6A/Sud8Galf6vZ2MXmWccjQtHcybPlhZcptzjuFJrQCn8ZrW51c28mnq0cFq7JOFbG6F0G85x2znPtW7421Oz1O/0Twrp+nwyQabLcAyN91ikOVZhjjGSw56/SrPxFuobDWrOyZkaDULTytyrxOcMh78ZJA/CsPwFHdf8I3Y3GqNDHNPLKkzr97gYHbuB+PNF7bgUfFtjcXH2WFY2hkk1GG0h/iDMZFyfcbd3HtXOXXg6G5+JNwQI72DSLA3awlcbmWQqM+g5z3/AK10PxD8cx2njfSLRAy+TeR36M/yoqIGySevt07iuW0HRdc1nxjNqFxLHZw+LrIFVGRJBaxNyW7DceeM59jxWfMm7EST5h8Wm2fhfRb3Ur7y5LpVmw7NuC7gCo/kK4nxPYw+FdBs7qL90rW6NcT9Gn3jIAHU8dq9K8e+DLXxHopsVea2jmUOkX8UiBgN3PqBWL4ogh1aHUZpLEK2kxpDaA9EEXAYjpnp1qnFMs8x8FeEJ9I0iZZE2tqUpZRKnzp13ZGeB0rm/FmhKby7jX99JGcKwX5Y17YxXoGsXSwf2aLM3FxNJaGZnYD52PJUc+4rjLxNW09NYe5sY4ZLiX9zhiwBPb8ORivPlFoLp7HGaX4yvvhzqMaxxyXNndOFe3ZsBWOfmU4ODz6c9K9G8PeN9N8W2zSWN5BOygq6KfmQjqCPb+lcV8Q/CkMmn6ZaW9x/pSsLiaUgkHkEr64HTp2rynxBp82n393Jpc01rJbtmWRDtJJJ/wDrVjOkpD5mj1/4z6xHPGwVvuqM+3Svm3xvfSf28ZY2aOSPDK6HDRkEEFT2I9am8WfEXxHBeyLJcG5jVRzIvzN+NYo1h9SQTXC/vWODgce9EaPIi6cnKVkfRnwL+NEPxI0l7G8aOLXrKIeeob/j5QY/eqMdemR255OK7K+RovmJ+9xjGMV8j6Vf3GkalDfWU0lrdwN5kU0Zwyn+vuO4r6C+GvxptfiHbLbXKra6wozJEAFSfHV4+T+IwCDnGRgnjxFO0vdOyPOjrGudyRsfl+bpmvsP/gl/+0DN4c8azeDdQuWXTdWAms03YWCZSdygf7eV6d1PXPHxlK+GYdc4x7Hv9DXVfCvxdceFvGGk6pbuY5rG5SdHzgDae5rKlU5ZBXjzxsfuZ4dvo7p7UhwH2mPB9+c16bHK1holtbj72N0n+0eo/SvCvgN40h8Yabo17CAyXqxSgEZILIGxXuN/Nvm3bXUE7QSuA309cV6NXY+flzLcYgBB3Dc3WsnUW3Xbf3V4Ga0JGZunHH41l6pJHZ29xdTzQ21raoZJppnEccSd2ZjwAPUmuCpBtrlNaMrvUI23qF/dosfzMzNtVQBySTwABk5JwMc4HNfmP/wU/wD+Civ/AAubxTcfDfwTeM3gzS5M6pqEEu3+350Yfuxgf8e6Ecc4kI3HHQ63/BRv/gqN/wAJpo+oeDfh/dTWnhdla31XWdnkyasOQ0cJySsBB5Y4c4HGCa/P3UviZp9tAWtY5bxlPpsjz7HOSOewrWnh5RXvHuYXC6e0kfYX/BMPxg2lfGa+05j+61e1DNGDtXemMEADHTiv0i8Fan5tysfzbsYGDX48/wDBPb4uRx/tN6KJo/s7vDPtVSNmCmMAkg9q/WD4LeKl1XU0f5du3qSvJ9Otbcjuc2OspM9wml+z6ekXJCjk9Bnv/SoDfC2tgqj5l+ZmL7RjHfisDxb450vwho02patqFvp+n2iF3mncRqB3PPXHHT1r47/ai/b+m8YQzeH/AAe0trpl0vly6gwCy3A3fdTDcAqQQeM4roPLUG37p6N+2v8AtPbvBOsaB4X1DZdXEEts19HJ8ocDlF469uvevlT9iS1lk8K69Lrlq0Oo6k5nBul2eYm/GQfYkc4rG0jw/qvi7UPCNnI802n/ANrPPqDxyHcEIYqWJHTK88/n0r1K60m61j4LyGwWN7zw+s5jZMr9piDFDGCRz8u1h7ilc6owjHVmz8MfB1nrWrWerXmjWtrcaLHPbjyV2rKsZGCBjH7wP0weV754xbvXZPE+ra14XhWZbua1n1DRZ153BcvCccY2sdrDPB+uK7X4barGt3piyMFjeNkkVWy27Z1xntz/AJFeV2895Yy6lMzN/anhvW7mxiuVjJ83T7l1b5OnzKyL1Hrz1pmyt0N39nXwxeeGtE09Li2/0y+uJL64R8R/Zt24+SD/ABAZwBxisn4p3l14K+IOircyCTSb7U3e0DjKTs0GfLI9VcEg9/QV1dz4mh0n4X+GobxjJcahNFDMX/dsHJHzZ6gnGcV5v8TNRk8WfE2TSvKtZdP0m2kmaeeU+bAdpMbxj13HknGB0z0pXQFN/Akmu+JNb0O+tvMtdctXvNMuPM3C1kaNiFxjorqR2yOeKxf2OfjZHb3dnoerRy6ktxbNaXtiOY1eOQjzAMEKV+X3r05/BrR/D6117TWSG7sYlmb96WM8LAs68A9+Rj1IJFeBfCHwpA3h608YaHb3kmv6tq0yrGOQ584gbB6jBpRt0A+jf2kfC0f/AAo3V7jS7d7iOGZLpZE5kgWTO/eD2zhvbNbtrq8Gr6N4G1t7NFUNbLcOJSuIZ1VCWwOMMqsB6qR3zWr8YPFtx4T8G6FeaglpZt4nMWmajFKNqHzGKYI/vZweveuT+EPh2b4r+APE2gNcXC3HhVJbLymAX7SFkZosEcnBQgcA/MB6mqe4Gzp9y3gT9t97SaCSbSfE1g0XluuYZxujcMOozuVhxzz1rb0zT9U+E2keLWZZ1Xw/q4cMGJe7tWKf+gI47tkL2zxjfEnxDZ2OvfDzWmhuY75pY9LlQkebG0jIOmcZynr3HvXY/G8arFrLNplvJd6pqklor2rHcsiJLh8fWIkMfQCqj3A0viJ480OPxJ4d8TWt9J/Z+sI2j3WVKwpI4AV3xnGCOvbmt5tMs4tIj03VLxdPgnVTayJLujLDBV4xwA3Az3I4715j4x8L2PwK1/UvEKQ3X/CP32myrJp+DJHGwUEsqtwMbQeOfp1rB8D/AB70Lx14D0mz1O/RrXWd32HUp+BC+4rtcITtI6jGen4UOo7WYuVHvMM8U2jyWd5LbzJeOYTcMQIreU/dcjOAN3PXiuQsNW1K/u9HuZtPk1DULKX+ytbtNmJYGVuJ427A4645HPbngPAXiTUPBdveQ+MNJ1tY9IvWsNbiiJaNJs4jmyxA8tkKHPHU17hc6BcaToU3izQYbi90vbt1CIj995Q6Sx4yGI6dcEDrQotvYexL4jtNV8aR+KdFvLURLpMafYJZCrLMzpvRWXsAMryecg8dK579nXx82ofBm60xo5Eu1jnEgkj8v7VKnyvbA9TIm1TnnqeK73S9MtZvH9pdTXfmQ+JNKijicH5blUA/DeOOnPXp3858EajNo/ibXI/s6Wel+HtWv3V3G4tO6qWIJ/H9a61dGZ2vww1V/GuiQiS1k03WmtTFbrNxK5XO2NuByABz3HavG9S8Cammo3CzeGZ5pVkYO4ucB2ycnGO9e16bBHr3h+e+hkls77T8tdBRv3ts3xyRntkYz0/EVtaN/YetaPa3k3nNNdwpNI3lYyzKCT971NKylqLmsfFkfjebXNR0uy07VN1rLCouLS5tdjWKEZDF2wOct8ucjH0FWfCXidfDWmeM/B8d3ZJDot4lxFIZHiuD5hKbY4gdxjTyWUsAQXfkqNtV/jXrrfDzxdp11Y2em6pb3lptu9Ok5kC4YCZ1AJ3DggHrXI+Ifhvea7e6PrF5Y31j9jjS0smtIDHIwAXzUYDMhIwvJXJ64FeFY6fZpnZaBZWei+Iry+lS4uNI1SaMzXvnBAjr91XDc45zlQffuK6JdM03wH4Shkmks5lkt5r+K6jUjDsCm1Y8lgq4YAkDkn044X4g6NdXdpa6dptpfNputSRywCL98xffsZxxlsENk9B8wPQ10Gg6vL8OPEesaHcSSLp2oQw3IivbdfNELZiLbmJ2x5R+F4BB5yapt2Kk7I4HSdbj8c+GdfmisLOTVNV8opNcS+RJDIgfG09SvIJEYzg88YFd9reg2o8UeHtUvt/2tlhtFhixLZXk8kalolHUdOCcDrzXLfEto9P1uSHQY5IrQPvjhhXzUAKkK6DH7vfggZHOeCeazNKuvL0j+0FuoYVhbLQq5zFJkFX6jbjGD0GDWZPMyj42Go+D77UPD+iWLSIy7pT9rGLdvM27dowuCw29W6nnpXRX3wlvIYryZ4rdtWtbJbm4tx+6AYIxVQeoJZcZ4B/CqlxZuLKa81I6a11fW6mzlLbWlLPxgfMx/eHPy5wFfPAOLGn+KvG2jJbWvim4kvI9S037LNHJboWglV0bdGyjIz8xVj3b0pFWvuc1pGrW+uReFtYWaSzkjvjPdzyQO6oFkdZD93HGAM59McAmuo8W2lx8KrfUpL5bjS7rUJEuIYZLbzLwbVRMysOiOFO3KggZzyaNKfQfBWgNb6tpMj7Uc29rcTCETxNM2ZAy8spByTxgnNZtj4c8YeLr/VNe8RXc1yt95CvBFJ50clsd/kkIVwWVflc5JOMdeKpK4cqvqL4I1O9vdI1bxZo91JY6Dp72kMWmyRPcLeyALuJcnFvkPGuBlt247cDAs/EnTNctNIur0x6LoFjeShdSsbUmSe0QKihskBCXwCAMkLjJHSqdh4sg+G+n6j4d02XULfULqTMVrbktaStEuxY0iBxv+YZwMkqCadoWq6p8N/CIsfF1jq1550vlTSaiJJmjUhXiKMxwh2nYR0OCeuRSaM1ozhdE13UrvWo7O5vY1061tbex0wPCIW+zpkIDzhiSCMgj+EYzkn1jSta8NjX9Xuo/tOm3l4sdrI8UpZ1Cly4K7MBk+Ud/vc9s83cix+IXxGh8M3Gl+XpuvCWc3cRCSWQjQOqmJiARuU8ZBB3delGpeNh4d0/y7Fri4aO4eKZDEsK30asuT5jEKrKCSAeu72pFSeh1l14Wk13whG8LNH4f3T3IMsvVRwfunOTIN20YwVyQOM+bHxHfaJ4TkszfJc2t3clLmNU8uS5AbKjcR/d7k4xkda0v2j/AVv488MaXqXgvWtW0HR5I2jbRLKRnjumO4yHIYhJN5yQRggk9q5V9PubD4UQ+dptx5OiwJLcLFGzuyKwy2/GXYdTycc9qBRjdXPij/gplb2uj/FLTEsVuIob6xikdJk2lGDuGAOAGGSORnNfNZOCMdc8/TvX11/wU4tW8TzeBtejeGeC606eCGZXDswikjYoQCedsq/5NfJ9rH50mfLZscDav6fy/OpaPo8HrBJCW1v58f+0Oee/Sp5FZQqr6dM1FYuyylS2VqaTlfMbcFXqf6fX2rmjHm0O7m90+6f8Ag278Tjw7/wAFLbrS5GxH4o8I6rbbSCQ0iPaXQ6e0Eh6j7xHUgV+4fjBmi8TTM3EkZCgHoV54/Wv56f8Agid48k8E/wDBUz4L3TMEj1bXm0ibfwAl1azW468cl049R61/Sb8Wvh1JJD/bFjC1xGqKJkjy5XGfmAHUYx+Vb1qbla58ni7KqzzG9jaFxu6Mcg+oH+RVTUUZZFwv3jtHPQYrXvLZZlTduHycgHp6/wAqJNFkuLdWwxRWzu28AH3rilFp2RhzHwv/AMF2fA9v40/YJ8QQSbZLq2nie2A67gdx59cLn8K/nzt7lLhI/m3fICcjHua/oW/4Kz+J4/E/hQeHLdkl0+CN5blkYMkkjfIORwcA8V/PjaWapfS2/lt+5kaE/Lz8p2/0r3MtbVNpmFb4rmjoUkb3a/LIyhTz0xX6O/sneK/+Eu+G9lZyX11ot5pOmj7PP5RYTAttAYNgNgg8A8Egnivzz8GaINQ1KP8AdyOqnEmM5Vfb39q/Rj4FfBC4+FiWkUti9nZ3yxmCHVIGgCfLlgpkGCNwYDGRnPoauto7E0zqrSKO/wDDNw01nD/aUEhtbfUZYPvSr34IIXqSBkfNjNb3gm61i2hm1VbX7aFf+y/3ih4PLAZ1CkjcoYhfoQQSORVmx0azXwjfW8kNqZ9ZmkvlaC42xI2DlUfBUEZAK8HPYHitH4ceI/7F8FWOiXkd1cNazTx3toJDJHLhnZJlX7oUB1BBxk5PNcetza+hyur3dx4esbO0ms11Kztpyz2sEPk/KSW+Z2OMqSD7hQB0xVL4gXsNzb3E7ahbxxsw86OPcFjKgBt4ON3Xt0x716P4BS3+IFnq11Dd2NjcaSggubWUbpp2Kk7E6ZQAjLYwCSKxfEfgnS4fE1jo+qXJg0rXFjlu73TYN7KpTlQT8ueMbgSBvBxwKrpYOZmX4d0T+wvA99DdXVwrzXQvLfU1u5FMDZXaqhcrtyFG3Ge+cZzzVvqeoeItfuNUsZ7/APt15mF756vHJtwV2jzAA2d2MgdMc1v+NX0/4beBfEOg2P2xvCCziRr+8kD6naq/3mRR8zYUswOByAveu20XxbpOra99n1NTqEcjxJpVzblGWZApZzI4OV6Ar0+6c4zVKyVgTZwb+Dv+JjDJJC98LyItI0twFbdgv8nb5GIJAIbn16db8MdabxB8KdP1Cx0XSZ7zwvcSRXUrHZeXG1SgX95kYYkEk8e4rG1bwf8AY/iIZIYZ9RtY282xBjI2RlSZmUgHcNpyQCewq/4g8Dx6X4gh1LwXrStqUkAEhWERQzsckwbgCHA4ypBxweOlTfl2KKusX/hS3sZbe18NsPFVxGLl3WczG7GBtRW4TYORtOOMGuR+KnguK607RLy2jXTYLyRrkvAEG0nkRsCTtwezY5yOvFX/AApbTa/YWUnlNb61BAXnFxdkZCthUjLIPlyTjGf1p9zotzq9nDY3Uk2kyyxmHTzHb7vMkHAVgcbiTjD9+Dnml7RgZfhLxdP4g0SF728jvLFZGKx+WzTFSxCsuwHI6Arndkfn3z6ppGj2kMc2nzxw2xYItxCUaUngkcYcE9icjHXsOXsbJdS8PSrBbx2t9p8rrlkK7ZFbDhkABVd2cEkdx2qfVvEuoTaNZtc/ZWlsZCj2kj+VdRykjCDGTwdpz905zjJNSxIl0B1gudU1K3tbTT7KW4VLaCTLSopCgEIPm2k567ehzVjwj4euEubr7TsumkSRrUJA6ScZ3K4zgkEf5yK3/i54M1j4ceCobKfR0udQF1I9w9lM0lw4LqZVBba2VzxtGAOeMjOXB4i0rxP8SoYZ7KSyhmsJG2WkzQLbMqqqSOSScjqRgbiT9K1jsMp+H/EMbMum3Nu2n61b3DQxulud+eMfMB2PJPYHPvWvc+F9QXXbqOW3tr6+O2S8vXjMkloFbCspVixDMTnA5wD05rGkuLfR7+D7VauyAtGWZpFeBwBh9zDHzHsR3OOcVc8QXcPiArq0NxdaxqF5IbZoILoSGKPIXeAp4jA45AyR1FUSpGPrfiC/8I2N811BZ3X9rTsLgl8GLYPlbcp4wCcZ7nvXyJ8ddR0rXNNNxYxzW7KuzyJSd44Bx+dfVdzYx6ro8enzWryzXTbIpDcGJox8zN5nP3ieAp5PpXyH+0Hb+TrEkkMtjHGnyqihdwxnkkfe+tdFPa4SPBNVumhv+NyNJy4AOFxWReDe20ZZRzg+lbWt6ws1xJD5Z8xWK7+OcE5xWZMzQyA7X54yR1rvjscpF5aiwkcRplHz7jpUd2y3bo0e5RnJPYHj8KHeaG5ZvL8y3kTBAPU8/h6e9QiaNrYbS+3Jyh6A8Y4pgfZH/BGrTvN+MPxiDFYw3w3hYucdvF3hg19+Np954u1OFo4ZjNdWk0EYCeX+7idiHyeoz2PavhH/AIIkaDN49+L/AMatOt2VLq4+FMgh54Ei+JvDbJk+m5Rmvv0fERG17StRsobibUW0Jxb2IOCspm8qeNv7rIdwxjPStOVNXZdLr6/oiD4oTWvhfWLVZJ/Ms9Ys0067j3HeikAiQnoPnYj8PxrJ8S+GR4j8Q2uh6WzQxeExHcTTquNwxnaxPdlYDp/DXTXvw2uLrVZPtV5BNa+ImS0RHj/eW84/eFfrww/IVqabd21l44mv1j8sa9ZWsyB12L5sIaGQNz1yAefWpjG5qZPxSkXQPCtjqtm299L8uaDna7REbSMn2JrjV8GXsWn38MMktrDPL/aVo4ZczA/Mw46H5m+9itiHwzd+M/CS29/eeVOs89gU2EF4wwdHHoDuA/CtXVb648MW9xFeeW0egrFayBcMMMrZPt8uBk96fO0Z87uYsei/8JPZaLefaPNWzacAMmA3XGc4xjn9ag8TQR3B0uDEkMUeZ1VemQrFmz6HinaFLc6z8O7jSbGCSZWAlafd1BJyF9Mq2KseKNWsX+Hc2rXAW1/sGJhFb+YFQqVxtY454/Ue9OT0NNzivGHh66TxDbXh8u4hsreyJilXIaM5eRPxIUk+grr9OjsfBWoXHiLVpTc38mjpp2lWo+7PM0vzYGP4QfbOBjNU/hj4hk8X6TDrkEIXT5rcbDKvUBNq4z14P40+Y/21480XUJGaSDw/9ojVtmRBPIo2HHoAW4rMDI1DT9XgsrjWtTt1t9QvGS1gSR1xHFxuwAT71yvii/uLXwxrWnxJJNqGoG4ldEwfKQyFE6H2z1rYg+If/Cw/GUdg1vJNa6aXUqp3FthPJx03EZ59ax0u/wC1/idfSiCa18uwWSeHB+95hwp9OuearnYGPqXh3/hGvCenzNtW82KluqnLLEcKTxwMkd+awfH8EMtlaxSXTJa2rEzoMB3k6Y/rkcc4q1q1vq3xC8bazY6aq3EOnxIoy4VNv3ic9/wrk/EGiR3urRwae8lw8YCTyht8Kc9vqec55rGpG5MY2dwTSzp+k63qUXlyKtoEjL4ZieTgZ79K4HVPAl3deDdPVFja4vLhJJiGG6NCScn2/OvWNS0t7bQvsU32VhC6jYuFBbqd3r7Z9qy4rGS/gkmkgEMdqTGQq/dkwNox24PShUVuRJt6Hzz8UPAax2Ie3jaMNI20kj59pGcjr3rzXVrP7Ciptw2Sxx3r6K8Twf2hoF5dLHudWeHb6Y64HrXkXjvwm0KW7N8u+NcjPc84pSp2RrRlZ3OJS9+YIzYx1qVLuS3mWSN3WRCHVlOGBHoe1VbmBobl1bduViOlPiOU7gqe9c0qdz1lK6ueu/D/APaHh3R2fiDzFAXb9ujQt+LooJPPce3Fe4fCaxTx9qdvDpl5p94rkZeO5QlRn03Zz7YzXxmX2Sbsj8atWt95bySRybZlXKuG+YfQ/wCFckqKvcuNP2kbI/oK/Zp+L1j4Q0LTI2uljaxCRKzEqWCgDocenTFfXHhnXdM8Wzx6xZyLcXE9usW4SsQIwSeFzgHOecZNfy3+FvjZ4w8OQqtj4s8TWaxDKrHqcwA49N3T6dK7DQv2hvG2q3QW+8Z+KrpJvlk36xcZcAEdN/piunSWh59TK23uf0afGn9r34e/ADTLibxH4k09LqKNnXTrUm5vJcA8CNPu9CNzkL6kV8cfthftQar8Uvgjd+LvEN3D4T+F6MI4tOgufMvNSmJHlxSEcvIW2/Kq7UDMWOMkfmZ4F+KkfhKKS/muLhTGCW+bdLKxI7N99j059RWH8aPjz4g+OGr2d1qtw0dppUIttK09ZWkh02LGCE3fxN1ZsA816VF4WjTc5rml0FTy+UZrlY34q/EX/hYHiGSZYVsdOVs29orbliHqfU1y73W51+hHpWfDctJKzM3bHPerUeZCMc8Zz7V5vtpSd2e1FWjZnb/BPUb7TfHC6hYXE1vdabayyROmMqdhwP619O/DL4/ePpvBmk6ja+LNUt41nKTTjaWdsZ2KP93B/GvD/wBn/wANw6N4X1vXNQ3lfsjxW8aL80jNgA47jkmvbfhdpMcXhxdPkt5bCRNVlkijnBDACPbswedx6+p5rXoeHiv3lRnrPhvVNW+OXjTxHZ61rl9r1quhC3t3u2IjaVlUnKnjPUZ4+79K53xHdaXZXWp6PJbxwrp8sF0+eGHybAARzjPv+ddx4ciPhTwVplrZyQHMiPCGfDyRlyHGc54GPpXC/EB4X+KV5eXlvutbiaO1uWVvuoMqpJ7AMyZzUSlZaGcYKOx6F8MpY9B1ey1K7k+zWeqacSi79ySMPM5PoSf51f8AhZ4ouv8AhUKtdTfZ5v7QNt5cmSgRmZlU+mQ3X3FeQeL/ABHdaJ4H8NaXJJ5U+nXk1uWCk8/aHi49PlII9RXaeILNdK+DUWkw3iSSX16sTXhXZyceXL/u84yOM471PMyjr/C8eqWeuapdTXUara39szLjEkAVwpOc5KsrcnoB6Vqx32m6D4+1qOGOHyby8We2lB4VZAcAD1G04B9u9cN8Uzc+F/EUWoTLdSQ2NxFpuqmI/u5YZAFiuHboAzZGD3wM5NdLrvgux1m6t7eGSSyaZ4pLK7hIk3GAK6xMenzAsSO9HMwIvjkBq3h2wvLe+e5a21GG8Z2Q5WFpACce2TjoaoaLqY17xvc6gPs0drJOLC7Vo900TlWVG/3SVXPYGr3hvWvs/wAS9C0+08m50C8hltLy5uVJSOSNgwhJXPzYBwOvyk4wDjotS8J2fhv4m3V1th8nWi+nuyj/AEdJo1yCR0BcH5fVs9TQlfVgUvh7ol9ceEYdNkt47e18QPNYBrcljY3abhk+kecnHIwRzwa5L9j3wND4V8cXXhXXI5o9W8E7dQWArwZGuH3sO2CHTHPIPGa6T9lj4uWg8VX3gzVD5Nxc6tOsNxuHzOSwK7eo+Vj71zOnanqnhX9sbxRb6xevHcWWkx2pkY/vJ4kljkRyTzgIMEnjC+1VGNgPQPH2jR/Hu91XT9Tumij8E+JItRHzH5rdNrocY7sxXiui8E3MXg3wV8QPGzTQx6fq+rC8h2DBiQPhhgZO0szDnHJzXmn7T14/wq8PeIZ7OaDyfGkC2YkRsF5fPUxsrDOQVxjB5xivZPBvgO2uPD1z8P7uGO4VvD1ut15JwrO3zYHuXDZPXP0qibW2PPP27JLjwrb+FPE1uiqun6hbXE+w7gsZBkVivXjDHI9BXui3WpeMNI8IeMNHja5vre7aHbGVXerxtk5OOdp6e9eA/wDBRXWofD/wqub23uFY6fpyafdpI5/cSABoiQf9kuM8V6J+zZ8V77wN8HvC9xr0a/2b4ssTLIhi8trCSERBXRW553cjjoT6miw9Sbx69x4u+NVho959uHhfxDpj3qXKKH+wzOPLZWHJ2hzhuON1fN2ufA+x+E88OpaZetdaVo+tNa69o0kymbTtrfLcxkfejYlTtUEqpJPQ4+qfidNd+G/H2papYBb06fp416wlj+62T/pcAX/bGHGMgMB9a85bwpp4+Ktx8QLh7RdH1C4ax8t4SjQzMFMUxz8rZPGGAyCBVR3H0PUPil8eLPxp8HNH1yzvLObQbqH+y9TuntWYzqHCEMoA9cdzXoP7KMmreGtc1jwPqEcaeHdIjjm8P6jb8b7N1xFFKCcgqFGMgZHWvJfiQNL8MpH4Lm0e40XTfFFjdXLxqnlW9rqQIyoXorswDhevzcCui/ZM+KkPiL4S6D4g1CaT/hIfDkkmh6hbmXa18YW2rgH73KkpwfvkCunXmuzGTsrI2/Hl74g+E3j6TT30+y1rQY7wXemMXVZI1P8ArUC5G0qzA9sggjIOad468B65pnwg8aahDDFrUerXTX+nNFhZJVkYZyOn3SAScdO3Wuj1bXdK8Ra/pXiBJ7eaxuFkt5rmVv3F1A2VUhj8odGAVufQduIPCGt3/gPVNf8ABKqLqCxUappMjn/WW8pxJBtPUoQfwI6Ctfi3KOR+Bv7QsXgr4zan4G1mx2ahdfZ5YgQXZraaPbGcrwyq2RxyB1x1r06/8J+IrO+mhh0FvJikZE2sNu0HAx8/SvErqCPwd+2r4b1OaF/LfRtSi0qXHDbo45BCWxjKkHaPTpXq+h/tbadNotm76zaxu0CMyvqcaspKjgjPBrPmS2JlTT1Z8j654d059J1K5GsTSSSSQ/Yba9ZVkkjJBKls9CMj64qN/HsOmeO9H0ER3Nu2sWdxb23npmOwnk8vCrJux84BJ25yVySc5p3inQ9N+IFtpcMMmLjR7+Owvb22V2tbe0+VZZ1OCrliGOATj0wab8RLTw74g1edfEkOoavp9msjaO9tYyR+RcHAiG5enK4BbAGTnivCWh3FOwsI/hxNqPh+8Pkto7RmPUo4RNIskjB2aIFi/CPKNvAJTNR+HPF99rdvbtp80ev32tCWBY7ldrW8KzNtkAY/cWPazLnAJbHWrXwg0z/hE/hHfzXt9HMt5cNDHczzjzIC2V2tjHVs4xxisT4e6lb+C/GniDW5bmTUrddLi0ZLy7gSQWiLKHkUKny/MCgGckFT3quaxMtjb1zTLfw1qlm9hM322+GGnkT/AFrpgsBk/KvzfKBjHOK47WPCsfhy0jMzLIusSPav5sR2hh8xZGz1AUjFdydf8200680vUYdUuNPe5i02wxtS4gcESKSy/KDkDj0qn4h+HeqeP7LVB4g0250m1jjVhFFdo3mFsDaDzwGb7pPG3NQ5XCOxys3w0vNQ1Gxhub2OOOxtle2YLtm244XyxkKMsDn/AGs1oeDLjxBoUj63ry/2rYyGSHRWiCtKhU7DuVfvBs4wQfu9a4HVPCmqM+hp+9s4dWuo4Xmf92qpuBIZlG05PA92FetRak3w7H9gria4DqsdvGm1o5BJ0l3KQeecjGOfrUlMintdR1jQtN8QTx3g+yzNHc6fcWwjzH5jKDGuOV3KRnpkGsnxcl54J15I9DkvNXnkJjvLZpY2jLKMuEAzwmR6HJwCareLfiN4i8X/ABd8Iq9iv9lWu8X5hlcRzTPuVAQRjh9rZ4+6OorQ+LWqNNp+neKI1urRbPVIzcSWqKWtmDBTKccFMtggZ5I6UGconK6doujPcTajc7hqhuIWuZIVMV1bzmQeZACcHBz7Z24rsvHXiu10m603Uprhr63s7crZNNHk7s5YHIzIQcgk5xjA4GK5v4weAdQ8JxW2oaT4gvr9tQ1GA6haAL9lvF3omJC3MYCjBORgA8V6Z8UvEOk+ALOPXtAvLHVLh4JLQvZTBjcKDhgJOmd6McnjjFVIpuyPI/DHimy0tND1z7YdQktb2XyLVIonyHTHDZORulI55GOgqbwt8OdX/wCEl1J/Etv9lW+LX9mkM0f+iTb9yBSRvXco6AHdtHpVjwr4Huvjl8UdJN3a/ZNQuoJbqTSbpfLmnto0Vmu+OqKXVeDkshqbxd8L9W0XU7y+h1e487TWNv8AvJdzzq5C/JubG4AR4znnOe1G6sh6OJY0SI+NJbGSztQviTw3G8Ekr/6shhk5B4JIIyCOprIFnNqEt9a20k0lxqCBXtYo87Gk4ZnVV6Ac8dAM+tb/AIin8P8AgS41JtEvNYhbUIt0kd+yLP8AaRhWcFQFJYcnqPl7V5vDrF9qepWaXWqWUnnbRdQW0bNOVZicFgeAHCE44IVvWpeglGyseI/8FOvBmnxeDNH1TR4I4I9H1c2E00TBg4mhLLJkHALGH25/CvkHV7Xw/wCJtHj3aZc6Xr0eFE9i0f2O993gYDy39432sSTsya+/P2hPhjcftC+CZPBmh2dvH4k1S7+0abawSCFbh49hjjAxhHbcw3HgFgT0Ne1fsj/8EBfCfhPQNO1b4sXVx4m1aRd0tpDOYdLgcjOwJHtmmIB5ZpApbOABiplJRVzsp4hQhaVz8dLuztNOuDG08ayMMiN2Cyf98gk1HqOialPbeZHpmrSx7Q6NHp9wyEEZHITBznr0PWv6U/Bf7Lvw3+FFm0PhfwP4f0dWwcx2ibpD6naFH6fjWL8Q/wBn7wb8QbSWz1bw9pkkcwPzQp5MsZP8QZf4h15BFYxxEb6lVcfKcbJH88X7Mvxgj+CP7VXw01y4+0WV1ofirS9QRZoXRnEd5ESACB2LV/XjZa+g823hdvMtZXt1IG4kKew9MY/Wvwj/AG8f2OdJ+EOpnTdXs/8AhIPBfiABYJbj/XZCgPhhyk0eSVYY4APY1826p/wXg+LOsfD1fh74s8QLcR6G8mnT3sDPZajrEcbFENzMmRJ8oGTwWIJOa7Y3rRTprbc8nEJyXvM/oA+NH7Wvw2+FWo3Fr4m17QdN1GMn9y8qmeXj/nlGSwxkA5APpnt85fEz/gph4IvLOWx0+61i4hkOM2+mGNSAOhZypxzX4xeHv2+fC/Iuo57VpPmZ4wJ0Y85JK49uTXp3wf8A2pvDPxW8U2+i+HZry+1CaMyrm0khiXBUY3uACSTgKMk5pyw/WwoNWsfQX7VXxTb4naTctpMN08TDcCwAO0EccEjse56V+TepeFmu/Gt4ixssf299uMbm+f096/SbWtG1j4lRSaLdzahpem3W61vpdMgZ7yNduf8AWHKoPmXcAM4LA+zfgz8HPB/hjwhPbr4ct0utBVIbl7yIyC8fBB8s9MjBx9aum+RWHI+c/wBkP9kuTxHr1nq2sWLQ6HOnmRRyLskvSpbO0g5RDg8nr6ivtbQdFn+H/ii48TWesz3R1uKZZbYSebLYqWaIPHvyCFK5OBwG6Dk0lnd65B4HmvLy1kkutBP2W2giy12IxhIsK2Q+1digDB5PaqniCW5sMQyWk82uxSK99F5irCC52mRXPJwSe+fl6dznVqOTuETM+J+lBfC+uNpCbUt5rfybWA7o8Snc/mgNlVbqAFB7HGMVFB4hh8L20OoWO+dVUJdIS6yOYh5bAr0HK5AzyMHqTXcfDW703VvhX4w0++t4rXxZbzPerc3MoEtxbOG+zqGByUG0nZ0BOeCa5fSbGO78BWM14yrNlnLEGRYXV/kV+CcFRnHv17VmU9TS+FHhHT/EXh+7mhvZpdakvS00Fq0TTeUyr+7UM4bOM9tvPBJyA3x34VsdKns7Gzml0jS7OWKNlugQw3EIQQMgMOMnI6D8G6d4vuB4r1OS6sbdY7zT4hC1upMPmBnLMT2KhR6D5uvAAx9Qu75tZnvEh8y6uoBmXG/e3VWHXgKeOe9Jk8p0nhb4Yajq/h3xJeyafNrX/COkSWwsIh5cSHMgDrJt8yQshwuSCgJ5NYniGw1DSPiJo2sapZPo8klr/aqhYj5MyyRuvy4HlMd3GATt69uLeqX/AIy82y1SbxBHp7WEQtE0+VvMilhbBBfHXAyFA5AL8iusXf4V+LN9a33grS9Q/wCEi02ALNZzyqHMTOZEAkOF3jYSM5IXOeMVcY3Vyjgr6TUr7Sr7XPD0lncvMkSW0KSt9q0tG/dzOq4IYNgsUIIIGO9eZeJP2o9dtPiVp/gfVIbGPSdUlEFjqqxmN0lZWYuyqDzkHkcDPIr02HSI/Bvhq80i1sy2sNqdxqUlnc+XGtnCedkLH5jhMkAN95enSrHxg+BOm/Ei48MNp1u3h+4aCO8uI7iMf6vbsLFFDF5FSRmBOG6gdcVcYpCauY11qklrpUTSR2ttZpcCcEMwb5sq2zksASjDPUde1VvHOvS3viG1vI5J7fS5LqR4pFuC8mc/OGJLZ3DOTknk8966ub4eW50VdCW2t7rVNLYSRXljHJNb3MXTMpZyDxnPXoKi1j4e3lisslnNda9HeRKLlLPG23QfLKBGSOQPcYI6VnKNimrFnS/BP/CRaTYaxp95C1rBLH82Ak9wCxGNoBViNuMnrg96TVvEdj8T/h/fY0WZr23vpLG5lNsrSpdRNncG4baVKDYBwcnPOBZk0fUp/D19Z+FHvtO8PXVwIJYr+5WOW3kC53sAeQ5HmD2cCp/hL4ik8Z6ZqVz4i0nQ7htAgXRra0hBVb6RFMxuGwNr/I3IxuBAJPSpEb/w98X6hc+FLbUten1y8gvRLbXEFzYRIbIjC+buJygJ67ucDvg0W3gKO88SW+k7LGG4uIXle+eRZraADA3M6nIwWAzgA9q888UXlx4MnhZGlGl3UYult4yPLAJBKEk9PqR/Otfxx4403wyxm0Oxs7Wx2Kpjt5yzTrLHmZJMYLKzEHpjcg56VaVieYvP4Z0+/vlkuLiTUbCFmf8A0LbJudTwSW4JBUNtOOnrwaL3kHhvxHObW3t7qTy4/NKTgIrbDuVWUdy3CN0OCcVlXGmyeFtGMtvbvHMpMiTfacKVwpC7QORxjHqaml+LCXyeZfaL/ZT3iiTZbR+YqygAEsMAruY5BIwB1NVzai6XMr4r+Gbm28I3F8Y7ez2tFJJtYSOGbcQrDn/Hivg/9oHxFbJqUxhVhuOCzE/OOzc+ufyAr6++L/jCHT/Dx1iadptUVTBIkkJZI1I4wenGBwa+N/jFPH4hu5LhrWSSaNA7En+IqMsPc9PwrshsI8flu1vrlm27V4KkDOc9KkuDMNq3UIhZfu7W3qT6H0P+NTSNvnkeaJvLUcH0IqFL17lch1WNv4QMbgOOa7o7HOV7mXmOMADcT8xPA4qqVMcg2bWK/eX3/wDr1du1zB5zfw8ADqDVS5vI5oG+YqewI60xxV3Y+9P+De7Slm/ak+Ko+f8AffC+UkL1OPEvhw19teENL02X4x6rqOntuGuaXGzqwxHFcK7+bIueBu2gkDv9a+Hv+DfnTrqb9pH4urHPuX/hVtw0BjO0o6+JPDrDk+4Ffbvw310658YvFSR2syXlzpyTpZMceUwZg+MfwnBx+FTOpZpF01a68/0RdGpXGpaL4b1DzFX+y9YtLy5GdqFHYLkH1JLcegrN1T4faprOpaxsuLiKHR9VSWwmXHl+U+JJhz23Hgf7ParKrZeJdT0/w+kMn2W50GXVruGTIjSSTasSDpkK4fNdJ4k16Hwp8M7OymmWa61I322QMedm7AH0/rWkZWNDmvF/iqO5+KPh9LGH7R507RSITtUZ8wBz7Aqo/GqWs3K3XhbWl1Nkja7M8d66N/rnUmJD7bT+tHwf8NDWrix1jUGkhkl01JNPi3bZI2GXaX2xlhzVqzDeIPAl/Moga3vr6JBAG2+RDbTv8nuSEJ59aHK+gBcXNv4X1LTbWOHbDBpFtcxKGO1isi5Yjoc5PWvOdWT/AIT7Udaa6kFp4RggY30sY4LMx2Io7kjmo/2trrU/Evj/AEMaQr3V94hsbm0ggQ/u1CbcAfQCugv9Cfwp8GfDOgxwtI0niCOxMQxukkZVdwT6ZAHtmpJUtbF7V/Euk+G/BF1HBatYt/ZMkumWn8UPloNquOzHIz1rNg099P8ABukWe2YXWq+VJdzk4zIyl934Yx+daOnWieMvhN4+1KaGZWkSXypAfnnljaRh1652Y/P2rA+IlzqHi+HwXDYrum1KwD8f8sFa3wWJ9s1XLeNyjzn4Xob34Ya1q100iedeSLujY/Oq9TxyFyNv1BzXdW2iWFl4k0Oz09riGPXrCP7dJKxY7xEXPPYAg49vStjUfA0GleC7Hw7o9rJHbzXEMEhUg/u926SWQ9MnrXO+P/H0MXxNaawZNSktdKkaOXd+6WY4AXPThcE1nGNgK3iK6s/g/wDCPXpljWTxBqd6y8/65o5GVYv/AB05x2NZ3g7wNfaJpEWl6gkdpp8Mh1K+LDM90iZCqT1ABwMd6d4A8OSeKdctfE17byXV410ZI4z91GjGwHHfrVv4m+J5rT4m6b4buVin+1WBm1CRODJIJDuTPuSBj0ranuBzPhjRofHdzrF9dW/kWrXAEUS/KdrF9r47kALSa1paWOm6wnk4a3hjnaOP/nui7WP0JWuq8W6ZPpvie00yJY1u5oEvLp4eEjUbmCr6BcBce9YOs6zDYazfRxBpmvFe4cqD/CqsD+PT8KqTsKUrHjPxdtZ9K8P6bJZxRhtWmKCNOskjsFH5c1R8dfDH7Ja2qhY5FjbyEXr5mB8xz7Ej869Eg8JN4ghttS3RfZ7HVE8u2/iwVO1fpnJNOaKx1SK+nktZFnsXJXbHtXBOM/gaz3ZHLfU+OfFnhuTSnuLhlKRLOVGexzjFc/JLyx/hxXvvjDwbB4m02+h/1X+lfuh0z85P414n4t8LXGi6hcK8bBY2wTj7vA/+tWVWnrc6qUlszKNxuGf8mnpN5Jz/AHh0qvgkdzznpU8g/dSr9DXJNe6ejTqa6Glb33y8L94ba0rS/ks28zjzI+n8v6iufG24CH5Y9qj5V4z71reH4ft90qh9qxtk4/i471zxi1sz0ea6Ow0kz3cnnTydsqh7f/rq4+oKT/nis8Sfutq8qO9OaTzm5/hAAqd9wlL3S2u2VfvV0vgLwk2uXEU0p26fbN++lAznbhiAP93Nc54b8O3XiCV/KV1gjB3TjopBHA9TyeK+ifhn4BuPEVh4T0+K18hFuYo9QS3AWNgy5RyPUgAZ9c9K3hTe55eKxCtZbnbfCTwbF8RdU1S3azmFlaofs9ruCxysBuA4/DrXpHiDTY447dtPmeRo7uSZyw3eXhuhPoMMK0Ph74Ot/hp8U9XtGmnXT57Bru08zhp2kOxt30OR9BXVW97N4Z+GKvcGNrzXPtNva7D88ZlztB99xwT6+lannLXVi+IdJs7mHR7G62yRwwyPBMCQqhyQqnHXBIP4Vx3xEjhOn6Dd+W1tHqjT2F9HHn57mOMlCPqyqRU3jaefw38N1tbxVj1q607ZtZcskkc5zj8Mk/QVD8V54/GvifwJot9m3try3i1m/WMgyWqxqw/PdsFZ1BlabXYvFs62pg23GgrZTXMZT5pVmkGXJ9R8x4/u1Z8beNZbnwx4euJ/IuTaXIE8C/KjW8eSSD/fK/riq/gqe8h8U+PFV2uLO8tQsE7jEkuyKaX+Q/SsXWb6zv8A4OeH9SurPy5wJ4mIHAyY2R/+BAuM/WrjsB7B4KurPxJ4f1a31m5kW5+WwZs7vOgEnmRE/wC2MEf5NdvqGnW/hLXdFhsS9wouYXw//LPZg855+ZDj6HvXyd+zD421CX4jWiNb3rW19dBrtCpUyRhiIwg7jIyT06V7x8aRfat4us3sLjy9e8HzxPNEg/5CFkJEJY+6oc+uMjqaYHRfDPSL6Lx7r2mtFbrbaH4r/t2KUnCz2txE2zA65BYhgeAVrS+JM6W/gvxpaZMes2s1rr1uo4KIQuF+jbHBq5q9rqekePda1DT9PtbzS7rTFu4YN25nkiwXVFHJVg2761m+AvsvxV/Zj1jXbOXzLm4M+m2d7K253TH7uKX/AHWdk59s8UAec/CGz0j4eeEF8bSWxbVPEV/c3/2yZixs5MDaiL9CCSPWrHxn1q0tvFtl8QfPZLjU/CZW5EgAjkdp1Drg+igD2DGmfBXTo/iN8JfD/hrUre801sC6uo5BtlaQB0RlHdHXKgjjKmuz8TeJrHxtf66biGG80Xwjc2cpRgv2eJk2q8YPP8KbiP8AZoAtfGH4ITfET9m/w/pdvPFJqWh66t1veTCywQkTPGG5JPluhAr0jwn4wXxL4es/EVrDJbta6bDd3rRKfMiRJHUqB1OCDx6DNV/iLe3vxBTQY9ChjbS9Vv47jzVYx5iiglBHPIDKFIFaHiLxnY+DvgP401i3Zvsq2TKNyBRCHXy9gOOeWPOetaU9xJ3PFv8Ago3py/Ezw3o/iLQ7n7Vovie6s9Ov4wCBky7VkyBgYyVJPTIr2X40GK/8I6zpcKxx6l4LsFkhjHy5+zyxy7hwPvRbwR0I61n+Bb7R/Bf7M/w30m+giuNL8RxfZ388geeZY2mwCB97kYHXitT9nvwYNT+JviDxZ4g2touv26eHrC5Lj97udxsI6ggHbk+tFvesMwP2TvFEnxx8YaxNeTzR6ZZWF8yxyDH262GP3YOOOP0qL4S/DyHxB8D/ABNBI99qXh6NzqVpAXImggiBPl7j/uhc9vWj9nf4Ut4O8F2Oi29w1je3Md9ayy78vHPDeNAAF7ho5MY+tejfAX4l6fq8+j2lxDGNZ1OO6h1O23FSY7ZmgdQM9ehGP1q4011J5jG+I3jQ6t8EdD8TSLe6fpet3lrBKm0StaMJlEUyHnncBn1wc8mu3m8Jf2B8K/EUjRLdLDDca1aXSJ5eJgc42gD+vWvPpvhrpPh74If2TDHcab5cpu9NeeZ5Vtbu3l3xOgJx5UgVGKnJ+Y9K6v4HfHS68dfBPW4nazOrare3duEnB2RTkkPDx91Du+XJ6EVUqiTsSdL4C+Lui+Pfg54b1jQrO2tbHXHxeWsicwNKXxxyMsyMcDjg1o6Hqek6xrVt4g1GxmkvtLRbb7UBhUQtgjHQjJHX1rw/4IeGpP2b9L0DSI47tdS8Uac7+Ysg2o73jOYOeMqCMj8a9C8L+II9KsPH3h9JHZRrF6Y7cL81nEYkBYN0x5pB47GlGd2Oxz/xS+H8er/EXU/D0l3ezQ/ak1LS72JiZLOOTBaNScd9xwT0JFd/pn7MPw3bTbcyaSruYlLM0kuWOBkmofA/htb34deFNH1Saaa+vrdMlJQxQBRKr8dCfmH4V83eLNH+I2h+KtSsYdDvriGzu5YI5UTKyqrlQw56EDNaKgnqI5/R7Txdd/BjxN4H0HXNDXTdEVts6aUtusrSyBpFZg4IZV3KAM5UBehroP2dfD2pTfCI6W2pR6lcNaxxFbhN02pGBSkjsSxyVkzkkfe+ld34/msX8Atqk0dlo8l5CkptYncSOuzLkgAKMDoeOR17VwUE8fwwsmk1K7ls42Rn01ZFaaWRJBuAUKAW3EEliTkkc814Wp6VVbHDeDLmPwz4y1bR9euNNs5JA1pHHeRIWCMRIRbuR8zheeDkFSB93iz47v8Aw/4a0/UrG2sr7VLAxvPBFYp5clzeE8pIMcZUqcLg446V0Wg6DdfGz4K6N4r1bw7camt3ZTfZLeQCLeEmkjLFcryzRk/OR3OQCK4nXru6+Gdrpvk3UMtrqVsjaja/Zkby59zgmJ1bAZdqITnDDjsDS5rGKjfQ1vhpLoHhXwFJem0k/tiJArRP8wtXJLeWCMgKMc4PIIz70/hn+0DY2vi+30PxzqNxHY2drJfQahcQyQR38pk+WKSU53YEjbVftF6CofE3htYotGuUnurHw94guwkk0qkyTGVDkRhhnJKqMj7uB0zy3xZpGgfFHxXdeH9PmuV0/SdFea4ilQvPMySpEImVvkGc7wQAcKOe1HNcrlOyl1X+0PGGk6YPDF5qemXs0k9pDa3Ai83y1LF35G0ZAI6liuBziqvjmy+0fHDwzazbbWO8vCsisGcQZQ5DEAHcmAFxwCO+MVe0PTr7wj4CHiLV7xrVdLtXaC/KIDaw7csztwNuCck/TpxXF2XxXk8O6lZ6xq11a6wdYQQyXQh2R+SxUicMDt4yGIx271nOLtoT6ml4nsLr4U6H4wmsta+wpqFzDNEUikuLeV4wASVfKjCgEEcZ61yOi6hDqWs/YdY1K8uPDdrGtxe2Vy7EXbMNw24AG37rFMYJXntXp/xG8ZaFrttF4PjuZtS0mGQk3djCJF2yj5gCzYYEdzyM8YrnPEnwlvPDfjaO+sZbfT7fUwrm2VzP5ciKD5REmcfLxkcZ7VQ9B9t48m134NXOn2unWqm6imV57lY5mvEedxC+GX74jAJOPlPfmtfw78PvDdj4Y0zS5LzWPFrzj7M9x9lPn6ex43Kilsjcc7s89TjJxxN74umGratdaDZSWs2iq0bBZ2nF4xT5mRfLARcncF5PPLHGK9AsNV0yX4MS+LvD9jr2n2NvhGkghO6a4TfGchnLMnyP8wwuT0BHFc19GRUPPvDPhb/hLbLT9UuPGd9pWrQGRrG6knkW8tYwjEWUiqCVLmAHjgbyCOtdX8W9b/sDwzo+oXd1a6vb6siyyO0O7zC2FzjHUP16EEA+lR/Bjwhfar9o12ZYI7yCExpGIx5bsAf3sfygbfnwMYAJaud8f6O9poUdnJbwa14k0e/Dib7SytEZj5iBfm2soIOVPH0zUc2tiYxe5n+ItE1j4k+DbDUP7Pk1gKSjzxRYuYQoO2Qp1A5ILE+nXPEfh/TLfxlc6XYxx6bb6hLCY727l/czQrGWfcYx1yNqYzgbuPSuiufEcPgmwvvEFqUj8ZXqiDUoJyLe3WDcru21MLwYwAABk4HSs7XLSH4w6VJqtnDPYyXXlWx1qK2jcWgcgOywtw+MlhuBGQM5GVKluapXPUf2F/hpb2v7WCxzRNefZbK7ltL0jy9ykQhQqknkKz/MvOQe9fbmrOl+pVNiqnyhQAAP85r5e/YI8HWvw6+NeiaZNfLdXN1bSJKy/uluXMUjlxGMBQ43thBgbTjAwB9HFv7GurqCXc3kzMCc/dGf8MVx1r3Ie9jMvZywHLen0rn9T2+fjjPVc9W9a2ru7E77I12oh/WuevpfKvZPl3bGKqD25rEo8a/bh8BW/wARP2dfFMM0arcaVbf2lbSNHueJ4sE47ruUkHGM8V/Nv8b9MOn/ABw8QQBWWNrt3VT/ALXOfxr+mb9pfV4dK+BvjCa6UMs2myW6x7jl2fCBfx9q/nL/AGwPCTaX8e9UZTtkkjjfaByeoJ/QCvcymVmcWMi3FWPOdMsZY7v95u+zqRlA3DnsK98/ZJ1u30v4h6atzayNaXDLCRbZWQEupDgryGUgYYAke1eJ2lhLdwLtkMZPXivpD9hixsbf4xaQJL6O0VVLrP8ALvQB03MAeGZQSQp4Jx6V3YiV72OelTa94/Tb4M3mmfDTTddhVWulCr5P2iLz4gWBDuzMQCzbk5OCCAK5uy8D3AFnMsratHrlyEudO8zPl7SdpGQdrDec46qgGV4Imv5Y9E1K48Pyas2oKsrajcwRuxS5DAEliuDuG0Har7Qy8cZFWPE+m6bpvwhj1DTZ7x9SXVB9imgRZGtJWARQr5O488qxK5Iz7eZPc647FDwV4Z8SeG/Fd5rDalMmqaXeJd2P2/eQ7AnMUhzggAKePbuBiPxNqGq+LPB8eoSW8VtDNMGWO1k3tAinG3GA2Mjp05B7Vv3HizUNa+HkVv4ztLfT9Q8w3JvIDG0cqPnE6hRjzMkDaeOeOAKdaSeGLDQtbl03VtQh0tX/ANChkQTFPl+bzHcbyxbB4woB4HYQUee+EYF1DWoY5pLWRWlZSBMwdYwxJjbIwu1f4u3J9zuaPJY65pCy2rfaodkljNGXHADMC/TDkdM9xU2j6W1tLdatZpYzRugLoflMhJwwHyk8jPHT6V5PpOt6h4F8TXEFvNHb+G72U3dwsilxpZ3fOMdFRjuIznLZzQB2mha/daJfNrXh+RIU0+T+zZ7S5s99jKSASm37oGGGOCAVzWj4x1ubUfFNiGguNOuLqBIrZxIBF5gX5E4ODhQQoxwMgdTmXTNEs7/wpe3UF1Na2l7cefJNFdSeXcMAMgLnGcDOBgEFeKk8ReFb7X/7Pj0bVs3VxbEpFcxIyQ7MAeYeWUAMDkHqPWhFvYwfEF1PoEmoaD4k0uP+0dQQz+eIMRBQOMf3MAnhcEkk+tbnhDxLqWg+A7W3t9QumbQ4RNLNe3H2hrouu2MgZ+UDnqecLR8R/Gb+NdKt7HVLaGLWVaSF9YhnkWKWRUC8g4G05yVxjcR6DHl89jPfahHp5knttS1C6T99bOVF1txgBehyFIx7560EHon2zVfEXh601f7U7PrO+SFf+Pd4Tyoxhsq3JIHGM8HnNUfE1ncQ+D7jT/OllNmQ8F06eY6Phm2o27/bPzAjis/xX4jutC1zWNBupJprKTTGL201qqlGUDcN4HyvtY4ODg8kGtLUPE9lplhGdNMdrp80aCCBy1wcgYbnqWznqeOgx0ouwLnhXxFdeGLhrefULaSOaBYVls7dvsrYGQrqxIHzdRjufU1Lo2u6hoV1rk1vcLBqUkJNvHZljBLA2Cy7FwEyxPHp64qDxDYWt94ftLeHUodW0/UP3gu443XyH+7zk57Acn+tSfBR5PD3iq60fU9QhP2qETWmoiNPLlUcCFxnhhnr0PPToKcmymx2heMLXwzo0mqNJJNqNxIv2m0WU7ETgDY2c7hjHzdAMYwBjU1a1n0zw1FtvrObTtZjF7a2dqoPkTncedrD5goAJAzjjOOK5zxb4Wsba7vJtLvoxeh3klEDjcCTxuAOBx7DqOvWmfAaOTx/4Sube8a1ZtM3yoJAIlYjAHzKwxjB4HJx+Zy6XJLGl+Ho/Efhb/TpLpdTsYyzpu+VCR8wXHLLwME4Jx3xWVeiefwp9vZo/M3q6htrKETdng8ehx+NbN3rv/CawPZJZQxyWblJdhAeYdgNx3cY9Tj+dPTvD/8AaOmwRme4tbeN3jvWkI8vJBOCoOACOcgA5FSBq+FdRtrfRtJit44NSvriB0uTdkSGEFeSqkkheozwcE1Q8G6nY2lner5+5WlMFwl3hoVQEHMWSf4SQPU/U0yx0BdL1tdSs5LUXFjbqId7MY7oFirDG4AgqT6dMjkCn+A7e8+JN5rS6hp8Om2dlbfbBJYoJApUnYF7g555yOO9VF2Jkee/Haa10aKVYbyKT7Rl5oI48eQh5yMcY6Yx2r4g8X6neQpJGVk3LnepGImXtj0/CvrH44+JdQs1vrO6tZGk+aNLuTPmYBwdy9Pyr48+IviBnVoFuvMghVYhIEGWOeM8V6NGDcTGoczquo/6tVCKM5CIep96rJErvg5i9ff6elQ61bt9ot9mFuZBncpyq9z19KZc26vaK279/uBLhjyBweOn6V2mZaWBY1UpJx/tCqktur3WG8uZW4CMoORyD/SpoLzEu3b53PJxVW/dhc+XF8vXAA7DHegD7p/4N+2mg/aZ+L0UO23VfhjIyq33VH/CS+HN2R06Zr7b8I6xY+Av2gfL1CaFb7ULWdoCV2NHFGScOT1HdQOxGK+GP+CCN0th+0Z8YpJFaYL8LpMpu+Zs+JvDnHP1r7N8fi3f9ovQrXUrloNX022l02+ZYw32wFgsDMB0BVsfh9KJrS/9bm1HVO/f9Ed94N+HV4S2tX15HFJDYzR25Q7Fa1SYyRk85I2uML0FYOvxXvj3wJqWk2luqXun3TLpryIC2HbMgDdf4utaHxr+Io0XxZZeGbNZIZdU0xY1mLYULLOIgBjpjy2OBjgHpXR+EWg8NpHHE8dxdWlpdXpYYO18uEyO2Qme9cvMW0cRpks1npmtSWMrNb6eLbQLfJ5DpJtbA7gqGJx696kvtLk0jwf/AGSsatcSa1LM/kjBZJQ0o/AAuSOcAGofhDoos/hxpP8AaT/6TJcTazNG55DlUAZvUl2LAYxg+nFa3h7Sll1PxBdalM8a289pdacGV1VN0c0bdeD99h1IrpWwiv4T8J2cvjzSNTj23y6TDMlkTGsimUqBIEPUEq69Kta74/0e58e2/hmSygmjht38QNJDCGNtNGUUgEDh9yglhzj61rS6VY6Ppd8kdxCkUN0txaCPK+U2wqzE/wB0mMcDPIrldV1DT9I+LN1qJsUjkuNFN1EmT8gMqrKNvQAj164p6gZfw8uJ7HQE0VpI3j/tm5tJ8HjyJXJjbHp8wAHbPtWat/FoPjLSdLS3ihmtbS5sfKkX5gySgAE+6rgegOOlaHhjSpvDGk6+yru/tjUI7qwRQTtjH3Gz9ccVzfxpuJtF/aTkt7hJPtC2P9p4BGXnkjQIg/4EwbB7A0AaWn+ItW1fwXo99aW821fOuZYNv7m7mll8uKNmI+4itu9MrmmfFrwdG3iLwd4fFnbNayme3upLVQFklckYGP4Vz+G0dhW7418PzeHvB3gfQdKuo7jUtW1KUM+0qttaQQv29iGOSOS3OeMXDts/ixNPNBNbt4Z8OJcso5Ektw0gZvx5P4VUY3VwE+Emj2XgmHwnNqDW6wW+n+XdK+NnmtLcKzn1yyDr7V86aG114s+Jn9q7vtU1xqk1vBv4Z0J3hgf7oGPxr0jxVpusz+LtP8KyXlqNW1qzkuJArAw2cL3TMnPdtuD06kjpVZLLTvDNrG0AVodFvf7Js7jlmmCLKrydf4yP07USfYC7qtjDYa1rniS9maNdNkntERdoV/mz36rgHpxmuN0OO18Sy6XLbuJYbjS5orqV8EAOwAUe+eB/vV0Hx30ua9+G3i3S9PvLUx2mm/2xgqzPcKCNwDdvlOc89PrWx8LfCS+FvDmp+GbS1hW+k8PRvbzzLvEVxsWXbk8DcGGD6+mMVIHn/jvRo9G0PWNPs18mOwnyu35SXVQxGe5wP51zvw78Yrrl34imuPLMMenP/rPmUsq7uAfcCpNFvtU8aaLqWrrcRqdP1CQNbTR/NcrMnkmYHGCq8np24Fc94b8F3XgXxT/wht5btqV9Nam4uCg/4+EdsfLjjhN3fNEZJMDO8NeFRHqtjdX9q2oItu9+8a/ctwmQAx9yeh9a5/SfgrceMtKvtQ1CyMlvCguJF8sLHNu4IHGDg8e2K9m8feDW0HTfDN/p8F9a6f4htF0a/s/LIks3XLB13E7kk2Lznj25FFvomtaj4PSyZrW1ea1Wz8uQ4Xeq56Duev1Nac6A+KviR8NJvC17cS29vcGyjlKKXTlSBypPfjH4Vx0jZH3WIbvg819ZeLLS48XPqPha3tUvZo7qOWOMthQ5BRyT6cdOnNeT/Fr4M2fhnxP5VvD5G4CDy0Zm3yBQWbk8DPpxz0rnqU7v3TSjiNLM8fRG5+8FBrqPB1myWe8f8tAcknpirw+E2qT3dqUgZbe6kEQmdT5YP+SB+NdDF8Ob7wj4kfQrzy0uIpUi3Lko24449ifywa55UZvZHZDFR6szI49nU7efWuj+H3w01D4h63BaxM1jHNE8iySoV8zaOig9fr716l4P/ZXj8V6fdR2Pmapr2mzRrc2Xm+XHgvkop68oVx9TXoPj+1tfG82gt4f024tZdHAtbzyUJ+zD+JWOcYbjBxnilHDuKuxVMVfSJy/gLwDpuq+BtDtIbeSxeVjNdJG2fOIBQMo67t2PzNelfst+GbzVPiNebbu50/T41ht7cg8zvFktwfThc9skcUz4J+GW07xV4EH2yKG7steSwvCFDiNHkBjyDwOQQSBn5utei6LpWn+EPi74Vsbcz3Ml9Z6kZLdT/qpmuGeRgQc8EEjJPTHAreGkbnFLVm98TPtDfC6TWI4Y21XQ7lb63BG7zrR58NE5PZSG47dam+Mbw2vhvw7Hblitxq8Goxxxty0fmBpIwenyncAPfFdJ8V7v+yfAHjCe6st1jdWSQ26I20kqQX6Y6qS3415x4vvJtd0GWG24m8PhNatN6sMocbkJ5HKrnDccVnKSeqBXN/40Gz1fxLrEc0r202jn7dp7Yx9rjmVco+eSAGPrgiuQ8VeIZPD8D+Mb1N93cRW+n2I3fLLGcFhk+jdumBW38Z/EFv4v+PPgE6bNbrDqVtJdXLH7n2ZY+Ceo46CsP493tv4z1D4d6Xp0Lf2GurRwoHH725CRgsOOP/r5rGUk9EXY63wxY3ls9va+VDdLr0kE1tK42Ft8UiSR/TD4HY4xWB8HPDsfxE8JXlnfQzHRdP0ae1gldgfNuYbhh0/2QCoPbkVvftJ+LY0+PY0XT4ZLWHQNISWNEHNvJtDrx7AYOaxvDmu3XhH4U6XcabK9rJcSm5dCfkczyOGT5s45ZHOO3p0OgjqP2cktLLTVlaNBd+GrMf6RMg3EmcokZJ5wVbP0xXo+vRWVzqEXiaztjbyx6a11PMEH2o2cmN+71+zsO+QVbjFcLa6PdaZ+zB4t1SRQ9xJNCpl2bWd7SVUdj6q+OnYHjGa9C07UodU8OaN4gaXyVvLc26xytlDHcBWAJ6n7pGOQec5oJSMHRfHup6L410ldLtUvBo83k39q0m1okdQyOpJ/1bjIU+4Heup+AXwsHwe8J/FbwDc6k0um6hdf2zoE7ttza3cYEeDntNGVOO5PrXA+H/DOryar4m8UxeZFDo1mfD7RFBuu7eOZSkobPyywsoTaeqg5zXoXxkuz4m0LTWs7yNf7NnkhNyEA8+ykAkjQDp8sq+nG7igHc8n+Gni/VPEOl+JNakWOzvvCGhxWsSEj5JY5mcI2eNp4JHuK9H+InhVfC3h/xVprabBbr4o1GPxLEkKgCKONAZ4enOSJCB3VsYrM+L/g59X8LWun6ZHbWl/4huDo1ykR8tb7bEkqyFu25SMEc/N7Cux+OGoG7bwv/ZcMv2rw/fwWtyNnmFLWeJwfMyeVKleeTz1oKOytfEy6f4judFt1k+16PELpIEGUSN7PerHn5FZgQOACWx7Vg+O9CTX/AA/4j+H2n3kUNjeaFHciV13fZ/NdpF3dM88DPTiqWi+KLfwl+11r0kUkluU0XR9NaApu8yyLSYlYnjcjNj1wvWs3x54vRbj7RbrDdTeNIYtCCxvt2vi4ihYk9B5ij8UI6ddvaJEbHY+L7ex1v4aeD7ya1RpPA15Z3c9iFB8tPs8sW4qMFTHJtPTGMHPArI+HvjS2+Jfws8VaXZpfQ2f9pDWNGMERXzP3scska4A+dGDoQP4XA6cVjeDtX8QeA/EHh3UNXtf7avLvw3Nouvw2w3R3MsUpCkdMM0TOvGOY89cGnfBzwFrn7NNtDHf3dhrGm6x4ja60a6hO1fJMSu6PkDaZI/uj+9H2zUy194ep6J8XdaPgzyfFWh6TMf7NvRd3RRf9WvmKZ5dgwWLH5ycckZOTzWelzonwt/aS0PxBfQwrbatp0rWmAqFbiWVDcEjA+8MHpn5q9J0i3VbjXAiySQpGUuZpCpjERBVyVPA25GcdQc4zXz1+1xZWPxHsdWtf3yzeG7AXOl3kRdZLKYDPRTh1dV24bOOw71mUVfEHx8uPEPwb1a+WTz7nw5ehihbKqEZVdMf7pU8e/pWx4T+AmofBrw5rnj7VvE0dj4e8SSJfXmkojkQu8aShoju+WYFgMgcgVxPwFgk+G9r4f8Na0iW2seItOuNa1W1b9550dwR+65H3mjA+nYivoD4z6boWp6X8KfALXV7dafqWoQXSlvvXsEUKthivPUbc5HGOa0jT59gvYS216DxRF4QeZrxrz7Qmp2kkiL5sMAyGkcYyqsxBI/iwp57dHoD2sHx78UW6Na3kFvp66n5bkKb3zfLVwQeuTwc+2a83/Zz8T6T8RtR8TeKLnT7yW/h1uK2u5LgHEUeVKRqvTZHvAOB1XPJJNe2aa1jrHiW1muo7efUo4rqznmXKGS32tvB246MEYdwBmto03HVkNlFNCW21vwj4ltLpo7LRdRltLqBJs+XCwKRq4PQI/wAo7bX444r1h7Tw/I7N52lfMc/dU15H8GNZs9Z063tfsJtrgAWOrW7ZbzSJHiG//aDoG3HnB68VJqthdabqlzbpNhLeVo1G88AEgfyrZSVhHzv4v0e6v/CreGdUuPsdrq1tIBfSyJtUMCojLk4XGSc47n8MLwhpMNpbX0mizQtqXhtpNLtZ7hjc+dGsOMpvyFXB7cDsRXEeJtVvvjLBb6fqE2k2um31ssptbZZEt45TjD4JDkbgAVLc9zXdXHxTt/Gl/Jo8jWdrrWi2cFqzWJLRzBAEDqucJu2k4O7Gcc4r5x76Hocs3uef+O/E3ibw14G8M2NlY39jYKgjunScSC537pNoX723LdByT3A4rY0jw1o0cvnapf32n2d6rf6KkYVYp8AoCvzYDFQT06e+a4vUtduIPH9nora1qTWfhm7Ey291FIYyrP5mEcnbkFs8dM4r2bwRq9n8YbjWmitoY20m4Uq11IGLssalgoxyvI5JPJPpSM7tM5D4h+M18caDpuk6sw0/TdPO+a7tcolv5e0xkE5wHIOeQfk4I5BZrfgRtVg8NabpuoWi+Jtbv1jkvPKLgWkpdmlfBG4KqjbjGScYOaW+0DXPiDp3lX2grpWi3MDTW81vLmPVWLuAhtztEWwMu2XdyJWO0YGaMOl6faWtvrEfzabDLFpCQHUHa8tEBbzXiVQdxVwAAzKeCe1AXOmfx/pPhjUNY8H+ItWs9Q06xsANM8yEQLfsx2mMjJBxktg8EdQRkHK8U2lyng240/VoZG1XC+RLLbq0SjCkZP8ADGCVyR2Jx0wMHXtY8P8AjvwrdeFtP1Ozh1jS1MdhqkyBZmw/mAMznc7B1VAPVvwr1L/hPNJuPCAbUlksdctdKDXFhOhNxczohYoN7bXZj0CjHI4OdtVp1FzJHDeC5NP8FeHxrF/fWbSSLJNfedAzESD7qoyADkdiG7VR8V63N8VdS0vWofOttL0XbGkBtihZz3cs+CTu6rgdOOpNvwLougwNqkzeH9Q1uBoWGnKPlByVNw4JKh2H3Fxxz+FdFofi+18T+DNSTTJGvl+1tFa6fqcT+bBJ5W4I7AEkgBeuB8pJzSdugziZvF+radqOoR6Xb6dZ3V5aJbr9rXco6ZYhDywIIyOcgngGoLu31bQdNsdF0bxEulo9xPZ3bXjC5jMGWd2MQUZDSEnhiORU2nSahoOvtD8QfDcl1ouo3Qt5Lyxj3NbgjbnYilhyyqCo4Y9Mc110nwJ8P3V3otxJDfWt00yRMuoWUivCiyYG7a/zqAc84B49RSBlTwz4puvCPgfUDY3EP2PR9ljBbrChkvFdRunVsg8s2AgbA2MSD1rGS21ab4ex+JNLgElxbedmK7j+dxH8rY5z82MZB5A46V0Pxu+I+ht8Tm8KvrdldNdWov7Z54pIbeF1LDa2QwzgjHIzj7vGTi/EfwnN8JPsPl6hf3VvrCDctm+6O1c4IaKPrsG7B9cZ46USj16hsYZ8Yt8TvCtrCunTabqGmwEX9vPCI2k3Bm3AHOU5AyMnrWx8JPGq6/baX/wnEFtpawySafZSaVhorl0jDR+ajAsw35wAV2sA2SBg61npWpReNbO+msY9ejjs3tJVb5VldiuJi7YG4KCm3B+/6CuX+MfiVLHSrJrf4dLa3mmSQzRYn2w7llDM5UKeWPTHfHWs+V7g5Wdj0z9jT4kR2Pxw0NtUWDVptJ8YR6da3s9syS20E7+UG3MAOFmGSny9eODX6AfE7wVJqttLqFjGZLhgPtFso5GMjcnr7gnrX4V/Gn4o/FjxJ47uNa8KLpekTMUmg0S2uPIZ5oyXVo2cYdy+DtJXnAyM5r9Tf2f/APgrN8Jf2jPhrcapceLtJ8FeINFRDr+g+ILyKw1HSrjGXO2QoZIyx+V0BBHOATisa8W2rEtO9zqtZaa2Mdu0ZjkA+UEEM9VbnRLiG2NxMBbwhNzTS/KuOvTqc+1fPPx8/wCC+Pwr+Hwk/wCEej8S+MljXclxbL/ZtlK3bbPMpkP1EZr4q+PX/Bwn438eTzR+G/Buh6Pbspw9/qE2ozqTznO2Nc+23GfyrGNPXU6qeFrz+FH2J+2X8S4dc0GTTrdzHpdkxky3+sml2Y57AcnHtivw2/bruY5vjDa3lrIq7ojA5B6YLnH/AI9Xo/xn/bp+K3xqmma+8TtaiTIMFjAluv8A46N3TvnNfL3ixNU1TUWXUbq6kk3F288lmYnuTn9TXs5dyJ7nHjsJVpq8kS6PcrY3Eas26NupJ+7/AJ/pX0R+y58cNM+FFzGs1h9uXUJRBcyyDc0UbMD8vTC8KSO+McV8rnTZtNnP7wtHnAYjhj7fnXVaV4slgtljbb2AfP3D6j6V6lSCceY8+M7aM/Vr4YeNdQ8VeI9MuoV0v+zJoJ5xcRpIAQFO0DDYGSw4PPTmvTvDnxI0fwt8Gl0eOxvdMuNBiubWMsh8xocF4pQxzv6knAH4HmvzN+DX7TOreHdPh0OzvvMtZH/d7mV3gB+9gnlR8oOPavvL4d+LWitdL1K5ki1Kx1vKGIRMrKmIx1OcuuDtPAJbkcV5lWmrXOqnJM9J8R6pZ67e2EmkXEmyOzhguHM/nfasBWDOSoG4Nk8KMdM1wWn6RJ4y+PupXOtTXFvDb2kYt5IVDpPKQSQxJOCMexruLTwHp2raZ4i1LSbmGzvoZIl0WNoC2JCFYvKWKkAAn5QCMj71cfpo1Tx74gaa4jngsBcyRaleKNyag3lkFkXA3Dceo9OprlNHboV9Zebw7eXRmuhItywJKOfLyCxPc9e56Vyt5oczaQsi+TdPq07lmhO6S3j2sGD8528bvbJ5zXcWuo2euwTaPfXlnF4f0q9S1ur14gt3bQAbgoI2rjhV5zhVHHBBybXxRdB/FGg2cOnTLqE0MdjqCJvvLkR5SJVG3hfmzhcjcMkHPBYmxZg8PNpHieG8uYYb2xvobcs9upfepJVZFXOA6gEYYngAnPFXfGmqN8PH1H7ba299JMBJa3aI0klorLnZsBwxOVbJG3DDjIJMnhHwvc2NnIpt1j066YxSxpF/r5VAYS7dvytt2krzxjnHAh8ehfE91bS6JbyWNjb2vktHJOlxHC6Jt2qVYYQEYUEDIHU9groSaHpl34ggttPtbqwWzurR9XN3LH5Yt8FI2BUsCQd/Qc5z14I5qDw4bLXbHVJLxbldHVmilEuwqoQhipHOQCW3fTg11HwW8KRmxfX2Gl6lDqCSwi22nIRZBuUMGPlkMhO0rznqMcwarbtLrtjp0Ij0+wZPKUh9uwfd8vDHbtbgHOeMjPQilFisee2vxq0P49DWLvSpkuL6YtGbiFzLI4XgsS49/pkemRXdaaukan4U0eHUvsWjx2FuUWJLZhJuUFi8vGSGbLFwT68CsP4c/CPQvh947uNN8M+H7NYL6U21zHBI+1JncgsEBICEnkkgDHpXQ/Ez4dTeG9T0nTZNQtZlvrhbW0Mx2snmOFYM3OQPmHfCj8KppfZFY07b4T6xIbixka8sY7i1FzA99OpmkckEBhjByR6DI5rzvSdTvr3W4Wght4ZrO4ezvBLFmOMrJ83BJ+YAMe4r1zxD4STUvEOoWmla5q15pkQjVLm4umZUReMkOQ2CgB25OPU9a5e+0TSNM8J37alNqcF0txuiks4i8JTyvvMygkHgfKRzk8nGazA1vjIqalr1rNounxWrKkcciwAb77KDbuC8seRgdhgdq5yPSl0iF2urCaBFZnnSUnzpB2yML90sTgd61vASyWHgex1PT7tLz7KMuYrrMwHzbJNpJ24wODj7tZWuXepeJPDEf9rWF1a6kud0zxlftXORJgj7vBGBkHBOewOZgaXw5+Ey3Aj8ZQ6m00NrNKl1DE+2ZIk4BK8ls7sZwM4PWqPizwnqyaFrFxpCXU2l2r75GmdcliM4wOuATWf4R1X+yru9mksfOWa1ChbK6YpMMtlmTP8ACcHA5Oe/bV+EviT7ZrmtafNNeXWjuqyRwlz5cUhO1uDznb0Bz0NVpawE/hTToz4ahk2nUZ7glrf7Mu4Ku0EjaOQep6HA5qzbaZqSaRrlzaXlnZ/alZHtmkV5mEaEqB/dyTjn9aZpuoW/gz4htHb6hDbRWMZ23IYhUZtysGHYbTjHIyfXpkr44/tbUNWuIFs5LQSjz/IicfaEX0GQFJA4yp/GpJaZ4D8Udfvr+DUre823FxZ5DrEv3xnGck8nr+lfHHippIteZW2yLHKxEbdcHue3H9K+tf2kNTW5128jsDJa+ZltvlkYIHccE/mK+P8AxUzHU2kuJGNxIc8fKMcfX869KjJpHPK/Uz7lSYfkLM0f3dwz1qiwmzub73UBRxVq51NrSx8jZu84bfMR92w+vTn9KblYZ4185iYxjH3dx/Ouv2kSSvb3rR3agxjGzB4+tNuoH+1CTbhlPGe1Ont8XaMpcq5+bHek/tOG2v5o5Xb5RnnvjNVuB9vf8EGBJN+0T8YHhh8y4X4XSSbQm4ShfE/hxsEDtgYPtX35pv8Awi/xF8e+INagX7ZrFxZwWkc03+sSMDzEZCAAWCOEY46r2wa+Bv8AghvrjeHfjl8W9Wt4l8u3+F7TMN+AYl8UeGzJz/u7v5V90eEptP8AGnhnxM2hQ3Udxom7UoL1SZDIsnLW5AIOOHAI4BI9auWyNqGz9f0QfHD4Uatr/wAW/BOtWsIbQfCNjOLhklHnO7MGGR/FjduBGOp9BXVeJF0/wxYH+xbVmkupIrO3lUB5pN5WW4Rj0wgVhnA+8OvNYHgb4kjxl8INI1Cxl8691DTsz2rN88U8MIilhf8AuHYVbBBII79ayrfV28BfDfwPp7Xz6jqEupXn2+4Odwa5jkZRjJII37e+cDp0rnfJexfKxvh/SdR+Iun2euQ3Md22m602jXUK/LJPGqgp3wQAq9Mda7744a6mhWEKSQ2zS3yboM/8s1YYwPdckdDzXH/AXS77wUmiwaky6bHqAlvrlo4zJ9m8wLHEzDjqY89Rw/tzS/bJt5tO8feAYlvGCKbhZpcfuyVZGwzA45ViVPoK2EavxJWTV7nwbZ6dcvNN/aKafP5gI3pKrOW5x93afzPFcbrE994z+JHjRo4/MaLRLG2klJ+WKB7j98456hVP+BruLvRml8P6pqSSTNqfh/VnewZAFMqNEcg8kDKlyp7GM+vEHw00Swu18bW+oefYz3FvDYlkjIm8gHadnqQZM+woAyvib4tmvvhl4f1XR2NlaSX1lbWoBG4WyzbCT/s7cZPT8Kq6Xof9s/tbeLNWvW+1WfhwW9xJO2Dvmnij2Rgf3UDFvXCjmuy8J/D218MeB9N0uWOJrfw+jafJC0WcxnguDnPBGe/Xr3rivh5c211Y+Mre43TvB4hC3smdrSxQKsJb/dCjjmgLHRePfFkFz8RPG1/Csby6Por2Gnxx/wALGJ5mkB+mOmOAPcnzpPF994j8MeKPEEd19outZtYbNIYRjbbwxFVcDk7mkLeowRwOp6z4m28PhWez1AQsLnUNBlGyBhvuGkdoYuufm2fKM57Vg61pll8LtAsraFXjXQ9Pg06CNgGmtpg4WV5So5dfl+8OeOBQBgPZX3wt8A2er6mUudW0+1ksZHgJaSe6kCkDIydqLxjrnJyRwMjQb+LRfDXhHTbVW+1XmpxvcrcqWBBhSZyvQj5ieucZ6VvePbO6ufHPh/w5HBLHDNbt4jkS4nMkzAbuD0wWwCD2DDgjirWo/Dy41u30e4tnm1GTw/eC+dYY8PLbSxqnGD1GVA69PfiI3vqAsXi/T5/EEN0bdWtNavY9Jton+bKQedHKreo/eKT0BxXMfEz4l3PhfwRrWLdre7LpbvIMrujjBSMgZ452n8+2BXRafqFhH4p1C1uFsZtN0XULyW2/cAtLuWOWOYHOUYZBOO2c+/mvxYs7zxAj2uuTeUuvNJ5d2rho1Q9MHgArgjGeeDxmipKy0KiaGnS32k+DY4ZLiG4nOi3Ch1XnzEUyBR/tfNnn0NUbXWr7XPEHg/WtN/5DVhPDfJJGmDJESBPEpPXucGtz4e2nhn4ufDK8s4ZLyz1KCUi2mVtssEoGAXyQHV1VsZA5/OrX7OPhWa3+HUWoXV7b3cmla1bXNvKV8to4ncxSxkHsep9wKw1bBnG/ETxzql/8U7PSlusafb31zf2fmHcREI2dovba+Rj2FdV4nEWiWlqqs0014YpVYfMsc8ZUhR6Ejj61y/xf0/S7T9sCEXnmLps4afyoxgQnP71AP4tzjbgf3vavcfgR8DJ/FPw8kbXlt7XWNakGsWMEnzfZdkuUTdxydqg9CM9OObSl1EcF8Bf2dW0C3t/G2rxSfbNd1drW3gOBsQSHBbIPUk+/Arm9J+GOm6P8SvFl1fyR6lqGjzywRKy7vKG7DMeOrEoB7A9eCPXrX4iXPirT5rO687SbXR7sSHKZ8uSKcsQo4Iycj9fauN8S6fZeF1vNe2KP7auUvLpioUv8xCoR3yzA59hxWntEhKPY858M+BrjUvFVxo/2qEWtnDJqLIFzzFysf1Z9q4A/Gu2m/Z6j8e/tA6f9otY3jtbXTbjUN2I2haZmO1c9w2Rz6Cuq8BfD2TwL4+vtQ1mzt9Q1XWb9ZpPnCx2FoI9wAJ/iLAMRjjjrnNV/jn8QJPCfw98Va7aNcW+veJNat9PglLFfNVGEkaoCOdvTIxxnrSjUjbUOWxm/DTwsnhn4265q1k0tlo8hna5nkIaMzQyReX06gjg49+lavhzw1oNv461g2f2y3j8QS3M3kSZWEkbvujaCMbxj0I5zWdcahD4Q+BvnXF8NRnsfs6zxkgASbmdl4zyWYgk+ldfNHdeIPH2ntZ2sU01xp9xOsx+VbQui8dDzkHsM89MVM6iasikjM8Ao+q/tHtDbtBExgtzeAqWVZUcAHGep+bJ/lW98LPB0lj8UfGWoyQBV8O+fZ6fMTuaQSOJifTIyVzx/MnN8DeF5NU+OXia6jVrN5rh7hrsAFQUTZtTjBHmlzn1XpWvrksXw7+HviDWNPuLi8n1KeFbh3fO5miwjqO2QQCB3Gc9qxvoVYf8AELxRbeKfFHhvw/8AaC0N/OyeU7Ycq9sT+fQD3rz7U76+vP7L1YzeZHeW1toslkABuCLj5werdiTx7VheIbHU/EnxTtdchu3u/wCxtRjtLa3Ee3zZY4Rg5DcfMQMYP1rqHvrSwbVNaa2ja4sUhE8Yl3pFNHIQ7lccOWJAHJO3rWMp9gscz8WtYh0oaEumxst3Da3GmxLGC37gkMMADjBJA9h3PNdF8ItE2eGPDWqXS3CzeGphHZW8x/eSSS8NMy4B3AZ9BXHwfEltC1e78sTahdTeHLm2NvZxGSRLuS4zDjbnlY8EkcjNa3w08X6h4Q0vWYb+S6k8R3EbfeH+pCjGC7EAEnBOAT25qb2K80XvFOtNe/F7xx4iuLgah9str2K2fbtJWCPbyP8AZfA69F4rmr34nw+IviF4R0W+mkg0WKaa7vJYxn9zG6soUf3mHyj3xXNeM/FTaDY6bHbRs2o3Fm1qlrFJuZ2Zt0jseepJJJ6V6z+yh+zRb+I/if4bbWLGbWNXvrJr+1jAIs7FVlAAL8hiX2jG0cZPNaU+aTsEu7PpPwCsWqXvjTTNRjtrLSdamkjt1ljJLmdRtLDPQZA6gg4+hx/2f/ETeJdfsfC+sQ2qrpYTS4oI49wIhTd5bZJHOBhscfStK08W6V8QtT8TWum6la3f2HXYLLzk486VJoi3GeBkYwPeqfw+F5beNvGWtSaHDZ2Wj64+pWV2HCzl0BW4hZcBtpjZih5B/u8VoZ67oNf8bN8NfjL4m8Ixovk+Jrv+1dIUn5HaWPc8ZzxjzN69iMcmsnWD/ZDXnha/juvK1q1h1TRCgy0dykvlT26+6sFfnqp/GrH7Q2saTN8W/ArhRfahbm7jjmJCm4jmieRGQ+zqMjqpJ9Oeo8Trod74r8NFr63jkvLwaxpFxJHuWNm4ERb+7MgVT04IPJHIUhPFv9l6N45+Gk/2q7h2vBdG3JG151tViwABkDchByc/MORznr/2d47rVviZ4wurpY7rR4Ra/Y3VfmukMZDHcTztHykYHQV4aviFdftPGmuXq3ENt4Z2R2FxtO6xmLjzTFnHyquST0O3qOtewfD7XH8GeO/FWj2eorHHpek28tpKo/cG4RxEWzkjy5lMbMBnaST81AM4n4i+O2+G+u+Nry8tYGt01S00/T7knc7wQQrLCc5ztYMy49R2rYh8B6Z4Si8I/wBtafBJa+JJndJkjZpIryRmuIihz8uBctjOceWOSc583+J6w+KNF8NeFbizjstW0/xX9i1S2nfcxYyecFfPzYClQpwcAj6V6z+014qk8PeCrRrURzan4SuLbxCloTjdbxXEYdFxz8sEr89xERgdQWFbuXf2h/7cstB8zwvbw6lq3hmFL68hjUedLDFmRWCZBYko6kAgnfx6VzsHjHSLv9nXR9Gvr66l8M+IpY5vt4x52kRXUjvZTAY+9BdKFbgjYCMZBNdNpmoL4t+Oc3hFb6bT7Xx54QN5p3iGGRUkjQyr5ckWPvSQzOv8XOMEc14T4ri8VeLPGY0WPR7eOy0FoU1+yi2RWfmiRnI2HopkWWQDoglYdB83QleIrvqfRFt8TvEPgyyvLXW3hjutX1Z9PvorYMV2S2zAyoT/AA+ZG3XPyOMk9a4n4XfD/UtLt9c8K32svPaePLea30PUIpUEkNzBNvSBtykLKq7gQeCAcdq6r9pfbrfwk8LXGgM9vrnhcWV/d4AeW505wYbnfjAZ4lYsRycL2ByN7RPhzZ+MvHl4159qk8P6hbwXdleWbhBY3yAN5ykE7XmjE2DgZxt53ZEVKbb90FsfOvxuudY1rx94I+I9os0kdq0vhbUUUbZLG/QOIieScSMQox0Ir6o+LWt6f8G/ht8O/GHkr9o8C3dlppATcxtrwLDcKe5+cIRgjBz1yBXmfhr4Ya1feBNY8P64rPcL4tk1GLUI41KXUe1biC82hsgbkkjbk4YHk4xXsHjTSLH45eFPDuhzXn2O18caIb6OaJEZra4t40mVwT3EmDjj7nvxvRjbUzk5X0OkstDbwX8LPEVrp9hZiSUXOoRNs2NueQzFSPYMevbHoa8l+EXiO68Z+GPC/jS4jjh0HWLtpLZtxjNvP5jKhwSSVbBQ9iHI64x0Xj74p61P+0jouhz7pT4j0VL3UkR2Ece1DBKy9uWAI9yR2zXCfACxm8LeFdY+GWvXEiyeHbhxphPymWzjlS5jlROfmOR0Jwc9cV0NX3KNL48a/cQtZ6h4Uh1ST+3tStU1P7MjD5IblRFcgr2DFkfI64z616T4w+OtnpHi3VLQrasbW8lhLZ67XIz972rzHwXrV5Z/HzS9R0HVbyHwxLf3FulzGFgaOe6lEhtgDu34lQq2MbhL0Hf1/wAS/sb+FvGviLUNZuNShjuNWuZL2Vd23a0jFyMbuOW6Vz2u3rYq8ep+dHgHwtrHxP8Ag7Dry3UlrJeX0lnCoc/ukibGRtBOGO453Zz2HWu/8AzW/gzx5izaxne0gF0/nK5kuk5IDY+bzDg53HHXmp4dCtofCmtWmnwLb+H9KuDcWzSK0LGRgrsyoBhQG3d8n8qzvGXgeeX4Laf4n8ORW17rqxfap7oA7pFDBWJU5Lbd+c7QPn9DXzvM+Zo9ZFu8ks/jl4x0VZHXQTcXmLu4uVjWO0QZZhzzuYYI93XOM5HSah4d0vwhqWj6d4fuI1s/EQaOW+huPMlglwVMr84UMoUAc/eycVyPxQ+IOrfDH4k2LXSWOpR6xLb2G8x+XJHMEGGXaOMhcZ4BCqSTmtTxRF/wr3xJfatp80zNLEFEfHyEkhtoxtxjoevXnGKo457s7TULqPUfhtb6LpllrEt9p16NLs70YMdkVKpl2Vs7erEgHgCuL8VeGdRsRb+GbXUP7VkkuHuFvpiUlkkkyzLIceuSrYGFABOSasfCLxDc6JZGbwzt1K6klMuo2mpXCopk2uqlMpubd0O04G0ZHQ1x9l8TrhPD0mla1ZLqljd3MhlVYN01vd+YGaF2BykQxgFSXA7EEmgmJ2WheEIfDLW+oN5djrWg3E0kUqReeW82Nox5gbjG1mxwSCQcZqTwb4stvH+p/wDCQatpckrWkcljBbJEr3Fq7KoE7oOdrEDb32tkY5xhX3iPTfGHjaP+zra30W1ZminNsNygKrbcq5yxzheOcAnrS/B2LXLHX/F1no+kya/rFv8Av7u9cR21vJEVcQ4b+GQYG0Z5A5AHNAcqL1v4zk8N+IbWO107UZrextWEdmECrbAtuaZgwGUycgqSSOODXJeHPF1rc+OE1LRb6bw3Ha3O97ZozM182wkOA25ZF4Kckc4HTkbvjqJtU8O2+q31q0dzeQJE0Jkb/Q4shmjwBhmDYBOTnA60vwb8O61420u6aP7DGtrdm3sQYXSRyFSRiWb5WUggZBOSp6Higo5z4zfEaLWfiBa6tcXupWtlbxJOrzQx2YsJ4ySdyrwycKcjOSDxjGeo8N+Itc8SeMbfVLTXGv7h4FEsbvEqoD/EQRtGcLtCnccg4GeOd+M3hQRXGmzNcfaFvHWa7UruWN0f97jB7NwAc8dTXQWuj+H/AIg+KtPuJItP0mOTTpI7iNiqPPIzAL5ajByu3JPTLHBpRlqBa8Zw3HiXxjo+m6d4enk1m61QWwtptu65Y8qGGOCoJZj/AHU4zxn6u+E/7JS6fFEL2NLnUvLBmuJCdseSSVTIBwDnHAOMZxXiX7LPg1NF/a8+H9lbTW95pdnc3CNKsyyuWFnPjgNlgCOuPx9P0T1LT4dLsbeOPb+8UyN8vJz0/rTtrcxqVOh5fpPwH8M6SP3mmx6hLtI3TjK44PTp+P8Aianu/hD4W1lGjuvDOiywt8pSS3GD6c9eDznPBrtmdFcn+I8HAPSmNtkRtvzcjtQZ8zPhH9vz/gm5bXngLUPE/gKFra402GS8u9OaR3EkSKWZoWwSGUZO3oQDg9K/Ff8AaMvzqXxl0HUNSt4je6XZzKLp0Bkk+ZFXcf4ioYgE8jiv6kZYfMtpI9u5XVlI4wQQQf0Nfzqf8FdPhBD8Gv229W0mzhMVp9ru0txgYCukdwigdRhS3X+76kCrjs2ux0YP3q0UfMfi/WZNUlYyHdnt2JrBgfL8+hz71o69CTL8rd+cfSqFvYyTPtO5eM7sivO5kz7ZRsrIcxAmz79BVHxFoS+IrJkC/wCkKP3cnp14P+e9aiWbRs6fe7An8DThayxZZcgrzkUoVuWV0Z1qKqxcZHiupXE0e2PG1o2KOG6BgeasWEn2mTtgDPPrkU/x/Hs8V32C2PNznPc1UsJ1BZdrbtp4x34r7CEueld9j87xEXCq4LY9A+HjQxXarcNttdytIQcZXPQcjn05HNfot8KPHl0fAXhPUbeykuLewed4/toZbO7hVDsIxk5G0AEDnI5xzX5v/D+J7iZcDcpbIXGQW7DHucCv0i/ZDNxqfwbfwzqENnJqNrbJHZzIMRmKTLMWB+646EDGccZzXm4my0R0UVpY9n+Gd5pF58HbSZLFk8SafAvnwPcTK8vmbnRg8ZXMe1hwRkgdOKrazfJcSWv2qO4WPT7T7JtCriHBJEpYfNznvkYHaqfwn8DW9tqwhuBq2nxafYCC/S21MpDcKsp2SgAElgnGzsB1rro/hxfwaCt5btZ3ct9BLfPdFvKZICMIzEnaWAIG1VBxxgkE1wG22x53bvq3hzw7qVha3dpdafqcP+kfbMb51kzkKVGWCrxk+o96sTRaX4AgtI7V/tUyuotLyV1FxasnRm4ARtxyNpIx1rW8Ra602q6WvibT/sAhk3Lc21x+7mj4CBcjjoNy4BHtzWR4z1G6vJbq3MS/2XJ89vNJEokJ44J7jPf2qk+4b7k/hvX7G/drHVdYvJLqHV47qOa1jYyGCaLaVVmBUt5kTZB2gB1POSB3njK10vwl4DutUtZriaS7lAkjkliLLs6xydOVaTI4AweM8geK+D7e603xDdTDUIbK6tbLdNCIypki3OkbBXGTlxIuQe3Tub3jX4d6brXiLw3qmmSXFnq3ktMlxI2H3bdxV+q8PnB+8RwRxUh1LGjafceA9durq1vLe00i7V9RKF9qu45AyBzkbuOmSM+tej+bavcxXkr2X2W6CMVjk3Nbsy91OVUMSg46Fe+SK8+0m7tNe0C10u6MKa0rC1vGRmEbzPlkZXOFQEdiQBgg44ptgJtF1dbDW7W41DTreJSzecY2ZcgIMjsMDrzjiq5mtAOisNT0fwj8T9L1O6s724msXC3nlooadiG+chmAUj5SME9M45xWv441PTfFHxI8P6RrkeiTaD5kk8MkrADdKMFC3DAB2XJ4A7HvXK+NvDmoeI0XV7HVtS1lcLLHA7jbbhCP3TMd3XAUZxnP41t/DCPWPEGt3nk6NHJqH2YrDE6xyrKM4KMrcDk4ycdvWkpNEnUeOdJ1/wAE+I9UmtxaQ+HdXBiksbpnlluZCMCZHzlR/s7j0zXA3N1fJa2drDq8LXd1Jsnhkk8uOMKD1BbLP6ccHNdDY6iuneEhod/atb639rZliL7pHifaEZWHBOcrtzkBenc53in4e6h8O5I2k0/T9PvktGjIuisktyON0wIY7ZM9iMgn2pFNaFXwXYy6drV0rQNYzW8USi9gQSWx3S7QLpt25UwN2cMcAduK6L4q+No/EOu3X9sLDouoR3CCFgj/AGWe3IXeyMeAWw/DBQDnB5xXG+BdXXw14p1NtS+3Rw3VqkcjWsfy3XdfM5x8uSMeg9cVP4purPx3EzXlrcR6hbwxSSSx7mhnKMcFhnOcY45AwKAja+pX0a0h1u61aa1uIXh0FYw1pa3JEm2RmCyEYy3K/nntiuXtLe+tfHzTL9osWt2Et1uVTuQsOhHfDDnIIJ9zWnqWn6tqdguvWLOusabEwicMymzVvlMuNpDKTgHrjrgDmpdDv9Wv59Jm1i+in08p9lnk2gFGI+QZGBgkEbjwOM9c1pyrluNpdDsZ7a1urq3sl8tV1B9ks7qmAq5kOTkkbsYGe9cTrmmW/hjxvd2sEl1dWf2drm4gguAeIwSCNvB+hIzXWaLrNvoN74ifS7q2uZZCtvLbsdyISB8mOq4HORkDnmuZi8OaYwt5rOCS4utPVr+6iV9wTbzkL1KBscjJI7cVKi7kHzt+1BZtoF/9tgbbcXaeYFEnmKMDpnsSB06V8ma3cSX2pNJdeX5kxBUL0xivqP8AbKnuri8W8tYfsjyKE+xGUNnJx1zwTj1FfKniW4t7TVYpFk/dxH5VPLDPTPvzXoRVonLJvqYk+62vGZt/lyEhV/hWny6aDL5wlj3YyAWP09KZq959rabEgkWOQsuf4hk/SgW8bW6ruUyMu4kDp7frW1OKbswkkloT2zeREwdk3scrz1qndtBLI3nMqHkgnvUFyZDfMyw+Z5bfJ8w+X9aS5tl1BNzttbOSuOldG2xJ9of8ERxu+MHxkVobq4tm+GQWWO2ZfNMR8V+Gg+Nw2/dycHqM9K+6Pgz4puPBFhcXWrLo+oMITol9fwMYPs6I+LYzLtG3cpQFgD83scn4N/4Ima6vhv4p/Gq5uJpIY4PhkqysASSjeLPDKsOAfvAkfjzX2h4Sb+wvhnDZ3Ct/aHiGfU4byRl5kjjUxw+Wo5bcVDAeozxU1JS0S/rU1o7P1/RGp4dubX4afHPWNN1S+jt9M8QWsusaTPboXtH86JUlKyYHzbowxUgd+xGeu8a2ZvviFqUOlus1wkVrNBHLArxtMIFcEE44ZEcdAckdskcr8S9EsPGPgGTTPEWnWFrHbWcdylxYTG2utOWVAkk6ozEqpYbGV+AVzkZrRh8Iah4d8K6/Zza1c3utQ6ZFcWVnPbLG139lZjFPBKpxIrKTGc4+8OvbOFO+sjXmPStR+LGhrrOn6kpi+w+KdB88PFtMW+3YRsoI4DFJlIUkHKHsMnnvipqNv8QPF3gvRLmOGSLxDaNeHYPmjEQR8g9s4ZcD1rg/BMdn8X/2c761sytgtnqVvsRBzZwuAkhZcj5gWf34PXiu2+JegXXwe8XeF7yF2vINF0250VblwHO/YjRyYH99Qw6cH0rbYkz/ABffTReGL7RrW4azOraxJaTXAzm2gZwQw7bxHIVAPH3uemelmvbfwVo5M/2i+jtHSKNmCmUlZI0kJPHOJAefSuQ+LEV+k3iSKzs1kt9ag0+HT5Y5R5kl6wEmcMflDDjPT5SM54qx8c7+80j4ca1qMi7JkZ78EH5XiAt4/lx6lpCQQCNoyBkUAVPEXx/utB8WeLPDc9ncNqlheLbsgGXuEIc7l5+6QQc8dDTrjw9bfD74ba0u6SWZpIG1AkAuWnWPzQOAdods59AarG2vPFn7T11qkFt50OoR6TphnbbshiuGUzzA5y52qYhjP+sJ6Dno9NX/AITjwNrjXluF/tbVLo2+/wCYeSQ0MAB6HhTjBONp6cUAc8bO+1jw9B4yFqUh8MafcXMEL9JhGXaIkevysRkgcr7kUb/wHN8SrXwxatrjCPxJdtcX1wq4EkkuZZY0OMnDoAMgceldx4zhhtfCev8AhuzvPskWo2dvbkg/LCu1YAoU4Azz+JPpXN+MtT0bwp8PPC8IaYaf4Rv7NHdiQ84IcszcZBdTux1yMdeKALGtX2p6B488Sa1Dpcd5ealp9rpmnoJNzLHLaBwfmPy7WDggdsde2P8ACPUI/D1+tm3nWfnWEipKT8olWGIxgHJyA5XPX7w603TNC1C80O4lhaS3XRbtJpJWf95dxBiEG4ngrE6k/wC9jqCBj/GbTNT021sbXSbC6l0fTr1LyO4jTm0hdVhlR/psgOR26ZGcAHNeE9Ujk+OOv60sDR2t84eKzlXy/s8xiKyhhkjh9y4yelQ61Hbx+CZvDOr2Mwuv7SuP7PeeIYZHjDwSI2eR8jKSP4uOetdd8aPAmk+BviE4s9RNvBqVwy3TzEyqtxKivbyLgDCsWYMOuQp45rlv2o/iLeeMPBek2osbddZ8JazY3MEtkhNvdwurJN0HH70BtvB5bg81zSk2y0c38EbK9m+DqzTyaeUuLIaTPKrss9rOl5ujkA2gZRsDOeVZhV6/sri58PR/2XPHZSeILhtD1G2kzGlhdtl0uCRzsbYSD69uc1yXxV8SL8M9Duf7Lkkmj8Qb5EjYj9zdFkcoR6BgpH0PNdz+0uG0n4P+CZpI/sviaa0huLsxuCrvEyGWKTHG5Q8ZX/gQyela04Jq4m+hT/aO+GF54L+Mmi+MteljW12FXlt8tF5oVT35+Zhu6Y5HPp6X8LPEg+JltpFva6zcRXnhWC2nZLePzHuEkcCVSfXqeal8QadqvxR/aR0HwjNarqnhvVtD23csqE29mWiwRg4GQwBUev4V337MXwH034G/DhbK1+0SahrGoTXUwuNvnvFbkAKxX+H5cduc1o4vqScp8R7MaN8S59b8gQ293Mnm27DCgqhDM3+9jP1PSuC1Xw9L8TPj/pPgLTZFmhtZ4tYlklQ7Y7cqJFTIz0DcAjHHPYnv9f1PS/jr8Vda8O6Ov2r7ZeWEMBIKrBKoJuST/cAIHvjjNdnpvhDQ/g7/AG54oivmXUfEFwNK04yOGkMEYELyKAM4BRuv+FZuESonMeOTp/hXxDB5M/2jUdQ88xCZgNwM2FwPovHbGK8z+Mejap8RIm0uS8gRPCN9FdEBd3nXNywwnTI2oBnjIzxmk+Dej6p498ZXmvzXlrcebd/2DpUcpDeWQpJkPPXGemK3NL0ttf1u1jSGOKPxdfApKUYsfsxRJJOv8bFhnHGBmuco0vCXwo0mbVv+EfurKLU4/Jilvo0kJUFpBnHT7oOckg8/TOt4X8zRfDlrfQhFvLrWk0jTXJ+8sKySzk+qgFBnHc1T+K3xK0/4RaJrEumxrb6xqllDpsaupAieSRWVifTAK59ue1WvHOiJoPxNjtrW9muofDcljLbQOQiyNOJDcyBj1yI1HHZj1oAtzTXGjeIviFHHLbwww3UUNuD/AMtGyJmPT+63b1ql498RNp3je4s4oIWsPPtr2a2dPlQMgUgfUuMDt7dK2LqeC8+NPia1kkXdfSQ21p5b43O1q+Dj3ZVG4jHvXG+HNXt/FfhCDVrmMx3OkP5q3aH5bpoZtkqN/EflGR2yD1yKAON8ByHQvGviLTtQuJI18L3t1qNw1sQzpL9yNFJHeTA5HAHfobJ8HTfE2w0Pwrov2e00e9abxD4jklcxup3N5Mcj8sSdjsfp06ZzvBHg61udU8RGa/vDqfioG7l3NtAZ1aVVU4PRVXr3Y1Yk+LY0bRdN0WGGPdLo7+Y8ceJHkjeNslgOcfvOc9yO4rL3U7GkVoZ+l+MbfwxoE11pWnrpN5qFv9nhuGXaYlbdtLDruZcls9AFxntheLPHFt4ktYhYxapNHp9sZbmZomaS8SNSZHGB0OMgcYVR61s/tb+H7S7+FWn6p4etWt7PVLjYYVzuDLlBknn5hgjpwfanf8JdPafDXU/Euv69HpLJpqadp+nWcYkbcI9hZm3bBvXIJ9x25qOXm2I2Of8A2YbKPxxr1xqF9DHJJfM4hWdVxDaopeYnI6iH1OAcc9M/Rngv4sanJZfavD8d1oz6ta3GoCeJojFp9laxvHCyg5zmRRkd+CMmvEfBPhLQPBnwzHibXlvLPTdctvsUtvFP++eBvlEEYXLbpW5LkAfdGcDnsvBmp6T8PPHdxZ+IPtUVlceH0iS2jG9bF5SyQwjaDnbDvJ75Yk9K6I6IUtTpv2JvBz+FrLwjqyh9U0rUbaXWrx5NvmzyiKRRx/E4ufKOOBtJJOeK9evtfi8Q/DPxffaWzK2u3Emo2sjjaLyNrXzUjVc8Mq7lx68Z7jyP9j9J/Dmv+FrO9vLyx+wxTWTQvB8jXkW52VFOGxsdMkfKc8E4OPRNO8NS+EPj/b2Ek0l/oN5bnxHoF7DgRzSWSZNuUP3f3TshUjnIIJwaPMC5f+K/D+veAfAuvapp8NxpYtLdpJi4Sezk8sFZR2GW7Z5zz1NcX+znDrmlfE7xRoWuTWtxJa6ZGvhu9RMxXWVlgQAkDkKycdQRWnbarZa98VbDRNAurebw3rVlJ9lsrwZQyRZJgYH7pXcy/NjIK45WvO/hVqV7oPjTV9FaUfZdFv4LxBcTgN9kxGUUHPGCcY46dO5AO1/aI8SDwV8Mdd8Pz+Z/bF1p1vBcgfLGtzKCoXLHk9e3eur0ae80zV9Ijjs3XU/DqWSalY3nKXunzAW1zAWByzxukUykD7rHkdC39oz4K2v7UNvqF5ourNHrng/Wg1zBJHtS4j2+ZZNIT6OPLY9fm7VhWnhfWfGl3a6pouqXh17S/C1rfSWzfKu8M6EMT32/Kc46U7dgL37dOhN4D8e/DvxfZv8AZ4LnVI/DWsyyndI81s0oguXP95osKW4ztU4Oa2P20/DXiDQfj98N9St5LX+z/E9m2k+a7N9nMyvEyiTjBDxynb64YEDvH8W/C0n7Rn7O17NLrF3Jb6l5etWtuEXda3aId9qxZhtDKjFW7Hg9xXX6p4cs/j58NF8J3+sXun6h4XurWXSo5pAlxDKbcpGkrsNudweMuDtztJIBDVcYXepMnodtP4W0/wAP+D9JvjG+m3Wny3C2O9Nv9ml9ouIOPuqZohKuMjaexyK82+HUtrqXxB+P2uTK0MeoNbzyWpBPl77SQGROOhK9vUepx6F4Wnk+JHwuupvFTLZNqWgtp08cYxNFdW0zRefu5AdcDIx2yc5GeR8LeIf7V/Zmb4gmzjXVvDIbwz4gFttP9pwOY4BLgdSrNHKhP+0Dw1dkYq1iTpvgEkT6H8O49alhmv7vSntLkwp53nWtwoaOUsB2kUISQMibB45qv+z14gbwTo/j7w/ZJcXtx4buJbfdMu6O9sRKZreMHg7xDLIgI7xkAnAzo/A/T7PSrnwzDaibT9b8HaLNpF7Cit5ZMMiiZHTk7SY1ZAM8E4zkVl/s5eG7XT/jH421iPNnpuqX1tZTafNNu+wXYkMkTK/3Ckg37OSMPgkcCiUUkB2XiDwZqHw7+D7a6urf2k+gy+fbT2ybV1DTpJllWLDf8tExIQeA24DIzisr9mTW5F+EdlrV9OZIdI0I3ghhtw11BZyvI8bgcA/uyytz2xzwa9Kv/CumaT4EutBW4VoY7ZrKMgMA0L58sezDhhnoVIxjFeN/sq6vN4T8fXOl6hbmNvDCf8IhdOyN5cQ5CMSOCjbfcdelS3a1iY3e4349eJNL0n4c2fxQ0yWFrPSVjgN9CuJ2spCnmsUJwQsiRNjtvYc9+m8QNJb+M7PXtHXTrqOHSp2juSn7yWFR5wVeodtgJ255XvwRWJ4Z+FGm+K/hP48+Hszf2Hokk1za2MssbLHaTvK/lyIWBzHuwrA9QBjoSMT9kvxl5n7Pa2+sW01nN4I1q08PXMEYD7rjyoYCQ5yAu/o2dpB6nJrW5RseJPA9w3hzTV8KwQ3VzqV0fF8lmZhGuoRp5ZmFmD8zSJhHKcDlhnJxXvyaFeami3KaXDcJcDzVlW4VRKG5DAEgjOc4rx/4ceNbHw3rniDR9SVry++Feom80qDyh51vbXEYeQxv0wUyRyMmMjmvYG8B2PiJjqENtI0V8ftCFL2JVKv8wwN3A56VPKiZRT3Pzb+GvjbxJ4r8IeFxa3C30OsQwxTTrCiRW9oThp5VAx8q5bPGaj8Sa5PdfDw6PZvp+oyXV9Ha2McEaLc6jE7bCVQNujCAnOODsJ4xxznwGn0i50q6a8vLiC7h09QlvCrRxsdwUIVQbUA4PIx2rqfhP4W8H3fxAmTxJe3mg61o7LNp7SJzLLySUYgghDgFeBzXy8o2kz1lUvseg6z8LdBlsr621bUNUuLGzt4Vhn1S3ZprlhkFVcYzjnLj7oIB7Vw1ra6l490++Gl29xHpGk3S2trO0e+WdQoGTJuC9T2BGQeeCBM3xOvvGer3f9oXUN9odhIYkngYngKQxKdAdwPPrXT/ALRtlqnwJ+AdhJpcMs9xeTwTpEVGN8mWRPxLR5PYN7inE56m5B48v/8AhFZNMW6t5G1CO1Z7eKBhvkkKj52KjhVADEfnXF/s+/DLUNa0fxlrF9eKo0++a6EUxbzXlkyQCCcjCq2M44xjg16d4ghk1Hwd4N1/y7dteuLWSa6lLLmCQoAyAcBVIcgeuzpXE+Ab4SfFTUNK1rV5LbWPFBDfZ4LpkRIo0I3r6NtI6DjOKolSsadnNpPifw4moNa32gtEBG6SwhVWQkcZznd0z35XPBFRaH4A1LwlaLqF/qlncafqRDwThGju4yV4Aw21srkLk5ycjg1qeLpJPh18Ol0mws9L8u1uC0MbIzblLAtJI27I4yc9cj6Vx66lfax8Pre6hkmurprgz2lpK5lSA/dG4cbVx25yucelZSk4uwdNSxaeLIPFHiGPS7rxBa2FxY3UgEd7L5dnIqp/qg6llVzuDAMQx2kAHHHommeINOv0sbjwzqEWppC5tFMS/wCrIzvjyR82Dn5hkEA+nHj9nr1hc+PtYt9a0HT4dY1iNfsotkRbPeG4duQQwDKBnkZPSsHSLq4+Hlsz6Xa3ui32l6xJfSxQwoY3aQ7Rt5CjKP06A/ga16XCJ2PjrxrZ+EPGy30dm8mntK630sOZB9oOMbmBIChs5HAPJrudQsNH8WaNpfiK6ks47y4l8mRDGyyQIhZfmcrj+AMPQMK8U8V6m3iVdRsrq8bzLyUwzvEeHkkbaWBHBJZuCM9c1L47+MurfDHSNJs7xVl8P2ckNhBEGCukrRsRkD5mJ2uevPzGso3b1L6H0/8Ash6Ssf7W/gNtLs44NP0XVHS8ZEWRZGlt5VOSPeReTjr9M/o/41mit7m2Zl+WSHqrcfLjP86/KGw8QWfwt+MXgG88O6h9ou7fXdNGoQ/etb6FrqOOSQ9CJUVuPUIB0xX6reJLW38UW32cTeXeWu7yCxwr56gntkBfatJaGFWPUyZNXhjO7MbL6cVENUjmlBJCq3vxWBLMunXLQ3CGOdchkf7w6UJqSIo2gMucDHU1EqiRidK00aJlWTpkEkYr8Cv+C3Pi208Z/tdN4gt5IJbMeKBZBw6srobaSEEMOCDtXnpzX7EftHfHdfB3ga+0+ybbqF5A9uZFGBAGUqWyf4hnIA71+FX/AAVR8SRxaX9oZh5llrNvcbzwXIkyT+Rrpw8XMdGXs5xl5nz5428PSaM9vN5cjWd2GME5HyS7SVYA9MqwIIB4PBwa59bgqR8y45x/Ku18O/FdbjwLeeGdShOoaHfXQvooslZrG4KKhnt258t3VERwQVdUUEZAI5648OJcS/6PcL5fYzIYzx1z1/SvEVon6LRpucbozTMwZmX7xOelLNe/ZNOuLif5VQfKSMBsdhVu806SwjxuSQj+5yv4Vg+IdMvNbRljS4mRcKUjjL8j2H1FVhoc9SzOXFVvZRdzzHXmbU5prhVBaR2kPtk8VP4U0iS6uNzL8zIeCO+R/wDXr0nwh8Ata8X3cNrZ6PfT3V4yxwQiFlaQ5APJGOMjrxzXunhv9gnX/B0Dya9ZpZSW0yo1srCSQbh1bHCryBnJ5YV9Yq3LDkR8HUjebkzg/wBjn4RyeJ/ilp9w0N/DBZ3KObmO382KF24Qvk8ANg9O1ffOrfBfUPC2kald+GWMmoWlv50TwW5iPnINyny/7hfHy9cEjvWL4K+E3hbU/gtcW/hmKTwv4g8Mm3j1ZHlf/TZDLnco53YXPPABA967rxb4ssfh94g0i88O6zqVpZXSLaytdyl/NVgEII55PAAx1A9TXn1pu9jSBqw+Co7640K78MeI7O3utTh8+8t9WUBDKEIO1t2SCcnb+NZfhH4tah4W8Wy6LOtnex6TBiewBIBRtyqQCcsA2TgD+EVoWCWvh7wG0cdpDP4juNRaaWR13cPnGxuq/L/DjFeb6l4U1Tw740Hnw27yXEq3QuVmZUlVeTFjbuKnrjP865zSmeweIdAk8Yx2Mdxp/wDaVw1rDc2z2mLhEJjDHeqnCsQRuVs4JGe1VdP06x8RXFsupS6fcXGoJK1paR26xtZtC214cKA27cC+DyADxgZqO98T/wDFLaffW37l540+0RxISysoPmRnb/CpYbR6L9axPhmui+NBqG/Ula88+WOyDRvCluzE8hjjBJVu+Mk9TQU43KNjYzX3iDM9wfPhs5IZLW7kYG6UsSmwYOQCGPHQHPAOaxNL128u/Dqw2+mTTR280zTyYXy12upwMndzlsEfrmuy8U2FnHq+h3dxqkVlLJpy3FxcXFisvnykSbIozDyoYDjI9SaozRS6i32Nt63PyS3NjLL+7mz9wYwGUY54BJ79KDPZnOosdlqupTALbWGoWrCRXG0lyFGBxweDgnpjPWum+GfiTRdVtW0zXLi8kssMY7kt5kisAHMbuWBydpAznqKp3nw//tXU7uS8kXT9FkZRL5bgRo5wMLuYccHp3xW14Kg8O6h/wkhtfON1HbI8LWg3K6tJtYTEt8u1kBA2nGO2eNOW+oWuVfEFhJ4ZvLy40+RtJh1C3+0CJZUk3wHkK6jqyqeFHzZ7U34fyW/inwKv2E6jHq1veNtvrG5KySLuIO5EBbI46jpTJPD7Hw/a2batHdMHMW9UUG3XptPPJxjbkDPGcVQuLiL4fpLcLzbEFWmWFGVQflcZH3eeOMmj2YcpDqXiRvFmo6bqel6wsjNc70NyhkWNGAyBhgcDIOelavhrWLjxj45trHxTNdTWbXTWqPYwtKX5JDBsllD55AB6YzWB4cv4/DtlekJHZyQTzRGAqWDAp2OCVzxzx0Fd14g8CNa+C9D8YeHTDpl5Y2/25GMkrR+YFBcOOjA5YE5FHxaFSiZHxT8MQ3E83/CO280jttKiKCRpFCrtbdx8oyWJ/Csa90yTQYFhlkheWZFgWXeWYZ6gYI4OcdDjGa2PhV8XDbrd2etSi3l1DZFaSxhmIlbapw5JXn5sZPUEcYrZ8V+Ho/Dljf6ReG4+3MY7iAsvmPb9A7I4xs+YYwcg84zzVez0I6nK/Y9fgv5rGZVsZvsCyBFJkeeMZDAAY+UEj1zlfSqy/DC88deEry101lNjDasTcJ/qy4ZVMbc8EE9zxiqekeLo/F2o6tZLcS3GuWFwFWWRWXMcec5JxtPp9T1rQ8QQXmkQR32h6pdTXTFXlsJmDAuV+ZhgZJ5ySc/XtTSsrFdDR+DHitfhRo9rc3TaDqs0kMtrPFHEscoJUrvbOWIHrk8d6xtct5vCd7D4gvvD+qDRZIZ7pEjlMcdxCVJJUjGfl5GeMlavaOzaHYQ2+owRrpOrbRqMpQPIsiKSfLYgMoP3cZ5B5xXG/FfXdQ0Lwyscmuw6poN84trW0BKzW0JABJI9B1A9KZlI+Xfjj400vV7/AFCSzt9RVo5pG/0hWLRqem5j9eDXzfqWleYRI1u3mgffYHkYr3b4uam1rq2pY3Hy2EXmnlHAAyPwz6V4r4k1JruNtw2yE9Qfuj0xXZHsY1Dm7V4p3zHCzqoy3zYxz096Llzptx8vzRyNnkcpViYQ6XMjnbCkgxkngt6VVvJ/tBLL/q2GcsOnFdEYcruZk1k5ii8pdu1iWy3OcipPsqyFWQNjGDx3zzTobZ/sEc8ce/aQCh+8uRUltfyS7jtjQLjKhuQec5rQD6h/4JmaovhrV/jndMs0ULfDOGKZ4SyOqP4v8LqxUqM5AJr7c0f4aal4T8ReAdQk1KbUtJivJ723v4rXy5RPBbtKkUg3EPHLs4HByrZFfEP/AAS10a58Sax8drOB5pZm+GSN+6JPl7fF/hhmOR0AUEk9gCa/QnwBrE3iz9i/XLq+WSO40NGeCefc3nCP5g8eeoCswyDghj2xVPoXT2f9dEZPxh1aebwrpfiSS+1FG0m4iv8AUJLmGLzrzTpQovIgFARlTKOUOSBkkjnHffFzXLH4Y+ArHU7WSDUp7Vkktpo0RTLAB88SEEnOza6gAbgpOD1rxHwTr2o/E39mPzNPnnt9V0Oa1mt0kxJE8JfyJd0ZByrRMMqRj5favYviX8Mrjxxf2tj9q/sXztKsrkW9lKGgtXtZFEywkAfLLA+5eOisOoolK4/aMh+F/guH4R3njDWpriOSx1u3ttQuLaOIExxkrIHBDEMsi7g3AwQfQ11V7q8nim+8HyzSK2jyTXc6STtsW8tbOXEUxB7OCBk8HA55FcbqmsSaz4RuLWN3FxdyLZ+WE8zFvExduvoJ3z/u1Z+OGj3Ph6303+yb22XT/DemXlmI7mTcZRG0Tuy46ZMcgAPZTUmhZ1fS5LSCwtVvLaG81DxLEsdxsbEbGK5khjw2OAYwOw+YVzfxSl1L4i6XpNrDZiK0t7CGS9SNDuWC8DWs29ByTFcKpweRkdK6698VR+IviVZyRWMl1o+k2o16S448tLiOJo41XP8AGRMSAP8AnnUWs6ZeaH8OL5be0kbxZK8lhbwAfMFku3kAI9mVD7AGgDf1nRrP4eadb6TbwodYtoLFYOduCT9322sFOeoz71g+LPEGn/Cv4YyahD9oXS7PXUtbKESZ3M7syqGPVQzv07D2qp4yh1r4g/tvaLpVnMF0zTfD8OpXqSZ3MjSIQG9Scj2AFM+IPhG4+K+p/D3wv9iWHTL27g1HykbYxS2TfK7fVGz/AMCoANaZvDXiu61jWGgZtf1lG0+3Zs70tofMCFcdBI+8kccGsPxB4QtdSm8eW+orcSXN8lxfI08floZFeEW+0Z+6saMQeMgnpzW58ZpbbxFrukzWQ8ybQ5tMv1R/+WFvcybJmPr8jR5+lS/FC9Wz8Z601zAs0qx2Fldx/wACGYXPB+qgflWc6ltEUkUNTvUim0ezeP7Ro2p2U0t/codqW5m8oq2R1xmMdf4W6YrM+PfxZvvh5YwXFuq2dncQJHd27Z8vzkkCTR+5AOcemDWNfaf/AGt450rwzJI0umW5SdxHKE2QMsmEAHXBK4HtU3x0+H+ofFvRvF/hbSC02saHc22uWsM3ytcxtCyzAY7lULbemUNR7VhseefEXW5NZ8ezLcN5839o6LJp0Z5+1oJ/lBB+8djujYByY8n7uK2LaLSfAvja/wBB1Z9j6tpl9qMsrylPJnF15ccIUnjYAzDvk9KpaVb2vi7xNoN1BCZo/D0F3quxcAgxWQdFye/mGM+xLetXPDXwafx7e654se6nvPJjNvbyygbGZo/td1IDgf6vAAAyMmlGPMHMbXxB8Lf8Kt+FXg3x9cRWWoDT7lTrtqVBFxaSStGsoBHG0tHv4+62SQOaueMvAepftG+HopNUZTJeKy+FRHdN5xWSVGxKAMKBGMZ9h6V0/i3TV+LfxRTwdcQ3VnoeveBIb3Y5C+Q/m3EaE9xnYrfUCtP9mzw1/wAJuLO4t2u7O18N20mmxkzGOSO5KlmlK9wAhAB9RXRTjbQk9G/Z10+bTrpdX1CG3jmm0gRKoKtsaIs+7cexCcHjP41yHhPxPN4f0c+LJLpbqLxBqUyWkakg2sLuS5yf4Wbnjru9657SfGWveNvE48H+H9QurPT4dFgV7ySFR0Co7A+jMzLnqfStrxjBpekfs96lbW7s1tHFPDpu5fnyAUD8dztBGK0qTsBxXwd8KR/s6eFPEfjaJbjU7u9R7fSodpL3NxO5PynOdu3POOzehrOudFvtW8RaPoF5HNf6lbRQR6hcxjdHayN+9+zQnphmOGPHQmtXwlJqnjTwQPF2rPNZ6FpFnDpWkWUXO66nKEXDDp8q/IM8/ePU1D4w+Klv4D1jU20UCbUNWunuLSUruUIlskURzg4/eOSTjqK4ZVO5oS2eiab8N5fD+g6NItvNHq8mrX4jXcLdpGY+WD6qrKmO5yRVX4AatHpfxW8L2CN9pjtrcSRM8g8uNppZmaQ57cjgdx2rP8DXmoW/xV0zw7Z7NT1q2sYb2Zc/u55lSVizsfuqSVO7kYQUa94H1D4LeE5UvNr6pe22mvpskXWMx28zOCT/ALRz6HA7GpAy/HGoXXxX/aE8VWGnxR/Ym1aHRbd54vNWOSL95JJjpjOxRz/GOa9U+L/hm3u9X8HeJbq1ns7q98jS/EmnIrK1rGkU0kN3jHyKMuj5wAVPPy4qS18LaZ4S+Fi+OLq4nttSkMFzqAABVDcXCEttPRj9nAyOzGovi94nmX4tqrLdz/2zbX9q8MTbVa2SOUhsDnO2cAHsBQBQbVtNtPF3wt8UQ2a+dd6TJJcKMKu+JERTJn/rpuGew9Oa4fS4l8J/Cq38PTMsd6mpX9sQeTIzXR2be/zcY9Qa9GtrIxeAl0c2Mj39xpIuoUY4aBbVBFKijrygB467a+fx45Txh448N6zdR/aNNupLrzS42hHD4iK8jldqtj1IFAra3Oi8U3V14J1TRpLyx+z6lZzppuo3St+7lDQARtj+EgcDPXp3rzfVrm1sfEkNrY+Yt5cWwH7vLmPeP3jY7HOfp7V6Z45tPEnxo0nUbzT7b7Zd3sNtFcRR8S2zPzbysG/hYsApHJ47V0X7OHh7w7Y3d5qlwbG21Kyi+xaldNJJHJZwqu5p264xJ8p45BJ+uXsW2ae0SRyHgq6b4j+CZ2+wwz6XqUqaZD9sn8mK0IHyzx7ecja2WPGCPWq2u/BMeDvHXhHw3qdxpesRaq5a3RgfIaYK4MbE9QmAT6gZ4Feo+ENEsV8FeL7zTbS002LQNKuNMltYI1bYLkbo7hX2lmAlDjcTkbuK8g8A6leaV8GfDestcvL4u1exu7mCa4xO6sjiKJYc8Izlhk+i561rKiorcy57kmq+IILDwNb3d9Hb3lxqmoyxJaEK/wBnW3ZTEU7gE4wRgfKeuDWhpnxD8SeJPFsF7b+GbzWrj7JjTmigMsMlzuEb3UhXKjyc7fm6bxnG6uD+I2myeF/Ffg2xvZI4bfT9OW51Dym3NPdscMme4LgD6V6NqvxOg0HwXZ+FfDnmNrOrr/p0to5WLT45JFklYk9MKqr77hUxlrYo77wV4zvPDv7SOj/2trL6hq39tyWunyy3IIjRYUEzAg7SDkAEe/pXb+KBqnin4HS69p00f9oeE9WvntRa/KuUV1mtGx0MkOWUDglRjnFeES+JLLUf2l/hjI0Mslvai7j/AHeA00irjJx1ZmK/56/T3wFv18Z/DfWPDyx29pqOpRz6heCUNtt76SWRETIGNxIAx0xmqA8j+BUd9YHUNca8gWz1rwi2oWETrvMV1G6ssuT0LiM7sHncc1rWnwck1bxdr1/bx3MM2p+F7HX4oJodl3Cxvdk8UkZ5ygAYZ6qUPRhXG6VrerfC+w8K6Dq3/Ep17Rpl0mfyvnVcTAg5IClGUkEHqPrXv3x+E3g/9pvwz40sZA9pHYG11izhOyOO0ZVi81gT80YPlkgDjGcVpGF1cBdC+MWkjSrfWbe1hs77xtaxA7ZFVLy9tE2TWczZ+Uy4yueVkXkZasG0u7PwZbahrui3l9fSa/bTogtztutNuEuC4s5YPvBiGO3H3tj4HymuA+Lfhy58D+OtMiih1JfCl54mXxrp1zBCZPsllNKPO3gfwwyZVv8AZYHrXrnwi0LSNS+MHxQlsdYt20nxS8U+mSxTNthV4IbhZAhOFkjnkmXI9GHc1rGFieY39W0PR/hB4fv5orma1s/FVrbXdtpVxCd8F4S7HCHkNIC67cfeHGdwrmv2WtLbxr8XPFGvaxNHHrFrplqmqafMwXzImkZNxXsw8rOMDG7PXmvQP2t/EsPhj9mE65LHaN4l8NRWi2lyqZE8Uk0StjnJXMiSLnkEMBxVPwNocmjftBeLvFk9vClt4qitNJjfbtAljZIyxHQjzLiE/n603vYl6qxD+1DH/wAIx8Fr7VNCMlzJq1+1jfCDMwkklxDFMoXOHE0CKcDksAeTy74JeIrXxL+yfo7s0kcZtI7fXbcELJIZVaI706hlfDYYZGCe1dFoXwnvtU+GHxS8N3V9HdzXGzxPoNxCwUxgSLI6Z4G6K4jIHYhgaZ8O9A0HQ0+IkhmUaQur2ksuVGyCC5gh2MCOT++kb6V0ct9SYdmQjTtT8L/ETxcyzLPqGjpo+uxLHCTcamI0KSnA+950CuCvOHAPJq1c+ErGDwv4i26lNaw6oLVnlkbIi8oobSY9MFWZEwT90n0ro/hxdJ4v+Jtr4lYL/wATDTL7QWRgDIzwOrRNnA6Yk+gNO1WLw/oOharpmvefJDrEEmiz28Zw1xEUDLt9JE4Kt1O0c05bFG/q+oaf8WvBmvp9ok8P6tpd7iSZJiFtpI8spZeMqNxH+cV4L4t+I32P4j+JtD/tKOHUbprXxPBiQOl9HHGkUssEmfndSN5XnIJ967T4P6T4qX4a2l9eSabea54j1DbqUjowhnljdonn2/wh0JHy8bipAGa4f43+D9PtPif8KPF2kaPpVw0l3JZ6lp9z8sepIiKk/lr82HC9BwCQvcmsAPZfiz4x8N3elazY6xqDWNn4nhm0w3W3ZEjZz5qtu4aNsNjg4DetcTpMuj+EPg74k1S+0+z0m61O6Sw15LZkjEkkQSNrlyxALblDBz2kRs9DXUfBjw94f17w54u0BrePUrnQ9WuXm851eS4+0L5vmAP90GN1Qr/CYz3Fcv8AtH+CLv4ia98NbDSLGGKHWLtdH1S0uHBj+zxK0jSuRkHKICCOuR7VUZWGtTlfipcSWnxt0/UbeGS68N+KbWNRqtrGivOUjLmB3BKibcW2KT8x3Y5BFd14e/ay0mfQLF4fD180L28bI0lp85UqMFuOuOtTeKPDel/EPSL7RdL+y6O1xFM2niKAyLpt3FOkcMzKRlgsu85HRGfr0qnovgPRLTRrSK68Zm1uo4USaEJEoicKAy4xxg5GPas2m2I/Jf8A4J0/Fm8gfxBcPtmk8l5razZMNNI6KGO7sNufyr628KeK7PxN4h0XVmulW58yP7ZFJAqpE2OEHTdtzjdyOM55r8yf2Sf2irb4b+Vb+XqV1qHmFRceeUEHHBG0Aj0696++/BfxGuPjJ8KvDOh2ekLp91ryrDJexxbfIkVVL3BYYY9cgD72cZNeTiKdzogz034aX154C1jx61vDDDpOoXQmggdArQz7Qr7W6kPtDH0LD0rVl8Vah4t8HanaeLNQutU1a3hD4lmFvjAJVEyQGwo6knJzXK/EG0bwzp/hzQdLube4sfCri8v2VWjk1RyDlZ3Qg7SQCclsDaOnFX9e8Ktr8+m+JLPVI7zR1IXUbWdgsHmS5DBzuBym4BedgABIOa5YxsaNdzlfAnxInvPhq19oN1t0O1mEV8J2P7plcYCqx5zubnBHynpW1+0jptnchfGK/aJF8tHS8yUZWCbN6njGFBX6Zr5x/aW/aL8H+AvjrYrpNyPFml6T+61HTdLmaGzuJIjmNFnXKuFJKthcHPfFcf8AEv8A4KSeJ/HHhqTSdN8GeHdJ0tmxFHdX81y8IPUAqEGDk8YqKmp00cDWqq8UfZ3wQ8QaT4x8OWd/rx1S10q5tmmsrZw204yVckHLocbhzgEZ56V5b8YP2nPBPh/UsSXz3UMJKyR2RHmsvV1BI4bjA4OD618Y/GX9s34rePtIhs/tGj2elaeqCKz0eB7WMqgAAKhvmCjoD05r5/134v6nrupLLqO2aRcAgErtK9yCfx966cPQ5tmYYihOh/ER9ifFD9v+8tNcmj0SytYFb/V/bHM0sSdQoIIwa8p8Qfta/EPxL4omt7jVpmjv381bezyZGcdPlUbhzntUP/BP79lDW/8AgoZ+09pfhGzuptN0y3tnv9e1RF3tp9irKrMM/L5rsVjj3cbnJ6KRX9EHwJ/Ya8H/AAX8DWun+E/C/h/w1YW8QjDWtjGlxKOmZZQoeaQ9TI5JYk12vDqEdTj5j8FdH8S/FHxdpaXMfgnxnql5EcW8v9jXUm5mx85OzLA9c985716n4c+Nnxo8Ea1eXGp+Bbxrye1S1glvbFF+zHau50RyCGKgLyORz3r927f9l7wjdQbr2zmvrj7zvLKQGJ6kADjnP515r8ef+CX/AIc+IHh5rjwrNJp2scskd25lhnPIC7j8ynoATnHA6dMJU4rYqNRp3Pwi+ImqfFLW7xby10fUtW+xXUd6sU1xD8skb712xl17jjAr9g/HX/BwX+zxp2haffab4om8Ua9eWSXlzpVjFJHNYykKXhmMija6MSpADcoeeRn8+v2o/g7rXwS8Qala31rNp+pae/lz20infHkn5vcHOQe4xXwzcX1tD4n1rVI4Y2m1CVgH8sBnClhyccZJOT3461nOKa95f15nTRp+0e5+onx0/wCDoLxcJpIfAfw38P29r0iufEFxJdEHHBCAqM9wMdM96+Wvif8A8F7P2oviVFNFH4u0nQbWTA8vSdDhh+XOcBmyfb1xXyWNt9K8khLztyzMeB7D0xVWe+ayZl3NgY5BJxzWceVdDtjgYJHdeOP2+/jp4rt7iS++JnjSRQrEiK4jhUcHsqfyNfPPxP8Aif4m+JdwG17xFrGtFWO37ZdNNg/j36+lelAw3WVG75h82B6+tcD8TvCkemySXEKKI2wWKL6ivQwuIV7NHDjsHaPNEr6H8dr3T9MWGaNZpFJIk6FgfWpH+PetXOY45o4U7BIgDj69c471xMVr5D4bH+FOgtzNL/CO1d0sJRbvynDHNsVGKgpOx1i/EC/1fUVWaSa4kZhgtKx54/D9K+wP2TfhfqOv+G7nX9Y0m+1bw/Zr5IW2nkXbIVUjCr95RuBP5V8WaFEkN8ryKNqHkqvzevUc/rX6K/8ABN/xlrnjrw7Y+G7G7soVsbgzW1hPDGkNxGEXzDNu+aRVGWX0ZV6cGuepThDZBHFVKnxNn0f+zT8MLXSvgLqmqRSaUup3yJbppF0A8phSTEbKMhkk6nIPck9K9BHw3tfEFlDcapquof25JHFcmOTcZGwSu7GAdq9FP+0a5zx54iv73XvBtqYIfDdrb3Mlu+pNbj7PbrsWPJcEFvMySpB+UrjuMdhqniPT7Dw3Jaxyz32rLem2t72MnZaJ5ZfzEPI2jYQR0ywrzZ1Hc6I66nGfDzwTJ4o+HSaK02mWtkb6/trLVBDJJeFEmUxtMu4DcAu3jHHPQVn/AA68O2lx8P5PAHiC+0u+1vT5pbT+0YWkhNwjFgZBGTnco6AZHTr0qv8ADvxPrPw4uvEGoXXl6vousXE9zesJjGyMFP7wIvDFc5P1J7VpeFPD1v4g0j7WgfT9e1YyXVvFdW5t/tiH5kMeV5UjIYrjGecYzWN29ypROK0TwzqPgbV9T02xvpLq1iga4tzeyF3mHQqzZBVRjgdOnevRlFvr2nR/25DGt1LL5ULK27czEgEewBNcf4z8M314n9oafHJZ61cxeWgkkUMn7xRyxzgcnhuMAGro8QJNoxjvNQWx1zQIRJcxzsNqhTguMqo+YcZXknrnNAc1zrtMfTPD3w5spL2O3sxZoljeXHlPtZpZtiAkK3ztlTwMHAPGc1n+FfhdqUeo+MLPSLiP+yZvLuJY58rGZCm1XK/K3KkEgHHStbxJ4XvtB/Zq8CfFTRbu61T+2r5bHxLoklj5S6RNKGzGNrZZEKYXcM7ZASBnFUb/AMRah4Skg8RWcl1DdanDLbSfbEJt2idio3ITyVPzhz0yRmnJW0ZPMV/hx4Ij/wCECaJpI7bT/D+oi2WL7URKZE2NlCOWXLDleOPwrW0TwN/wl3jmPWnuLrVdLtNzXsIBe4mTJZ1MuQw+XOMkElTjPNSfDfxtB4t+Eml6Tq2qi3uIXeC31G3Tb5JhyRI7oSXDnrnqXJOcU3XFfR7lb7SrNLO9uLYXv2iALu1RmwWjcIAJuWx83QHjhjhA11NbTNY0P/hGbzR7vTbabRb3/SbK2KbmhnZghA37huwSwPsR3rgf7avtL19da0W8az/0ZbaSJwFS/XjcCrKDu+RTknkqT6CuwuvD0+heAG8SiyvptJhhMbeY4imSRWBZ1TdnC/Mp+uK8w8Qae2r2sem/ZBHNa+VqMgdt8bjzF+TG5SwY44AI9RjNaR+EI9hng/xpcWd5rViqW8zao/2kw4P7xhltm4/LySOvHXORW74gvtH8RaDarpdzqET7PMnu58R3KyZBkIOMZzkYAx0rmvGXhm6ku7fUtLuF0u4kdWeCS2HlhcfvNqg4GOwzjtxmtfTrO++K1va6WZrS5k8PSrO08Z8pUh5+Yjqp5OSvoPqCmVaxd8B6XpVvq93Jq8jyItsIbRFcotw3mZ+dQQSTjHHTJzmtaL4o6fpvg6303UkvdN+wRR2sFmsZeOTIwqB9p2gY24Y54H1p1pqGk2mrx7dGXbCjQXttPIF38khlDHaCOCMAcYrEHhxrzR7h7fVNSswpMlr574WRVLYjzwG24I3cj86pSuVI6Ox8A2d1ZNY65DDeaXcXJNnG8gEcayE+YpHBLZOQRn7v5TeCvDDfAr4xatpOkFrh9W03dbuLjdJagFvlJJJAwPu5HXPeuZ07xJcaf4ZLagzbbGMyQ2ctwZo5iJN42q33SwJHvk+uK0x4wbXNEk1K1tzo9010TJbp811ZqzY27upAyMY7EjoKok5y1nubDxZc3kQhXV71njaLylZAwJO4d1Jz+JArHsfiTD4T8KahoVxDN/aU1zJcXF95TyGIs3Ccgqqrubp94lTkY51Dp03hDx5ba1dRztMtw0oiuY/mmT5d/BI68YAqb4rywXXiifWoo7e1sdQtwlzGkeYnH/PJz0JJBODnoeKAK/j+xt/BVwup6ZJJ4g0trONQ7jy9zHacBQMKdzL9ciuC8U3rz21wZtJtYU1GMpKjSFZYlKqeG/hbPen654VW80/R7y0W8tVjQSE+awGFP3evyj5QRnjpXF/FLxjpPjfRde1ExslwqFYN0reZlYieeR1I644qktTGofOvxcvprPUZ7WbyLb7KxCL5pkJXJK5bjOBjmvEtXsGtp28vay8KMV1vj7VZrm4ZLhmwfm3/AHpAe3Pf61xerXDWp3KzHccgda6jlIcFZ8nbMm35954Ht9KJlN1YKysNqD5cjpg9P6U2G3XT4mkZmKumemeaatwWt1YPu83OQzY7/wD1q6Kcr6AWBF9rgjZV/g/xqOwd7UM0hbzGPH0HT+dX2njS0CrGFJ6Y7VUM/nDdG3zL+Oecf5NaAfc//BBabf8AtI/F77Pctazr8K5WhmjIDJL/AMJN4cMZGQRnft+tfX+k+ObXxH4B8bR6lDa6Xo/iC2vNMuhBORbw3yRyo80afwqxKNg9G3AdhXwP/wAEmLm60XxD8fL/AE+ZbbUNP+FDXsEvnGDZJF4q8NSL8w56rj36d6+wPil4W+3/AAG8TfZ5JLFfFNsLsKAiJa3yLFNKFAAIRkbAzxy3oK057RKo9fX9EUv2Y9Oh8Ifs1Wgs5fsmoajHHdSyxPiYxxIZVK9sGSNcjHO7r3r03UfipDf/ABF01oLd7i8s4rm0lZN26O0lh3jBHBZd0W0Megkx0wOH+B/hJLv4Z6NZM0UL64v9lw3ocMdNsLWONrl4l7SMh27gQRnGecVY8I/FHTfENxd6pa2M1qfEmp28KQJkiEGWOK3QKARvWGN3LHnMhrilNs2Jvhr8ZryPV9D1a7s5bO5uZ47WC0mjOGgkjMrysB/fWSNV9lJr0Lwv9hvNYEmsahZ6hf6ves8Dpu22tkgYzlgePmZdufQ8CvD/ABJqEnhrWbvR9Ltzeto2prp2kSuwWUCGIqQWx9zYq8dMqBxxXrnw3+E1tPZX1rqKw6l4g0fw6ytpqlo5jFKTJPLBIDhpF3AbGUlgvB5pRTYHsnhvwzpdrLqGh2HlwLqUInmw+5g9wpiLe2FWfaP9mvM7P4t23iP9pLSlVmbRY9Ci1e5ZYzslumWePYCfRQrY/wBsVpX1wfhh4H1rxCupNcXWg6fDqrXkibTfI1vdpaHjnCy3eOeCYhwCBXn/AOzXq9j448D+Lpb2GS8sbW/TTbG2Q4by4oIgz78hly248dCT6mtpaR1A6jw34pj8Ea3rHjLWpZrSbVPCcETpdEBYWguJldyMZChFVjn1P4R2XiP/AIQz4ZabrmoyrK3hnw28gUNlZWurV4Ix9SzoeOgYZxzXB/HGx1j9oDW7rSdLs47iPSrf7JqMWcpdQzxs0YYjjlkYODncG5zxXTfFfUrO5+Dtro8Gl319qzR2dvd6dGmTFHh4WUgA7CrxA8dDj1rP2itYrluT+DvFkmo/D2+1CWNppLhrSSfbEzPJFGYITGrdPki8wEfSsr4q+OtP8Pf8JFcXStFeWviiO2uImbetxZ2unztBIR/uy7fTK/Wum+EHhz/hX/wgkjkk/tj+xLm3E3m5jkVPtKCZmHXesTqeeo59c8FJpem/E/8AbL8d28ypdaH/AGZBfyI8YeMmaHa7Yxy+0HB6gn3NZhynY6r4ifSP2drXX7yOOC9msk1TzMjzBsKyIme2Y5JD+Fa3hPwpY674vXxxa6lLa6pq2hWjx27N+7ldZpv3h/74RR7ZPeuH/aQ8RyaX4D8Q6Xbqt1bfbtKtdOtgu4vZSWbogx/FkKpbtkmuV+BWnaxpd54R0/VL66W3TSQLiDzuII0mIAZhnIZmbGegBFAuW4WPhGHU/wBpqPwXpv7nTfFkilpdxxHZuFLqMcjaBIp/pXrn7UXiXZ4ks/A/hCwMem6Jp1tYSyAAxWdtPLHJNJKemSuxQOp3tXMfGeNvBmlaprmh+Uuv6W8UMckD7GjhuA+UV1G4lsgfV6f4NhvLT4caTYG3vZvGXiLU/teoXEmWa3QBo4POOPuLtLKrEjMa8ciqjJx2Edp4Q0LTdS8cQXXl32oXXin7LYW81w3MtmspZp2Xt5knmYB6AL6Gq3wJ8Xalrfii+jjmis08SazrV8lvIjLMsauJY52z0UKMLu7HvXrP7GvwMX42/tr69o66kx0Pwbd2sd/Pjf5kdtaNuCtj5czS9u/Nb3iz/gqFY+BL3WrD4W+CfBnh/RrETwWpuNJVpLkxypbpJcMMFml+cbcljgfMea6o6w5pMxlWtLlSOC+HngSz1DxdaaVHcKzI2n6IsasFjEMES3Ernjp5jr0PJbFeJftnfFOxv7nQNDtJ4obS7tJtXvCjBVhs1kaMZ6cnYwGP1FfTn7TPinwf8RfhPoPjLw+2jeB/idbah9l8RaRo0T29re5USNdQbcANuSHec5OGU5Iy2R4A+DvhT9gf9nPwX8RNS8M6X8SvjF8VGi0/QF1+3WbTfDtgn7w3CwkHBCZkJGCzuFyqgk5yhzPfQPrFltqfPPibxRqVj+ztZ6fDbxSXltp0/iWOBpVWZLOKMBHK5ySOM+w+tZUniHS38B6D4c3wN4itrCTWbq7I+ZNpGwFvX5zge1fYGh/8FKNL+I9nqVh8WPBPhHxd4HvL6LwtdPaaMsbxpcARytbuCWKKGLYXDYXhgcEfKv7dP7Nln+wz+1v4x0Wy1a81rTtQt7fUtHe7bzJLbTpYzlXfncEkEyA9wF+lYVqa5eaLuVCs72mYngb4hw33i+0uo/JtV8QXL29u2395JZwKsTTMcghppsoN3RUIHFdT+0DBceMvFvhOx0dbddDuhe6bDcO2RbT/ACIUPOdvlqD34H0r7b/Zu+Ang3WP2WfBfgHxF4X0IfEL4yeGtQ13T9TurWI32nShBPZIsjL5iZUSzAKwwyTEckmvj79ln4e6obnwCfE1streaN4+0iS8tbmPzdjS35sbi3dW4P70BWU59DnFa+xdlqKOKTvZHNfGvxVqHjn4NeGtQm8qRb7xLp/9uJD/AKlorVHEC885IcE+uwVn/Hl7fxJ4ytfEOk/atRv7ixXSxYWox5WZUMkp9FEfX02V6X/wVq8M3HgT4q+NPDvhKxt9Pl1TxzZz2drp9uIINPtYdMgXhI8BUMjszAAZJJ5JNcDbeELj4Q+GdSuP7Qj1GTULm1j1DUsBbixsrgtD+7Ug7dzLtOepYjvms5pp2ZvCXNFSO4+E2sR+JfF+u6utxbNZ6HDCodyd1tGbhFLrz0Khxn3GcV892cq6XoXxEikVtvgTxgken2m4Ot25nxIF9R5Zbp/+v6Q/Z/8A2fZPDnhDxRoN9H+51XTdQtVlUs0tpcOqz27Ek9SsbEHjDKCMEA15j4f8Or4Y/bZ8bTQ6dZ3fh1tbfUjGwRkja9tEERCHIIR2c57bs8VMpaWKPTvD8Gn/AA78E6bYzPJcz/8ACPym/wDLzjULCKRhcIfSSKJ0lUDn90eoryH4PaxZ618RNU8RalqF9e31vFqfh7XWiwI9VjJKrc4xxutwjsR1PPrXb/E7x0PgXpGkw3Aaa81K/lt9pO/yUubXEnH93emOOvOa8f8AgTa3PwfN3DPbm41C81yaCWJ03bLaSEeZ8rdGCNgj2xUyly6ijC7PU73RNQ+AvguHwW3l3Ka1pkp07VposStA8m66s5cE7tuVYZ7FWGcmrHjaWz8EfEuH4dadb4/sG2t1tEnbL5XczO57sWIOSeoNYfx28aXGo/CCxYziTXNOjsZreRWJZ3SMWk4VuvzxiN2x1ZeckCsTTPEN18f/AIzeJPHNtcLaySanJo8Bc5e8YCLCJn7w4kbPfn0JrGpUU1dGqjZ3Z43+0TDNqPxVvNP+YvbwrBIyLuYMMnCKPr/Ot/4NfBr7Dc6fpGoRz6amqSK0MksyNc3D5GPu5wD6HpycVm/Fa3j8P/GHxJfNdTTNbvBbOwPEk4iG5R+mQvXHNdJ4d+EeveN7aRbe+l0nUJnFn9vulZY7BvJMzjy8g4MTA8Y5wOtEI8zsD3uj0j4L/BjR/DvxOh1C8vI7xbO4mFpibcLKFEd3ZpAACSI5BwByBjFe5fCH4t+GPCGmR65HDNH4fuIIbkylWKo0pJOxv4tjBTgc8Zrzz4W/DCw0O6+Gml3drqUj6l4bmv5ZbGZwLtVMkBWUgE4MU0vPQ7QDkUvwM8JaPf8AgvxV4B1jXtSGn6NqV5ozTySfJZETSRWs68/LxtJQdcke9elGn7hnfU6S3uX+LWp/ErUp1+yrKttvVz9yeB4rjeoP8DpHkHphsdc1cks9L8eeJPDKNdXVrqGuaTfaLpy7zHb/AG6CVknRvlO4SxurdQQSTnpVD9hnwfa6Xpvi/S/F2qNfXyxXXhjxHEJjG0CQ8wvHISCD9nZnRjniJQDgVzWsalN8O/GXwv0+4t1W60bx/f2d8g2xl5jaRpIwI42SgJMB90h6IRsiWzvPCMD+PfAnwU1LUoFhuNP1698C69bJIZWtTcbrRGbOduZ4Yww6DOe/Gf8AsxEa38OfC3h3ULPN3oN9f6TZ7+G8+06OvH3nV35/2frWhrWnXXhfQ/ixY2NqPDuojVrjxZpkyyYJuAwulctnHzzQywk9ckdyM04PHcMV38N/GWhLJFovjXxrDqaokZCafLcq0F1b54zucM6g44JA4Bqidzsv28vh9dax+zVfWNjPLfXHh6z/ALQmJJGLVXj80kYxkNtPXOCeKseBPGza98LfhGrGK4h8TiCC6JjJ2hoZZA+PQG3UZPHHFTeJtb1bV/h948N40d3daxqGp+DYbcDAaF7FJ7dkHclmwe2NprB/Z98RXA+Efwxt49LSX+zfDei6bLcmIF4p7z7bHGRxnerAMwzk7+a0UXuHke+/D74r3WvfDeHxKbGOPUNBE9lPBApP22zyPM49QURgB3U14h8EdatNd8N6po+oxvfeG/HU0+kwGFlVpYIW+zuefusBsx/u8ivUv2c7ObVPgPNcQn7HfDSmtjEekOoRLJ5wAPrNuHvg1iaV8JIdA0rwHHpT2mh2Wj3d1eX8QjX/AI+JGhYnb02szpuAxlXYnpVXewGr+z1J/wAKv8V33h2e6tmh8CnToI13bpnhl8xWmZs8/uyo4461yf7Tni3UdU+Md54Rj023sLjRx/wkOnzo6sNQEMX7yHPQEZJ55AHevVotNt4virqmrGNWnvPDXkTRbR5c0UE+/ftxhsK+0deMDpxXmfw81iw+JHxo8f3k6Wt5e33h1ViaeNZjAY1Acxnqu9MFgOueaznJp2A6X4xrrHjPTPHWiQx27WfiLTbiXTL7cf8AQL2JkeRDnoC8cR+mD71zXjPwno/ijxN8MbXUYJLbVJNEGr2VvE2AmpQ2aRyRMMHKs/DcjOBR8FviJ/wnXwc+GZmu7NtW8T3zOy3UgZnjDvHcZB6sYAo9SQuegrUbVdN1G4+GdzePaXGv3j309lNb7WI8612yxuM8MhjRweoYOQATmpiB1Gnaangldd1e1jjbWvEllba0wYkR3M1pGwYgHBaTy96MBn5oienXl774taBpHxT0vVLO4kl0+Py7W9uShZNGkkzEkx4AETCRVz03Njiuosdc0+L4r+G9P1KzupodNsrvUftMhLx2Mky7bhgvRFQlwV7mTPc15b8PoI/hInhWy0eCPVbrxBb3ej6hJcRq8EqW2JlV85yDHCSqkYcyMe/NSnfQDi4fEetfCT9oTxlJrkLLN4VFxq2nhCDDqdrLPE8jgg9EQuWGMjn616B4n+F3g/xP4l1DUrybRorzULmS5nRdQXakjsWYDjoCTXc/FXSbXU/GOjx3nhuK61LW7W90WVU8rF9ZGEbL1CflKxhmR1Y8qQeuBXxfefs6eP47uVZFvHkVyHZLadlY55IIGCPcVz8vKyuY/FjwH4jFlrcMsMgMLOpdlA3e/BwK/Rz9k/xjpHi220/TLG4uPKlUBNQVyZrMZDEYBGw9BlenQcV+WEDrpmtSLuZVjbAweK+lf2SP2gpPB2tfNYxXxS1FvboF8sIwXAcleWYYzknBPJHJoxNGO6Kpy7n6eR+Ko/gr4fvvD1zpM2oNrJ+0w3qHfLKd2H81pPmy2zjGAAy49a8N/wCCgP7SeqfDfwRp/wAKfD80djdeJoF1bWpYYsFbWQkRwq2TjcYSzYwSFUZ5xXSfs8+Jtc+PngnT/FniLxbbXGrSahLYLY+dnyYY2YeZ5YywUeX3dgcjgZ4+Wf2qPGH/AAmX7THjDUre6e8jtrsaZbzsD88VuixAgEDGSrH05rwcReK0PaynDqvXtLZHnul2cccipKwkknODIxwFxnrjnv296cNNzK25V2qdxzkD8P8A69NPnLJE0nLZOF9Bkf4/pSufOky8hx1KkEc+lcHM3ufZxjGK0Jnt4o7bcqs3mABd3qTivNfjP4OjUQ30SgMZPKuGHfPQgfpgY5rvrmU3E4ZcxrGSQvXPYVz/AMUZDP4SjWQqqyTInTuc8n/61dWDqONRNHBmdKFSg7n66/8ABqd+zlar8BPiB41mt4Td654iTTRcdX+yWkCSFAd3AM82cY6x1+tcrs1isa+nCgdK+Mf+Dbjw5Ho//BJ/Q7j7KkEl1rms3AkVcGTN2yE5I5xsx+Ffas0BXaxHLDtXr1Kjmz8/lFJ6DLNS1sM/M2CG+tb9jC0dvC3TbkEe+TzWLBD5UB4OOua6Cz/eQRju1OmZy2Pz1/4L0fA+O98F6L46s4V+0Sf8Se9YDPnFQ8sLNnjOC656njOcDH4A3SyW0SI33mLkcdfnbiv6Xv8AgsiVn/ZP/s+QK6z6nFNGCfmDIrDI/BiPwHTnP82vimxayPlncNryIxB4wHIwe9ZYhPc9LK+tznbhwE2rwzEj8aoxvM82Oqtx82Ofxq1dRfLt6fXjFVXlZ5GVecHDe9cp7BMiLEyhZMLnnjBrL8Zssmi3UeN20fKfQ1akDDruHQ9ay/F2obNLaONhvk55ralF8xz4hpRdzzO8XZdYHyrj5feltoWhbcFzx096n1W3PmxtjnOPwq5ommNfzopXv36GvoOZWufIzhrZFzw5F5sq5hkkkzwqDdyD+VfoR+x98JNY+HOm6P4jbULiC61q1+zyJCuJIraZhvRQQOGjT7yng55B3E/MP7OPwJuPHnimztljnyz5YxxkgDdjHIxkjn0xX3xp+lL4AjGh2MlmbmaP9wwVt1uyd4+cAHjjnOD0ry8RWu7HRh6Ti7s7y1sx8ZtX1ra839n+FUt7NbOUbpbsPvVHYZO3GzGeTk56jNcvrnhfXvhZ4yhVNYtV0nXFFhBbBF26VM4DNJcbzk4KhvlccKRkZOfR7L9n4eHvhDbeME1yx/ta8tnlvLK0lZJIQkmEbBxmQlHIT5tpx1781401qPxTA1xrFre6h4gu3+z2UMbmOO9dj8mOvI2EdRwTz6+fJ3Z6Ct0I/EmveFtLv7LST9qjaSAJdQXSlA5LbfNDK56kZwCQeldx468XXltpPh3S9Zmhjj01Xaw3kDapULtBI+XseOvPvVL4iWt5438VR3ttp39mjSLBEntLuRZpPIY8iIx7g2PmPIHT1qh408L2HjCzj0ndJc6ffYtEkKEzwuSqhkA5WT5sYwc5GBkZqQkR6BqVq2kaXY2et39j5t60Us4t2u/s0YZmfY8hOScbQWyoJHTAqDQRosvxJbVNe06HWLLT4tunEu0MbrgghhGuGKH5gXJ6isDVdZ8QabqNxo15q1tN4Z8KwKLK8ltZZLwBwWVSwdVwg6ZQnJ5JrU0qe6Tw+0MEEa2163mx6kSJI1UrjbhT1znqBgnn0oM3odFc+KLn4p3UBsJRfaZZ+bJdQo0rrbptXynY7c5AOASecYJwKuajqem678I9UXVru6jvpZxb6W5j+SK2Cku6Nnn96SpQqDlSeR11PB/x80G98GQaL/YkOn6tpOlvp+q3gsx5c8COFtycAEvhHyW+XJGFA4rz/WPixDa6D9i/sm7bSridvIcsjb1ZRg545L4I5wAMY4o63Fc6H4HfCfUvHct0slppjRwWYZLRS1qYJATuZPL2jJA3bTn73bIq1rPj/T7y+b+zpJ7W50O5FoTKgZY5kb5QW5XcVBHPfv3q58PrGL+zPs+n6266rdZieO4hMaqJFOdreoPfcc+nar1n8PoL/R9R8G+LtW1DT9MdWnkmt+DPKTw6nPySZAbPOcdOKrQ05kZ3iDVdR8OeHdf09dNnXT9YQMiXisHjZotpmjdxu2EtnbnaSM4zg1zOtXNzf2Vnq0umQ3lwtsHhkQvvjiOUAycjOcg/5zY1G01bUNHutBury61aPSibdb9n3zNbBvlJJ55GOcYJxwK6i80XUPhvFHb3UKzac0CTILgBnEZcBAeOCSQMDjnGKqMlYatuij4UtF8XaBb31o8a6onmrLAYAHKgAFlJHzLk9R3rmoxJ/wAJHp9/p62dnLaykKpBTzywIbcACAODzxgiulbTrjxJ4/tNRs7y20+6jtnltXkBjjYNtGzaAQ24gAKMZrD+H+ua18KvE3iC41Awakl1ILSWJlIEch3OGjPG3g+4z+VXGz2CRo6xr9nrM9611p7Wt9fPiORphIbUKMIinA3D/aY59aoeC0j8D+LWkuo7bXEuGMMkciI5hc7WLqCAvzA8Z9frT/BVzb6Z8QW0nXYbSx+2WwmjSONpo2IHGW2/L/LtXU/DvVNA8I+IdW8K6+qy3Gvf8TC3mZM4cfI8UZ7YUoylsEZIOTwIpmcTz/xZ4qtNf8Yz6XDJoulWsl7DLELgbntWCFt4QE8Mc8HK5PQU7x5p2paPJZzaa1xeahDOL99Q81WkuZAxJR12hACApARQoBAA6mtDX/g1a+FLFfs+nuLqaZ/sfmDz5ZVLsRuZRkDPYccdMYravX1jwr8LpNPtb63hW4xcu8smZopemyM/3flGAQeS1bcocx5x8UfjDqHxDtbXxBqFrLbvIZYDmEJDuGAcMu3kMD9M9q82l+JX2PULiPXbm1NjHsYeYx81+GKnK4yAQeueoFc38aPi/HoUN1pourpvPLSXNvdoDIsjZ+aMgDK55w2SOx5r5n8c/E+5Fwrm4juLeFdu1FPyk5IHJJ9eK0jTfUzlU6o+hvi38bo7q7ZrRc2qrueHzGZLhgRgnpgcDpnkV4d8QfirHrhk+xq1qXJeSHeChB7D6dOteaJ8Tb6wkaaOZmWQndE4yvTaMc5GAe3pWTc6/wDaZWm2bdww2e1bqndXRzyk2WNf8SCaUiePdt/iXp9O1c7Hftqh+Zdnl8AsMbverN9dQ3kY8sls8sT61R0/T1uLlhIxPAO4nk0iS9FNC1uqwyR/IoVg3zZPcc1Fq8cd1ZrtiETE4+9kr+X50ljaLZmRWXhjuHccUkbh5yskbRrvwDt644+lbU4tO4DhHJBa7VO7jPFGlp9q3M0ZiZR8o989aSHVIprqSMmTaq9h/wDWp2qxNpiq0Zb5kz8/PP4YrYD7G/4Izagmg/F74z3ElmuoJbfDWOSS2ZQwmX/hLfDG5SGBHIz2r6b+OVleazY3ngvRVisdSkvIJ1zIvkxKLREldto+Tgrx0ORxmvmf/gip4fk8UfEz4yW+/a158MfKBVtuf+Kr8NcZ6jPTPbNfTWoEr8WfFE9ybVpPEGq6a9lG8pjxZRW8LERnHzEunIzyRjisqz1Vu36s2orR+v6Isfb9UsLrQvBfheRLu+0i1u/B9p9qHkwx3U8ai5upZeW+QFxgL0QYBNVYZrTwTp3hvw/p6NI3h+0k1NLl9ym8nA+RumSEXdxxjNY/wY8Na14jvNQvtLhm1jVrjxRqdqYCyArGJMyv8+ACUcDJPAXGM169o3wvuJfGkN5byRP4d1mK602+TyVik0S+8iRY49m793HLGxYMc/NGAcZ5yjG5pKSW55J8aNDv/DWpw648kd3Y6pqdyP3K4X7RLKszIARwPLCAHP8Ae4r1/wCLUb+MvAWheMtMkj8O634bjj1O3ka6Zl1GJtoEUjluFDI6bsHazx9AMj5tt/iJceN/2Or2G6jaGS3Y3EUiZL7YisRkzz/GFGfRj68ewfDe21K+8G+GryO7j1fUtL0iHxJpKwoXsb2AxB9RsZY2XcCcIV5P3T1ByLp7knrXgnx74f8AibqF94S1VWs9L8Z+GpINNujIHjABE/kOx5M0UrSAHgFQB2NfPX7PvjJNN1h/BtvdWtrfatqskDiAjEbKhD4z2G31Fe3+H5NJ+GgtfEmkWMjeEdatLi9e3VWDRW2Y/t9lJkj54t73ELHnyY2T5iqtXk/w6+Fuj/CvUvidqVgYtavrdjB4fmmZjMlxj7QvI2/fj3IT3JHTvpV0Vhx3PTNL0z/hVafETTNJivNW1CG/hWGZQI/NGFUjHdk3ED1LZ681peAfFGj6WvxE8ZXDXF1/Z/iRLS4xGVNvE1s0iseeQZHVT3DAe9ecWvji58Q/D+/vNNnkXUPEGrz+QzMcJMBbzREnqFDCTJPaul/aW0G88H/CLxpoOkx3Fx/wnGr2cM83mKVgvLto5NqHAIjwBjOSB3OawjFvYbvc6LwRef2R4CvNakuk3eNpdZjtVMfmCZo4lFvsA6lhA49815P+wjDqEeuSeLtShWSy8QLc2ex2+eNLKFAQ4GPvM+PY17V4503+yfiX8K9LtYf7Ps9LW9s1T70VpcRbYo0HYMVmlYZOSEz65wfC3hJLrVvHLeG/s9vb3niOX+x/KiKhmby2nhQ90YxuSfXIz1raELLUFqQ/BXwdPY2Wh6rrU5uJpPEdxY3T7v3cNtam4tI1HqC4PI5BA5rm/hFqmn+KfhNrMlxJ9lurK3tnlU4Vo4o5grE+i7sjjrknOa6jx/qkdn4D8evoTSSDS/F8Wm6bA3K2x88+a/AGd15JM/0C9ep5j4kvpfhv4Xaloomkhi8aataeHYZox5lw+iaSqSXt1kDGZbl5VHGGI4FVyxXQk1fDgvvFHiO41K8t7WGx8UTWtzpFvE7bvKtyFBJxz8oi4Ocm4XvXqXhKxj8T/HG6jhijs9B0WKE3Vw/zAQW4YtOSRltziQAE/wAR9Fx5b8X/ABjqV7efC3Q9J06Ox1K+0m8v0ilUxjTBJMhhVhnISOK2jOM5PlkZ549G8CeHm8V2PjTT7e4vbjTbHS7K2tIYpCral5ccUz7wO7ujgDjIkbOc5o5UwPoP/glFDomh+PvF2m2NuLPXPiFbanqcJdirCaXbKsROTtZY9owOnlOcdSfmD9nDwnffCPxbda54y8LeFdUs9Quna50TxDHIBaASssYYAZEquzMAfTJyQK7DXfil/wAKX8OaD4kjmmj8U3WoRXGjWmnSFTb7CHlOVzyctGzHICyFcHqF/aF/4KYaLo3hmbxH8Qvgb8NfGXxBjmgsrC6VZrMapfFeHkiHmEonUln9sjNDcVG3U5Z0p8/Mloz37xZ4o8G/E79hLxp8Qf8AhXfhfwnp/hvxBBZaXdWMB/0yKOeJHYE4yDI7J6EA964b9u/Srz48/BP4A+MvDDXk3hvUNBk8N3F1CPlsrhXiQqy4+Uu0cqE/7GOc4rx/45/tzfEjxz+xRrH9u2PhO10vV9V08DTNOt5LW00GK3YPFDZQiQ4UyKqyGQsW3MRtO0DW+Bn7YPjL9j/4XXXhb+wdJ8Y2WvCKeXQdTDTW1w1w6xBlw2YiZCAxww6Hbnmp54u6fVCjRmrSW6fc43T/AARJ441bwzoOiw2sqS6pLo2iwISJLudH2HaO4ClRuOSMEk5Oa99/az+DXhn9rr/gq/Z/DvUFa/t7Hw1p3hS6ltZCrsYlkvLptyngJFIiseqsccGvMPHv/BRX/hT2m6tpnwZ+FPwv+HOtaQzaXLroV72bTJ7iRzcG1DbERhguGbcMldyMBg+I/sj/ALTPi79mz4ieJvEOjS6LrXjDxXot1ZNrepyzz/2PbSMJJrpCpUmdmRGYvkkqvbdlRlFK1y3RqPWx95ftIfHn4L+LvF+j/FCz+LviHw/b+EfF1pbWb6Z4WNzDALAyxtZqOnkyAzJ5mORKMZ4xyn7fdlYeEf2i/hTqHheCO60X4o/ELwv4ktrmN2VXiuNRiuHZcn5t0sYkIPTz/TivgS70mb4X/sWf2XrF5Jam41eHVbK0WUMn2UwzBZGPVcnB2nnOO4rudK/by1PQ/wBkn9n7Sta0bSdc1n4d60n/AAgr+ZIlzdi2dGxdkSYMOTCoVAjbUGMckt1ubcj6rJO6PTv+Cg/jq31T/gpF8XPtUzbdD1i00mzg3lVQyaZbSyP8uDu3yIecnGR04rhNYk8O6p4+8eXmuW819pcNzc2ltuZlj8m3iinV1HTId5CCeQT14FenftH/APBSX7H4rtdQ1D4C/AnxF4s1mM6n4huLzSJLm4tfKQRvI7ebubH7tVJP3RjtmvLfjvqXiT9rrxNdS6bo3w5+GraLa3eivpGmQvZWl7KpeTzjywLOr7d5O35Bx6xU5X7yZ00YzjaLR6d4I+J0Hw0+CPijxDe3jXmoaXePd3rs+77WLe5ltRGmeArQgc4BAkyOeR81/Cj4k3eq6lNcSHdJqV9Yi78zBVIYY41VMgddwUZ65HfrWN8OvFc/xT0uPQb2S40u01y6Mc89wTJbXGZeTnsC69enJqTR9Qj+GHiP+x5Irf8AtT/hI7ex8lGDLshGQwwRkZx29PeuSUmzpe9j0n4t63NqP7X9nb6tLC0On/abnGN0cayWzrGCP9ky59j9BjO/aB0JNB+IesaszSSySNb3LKCVRpZoVVnODxkDJ7DdXGN49t5fHuiXTTRtHf6hcm5vZE3OLa2LEd+4C57egFeseAtGh+Kfj7UrXxhI2iTX2hhvCkk3+jLqksaMw8wMGDI6kAMMDcG+lSnzEOXKed+JPht4w+LWiWNjozWCwh/MiAuNtxKh3MijcPvEZA9dp/G/4f8Ahh4m+EPgOz1vXNN0nT7TS5QYrX7RI95G8o2LM+Dj5skHB6joM0ngnU93wf8AiVrrNJbt4PstOtbdo2KtBdG8dCR7iPOfc+nFdcfh9Z/tAaRqXiTUrqPTbW61GXQLlbpd0yRNb/aYL2L0ZvKwCwK5c9DWlOgmrBUnrocZ4j+DOtXXxj0XxN4gsVuvB8ckV1eSQJutrOWcoqvO642k4Y88cV2Px98T2/jyPxH4JHm6PeeJPE2l3dncFSPsQK/Y3JzzseJopQO4TjvXrH7N3i3WPHXwL+IPg+xvGuL3Q9Ml0q1uJUjxOAJHtTOvcTxsoB/hZMjA6Zfxd+Gr/tZ/s5aZ4y0nS2tvH/g2CLVxYvIGW9tEBleyJzydyvJDyMfvoz1GO+jBJEczG+GrHUdF/aK019N1pG1T4fWFt4Mn02ZBFFJHKTJBcIOdxfcUbceDtxwTnJ8Q+DIfhNJ8UJFmury+8S68+tmFQpFrbxLJLI4AP3UYrnPI2H3zN8arm1s/h74d+Nui7byxiuLHTdWDMWjurcTRy2N0/QiRHMkDc4wsJwMMW2v2VLTSvj9q3iDUrqO61SPWJ3uNR87DLZK1rKZQoA+VJGkmAxk4YAk8kuppogW56F4M8HaNpPwd0z4ga3oqWurXvh+3l8R/Z3Pl6iJFW3MsiKdu5AxO5QMrJg5AGPL/ANoaXw5rn7R9uupMwfwjoM2phrfMayXluLYoTtADsI2IxnBG0EEAAenfDH4jeV8LNV0vU7WC0j8G6d/YWsWJJkaGFZPJLHnkLsRs9MHOOhr598R/EqHVfEum6pp6xzWLeNZNQsZrkbo5LZLGFJ+mCU3R9Djn8KzdRLRlnWfGP45H4j/EDw6ukxzQW/ibWdZiuZ7qMqrWck0TiJf+BhSuMEFjjHzZ6z4V+Ho7D9kfxc0sci6X4fh3aQMZEdxYXtywlDcNvKpICc9Hwc8Vi/DbwrNafECX7XI+pWujvKbO0ghEiPdNcIGkJPGEkuMDAH+qbOQMV6FqFrYxfsRQ+FdNltbJdYjt7WBwwVUvJpnkmAORkOzyZ993IxRTk5E8yJvBei6trvjK9j1iytbWGaO/1K1Ctubz1trGEuccDDQyYHXBPGDzoWPhrTvh34Vvo22wWMPiuG8023hkZfIFpaPIq5z90OzMBkjIOeK6Txjrdj8K/gVoviVoZ7i48L3ljq929xhZ5LW7Jsps8AcmOM47Ecg9K4/x7ZLp/gqTVrrybjTtB0XxKk7RMWX7dNBbfZzgn5iEk4P94sQccV0R3sSdT8E9Zmv9I8XLazstvrfi66sLGd4yI44rjLxzp3YCSUnPcBvSuZ/bH8Vah8MPhXea5emCPS5dHjy0LHd9vgItJ8dMA+bbyA9CqP0I53PEXizPwrsLnTbS8hvNW1HSrbRLdQoQNDbiVU3E8L5SszEnqRgjpUv7XXg3Tvjh+yx8S9At1uG1bQ1le1jdfnS+QCbbnbgq8JPA5O7qOKqWwBqPjvT9X8G6H4+hWaCbxBow8OLK8hbBdWKMik4+cquWxnMfuc+Z/wDBPrUtW8QTza9c2L3UySHTPtDIRDcASC2uFBGMEluO37vjHfoG8Mab4n/Zr+Fvhye5lhsoUsL6Eb/Mk/cwZdiw4/jk/FKo/sopH4A+DdpJMzRzaT8U7uxaMyiPzoohP5gUHAccB8D0rjkpXK3QfBfwf/wpf466t4O1WH7UngPXbBdFnYBlEV5KzhxnoxjZQeDgqelaPxUs10K7utB0GNbS98M+PL9riW1ZI3t4XEssC7nBIHkuQ3qE5yeaj/4KVfEH/hEfihpeoabIVur60tbAJAgEksy3JMUg5ycLKqgnso44NWvFnw3j0bUPEthFd22qa3J4bl8Q6pJLN5jTTrbSwKrbP4lCyZwc8r075vm5rCOs+J/xI/tjxHN4kFuv2z4Z63FqdoDNg6jBPCBdW+E4ZGQd8EOgx1Jq1oOh2viy5m8NQSNYw+FNeGrafcrFuNxbKEljcyZJw8DmLDZ/1TDgtmvNPilp7T+P/Ad/dLGkOvRyQamsKhoY08xGeU7f7nmMi9cb04PLV7JoHjWwvG1fVoZWs4ZtbvPD2qsyhjbNAhtxIhHbzFiIyDne2eoI2hFp+8U7HRfDxj/wh2l6z4ijhk1DwTq1wdLmeQx+bCW2RknhSjRvsOcgjk5IzXxp4m+Ner2HiTUILDVtWtrGG5kjt4VKsIowxCqCck4XAzk9K+k4vEuoeBvhpol1qGm3trN4Vu/7D8U208Z8m4tZLplt7yMgsHj2u4J4wrgnGK6Cb9nHw75rbtN1ZWyciJTsB/2fl6entVSa6kH8sHxA8ONYSx3McflxTY+70U+nv0zmjwj4qbRJ4Vjm4f7wx1BHT8K2/GU819ahHuFlSPJB28HFcXo8X2W73SbT5Q3MAOnpU0Pep2ke5xBhIYfGNUtmfVH7OnxUbwdeWd1aXEsbWpEjohwXXzNzduB2Ixz1rQkl/tjVL66b7l9dS3BwPlVnbdj8MivDvAWvIuoQ7Sbfy+rP91/mzzjPQHHA7V7P4Y1uOa4a3uJIYobp1jN0+5Y7ckf6xiAW2f3sKSAOhNeNjo23OjI5WqNouT6fGyqLceV5R6k5JJxzgfSucuY2S4ZFPm9TnGOf8969T+InwL8VfDzQ7W81Kzj1DQ7xR9j1zTJ11DR9QHH+qvIQYz94fKSHBOGVDxXD/wBiTBpW8nywnGGbGT6Y6/04rzpQSdmfTe0ckZ1vYTN5ZCxnPGN3HPv7VwPxE1JdX1iK1R1kjtHYOFOVLnjP4V1vijxE2lpJEoaORkCBlPCjPP58j8a80vImvbvcpKqxw4zyT3NdmEiua55OZVuWHKf0v/8ABuJ4otde/wCCUXhDRVkj+12Oqa5CyBssP+JhI4yvbPmgY9vfA+ytQga2mjVs71UAgjG2vyF/4Nfv2oLf/hV/jD4ZzXix63oupnxHZRY+e8tZljScqP4ikqgleBhgc5yB+xEvifT9btIZbseXcjAaZQNrD35z+lehKV+h8ZUUvsmdyYov4uMce9atorKsKK2GHIbH49KzdU8T6DpFs0zapA/ljlV5J+grxn46ftPrb+H7y30OVrT5NjSzKokf6DcQB7g59qlOxnGMnufOv/BWn4pp8RLhNJtZt2n6REFZg3ySzZfOD7ZUd84r8HfFUn2TxDqVrJktb38sUwVdzIfMY5C98A5Azz096/TT9rz4sapJ9ot1X7QrbseWvmb25ycdevoK+DLH9mTxB8Ydf1S8sI7eO51C5GLOdzDJuILFzwSoAHO7HXgHnE1JXjZnfhp+zZ5n4/8AhxceGwk8d7Y65oN1s+za1ppM1pKzKH8ljwY51H34nwykH7w+Y8tJFFbKepf26jHr6V7Vqf7A/irQrorqF5DYzTkIojTzTIf97IGPc45+tdVJ/wAEufEeiaTYzatJdNNqRDRxw3luxMZCsHPls5A55J4HPcVio9T0I4pPdnyxeauTZY27vLBIwOuPwrn7yM6irblK56Z5xX2n4N/4Jop4h8PzahHLZXHl5V7f+05nlUqxG1lji+QnGODnnr6avxI/4J6aD8MLOxutSjs7bzpUSSISzTSIxA7OQT19PWtIzjHU569bm0Phg+BZtRuNPYW8hhumEasEzzn09PrX0l8Ev+CeupeJZJpdS1Cx023hiWdUZRI8wZNwX74C56dz7V9L6J+y5oHgj4eQw2a2tuyyR7JQzAW8jkf6wnORuwPYH8K910nwRPr1jpvhltMtWvtMtZDNPp8gaN3g4llByu6MsHKbsMQy5UcgaPEO1keb7BXuzzn4N+BtD+C3gDTdHsfD81vqE8Ul2l+Pmku5N5CjplFKgcEn9a2NE8G3nizUdQu9YWHR3t7ZFszjzjliWGVIX169zkY453odY1KKe9k+xvcavb/urZZExaqmT8x3fNuxg8DA9KdonjTwnda0PAur3lnouqalefbb25mmm8y2VARtVlH3ACvHqW9s80pORXKHxG1fwnq/hHT47a8abXtKZYnEc+GjkLp5i4yq/Pszgg42jk1k6n4tbQ9ZtdWU3hvFvoYrMSW4UpGDwvJI3ZGS3GRx7nc8SXmk614T0u+0u+gk0+6nmWGYlWjupIpNrOoKhxkjq2Ccg4HNbniXUdstv/aWlzEWCqftHllYizY2nK+nT04PfGYNo7FnWFvIvHF7J5evWf8AaFoLeI6W8axoN2cyRfMDlifTO7tjNRfD7xno/jH4Nagt/pDWviKOedGm8sie5uU3YRTw0UgKqQM9SOmM1j658Y9Ut/E19YQWtrZ2t1bxWxa6Qssckas24EEHJwDx61zniaaxsNAm8/UETWpDJcpLbA8vgksQ2OufrjqaCrE/wx1TVPEurX1xZ26w3VnFHDPpt1ArCYMw/eZP3toHXGCTWlpN3qVjrd5ZS2uh2th5Ykji81vLmkI2uCyqCpx827kZJHHaitxD4b8QeH9Yt7y1tWZDAk9qCqviMsVIwc5IHbg+3Na2q+EbrxX4b1TXL42SalEwYCIMoeHPzOMLgY9M+nBoJ5e5P+zl8RG8FeD9VjutM0m4j8QzSs3l3S3FxbopdVicc8qSW2scjHNYl5oUWteDfsV8i+TZmVrYxRiKaA+YWXKjJIJJIyehH0rY8OwW/wAO7eO3tVuLq11YxyYVVy14wXB3Kf8AVkjnIx6YA52p9Et7DwlMdQtNU0/xZasyXpd4fs8gZ2MZTyy3HleWCcA788HrW3KiOVHm3hO51bUIriH7dapDZZI+0MsSybcEqGzndk//AFq6/wAHeKtR1Ox/tBbS+vrexc2l3cvcAi2kQsc7e+ODk9sVhN4af4NX1nfTaSNWsmuFupVt8tLCsgwCeUGdwU9a6XUtdu9C0Fr63jubVdUab7SptV8tmGeJBkkHJ64GRn8D2aCyM34r/Fe88L6NZ3djC1nBNaGIxWkY23O52LNISCSDvJHTGABjirmp+Ita8efCeG81i+huLeSKKOSKPcJ4LbcCPl5IYHkBv94HgA5OpT6T4zsrWKS7tdP1TRot01rNuQONw2gAZBZsgr29SK29QTQjLp95pMiecbXffW0hMgmI3demMEjpnHbNZuOugrtaIqax48j1S3061t5LGTyVREltYABEUGQpXg5OAO2DzzXJ23hK11a8uWu9Uum1qK5Lid5XHmI7ZEallIGM9ic4HHGK6uw8Pt8W7WPVofsOmwabcCGR1C/vYRjcwU/Mdv8AewR3JxVrxXa6T4TSS8t9Sj1JrdwBGm3apx0IyAe5+lFmguzjvGCrpenr4gDW95Zzs2nqZFMcsiltpI+9sweoycEdTTvDUkdrrV1eSrHqHmafMsLGPzJldiOA2OpIx75qHwpqGn3ljpcYm8nWIb+We4k85ZYFVm3fINpPsS2ASc11WtaXovhDwVf6tb3lx/al3BJ5i/IyBmzlNg5yCc5B/wAKJW6Btsc5N4x8QeEbO3m3rcXdxCh0qQ3P7uKUHIRlYfPk5GG2gZ74FeZftG/HL7Fdi80e6vtNumjH2+OYBl8wuXYqOgXJIAHQ5r0CTxTZfFy+hs4jNa22l2MNrdGNSxN5tGX4OSO/3QenB6181ftL6n/ZJuLa6s77zPMMe50AyoJxkHn3+hFdFPXQk8K+M/xc1Lxp4hP2xnmABQT7uWHv6dK8zMq3lxKqXGWQ/MAOG9/6V1HiuWa3s3WVdscjNtcDjnHXvXGWukxz6kfMkZNwJbBxzxj8MH8yK9CChbzMJXTstircRH7YyZ+Xd8rDn8DVpYDaR7dol4+8eM0txp0cKssUnzSHK7j+PNVClzHPH83DEA9+9aKKWxkRXMu12Zo+GPG0dKfF5Usi/vGTdwMD/wCvUF7BLDcqrSbllJHA6DBqzParp7htzMqk5B70vZxASS/bT7tYJMzRsp2sq/ewe4qb+0HkCx+Qyxqcb9+Rn8qbfSCaJTGuzGDnHQVYtZMacvzL8oJwe/JqwKlxM2lWk2NrecQTzgj8KnEsusWRaSRVDdsdB+fP6VVEXl2rRMpUt93Pp6/zH4VLa6gsWn+TsYXCt5eOxHrQB9of8ERp9J8P/FH40XWqPJBptt8LZXuJUJLRqvijw2d4x6HBx7V9L/HX4R3Hw20/4cnT9cTxNDDqIsb2+TZDLCJGaWNSMsGQs2FYEc9iDXyP/wAEpPidN8MPFfxs1i0WNrmz+G0ESrKoMZMni/wvHhgfvKQxyO4Jr6M8ZyWtx4a8dafZyRhNPuLPWtO+yFmQ2sM8Re1QN2UEspHOAAcDmpqRVrs0o9fX9Ed58OtDufhZ8a9B0mPWrmax1Se6u/3Si1mMk9yI5YCqkqzoQMtxlcHArvP2qPjTp/wJ1bxBqFxJtg8WQr4f1/ywq/ZbkWzPaX8YAJLEP8yg87Adw3cedx+JrPxZ+2Pq32jfcWNtPBr1gkLDFsjPmXBBBU8AnrXnfx10/wATfG34p2el2Nnfap4b8N6pHLqE5gJDsPNW2Mnu0aEenyDJGa51JrY1kk9y98PPAsPh7SfDnha8hZrW90KSO8+fCxgBS6liOpkKnHBy3fHPRfsv+M77UP2Wtd8Pq0um694f12ZPC86MzzWssW6XbGSBuiby9jfwshII7HQ1T4Wwahp1r4j0XV7HxNb2lzNPrMcZNteW1tLsXEcOSssMcik+YrA4JJAAre8W2Ufw+/a98Cw2sMOn6Hq93b3VsVbyxOYkaFuvyjKqhJ5yGPrV0VJO7HpY2PiZ421y7/Zv0PVPsdld3uhapBrN1FaTeZBdaZPHNBeAFV+fZ5zIUIBUMpxhcVxP7Jlu+seBvFJV47lfB+pBoXDb3uCsYaEA9iwh2nJzllAzmvRvh9deG/GHxGuLdLWbSfC+prJZWu5HSOOUSOZERvukn96BySyCPjrXkPwG0tvg38dta+GqrNME1C2h1CT5v380N3HKkhP3gpQZYkAEHFb2Un7xJ6V4hstD0nxN8Ib7SbS30/RdQ8YR/wBo2aYTNnfCVopW45XiVD6BB9BoftOajHp3we8U61dT3VvNbXvhrVrSJn4khcQQrtXGSxCOcj+7jHetrS/hHHqPw6u9Unljub5dIkj0xk+7bvDLd7SoxjASdecZGOneofiJqFn4y+Img+GzZNrXkrodzf8AljEK21jDKDls5I864jH3R9w+nOnLBaIDB+OPjvW/id+0X448MeF4Ws9L0fWYtUm1AtiS3UQAOoXs7NKVVc5yOwOa9auZrH4Sa/4TsI2WQrJKbW1hQB7hbKCWHPfLyXNwAW7k9OKzfh94YXwpoekzavG1rrmsaxrPiDxAJSu4yJtA5yR8u+PaOmQOlcrqeq3Xi/QvAvi2zgaTULfTL1LCKRcI9zdFZpZwBuJSFwvUDLnHTmj3QLEWh6f8KfBviSxuY45LzxI91Jaoq+a91cgmaa4VeuBKwVTxjbn2Hmvh34C2o0nwQ2t6lfSeJryDTgIt58m0hmEszxGP/ZjETluNzSZ47+2/Db4QXF342svEHiS7ln1TXnHhnSoXbZDplpGha8uhzncfKcb8ZLMuBjDV5d8NPiDa/FL4leIviHLbt9nv9Tmt/CmkQjZI8ShIoJJQPupsiiO0MS3sMmsa2iVgN5dDXVfj5441x5Fe2aWPwpo1xLmRrS2towswhA/jkkZtzdcYHHNdovjb/hD9CXWLWMaJbjWptQlGw7ry1t98BdsD5UzEyKDnPXABrzp9Ia7+IGm2cEtuum2t7LBbxWkrEGSH95d+Yc4Ulgu45OSzduvPfGr4ga18WPDO+0+z2OhvrSabcXat+7it7a3eeQjg7l3Mc9fm2DkNkc/tJFWNL4aarc63qt5qiiTVNQZzPHDcqCunW/LRooX5UXAb5eOmTkmsnwN8MNX+I/xmi1DxJp8Mb6Lm9j0+cebJGWYABugWU/Kp4O3dnnivYPBWlx6DaeAre1s4dOs/Eeo3FzOsihZp44INquw3HPLRkgkDnvXl3wE+O8UXh/xF4kvDdQ7b5BiZAZnkWUNGp+bksqMdoz1HtRKLeoadCt+0jrn9rfDzQ/Dky+XdSKTPGvzeW63fmkBVxlvlxj8Se1Wfif4oj8J+JNK1ybVlur/V7QwrAibvIWG9hlXBB5DeZtxgYCE85wOQ8a2+s2Gt+Ib68mt/7c8QTx2ehwQkiSz+3ATNLICo2mGGRlbqQ4xg9a9L1Hwb4Z1LxD4m0uO1habw9p8elSONpNvNJZzXAcEn5W3eT15JUjGMEkYt6oNTzv45fCuTwv8AAa+8QRpJFcXHieW4urJczSXJuM/MehHluhU9c5z8uKT9lP4K3XxE+Gusa1qkx0qTWoJ/s7O22W1htiQxI4+ViNrDjrjmqPg/x5eeLbHw1fa3LdLZzeLprGdZoysUkMoG+Yt6Z+715z3zXSftS6pJ8B/hHJpGktK3izxU7aTbJG+SlpcSPO5SPPA8sQgsBnJ6Y5o5WnqVc5O2uIv2j/H7QppNtN4Z/teUTQpKhS6VUlMUSghcqqSIcAgDHuBVX4ieLdF0mwuptL0V2XwzrI03wxbQjMMVwwQTnd0HEfuBjOTjnPsLex+FXwqTw+l/I/iKEG41OZB8tojlQyg8fOcKoG3v1FampXsel/B3UprdrWRtA1WS9vokjPm/aLhBABgnG5DkHoPm4zWcpW2BWG6N4Os9T8RWcerakbrWfGGpwxXV0AYlgsSpaaNXzyoaPG4Y5GQPlrD0PxDFrGrap4u1a4uprFkvLkRtMyhbViYLaI8/eduSSOhX61zmr/ECO6vvDl00k27TdJazWGNgJJ2aZyOM/KAM5YkEdga7vwx8ENe8afC/Wtebw3cto+lwJdXVjY3IfMcYDIXi3byMgZK+h9Kz9pItWL0HheH4c/D3wlBcXz3GqeILVryWOVgxtVUgIigAdM7ie5PQVxt546TTPiN9ontY73UNDQ+W8EW5764KKU4GdrE4JPzdzWx4c8Oap8atX0LUrqSPw/HHZ+VYTzW7iHYQSMsCQM4AxjqRzWj8Dfh/rV/8SNKh1vS7ufSdS8Q+RHc2yDbcxwRiF5FJ/gBIOT2596IwbeopPW5r/A34L3S/FvwpH4qtofD7ra/aE0xnilNyhVp2AZjgZMQVjjpuBFe2/tIWl/c+DNF1ixufsd58I5U1COaW2WdpdLubh7ZWikU4dF2qQu07QcZPWsL4RfEWz1s3CSaNZxTaVdxmW1dzHNbXEN6xlOW6RvBI8bAY69O9dTp2h6H4v8G+LvBOpX00mo6dJrXhC2aZhGzReT9stPMK8Hd5WARn5n4yDmu6lTpqNmZyOT+MHwzk8deBfi94V8Hw2mr+JdUQa2z28IjfWR5aSMqjdteVNxYbQCQxHufMvgH8aNFt/DWgXGraWupM/wDaGhyWUmds8kNqLiBnAXdv2zyx5wCCFJ9B7N+zVBb614U8NTaNf7fF/wBjh1qC+lb95KyGWJUk25GwKoRgM9U752+U6D8MtLvrPVPHWlW86tqkuvahaWW5V/szWrWOPzgo+7sltpZztOMNCegGa0slsSdh4Pv739lO63aN5lxe614dttd0y6kQq3iSwhkLCKUbmCz2qyyxbudyyK2AMCvO/wBlP4+6p4Tv/Ez6bqlx9j0vzMxS5lSa3a7aZJv96PzdmeQRuHFepazr+ofDLQ/g7deI5NLbT/DVzqejzPaXHnlLa409pIFBwMh1GR2/1ffIXxfwV4o0XwL441a3v4fM0O+g8qOaMKJ/sVygYPH0B2+YAQ2BuRhkYBObqWehUfM92/aC0D/hTv7I3iLwjZtJqnhfxbp9y1vcRorrp13HJBKYG2jClWCtGQRlZDwNvOt/wSTWOH4TX1olzHZtqljbWUsztw91ICscLHt5hOwc5BYdelQ+I/C958PP2VfitFq2pWWuSeG7PTViljjZLdnlkhhi1BVbkF0O04yMq2egzT/4JueCbV/2ffF1lqlneR28mp2Woq2Ww8cTNKhifPIUxK3B645rTfUdkbvw8a5+K3ijx14wtY2t9L8T+ALrSdTQAKF1CCIo0xTOcsUX3yDye3lGi/DuTT/2aPBMbWtxNex3NjcapGEy1laXzKCmM527fKOQBkseK9Q/Z/aa18NaR4Fa8mj1T4jeFp5FuEKp5NxPCZ2JAHPt7k/jwR+Idx4/8KeK30+3jU6vd6faQ7U+W2tIJoYI2A5JzIMADJJwMCseW7uM9J8D+MY/g3400OW3+13UskMk91unPypJe3TxDOCCxADHIHDDnsPVPE3gbTtAsP8AhGW023b/AIR20t9aVmAPlSxXczbRxhWMdy5B6jAODmvIdL8Jad4f+EVqLrbqj6frdvouqS202ZrqCC8LB45MA48m6Q5ZVPqOOfRtS8Uv4q1vWrXUbw/2toOk6o92sQDSXp+0iNFAyMnbsdR0wwHB4N+7EmXkYv8AwVQ1m8039myxnF9PFcSTWulahFjm+tnuleNuTyyzxfMB0ErHPXJP4f0n40fD3wP4bt9QmurfXtb1WHVUjuJDDPcxwwRqSARhQYmYLnGFx71i/wDBVe5W7/Zn8JTXE0cuseHdZsRc2+4r5xaIBio6khy5AOO+cV6x8G9Hsfh58JLGJprW1l0TULf7DAu5S1xcQnzGbIzl1aQjJGWb0xi1J3uSJ8RLu48B+CINaa1guNN8N+J/O0+2RQqtA1lLZ2yhecHzkRM9Tu6DHPf/AA0h1TT/ABStrrEVvfTalJHe6hHEgVVuorWFXJwTncqbcHoB3zgc5pPw8k8c28OjL9ml/wBDjuYLGVpBGbu31VJfOZh1Kofu9yfxrM0v4mHRPCvjHxlbStdXE/kzWaSYREUyshkxwFDKADk5xg4quZsDxLxJZ/8ACCt4F8zUZtPsNF8T3HgnUfNZpEjUTOUKE4GDC2zHOPU5xX0jYfBawvZfD1jp9xNHY6LrN5qtkcc3dzPbyo8kr9mxIzD1LdsV5r+174U0rwh8GdNk1GMXRi1yLxVJGCoaaWFY5TubHIbymQ4HRvwr1D9nnVZPiLqWs3lnZ+Tp/itrS6sJ2kG0xLZQwyOuDhV+0I6jucE4GcVnzpSsF30Pnn9v2xuIf2pbi+ltZr7SfBmg6RqNsMHYZU+TMh7/AL4g477VHfj1zS/DGn/DT4AadNrkjLqmpeGdRgnnCf6XdXV0piWINyxw7jHHfsc1zHxH8AxfHD4l/FOG9vCsdnFY3KwrzNC0Xls8DKeiuykjBPOD9D4sfGDUpPDS6pNbx6jJ4W+J72+n2kKbzfb3maKPI5Hll4mA6buDgYNTypyuylqtTlJ9ck8Dabonh24TVLnxL4buIZ9zF2up0NtZ3NwAOgVpk8rPIHllsHJUenaFDqGhaJrUH2WO8ufGHi83gt5PuWtve2yzzoSActHujbfjGSD1rL8TaNf6J4t8Z/EDW7jTbWGTSrXT9MkikdpLWcxyAliUAKyNbIeCxwegJIrW8dW0+u+IrPWLeKaRrPUtKGkQ7jbot4wa1uYJQCRkRx7woz0TtnGgSO08a/EG30C6eaNZLzS4bK1tdTadt5v7CRxbkPkZO3egJxkBt3au3SXWdi/ZNdmNrj9yTKSSn8POPTFfP3i34mXw/am8UaLb3Ui+HdDfyr6KdUMM0UStHtfjKRyI/LAnBXIyRR/wqDx3D8um6tbtpq8Wpkn2uYv4MjHB24yKmUo394mx/Od4jspEK+XGqxg+WNxzk+/b8a5jxBbR2sXyqq7uu0dTjmu88a6fImpSwxsojhAJB/5ZZ6Z965O+0oahcmJmj8zlRzwfevOwtZ9T9E4kwKnflWpDoWqrDHBG+2TduDKwzxn16V6L4c8bfaZdsascAfKg4xj0ryJ/9Bv2ibICsVyP6V2Hwt8TNpWt58pZF27cuPl2njqO/WunFUY1InwuHxNShLTc9m8J+JNeb7SmirrCLcBWuvs7yIJDggFwpwT1GWGevvXdeHPhh4h1hPt0kLSySDOJA7M5HHr2rd/Y8/aBvPA2naxovh793qetRrEhMhUMo3fPwQP3e5uc/wAfavujQPFujad8JPCeoSPM9reL9l8m7mjaKa4dzGN6I2/lzuJZs9sc5rwamFtKx7VPOKvQ/P7xX+yTqlqsN3qU19BHdDMai02rjn+Ik55B6VH4Z/YNu/EUiTC6vbe32qdzxoq4PIOOWJI/Kv05+Ivwg1Caw0NdPgWTxVHHst4rydYLeMKT+8CuD1GQATg5BycVifDLxhBrPw+1q8vrIR3ehTPp15K6AmCUfKY92e5z06Yraj+7VonJisTUqv3tT5l/ZJ/Zuk/ZZ+Ovh/xHaa1qWnaxpd95cl4sEptLeGRCGWXywdysCMgD+VfZXin/AIKkfEaHxlJYWt14Jh8NwoXmu47J5ZNpIGdzSMDj0VcjvxWH8RPg5p8vwV1a6tPt+l6ZJbSRxySOwMrYyXyx68Eg+3tXB/Dj9n2w+E/gTw3o/ixbO4OoKdSe12qqWwIC7R2YHBBYY71pKrK1zlp009z3jW/jr48vBb6hLqupR6Dryt9nul2RwIGG5X3qOjAgjsc1w/jLX7rwJ4x0tdQu7zxhBqyLaSxyMZphM4BRyEO5AfmUHGMAEkV2Gp/YPBnwNs9KvL2+utFQ/Z9JskuGmW2iLZgjiHUqqbVUDPCimeDfhFda74z8WeID4s0nwzrml6asWmaPqZHn627/AHRCnmA74wFXCq5GcnFTdkOJxF38MtPv/C9jqcaXemXVzOySNOk6ta4ZGQKMbW3hiDk5we2KPE3wls7DRJtQvf7LuJtQkCNsEizws42MBxg5+U7gTjHfNedfGV/GHg86/qF9ZX1xrFuYlWS2jV1lUR7SgOTt2ksMnBJz2wa9/wDAOvaX8TtJ0+bTYWvrGx0dRqMkbBpbYsOHmUNhWyrbVxkgE8Ag1LkxezueK2XxN8LaDp/iWPxHo661rVlFHDZm3ikaB4wQuCpUfNjaQX4+96gVx3h610/wn8L9LhtPD93a31vfut7JcSbmwxz5caZIMYOAFXGNgPNdX4pS4g07UtS0uOS10+/ZvIiuoFL3MilgqPjkbuCBjPAq4NKn0G01A6pZquvSXm6DSbgD5AV/eO4HOPmBXjquOpovfc1p0ly6nF21lYWHifQ9Rhs2h/4maFFjTEhkDhy2AeVG0k547V3XjzSNN+Jsd39s1a3NxpebhZmXy1OHwoCnh24HDY4PbBxn/DHUm8T6q8l5p4s59AuQ6zIhyhUH5QrLkAg8g9a5T4h6h/afim+hk/1Yu8kOm5plwe2RhffpRGNjSNOKdz1zxR4S0fXfAsk2ntG2pXMYlmLK6rKcgnJbGPoVB715l8DPCuoXcept4Zul/tq81Mw3dpcEiMxxyEOkZIBG5dzbwSMnIyK3PCPxPOpaH/Y0dhINWuIU859ha2h3D5ipP3XxkHPOM1R+DHi27+F3irUF0/T7W81y1ldUM6fLfCePa204Jyo6dweODVXOc9L8V+Gbi5tdSkt7WOxtZLw2VsYXVmikiA3OUZtxBw55xuABHUCvPPJs/hl4/nk1yG31iS3tXgt7yeEo1vE3LjEYOwH5iGb1xnIArS+JviGaREs203VLTVJvLE+ycKsc6jl8EDKkHGQeMe2Kzda8YXmhx2trZaXeahr2sRvaxwCCSZrz5Wzh1Uhsc4XhgRnGCKForILWKl9b2Gl/2bqlikMtjJMWYCZVjjVzjeoJHzd+cZI7812nxO0PxNoOpWej/aIU0qa0juY8vGpaMlRvPJBBJ5Gc52ntXPeJ7iz08aH/AGlHJcQ6TpSwCJ4txV0hVOUwTu9d3IPvWH4G8XSfB/xdbzahJeeJNL8RsunkStuXSo5WBJiUg5x5fQYxknIwclrAdt4X8I6J8SNA1GTXJJjqMdx/oxhfcqRiI4P484z6DvXH3D6M2mX2l669jMuo2Ultp0sjssy4jIYnaCFwCPvYrdgFzpnxR06Gx0+3aHVvNgt7aA5V0j3EfxEl03FyRxhK57x5rVzc/GSw0OGzs9QtYy887tEE8wAYdc4OQcjucgdKALn7Nnw90vTfE13puqahGtqtg91ZQuPm+0bl+6cc/KxOO4HFaHirw94k+GniPVvD+qXmsXGg3k226tlufMSBgMDb5ZIBBxkc8DoOap/s8eMNMn+JOqeH5reF7Ro/s+jrOqJcWbkmRhCWIcseAq/e4IAr0258QyWcO079Qna4Bj0trAx3UcDbUOBkluAzZ5xg1UQPLPgPruoeF/iRp0Mb2+raet4NJNneRnybWKQkLNuA3BlwMZzlfQ5r0f4xfBq88JyXF1cXmmwPqTyta39nL+5zyqgKX7YwTj7wP1rlNY8SN4X0fxd4ZttRvtH03xfbtIluGj3SrHI5izwrghGUZBIJBPTFU9d+IsfxC+Hnhm8msYLSGzdY5kSRpvI2s6Nu3LhN5Xf1/ip87Fymv4ckTWPh1JDeTqurEq95FcFZY4UVs+XlFJKhSXwoJHHasyw8V3nizwzf6bHbyJb6dJJGGm+ZbgNgsAxAygBUDPJ5HNSweLbH4rfD2SMX0cetaXEhh3qfMkG4kcJ82Rk898AdBVS41K38GaPcto811qGirCz3bSSfvVlTnKkj+Ig9SOgPTpfNpck47x54hu9Q8OaRZ/ZdPt0GIzcBFWR8BwoJQHG5ecMR9z1K1u+FtFuvFniHwzptzdalFp80RbybcqkqQLnzY1kZRhvutgknAxirtzpNjonhz+x7PS7pIbki8jkmsN0xJwcnBJ4LZB9D7cs1Se41e7sbvS47qG30AiSVDK6vBuwZHjOGPzbWTIBHzH0OCLutRtIb8ar/AMm+8jS4bi/g095YtNkvhGlwkTKB5TsijIGw+mcnk15n8XvjfL4J8FKtjodi1nfeUl2wG2a3uVUjMYPLLkkMRnoDjHNdVpfjhfHV9fwm4k85rkmBI3EoKlioZ2Q4UEnpgE/hUWjeG7WfwTJNrWn/ACvqTrZXksReCO4XcFiZDwwk6bT1xxmqJOa+E+twy+JLWSO1VZJbZ4VmZQUl3Aja4B5+oJrtrm/n06wt/KaK6m+3xwztG3nLGhYb4iM/KMAjseKy/hx8DtY8DfDK0udSuLVpIbjM0KQbXtVzgAEZP1JA/GvQtP8A7JfSmsNP0+4OpXJIu0ZlZYmVt5mBGDlgcg9Bms3BJ2J5Ti4/CcdxfaikNn/p7boGhtdwwAc9c/OePfH615X+1r4Um1uTT/JZbx44Qs0CDJicbtwb1IGOQce/BFfRfiLUJP7Ph1HSmbzLdzHLcABURudyuVBGcdATnABrnPiv460nxX4YtNINh9l15Jmkj+zWyOtwCNytvHYksOe9bQdtQlGx+b/jP4d3mp6gytbyJFEd+0leMd+vOMdq5G3+FV9qeoSGWOSNSuN+e2R6V9nfEf4IeIL+ezurrQ47aG8VZzIinzUU7fvYHAJzgjjg+lZvjT4GXGiaTbxwukOoeWWENzayIdx6FZMYJxng89T2rojUW5nKN0fGXiLwovhzWY/tB+USZz94Dt1H4fnUF8IZLzy4iv8ApEZkx024HIr6O8RfBJtV8LPqMht5vsc3kXKpgtGcjll6j71eC/EvwJb6d4jtGK/6NIRHvRuA2RgHHY9K39s3sc8o2ObuZYoW+bbx8wODwap3YaVNzN5m04KZ+laj6at7dMI2Uw7QBnonvn0rJnDQXfyYB69OKuEm9yR10reQsa5ZuOe5x1plnbPcRyIS25OntUv7xIt2MkZHXpVewu57O2aSRVPmEjPoa0AbbBJjLHv+6Onpn/JqSGNYIy27coHzEjoKz4LX7bebg0gk3D7pO1uv+NaiQq0cisCo+6y4xg9+f89Knm1sB9Sf8EqLGa48V/GJ7dxbyW/w+tLlJA4H+r8ZeFn7/Tp1PTFfWngjxdY+PNc8R3WuiY6Trc8gtltb7yCqsqrdKsa5fIUqwJGPxBx8k/8ABKGS2sPFPxm+2N/oyfD21G1nCjnxl4WxyfU4FfU3w1h0vTPE+oQ314bOx8UXVzfrcWkO2DSVi6tnH3AB5jDIGzHas626RtRWjfn+iH/Cvw3qPgr9t7StP8Sae+nrqmNNiM8quL61EGRKu09H5yMAgg8Diuw+C2pw+DP2n/iF4Mmu7eY3ptrrw/PMpMSwNF5yJvGARvYAg4zzXMftC63datqngjxLp9wHm8PXws4r6RlX7TNiNWMeMmRWXysAf38Z+apfAvg698Jftq+F/wC0R9qt00mSS1n2lUYGWMLkYPKb3Ax0AFHIrcxbdzo73xJ4bvvCeo63Yyzaf/ZvimSzsJF2q+hefCrvBkgM0TSB42U/L84HGc1wv7Wn9qfFPxZ4K8HyWsLa5ZyRzeHtUjDx288U2wNl8HhFjwR1BTB4Ndzong6z8U+BPEl1qC/btI1yXVtUMcliYRJNLuWOYN14a3YxtwDyQO55/wAC6Tdaj8Nfg94lkmjkv9DiWS9SVxvKXaSBZWOeEiKR9By02OMVpGSsI9q+EWiXHj3RvDur36wf2To+tC4s/L/eK62Ns9s5AYDAdjJzjOVU9CM1vhT4cmu/jV8VfG+pWscMmsaZpV9ZkESK0c05AXcD1/0UrjsHyRgg1S0DxrYeAf2adGjiFo154b8MNeneXiEs7XU0igL7lMsOpyO1b/xnu4/ht4Pm8MWqyT61NommRQ21urNLNJCxL5GdwAxI/I4XJPAJqgI9e+J+meE/AfiG4hWaaWPUrzQdLSMHbJeXSkQQKD95gxy3bPpg1T+CehQ+DdL8YeKrm6mvLPwzpyaJBKxDPeTWiAzudvY3bbfT5OprB+DXhXVPFfgb4exaxai1um8SSa08mwNJFNdQuY5th5BRSSDjjINa/wC0jqJ1DWZPh7otv/YOitbRaxqd4i7Cd7BktkVRzJNcsSFHLY6Hmi9gMTxlJefFfx/cXTahcWvg1bVdNYxt+9vrhhE06xhhlQZdgLEY+UkZxXdeKPFD+CtLuL6K1Vrq6uF8MeGdPj/dsmn2jr++IPI3SZc5wcBM4ya8f+PPxXbwx4R1lPDMMbWfh+6t/D2lTRSLKPOZyrT7hnzHLFpMrnAUE0fEWHxB4X0vw7rCWkuqQ+FfD1np9tDK5WRJbiVoppnI+YF3VVZiPlAAPXFc/tH0Kasdf4y8Tal448d2ujxaxPBFpVlJaalqccG1bWa5bEdraKeXmW2WJQx+UEEtg8V3Hw38AS6PY2d3Lbr4Z0fw/a3NppVjbSic2tpHCElu5XztmlJKqXU8Eqq5w2OH+Bvh3TtF+C3iDxVqVx9l0cWs19c6hKBc3c92t4sErR4TPmEs0Kbe6gH0rufiRpmoeIPhpp+jysPDcHiEq+uPJNtXSdJijVktFfGMlmGTkZbfwacY33C/VHlc+vzeI9C07S/C1rqM1xJBdadp6ALGsULxql1dyk8BipaQDOcsBgsQDe8S+ENJHiHw14A09Zm/4RmEJdRE7Ejlmmjzu7vjOSRxkdeCK0fGXja6tfDMWn+F9Es9K0uO0Jhnmf7Lcz2UWSW27QY1kcK2T94GM9cVleA7h/h74DNwks15r3i/UHsLnUNnmRzyyKCtvCMGRyFKrknOR0qLag9je/aN+O+n+F/E/h3w/o8j3GrQadNd/aQCfssMjAksw9rfIA54HHK5p/DfwAvhn4reH49XhNv4X8IaS99qEzbZDNqPHn8D58IoWMNtPO4jNU/E2p6FpH7Lsd9dR2cetXVnFq85JAma1ivLdEQt12t5JUDoQWxnmsfwlFrnib4e2Ml/qV4s2tWMd5rRTD3EcF2ZZiY1P3nVTu28HEg9qrmdrCRsfBy1/wCEyjPjHVo5Ibnxhrl/dzecSBp9ql4lrCoDcgmWZm5xxCO2CeV8eW0+oXPjfVJIrq2tdU19vEesQxRkTQ/aJI7Sws/qiSZYjOTk8DGL/wAe/i3b618Hjotmyx61qWvf2HDIjBdkclzvDnOMDaQ2D0PvXS/FHxLpXhjwolvp11DjxJ4kiW4ywbDQSvBGWHfgRv6ZGaqnpETJvhL4btPDYj0xoI9J1C8v7HwvpkU+1mgkS3eYu6tkZaU7Dgg8DHrXkms6kvhT4ya/8RNeuvtdxDZoujWspBWS9niijbanXYjK2FODhB1zWv4z+NmpN8W1lvFSzj8daTdRxxbgzWepWqhGkHA2urRROjDkiQ9qy/EHifSfiZ8Jn1q9too31ADW7Vwu2OKSDUhDiNjwFKyMpI7r7VnOonuUkifV9M1uHVLOa+azvBe6fcR24f5pLiVIjITKoxyXG7g/KfbNeeW/hG+8L/CjxRdajqjRLfRWV/eRW8MkxMRG9F7/ADA45x1+len3VxH4t+KPhOa4aH7PdW7Tw7BsSGOWyJJkJPH7wTRE8Y2+2Kh/aTs5NH0Pw/Y2Fvpum6P4ojt5luLOQTW9+4l/fbWHWKEqiYxj5s5rH2ZR538D/gra/EHULhriSGK+jtfLWW6in+zJxvEJeNSEcrzuchACwPJAr1nwHoeuQ/DLwz4gbX9D+H+tPa3UV6Bctcza3FE5V1REYruRWHHDD0IzXsGiHTPgR8MdN1DTtStZLXUjNaSX77UiuXkVlkjn5KsEJVlzzs3DOBmvl7wz8Dofi34W1qRdA1CW9uLu8uvDNnZ6kYzbMypGZ3HQxeayqDjDCNh0Bx0Rox3Jcih+ztY+NNY8SyWul/8AE40i41QWjOikxTwjEocgkbFCk4JA545IOPfP2Ttdtbb9oHxlotjI02i2+v2kVlLKxcRGaB47iLOMjDxcEYB65Oc1P4rv774LfDXR/Ek9itxZ6D4g0HQtOsrYNEJApnhvUdQQd4fYpBHJIxXnmh6X/wAKm+Jv7QHhOyivItPj0m68R2lw4KG1K28c9u5zxuBl6ZzuU+9Wqai7hzHqXwY8U+HPid+0x4y+IkOjx3y2FvLm1uUMUN89vAkUiupxt3yqGbAyMknGOPSvBttr3j74c3niFrfSYb/WJD4k0izEf/H7JbBXWMOf70SCIBsZD/XHlvwTtbnximoeKjarpb+JvG1xqy2phMMh0/7JGrgKDkLNJKTjGCRnnFeofDPxhbt+0jN4GSa4t4/hsGtLa2dXCm3jchZuB3jjQ7m4IPXBrRW3RJ57+zX4X0SbxzrXjjwfHdL4du7b7JpMToYoLaJ5kuZV5wx2yDYCBgYINSfBLRLXX/ih8WfCH9myfYX1fXPEOmTkhUtoZtItzBLF03ZnM8RK5B3tnAViNT9jLwjbeBX8SaZLeM+h2ms+J9JAKgfYJI7lPJHBO4MZt+B0wa87+GHjzV9GttH1RFmWDTbI6DqZiYh2MyXE58pT94/uZTgc/PTK5TE8NaRb/EHxl8JPCl5cvJY+MvDulCdGkO+OZbZ0jxngbWRMYySDiuK8I+E9Vg1TQdU1DUrSSxi8PXlpHHjdI7ROCIjHkFQQ8ihuxXp0z6x48+KVn4GtfGfhfwbpttqOs2UmkW3h67kxvisobVLlJ1l6rlXUkj+LNeb/ABD8M2/hi68P6RaMt1q2oiLTprnzfOSKRo8OybQNoLPkjk5Of4qxnC2oXPpDxXrd78Rf2bNN8OtprwaprVw3hK8WQbGvLOC3nujy2FB3RrIpPAJIBJyB6XoHiex+F/ia8itFFt4euvDerTx2qRlYXNvEjjC4+X9y56c8Y6nFc7Lp0yfsa6D4nhtZrebw1rksreaCGv4jE1kJM9QSJmIBBz5Te9Z3jSG98V/DnXbpFu7m/t9Juo4ktoy0ckt3bRq3lYGT5iIDtXJUpjvW0JPlsJ7nnP7Jt7qHiH402N5c29xp154V0HTI4mxu2hxE21T0yU465wxrqI/hTp3wd8ceJPszZ0Wz8THTTG27dDnWHlinGeAgjjTAI69q1bqUfDP9j/xtrlvvs/EEWviK4cLtuhHDBEkYA+8oCHv0HFYnxA8WWuq/tN/FPSbzdqfhi4uLrULSO2xNHdzDyY44Y3U/eWYs2Acgk9KqMVYfMzU+IXxX1DS/hhpepTaPNqGn2OsWursiOJpP7EuAiTLlf7kgPAycOvXBI6r4HeDLrxH4guPFGrW8mmr4o0j/AIR68a6UK8d1DLKjPjOUzFBbsDjkk85FZv7Mfw3tZ7fXbfVL4T6jY3cnh2HzpTtmsRtijBjJ2yYkDZ4+8gB5q34P1nXvG/7MeqW1vdRjxFY3N3bzXEsf3tQivrhljzwQHjdwO+FHB5NZyjzbi22OT/bT8F/8LX+AMniuNbqHUtF1NHktQ6+XIlqFWdsnq0ZdmGDkhjgMcV2tl4otvGHwn8Ca9NO8f9oeIbG41maJCzwIzyPbMyjohiSRS3uo44z0kNpL4k+DFjLcWsyyw6dvMEybWvZ7m7jjmdl/21yMdjxXJafd2/hrwR8U7O3mWz0rTrTTzHI74jtoInvIBGS3AJZNoHcg+xqZXS0BLWx7P4B+Jh8SfEu30uJRcSaZdG4YOdu6xvVLxkEj5gCRwCTzjAIOPm/9qjxfH4C8I614TWY/am8ATReSql1lvbrVzGhwBgtH2Az09K9O+GvjOHQviPqckxjOvFbfFhavumtbP7BAYk8vOR5ckw3Z/vgcZFeZ+F/h1qHxW/4KHeJL7UUuI9D8F2lmzI0bJ5000jzQKRnjaJt7YBwqZOBzRzPluOyNv9rGwb4q6J8J/CKvd28+rxCHVBMGSWPTYY1Lu3IAaVoygHU784wCa9t8GX158PvCrw6Pbtb2+h22myWdrHARb2sToWKAAkscmQdxjr1Feb/to3mpeIPhN481bTbVdPkjltoYtRiO6S2sJp1hDBuCqxRvcOeQFwBxXpfwA8Nx6F8PdDN4biOXVNJF1OLjJmmjaZIYHP8Ad+XZ+LZ61nq5Jj6GTqXg4fDr9qb4i+KLxWh8O+JtLg1KZs7reC8hk+zzREAk5AWOX6T98HHDaLc2uvfGHSYbpVh8N+CvF0dnbLC7Rx32q3MqSTytjO/y4UVeRjdI2CSDjS+OfxW1q4+H3xyjha6hvvD50trWNU8wCRXMVy0ZAJyQqE8HG0muH8SLN8GvhB4Z1bXJpLxdJ1GLxbrk0zbGvrqexmvfs4YjA+fZGWPIA6c0ObTsKJc+P+v3vxP+FXgXwz4dNvdTXGtWk19ApwqAy3EyjJPzDy5ugORyDyCB61+0H42vvBklxczSW95caD4isdYFnDEFEx+1AzMfXZFcMOCCTGuM4IOT+xzDv0vRb7U7NZPEWpQrqLstoFgt3KGU5bAwxWQNt7LtPQ5PP6xezfEb9uyztWhkfQbLQ01i7E7/AOtcFl2Oh5ADkgk91IPJxWlOTb1GzzPXPDV8fj9Yw+Ire6ih+J2s3uoak33vM0G2dNkXH3F8tZGYtzmVvY16FcfEH4iNO50PT77+xSx/s/bCzD7P/wAssEnJ+Tb15rQ+KfhqbUPFeizTTNqWsaxbXfhdI45vJitLZoRLcGZgpYbVlXeSRt2AHBOK4HXfBup6Prd5af8ACyl077LO8P2SOEFLXaxHlqd/IXGB9KJU0yVI/F7xP4Ln0rRfscUm60klkkUKg2iQqqnkdOgHNeePo0Iumhk/1kmQJR93d0GP69q9kttIs/FPhezNvdJ/olrhIkQlpJuTl8DOTnoMADrXjnjeFrDUriKRU3b2YohwqjsAfxrwKFWTdmfvmdYenGnzrY57xn4Uex023uvlLCTDEdW4Jz+HSl+H1r9t1O3tyxUmT7oON1WfEepi90dbdY5FaJQuwfNj+KszwXcTwanHJbs0LKwcsvDcdQK9um24e8fjOd0oLFc0Nj7J/Z+8DS+HNfjvbu1ENw0YCPjcMD+HGR13DP4V9eSa9rD6N4HmstLtDa6Zcwzi0ijOZmWXI2jBUZ5JGevOa+ff2a/iSnxB8RaLob6XC017B5iXU7ZEIUDczHHPbjOTgAZ5r6n8LO/hjxHayRxafrWm+HX3XIZjbR3EmMptBAyQ3HrgmvLxG5xU1oes+MviBqXibxjDrlrDf3EPmslnHOgVzGQVLOBna47Dnt9a858A+Bo9M1PxDpYvr7UtQ8RaodSWzguWX7G27epmHyjccMcDOQK624+K80/ja2sbj7Po84gmuoLZXXdcqMfL6YUHcc9gcc4qD9n7xVqHxj1G+s7m6l0a3sZptStrnCSTajKZWMZJYYwgGPpWOzOiNToZPibWpvEv9l+FfE8yxzSI2bV5mENyegJ7A89R61c8K6tYSW2r2Pl211/Y9zLa2K3LPKXIVC0Y3BgMF89cVe1HwjD8SfHOuahr0n2c+H7fy7U28O1JJGdVaQ/w52k8A9easfCq5sdA0lo9WFj5hvZruPymxkEAIX467QKicjY5HwD8R7S08D6to+pH/iaxTSzwWLo8a20m4nbkf3cEjb8u08V22ifG3SPit8QtN8XX1rNb2OjxeZALULJI+dqEhyQQNy5yec5Fcvpemjxj4j1+Jbgf2LNeuzoVLTBFUlsN3GcgAdMHNP0bQNM8KeLLmLw3avcWbQhrq0S5IlRwmd4JyVOGPTPJNTGVwNf4nXOuaza3EdjctNaXHEzo2xrhFYsvmEHhtxPTmuN+J2ur8FNBbUpLufTby7RLe6W0ADXEeS7oVAG5sHg8kZPrWsNTPiYbdEbyzp1lGNRY5MkSjkA8DPX9a5z9ojTbH4xaFGklzdafPpZS6lktiMzOmPLwMEdmJBz1Fa20uB0/gLwFY/FnQ7iRb2TTNNtLVdStmnmMONm0jcQCyk88jnntW34k1PRPFemeG7lbi4bXGjzbiT7xlILPsydz4QO4LAnAP1rxy0+Nus+H7Xw7JqGtm+uJLg6a/wBoQRgR7WaPAQD5lZB14OTxkjHW3XjDWfD3jeO41Syae81ix+z2864Zohkk7tvIB2n8SKkUtjoPA/jvw3da1eeAb5bix1aSGTWJ9YjhBF6yyKixMU6MDy2QPlPevNfip4L1LR9YvLxYowts6SxzNGpNwgG0KowQzHPRuK6fRNW1HxDr9xZahdW8QjZltpd4bAbOSxz1U7eo7/WrM3xKtdV8P32k3mh37HQJvs8WpdIbtnj8ztkhVLBc8DjrmtDkTOA+G0C3Xhq11ex1y1vbqPUXEJceXnBHyNxjaFPfrjpXrWnxaKZbibxFarb6lbMjXFxbRmG1vpCN4kQgBhkHp6NzWb4F+BS/Cvwvq3hXUtWvNLiW7a7t7W3CsI5nQbw5IO9cD2OSTmjwLcXmp6XrGi3N7a6gdOjaGWWaQnMJLhVViOFC4O3qAQKl6uw1qcz8Y/H2l+MLBLvRbiSK/wBNvhFGjW+POYqGBRixDL/CenPFdJoGkWnii+MniTT1t/Ea2gmtXH7q7s0LEP5YGFHzYzjJwcZrB1/wNceEZNNuoNPXVNPtnje6j6ttXJ4AwegJGP7ua7It4d+KusWcNpNJqFrqk0ZlIXaWXA4c4yrD5Rz2wKoRyOh+AmuvHt1aR3TyR6l+73ysN0bbmJU5I67gOTxtq5rPw0TRtYbTGuGuNRhiWaC2VssyK3zMCMZ2jtnqfqRQFp/wi3xB1Cxk+3afHb3EclvcICsgYOfL2Fhg/MMc9setd38OIdH8X/HG9bX9QuftFno8yyQELbymISxn7xADBgIsgE8gnHAIB20ueVxeKdJHja31TSXuLq/8LsZXO0ptyro4UEZZvLfHp8x7gGt7wp4gg8UaB/wm1tHc29nCBH588Zi3RscDAPOTsbk9RjnrVD4q6Zb+EPGWo31jbvO2pzxafs3De8bD5Mc4+XIOR2HHv0PgHWdX8L/BC+8AeJdNWZtFtrk20cVyHjk3L97BG5cnYOp71MZXLhPlVhfCPw5034l6veaxNYxyX2953mg/cynAG1gVbHCnHHHfrVK4n1TxjoV1b35uFvNJZorO9SR/Otog4Kq4J+fBGOc5BznrWx8M9QXxP8Kby70+KO10nTYlhLyARyeYPvArjJPX5sYJrX1DSrjRvF/h+NfEk+i6b4stYI7+WGzdmjMcZLqzMOA/yxd846GtOYmpVOY8SaldfFXS/DbTafpq6xpd5FaRzQKVaVMCM4J5wcA4OetLqF7eaJrc2jRafYLp9xMHKxu33ym3fnHzBsrkBeCT1rqfil8OZvCek6JcyabHp80JMl3eWrljKG2upYMB8ytkgrgkMQc8Vzt/Lavr1xJJN9usdQtv9aqeXLGSgIz6EgevQ/hQ0Scj4G+GNj4m1qeGC+jsJtHthcLbtPJDJqaLIW8tvLI3BWD53DHyYNdP468BoL/+yb/TbmxsvEERa2ZnCW88mMMNwOEADDk9d3sa6TWfhFo/xf0HQV05pLRft0cMTXEBQ255y5J5KqOSehJ4Oai/ag+GWqeDL3w/p95NdeJdJW822eFUOXhRT8uDtI3MvoRkZqoppcwrnMT3k2leC7fUvt1xqem2t3JYGR16SRuIyoJ46c7s4wa6T4gfC9fh5fwrYzSJeXlsbxLwTFxtPHluBkBAC3C9yKr6z8E7rxhoTaRpsN9Nawst3e2dwhj2bQQeGO3OWGCO2fWn/D3U7bRdC1mx8UW91dxpEP7HuRKM2C5CmIcdQSG5zxQp3IlLU858M+HpLhry/wBJ0i4jMyJ9rlhI2xlTtJXONy5I4Pp+NL8U4r6w+GohtdQvtTa6u4J5RMiRIZYxgOuBtDA8c9c1mLqni74SJeaPpGsC68J6lYtBJJMczWgeRXkx90bSR05HJGD0r0bwd4Ym8Z6JeajqF9Z2LXFuhtpN7RxZjxnC5OGIz9aqUrBzWOf+C934Y+JP7PviCfWrjVf+EmS6lieS3mkVYfmG0iNMLz/t5HPtXO+DrnQ9CMMzXd9/aVmxspVknO64QnDN/u9/THHQCvUvDngHxV8Evg1FqljHa+IvB/iKaS/lW4f/AErTppwpYoRw6Fv4QMoFx71xel6b4dszrGpawbQahapbzLDMuEmtpy5BXK5LAqTkHjPNEk3qVHc9C0Hx5N8M/CraN4iEi6TfmVv9GKjfJImYwR29MjggeprmvD2kx3N7pMcoDqYjJBeXMZMRkwzIHKjgnaF5HU1Dq/gyz8c+GWtYrKS+tVdBGkbBljQn5icDd1zjPQVrap4bi8Ja9NY+dcW0ccFvOYBcF0ABBDo2T8hPyFcAgxsO9KMnsVLQr+AdWGm+ObnRNcvI7LRr5JZlWWNC1pLGDKm1sZC5JypJBGR0qf4p/B/Tdd1NbqLWhqNnNbPMRKoZpWUt8y4GR8p4FWvjb8LH+JPgKHxBda5pdxrizT3Npp+mwr5bxCaSOJpVbkyERZPpk1X8W694V+J3hOwh0to7W+vrVGcIHtprCYrh1bIHAYnngce9a8xly3Z5j8RPg/b+Gvh1JNe6VHa3UcMlyDat5n9oFjn98DtZQEPGAeVHNfEPx/8AB0Om3lxi1aDT77PlFzny2xw2ewzjv0r7i+JMPijSfCkmm36nU7uxtDCZkcsrDPdzgljnH0Jr4d+OGtaiVYXk8gtv3ixohBVFKkN1Ga6KBhUjqeK6hpclpqK2tvcLIsa4yo5Ix0NZp0tnSNoW8xuF/Om6vBtvHEdxJuXOMVDJe+Z9zemCCctu4rsjPmOcgvWntpJFdGyG+ZQfXpRZT/aLxUyVVV3bCeM+9XJLZmn3bmA21WljxLnPzdzVAP1G5FlAZB8pUjnHamXU76jeRm3k8ttu4gHqTj/69OsLdsSXAZWWMhdp7n1qe01Fbq5mby98b89Mc9/6UAfSX/BNqJYf+F6SNI6yQ/De3leTdtOF8Y+Fzgdu1fUlh4ksfDnw9+HupyxxtJcWd7YFFj+Tzp45IWZlP3vlcDnj8q+UP+Ce9vbpon7RSMjs03wqRQN23ax8X+Fwhz2wSD+FfQ3wS8PTeK/B+lW+tKy6D4f1/wC3yzSgosmFBMII5yxXn681jW3XoaUZaNef6I9e+FnwCs/Eng/wL4V1LUG3aLoM2pQvKeRc3IljicEEA+UIosBuPl/Kx8afHY8DeAfDniCK3I1GCSPStL3qGKiaLDA/3iHt9y88bvevL5fiXe6XrPwzv9QuPJHiHSkgnRMqyxrcOFOOedvf6V6FrHw2u7nxX4P8ZGFo/B9vdmeWymd5JxeJEiRCV25wVGEGAB81Ymp0i6lfeBPDei+GdShm1HVJtPtp7yNQWcQLHOkgOP7p3Ox2jqenSuB+B8Ov+F/BWra5qSxxaP4c8K30FtC4Ei3lxA0cMJ+78qmR12nqcg1D4t8ft8R/2h/FcOneUPOaLwnZGGT5I7eE+bqEinsuxpEHHPmbeua9JuvsvhjTdJ8JyO1mNWvINKaFFd4THDdC5ucEcAeXatHnJAxt6mtKcfeAXw58PbSH4i+CbNpvMtYxv1GzmOVumiiSTdj03zFSOxXpUGg+Nn+PP7RnifXppAln4Z0n7Gs4cAGbzJ5ZCT6iPIH1H4W/in4wu4/iH4U0u18i3tbKz0yGV8ZeeS5jM2xB2H7xRnp8prgfgxLpNqnjS1juF/sdbm9v5kEgZrxliYsg55G2NuO26qlUewj1z4E/Ee41n/hI9eiRF0jQ7Sa2tnUEySG0gtgX5GMAKB6/OfWvFPE3xM1b4p+ONR1Oxh1B9NTWZ7yU2kZMj/ZgLaziXjsu+TJOAcsckV6p4Ys/+EX+BfiTR4fsv/CRapqY0yXyLYRTRyS2LXs6AZ+X5g6D3jFVPAWhW9toWjeRFKum3Hha91GeIPuYAQypErEdTk7jnklx3zWTCO5xPiH4WQJqPhHw7abv7MFxHfW86y+YiQodskztjl3lDooJ4UDvmvZtEVbP4M/GLXNQt4VsbXwjDqtiXUuArNfTx5HZv3C9Om4cevkU922j/AaPxN8sl54d8L2NlA6Hm4MQleXA7jbPIT9D71rzeNbr4s+CvFnhdkaPSm0zQtB1G4jjLR+VZ20VvMA4yFLiSYqWAyBTi0ipD/A/xCj1LxP8Pvhtaxx/8Iza6NY+I9Zd5MGW3ie5vJYmx/euLtOnXylzW/8AC6PXP2nviNrl9q9401s15O1xBj/RrWJcmEyRng7WKkL0baM5wRXl/wALdN0vxjr+sLpelrqEaS2ukeY8z/abu1X53TOehwF4APB68Y9w+K+tXy2djo3gwrHb65ol1p2i2UkyxpLKq/LEnmEcrbyMVUnJMeOpqpS5tAsc/wCEfAsPx41xbxb6+t9L8Uakz2bM4WTVdOsJNs1y5A/do742qMbiwAAArU8QLceJPHfwy0eG3hg0uxtdY8Vi3jTYo2MI7UnP8QypyT1GOgruPClgsvw2j0OxC2s2vJa+GLK5jbzPLslXyZZVAOV2xfaGGe7rjpWD4/e+vNO+IXizw/btHJJp03gvw00EZRbfT7cNPeXILdWCqsanuzkYODWkadncGzzXWLeH4ka1oukSWS3mhXtlB4Vd45Ny3cdrMNR1WQDsi4WIHOTk9K9C+G3h/R7Twjpuk6fZqJ/FmhahelZXaRlltYtkS5Ylh+7YKB93gVN4b0+18H6JpN81nZ21pp+jx6XotqJgzXJuJGa6lZAQWZgsQAyOmehrL+F1ld6R8GtB1zUIFtdQhlXVJ7m54+yFJJkkZBnIRY0jBVfQntWhJ5L8NNH/AOEz8e+OP7TsLe9s7HWre5t1m+UGTfDMmNvcoNvT3o/ab+Bceg3HhSz1TUNW0+HW/EX2yLVoIYW0uEvMsot4R5gkZ1Vx1AztJHHNemaU2kfD+DxZpUP+m32sWouGuWXbGrCKTy1CnoA0YIzk8jvXnfgL4yXfx0+GvhGOz0PVr9DNq1jeT7kaKTNu8kannojMT5nbOOoqJRuGvUi+N1ivwe+Jvh1fHGi6bqlnrWmSy2V1GWmjKyA25njHBjk+Ubl7MvvXQ/tNH+0vgbN4bjhtdL1TwrdQ2sKoFhiFjdWsM0jJjjyzKBKnJ+/9a6n9sHxZZ+H/AISeEte02y846dcXBSO8gGIkmhUSEB8lRIkRYE85UZrz7w3bwfGjxP4dhdob2bSdOtrLULC4tpN0lqfLht2zt2MR54cZOWGfSoVJFRI7rwFJF4N1Kz8OLqPiSTRYpbWO7dQsojx5wYR7vmQRz7twGetZLeK7f48fsk+C49Nhm/tzwcktmlokZkkuWlk5WPHzZ3RqVB7E+lXviRqOrWPgXQfFVnq2n2Mnw71CPTtW0yAG3k0PymVBKYz0jcMykjIzjPWpPBPhbSvhx/wUGkt7a8/sbw7qlpZa+iw25uEd7pUuEKKD9wM5YkdiTgYwD2PmUVfhBrD/ABo/Zf8AiB4X8TTTabNpM0S2ryMyPbahlhAuAOPmEqODxtbPJFc58GvilfWXwzuNFn862uLTSxo3m/8ALaIRI8v3u580MM9q2/ix8P8AxE3xI8R+B4od1rp2pz+JtevbCQLC9lcSxsskbE5PyqoJUHBP+zXnfhzWr74raT4wXSYNPgnXTXvVgLpFC8EDPMR6NIUQ47kkDFY3knZE8vc+n7Pw7a/FKLxB4d1B/t+j3Wlw+N8lw0+mXkdiZjOoH3d0pOQfl+bpnFeT6Ct94t+Cl1fWa3EviD4q6t/ZN86bpbi9ski3xwDdkDIiZSwwMHJ4rq/ip8Notd+Lnhnw/wCFdcs9D1zWvBEemaqweSZYI12GSUrnI/dbvlHU4q18RrGz/ZR0v4WyaPqlve6brGbXTZ3tfL3NFFJaXbYYkryWHPaUHnrTs+pW2x9Hfs2+GbeX/hLPDupCBbRb6e2LQBfKVChtXCuMchnBA4IMa/WvHf2dtbvoNT8QfFG+hMc3xH164sFlldTI1jEos1KjJxlhI+OpytaXj7VW8M+ONa0+yjvLH+1/Ddz4l0g20+/7U3kSGNSQCrEtBgjHOcDBrhvg54E1zV9I0Sxt/EmnLa/DGW2jCEkKxupFDSNxkAHA9PmPoacZNbAdH+yB8TbqH4Z+KtPkXGvWHivUvtMoUM1wZGmUKODyZUQYHUAZxiofjJNbeGNauNGtv315J4otZomOfnij02Tzn9Bh7rOB2Fc7q+uWHhP9pnxRGtxqGjaLpps9k0MZ8z7Q88RmZhj5meRpD04wPSqvxq8MNofxN+IXxKtbyaTQdN06JdGu72MJKl1fXAjuYGAVfmjYdMcJIOoINEp8wm7FjQ4JvBuoT+ILWCy1CfxFpK+FIjqTgxKouXghfDYCkwRg9ecKOhq98atB1Xxv49XR4dJmbXtL8SW+tJbbkBgtoLMBvm+7g7d3DHG7NO+Emgn4h/HLS/DVu88ej+JbceH7S8mg3ebcRwbpnUZAYq/HI716v8HPEdxoHxD8f+ItYWzm1zxdeQaZpttDMkkyxwwGylcpkeUrPbyEE85z7VpCXQm5r+KLa6/aA0T4meB/DmqSFNJ0/TdCNzJKv2eO9jEs5dQnC/v025x3Pas/9kjxT4gmJGrSQ2kEMkGiXkVzzJHqMKSeX8uPuvtnT2YKOhq9+z/4NsfAvx28SabeXEl1qWsRxavZ3ZOV1C3KmPyHGeXjKsysMHd65rn/AIeePrOL9rP4mWTSQqscGjajbx4DgXUcpk3k9PlMJ6c/MPx1bshHN/tKeLbz4r6zp/h/w/YTXkeqXMutXVtHKtudWeGBZFjJ6c+WpIHXHvVz9nr4cXHhXwhc654guhNZWulLrV1qMKD/AEq8F7L5qnK7gqtGBjuFqTwH4fvPCd/8QtXlkfT/ALZqz6folz5is1vYTSk+YoGcHcEAzgjYRwQa7b42Tf2heaL4atdP+z2PjZbnSTaB/LMwkKPCzNxguZByO5asJSvK5XMdJ4K0PSNQhj1PSbzyZtOvL3SmeELIskkFzc3KNyD8skbR8nnAPevPf2e/H7f8NneNvAumrHcWPiu/i1zTgzf8e++yZpWJ5/5bM5x3xmtj4h/Fh/g98HPA+tNNBdNcXtvaaiokEn7uKOWOPkHJASXbxjOFwO9cv+zPZy/C79qi+8TXDTadH4gs728trieEwmSKOQbDtYBkw5HBGeK36EnZfst61f8AxH07xFeXFw11Hq2rS6dA0ucLHbLAkWzsuJtoIPPzeoqT4rxRxt8TIZpbe3tdb0/RbyaAZ+ZI9QvJJhjHQqyng85NdRceFNP8Aa5q3h7QSqGz8K2E+iXG75JJGju2IGezTWqru9cDis/45T297+y3qXiqKzmW5k0uK3khDfIbW7nWWHIJLN9na5nTtwxzwKY07O5R8BXWj/Cb4hePPGXiQslx4xvbLR9BZEM0jRSWyOIwMfKzmOIH02LgnpW9pNp/ZH7VGpWtrcTG81TwYus3pmJy7rZtaoBjgncFI9yPrXFXXiOfxl+2P8IvCdnHHNo2h6UdavIGbcu8FoYt7f3RIqkZPf0xXo3w8nsr7U/C3iS7s1juG1vTNGuJpSGmfDzi4jPP+r8yCHHoc9jQI4z9rPx3eaZ+yz440+a4XUpE0+10yW5XaBeGW1E8M0ajpkSNkngFRXsXh+1tLvXvh3NctI1tJ4Lv2fL4LvHdWspBXoSisuCOPoK+cdb8JWHxQ+Cl3cTR+XY6V4RvLq63SiHfPBPevabF/i8uMY47V9FeF9fks/Cmj3kca6h9lsbTV4IYV2vAfIhhuMAYGDHK3fjcOtT8PvAeUR3kuhf8FBo7qT7RN4f8Sacl9NGkLFUl8wBUkGMZ8yWJhyfvVJ+21bT/ABAn0fwTb2rXV94o+ItroxVfmCWdtE0krk9RmCVVHpzxVD4EnUvB37Rkmk61bTaZJoOjrIqkZm8qFpb0buSNmHXDc5wB2NejfFCG18BfD/wj4qvIo7W98Owx3t4VXLedPbPGrt2P3T+Fc8pXdwMf9n7xYt5Db+aywr4mvtT1pC7HcIP7XNjHbBVGcsIUVRwAo69q9C8K6Baj4t+L/GcgtUjvPDolFurZVIxvkYFMYIEiS9+/414v+yv4Q8Qa14CvLrWkGmyabbrNbaV5CNIEkNxMkhlzuHmGRSEJ+XI9a9y+B/itfHuhN+8NxNdQjzF3LmWNbmaGX5RzsZTKobnlVOa2hO4Pax4j4E+KDfFnXfEiWskUF1pOhSJktueym1CdvOlGRj/jzjiHXjivaNH+CS+JNItdRuLrSnuL+FLmVmHLM4DEn5fU18//ALHbaX8PIviNfXf2O7h03WY9Jt/L3FZkW5Yw7dwBwUWM5I/KvaD8CNY1c/am1ye3a6/emIOoEe7nbj2zihVAkfzx/CzxAvg7Q3fXrSeG4u4mltk2GOVo2UMkijgkMMFT3HI9a5DxFpTTKuoTxhIbp28qORh5uST8zE8knrU/hjWI7TVdJ/tD7YsduX8meRDuXjC8En5Rjp04rqviPolrraRTae015a2yrmeVSplyPlwBwc9SevT1NeHKHLU5kft1Fzr4NUpO/L+PmeUTRDTH/wBJ27ncFiG5C9/zB6VVWwk0zWW2r91PMyDwykcH/wCtWhq9hLe3iwqF8wf/AFsVQ1O+kk0hd0bM8LmNn67VGD7Hr+GMV30tT4XN8OnCV1qj6W/Yf8WXlh4+024vX3QwwsIp5GPlIy84XkAMMp09q/TPwI+lt8K49NbyrXUpLn+0p5ICF3hgrIxx1xlQAScFq/J79mvXtLttMhs7iaG2m1RmSeQnJi9CB2xz1PcelfoB+zd8VLpvF2m2+qawmseD7e1ZU86ERzyPlQqsVP8ACFYDB5DZOayxEWtT5KOmjO98T6Deax4nvvFEc0dnJYwyRPIY185UkUBVJZTjJBIwcnAzWzc/FTQtIvPCt1orX1npdkqXGqySSGOaSLYN67l+7ubJ9sVsrDqEtlPPJpRtY5oytiXBuPP3HafNUjaAIywBAJyR061a8QeHdL8SLa6DJ9nhh+xMs6CMIJWH3wAR2568V58panVTjfU2fDfxa8Np8GLjS0hX+1ryWQGSXHnaiWyyEZHC4IO0nIxVfxD4JuLSW8+12kd00UKxxsVBUzbRl1yMHOR0HFeJ+GPCE1hJr9hNetd28++HTI5NqyBww+fcRndxgdsVvfEHXfH3wa+FsMd9Z2Oq6tagI7xSPMQW75YAvsXrhRnFKUW9jc9N1L4XL4f+CUMmnXl7YaprE5iNxbSHbFI3DKQuNzIxJIJyT1z32fGnwPh0y+0q6094brVobWOJrqJRDcTh41MruEIyQysgyOBn3ryG11+3tvBGi6lukbWlij1FmV32mZlVjIEICDJYkjaeBzmu08Uao2peJbPxDY6k11fSBVWeI7iucjaVA29QeozxRLRKwHN6g958JbnV7nyVtLXXbf7NCmCGIXO1txBLlSWPPXgY4FZfgvXoJ/Bemi88u4vpbq4UysAZZ142jb1OMrzjnn0roPiGmqTWcdxr0Un2e2iaSLzWwruOAq7cdc5yenNU9d+zzajol/p6mDUtOhVIXLb0yypuBGORkHnnmqVRWA861n4GTarr9zdRtCzaftubp5WHyhjgKoPIfJBHHAU12fw38V3Hh7RvtF9p2oa1HfxRwNc36s5hUTA7gSAcDr2yAR3xTtOvfEHivx1cWcNit1cPA0jgsqyNCuxHbHGVDPEMnuw962fHDasth9kkhmmXKW7gqu0KoBxwSfc7OwJz1q1JNXQGZ8fj4X0b4ufDqPT4Y9PtrjUoY3vLGQxSXpLpIwYZ/wBWGAJwehNb2ieEPEmj+OfiBNp+raTY+E8nNtqEZCtuQMEjfd1C4IyOvTpWf4G8P6b47+A95pOn29po8Phm6MiQyv5s9yW2soR5CZdvzMhDN1PfGa5nxXoOueGvAviJk8TSJb33lO6IiTQxp5Sx7l3IxDgFweOuMdM0Rv1M5077HafDbxvrniHWL9tctbGa+uLE/Z9qKxztb70nB6YOT6e1Z/grVNU0qz1KO0azjmlLR3UMsm5LpCcoV2kHdtXoQcE4r0PwT8NNP0z4ftqEusXCyXsZhtZDEFh8xRnyyThiVU5PPOa8y8CWVv8ADo6xdarr099rX295NPzHFHFCsbHylCDgqVIOSCSeMkVMVJu5jGLex6B4W8WXC6TNJe6UjXEqoqxx2xuLlZFOI9vBJG3PI5XOOK89+ElzYad8UfECNeeTJfxXLS20MbMdPkUq3z5GRg9DwM5rtvhJ8XtQ1jX117bHdXWhnypLu7iW3jkMqlmXCKq8bQAQM4A96dpHgDT/AIoeOvFEOl3en3OoXOmRLqE8M4UwSSOdidPmO4KcenGR300saRpuLuzg5Zo/EfhrUtLtLe71jxBdXUrWlokIuLq8MJ+UxgcsQOevBP1ra1fwXc/EfX7a0b7RoPiKx0n7WkF9AY5bMxEKyHfy6kfewSMNg9RXJ+BvB3ij4X+KdL1bT9WW48TaNdS6dL9rjEURWRgPm+T93yRyeTxzxx6J8Q9avNW8TWetPp02m33heCVbwI5mi8znKeZhgwlGCMDsRxmpjJN2NHZK5m+MdP0fxar6frWl3cPiiOGKT7ZEm6GRgTseIYAA4+9nOAB6VhPcSaJqGpQajfTXU18VgXyQV8zJI2Fcn5SxUkAEE4OCa7Pxt4L8O+MbfS7rw9e3sPiiw8uGa+a5uGinyFDxvHKQqqPmxiMHk+xGDH4Ju/Dn7RFxZ/2lbxzS2aXtvdPEJ7fEZDfOpHCggsSQRgHIIyKvlOUwvGHwvkt9G0+3juHto76Nkvxbu0RZcnPQcngc9al1O51zwd4bW78Szf2zDHb+TGLYbREWO+N3YM2ZN23JBweRT7r412+rfEtLXxVo97cRTLcRtcWH+pWTGE2+WVAzjIxgetXfiXqNx8GVtdPa11XWvDthaw3t7cQQJJtjOAq5JGCcAHJyMHgdSgPQL347f8Lp0nw7eatpN5Y6XdXDaJqczWpitVmKL5aDcSDuw2Dgbsr7V5h8c/h1J8OviiraVbtbWEgDRw48xJIwApjkUjBOQTtOcDHHFdp8KPiPpeqLc6atj9h8A+LJLUTDWSB9nmWEl3iZZORuOFJGS3JPNZBkn8PeK/EOj3V9Brel28yy2M0sjLJFDtAwTljlcgZyTxyc5oAqXfja+NhqGhaXbtOyQB3aCTABLsVU7fvqpVSoIwGBxjHGRZ3mqTfEPQLH7Xq9tO+yT9+0j5TBBUqThfujLDoeee3Q+FLvSdW8SXVrpbLBe3w3ALwvmkEMHfqBtyQPXJ71keLtL1D4P6t4N1LxB++0W3vjbyhGIuBbFvnjGCA/OOTk+4ou9ieV81zqvEfxFXwZqc0lpfXSxa5LHBdSMRm3kVSg2kY+U44HbB9a4/4o+HYo/Ddxqmm6rcSzaWJEntQu0scL91VONx5+6Oc471a+IHhFbqH7Rbyt/ZrHzY2X52lySQNufvbRyScDb6kGuf03wtqS+HdP1Ty7OO1uF/eRnEkjDkbW7KQcN9OKCJbi6n4LtvE/gyxmVlR5rpC6MdsLKxUbWPUY+br056ZrsfCnwus/Eeial4Vks7i58RWdy9zpwWaUpNb8H5AXwyR/7QyFz2FcP4g8ITfDiytvJaSbR74Ced1Y7bQNwWxgjA5roNR8Z6l4b8Q6HLDqQuIbNfsq7LbbLdCUHHzrgkYJyeCaTkHK2aXh7UfEA8LalYadrU1x4etxLY22nXm92tnLFiUDbghbrhSMbjjqa5e58Mx6db29te2KyG8h8jZIoRgFJzgkDPJJx/tH1reSfWF+IzWPhe3s2bUGa4LXPyRIY1ywCNgFtuOhz0rgPiD458SeIPGVjNNqLTW+nTyNDHtQDaW5BOSQQB68VvGVyrHWeHfBtq/hJdYs4dOt42imimRpfMSXY7gCVFIBJxjJOQMelat9Jb6zrlhNHbW9r9oIiYtcLucHoSoGGAGBxz8orxnQ/EEHir41Q6RdNrFnZ65fLZ3WmqSWjc4RSiqDu3qobqfvfXHrGseFbH4WfGeFtN03UH0jSWaZLLUyViLDKup+7J8wUEdQPbkCh3K938Om+HfiaG2sZbi3s7pneeNmJXYTuyAPugs7cHqTXN+ItGk8La3JqGoaVHrH2eNnVZBnYMAoeDycZGCOvOeMH2bV/D6eOYLDVnvEttNnVbmaK3gE62e3B8ojI4fpkkkbcjBri/izqVn4b0eTeVmvdPVB5ibitzE/zgbDjDrjB7dRk0bIk5X9prx9Dr+m2/iLQrOTTLW8skW2jjYfdVicEf3srjivzz+POstq95cSbUhIVg8IXG049O2Tk19veJPE19H8OCtuqXFtp7MYvP2+YnP3P4v7zY6cgiviH4z3a+J9aupWj8mXDKqMDGQD6jv9T07V0UO5jN3PHzEtzqjL9nZhGPnI6D2zVZoIIgu3rjJz3pkmiyaHrpaO5eNZFwyfeVj9Tk1GNzSNIR1OWB7/AOfavQOUtQSPfmSNMKYyRuYelU48ncpG51J3ZOaLi+ezuMxqU8wElCc4496r2VzPFdyTMfLD/d3BdvHH9KALdjDuikVm8tcj2qot8kE7blbcflYjjOOn8zVifULh0RmG3jO7YMZ/KpLmwaUpJInzMOMDH+etAH1J/wAE1NHXxMfj5ar5O24+FSCUyNswg8W+GCSCOdwA+UdzgV9DfFPTr74bf2v4LhvWuGa+WeOQlh9qE3zY2DI3nJJA6c9q8Y/4Ivy22m/E/wCNUtxNthX4YAFiB8pPivwyFxnvkivf9DuYfiZ+05Z+PLfU7e6j8+XT0swu14vs9rGTc+jEB24PGU7VFSm5Wa/rUul19f0RpfEP4dw678b7fUrCytbyz8K6ULWG2uogthbYZQjwhR95iJAMY5XOa6X42fGhoYviL4HsUkknGlW97p3kgyulzbxRSEAAZAIZst6jrk843jjx3Z/Bjw3rlveXazaheW15Jg7maRWlZ4WwVIUAk4IwQc1wHw00258VfEbS9avn+1ah4gskk1OGPkWunxqCRMOMGQIhwOyj1NYSi47mp0fw88Ir/wAL+8T30MP2XT9Kt40tRtKssTxPK8uW4VnMe445JK817T8ItJhX4IQzXCWtzd3GmjWpZiP9Vd3Sy3IVXxkKqnZgepPTNeG698Sr7xl4muYtFjvlt/GzwRW1z5eGjSOBUmPAJCoiucfd5+hrp/jt8WNSu/DHhTwX4Ll+2314ktrAjqsaxxQC4t0kdjx/ATluvA70k7AcP4R+K2q+OvE9x41WzmvI0ni0nwzYnIM93DaOsZVTwAg8x2IHykqTjrXQfsxfDSTQfiBpfgy8+x6lfW1qt5qcNxzBfS3O7zIQp+X5Yi5LHqAfStCw+FK/CDwlNC98ZLrwXoYikeFQYjcXsJDojEjLkNjPJAPauX8M+Ll+HfxJ8ceIrrUIbi8sZG0m2SPmG3ZbfcT5vcYHlgHkhiSeM0MfKz3vUvs/grVvHOqKv2yzhuYfF9rcXgWVYzbyGF9rDgNiZ1Ps4zwa5i7tr7w/+z02oaXuhs72KbTI5pG8n7Jb3E0LQZPAACyIvUYDAYxxXDvrPjL4gfs6XcHh+xNzN4phka4mlkQC4WRgs0QycRswjiwFAXKDgE5PqXxM1C31X9nm70O2ZcXjzRIqHllis7d0UZGPleItk5+7+FIRZ+MQ0TQvhavhG3t/tTafol5BtdVzcFYPIjXnoTLdOck8eV+XlcHxCh+HOgeKLqyYtHcXrLd2cMh8mS2SNYgjKpwSY16Huxb+I56T4y+d8RP20dY0XEcVnrOtWGnRLv2+VpsEK3d3Mo6sXWWRRjPOOhGRD8eJdPi/Z/1jVN0djfavPHcNHDH+7szeyuYIhkDCxQxqnHXOTk/NQP1K/wCwhpE2geL4deeK01DQLPUjG6Q3KecZY5ThXXru2tuA6uMEdMC/8QPjVdeA9E8C65dabcabdeCPE11p95avGytLA8cZSTBGdu2IAYOAenWo/gB4QvfGngS1ttH1PS9F1q3gXTI2u4PLj1kojPCtwVU+W5kClZ2y4Py5wWFa/jewuP2tLSPSJNJa28Y6blp1EyQSh4G2yW88bsA7b4yAyncc+hNax93Vj32PQf2pvixH8MfjFb3mlraxaa3hxtS0WKAnclzcRlYyI1wGVJACV6EAg9Tnj/gBo2oeLfDGj+H/ALfcWd5rcP8AZcCXETMbSxjmU3VwwDACSeV3kY5zl1ByRXL6P4muPEwtbrxdptxp+sfD3w9daNMLmT7PJNICTHjBGFCbhkDndXffs/8AjKfxX8IW8Sah9k029163Nnp9tAuDaaf5hQCIcnzJC80jMTxkHjC4JSurBysp+FoNW/sLxJ4obUre68u1t4NGsJoXRdN024uooIio3EJJMsRJ4ztck8E1e/aB8d2+jQeI/Dj/AGqOz8K/Dq3twsZK+dc3SSQGbnncz3O4t1+Uk81V1fX4dah8ZaLpksa3GqX0GtW4YbxPaafNbgw7gQUAjUyL2+Q+pryz9p/xO3jPxhfR2LbbzWrG2a+kXAhFnBckJux0zI2QO+MHI4GPtOgcpf8AirpGqeOPHN0dH1Mww32lpJKkMR32tuo2q5YEbWfkKOCcsfWm+EPgdr3wthtdH0mxvJmsppLEx2ykrbrtMhkwvLM8rYY5JOzB4FZfw98dy6DN4+1y7tYobq8v4NFsVb5pEt7WEQs4U/Lj5c85xknrk0w+Npz4j8N3jyySTTWl34i1WeNsW2myTmRlicDjO2PIHH+tHfo4yuUbP7Tnw/8AiT4k8LazdaxoOtaH4ct7K1jN1JYzbZ4kWNfLVgCihfmI3YwPpVnwTfw/BD4ZN4s07Upb+PxRq2iwwteOJJY1055IDG79cEiLPYBhxzWh+wX+134qj8S63Hcareat4P1F0sbjS7tkkjuYJTKoZd442qo+6QSMHmuIvPDdl8RPhTL8P9EuhqWl6X4/FvYSyTfYHt47iEum5mB+UEYwxJyM+lbUydtzvNZ8JeHbv9rD4laJq0srWvjrw0dOSGAeZLLcedGshRR1cKqOB6jOOTngNH8FSeBtCvtU1DV7XUvFfg/w5pmjwDT52a402exnDNlz91tjKGMZ4VEzyBWX8cr3XPBfx48Iafbxw2XjtHiiuPm3LE0Y4kBAxiTaXJI+6F9SK7H4yapN4d+Jni3w3p8bNdaT4fvvFV7bgbZVmvrVGa2QKMP5e1jk87RnOa2ByO81bxhp/wAWfjd4c8SWcNxp+n+HbW40/UtRiRvL1q2FvBI0cny4co85Rs5DrgHnBHzJ8BJrKf4ueJbxVuLXRbWSOaRIbf5IYZZRbom0Oh+WaRflzyDjpXf+JvEtz4R/ZRSKSa60/wATeGtLttSuPkdo7+w1GNsq38AkRxGwx8xU88AVyfwa8NSeF/GviS11jT7FrqPSpNcluXl823VYYYbq2XC4XzjKI8BsqGfncOkcybJPePBunwXn7eviLXPEmm3S3lxrNroXheUeZDbzXES5nnQkDcschTcvRS3PrWt+0T4b1D4i/FL4a+GdO0ux8QDRxrmpzQPFvhWZRNFcZ/ulpEidccFsVnrDqlp4u1vxJq1/b6pa6Pb3vjrSZmgELvBPLZzb12DKM0MjqyjKbipGDg1D4T+I194H+Efjv4taonl6bLq93omkecWkmuLWeW6uHYEjaVby0XcOT5fBG47qlsBY+JniyGy8UfCOx1G11LSIdF0GxsJSV8trhJRh0QjP3DvZupbzORzk9B8Mvhf/AMIX4M8WRWc11dSeKPC97dWkE8u6aW4tYZ7i2RAMMSyFRu6l8Ec1zumW0kmg3VrqDWt1b674VOowLG25gkqpawBWZQQ6zwK+4EHpyQSK65fFNvrPxj8A6naytDp+j6DdShRIE825tLNVwPlIbIQlieCB0OTnCMktwOH8RX+j/Fbw38TvGXnWsy3F34e16GJIEWSDzpoVkiaT7xUmUsyk4O0HHU16B4f8HXH7TnwN8caLqt1AjeO9HOr2MMNtlrO508JFbzE+sklntbpuRj1zXzp8DdctdA/Yr+KV4Y13X3jXRNAiBkKt9mW4AYKevRME8EEDmvtf9mPQbTwr4L0XXtHitbTT/wC1YdMgMpNwTYvNvlSR87t+x5cHJOSOR1G2gSkj5H/Ye8YrqOseDde1SO8tbbwffalcyXKFz5F5IimPJUZyW4PfLDvivRP2WNQ8WeJE8ea7bWK6lrF5qvnx6a7FdSntDK22WKB1BZRcOEJA4Zye5ryj9m/Uhqfwz+JGm6PDe3n2rUNQOkeVBia5mN6zxHbntFEhYA4AUnjv9SfAaC+1T4ca1qV9De2tx4d1lL+xZ1VZZbZXU3VuFBGVZTeq65IPlq2Nw3Coxu7ImMk9D0/WYNQ8EfDTwj4mQrfX32oWk00sG0vGZG8qKRSpI8uRmGD90lTgcGvkxbG80/8AaR+J3jJLaPTdH8TQX9ppxE6lLW4SNd0AA5jkiEpkXGMZGOa+3fiFeab408I2nhnULsW934uR7zT9QL4+z39r5W1zk4BeJ1RmHXBJyenw7P42tB8PPGkklrMx1fxndm0ErMPJuV2W5IySB5sTvyemwHtmpq6ItHsnxF+w+N/2fEj0e4eyk1Dx5pVtq13HJ5ciRXUsbsyyDBVUZtoGffio/wBpO/8A+Fi/Gbwe/hnUbKE+FdU0+MW+13kZzMyktt4XbEwOTgZXHavMvhzrOqeNv2d/FTzr9lutUurXU9O04Rl5DJZ3ttKsYYcsREmCeCROCQdpx6z4B8H3PhL9nrxFr2tWNxYeINWuW1HVIrrdFLayNdxQxxMAAV2x7+BxlicdCOZlbbnlv7QEb+JfhHNoun3ATTvCdro+oNbmAyMrTXiwJbKSc+YEjKljzwB0Irvv2w7bUvhh8INP8VXCOtyss9lcy25Zfs0F9blnbcvO2G5tmXsMy5rlfhuh+IqfGu48M6NdrbaidFs9JOGbzTHPKZHQMSCwIyo56V6mmmTfE79n7wNoFzBa61oesyXGn60lw5j8+CaSdlfcp3BhLAxBVhgsv0GtEUjq9K16x+KVzeappa2smsaTFBpOxlKJZK229t48DhSl0fJ9hdN6HOHo/iyw8Y/s+2NvqVzNC3iyOS0eEyFVs3mnkNtEw7FHVkyccjFUv2XfET+C/iR8Z/AN9Zm3tbfV21OK5twPMeFkiaJ1JAO8K6Hb90Mhx2NebfBjRL7wt+2f4s8OeKrRrvTvBNvf6pGAvy3MM07S2kyDoSVkVlx0P0rSUrIk0v2cPC194C8QWuqeIGuIfFniCxNo0N5CY302KC5R44QW+bbIqsznoQw7gk+jC31DSfhl4x0uzSO81LQxHriyuxjxcxakhkBDL8v7pphkZJU7T15zfixoeoeKvjf8I2vGkKX1tC12zoW8ki4lLFsfxYdfzx0AFeqeJvD0fhzSvFEzW/k+bomradjewMsjW80qdcYbzEQn24pp3VwM1fh1pvg2w8VaDPcNJb6V4Gl0iGN41cec0NxiXCgKx8y4QA9zk9RWT8KviL9n+BtprF2stq/h2WRr6eRtstxaB0VYcgEqrpEc/wAJKAHoMa3jHSF1n4X6H4ka4NwsVibPVn8z/X248m+EpIIzt2AZU8iUduByfxQ123X/AIJ0a5dWsNuLd4dMsYmikbLIriOdmb2leUgcnBx0AFZ1EyuU7r4q2dn491n4ceJNEmbT5PGMDeENQuQAsrWssMhgIBxypUAHPAdsc1yP7Vtv4p8T/BiDw/psOm3j6gnhu3ubiVWWRbZ7n7Ix3KTv+fJYE/8ALRuOSa7a007S7bw3puqSTwXeneF9U05bA27MoVoJLS1nkUKQOI7pjg5A8snAPNc1pvxUtU1/4jaP4guobePwjfG0VHT5jHHeedG4z1xw3pWArWO5+Cfi638T6hrFrYaVtjkthdRzyoFnunV/s8a7Sc4XyBgHJAVeBxXi/wCyn8R4/A/gn4ezRxKuq+JtObzvP+XyrRLlo1J/uoZEkIH99m75z6Z8EPFc+l+GLzxo21f7P+2J5UO0xLHFqEyqxH+yQgJP3uetcZF+zVqmiNFfT2trLDa2miaVp1rAzvNbw/2w93KX6naFlGT19615HbQaZT1fwRD4H/a10rwXZQ29to3iK+bxZeXpiPzwW6IqxKowpxJuXHoFHtX1vOmmmd99rqEjbjucW4+c+v414b8c/GWjz+K7LxpZ6XJqF94f1S80zT7mHftMf26JLxFT/lpwIypIO3EmMV6UPh3feIx/aEfj7ULWO/8A9IWH7QF8kP8AMFxs4xnGPaqhFrck/lfuI2toJtv2y4WCTZZSj7jDPzBhz/D0x0JP0HoXhPxta6v4Yt/DyWCRtpxLyyN8okZiOCe6AjKgjPPJavNdT1q4s7xVjkKpHJsRf4UGe1dN4S1648L+JLWS1Kj7dDEZ0YblkyO/5151TbU/XcvqShXtB+pQ8U2cbTTXMdx50zHe5VQig+wx0Pauf0q3uJUuIIzvWQbiB0k5Gev0r0HWNNg1eC7upY1E0in5lJGAHK4H4AVyUcSxaxAiqAqs0Y+mM/1pRk1EyzTDxdW0jAiv5Idda3w0MckmGwArDBPfp39K+3v+CcNnqXieyvtNt90ttfJLBvuUEi2qI8bErkfKxYAbhzgkDGc18M6+gj14Rr91Qxz3JzXvX7L37QPijwRbabpOmXy2tpLcsrlIwsjgnJBYc4zg46cCumrrDU/N8ZFRxEorufpl4S0K50WW8Goa3dSXUcyJEv2g52jbgsR365wAMdh1rqtL0ZdTvLGaaR5r95mNsMjYQ/Y7snGfU1j2PhCw8T/CC21i8h8y/nvp45JAdu9U2bQQO3NdB8JvCNrqerNau0qxyWslxlMKyMqhlCnHAB5xXj8t737mtPY2dU8O6P4Z1HSbNrxv7YO26kvpZF+SVuiFshQB0BAHHXPWtuaw0u7g1CDxNLdR6jo4SeGJ12oW4ADYz81cbY/8Tzw1e2tyFkFvMVSQqPMwGIGT+FMnuxbfDXTx5MLzXxZ57h13TSnjq2az5nexNSTWx5z8Ur6EandR6a3l3UjuqSSOB5Kkn+LjjHat7RLezmuNBudJOyLS1+0zPdfLHMcEEk87iCT0I/pXlvia2GrzNHIzKJp1Vip+bBbnH516N8N7WHwXpmqWVnBC1rcF18uVd6x7LgqNo7HjJ9ya2MueR23x68Q3HjG302O102G+0nT7QSypCRy7O3f/AHV6denTvzHwhd7++bXPJZdMtwha1mI8oxKxBKEdScgZJwOOKvfFPxjfaH8MLwWsixK9qzbVGADyM8ewH5V6J8aPCtj4J+BfhO20yEWsK2FlDtTjIkiidyfUlmJzQoq50RvbUxdWi0ef4k6X4msXj0mGCxdpNQ37JJuQFtyeS65+bgAZi6jitTRdHbUPBWorNZ3Gp+JL2Z1s55G8rchYALGpUDKjc5djzj8K810Ly77wdr0M8Mc66LarPaiUl1XEh+UqSVKk8kY6/U17pc6jNrHgjUPEcjLHqGl6bbT2/lRrGiM7wqcYGQMO2ApHXHQkE20RR5v4asNFvNJ8aW8dnJDqWnzQTah50ys++NCD5btwvzfMwHXtxxVHxr8N7rWtAt72CGaxV44TPbSy74XCHcQSpzg45wRwT0ODXP8Ahq4a4+GvizVphHPeXl7dLIZY1cf6xlBGRkEKAAc8dsVD8UvFF8vw+mvorh7eZbWGILHwgDBQTjsfcVcdgOl8VfDq/wBX+IdrtksbRdRV7y7Z0fau0AhAODg9M5yMc15c2oHV/HM2o6lpb31lYq0cKHdJHKQ5CKF6+gznpXvPh7Urmf4eeGb1riU3dxYIskpO5nDhQRk5rl/jd4RtvAPiK3bTZLiKK8t5GlgL5hdhyG24655pxSQKKSujc+AmnaLdfszP4fuJv7O8TQ6tdz6rZF2aRleYyQOWO6PasJVFVdvAJJJJNYVpdXHhXTJvGXh++ebc5021gjO5NTC4kBZxwpGAFOcZD8HIIy9a8CWt/FNqzT30c9vGkzxxzlYborgBZU6OuBjB7Zrv7/Xm8I6ppelaPbWmk+H59Ohc6NbIV0+OTe/7xISSEbD4+XAwBxnJNcqM6nwj/h9YeE/iprtmsl02geKNaJmmgu7XcWneQbt6OhEkbYyD97g5IziuR0fT0u/Gmp/2jJa3lto95PDdW8dwFkcRMilyAcL97C5JHPtXfah4G03xl47j+2QDzUuYraOZMCSJCzD5Sc4PA/Kvmv4h+D7XwX8ZfFVnYyXCReaIiWfJKkqT2xyQDnHajlV9Dncnsz6f8Y+P9J8CW2ra9JYXkel3U66faarezi4i89ZBHt4BOZI/lBGVJYDjk15R8RPg7q2q+K/Cvj6wkS11STUdti3n7Y4rZ5gxjby8ZHyIcjoMjvXUftG6/dXHjnSfD0ci2uj3CG9ktoUAQvHApTAIIABGcDHJJ714/ofxI1vxVrP9k3moTNZ2+rtbxKmF8pN5XjHA4HpRID0z4EWel+LPhdrWpNqMNp4itrq42wlPmvQpIOQwyVJHUEZ4wai8N+KdH8M+EJtO1SK30VNUkNxbmASSLMZCT91mbH51jadrFzpuvW8aSlobu68mSMgBShz8uVwccetW/j7pNtpn/EvjhjNrBqMXlqy5KZznB6/rUgdhZX+g6hpMsItbe9sbeNGMYO9lGAQ2O3GCcqfwrzfx/rbaVrVnBawpdaXcX37q9W3eOZPkZjG3O0JuPOQclQc8893pHhbTvCPwm0nUNNtEtb24toIZZVdj5qiPd8wJwc9M46cVwN74im+J8l9BqEdvDDapBHClrGIhF8gyR15JJJPqaqWwFEeALi98TPMuoto8+obZ/PkO5ZG+YBAoHBIx69e1dl8FdauvFV/ruk+IJE1Wz0OTyIx5hkAd8qqlcZUiTPQjIz1xViw8HWuoaZqHnPcPLo8u23mMn7wDzSnJ78AV6v8ADn4J+Hdd8LQ3Ulj5V5ctLJNcxOVmlffLhyxydw8tcGnHexL3PnqfwxrWl+Lfsd4+oR6TJKwksorZpouFPzhQ2VwABn3PPrh+G9Vm8O6dqVujSWcXmtut2y7Q9yNv3sEe9b1h8WtXPjO485ra6me01C3M00WZNsbR7ORjkVa/Z9uF8U/GD4i3V7DbyzQCzEfycLuj547n3OTSkknoFkx/wp1GT4w+OtX8N6C0lxdCyWOeK4O3yEJG5gCVyuOeQ2Bn8Kc3g/7Z8QV0CZlN5pzAwzAHy3kUg4XnoeQR78YrZ8X+EbPTvitpmrQqYr5pDbNJGBHvjlwrKdoBOAxxnpmhdJtdL8aWV01vHdyX8XkP9oJdYwTtJQZG1sHrUxScii18Xv7Q8Q+IpPEtrNuuLOTdtQeTaxyDgny8DDEcHBGcDpXjHxJ8Z2Mui3V60ECz3iSST28akFWDHccZ7HPcmve/hdpVvqOkNZTK7wvqdzI+ZGy4zHheuAASTwASWOSa8j+IPw/07U/jjaxssqRr9rYoj4WQ5Od3rmtKYHP/AAbuln8X2N5bXwg1KMpLHIgzcIqAssqE5GV4GME816z4o8Wx/EHT75Ne1Nrq8kmD2l7LCwniLfKwBVQP4QeR3NeZWvhazT4n6DYrG0cKs7Bo22ScnpuHarE1/cN490/S2uJns7mWSRkZuVYMV4PXoBWhO7Nz4n/Gu18CeE7jT7Nbtr2NoYZHiZhC0blgxwvLZOARjgdMZNZPiX4nafceKNLsb6Fprtk8p/3m7zEXG0LuGRgZyCSfTFV/F+mLrVrr0dxJMy6YR9m+bPl+/Pc4HPtXz9+0r401LV9QsvNuCJUuolWZFCyABCODiqiuZ2ZnLct/tH+JYfB/jG8vdFvGjsZJftQsmcsqt0OV9+eAeOOeK+UfiJ40u/FOuXF/I7QswYgYHPeu08TeK75rgwSTNMqrIu5+WNeC6jrFxPr9xG0jFfMZMe27pXdRppKxlIuak6z3luJriR42bkZ9BU00SR3DCNiwX+8ah0yyinA3orfNjnmobg7LKRx95QMfia6DmGXnzD52zhcrz1xxVZrNpXRmeNUXkA/1FSNax3N3bxSKrIo2gEZ4HSpPJX+11j2jYMADHHagCaaNb5dsjbW7nd1/z6e1SWjy6ejRt++A53E7h/npUmn28d3YtJIqlskZ/Cs1bhrAboz3UEHkEHNAH17/AMEjZlh8ffGa4aNmjg+HVvM4XrhfGHhdj/KvcfBOkz2etW2oWd19qs/DcuqatdPEdvnyXbMFh3cbti7QR7ivmT/gnzrVz4d0H9oS8s5DDcf8KxhUOOqhvGHhcHH4Gvpr4x6jN4B/Zw8Rf2a3l/Z7tNKj3c7YBG0pHuWf5mJ6miWxdLr6/ojlPjlr1x8UvjR4XsVktYbi00bTX1KTYZAUOZiCmTmQI2D2OK9I0Tw5qml+AvH+uWtgkFnf6Z9jsnu7tDqJeOB9iEodqSnBYgjYq4X71VfCPg+0t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tionship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47463,"title":"Slitherlink II: Gimmes","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 531.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 265.833px; transform-origin: 407px 265.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87.8667px 7.91667px; transform-origin: 87.8667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink II: Gimmes\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 293.283px 7.91667px; transform-origin: 293.283px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.683px 7.91667px; transform-origin: 363.683px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\n\r\n[sv,valid]=pcheck(s,p,bsegs); \r\nfprintf('sv  init solution\\n')\r\nfprintf('%i ',sv);fprintf('\\n') \r\n\r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n%Author Note: I found creating the complete set was time consuming\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge  add by raz as a Gimme\r\n\r\n [nr,nc]=size(s);\r\n %Example Zero processing\r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  %enter setting of p for 1s in corners\r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  %enter setting of p for 1s in corners\r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n %enter setting of p for 1s in corners \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n %setting p for 03 adjacent cases\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n %setting p for 33 adjacent\r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  %setting p for 03 diagonal\r\n \r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); \r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n  %setting p for 33 diagonal\r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); \r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i); \r\n  %3-0 adjacent set segs to 0/5\r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n %Slithering Starting Techniques misses the 13 edge Gimme     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 2;2 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5;5 0 5;5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 3 2;5 0 5 0 5;5 3 5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 5 3 5;5 0 5 5 0 5;5 3 5 5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T17:23:06.000Z","updated_at":"2025-05-02T19:04:22.000Z","published_at":"2020-11-12T23:27:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink II: Gimmes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check 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your own Test Suite (part 3)","description":"In this task you need to imagine that you — _yes, YOU_ — have developed a problem on Cody for _me_ to solve, and now *you need to implement a robust Test Suite* to check whether _my_ submitted solutions meet _your_ requirements or not, and to be fairly assured that I am not trying to 'game' the system.  \r\n\r\nSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!  \r\n\r\nThe problem you've set me is to:\r\n\r\n* output the *sine* of an angle, when the angle is specified in degrees as a (scalar) \u003chttps://au.mathworks.com/help/matlab/ref/double.html |double|\u003e, with no restriction in the domain,   \r\n* *without* using |regexp| or |regexpi| or |ans|, \r\n* *within* less than 0.01 seconds (10 milliseconds).\r\n\r\nYou provide me with the following example for the function defined as |s = SINE(a)|:\r\n\r\n % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\r\n\r\nNow I have responded by submitting a large number of purported 'solutions', some of which are _reasonably accurate_, and others which are either too imprecise or else logically flawed.  \r\n\r\nYour Test Suite (contained within _your_ function |testSuite|) must address each of the elements of your problem specification:  \r\n\r\n# Check that my submitted code reliably returns *sufficiently accurate* values for sine of many different angles.  Use the \u003chttps://au.mathworks.com/help/matlab/ref/assert.html |assert|\u003e function.  \r\n# Ensure that the returned *data type* is suitable.  \r\n# You *cannot* use (or mention) the functions |sind|, |sin|, |cscd| or |cosd| in your Test Suite;  any other functions are allowed.  [ _MOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!_ ]\r\n# You must check that I *haven't used* \"regexp\" or \"regexpi\" or \"ans\".  You could try using the \u003chttps://au.mathworks.com/help/coursework/ug/assessfunctionabsence.html |assessFunctionAbsence|\u003e function in the form |assessFunctionAbsence(..., 'FileName','SINE.m')|, or else you could try 'manually' opening the file |SINE.m| that corresponds to my submitted solution and then parsing it for occurrence of the prohibited function/variable.  \r\n# You must check that my code returns a result within *less than 0.01 seconds* (for a single input of an arbitrary angle).  You could use \u003chttps://au.mathworks.com/help/matlab/ref/tic.html |tic|\u003e \u0026 \u003chttps://au.mathworks.com/help/matlab/ref/toc.html |toc|\u003e or \u003chttps://au.mathworks.com/help/matlab/ref/timeit.html |timeit|\u003e, or a variety of other MATLAB functions (but not |cputime| in this case).  If your Test Suite claims that my submission runs slowly, then you must be confident that it is truly caused only by inefficiency in my submitted code, *not* just general/erratic overheads in 'queuing' or suchlike on the Cody servers.  \r\n# Your |assert| (or other) function must throw \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44357 errors/exceptions\u003e with the following *|error message| text* contents.\r\n\r\n* 'Incorrect value' if the output is too inaccurate.\r\n* 'Incorrect data type' if the output is not appropriate.\r\n* 'Too slow' if my submitted code is too slow.\r\n* 'Banned word' if my submitted code contains the |regexp| or |regexpi| functions or the \u003chttps://au.mathworks.com/help/matlab/ref/ans.html |ans|\u003e variable.  For _this_ infringement, _additional_ text can _also_ (optionally!) be present in the message — for example, 'You cannot do that!  Banned word (regexp/regexpi)' would also be acceptable.\r\n\r\nWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!  \r\n\r\nSee also:\r\n\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44631 Problem 44631\u003e (part *0*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44617 Problem 44617\u003e (part *1*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44616 Problem 44616\u003e (part *2*)","description_html":"\u003cp\u003eIn this task you need to imagine that you — \u003ci\u003eyes, YOU\u003c/i\u003e — have developed a problem on Cody for \u003ci\u003eme\u003c/i\u003e to solve, and now \u003cb\u003eyou need to implement a robust Test Suite\u003c/b\u003e to check whether \u003ci\u003emy\u003c/i\u003e submitted solutions meet \u003ci\u003eyour\u003c/i\u003e requirements or not, and to be fairly assured that I am not trying to 'game' the system.\u003c/p\u003e\u003cp\u003eSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!\u003c/p\u003e\u003cp\u003eThe problem you've set me is to:\u003c/p\u003e\u003cul\u003e\u003cli\u003eoutput the \u003cb\u003esine\u003c/b\u003e of an angle, when the angle is specified in degrees as a (scalar) \u003ca href = \"https://au.mathworks.com/help/matlab/ref/double.html\"\u003e\u003ctt\u003edouble\u003c/tt\u003e\u003c/a\u003e, with no restriction in the domain,\u003c/li\u003e\u003cli\u003e\u003cb\u003ewithout\u003c/b\u003e using \u003ctt\u003eregexp\u003c/tt\u003e or \u003ctt\u003eregexpi\u003c/tt\u003e or \u003ctt\u003eans\u003c/tt\u003e,\u003c/li\u003e\u003cli\u003e\u003cb\u003ewithin\u003c/b\u003e less than 0.01 seconds (10 milliseconds).\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou provide me with the following example for the function defined as \u003ctt\u003es = SINE(a)\u003c/tt\u003e:\u003c/p\u003e\u003cpre\u003e % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\u003c/pre\u003e\u003cp\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are \u003ci\u003ereasonably accurate\u003c/i\u003e, and others which are either too imprecise or else logically flawed.\u003c/p\u003e\u003cp\u003eYour Test Suite (contained within \u003ci\u003eyour\u003c/i\u003e function \u003ctt\u003etestSuite\u003c/tt\u003e) must address each of the elements of your problem specification:\u003c/p\u003e\u003col\u003e\u003cli\u003eCheck that my submitted code reliably returns \u003cb\u003esufficiently accurate\u003c/b\u003e values for sine of many different angles.  Use the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/assert.html\"\u003e\u003ctt\u003eassert\u003c/tt\u003e\u003c/a\u003e function.\u003c/li\u003e\u003cli\u003eEnsure that the returned \u003cb\u003edata type\u003c/b\u003e is suitable.\u003c/li\u003e\u003cli\u003eYou \u003cb\u003ecannot\u003c/b\u003e use (or mention) the functions \u003ctt\u003esind\u003c/tt\u003e, \u003ctt\u003esin\u003c/tt\u003e, \u003ctt\u003ecscd\u003c/tt\u003e or \u003ctt\u003ecosd\u003c/tt\u003e in your Test Suite;  any other functions are allowed.  [ \u003ci\u003eMOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/i\u003e ]\u003c/li\u003e\u003cli\u003eYou must check that I \u003cb\u003ehaven't used\u003c/b\u003e \"regexp\" or \"regexpi\" or \"ans\".  You could try using the \u003ca href = \"https://au.mathworks.com/help/coursework/ug/assessfunctionabsence.html\"\u003e\u003ctt\u003eassessFunctionAbsence\u003c/tt\u003e\u003c/a\u003e function in the form \u003ctt\u003eassessFunctionAbsence(..., 'FileName','SINE.m')\u003c/tt\u003e, or else you could try 'manually' opening the file \u003ctt\u003eSINE.m\u003c/tt\u003e that corresponds to my submitted solution and then parsing it for occurrence of the prohibited function/variable.\u003c/li\u003e\u003cli\u003eYou must check that my code returns a result within \u003cb\u003eless than 0.01 seconds\u003c/b\u003e (for a single input of an arbitrary angle).  You could use \u003ca href = \"https://au.mathworks.com/help/matlab/ref/tic.html\"\u003e\u003ctt\u003etic\u003c/tt\u003e\u003c/a\u003e \u0026 \u003ca href = \"https://au.mathworks.com/help/matlab/ref/toc.html\"\u003e\u003ctt\u003etoc\u003c/tt\u003e\u003c/a\u003e or \u003ca href = \"https://au.mathworks.com/help/matlab/ref/timeit.html\"\u003e\u003ctt\u003etimeit\u003c/tt\u003e\u003c/a\u003e, or a variety of other MATLAB functions (but not \u003ctt\u003ecputime\u003c/tt\u003e in this case).  If your Test Suite claims that my submission runs slowly, then you must be confident that it is truly caused only by inefficiency in my submitted code, \u003cb\u003enot\u003c/b\u003e just general/erratic overheads in 'queuing' or suchlike on the Cody servers.\u003c/li\u003e\u003cli\u003eYour \u003ctt\u003eassert\u003c/tt\u003e (or other) function must throw \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44357\"\u003eerrors/exceptions\u003c/a\u003e with the following \u003cb\u003e\u003ctt\u003eerror message\u003c/tt\u003e text\u003c/b\u003e contents.\u003c/li\u003e\u003c/ol\u003e\u003cul\u003e\u003cli\u003e'Incorrect value' if the output is too inaccurate.\u003c/li\u003e\u003cli\u003e'Incorrect data type' if the output is not appropriate.\u003c/li\u003e\u003cli\u003e'Too slow' if my submitted code is too slow.\u003c/li\u003e\u003cli\u003e'Banned word' if my submitted code contains the \u003ctt\u003eregexp\u003c/tt\u003e or \u003ctt\u003eregexpi\u003c/tt\u003e functions or the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/ans.html\"\u003e\u003ctt\u003eans\u003c/tt\u003e\u003c/a\u003e variable.  For \u003ci\u003ethis\u003c/i\u003e infringement, \u003ci\u003eadditional\u003c/i\u003e text can \u003ci\u003ealso\u003c/i\u003e (optionally!) be present in the message — for example, 'You cannot do that!  Banned word (regexp/regexpi)' would also be acceptable.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!\u003c/p\u003e\u003cp\u003eSee also:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44631\"\u003eProblem 44631\u003c/a\u003e (part \u003cb\u003e0\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44617\"\u003eProblem 44617\u003c/a\u003e (part \u003cb\u003e1\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44616\"\u003eProblem 44616\u003c/a\u003e (part \u003cb\u003e2\u003c/b\u003e)\u003c/li\u003e\u003c/ul\u003e","function_template":"function dummy = testSuite()\r\n    \r\n    %% Test 1 — test outputs of submitted SINE function for various input values\r\n    % RATIONALE:  Computing SINE requires relatively simple code, \r\n    %             as the focus in the present task is on implementing a robust Test Suite.  \r\n    % MOTIVATION:  I could be submitting lazy code that only works for \r\n    %              a small number of angles, if that's all you test.\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    \r\n    \r\n    %% Test 2 — test for use/presence of  \"regexp\" or \"regexpi\" or \"ans\" in the submission\r\n    % MOTIVATION:  The functions regexp and regexpi are sometimes pertinent to efficient solution of a problem, \r\n    %              but at other times are used to artificially decrease the size of a Cody submission.\r\n    %              If I'm using regexp or regexpi for *this* problem of yours (where neither is relevant), \r\n    %              then it's probably because I'm trying to 'cheat' to get a smaller Cody score.\r\n    %              Use of the ans variable is also generally employed purely to decrease\r\n    %              Cody size, at the expense of clarity in the code.  \r\n    assessFunctionAbsence( '' , 'FileName','SINE.m' )\r\n\r\n    \r\n    %% Test 3 — check the speed of execution of the submission \r\n    % MOTIVATION:  Some code that is rated as \"small\" on Cody is actually quite computationally inefficient, \r\n    %              but it is possible to 'manually' enforce a requirement for Cody submissions to be at least \r\n    %              moderately efficient.  See e.g. Cody Problems 44351, 44356 \u0026 44383.  \r\n    %              So this will balance the Cody focus on small code 'size' with \r\n    %              the desire to ensure code is also computationally efficient.\r\n    toc\r\n    timeit( SINE(45) )\r\n    duration = tic\r\n    assert( duration , '' )\r\n\r\nend\r\n\r\n%{\r\nNOTE:  \r\nThe text \"dummy = \" was added to this Function Template, \r\nbecause Cody was generating a spurious error, namely\r\n        Your problem cannot be published until you:\r\n        •Edit the function name in the test suite to match the function name in the function template\r\nHowever, in the Reference Solution it was confirmed to be unnecessary.  \r\n%}\r\n\r\n%{\r\nFOOTNOTE:  \r\nAlthough 10 milliseconds may not seem like a slow execution time, \r\ncomputing the sine of an angle is a very common task, and a general program \r\nmight need to compute it hundreds, thousands or even millions of times.  \r\nIn the latter case, if each computation of sine were to take more than 10 \r\nmilliseconds, then the hypothetical program would run for several hours.  \r\nBy way of comparison, for a 1×1000000 vector input MATLAB's built-in sin \r\nfunction is currently taking a total of about 10 to 25 milliseconds on Cody, \r\nwhile sind is taking about 20 to 50 milliseconds._  \r\n%}","test_suite":"%% Timing\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfprintf(fileID,'   pause(0.012);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Too slow') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Use of ans\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function ans = SINE(angle)\\n');\r\nfprintf(fileID,'   sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    e.message\r\n    assert( contains(e.message, 'Banned word') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Possible false alarm for use of ans\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   % This is a simple problem:  the answer only takes one line!\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Use of regexp/regexpi (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfprintf(fileID,'   dummy = regexp(''Test text'', ''t'');\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    e.message\r\n    assert( contains(e.message, 'Banned word') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Use of regexp/regexpi (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfprintf(fileID,'   dummy = regexpi(''Test text'', ''t'');\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    e.message\r\n    assert( contains(e.message, 'Banned word') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Ensure no \"sind\" or \"sin\" or \"cosd\" in _your_ Test Suite\r\n% NOTE:  You may notice that the user function has been named \"SINE\", \r\n%        in uppercase.  That is an extra precaution to avoid accidentally \r\n%        triggering an error due to a banned 'word' (sequence of characters).  \r\n%        Careful choice of code to check for banned _functions_ is better!  \r\nassessFunctionAbsence('sind', 'FileName','testSuite.m')\r\nassessFunctionAbsence('sin',  'FileName','testSuite.m')\r\nassessFunctionAbsence('cscd', 'FileName','testSuite.m')\r\nassessFunctionAbsence('cosd', 'FileName','testSuite.m')\r\n\r\n\r\n%% Exactly right\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\n                    testSuite()\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * pi / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = 1 ./ cscd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * 3.14 / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * (22/7) / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + 10000*eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Completely wrong\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = cosd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = -sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle)*sign(sind(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Fixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(fix(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Unfixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   inc=0;\\n');\r\nfprintf(fileID,'   if mod(angle,1)==0, inc=1; end;\\n');\r\nfprintf(fileID,'   s = sind(angle + inc);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int8(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int16(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') | ...\r\n            isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int32(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int64(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') | ...\r\n            isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(double(int32(angle)));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = \"ratio\";\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = \"?\";\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = {1:1E4};\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Wrong data type\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = {sind(angle)};\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2018-04-18T14:04:26.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-02-13T12:05:07.000Z","updated_at":"2018-05-11T13:20:58.000Z","published_at":"2018-02-20T13:11:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task you need to imagine that you —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyes, YOU\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — have developed a problem on Cody for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eme\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to solve, and now\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyou need to implement a robust Test Suite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to check whether\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e submitted solutions meet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e requirements or not, and to be fairly assured that I am not trying to 'game' the system.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo the tables are turned! You are now in the role of Tester! I am in the role of Player!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe problem you've set me is to:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of an angle, when the angle is specified in degrees as a (scalar)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/double.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edouble\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, with no restriction in the domain,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewithout\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e using\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eregexp\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eregexpi\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eans\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewithin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e less than 0.01 seconds (10 milliseconds).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou provide me with the following example for the function defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es = SINE(a)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % INPUT\\n a = 45 % degrees\\n % OUTPUT\\n s = 1/sqrt(2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ereasonably accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and others which are either too imprecise or else logically flawed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour Test Suite (contained within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etestSuite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) must address each of the elements of your problem specification:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck that my submitted code reliably returns\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esufficiently accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values for sine of many different angles. Use the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/assert.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEnsure that the returned\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edata type\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is suitable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecannot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e use (or mention) the functions\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esind\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecscd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecosd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in your Test Suite; any other functions are allowed. [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eMOTIVATION: You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou must check that I\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ehaven't used\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \\\"regexp\\\" or \\\"regexpi\\\" or \\\"ans\\\". You could try using the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/coursework/ug/assessfunctionabsence.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassessFunctionAbsence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function in the form\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassessFunctionAbsence(..., 'FileName','SINE.m')\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, or else you could try 'manually' opening the file\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSINE.m\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that corresponds to my submitted solution and then parsing it for occurrence of the prohibited function/variable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou must check that my code returns a result within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eless than 0.01 seconds\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (for a single input of an arbitrary angle). You could use\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/tic.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etic\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e \u0026amp;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/toc.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etoc\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/timeit.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etimeit\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, or a variety of other MATLAB functions (but not\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecputime\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in this case). If your Test Suite claims that my submission runs slowly, then you must be confident that it is truly caused only by inefficiency in my submitted code,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e just general/erratic overheads in 'queuing' or suchlike on the Cody servers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (or other) function must throw\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44357\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eerrors/exceptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e with the following\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eerror message\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e text\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e contents.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Incorrect value' if the output is too inaccurate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Incorrect data type' if the output is not appropriate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Too slow' if my submitted code is too slow.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Banned word' if my submitted code contains the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eregexp\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eregexpi\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e functions or the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/ans.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eans\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e variable. For\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ethis\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e infringement,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eadditional\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e text can\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ealso\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (optionally!) be present in the message — for example, 'You cannot do that! Banned word (regexp/regexpi)' would also be acceptable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee also:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44631\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44631\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44617\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44617\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44616\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44616\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44616,"title":"Make your own Test Suite (part 2)","description":"In this task you need to imagine that you — _yes, YOU_ — have developed a problem on Cody for _me_ to solve, and now *you need to develop a Test Suite* to check whether _my_ submitted solutions meet _your_ requirements or not.  \r\n\r\nSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!  \r\n\r\nThe problem you've set me is to:\r\n\r\n* output the *sine* of an angle, when the angle is specified in degrees as a (scalar) \u003chttps://au.mathworks.com/help/matlab/ref/double.html |double|\u003e, with no restriction in the domain.\r\n\r\nYou provide me with the following example for the function defined as |s = SINE(a)|:\r\n\r\n % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\r\n\r\nNow I have responded by submitting a large number of purported 'solutions', some of which are _reasonably accurate_, and others which are either too imprecise or else logically flawed.  \r\n\r\nYour Test Suite (contained within _your_ function |testSuite|) must address each of the elements of your problem specification:  \r\n\r\n# Check that my submitted code reliably returns *sufficiently accurate* values for sine of many different angles.  Use the \u003chttps://au.mathworks.com/help/matlab/ref/assert.html |assert|\u003e function.  \r\n# Ensure that the returned *data type* is suitable.  \r\n# You *cannot* use (or mention) the functions |sind|, |sin|, |cscd| or |cosd| in your Test Suite;  any other functions are allowed.  [ _MOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!_ ]\r\n# Your |assert| (or other) function must throw \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44357 errors/exceptions\u003e with the following *|error message| text* contents.\r\n\r\n* 'Incorrect value' if the output is too inaccurate.\r\n* 'Incorrect data type' if the output is not appropriate.\r\n\r\nWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!  \r\n\r\nSee also:\r\n\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44631 Problem 44631\u003e (part *0*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44617 Problem 44617\u003e (part *1*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44521 Problem 44521\u003e (part *3*)","description_html":"\u003cp\u003eIn this task you need to imagine that you — \u003ci\u003eyes, YOU\u003c/i\u003e — have developed a problem on Cody for \u003ci\u003eme\u003c/i\u003e to solve, and now \u003cb\u003eyou need to develop a Test Suite\u003c/b\u003e to check whether \u003ci\u003emy\u003c/i\u003e submitted solutions meet \u003ci\u003eyour\u003c/i\u003e requirements or not.\u003c/p\u003e\u003cp\u003eSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!\u003c/p\u003e\u003cp\u003eThe problem you've set me is to:\u003c/p\u003e\u003cul\u003e\u003cli\u003eoutput the \u003cb\u003esine\u003c/b\u003e of an angle, when the angle is specified in degrees as a (scalar) \u003ca href = \"https://au.mathworks.com/help/matlab/ref/double.html\"\u003e\u003ctt\u003edouble\u003c/tt\u003e\u003c/a\u003e, with no restriction in the domain.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou provide me with the following example for the function defined as \u003ctt\u003es = SINE(a)\u003c/tt\u003e:\u003c/p\u003e\u003cpre\u003e % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\u003c/pre\u003e\u003cp\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are \u003ci\u003ereasonably accurate\u003c/i\u003e, and others which are either too imprecise or else logically flawed.\u003c/p\u003e\u003cp\u003eYour Test Suite (contained within \u003ci\u003eyour\u003c/i\u003e function \u003ctt\u003etestSuite\u003c/tt\u003e) must address each of the elements of your problem specification:\u003c/p\u003e\u003col\u003e\u003cli\u003eCheck that my submitted code reliably returns \u003cb\u003esufficiently accurate\u003c/b\u003e values for sine of many different angles.  Use the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/assert.html\"\u003e\u003ctt\u003eassert\u003c/tt\u003e\u003c/a\u003e function.\u003c/li\u003e\u003cli\u003eEnsure that the returned \u003cb\u003edata type\u003c/b\u003e is suitable.\u003c/li\u003e\u003cli\u003eYou \u003cb\u003ecannot\u003c/b\u003e use (or mention) the functions \u003ctt\u003esind\u003c/tt\u003e, \u003ctt\u003esin\u003c/tt\u003e, \u003ctt\u003ecscd\u003c/tt\u003e or \u003ctt\u003ecosd\u003c/tt\u003e in your Test Suite;  any other functions are allowed.  [ \u003ci\u003eMOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/i\u003e ]\u003c/li\u003e\u003cli\u003eYour \u003ctt\u003eassert\u003c/tt\u003e (or other) function must throw \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44357\"\u003eerrors/exceptions\u003c/a\u003e with the following \u003cb\u003e\u003ctt\u003eerror message\u003c/tt\u003e text\u003c/b\u003e contents.\u003c/li\u003e\u003c/ol\u003e\u003cul\u003e\u003cli\u003e'Incorrect value' if the output is too inaccurate.\u003c/li\u003e\u003cli\u003e'Incorrect data type' if the output is not appropriate.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!\u003c/p\u003e\u003cp\u003eSee also:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44631\"\u003eProblem 44631\u003c/a\u003e (part \u003cb\u003e0\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44617\"\u003eProblem 44617\u003c/a\u003e (part \u003cb\u003e1\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44521\"\u003eProblem 44521\u003c/a\u003e (part \u003cb\u003e3\u003c/b\u003e)\u003c/li\u003e\u003c/ul\u003e","function_template":"function dummy = testSuite()\r\n    \r\n    % Test A — test outputs of submitted SINE function for various input values\r\n    % RATIONALE:  Computing SINE requires relatively simple code, \r\n    %             as the focus in the present task is on implementing a robust Test Suite.  \r\n    % MOTIVATION:  I could be submitting lazy code that only works for \r\n    %              a small number of angles, if that's all you test.\r\n    \r\n    %% Part 1\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    \r\n    %% Part 2\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    \r\n    %% Part 3\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    \r\n\r\n    %% Test B — test outputs of submitted SINE function to ensure the data type is correct.\r\n    \r\n\r\nend\r\n\r\n%{\r\nNOTE:  \r\nThe text \"dummy = \" was added to this Function Template, \r\nbecause Cody was generating a spurious error, namely\r\n        Your problem cannot be published until you:\r\n        •Edit the function name in the test suite to match the function name in the function template\r\nHowever, in the Reference Solution it was confirmed to be unnecessary.  \r\n%}\r\n","test_suite":"%% Ensure no \"sind\" or \"sin\" or \"cosd\" in _your_ Test Suite\r\n% NOTE:  You may notice that the user function has been named \"SINE\", \r\n%        in uppercase.  That is an extra precaution to avoid accidentally \r\n%        triggering an error due to a banned 'word' (sequence of characters).  \r\n%        Careful choice of code to check for banned _functions_ is better!  \r\nassessFunctionAbsence('sind', 'FileName','testSuite.m')\r\nassessFunctionAbsence('sin',  'FileName','testSuite.m')\r\nassessFunctionAbsence('cscd', 'FileName','testSuite.m')\r\nassessFunctionAbsence('cosd', 'FileName','testSuite.m')\r\n\r\n\r\n%% Exactly right\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\n                    testSuite()\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * pi / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = 1 ./ cscd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * 3.14 / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * (22/7) / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + 10000*eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Completely wrong\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = cosd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = -sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle)*sign(sind(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Fixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(fix(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Unfixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   inc=0;\\n');\r\nfprintf(fileID,'   if mod(angle,1)==0, inc=1; end;\\n');\r\nfprintf(fileID,'   s = sind(angle + inc);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int8(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int16(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') | ...\r\n            isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int32(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int64(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') | ...\r\n            isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(double(int32(angle)));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = \"ratio\";\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = \"?\";\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = {1:1E4};\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Wrong data type\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = {sind(angle)};\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-04-18T13:09:12.000Z","updated_at":"2018-05-11T13:19:31.000Z","published_at":"2018-04-18T13:51:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task you need to imagine that you —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyes, YOU\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — have developed a problem on Cody for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eme\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to solve, and now\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyou need to develop a Test Suite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to check whether\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e submitted solutions meet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e requirements or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo the tables are turned! You are now in the role of Tester! I am in the role of Player!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe problem you've set me is to:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of an angle, when the angle is specified in degrees as a (scalar)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/double.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edouble\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, with no restriction in the domain.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou provide me with the following example for the function defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es = SINE(a)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % INPUT\\n a = 45 % degrees\\n % OUTPUT\\n s = 1/sqrt(2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ereasonably accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and others which are either too imprecise or else logically flawed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour Test Suite (contained within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etestSuite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) must address each of the elements of your problem specification:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck that my submitted code reliably returns\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esufficiently accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values for sine of many different angles. Use the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/assert.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEnsure that the returned\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edata type\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is suitable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecannot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e use (or mention) the functions\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esind\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecscd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecosd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in your Test Suite; any other functions are allowed. [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eMOTIVATION: You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (or other) function must throw\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44357\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eerrors/exceptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e with the following\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eerror message\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e text\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e contents.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Incorrect value' if the output is too inaccurate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Incorrect data type' if the output is not appropriate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee also:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44631\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44631\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44617\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44617\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44521\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44521\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44617,"title":"Make your own Test Suite (part 1)","description":"In this task you need to imagine that you — _yes, YOU_ — have developed a problem on Cody for _me_ to solve, and now *you need to prepare a simple Test Suite* to check whether _my_ submitted solutions meet _your_ requirements or not.  \r\n\r\nSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!  \r\n\r\nThe problem you've set me is to:\r\n\r\n* output the *sine* of an angle, when the angle is specified in degrees as a (scalar) \u003chttps://au.mathworks.com/help/matlab/ref/double.html |double|\u003e, with no restriction in the domain.\r\n\r\nYou provide me with the following example for the function defined as |s = SINE(a)|:\r\n\r\n % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\r\n\r\nNow I have responded by submitting a large number of purported 'solutions', some of which are _reasonably accurate_, and others which are either too imprecise or else logically flawed.  \r\n\r\nYour Test Suite (contained within _your_ function |testSuite|) must address each of the elements of your problem specification:  \r\n\r\n# Check that my submitted code reliably returns *sufficiently accurate* values for sine of many different angles.  Use the \u003chttps://au.mathworks.com/help/matlab/ref/assert.html |assert|\u003e function to generate \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44357 errors/exceptions\u003e if the values are not sufficiently accurate.  \r\n# You *cannot* use (or mention) the functions |sind|, |sin|, |cscd| or |cosd| in your Test Suite;  any other functions are allowed.  [ _MOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!_ ]\r\n\r\nWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!  Therefore the above specification is merely a _starting point_.  You will develop more robust Test Suites in:\r\n\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44616 Problem 44616\u003e (part *2*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44521 Problem 44521\u003e (part *3*)\r\n\r\nAlternatively, if even this Problem seems daunting, you may want to start with \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44631 Problem 44631\u003e (part *0*).","description_html":"\u003cp\u003eIn this task you need to imagine that you — \u003ci\u003eyes, YOU\u003c/i\u003e — have developed a problem on Cody for \u003ci\u003eme\u003c/i\u003e to solve, and now \u003cb\u003eyou need to prepare a simple Test Suite\u003c/b\u003e to check whether \u003ci\u003emy\u003c/i\u003e submitted solutions meet \u003ci\u003eyour\u003c/i\u003e requirements or not.\u003c/p\u003e\u003cp\u003eSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!\u003c/p\u003e\u003cp\u003eThe problem you've set me is to:\u003c/p\u003e\u003cul\u003e\u003cli\u003eoutput the \u003cb\u003esine\u003c/b\u003e of an angle, when the angle is specified in degrees as a (scalar) \u003ca href = \"https://au.mathworks.com/help/matlab/ref/double.html\"\u003e\u003ctt\u003edouble\u003c/tt\u003e\u003c/a\u003e, with no restriction in the domain.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou provide me with the following example for the function defined as \u003ctt\u003es = SINE(a)\u003c/tt\u003e:\u003c/p\u003e\u003cpre\u003e % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\u003c/pre\u003e\u003cp\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are \u003ci\u003ereasonably accurate\u003c/i\u003e, and others which are either too imprecise or else logically flawed.\u003c/p\u003e\u003cp\u003eYour Test Suite (contained within \u003ci\u003eyour\u003c/i\u003e function \u003ctt\u003etestSuite\u003c/tt\u003e) must address each of the elements of your problem specification:\u003c/p\u003e\u003col\u003e\u003cli\u003eCheck that my submitted code reliably returns \u003cb\u003esufficiently accurate\u003c/b\u003e values for sine of many different angles.  Use the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/assert.html\"\u003e\u003ctt\u003eassert\u003c/tt\u003e\u003c/a\u003e function to generate \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44357\"\u003eerrors/exceptions\u003c/a\u003e if the values are not sufficiently accurate.\u003c/li\u003e\u003cli\u003eYou \u003cb\u003ecannot\u003c/b\u003e use (or mention) the functions \u003ctt\u003esind\u003c/tt\u003e, \u003ctt\u003esin\u003c/tt\u003e, \u003ctt\u003ecscd\u003c/tt\u003e or \u003ctt\u003ecosd\u003c/tt\u003e in your Test Suite;  any other functions are allowed.  [ \u003ci\u003eMOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/i\u003e ]\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!  Therefore the above specification is merely a \u003ci\u003estarting point\u003c/i\u003e.  You will develop more robust Test Suites in:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44616\"\u003eProblem 44616\u003c/a\u003e (part \u003cb\u003e2\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44521\"\u003eProblem 44521\u003c/a\u003e (part \u003cb\u003e3\u003c/b\u003e)\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eAlternatively, if even this Problem seems daunting, you may want to start with \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44631\"\u003eProblem 44631\u003c/a\u003e (part \u003cb\u003e0\u003c/b\u003e).\u003c/p\u003e","function_template":"function dummy = testSuite()\r\n    \r\n    % Check outputs of submitted SINE function for various input values\r\n    % RATIONALE:  Computing SINE requires relatively simple code, \r\n    %             as the focus in the present task is on implementing a robust Test Suite.  \r\n    % MOTIVATION:  I could be submitting lazy code that only works for \r\n    %              a small number of angles, if that's all you test.\r\n    \r\n    %% Test 1\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 2\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 3\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 4\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n\r\n    %% Test 5\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 6\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 7\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n\r\nend\r\n\r\n%{\r\nNOTE:  \r\nThe text \"dummy = \" was added to this Function Template, \r\nbecause Cody was generating a spurious error, namely\r\n        Your problem cannot be published until you:\r\n        •Edit the function name in the test suite to match the function name in the function template\r\nHowever, in the Reference Solution it was confirmed to be unnecessary.  \r\n%}","test_suite":"%% Ensure no \"sind\" or \"sin\" or \"cosd\" in _your_ Test Suite\r\n% NOTE:  You may notice that the user function has been named \"SINE\", \r\n%        in uppercase.  That is an extra precaution to avoid accidentally \r\n%        triggering an error due to a banned 'word' (sequence of characters).  \r\n%        Careful choice of code to check for banned _functions_ is better!  \r\nassessFunctionAbsence('sind', 'FileName','testSuite.m')\r\nassessFunctionAbsence('sin',  'FileName','testSuite.m')\r\nassessFunctionAbsence('cscd', 'FileName','testSuite.m')\r\nassessFunctionAbsence('cosd', 'FileName','testSuite.m')\r\n\r\n\r\n%% Exactly right\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\n                    testSuite()\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * pi / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = 1 ./ cscd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * 3.14 / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * (22/7) / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + 10000*eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Completely wrong\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = cosd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = -sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle)*sign(sind(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Fixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(fix(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Unfixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   inc=0;\\n');\r\nfprintf(fileID,'   if mod(angle,1)==0, inc=1; end;\\n');\r\nfprintf(fileID,'   s = sind(angle + inc);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int8(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int16(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int32(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int64(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(double(int32(angle)));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-04-18T13:35:30.000Z","updated_at":"2018-05-11T13:20:07.000Z","published_at":"2018-04-18T13:49:32.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task you need to imagine that you —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyes, YOU\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — have developed a problem on Cody for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eme\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to solve, and now\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyou need to prepare a simple Test Suite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to check whether\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e submitted solutions meet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e requirements or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo the tables are turned! You are now in the role of Tester! I am in the role of Player!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe problem you've set me is to:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of an angle, when the angle is specified in degrees as a (scalar)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/double.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edouble\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, with no restriction in the domain.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou provide me with the following example for the function defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es = SINE(a)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % INPUT\\n a = 45 % degrees\\n % OUTPUT\\n s = 1/sqrt(2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ereasonably accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and others which are either too imprecise or else logically flawed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour Test Suite (contained within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etestSuite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) must address each of the elements of your problem specification:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck that my submitted code reliably returns\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esufficiently accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values for sine of many different angles. Use the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/assert.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function to generate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44357\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eerrors/exceptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e if the values are not sufficiently accurate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecannot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e use (or mention) the functions\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esind\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecscd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecosd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in your Test Suite; any other functions are allowed. [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eMOTIVATION: You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ! Therefore the above specification is merely a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003estarting point\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. You will develop more robust Test Suites in:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44616\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44616\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44521\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44521\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlternatively, if even this Problem seems daunting, you may want to start with\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44631\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44631\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44631,"title":"Make your own Test Suite (part 0)","description":"_Have no knowledge of \u003chttps://au.mathworks.com/help/matlab/matlab_prog/floating-point-numbers.html \"floating-point numbers\"\u003e?  Read the documentation, and/or try \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44690 Problem 44690\u003e first._\r\n\r\n——————————————————————————————————————————\r\n\r\nIn this task you need to imagine that you — _yes, YOU_ — have developed a problem on Cody for _me_ to solve, and now *you need to draft a naïve Test Suite* as the first step in developing a more robust Test Suite that would check whether _my_ submitted solutions meet _your_ requirements or not.  \r\n\r\nSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!  \r\n\r\nThe problem you've set me is to:\r\n\r\n* output the *sine* of an angle, when the angle is specified in degrees as a (scalar) \u003chttps://au.mathworks.com/help/matlab/ref/double.html |double|\u003e, with no restriction in the domain.\r\n\r\nYou provide me with the following example for the function defined as |s = SINE(a)|:\r\n\r\n % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\r\n\r\nNow I have responded by submitting a large number of purported 'solutions', some of which are _reasonably accurate_, and others which are either too imprecise or else logically flawed.  \r\n\r\nYour final Test Suite (contained within _your_ function |testSuite|) will have to address several aspects arising in your problem specification.  However, for this 'first draft' you are creating a naïve Test Suite with only one requirement:  \r\n\r\n# Check that my submitted code reliably returns *sufficiently accurate* values for sine of many different angles.  Use the \u003chttps://au.mathworks.com/help/matlab/ref/assert.html |assert|\u003e function to generate \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44357 errors/exceptions\u003e if the values are not sufficiently accurate.  \r\n\r\nYou *can* use the functions |sind|, |sin|, _etc._ in this draft Test Suite, if you want — even though this would be bad practice in your final Test Suite! \r\n\r\nThus, the above specification is merely a _starting point_.  You will develop more robust Test Suites in:\r\n\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44617 Problem 44617\u003e (part *1*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44616 Problem 44616\u003e (part *2*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44521 Problem 44521\u003e (part *3*)","description_html":"\u003cp\u003e\u003ci\u003eHave no knowledge of \u003ca href = \"https://au.mathworks.com/help/matlab/matlab_prog/floating-point-numbers.html\"\u003e\"floating-point numbers\"\u003c/a\u003e?  Read the documentation, and/or try \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44690\"\u003eProblem 44690\u003c/a\u003e first.\u003c/i\u003e\u003c/p\u003e\u003cp\u003e——————————————————————————————————————————\u003c/p\u003e\u003cp\u003eIn this task you need to imagine that you — \u003ci\u003eyes, YOU\u003c/i\u003e — have developed a problem on Cody for \u003ci\u003eme\u003c/i\u003e to solve, and now \u003cb\u003eyou need to draft a naïve Test Suite\u003c/b\u003e as the first step in developing a more robust Test Suite that would check whether \u003ci\u003emy\u003c/i\u003e submitted solutions meet \u003ci\u003eyour\u003c/i\u003e requirements or not.\u003c/p\u003e\u003cp\u003eSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!\u003c/p\u003e\u003cp\u003eThe problem you've set me is to:\u003c/p\u003e\u003cul\u003e\u003cli\u003eoutput the \u003cb\u003esine\u003c/b\u003e of an angle, when the angle is specified in degrees as a (scalar) \u003ca href = \"https://au.mathworks.com/help/matlab/ref/double.html\"\u003e\u003ctt\u003edouble\u003c/tt\u003e\u003c/a\u003e, with no restriction in the domain.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou provide me with the following example for the function defined as \u003ctt\u003es = SINE(a)\u003c/tt\u003e:\u003c/p\u003e\u003cpre\u003e % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\u003c/pre\u003e\u003cp\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are \u003ci\u003ereasonably accurate\u003c/i\u003e, and others which are either too imprecise or else logically flawed.\u003c/p\u003e\u003cp\u003eYour final Test Suite (contained within \u003ci\u003eyour\u003c/i\u003e function \u003ctt\u003etestSuite\u003c/tt\u003e) will have to address several aspects arising in your problem specification.  However, for this 'first draft' you are creating a naïve Test Suite with only one requirement:\u003c/p\u003e\u003col\u003e\u003cli\u003eCheck that my submitted code reliably returns \u003cb\u003esufficiently accurate\u003c/b\u003e values for sine of many different angles.  Use the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/assert.html\"\u003e\u003ctt\u003eassert\u003c/tt\u003e\u003c/a\u003e function to generate \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44357\"\u003eerrors/exceptions\u003c/a\u003e if the values are not sufficiently accurate.\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eYou \u003cb\u003ecan\u003c/b\u003e use the functions \u003ctt\u003esind\u003c/tt\u003e, \u003ctt\u003esin\u003c/tt\u003e, \u003ci\u003eetc.\u003c/i\u003e in this draft Test Suite, if you want — even though this would be bad practice in your final Test Suite!\u003c/p\u003e\u003cp\u003eThus, the above specification is merely a \u003ci\u003estarting point\u003c/i\u003e.  You will develop more robust Test Suites in:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44617\"\u003eProblem 44617\u003c/a\u003e (part \u003cb\u003e1\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44616\"\u003eProblem 44616\u003c/a\u003e (part \u003cb\u003e2\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44521\"\u003eProblem 44521\u003c/a\u003e (part \u003cb\u003e3\u003c/b\u003e)\u003c/li\u003e\u003c/ul\u003e","function_template":"function dummy = testSuite()\r\n    \r\n    % Check outputs of submitted SINE function for various input values\r\n    % RATIONALE:  Computing SINE requires relatively simple code, \r\n    %             as the focus in the present task is on implementing a robust Test Suite.  \r\n    % MOTIVATION:  I could be submitting lazy code that only works for \r\n    %              a small number of angles, if that's all you test.\r\n    \r\n    %% Test 1\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 2\r\n    assert( isequal( SINE(45), sine(45) )  )\r\n    \r\n    %% Test 3\r\n    assert( isequal( SINE(45), sin(45) )  )\r\n    \r\n    %% Test 4\r\n    assert( isequal( SINE(45), sind(45) )  )\r\n\r\n    %% Test 5\r\n    assert( isequal( SINE(45), sine(1/sqrt(2)) )  )\r\n    \r\n    %% Test 6\r\n    assert( isequal( SINE(45), sin(1/sqrt(2)) )  )\r\n    \r\n    %% Test 7\r\n    assert( isequal( SINE(45), sind(1/sqrt(2)) )  )\r\n    \r\n\r\nend\r\n\r\n%{\r\nNOTE:  \r\nThe text \"dummy = \" was added to this Function Template, \r\nbecause Cody was generating a spurious error, namely\r\n        Your problem cannot be published until you:\r\n        •Edit the function name in the test suite to match the function name in the function template\r\nHowever, in the Reference Solution it was confirmed to be unnecessary.  \r\n%}","test_suite":"%% Placeholder\r\n%assessFunctionAbsence('sind', 'FileName','testSuite.m')\r\n\r\n\r\n%% Exactly right\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\n                    testSuite()\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * pi / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = 1 ./ cscd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * 3.14 / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * (22/7) / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + 10000*eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Completely wrong\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = cosd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = -sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle)*sign(sind(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Fixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(fix(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Unfixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   inc=0;\\n');\r\nfprintf(fileID,'   if mod(angle,1)==0, inc=1; end;\\n');\r\nfprintf(fileID,'   s = sind(angle + inc);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":6,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-05-05T12:36:47.000Z","updated_at":"2018-06-17T02:29:04.000Z","published_at":"2018-05-05T13:35:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHave no knowledge of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/matlab_prog/floating-point-numbers.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"floating-point numbers\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e? Read the documentation, and/or try\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44690\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eProblem 44690\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e first.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e——————————————————————————————————————————\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task you need to imagine that you —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyes, YOU\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — have developed a problem on Cody for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eme\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to solve, and now\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyou need to draft a naïve Test Suite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as the first step in developing a more robust Test Suite that would check whether\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e submitted solutions meet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e requirements or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo the tables are turned! You are now in the role of Tester! I am in the role of Player!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe problem you've set me is to:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of an angle, when the angle is specified in degrees as a (scalar)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/double.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edouble\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, with no restriction in the domain.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou provide me with the following example for the function defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es = SINE(a)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % INPUT\\n a = 45 % degrees\\n % OUTPUT\\n s = 1/sqrt(2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ereasonably accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and others which are either too imprecise or else logically flawed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour final Test Suite (contained within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etestSuite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) will have to address several aspects arising in your problem specification. However, for this 'first draft' you are creating a naïve Test Suite with only one requirement:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck that my submitted code reliably returns\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esufficiently accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values for sine of many different angles. Use the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/assert.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function to generate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44357\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eerrors/exceptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e if the values are not sufficiently accurate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecan\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e use the functions\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esind\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eetc.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in this draft Test Suite, if you want — even though this would be bad practice in your final Test Suite!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThus, the above specification is merely a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003estarting point\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. You will develop more robust Test Suites in:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44617\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44617\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44616\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44616\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44521\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44521\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":49743,"title":"Determine aquifer properties: unsteady pump test in a confined aquifer","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 714.15px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 357.075px; transform-origin: 407px 357.075px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 382.358px 7.79167px; transform-origin: 382.358px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAn important task in characterizing the flow of groundwater is to determine the properties of the aquifer, or the underground water-bearing formation. One approach is to disturb the aquifer, observe its response, and fit a theoretical formula to the observations. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 106.633px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 53.3167px; text-align: left; transform-origin: 384px 53.3167px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 297.567px 7.79167px; transform-origin: 297.567px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, suppose a confined aquifer initially has no flow. In that case, the piezometric head \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eh\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 67.675px 7.79167px; transform-origin: 67.675px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, or the level to which water would rise in an observation well, would be a uniform value \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"h0\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 124.467px 7.79167px; transform-origin: 124.467px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. A well turned on and pumped at a rate \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"Q0\" style=\"width: 19px; height: 20px;\" width=\"19\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.9083px 7.79167px; transform-origin: 38.9083px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e will create a cone of depression; that is, it will draw down the piezometric head to a level \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"h(r)\" style=\"width: 29px; height: 18.5px;\" width=\"29\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 24.8917px 7.79167px; transform-origin: 24.8917px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003er\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 95.2917px 7.79167px; transform-origin: 95.2917px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the radial distance from the well. Applying conservation of mass and Darcy’s law to this situation leads to a diffusion equation whose solution for the drawdown \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"s = h0 - h\" style=\"width: 64.5px; height: 20px;\" width=\"64.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 79.3417px 7.79167px; transform-origin: 79.3417px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as a function of distance \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003er\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 30.725px 7.79167px; transform-origin: 30.725px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and time \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003et\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7px 7.79167px; transform-origin: 7px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 44px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 22px; text-align: left; transform-origin: 384px 22px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-17px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"s = (Q0/(4 pi T)) integral(exp(-x)/x, u, infinity)\" style=\"width: 117px; height: 44px;\" width=\"117\" height=\"44\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21.0083px 7.79167px; transform-origin: 21.0083px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ewhere \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eT\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 65.9833px 7.79167px; transform-origin: 65.9833px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the transmissivity, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eS\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 67.15px 7.79167px; transform-origin: 67.15px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the storativity, and \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 36.05px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 18.025px; text-align: left; transform-origin: 384px 18.025px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"u = Sr^2/(4Tt)\" style=\"width: 49.5px; height: 36px;\" width=\"49.5\" height=\"36\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 349.917px 7.79167px; transform-origin: 349.917px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that achieves the objective of a pumping test: to determine the transmissivity and storativity from measurements of drawdown in time. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 347.467px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 173.733px; text-align: left; transform-origin: 384px 173.733px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 497px;height: 342px\" 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data-image-state=\"image-loaded\" width=\"497\" height=\"342\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [T,S] = confinedPumpTest(t,s,Q0,r)\r\n  % t = time, s = drawdown, Q0 = pumping rate, r = distance from well\r\n  T = f1(t,s,Q0,r);\r\n  S = f2(t,s,Q0,r);\r\nend","test_suite":"%%\r\nQ0 = 0.15;                                %  Pumping rate (m3/s)\r\nr  = 100;                                 %  Distance from well (m)\r\nt  = [100 1000 10000 1e5 2e5];            %  Time (s)\r\ns  = [0.0402 1.236 3.664 6.276 7.05];     %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nT_correct = 1.042e-2;                     %  Transmissivity (m2/s)\r\nS_correct = 9.864e-4;                     %  Storativity\r\nassert(abs(T-T_correct)/T_correct \u003c 1e-3 \u0026\u0026 abs(S-S_correct)/S_correct \u003c 1e-3)\r\n\r\n%%\r\nQ0 = 0.30;                                %  Pumping rate (m3/s)\r\nr  = 100;                                 %  Distance from well (m)\r\nt  = [100 1000 10000 1e5 2e5];            %  Time (s)\r\ns  = [0.0804 2.472 7.328 12.552 14.1];    %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nT_correct = 1.042e-2;                     %  Transmissivity (m2/s)\r\nS_correct = 9.864e-4;                     %  Storativity\r\nassert(abs(T-T_correct)/T_correct \u003c 1e-3 \u0026\u0026 abs(S-S_correct)/S_correct \u003c 1e-3)\r\n\r\n%%\r\nQ0 = 0.1;                                      %  Pumping rate (m3/s)\r\nr  = 40;                                       %  Distance from well (m)\r\nt  = [300 2000 8000 12000 24000 40000];        %  Time (s)\r\ns  = [0.494 1.749 2.830 3.153 3.709 4.120];    %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nT_correct = 9.838e-3;                          %  Transmissivity (m2/s)\r\nS_correct = 3.4e-3;                            %  Storativity\r\nassert(abs(T-T_correct)/T_correct \u003c 1e-3 \u0026\u0026 abs(S-S_correct)/S_correct \u003c 1e-3)\r\n\r\n%%\r\nQ0 = 0.1;                                      %  Pumping rate (m3/s)\r\nr  = 65;                                       %  Distance from well (m)\r\nt  = [300 2000 8000 12000 24000 40000];        %  Time (s)\r\ns  = [0.125 1.050 2.067 2.383 2.931 3.339];    %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nT_correct = 9.838e-3;                          %  Transmissivity (m2/s)\r\nS_correct = 3.4e-3;                            %  Storativity\r\nassert(abs(T-T_correct)/T_correct \u003c 2e-3 \u0026\u0026 abs(S-S_correct)/S_correct \u003c 1e-3)\r\n\r\n%%\r\nQ0 = 0.05;                                     %  Pumping rate (m3/s)\r\nr  = 5+10*rand;                                %  Distance from well (m)\r\nt  = [4e5 9e5 14e5 19e5 24e5];                 %  Time (s)\r\ns  = [0.859 0.918 0.951 0.973 0.991];          %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nlogfit = polyfit(log(t),s,1);                  \r\nTapprox = Q0/(4*pi*logfit(1));                       \r\nSapprox = 2.25*Tapprox*exp(-logfit(2)/logfit(1))/r^2;      \r\nassert(abs(T-Tapprox)/Tapprox \u003c 1e-3 \u0026\u0026 abs(S-Sapprox)/Sapprox \u003c 2e-3)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-04T00:26:53.000Z","updated_at":"2026-01-09T18:01:40.000Z","published_at":"2021-01-04T05:23:32.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn important task in characterizing the flow of groundwater is to determine the properties of the aquifer, or the underground water-bearing formation. One approach is to disturb the aquifer, observe its response, and fit a theoretical formula to the observations. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, suppose a confined aquifer initially has no flow. In that case, the piezometric head \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"h\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eh\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, or the level to which water would rise in an observation well, would be a uniform value \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"h0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eh_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. A well turned on and pumped at a rate \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"Q0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eQ_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e will create a cone of depression; that is, it will draw down the piezometric head to a level \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"h(r)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eh(r)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, where \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"r\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the radial distance from the well. Applying conservation of mass and Darcy’s law to this situation leads to a diffusion equation whose solution for the drawdown \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"s = h0 - h\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003es = h_0-h\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e as a function of distance \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"r\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and time \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"t\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003et\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"s = (Q0/(4 pi T)) integral(exp(-x)/x, u, infinity)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003es = {Q_0 \\\\over 4 \\\\pi T} \\\\int_u^\\\\infty {e^{-x} \\\\over x} dx\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"T\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the transmissivity, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"S\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the storativity, and \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"u = Sr^2/(4Tt)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eu = {S r^2 \\\\over 4 T t}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that achieves the objective of a pumping test: to determine the transmissivity and storativity from measurements of drawdown in time. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"342\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"497\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45434,"title":"Weighted Names","description":"A cell is given which contains several names. You've to return those names sorted by their weight. \r\n\r\nWeight is to be calculated by the following rules - \r\n \r\n 1.each letter's weight is the ASCII value. \r\n\r\n 2.the 1st name weights 2 times its original value but if the name contains any middle name \r\n   then the middle name would weight 2x while 1st name would weight 3x.\r\n   The family name will always weight 1x.\r\n\r\n\r\n 3. Some of the names might contain some prefix like Col, Prof, Hon etc. They would weight\r\n   10 times their 1st letter. [each letter is not to be weighted for this case]\r\n 4. Again some of the names might also contain some suffix.\r\n\r\nFor example,\r\n\r\n  Ser Arthur Dayne\r\n\r\n Here, 'Ser' is a prefix. so value = 83*10 = 830.  [ASCII value of 'S' is 83]\r\n       'Dayne' is the family name. so value = 497*1 = 497\r\n       'Arthur' is the 1st name.   so value = 630*2 = 1260\r\n So total value= 830 + 497 + 1260 = 2587.\r\n","description_html":"\u003cp\u003eA cell is given which contains several names. You've to return those names sorted by their weight.\u003c/p\u003e\u003cp\u003eWeight is to be calculated by the following rules -\u003c/p\u003e\u003cpre\u003e 1.each letter's weight is the ASCII value. \u003c/pre\u003e\u003cpre\u003e 2.the 1st name weights 2 times its original value but if the name contains any middle name \r\n   then the middle name would weight 2x while 1st name would weight 3x.\r\n   The family name will always weight 1x.\u003c/pre\u003e\u003cpre\u003e 3. Some of the names might contain some prefix like Col, Prof, Hon etc. They would weight\r\n   10 times their 1st letter. [each letter is not to be weighted for this case]\r\n 4. Again some of the names might also contain some suffix.\u003c/pre\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eSer Arthur Dayne\r\n\u003c/pre\u003e\u003cpre\u003e Here, 'Ser' is a prefix. so value = 83*10 = 830.  [ASCII value of 'S' is 83]\r\n       'Dayne' is the family name. so value = 497*1 = 497\r\n       'Arthur' is the 1st name.   so value = 630*2 = 1260\r\n So total value= 830 + 497 + 1260 = 2587.\u003c/pre\u003e","function_template":"function out = weighted_names(x)","test_suite":"%%\r\nx={'Col Theodore Roosevelt', 'Gen Ulysses S. Grant','Cap Abraham Lincoln'};\r\ny_correct ={'Gen Ulysses S. Grant','Col Theodore Roosevelt','Cap Abraham Lincoln'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx={'Jean-Marie Sainthillier','Asif Newaz','Binbin Qi','Alfonso Nieto-Castanon','J. S. Kowontan'} ;\r\ny_correct ={'Jean-Marie Sainthillier','Alfonso Nieto-Castanon','J. S. Kowontan','Binbin Qi','Asif Newaz'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx={'Ser Gerold Hightower','Prince Lewyn Martell','Ser Barristan Selmy','Ser Arthur Dayne','Ser Jaime Lannister'};\r\ny_correct ={'Ser Barristan Selmy','Ser Gerold Hightower','Ser Jaime Lannister','Ser Arthur Dayne','Prince Lewyn Martell'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx={'Eddard Ned Stark','Rob Stark','Brandon Stark','Aegon Targaryen VI','George R. R. Martin'};\r\ny_correct ={'George R. R. Martin','Eddard Ned Stark','Aegon Targaryen VI','Brandon Stark','Rob Stark'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx = {'william','Ser Arthur','William II','William III'};\r\ny_correct ={'Ser Arthur','William II','William III','william'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx={'Harry','Harry Potter','Harry James Potter','Ms. Hermione Granzer','Albus Percival Wulfric Brian Dumbledore'} ;\r\ny_correct ={'Albus Percival Wulfric Brian Dumbledore','Harry James Potter','Ms. Hermione Granzer','Harry Potter','Harry'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":9,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-10T07:52:03.000Z","updated_at":"2020-04-10T09:59:56.000Z","published_at":"2020-04-10T09:57:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA cell is given which contains several names. You've to return those names sorted by their weight.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWeight is to be calculated by the following rules -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 1.each letter's weight is the ASCII value. \\n\\n 2.the 1st name weights 2 times its original value but if the name contains any middle name \\n   then the middle name would weight 2x while 1st name would weight 3x.\\n   The family name will always weight 1x.\\n\\n 3. Some of the names might contain some prefix like Col, Prof, Hon etc. They would weight\\n   10 times their 1st letter. [each letter is not to be weighted for this case]\\n 4. Again some of the names might also contain some suffix.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Ser Arthur Dayne\\n\\n Here, 'Ser' is a prefix. so value = 83*10 = 830.  [ASCII value of 'S' is 83]\\n       'Dayne' is the family name. so value = 497*1 = 497\\n       'Arthur' is the 1st name.   so value = 630*2 = 1260\\n So total value= 830 + 497 + 1260 = 2587.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46833,"title":"Roots, Bloody Roots: part 1/2","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1239.6px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 619.8px; transform-origin: 407px 619.8px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 83.2px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 41.6px; text-align: left; transform-origin: 384px 41.6px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eUsing curves or lines, we can easily spot roots from n-degree polynomials in 2D: wherever they cross the real line, we know there is a root. However, as we are well aware, roots can also be complex numbers. In this case, plotting curves no longer seem adequate since they won't be found at the real line. That's when coloring comes into place; by painting the 2D plane, we can visualize where complex or real roots are and get a sense of what's happening in a function.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 163.2px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 81.6px; text-align: left; transform-origin: 384px 81.6px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn this problem, we will color the complex plane using the color space HSV. Imagine that there is a vector spawning from the origin to each point in the complex plane; horizontal axis imaginary and vertical axis real. Each vector has an angle \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eα\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e (from the real-line) and a norm \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eβ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e that defines a hue (H), and a brightness (V), respectively; for saturation (S), we assume a fixed value of 1, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"37.5\" height=\"17.5\" style=\"width: 37.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. H and V must range from 0 to 1, and to fit them into it, we will have to divide the angle by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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width=\"51.5\" height=\"34\" style=\"width: 51.5px; height: 34px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, and apply the cube root to the\u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.codecademy.com/articles/normalization#:~:text=Min%2Dmax%20normalization%20is%20one,decimal%20between%200%20and%201.\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e Min-max normalization\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e of the distance, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg 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\" width=\"155\" height=\"46\" style=\"width: 155px; height: 46px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e (the cube root allows a faster transition between low and high brightness).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 62.4px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.2px; text-align: left; transform-origin: 384px 31.2px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eOnce we have the HSV value for a point at the complex plane, we can use the function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ehsv2rgb \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eto find the RGB corresponding value implement it if it becomes unavailable in the future (read about it \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.rapidtables.com/convert/color/hsv-to-rgb.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehere\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e). \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; text-decoration: underline; text-decoration-line: underline; \"\u003eAll RGB values must be rounded to 4 decimal places\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e to obtain the following result:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 608px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 304px; text-align: left; transform-origin: 384px 304px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: middle\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 75.4px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 37.7px; text-align: left; transform-origin: 384px 37.7px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eAll you have to do in this problem is to reproduce the above result for any matrix size, m x n x 3, given m and n as input. Moreover, the complex plane origin is always in the middle of the image \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"126\" height=\"34\" alt=\"origin\" style=\"width: 126px; height: 34px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. Notice that since the plane origin has the vector null, it will generate a black dot that brightens while going to the figure's edge.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.8px; text-align: left; transform-origin: 384px 20.8px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote-1:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eThis problem is inspired by a 3Blue1Brown's video: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.youtube.com/watch?v=b7FxPsqfkOY\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"font-style: italic; \"\u003ehttps://www.youtube.com/watch?v=b7FxPsqfkOY\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e, and I recommend that you watch his video if you haven't already. However, it is not required that you do to solve this problem.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.8px; text-align: left; transform-origin: 384px 20.8px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eThis problem has two parts, since it may not be easy to do everything at once. And you are encouraged to use the code for this 1st part of the problem into the next: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/46963-roots-bloody-roots-part-2-2\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblem 46963\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.8px; text-align: left; transform-origin: 384px 20.8px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote-2\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e: This problem was last updated 21/08/2020; thanks to Alex's and Svyatoslav's\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003efeedback. All solutions were rescored. Please, let me know if anyone run into issues. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function cp = visComplexPlane(m,n)\r\n   cp = zeros(m,n,3);\r\nend","test_suite":"%% 1\r\nfiletext = fileread('visComplexPlane.m');\r\nassert(isempty(strfind(filetext, 'eval')),       'eval is forbidden.');\r\nassert(isempty(strfind(filetext, 'regexp')),     'regexp is forbidden.');\r\nassert(isempty(strfind(filetext, 'str2num')),    'str2num is forbidden.');\r\nassert(isempty(strfind(filetext, '!')),          'Shell commands are forbidden.');\r\nassert(isempty(strfind(filetext, 'mlock')),      'mlock is forbidden.');\r\nassert(isempty(strfind(filetext, 'munlock')),    'munlock is forbidden.');\r\nassert(isempty(strfind(filetext, 'fopen')),      'fopen is forbidden.');\r\n\r\n%% 2\r\nm = 500;\r\nn = 500;\r\nJ = visComplexPlane(m,n);\r\ny_correct = [25 166 212 32 32 242 196 9 240 14 196 74 21 239 79 166 192 155 87 14 90 203 196 123 106 103 228 231 133 176 133 16 121 220 125 188 164 128 53 118 3 38 18 99 144 255 171 204 104 9 125 233 196 129 18 113 142 72 7 251 36 178 60 100];\r\nassert(all(uint8(py.hashlib.sha512(py.str(mat2str(J(:))).encode('utf-8')).digest()) == y_correct));\r\n\r\n%% 3\r\nm = 251;\r\nn = 501;\r\nJ = visComplexPlane(m,n);\r\ny_correct = [187 126 253 159 247 242 249 25 213 223 27 57 35 39 110 223 166 247 82 101 71 12 193 15 251 152 215 87 39 71 245 94 66 73 135 243 163 56 168 217 198 201 20 19 255 247 100 139 72 201 30 85 234 182 238 233 137 161 206 156 86 45 71 171];\r\nassert(all(uint8(py.hashlib.sha512(py.str(mat2str(J(:))).encode('utf-8')).digest()) == y_correct));\r\n\r\n%% 4\r\nm = 750;\r\nn = 503;\r\nJ = visComplexPlane(m,n);\r\ny_correct = [83 99 239 135 129 205 10 212 2 78 245 228 122 186 117 122 39 246 53 200 68 153 227 131 222 205 146 174 216 106 200 116 100 39 179 255 244 175 226 38 246 215 98 181 228 63 118 196 176 151 192 27 150 223 123 183 2 89 219 78 215 214 125 145];\r\nassert(all(uint8(py.hashlib.sha512(py.str(mat2str(J(:))).encode('utf-8')).digest()) == y_correct));\r\n\r\n%% 5\r\nm = 501;\r\nn = 250;\r\nJ = visComplexPlane(m,n);\r\ny_correct = [82 133 222 186 239 236 118 153 188 94 116 95 105 194 37 121 52 33 230 224 181 136 182 185 124 112 24 77 148 212 113 178 164 22 80 252 117 188 26 178 66 195 83 52 159 22 205 207 210 220 12 199 110 145 121 39 101 110 2 51 117 40 93 113];\r\nassert(all(uint8(py.hashlib.sha512(py.str(mat2str(J(:))).encode('utf-8')).digest()) == y_correct));\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":20,"created_by":443343,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":"2020-10-20T14:25:24.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-17T03:50:33.000Z","updated_at":"2020-10-22T02:43:12.000Z","published_at":"2020-10-18T02:00:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUsing curves or lines, we can easily spot roots from n-degree polynomials in 2D: wherever they cross the real line, we know there is a root. However, as we are well aware, roots can also be complex numbers. In this case, plotting curves no longer seem adequate since they won't be found at the real line. That's when coloring comes into place; by painting the 2D plane, we can visualize where complex or real roots are and get a sense of what's happening in a function.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem, we will color the complex plane using the color space HSV. Imagine that there is a vector spawning from the origin to each point in the complex plane; horizontal axis imaginary and vertical axis real. Each vector has an angle \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\alpha\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (from the real-line) and a norm \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\beta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e that defines a hue (H), and a brightness (V), respectively; for saturation (S), we assume a fixed value of 1, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eS=1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. H and V must range from 0 to 1, and to fit them into it, we will have to divide the angle by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2\\\\pi\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eH = \\\\frac{\\\\alpha}{2\\\\pi}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and apply the cube root to the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.codecademy.com/articles/normalization#:~:text=Min%2Dmax%20normalization%20is%20one,decimal%20between%200%20and%201.\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e Min-max normalization\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e of the distance, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eV = \\\\sqrt[3]{\\\\frac{\\\\beta-\\\\min(\\\\beta)}{\\\\max(\\\\beta)-\\\\min(\\\\beta)}}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (the cube root allows a faster transition between low and high brightness).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOnce we have the HSV value for a point at the complex plane, we can use the function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ehsv2rgb \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eto find the RGB corresponding value implement it if it becomes unavailable in the future (read about it \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.rapidtables.com/convert/color/hsv-to-rgb.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehere\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e). \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAll RGB values must be rounded to 4 decimal places\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to obtain the following result:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"middle\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll you have to do in this problem is to reproduce the above result for any matrix size, m x n x 3, given m and n as input. Moreover, the complex plane origin is always in the middle of the image \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"origin\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\left ( \\\\left \\\\lfloor\\\\frac{m+1} {2} \\\\right \\\\rfloor, \\\\left \\\\lfloor\\\\frac{n+1} {2} \\\\right \\\\rfloor \\\\right )\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. Notice that since the plane origin has the vector null, it will generate a black dot that brightens while going to the figure's edge.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote-1:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis problem is inspired by a 3Blue1Brown's video: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.youtube.com/watch?v=b7FxPsqfkOY\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ehttps://www.youtube.com/watch?v=b7FxPsqfkOY\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, and I recommend that you watch his video if you haven't already. However, it is not required that you do to solve this problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis problem has two parts, since it may not be easy to do everything at once. And you are encouraged to use the code for this 1st part of the problem into the next: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/46963-roots-bloody-roots-part-2-2\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 46963\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote-2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e: This problem was last updated 21/08/2020; thanks to Alex's and Svyatoslav's\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efeedback. All solutions were rescored. Please, let me know if anyone run into issues. 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2450,"title":"Back to the Future III","description":"Marty McFly and Doc Brown are \"back\" again. See the test suite to see what they are up to.","description_html":"\u003cp\u003eMarty McFly and Doc Brown are \"back\" again. See the test suite to see what they are up to.\u003c/p\u003e","function_template":"function y = your_solution()\r\n  y = 'use the flux capacitor';\r\nend","test_suite":"%%\r\n% Create necessary files\r\nfid = fopen('myfunction.m', 'w');fprintf(fid, 'function [a, b]=myfunction(), a=now; b=@()1;');fclose(fid);\r\nrehash;\r\n\r\n% Execute my function 10 times\r\ny=cell(10,1);\r\nfor iter=1:10\r\n[x y{iter}]=myfunction();\r\nend\r\n\r\n% Ouch! I have overwritten the value of \"x\"\r\n% Your task is to recover the value of the 3rd \"x\"\r\n\r\nx3=your_solution();\r\n\r\n% Clean the working directory (just in case)\r\n!/bin/rm checksolution.*\r\n\r\n% Build the solution checker checksolution.p\r\n!/bin/echo \"7630302e30307630302e30300005001cafeb3fb50000002c000000b7000000d0ef156cd482f6727568c9cb0434da4ffad9a709bcec12d21f8ccf6a6c736d54e232a2d8ed0f6c4af6e41cc8816d0521bc25f8f51890e51c1b4b151fd6b9731f0a501974f4c3b7fc8831644a60d74f49599303f43d51c9ce82b407b10a500ddd2e02933868a8815ce645d02a48c2c7fb5de99f2bc5fa313693cfc79482545e1aba66aa34776b8e2a46a370f472bfb3e2332bbf0ccc4043646014651579160c1ef8694310327422bee0059f02d33326f7b77b41cf2f273e4e\" | /usr/bin/xxd -ps -r \u003e checksolution.p\r\n\r\n% Check how you have done\r\nif checksolution(x3) else 'WRONG'+'ANSWER', end\r\n\r\n%{  \r\n\r\n \"I would prefer even to fail with honor than win by cheating.\"\r\n\r\n                                                                Sophocles\r\n\r\n%}\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":3,"created_by":5925,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2014-07-19T20:22:59.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-19T20:11:02.000Z","updated_at":"2025-11-22T20:19:29.000Z","published_at":"2014-07-19T20:16:20.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMarty McFly and Doc Brown are \\\"back\\\" again. See the test suite to see what they are up to.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":302,"title":"Convert Roman to Arabic Numerals","description":"Based upon what I see on tv and at the movies, the use of Roman numerals indicates something is important or sophisticated (e.g. the Superbowl, Olympics, movie credits). But who wants to bother with trying to translate them into decimal notation? I sure don't.. thus the problem is to take an input string array with an arbitrary number of Roman numerals, separated by spaces, and return an array of the resulting Arabic versions. Note since there is no standardization regarding some of the more 'modern' rules of Roman numeral notation, there may be multiple ways of representing the same Arabic number, as shown in the example below:\r\n'XXXIX CCXLVI' -\u003e [39 246]\r\n'DLV MCCXXXIV MDCCLXXVI' -\u003e [555 1234 1776]","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 176.867px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 88.4333px; transform-origin: 407px 88.4333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 351.5px 8px; transform-origin: 351.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBased upon what I see on tv and at the movies, the use of Roman numerals indicates something is important or sophisticated (e.g. the Superbowl, Olympics, movie credits). But who wants to bother with trying to translate them into decimal notation? I sure don't.. thus the problem is to take an input string array with an arbitrary number of Roman numerals, separated by spaces, and return an array of the resulting Arabic versions. Note since there is no standardization regarding some of the more 'modern' rules of Roman numeral notation, there may be multiple ways of representing the same Arabic number, as shown in the example below:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 40.8667px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 20.4333px; transform-origin: 404px 20.4333px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 104px 8.5px; tab-size: 4; transform-origin: 104px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e'XXXIX CCXLVI' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 44px 8.5px; transform-origin: 44px 8.5px; \"\u003e-\u0026gt; [39 246]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 172px 8.5px; tab-size: 4; transform-origin: 172px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 100px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 100px 8.5px; \"\u003e'DLV MCCXXXIV MDCCLXXVI' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 72px 8.5px; transform-origin: 72px 8.5px; \"\u003e-\u0026gt; [555 1234 1776]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function arabic = roman(x)\r\n  arabic = x;\r\nend","test_suite":"%%\r\nx = 'XIX';\r\ny_correct = 19;\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'I II III IV V VI VII VIII';\r\ny_correct = [1 2 3 4 5 6 7 8];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'IV MMXII LIV';\r\ny_correct = [4 2012 54];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'M D C L X V I';\r\ny_correct = [1000 500 100 50 10 5 1];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'IX XLIX XCIX CDXCIX CMXCIX';\r\ny_correct = [9 49 99 499 999];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'MDCCCCX MCMIII MCMX';\r\ny_correct = [1910 1903 1910];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'MDCCCCLXXXXVIIII MCMXCIX MIM';\r\ny_correct = [1999 1999 1999];\r\nassert(isequal(roman(x),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":1022,"edited_by":223089,"edited_at":"2023-05-04T07:35:37.000Z","deleted_by":null,"deleted_at":null,"solvers_count":124,"test_suite_updated_at":"2023-05-04T07:35:37.000Z","rescore_all_solutions":false,"group_id":38,"created_at":"2012-02-10T04:20:30.000Z","updated_at":"2026-05-12T11:23:31.000Z","published_at":"2012-02-10T04:21:00.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBased upon what I see on tv and at the movies, the use of Roman numerals indicates something is important or sophisticated (e.g. the Superbowl, Olympics, movie credits). But who wants to bother with trying to translate them into decimal notation? I sure don't.. thus the problem is to take an input string array with an arbitrary number of Roman numerals, separated by spaces, and return an array of the resulting Arabic versions. Note since there is no standardization regarding some of the more 'modern' rules of Roman numeral notation, there may be multiple ways of representing the same Arabic number, as shown in the example below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA['XXXIX CCXLVI' -\u003e [39 246]\\n'DLV MCCXXXIV MDCCLXXVI' -\u003e [555 1234 1776]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42633,"title":"Cumulative maximum of an array","description":"Find the cumulative maximum of an array without using the built-in function cummax (and a few others). Your function should act identically to cummax, allowing the same inputs.\r\nExamples\r\nIf X = [0 4 3\r\n        6 5 2]\r\n\r\ncumax(X,1) is [0 4 3  and cumax(X,2) is [0 4 4\r\n               6 5 3]                    6 6 6]\r\n\r\ncumax(X,1,'reverse') is [6 5 3  and cumax(X,2,'reverse') is [4 4 3\r\n                         6 5 2]                              6 5 2]\r\n\r\nAlso,\r\ncumax([8 9 1 10 6 1 3 6 10 10]) returns [8 9 9 10 10 10 10 10 10 10]\r\n\r\ncumax([8 9 1 10 6 1 3 6 10 10]') returns [8 9 9 10 10 10 10 10 10 10]'\r\nSee also cumin.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 378.633px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 189.317px; transform-origin: 407px 189.317px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 234px 8px; transform-origin: 234px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFind the cumulative maximum of an array without using the built-in function\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ecummax\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 107px 8px; transform-origin: 107px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (and a few others). Your function should act identically to cummax, allowing the same inputs.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 33.5px 8px; transform-origin: 33.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExamples\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 265.633px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 132.817px; transform-origin: 404px 132.817px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eIf \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 40px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 40px 8.5px; \"\u003eX = [0 4 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; tab-size: 4; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e        6 5 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 184px 8.5px; tab-size: 4; transform-origin: 184px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 56px 8.5px; transform-origin: 56px 8.5px; \"\u003ecumax(X,1) is \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 128px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 128px 8.5px; \"\u003e[0 4 3  and cumax(X,2) is [0 4 4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 188px 8.5px; tab-size: 4; transform-origin: 188px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e               6 5 3]                    6 6 6]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 264px 8.5px; tab-size: 4; transform-origin: 264px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 40px 8.5px; transform-origin: 40px 8.5px; \"\u003ecumax(X,1,\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 36px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 36px 8.5px; \"\u003e'reverse'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003e) is \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 168px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 168px 8.5px; \"\u003e[6 5 3  and cumax(X,2,'reverse') is [4 4 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 268px 8.5px; tab-size: 4; transform-origin: 268px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e                         6 5 2]                              6 5 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eAlso,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 272px 8.5px; tab-size: 4; transform-origin: 272px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 160px 8.5px; transform-origin: 160px 8.5px; \"\u003ecumax([8 9 1 10 6 1 3 6 10 10]) returns \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 112px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 112px 8.5px; \"\u003e[8 9 9 10 10 10 10 10 10 10]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 280px 8.5px; tab-size: 4; transform-origin: 280px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 164px 8.5px; transform-origin: 164px 8.5px; \"\u003ecumax([8 9 1 10 6 1 3 6 10 10]') returns \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(196, 0, 0); border-block-start-color: rgb(196, 0, 0); border-bottom-color: rgb(196, 0, 0); border-inline-end-color: rgb(196, 0, 0); border-inline-start-color: rgb(196, 0, 0); border-left-color: rgb(196, 0, 0); border-right-color: rgb(196, 0, 0); border-top-color: rgb(196, 0, 0); caret-color: rgb(196, 0, 0); color: rgb(196, 0, 0); column-rule-color: rgb(196, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(196, 0, 0); perspective-origin: 116px 8.5px; text-decoration-color: rgb(196, 0, 0); text-emphasis-color: rgb(196, 0, 0); transform-origin: 116px 8.5px; \"\u003e[8 9 9 10 10 10 10 10 10 10]'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27.5px 8px; transform-origin: 27.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSee also\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ecumin\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = cumaX(x,varargin)\r\n  y = x;\r\nend","test_suite":"%%\r\nfiletext = fileread('cumaX.m');\r\nassert(isempty(strfind(filetext,'max')))\r\nassert(isempty(strfind(filetext,'cummin')))\r\nassert(isempty(strfind(filetext,'cummax')))\r\nassert(isempty(strfind(filetext,'feval')))\r\n\r\n%%\r\nx = randi(100);\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n\r\n%%\r\nx = randi(100,randi([2 100]),1);\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n\r\n%%\r\nx = randi(100,1,randi([2 100]));\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n\r\n%%\r\nx = magic(10);\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n\r\n%%\r\nx = [];\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":5,"created_by":4793,"edited_by":223089,"edited_at":"2022-10-09T10:09:17.000Z","deleted_by":null,"deleted_at":null,"solvers_count":44,"test_suite_updated_at":"2022-10-09T10:09:17.000Z","rescore_all_solutions":false,"group_id":24,"created_at":"2015-09-23T19:36:49.000Z","updated_at":"2026-05-06T01:37:59.000Z","published_at":"2015-09-23T19:38:39.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the cumulative maximum of an array without using the built-in function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecummax\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (and a few others). Your function should act identically to cummax, allowing the same inputs.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[If X = [0 4 3\\n        6 5 2]\\n\\ncumax(X,1) is [0 4 3  and cumax(X,2) is [0 4 4\\n               6 5 3]                    6 6 6]\\n\\ncumax(X,1,'reverse') is [6 5 3  and cumax(X,2,'reverse') is [4 4 3\\n                         6 5 2]                              6 5 2]\\n\\nAlso,\\ncumax([8 9 1 10 6 1 3 6 10 10]) returns [8 9 9 10 10 10 10 10 10 10]\\n\\ncumax([8 9 1 10 6 1 3 6 10 10]') returns [8 9 9 10 10 10 10 10 10 10]']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee also\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecumin\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44248,"title":"Mastermind V: Optimal Solver - average number of guesses","description":"The following description contains a copy of Richard Zapor's \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses Mastermind IV: Optimal Solver - max of 5 guesses\u003e\r\n\r\n\u003chttps://en.wikipedia.org/wiki/Mastermind_(board_game) Mastermind\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\r\n\r\n  Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\r\n[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\r\n\r\nwhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.  will be provided. \r\n\r\n*Scoring:* the average number of guesses of all 1296 cases.\r\n\r\n*See Also:*\r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/44236-mastermind-i-solve-all-1296-cases Mastermind I: Solve all 1296 cases\u003e\r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/44237-mastermind-ii-solve-in-8-or-less Mastermind II: Solve in 8 or less\u003e\r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/44238-mastermind-iii-solve-in-1 Mastermind III: Solve in 1\u003e\r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses Mastermind IV: Optimal Solver - max of 5 guesses\u003e\r\n","description_html":"\u003cp\u003eThe following description contains a copy of Richard Zapor's \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses\"\u003eMastermind IV: Optimal Solver - max of 5 guesses\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Mastermind_(board_game)\"\u003eMastermind\u003c/a\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eAnswer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\u003c/pre\u003e\u003cp\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/p\u003e\u003cp\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.  will be provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e the average number of guesses of all 1296 cases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSee Also:\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44236-mastermind-i-solve-all-1296-cases\"\u003eMastermind I: Solve all 1296 cases\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44237-mastermind-ii-solve-in-8-or-less\"\u003eMastermind II: Solve in 8 or less\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44238-mastermind-iii-solve-in-1\"\u003eMastermind III: Solve in 1\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses\"\u003eMastermind IV: Optimal Solver - max of 5 guesses\u003c/a\u003e\u003c/p\u003e","function_template":"function guess = solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\r\n  \r\nend","test_suite":"assessFunctionAbsence({'regexp','regexpi','regexprep','str2num','!','system','unix','persistent'},'File','solve_mastermind.m')\r\n\r\n%%\r\nv = 10 * (1:6) + (1:6)';\r\nv = 10 * v(:)' + (1:6)';\r\nv = reshape(10*v(:)' + (1:6)', [], 1);\r\nm = dec2base(v, 10) - 48;\r\nmpc = sum(permute(m, [1 3 2]) == permute(m, [3 1 2]), 3);\r\nmc = histc(m, 1:6, 2);\r\nmc = sum(min(permute(mc, [1 3 2]), permute(mc, [3 1 2])), 3) - mpc;\r\nmpc5c = 5 * mpc + mc;\r\nL = zeros(1296, 1);\r\nP = [1:1296 randi(1296, 1, 6000)];\r\nt = accumarray(P', 1);\r\nfor p = P(randperm(end))\r\n  mguess = []; mpegs = [];\r\n  while 1\r\n    mguessn = solve_mastermind(mguess, mpegs, m, mpc, mc, mpc5c, v);\r\n    k = v == polyval(mguessn, 10);\r\n    assert(any(k), 'invalid guess.')\r\n    mguess(end+1, :) = mguessn;\r\n    mpegs(end+1, :) = [mpc(p, k) mc(p, k)];\r\n    if mpegs(end, 1) == 4, L(p) = L(p) + size(mguess, 1); break, end\r\n  end\r\nend\r\nL = sum(L ./ t);\r\nfid = fopen('score.p', 'Wb');\r\nfwrite(fid, uint8(sscanf([...\r\n  '7630312E30307630302E3030000D201C07E23FB1000000340000015B000001C36FCD46'...\r\n  '4336A6F9BD2918A670F86BF0AC534F616971CE31A66833ECF344BF30BF9F163C19F925'...\r\n  '0AC8280807727BFFC8E8FF46AA63FA75C2890672D48C5599920C846EB7E0E230FDC14E'...\r\n  '1EA1768A5F84951A22717883064D6B9AD895493F53F6443E63783C5BFDD36A65EE33DB'...\r\n  'DF0C38A4238848AF99B21B91FC5F8E0E31A8D33533721B36C2AB371D7B4D59A1036FBF'...\r\n  'E2A4A0233812C46AA22FE134B0DE0FCFDCDCD4A49106161DDD64A97EC0B1F14A8E1FB4'...\r\n  '81CE94D11A50006D5AA9536055488421EC1682EF9945B98FB175A9CE96624DE8C575C2'...\r\n  'B442A7EAD282A0A90E89D3F25C7B22AF0D1FA61A2F76ED95E8C6EF78A197B13C3B570E'...\r\n  'D2F8EA301AAA8795CB4B2DEF21140589C0FC80A0B75F896CCEB1B560C07273FC4BCBD5'...\r\n  '5D98ED8516915D9A344A91D913E1E106F8CB3C87EBDBB7C0501AE307F5B1807555F8D2'...\r\n  '65EE53842038231C5E6C0BFDC5EB1A33CA999C68E612C9DC17050B9606'], '%2X')));\r\nfclose(fid);\r\nfprintf('Average number of guesses: %.2f', L / 1296)\r\nscore(floor((L / 1296 - 4) * 100))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":14,"created_by":1434,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2017-07-04T07:08:13.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2017-07-02T23:16:44.000Z","updated_at":"2025-12-12T16:02:19.000Z","published_at":"2017-07-02T23:16:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe following description contains a copy of Richard Zapor's\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind IV: Optimal Solver - max of 5 guesses\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Mastermind_(board_game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666. will be provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e the average number of guesses of all 1296 cases.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSee Also:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44236-mastermind-i-solve-all-1296-cases\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind I: Solve all 1296 cases\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44237-mastermind-ii-solve-in-8-or-less\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind II: Solve in 8 or less\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44238-mastermind-iii-solve-in-1\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind III: Solve in 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind IV: Optimal Solver - max of 5 guesses\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42829,"title":"Number construction III","description":"Given a positive integer, n, return a, b and c, such that\r\n\r\n1. n = a^1.5+b^2.5+c^3.5\r\n\r\n2. a, b and c are all positive integers greater than 1\r\n\r\nIf a solution does not exist, set all three output variables to zero.\r\n\r\nExample 1:\r\n\r\nn = 168\r\n\r\na = 4\r\n\r\nb = 4\r\n\r\nc = 4\r\n\r\nExample 2:\r\n\r\nn = 100\r\n\r\na = 0\r\n\r\nb = 0\r\n\r\nc = 0","description_html":"\u003cp\u003eGiven a positive integer, n, return a, b and c, such that\u003c/p\u003e\u003cp\u003e1. n = a^1.5+b^2.5+c^3.5\u003c/p\u003e\u003cp\u003e2. a, b and c are all positive integers greater than 1\u003c/p\u003e\u003cp\u003eIf a solution does not exist, set all three output variables to zero.\u003c/p\u003e\u003cp\u003eExample 1:\u003c/p\u003e\u003cp\u003en = 168\u003c/p\u003e\u003cp\u003ea = 4\u003c/p\u003e\u003cp\u003eb = 4\u003c/p\u003e\u003cp\u003ec = 4\u003c/p\u003e\u003cp\u003eExample 2:\u003c/p\u003e\u003cp\u003en = 100\u003c/p\u003e\u003cp\u003ea = 0\u003c/p\u003e\u003cp\u003eb = 0\u003c/p\u003e\u003cp\u003ec = 0\u003c/p\u003e","function_template":"function [a b c] = numcons(n)\r\n  a = n;\r\n  b = n;\r\n  c = n;\r\nend","test_suite":"%%\r\nn = 100;\r\n[a b c] = numcons(n);\r\nassert(all([a b c]==0))\r\n\r\n%%\r\nn = 888;\r\n[a b c] = numcons(n);\r\nassert(all([a b c]==0))\r\n\r\n%%\r\nn = 19666;\r\n[a b c] = numcons(n);\r\nassert(all([a b c]==0))\r\n\r\n%%\r\nn = 314159;\r\n[a b c] = numcons(n);\r\nassert(all([a b c]==0))\r\n\r\n%%\r\nn = 1100;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 116600;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 16999;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 10000040;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 94940;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 9990;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":15521,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":34,"test_suite_updated_at":"2016-04-28T18:19:03.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2016-04-25T11:29:04.000Z","updated_at":"2026-04-09T07:25:23.000Z","published_at":"2016-04-25T11:29:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a positive integer, n, return a, b and c, such that\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1. n = a^1.5+b^2.5+c^3.5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2. a, b and c are all positive integers greater than 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf a solution does not exist, set all three output variables to zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en = 168\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ea = 4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eb = 4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en = 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ea = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eb = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47453,"title":"Slitherlink I: Trivial","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 540.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 270.333px; transform-origin: 407px 270.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 78.5333px 7.91667px; transform-origin: 78.5333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink I: Trivial\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 258.667px 7.91667px; transform-origin: 258.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.367px 7.91667px; transform-origin: 368.367px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s  yields R 3 R 3 R board values if trivial did not already solve. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.267px 7.91667px; transform-origin: 373.267px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np=trivial_solve(p,bsegs,s);\r\n\r\n[sv,valid]=pcheck(s,p,bsegs);\r\n\r\n  %show_pfig(s,p,c,emap,pmap,1)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns = [5 5 5;5 4 5;5 5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [5 5;4 5;5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3 3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3;3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[0 5 5;5 3 5;5 3 5;5 5 0];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T00:38:37.000Z","updated_at":"2020-11-12T23:27:09.000Z","published_at":"2020-11-12T23:27:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink I: Trivial\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s  yields R 3 R 3 R board values if trivial did not already solve. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"re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whether a player solved a Cody problem","description":"Write a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are  players and  problems, the first output should be an  matrix. The second output should be a cell array of the problem titles.\r\nI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through my profile page. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 156px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 78px; transform-origin: 407px 78px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003em\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e players and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e problems, the first output should be an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"37.5\" height=\"18\" alt=\"mxn\" style=\"width: 37.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix. The second output should be a cell array of the problem titles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/profile/authors/1887879\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003emy profile page\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [y,CPtitle] = isSolved(CPnum,players)\r\n% CPnum    1xn vector of problem numbers\r\n% players  either a character vector or a 1xm cell array of player names\r\n% y        mxn matrix of results (1 = solved, 0 = not solved, -1 = problem does not exist)\r\n% CPtitle  cell array of problem titles\r\n\r\ny = randi(3)-2;\r\nCPtitle = 'Verb the adjective noun';\r\nend","test_suite":"%% \r\nn  = 44340:44345;\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 1 1 0 1 1];\r\nt_correct = {'Recaman Sequence - III','Hexagonal numbers on a spiral matrix','Spot the First Occurrence of 5','Pair Primes','The 5th Root','MATLAB Counter'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t,t_correct))\r\n\r\n%%\r\nn  = 2100:2105;\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 -1 -1 0 1 -1];\r\nt_correct = {'distance to a straight line (2D) given any 2 distinct points on this straight line','Simple Robotics 1: On track?','construct matrix with identical rows'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t(y~=-1),t_correct))\r\n\r\n%% Grab bag\r\nn  = [46031 46053 46114 46573 47415 51451 51675 51803];\r\np  = {'William','Tim','David Hill','Nikolaos Nikolaou','Dyuman Joshi','Ramon Villamangca'};\r\nID = [173294 496166 10491973 7310613 2873 13897958];\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 0 1 1 1 1 1 1; 1 1 1 1 1 0 1 0; 0 0 0 1 1 1 1 1; 0 0 0 0 0 0 1 0; 0 0 1 1 1 0 1 0; 0 0 0 0 0 0 1 0];\r\nt_correct = {'Construct dimensionless parameters','Construct finite difference approximations of derivatives','Compute the date of a special marriage milestone','Determine the winner of a goofy golf tournament','List ways to reach a target sum',['Guess the card in Fitch Cheney’s five-card trick'],'Add 100','Eliminate redundant numbers in text'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t,t_correct))\r\n\r\n%% My targets (2022/01/29)\r\nn  = [193 375 782 2523 42834 45272 45274 47623];\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,~] = isSolved(n,p,ID);   \r\n%assert(all(y==0));\r\ny_correct = [1 0 0 1 1 1 1 0];   %  2022/12/29\r\nassert(isequal(y,y_correct))\r\n\r\n%% Problems with \u003e9 likes that I haven't solved as of 2022/01/29 \r\nn = [283 3075 42888 42996 43670 44376 44630];      % CP 375 is in the target list\r\np = {'William','Tim','David Hill','Nikolaos Nikolaou','Mehmet OZC','minnolina','Dyuman Joshi','Ramon Villamangca','ChrisR'};\r\nID = [173294 496166 10491973 7310613 3877541 9646924 12862873 13897958 1887879];\r\n[y,~] = isSolved(n,p,ID);\r\ny_correct = [1 1 1 1 1 1 1; 1 1 1 0 0 1 1; 0 0 0 1 1 0 0; 0 1 0 1 1 0 1; 0 1 0 1 0 0 0; 0 0 0 0 0 0 0; 0 1 0 0 1 0 0; 0 0 1 0 0 0 0; 0 1 1 1 1 0 1];  %  Updated 2022/04/24\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nfiletext = fileread('isSolved.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":2,"comments_count":6,"created_by":46909,"edited_by":46909,"edited_at":"2024-11-02T15:08:42.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2024-11-02T15:08:42.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-01-29T16:08:07.000Z","updated_at":"2025-10-12T06:40:33.000Z","published_at":"2022-01-29T16:17:43.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"m\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e players and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e problems, the first output should be an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"mxn\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003em\\\\times n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e matrix. The second output should be a cell array of the problem titles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/profile/authors/1887879\\\"\u003e\u003cw:r\u003e\u003cw:t\u003emy profile page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61285,"title":"The Genesis Sypnapse Protocol","description":"Ⅰ.Context\r\nIn a synthetic bio-neural network, information is transmitted between  neurons through chemical and electrical signals. Your objective is to find the minimum Metabolic Cost to trnasmit a signal from a Source_Neuron to a Target_Neuron.\r\nⅡ.Neuron Structure and Genetic Coding\r\nEach neuron  is identified by a unique Protein Code (a string consisting of bases A, C, G, T).\r\nGenetic Distance (): The base cost to jump from neuron  to neuron  is defined as the Levenshtein Distance (minimum number of edits: insertions, deletions, or substitutions) between their Protein Code strings.\r\nⅢ.Polarization States and Transitions\r\nAt any time step , a neuron exists in one of three Polarization States: P ∈ {0,1,2}. When a signal jumps from u to v, the state of the signal evolves based on a 3x3 Transition Matrix M:\r\nIf  is even: \r\nIf  is odd: \r\n(Note: Matrix M is 1-indexed in logic, e.g., ).\r\nⅣ.Sypnatic Barriers (Constraints)\r\nA connection from neuron u to v is only valid if all the following conditions are met:\r\nChemotaxis Condition: The Protein Code of  must contain at least one sub-sequence of length 2 that exists within the Protein Code of . (e.g., if u is AGCT,  must contain AG, GC, or CT).\r\nEnergy Limit: The total accumulated cost from the source must not exceed .\r\nTemporal Gating: A neuron  enters a \"refractory period\" (disabled) if the current time step T is a multiple of its Stability Index . (i.e., if mod() == 0, the neuron cannot be entered).\r\nⅤ.Metabolic Cost Calculation\r\nThe total cost to transition from  to  at time  with the current signal state  is:\r\n                \r\nWhere: \r\n is a weight vector correspoding to the 3 polarization states.\r\nInterference() =  (simulating temporal electromagnetic noise).\r\nⅥ.Input / Output Requirements\r\nInput: * neurons: A struct array containing .code(string) and .S(integer).\r\nM: A 3x3 state transition matrix.\r\nW_state: A 1x3 weight vector.\r\nE_max: Maximum energy allowed (scalar).\r\nstart_id, target_id: Indices of the start and neurons.\r\nOutput: * The minimum metabolic cost (scalar). Return -1 if the target is unreachable.\r\n(Optional) The path taken (sequence of neuron IDs).","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1013.33px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 333.5px 506.667px; transform-origin: 333.5px 506.667px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅠ.Context\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 31.5px; text-align: left; transform-origin: 309.5px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn a synthetic bio-neural network, information is transmitted between \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eN\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e neurons through chemical and electrical signals. Your objective is to find the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eminimum Metabolic Cost\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to trnasmit a signal from a \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eSource_Neuron\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to a \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eTarget_Neuron\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅡ.Neuron Structure and Genetic Coding\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach neuron \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ei\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is identified by a unique \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eProtein Code\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (a string consisting of bases \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eA, C, G, T\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 30.65px; transform-origin: 316.5px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 30.65px; text-align: left; transform-origin: 288.5px 30.65px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eGenetic Distance\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eG\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e): The base cost to jump from neuron \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eu\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to neuron \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is defined as the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eLevenshtein Distance\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (minimum number of edits: insertions, deletions, or substitutions) between their Protein Code strings.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅢ.Polarization States and Transitions\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 21px; text-align: left; transform-origin: 309.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAt any time step \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eT\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, a neuron exists in one of three \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ePolarization States\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: P ∈ {0,1,2}. When a signal jumps from u to v, the state of the signal evolves based on a 3x3 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eTransition Matrix M\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 63.3667px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 31.6833px; transform-origin: 316.5px 31.6833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 21.4667px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.7333px; text-align: left; transform-origin: 288.5px 10.7333px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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\" width=\"312\" height=\"20\" style=\"width: 312px; height: 20px;\"\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Note: Matrix M is 1-indexed in logic, e.g., \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"75\" height=\"18\" style=\"width: 75px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅣ.Sypnatic Barriers (Constraints)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA connection from neuron u to v is only valid if all the following conditions are met:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 124.667px; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 62.3333px; transform-origin: 316.5px 62.3333px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 61.3px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 30.65px; text-align: left; transform-origin: 288.5px 30.65px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eChemotaxis Condition\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: The Protein Code of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e must contain at least one sub-sequence of length 2 that exists within the Protein Code of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eu\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. (e.g., if u is \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAGCT\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e must contain \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAG\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eGC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, or \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eCT\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 21.4667px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.7333px; text-align: left; transform-origin: 288.5px 10.7333px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eEnergy Limit\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: The total accumulated cost from the source must not exceed \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"29.5\" height=\"20\" style=\"width: 29.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 41.9px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 20.95px; text-align: left; transform-origin: 288.5px 20.95px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eTemporal Gating\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: A neuron \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ei\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e enters a \"refractory period\" (disabled) if the current time step T is a multiple of its \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eStability Index\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"13.5\" height=\"20\" style=\"width: 13.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. (i.e., if mod(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"29.5\" height=\"20\" style=\"width: 29.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) == 0, the neuron cannot be entered).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅤ.Metabolic Cost Calculation\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.4667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.7333px; text-align: left; transform-origin: 309.5px 10.7333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe total cost to transition from \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eu\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e at time \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eT\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e with the current signal state \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"31\" height=\"20\" style=\"width: 31px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.4667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.7333px; text-align: left; transform-origin: 309.5px 10.7333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e                \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg 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\" width=\"351\" height=\"20\" style=\"width: 351px; height: 20px;\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWhere: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 41.9px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 20.95px; transform-origin: 316.5px 20.95px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 21.4667px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.7333px; text-align: left; transform-origin: 288.5px 10.7333px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEYAAAAoCAYAAABD0IyuAAAICUlEQVR4AeyZCWxVVRCG3+tqaUMpFJrQjdcWRJYogWpAJGIqYLSCS0IUEVCjBILBgAQUFSOyuAXExiYaVDYDKomGCMYEUFEgARWLYEtLd2J36CLd6/cfeh+v0vtKIcE2ec3MmzNz5px75r9z5p576+fw/XWKgA+YTmFxOHzA+ICxQcDG7MsYHzA2CNiYfRnTDWACXC7XIvh3uM2Dq2nvhCfFxMSEIBfCB+Fm2PKrHjJkyHb4Dut6CQkJcfAGfEph40d/NrYHLJ+eKDvLmObc3NxNTU1N97PgbNhQW1vb89hnwj8WFRVdRKa1tLQ8ROcx2BA+q/Py8mbBR42Bn7NnzxbAi51O53xU0R/8TMS2B9ljqTNgzGIDAgIqaPwFG/Lz86s2DY+fgoKCKlS3D21bam1tTaKzEYBeB7i/afdosgWGxdezcncA7YFhuoKKLQtBR1ttTxkdHT2APmXX3sbGxr2efT21bQuMFszWOCMpJrDOgnbS1xe2KNxqeMqgoKD70EfDadqGyB5PXoFh+7hrDJFcEXRSUtII7A/DFg2OiooKtRTJxMTEQcil8J7+/fsfRPYK8goMEaiuNCIdZE/swIEDw9RuZ2dzc/MztAfCLbAoqF+/fh3mpEALuHg6044fP96E7BXUIYj/rpjto+JaJzvtyIiIiJvUFpMJidim094BC0CBF1NfX+8Gj8dzFD4LAfVbnmKH8es+/U8jvAJDRpwjqEqtDRna0NAQqDbspBjPQVZj3wAbH0AIoh2C3RBt+UT7+/u/h6EZ7jXkFRgCqyfYckWD7M8jfLDayhbkLPrTKaxF9JmswhYWGBgYgXTgE4t9Lu2vcnJyTiB7FXkFhgxRfTHbhKisbHBnC5mwu6qqqh6ADHj4hNI2wFBbHkMfQAHfiOxV2cJ6vX+PKSkpUSackyNsgiYTEmk/CX9EJpSWlZXVkhmF6CIDXnx8vAvbfEDagc+f6uht7DVjFAzBnZIU0x5h1Rb0L2GLLlgN+ZAlT6EHs/U+RrbB10wUcHCOv+VaJxg7dmwgE0zQ+1135ugSGIJ0n2yZ+HZY2ZLOU6aEtiHAcPuQKSMxymdzdna2G1Rs3SaBwqCvmVOPe5rdJmdlZeViYnipuyO7BIZaoSeOOaewQJ1JVHM8s8WB/bTHhWfTbiGzNiO9Zgtv2OEEPwVO5bAYg54M6yDpZMvew/j9gB5GLbtiOyoT8B3H2EeRQ3ljvw2eyjzBjHMg+9L3Ae21rO8QJ2694qBepmHDhkXiY66v15bLPQ7vNUaObIdSpMBAGOqQLbJw4YtIFWqEQ+eZ9Pz8/Fyj2PwQTApB59A9qLa29gA34DX0nQAaQd885DL6EpibZutKAngQXeSkfS+ZcAz/4Yzdh8MK/H6j89nw8PDW2NjYROZbgz4D1k0S4Gut4AE9ljl28QVhFWMzmGcuT9f9Agp/Q11mDBfUljlvvB2Ok8gO2YKujKlCqlAjHKe5w5+r4Y1ZzEz6Q5CFKuCkezp6RnBwcAWfJDZz3e/QRRvZts/B36AIlAXInYxbg227xqKbN3zG7NfpurCwUIC/gb0WzsK+gDmXFxcXVwD6OMA4gv0CwLwIgOe5ts5eDdjc1CUwDFIK1mgEi9nKYgSUVDd7HgQxpvEksp5SqJ0Ti1Vd6oN8WtuCo8EpFrwqMzNTwQRwrfGMrEK6v/cQlLby29i38favG2S2KnPoBbeO8e6ahm0ofrL/yprNcYIsUa36DHst876p7cW563HaE+B3s7Ky9KmF7qvYSh7nFNtM4C43sRBlzAmA/MLM3MUP/ttwKYBnc40lWiTbT9uhjSdIFPYxcDYprruvA+MgFv8KthoA+ARp6p7e37CPQs/l2m5g8JmATUeMA0j5BiBX4qsX313MWwtQOmMtYy1PeAKNn6PLjFGqMrCQCT+1ywQCU1aV4ZOOj2qS5vbK+OnN/WWcWhi3kkfq3bQNsWg9nuNQjlp3kZRXMR6F7UBkZORJpKGwsLAxrG8cc2RYmUERvgnbXTiUsq1NxsXxhz4V1mFzGttoBWN28MY/im2mr4kCj+5L1CUwcuOCc0H0LbU7Y4GHTwo+qhOdubht2jbWpwkWpVqxhc5Q7rbOPk7aKt6TkUHwT7C2C3E69anVn2D2qY5gd5BZqg1LaPfF4WekCY5s0avLreiZAKAtqzp4M7rshwF5CutdwnqPWHPR14GuCpgOI65TKS8vTwkJCdmkabQoANFjvY6AB7MtQttBS6a/lACzuftz4Hj1Y3Pgbz0hndSHhdj0EawO+2mXy5XKE2cQIFkgnCBj9DHe88O7H0/aVsZZ5M/8MzTOMkh2Axi5Xz+z6Dh4IkGoELonJLDvybw66pXqy3A6zhDUdHz/4c6qFuVjUzaNB8Awxi8FrGj4EPZK5CR8h7JFK5hrIjZlVxH2+XxO/QVdTy7Nk4wtFd2f7esClC2Mi9U4bG664cCwaL1kaoEbCW4RWbGe1axi8SqEbdxNFUltiZEEUErK76a/lXHrkMfgF6grGQRzDhBfRddRIhLfZub4EL2FPn+kHgjTkOsp7JUU9lx85qFnwVu5dg1zvo/veq6hg6CuSdcluuHAUOjWsZAUeAa8iWy4E/kOi9ch0UF/FroLjoDTWKZZcLs9GVsA7ELfzqO9hvGPoPeBV3vMsRw9CJ5Mfx5zGKL9A7bRsBPWmFSk/p2jOmZ8rJ8bDox14Z4ufcDY3CEfMD5gbBCwMfsyxgeMDQI2Zl/G+ICxQcDG/C8AAAD//2ZI7lAAAAAGSURBVAMAlvfifkU3fRYAAAAASUVORK5CYII=\" width=\"35\" height=\"20\" style=\"width: 35px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a weight vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"72\" height=\"20\" style=\"width: 72px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ecorrespoding to the 3 polarization states.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInterference(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eT\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"119.5\" height=\"18\" style=\"width: 119.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (simulating temporal electromagnetic noise).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅥ.Input / Output Requirements\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: * \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eneurons\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: A struct array containing \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e.code\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(string) and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e.S\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(integer).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 81.7333px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 40.8667px; transform-origin: 316.5px 40.8667px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eM\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: A 3x3 state transition matrix.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eW_state\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: A 1x3 weight vector.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eE_max\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Maximum energy allowed (scalar).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003estart_id, target_id\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Indices of the start and neurons.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: * The minimum metabolic cost (scalar). Return \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e-1 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eif the target is unreachable.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4333px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 10.2167px; transform-origin: 316.5px 10.2167px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Optional) The path taken (sequence of neuron IDs).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [min_cost, best_path = solve_genesis_protocol(neurons,M,W,E_max,start_id,target_id)\r\n  [min_cost,best_path] = size(neurons);\r\nend","test_suite":"%% \r\nn(1).code = 'AGCT'; n(1).S = 10;\r\nn(2).code = 'GCT';  n(2).S = 10;\r\nM = [0 1 2; 2 0 1; 1 2 0]; W = [1, 1.5, 2];\r\ncost1 = solve_genesis_protocol(n, M, W, 100, 1, 2);\r\nassert(cost1 \u003e 0, 'Target should be reachable');\r\n\r\n%% \r\nn(1).code = 'AGCT'; n(1).S = 10;\r\nn(2).code = 'GCT';  n(2).S = 10;\r\nn(3).code = 'AAAA'; n(3).S = 2; % Will sleep at T=2\r\nM = [0 1 2; 2 0 1; 1 2 0];W = [1, 1.5, 2];\r\ncost2 = solve_genesis_protocol(n, M, W, 100, 1, 3);\r\nassert(cost2 == -1, 'Neuron 3 should be gated at T=2');\r\n\r\n\r\n%% \r\nneurons = struct('code', {'AAAA', 'AATT'}, 'S', {10, 10});\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nE_max = 100;\r\nres1 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 2);\r\nassert(abs(res1 - 21) \u003c 1e-6, 'Test Case 1 Failed: Basic cost calculation');\r\n\r\n\r\n\r\n%% \r\nneurons = struct('code', {'AAAA', 'CCCC'}, 'S', {10, 10});\r\nE_max = 100;\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nres3 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 2);\r\nassert(res3 == -1, 'Test Case 3 Failed: Chemotaxis should block this path');\r\n\r\n%%\r\nneurons = struct('code', {'AAAA', 'AATT', 'TTTT'}, 'S', {10, 10, 10});\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nE_max = 15;\r\nres4 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 2);\r\nassert(res4 == -1, 'Test Case 4 Failed: Should return -1 due to Energy Limit');\r\n\r\n%% \r\nneurons = struct('code', {'AAAA', 'AAAT'}, 'S', {10, 10});\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nE_max = 100;\r\nres5 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 2);\r\nassert(abs(res5 - 20) \u003c 1e-6, 'Test Case 5 Failed: Matrix M transition logic error');\r\n\r\n%% \r\nc1 = repmat('AGCT', 1, 10); \r\nc2 = repmat('TCGA', 1, 10);\r\nneurons = struct('code', {c1, [c1(1:38) 'AA'], c2}, 'S', {100, 100, 100});\r\nE_max = 5000;\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nres6 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 3);\r\nassert(res6 \u003e 0, 'Test Case 6 Failed: Performance issue or logic error with long strings');\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":4945722,"edited_by":4945722,"edited_at":"2026-03-20T16:01:53.000Z","deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2026-03-20T07:26:50.000Z","updated_at":"2026-04-30T09:14:03.000Z","published_at":"2026-03-20T07:26:50.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅠ.Context\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn a synthetic bio-neural network, information is transmitted between \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e neurons through chemical and electrical signals. Your objective is to find the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eminimum Metabolic Cost\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to trnasmit a signal from a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSource_Neuron\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTarget_Neuron\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅡ.Neuron Structure and Genetic Coding\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach neuron \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ei\\n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is identified by a unique \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eProtein Code\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (a string consisting of bases \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eA, C, G, T\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGenetic Distance\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e): The base cost to jump from neuron \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eu\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to neuron \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\\n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is defined as the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eLevenshtein Distance\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (minimum number of edits: insertions, deletions, or substitutions) between their Protein Code strings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅢ.Polarization States and Transitions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAt any time step \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, a neuron exists in one of three \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePolarization States\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: P ∈ {0,1,2}. When a signal jumps from u to v, the state of the signal evolves based on a 3x3 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTransition Matrix M\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(u,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is even: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP_{next} = (P_{curr} + 1) \\\\text{ (mod 3).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(u,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is odd: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP_{next} = M(P_{curr}+1), \\\\text{mod(length(} Protein_{v}),3)+1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(Note: Matrix M is 1-indexed in logic, e.g., \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eM(row,col)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅣ.Sypnatic Barriers (Constraints)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA connection from neuron u to v is only valid if all the following conditions are met:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eChemotaxis Condition\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: The Protein Code of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e must contain at least one sub-sequence of length 2 that exists within the Protein Code of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eu\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. (e.g., if u is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAGCT\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e must contain \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAG\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCT\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEnergy Limit\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: The total accumulated cost from the source must not exceed \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eE_{max}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTemporal Gating\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: A neuron \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ei\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e enters a \\\"refractory period\\\" (disabled) if the current time step T is a multiple of its \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eStability Index\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eS_{i\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. (i.e., if mod(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT,S_{i\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) == 0, the neuron cannot be entered).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅤ.Metabolic Cost Calculation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe total cost to transition from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eu\\n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e at time \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e with the current signal state \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP_{curr\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e                \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eTotalCost = (G(u,v) \\\\times W_{state}(P_{curr}+1)) + \\\\text{Interference}(T)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhere: \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eW_{state\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is a weight vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\left[\\\\omega_{0},\\\\omega_{1},\\\\omega_{2}\\\\right]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003ecorrespoding to the 3 polarization states.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInterference(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) = \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\lfloor \\\\sin(T) \\\\times 10 \\\\rfloor + 10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (simulating temporal electromagnetic noise).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅥ.Input / Output Requirements\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: * \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eneurons\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: A struct array containing \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.code\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(string) and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.S\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(integer).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: A 3x3 state transition matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eW_state\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: A 1x3 weight vector.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eE_max\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Maximum energy allowed (scalar).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003estart_id, target_id\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Indices of the start and neurons.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: * The minimum metabolic cost (scalar). Return \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e-1 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eif the target is unreachable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(Optional) The path taken (sequence of neuron IDs).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44383,"title":"Code breaker, Part III:  Operation Xiangliu","description":"You have been tasked with decoding several batches of coded messages that have been intercepted.  \r\n\r\nBased on previous intelligence that has been gathered, you can be confident that the messages were all encoded using a simple \u003chttps://en.wikipedia.org/wiki/Caesar_cipher Caesar cipher\u003e (a type of \u003chttps://en.wikipedia.org/wiki/Substitution_cipher substitution cipher\u003e), an example of which is the \u003chttps://en.wikipedia.org/wiki/ROT13 ROT13 cipher\u003e (discussed in \u003chttps://www.mathworks.com/matlabcentral/cody/problems/78-implement-a-rot13-cipher Cody Challenge Problem 78\u003e).  As in the Cody Challenge Problem, uppercase and lowercase letters are handled independently of one another, and all other characters (e.g. punctuation, numbers, accented letters) are unchanged.  Unlike the Cody Challenge Problem, here the number of letters to shift is not known in advance, and will vary _between_ (not within) batches — also, here you need to decode, not encode.  \r\n\r\nThese latest activities that you are investigating have been nicknamed 'Operation Xiangliu' by your own organisation.  A few decoding options are at your organisation's disposal:  \r\n\r\n# Test the candidate decodings against all words in a large dictionary — this could work, but it is very slow.\r\n# Test the candidate decodings for the appearance of a name or phrase that is certain to appear regularly (e.g. \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44351 \"Operation Phoenix\"\u003e) — unfortunately as yet there is not enough knowledge of the activities to be sure of a suitable name or phrase to use.  \r\n# Test the candidate decodings against all words in a short list comprising, say, the hundred most frequently used English words.\r\n\r\nThe *third option* will be faster than the first option, and more reliable than the second option, so this is the approach you will be taking.  \r\n\r\nYou have been careful to avoid using 'lemmatised' lists (which contain e.g. the verb \"to be\", rather than the various inflected forms such as \"am\", \"is\", \"are\") like those based on the \u003chttps://web.archive.org/web/20111226085859/http://oxforddictionaries.com/words/the-oec-facts-about-the-language OEC\u003e or \u003chttps://www.wordfrequency.info/free.asp?s=y COCA\u003e, and after setting aside \u003chttp://world-english.org/english500.htm another list\u003e you finally choose the \u003chttp://ucrel.lancs.ac.uk/bncfreq/samples/120.pdf list based on the BNC\u003e as the most reliable, and will use the first 100 words on that list.  \r\nThis list will be available for you to access as an input variable, |bncWordlist|, to your function. Note: (i) some entries on the list are morphemes (e.g. \"\u0026#x200A;n't\u0026#x200A;\" and \"\u0026#x200A;'s\u0026#x200A;\") rather than words;  (ii) some entries appear more than once (representing different grammatical word classes).  Of course, in the original messages any capitalisation might be used.  \r\n\r\nYou have been instructed that your 'certitude' (degree of confidence) in the decoding must be reported for each batch, and shall depend proportionally upon the sum of the characters for the words/morphemes in the decoded message that match words/morphemes in |bncWordlist|.  Being able to match words/morphemes accounting for three-tenths of the characters (excluding spaces) shall correspond to 100% certitude;  matching three-twentieths would be 50% certitude, and so on.  Certitude shall be reported as a percentage, rounded _up_ to the nearest integer, not greater than 100.  You need to maximise your certitude for each batch by appropriate choice of the shifting parameter.   If there are multiple shifts of equal certitude, report both options on different rows (arranged in order of ascending shift parameter).\r\n\r\nYour task is to crack the codes and report back in a \u003chttp://au.mathworks.com/help/matlab/ref/struct.html structure array\u003e: \u0026nbsp; (1) the shifting parameter that had been used in the encoding [as \u003chttp://au.mathworks.com/help/matlab/ref/uint8.html uint8\u003e] (usually scalar, but may be column vector); \u0026nbsp; (2) the decoded messages [as a \u003chttp://au.mathworks.com/help/matlab/ref/cell.html?s_tid=doc_ta cell array\u003e (containing \u003chttp://au.mathworks.com/help/matlab/ref/char.html character arrays\u003e)] (usually an array with a single row, but occasionally with multiple rows); \u0026nbsp; (3) your 'certitude' in the decoding [as \u003chttp://au.mathworks.com/help/matlab/ref/uint8.html uint8\u003e] (always scalar).  \r\nThe name of the structure array shall be |s|, with respective fields |shift|, |message|, and |certitude|.  \r\n\r\n*EXAMPLE 1*\r\n\r\nSuppose the batch contained two encoded messages — _\"Vomftt qvstvfe, pqfo op eppst.\"_ and _\"Ffmt dbo ljmm, opu pomz xpvoe.\"_ (provided as character arrays within a cell array) — and a (right-shifting) ROT1 cipher had been applied.  In that case A→B, B→C, ..., Y→Z, and Z→A;  similarly, a→b, b→c, ..., y→z, and z→a.  \r\nThus the original messages would have been:  _\"Unless pursued, open no doors.\"_ and _\"Eels can kill, not only wound.\"_ .  Twelve of the 51 characters have been matched:  \"no\", \"can\", \"not\", and \"only\".  \r\n\r\nThe correct answer would therefore comprise:  \r\n\r\n  s.shift = uint8(1)  \r\n  s.message = {'Unless pursued, open no doors.', 'Eels can kill, not only wound.'}\r\n  s.certitude = uint8(79)\r\n\r\n*EXAMPLE 2*\r\n\r\nSuppose the batch contained one encoded message — _\"Oa oqvvq'u cnycau dggp: \"Ctu itcvkc ctvku\".\"_ (provided as a character array within a cell array) — and a (right-shifting) ROT2 cipher had been applied.  In that case A→C, B→D, ..., Y→A, and Z→B;  similarly, a→c, b→d, ..., y→a, and z→b.  \r\nThus the original message would have been:  _\"My motto's always been: \"Ars gratia artis\".\"_ .  Eight of the 37 characters have been matched: \"My\", \"\u0026#x200A;'s\u0026#x200A;\", and \"been\".  Note carefully that:  \"\u0026#x200A;'s\u0026#x200A;\" should only be matched once;  \"to\" (in motto), \"be\" (in been), \"at\" (in gratia), \"is\" (in artis) and \"a\" (passim) should _not_ be matched at all;  and \"\u0026#x200A;'\u0026#x200A;\" will only ever be used as an apostrophe (never as a quotation mark).  \r\n\r\nThe correct answer would therefore comprise:  \r\n\r\n  s.shift = uint8(2)  \r\n  s.message = {'My motto''s always been: \"Ars gratia artis\".'}\r\n  s.certitude = uint8(73)\r\n\r\nIf the batch of messages cannot be decoded when following the above assumptions, then return the original encoded message(s) unchanged, with a shift of zero and a certitude of zero.  \r\n\r\n|Note:  Many Cody solutions are hard to read and not necessarily computationally efficient.  To direct attention toward efficient runtime speed of execution, timings are measured in the Test Suite, and reported back (if the submission is runnable).  Although the timings are not perfectly reproducible, they do provide an indication of computational resource demand. (Try resubmitting on another day if your code runs slowly, in case it is caused by a server issue.)|  \r\n\r\nTo provide some extra motivation for you to get your code to run efficiently, it will fail the Test Suite if it is deemed \"too slow\".  \r\n\r\n----------\r\n\r\nPrevious problem:  \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44356 Operation Orthos\u003e.  Next problem:  TBA.","description_html":"\u003cp\u003eYou have been tasked with decoding several batches of coded messages that have been intercepted.\u003c/p\u003e\u003cp\u003eBased on previous intelligence that has been gathered, you can be confident that the messages were all encoded using a simple \u003ca href = \"https://en.wikipedia.org/wiki/Caesar_cipher\"\u003eCaesar cipher\u003c/a\u003e (a type of \u003ca href = \"https://en.wikipedia.org/wiki/Substitution_cipher\"\u003esubstitution cipher\u003c/a\u003e), an example of which is the \u003ca href = \"https://en.wikipedia.org/wiki/ROT13\"\u003eROT13 cipher\u003c/a\u003e (discussed in \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/78-implement-a-rot13-cipher\"\u003eCody Challenge Problem 78\u003c/a\u003e).  As in the Cody Challenge Problem, uppercase and lowercase letters are handled independently of one another, and all other characters (e.g. punctuation, numbers, accented letters) are unchanged.  Unlike the Cody Challenge Problem, here the number of letters to shift is not known in advance, and will vary \u003ci\u003ebetween\u003c/i\u003e (not within) batches — also, here you need to decode, not encode.\u003c/p\u003e\u003cp\u003eThese latest activities that you are investigating have been nicknamed 'Operation Xiangliu' by your own organisation.  A few decoding options are at your organisation's disposal:\u003c/p\u003e\u003col\u003e\u003cli\u003eTest the candidate decodings against all words in a large dictionary — this could work, but it is very slow.\u003c/li\u003e\u003cli\u003eTest the candidate decodings for the appearance of a name or phrase that is certain to appear regularly (e.g. \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44351\"\u003e\"Operation Phoenix\"\u003c/a\u003e) — unfortunately as yet there is not enough knowledge of the activities to be sure of a suitable name or phrase to use.\u003c/li\u003e\u003cli\u003eTest the candidate decodings against all words in a short list comprising, say, the hundred most frequently used English words.\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eThe \u003cb\u003ethird option\u003c/b\u003e will be faster than the first option, and more reliable than the second option, so this is the approach you will be taking.\u003c/p\u003e\u003cp\u003eYou have been careful to avoid using 'lemmatised' lists (which contain e.g. the verb \"to be\", rather than the various inflected forms such as \"am\", \"is\", \"are\") like those based on the \u003ca href = \"https://web.archive.org/web/20111226085859/http://oxforddictionaries.com/words/the-oec-facts-about-the-language\"\u003eOEC\u003c/a\u003e or \u003ca href = \"https://www.wordfrequency.info/free.asp?s=y\"\u003eCOCA\u003c/a\u003e, and after setting aside \u003ca href = \"http://world-english.org/english500.htm\"\u003eanother list\u003c/a\u003e you finally choose the \u003ca href = \"http://ucrel.lancs.ac.uk/bncfreq/samples/120.pdf\"\u003elist based on the BNC\u003c/a\u003e as the most reliable, and will use the first 100 words on that list.  \r\nThis list will be available for you to access as an input variable, \u003ctt\u003ebncWordlist\u003c/tt\u003e, to your function. Note: (i) some entries on the list are morphemes (e.g. \"\u0026#x200A;n't\u0026#x200A;\" and \"\u0026#x200A;'s\u0026#x200A;\") rather than words;  (ii) some entries appear more than once (representing different grammatical word classes).  Of course, in the original messages any capitalisation might be used.\u003c/p\u003e\u003cp\u003eYou have been instructed that your 'certitude' (degree of confidence) in the decoding must be reported for each batch, and shall depend proportionally upon the sum of the characters for the words/morphemes in the decoded message that match words/morphemes in \u003ctt\u003ebncWordlist\u003c/tt\u003e.  Being able to match words/morphemes accounting for three-tenths of the characters (excluding spaces) shall correspond to 100% certitude;  matching three-twentieths would be 50% certitude, and so on.  Certitude shall be reported as a percentage, rounded \u003ci\u003eup\u003c/i\u003e to the nearest integer, not greater than 100.  You need to maximise your certitude for each batch by appropriate choice of the shifting parameter.   If there are multiple shifts of equal certitude, report both options on different rows (arranged in order of ascending shift parameter).\u003c/p\u003e\u003cp\u003eYour task is to crack the codes and report back in a \u003ca href = \"http://au.mathworks.com/help/matlab/ref/struct.html\"\u003estructure array\u003c/a\u003e: \u0026nbsp; (1) the shifting parameter that had been used in the encoding [as \u003ca href = \"http://au.mathworks.com/help/matlab/ref/uint8.html\"\u003euint8\u003c/a\u003e] (usually scalar, but may be column vector); \u0026nbsp; (2) the decoded messages [as a \u003ca href = \"http://au.mathworks.com/help/matlab/ref/cell.html?s_tid=doc_ta\"\u003ecell array\u003c/a\u003e (containing \u003ca href = \"http://au.mathworks.com/help/matlab/ref/char.html\"\u003echaracter arrays\u003c/a\u003e)] (usually an array with a single row, but occasionally with multiple rows); \u0026nbsp; (3) your 'certitude' in the decoding [as \u003ca href = \"http://au.mathworks.com/help/matlab/ref/uint8.html\"\u003euint8\u003c/a\u003e] (always scalar).  \r\nThe name of the structure array shall be \u003ctt\u003es\u003c/tt\u003e, with respective fields \u003ctt\u003eshift\u003c/tt\u003e, \u003ctt\u003emessage\u003c/tt\u003e, and \u003ctt\u003ecertitude\u003c/tt\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEXAMPLE 1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSuppose the batch contained two encoded messages — \u003ci\u003e\"Vomftt qvstvfe, pqfo op eppst.\"\u003c/i\u003e and \u003ci\u003e\"Ffmt dbo ljmm, opu pomz xpvoe.\"\u003c/i\u003e (provided as character arrays within a cell array) — and a (right-shifting) ROT1 cipher had been applied.  In that case A→B, B→C, ..., Y→Z, and Z→A;  similarly, a→b, b→c, ..., y→z, and z→a.  \r\nThus the original messages would have been:  \u003ci\u003e\"Unless pursued, open no doors.\"\u003c/i\u003e and \u003ci\u003e\"Eels can kill, not only wound.\"\u003c/i\u003e .  Twelve of the 51 characters have been matched:  \"no\", \"can\", \"not\", and \"only\".\u003c/p\u003e\u003cp\u003eThe correct answer would therefore comprise:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003es.shift = uint8(1)  \r\ns.message = {'Unless pursued, open no doors.', 'Eels can kill, not only wound.'}\r\ns.certitude = uint8(79)\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eEXAMPLE 2\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSuppose the batch contained one encoded message — \u003ci\u003e\"Oa oqvvq'u cnycau dggp: \"Ctu itcvkc ctvku\".\"\u003c/i\u003e (provided as a character array within a cell array) — and a (right-shifting) ROT2 cipher had been applied.  In that case A→C, B→D, ..., Y→A, and Z→B;  similarly, a→c, b→d, ..., y→a, and z→b.  \r\nThus the original message would have been:  \u003ci\u003e\"My motto's always been: \"Ars gratia artis\".\"\u003c/i\u003e .  Eight of the 37 characters have been matched: \"My\", \"\u0026#x200A;'s\u0026#x200A;\", and \"been\".  Note carefully that:  \"\u0026#x200A;'s\u0026#x200A;\" should only be matched once;  \"to\" (in motto), \"be\" (in been), \"at\" (in gratia), \"is\" (in artis) and \"a\" (passim) should \u003ci\u003enot\u003c/i\u003e be matched at all;  and \"\u0026#x200A;'\u0026#x200A;\" will only ever be used as an apostrophe (never as a quotation mark).\u003c/p\u003e\u003cp\u003eThe correct answer would therefore comprise:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003es.shift = uint8(2)  \r\ns.message = {'My motto''s always been: \"Ars gratia artis\".'}\r\ns.certitude = uint8(73)\r\n\u003c/pre\u003e\u003cp\u003eIf the batch of messages cannot be decoded when following the above assumptions, then return the original encoded message(s) unchanged, with a shift of zero and a certitude of zero.\u003c/p\u003e\u003cp\u003e\u003ctt\u003eNote:  Many Cody solutions are hard to read and not necessarily computationally efficient.  To direct attention toward efficient runtime speed of execution, timings are measured in the Test Suite, and reported back (if the submission is runnable).  Although the timings are not perfectly reproducible, they do provide an indication of computational resource demand. (Try resubmitting on another day if your code runs slowly, in case it is caused by a server issue.)\u003c/tt\u003e\u003c/p\u003e\u003cp\u003eTo provide some extra motivation for you to get your code to run efficiently, it will fail the Test Suite if it is deemed \"too slow\".\u003c/p\u003e\u003cp\u003e----------\u003c/p\u003e\u003cp\u003ePrevious problem:  \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44356\"\u003eOperation Orthos\u003c/a\u003e.  Next problem:  TBA.\u003c/p\u003e","function_template":"% Comments are nice, aren't they....\r\nfunction s = decode(x, bncWordlist)\r\nend","test_suite":"%% NOTE:  \r\n% This test suite can be updated if inappropriate 'hacks' \r\n% are discovered in any submitted solutions.\r\n% The assessment of your submission may therefore change over time.  \r\n\r\nglobal bncWordlist\r\nbncWordlist = {'the', 'of', 'and', 'a', 'in', 'to', 'it', 'is', 'to', 'was', ...\r\n    'I', 'for', 'that', 'you', 'he', 'be', 'with', 'on', 'by', 'at', ...\r\n    'have', 'are', 'not', 'this', '''s', 'but', 'had', 'they', 'his', ...\r\n    'from', 'she', 'that', 'which', 'or', 'we', '''s', 'an', 'n''t', 'were', ...\r\n    'as', 'do', 'been', 'their', 'has', 'would', 'there', 'what', 'will', 'all', ...\r\n    'if', 'can', 'her', 'said', 'who', 'one', 'so', 'up', 'as', 'them', 'some', ...\r\n    'when', 'could', 'him', 'into', 'its', 'then', 'two', 'out', 'time', ...\r\n    'my', 'about', 'did', 'your', 'now', 'me', 'no', 'other', 'only', 'just', ...\r\n    'more', 'these', 'also', 'people', 'know', 'any', 'first', 'see', 'very', 'new', ...\r\n    'may', 'well', 'should', 'her', 'like', 'than', 'how', 'get', 'way', 'one', 'our'};\r\n\r\n\r\n%% Anti-hacking\r\n% EDIT (2019-07-02). Anti-hacking provision\r\n% Ensure only builtin functions will be called.\r\n! rm -v fileread.m\r\n! rm -v assert.m\r\n% END EDIT (2019-07-02)\r\n% EDIT (2018-06-18).  Anti-hacking provision\r\nRE = regexp(fileread('decode.m'), '\\w+', 'match');\r\ntabooWords = {'ans', 'assert', 'freepass', 'tic'};\r\ntestResult = cellfun( @(z) ismember(z, tabooWords), RE );\r\nmsg = ['Please do not do that in your code!' char([10 13]) ...\r\n    'Found: ' strjoin(RE(testResult)) '.' char([10 13]) ...\r\n    'Banned word.' char([10 13])];\r\nassert(~any(  cellfun( @(z) ismember(z, tabooWords), RE )  ), msg)\r\n\r\nfor j = 1 : randi(10)\r\n    decode({'Pizza for dinner.'}, {'mozzarella', 'mushrooms'});\r\nend;\r\n% END EDIT (2018-06-18)\r\n\r\n\r\n%% Test 1\r\nglobal bncWordlist\r\nx =                 {'Vomftt qvstvfe, pqfo op eppst.', 'Ffmt dbo ljmm, opu pomz xpvoe.'};\r\ns_correct.shift = uint8(1);\r\ns_correct.message = {'Unless pursued, open no doors.', 'Eels can kill, not only wound.'};\r\ns_correct.certitude = uint8(79);\r\ns = decode(x, bncWordlist);\r\n%disp(' *** ');  disp(s.message);  disp(' *** ')\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.' )\r\nassert( isequal(s.message{1}, s_correct.message{1}), 'Wrong message{1}.' )\r\nassert( isequal(s.message{2}, s_correct.message{2}), 'Wrong message{2}.' )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.' )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.' )\r\nassert( isequal(s, s_correct), 'Wrong s.' )\r\nassert( isequal(class(s.shift), 'uint8'), 'Wrong class.' )\r\nassert( isequal(class(s.message), 'cell'), 'Wrong class.'  )\r\nassert( isequal(class(s.message{1}), 'char'), 'Wrong class.'  )\r\nassert( isequal(class(s.certitude), 'uint8'), 'Wrong class.'  )\r\n\r\n\r\n%% Test 2\r\nglobal bncWordlist\r\nx =                 {'Doo zduiduh lv edvhg rq ghfhswlrq.', ...\r\n    'Khqfh, zkhq deoh wr dwwdfn, zh pxvw vhhp xqdeoh;  zkhq xvlqj rxu irufhv, zh pxvw vhhp lqdfwlyh;  zkhq zh duh qhdu, zh pxvw pdnh wkh hqhpb eholhyh zh duh idu dzdb; zkhq idu dzdb, zh pxvw pdnh klp eholhyh zh duh qhdu.'};\r\ns_correct.shift = uint8(3);\r\ns_correct.message = {'All warfare is based on deception.', ...\r\n    'Hence, when able to attack, we must seem unable;  when using our forces, we must seem inactive;  when we are near, we must make the enemy believe we are far away; when far away, we must make him believe we are near.'};\r\ns_correct.certitude = uint8(95);\r\ns = decode(x, bncWordlist);\r\n%disp(' *** ');  disp(s.message);  disp(' *** ')\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 3\r\nglobal bncWordlist\r\nx =                 {'Elia lrq yxfqp ql bkqfzb qeb bkbjv.  Cbfdk afploabo, xka zorpe efj.', ...\r\n    'Fc eb fp pbzrob xq xii mlfkqp, yb mobmxoba clo efj.  Fc eb fp fk prmboflo pqobkdqe, bsxab efj.'};\r\ns_correct.shift = uint8(23);\r\ns_correct.message = {'Hold out baits to entice the enemy.  Feign disorder, and crush him.', ...\r\n    'If he is secure at all points, be prepared for him.  If he is in superior strength, evade him.'};\r\ns_correct.certitude = uint8(100);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 4\r\nglobal bncWordlist\r\nx =                 {'Ax qgmj ghhgfwfl ak gx uzgdwjau lwehwj, kwwc lg ajjalslw zae.  Hjwlwfv lg tw owsc, lzsl zw esq yjgo sjjgysfl.', ...\r\n    'Ax zw ak lscafy zak wskw, yanw zae fg jwkl.', ...\r\n    'Ax zak xgjuwk sjw mfalwv, kwhsjslw lzwe.', ...\r\n    'Sllsuc zae ozwjw zw ak mfhjwhsjwv, shhwsj ozwjw qgm sjw fgl wphwulwv.'};\r\ns_correct.shift = uint8(18);\r\ns_correct.message = {'If your opponent is of choleric temper, seek to irritate him.  Pretend to be weak, that he may grow arrogant.', ...\r\n    'If he is taking his ease, give him no rest.', ...\r\n    'If his forces are united, separate them.', ...\r\n    'Attack him where he is unprepared, appear where you are not expected.'};\r\ns_correct.certitude = uint8(100);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 5\r\nglobal bncWordlist\r\nx =                 {'Aes cèwo vo foxd bémyvdo vo dowzy', ...\r\n    'Yx wo dbksdo no dbkîdbo aekxn to dbksdo no vk népksdo ne csvoxmo / Vo csvoxmo ocd n''yb, wksc t''ks mryscs vk mknoxmo', ... \r\n    'Mrkaeo wyd, mrkaeo zrbkco nsdc kfom owzrkco / Pksd no Mvkeno WM, vo mywwkxny no vk zrbkco', ...\r\n    'Mkb t''cesc ex WM n''kddkaeo, ckxc dsmc, kedroxdsaeo zkc ox dym / Zbêd à pbkzzob ceb vo lokd zyeb vo wyefowoxd rsz-ryz', ...\r\n    'Ne bkz n''kddkaeo aes pbkzzo, ézkdo, wkdbkaeo od zkdkdbkaeo / Zvec no ckxq de mvkaeoc, t''cesc WM noc Mkbzkdoc', ...\r\n    'Vo zénkqyqeo ox fyqeo ke xyw no myno Cyvkkb ye Mvkeno WM / Do zbyzyco n''émyedob moms / Ae''yx ézovvo voc fyiovvoc, nèc ae''yx cyxxo voc myxcyxxoc'};\r\ns_correct.shift = uint8([0 21]');\r\n% TIP:  The original message was not English!\r\ns_correct.message(1, :) = {'Aes cèwo vo foxd bémyvdo vo dowzy', ... \r\n    'Yx wo dbksdo no dbkîdbo aekxn to dbksdo no vk népksdo ne csvoxmo / Vo csvoxmo ocd n''yb, wksc t''ks mryscs vk mknoxmo', ... \r\n    'Mrkaeo wyd, mrkaeo zrbkco nsdc kfom owzrkco / Pksd no Mvkeno WM, vo mywwkxny no vk zrbkco', ... \r\n    'Mkb t''cesc ex WM n''kddkaeo, ckxc dsmc, kedroxdsaeo zkc ox dym / Zbêd à pbkzzob ceb vo lokd zyeb vo wyefowoxd rsz-ryz', ... \r\n    'Ne bkz n''kddkaeo aes pbkzzo, ézkdo, wkdbkaeo od zkdkdbkaeo / Zvec no ckxq de mvkaeoc, t''cesc WM noc Mkbzkdoc', ... \r\n    'Vo zénkqyqeo ox fyqeo ke xyw no myno Cyvkkb ye Mvkeno WM / Do zbyzyco n''émyedob moms / Ae''yx ézovvo voc fyiovvoc, nèc ae''yx cyxxo voc myxcyxxoc'}\r\ns_correct.message(2, :) = {'Fjx hèbt at ktci gérdait at itbed', ... \r\n    'Dc bt igpxit st igpîigt fjpcs yt igpxit st ap séupxit sj hxatcrt / At hxatcrt thi s''dg, bpxh y''px rwdxhx ap rpstcrt', ... \r\n    'Rwpfjt bdi, rwpfjt ewgpht sxih pktr tbewpht / Upxi st Rapjst BR, at rdbbpcsd st ap ewgpht', ... \r\n    'Rpg y''hjxh jc BR s''piipfjt, hpch ixrh, pjiwtcixfjt eph tc idr / Egêi à ugpeetg hjg at qtpi edjg at bdjktbtci wxe-wde', ... \r\n    'Sj gpe s''piipfjt fjx ugpeet, éepit, bpigpfjt ti epipigpfjt / Eajh st hpcv ij rapfjth, y''hjxh BR sth Rpgepith', ... \r\n    'At eéspvdvjt tc kdvjt pj cdb st rdst Hdappg dj Rapjst BR / It egdedht s''érdjitg rtrx / Fj''dc éetaat ath kdntaath, sèh fj''dc hdcct ath rdchdccth'}\r\ns_correct.certitude = uint8(11);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 6\r\nglobal bncWordlist\r\nx =                 {'Brvdujcnm mrbxamna yxbcdujcnb ynaonlc mrblryurwn;  brvdujcnm onja yxbcdujcnb lxdajpn;  brvdujcnm fnjtwnbb yxbcdujcnb bcanwpcq.' ...\r\n    'X mrerwn jac xo bdkcunch jwm bnlanlh!'};\r\ns_correct.shift = uint8(9);\r\ns_correct.message = {'Simulated disorder postulates perfect discipline;  simulated fear postulates courage;  simulated weakness postulates strength.' ...\r\n    'O divine art of subtlety and secrecy!'};\r\ns_correct.certitude = uint8(12);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 7\r\nglobal bncWordlist\r\nx =                 {'Nv treefk vekvi zekf rcczretv nzky evzxysfizex gizetvj lekzc nv riv rthlrzekvu nzky kyvzi uvjzxej.', ... \r\n    'Nv riv efk wzk kf cvru re ridp fe kyv drity lecvjj nv riv wrdzczri nzky kyv wrtv fw kyv tflekip — zkj dflekrzej reu wfivjkj, zkj gzkwrccj reu givtzgztvj, zkj drijyvj reu jnrdgj.', ... \r\n    'Nv jyrcc sv lerscv kf klie erklirc rumrekrxvj kf rttflek lecvjj nv drbv ljv fw cftrc xlzuvj.'};\r\ns_correct.shift = uint8(17);\r\ns_correct.message = {'We cannot enter into alliance with neighboring princes until we are acquainted with their designs.', ...  \r\n    'We are not fit to lead an army on the march unless we are familiar with the face of the country — its mountains and forests, its pitfalls and precipices, its marshes and swamps.', ...  \r\n    'We shall be unable to turn natural advantages to account unless we make use of local guides.'};\r\ns_correct.certitude = uint8(97);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 8\r\nglobal bncWordlist\r\nx =                 {'Oa oqvvq''u cnycau dggp: \"Ctu itcvkc ctvku\".'};\r\ns_correct.shift = uint8(2);\r\ns_correct.message = {'My motto''s always been: \"Ars gratia artis\".'};\r\ns_correct.certitude = uint8(73);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 9\r\nglobal bncWordlist\r\nx =                 {'Bestow rewards without regard 2 rule;  issue orders without regard 2 previous arrangements', ...\r\n    '\"Let advance bee richly rewarded \u0026 retreat bee heavily punished.\"', ...\r\n    ' Qwertyuiop''asdfghjkl.   Zxcvbnm-0123456789 = pass. ', ...\r\n    'αβγδ — persimon–apricot hybrid.', ...\r\n    'aIanasatbebydoheifinisitmemynoofonor''ssotoupweallandanyarebutcandidforgethadhasherhimhishowitsmaynewnotnown''toneouroutseeshethetwowaswaywhoyoualsobeenfromhaveintojustknowlikemoreonlysaidsomethanthatthemthentheythistimeverywellwerewhatwhenwillwithyouraboutcouldfirstothertheirtherethesewhichwouldpeopleshould'};\r\ns_correct.shift = uint8(0);\r\ns_correct.message = x;\r\ns_correct.certitude = uint8(0);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% TIMING \r\n% Note:  The Time Trial section does not check accuracy; that is done above.\r\n\r\n% Acknowledgements  \r\n% Portions of this timing test code were inspired by:\r\n% (1) Problem 937. \"Rubik's Mini Cube: Solve Randomized Cube in 11 Moves or Less; Score is by Time (msec)\" by Richard Zapor.\r\n% (2) Problem 2733. \"Evil Number\" by Jan Orwat.\r\n% (3) Feedback in comments from Peng Liu.\r\n% (4) Problem Problem 1237. \"It's race time! Write a faster function than the test suite call of unique().\" by Jeremy.\r\n\r\n% INITIALISE\r\nglobal bncWordlist\r\nx =                 {'Vomftt qvstvfe, pqfo op eppst.', 'Ffmt dbo ljmm, opu pomz xpvoe.'};\r\nqSmall = 50;\r\nqBig = 1000;\r\n%cutoffTimeBig = 10;    % Maximum allowable walltime (in seconds) to run function in a loop with qBig iterations.\r\n\r\n% EDIT (2018-06-17).  Reduced time [slightly] to pose reasonable challenge.\r\n% Accounted for improving Cody server speed per Problem 44655.\r\ncutoffTimeBig = 8;    % Maximum allowable walltime (in seconds) to run function in a loop with qBig iterations.\r\ntRef = datetime('2018-06-17', 'InputFormat','yyyy-MM-dd');\r\ntNow = datetime('now');\r\nyearsElapsed = (datenum(tNow) - datenum(tRef)) / 365.24;\r\ndisp(' . ');\r\nfprintf('\\r\\n\\r\\n\\r\\nSubmission evaluated for speed on %s.\\r\\n', datestr(tNow, 'dd mmmm yyyy'))\r\nrInf = 0.2;   delta = cutoffTimeBig - rInf;  tau = 3.6;  % Data from Problem 44655, based on Problem 963.\r\nqBig = ceil( qBig * (cutoffTimeBig - rInf) * exp(yearsElapsed/tau) / delta );\r\nfprintf('\\r\\n\\r\\n\\r\\nTo account for computational power increases over time, number of iterations increased to %u.\\r\\n', qBig)\r\n% END EDIT (2018-06-17)\r\n\r\n\r\n% *** PRELIMINARY TIMING WITH timeit ***\r\nfDecode = @()   decode(x, bncWordlist);\r\ndt_timeit = timeit( fDecode );\r\nfor dummy = 1 : 10,  disp(' . ');  end;\r\nfprintf('APPROXIMATE time to decode %u message batches ~ %2.2f seconds, according to ''timeit''.\\n\\r', qBig, dt_timeit * qBig)\r\n\r\n% *** PRELIMINARY TIMING WITH SHORT LOOP ***\r\n% In case the submitted function has a lot of text output, \r\n% get an estimate based on just a few iterations\r\n% Initialise\r\nt0 = clock;\r\n\r\n% Loop\r\nfor i = 1 : qSmall\r\n    solution = decode(x, bncWordlist);\r\nend;\r\n\r\n% Compute and display elapsed time.\r\ndt = etime(clock, t0);\r\nfor dummy = 1 : 10,  disp(' . ');  end;\r\n%fprintf('Your wall time to decode %u message batches = %i seconds.\\n\\r', qSmall, floor(dt))\r\nfprintf('APPROXIMATE wall time to decode %u message batches ~ %2.2f seconds, by extrapolating from %u batches.\\n\\r', qBig, dt * qBig / qSmall, qSmall)\r\n\r\n% *** 'OFFICIAL' TIMING ***\r\n% Re-initialise timer\r\nt0 = clock;\r\nt0_cpu = cputime;\r\n\r\n% Loop\r\nfor i = 1 : qBig\r\n    % EDIT (2018-06-17).   Ensure each case is unique.\r\n    characters = ['  ,   .' char(randi([97,122], [1,23]))];\r\n    x{2} = characters( randperm(30) );\r\n    % END EDIT (2018-06-17)\r\n    solution = decode(x, bncWordlist);\r\nend;\r\n\r\n% Compute and display elapsed time.\r\ndt = etime(clock, t0);\r\nfor dummy = 1 : 10,  disp(' . ');  end;\r\nfprintf('Your wall time to decode %u message batches = %2.2f seconds.\\n\\r', qBig, dt)\r\n\r\ndt_cpu = (cputime - t0_cpu);\r\nfprintf(' ( Your CPU time for this = %2.2f seconds. ) \\n\\r', dt_cpu)\r\n\r\n% Display (default) Cody size-based score.\r\nall_nodes = mtree('decode.m', '-file');\r\nsize_score = count(all_nodes);\r\nfprintf('Your Cody-standard size-based score = %i.\\n\\r', size_score)\r\n\r\n% Report revised performance score\r\ncombinedScore = size_score + round(dt * 10);\r\nfprintf('Your combined score = %i.\\n\\r', combinedScore)\r\ndisp('     -----=====|||||=====-----     ')\r\n\r\n% Now disallow any candidate solutions that are TOO SLOW!\r\nif dt \u003e cutoffTimeBig, \r\n    fprintf('Sorry, your submission is TOO SLOW.  It must be able to finish within %u seconds.\\n\\r', cutoffTimeBig)\r\nend;\r\n\r\nassert( dt \u003c= cutoffTimeBig, 'Exceeded time limit specified in Test Suite.' )","published":true,"deleted":false,"likes_count":2,"comments_count":16,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2019-07-02T13:23:18.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2017-10-12T23:17:24.000Z","updated_at":"2026-04-02T20:05:58.000Z","published_at":"2017-10-15T06:52:40.000Z","restored_at":"2017-10-25T07:03:11.000Z","restored_by":null,"spam":false,"simulink":false,"admin_reviewed":true,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou have been tasked with decoding several batches of coded messages that have been intercepted.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBased on previous intelligence that has been gathered, you can be confident that the messages were all encoded using a simple\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Caesar_cipher\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCaesar cipher\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (a type of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Substitution_cipher\\\"\u003e\u003cw:r\u003e\u003cw:t\u003esubstitution cipher\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e), an example of which is the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/ROT13\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eROT13 cipher\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (discussed in\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/78-implement-a-rot13-cipher\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCody Challenge Problem 78\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e). As in the Cody Challenge Problem, uppercase and lowercase letters are handled independently of one another, and all other characters (e.g. punctuation, numbers, accented letters) are unchanged. Unlike the Cody Challenge Problem, here the number of letters to shift is not known in advance, and will vary\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebetween\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (not within) batches — also, here you need to decode, not encode.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThese latest activities that you are investigating have been nicknamed 'Operation Xiangliu' by your own organisation. A few decoding options are at your organisation's disposal:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest the candidate decodings against all words in a large dictionary — this could work, but it is very slow.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest the candidate decodings for the appearance of a name or phrase that is certain to appear regularly (e.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44351\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"Operation Phoenix\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) — unfortunately as yet there is not enough knowledge of the activities to be sure of a suitable name or phrase to use.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest the candidate decodings against all words in a short list comprising, say, the hundred most frequently used English words.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ethird option\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e will be faster than the first option, and more reliable than the second option, so this is the approach you will be taking.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou have been careful to avoid using 'lemmatised' lists (which contain e.g. the verb \\\"to be\\\", rather than the various inflected forms such as \\\"am\\\", \\\"is\\\", \\\"are\\\") like those based on the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://web.archive.org/web/20111226085859/http://oxforddictionaries.com/words/the-oec-facts-about-the-language\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOEC\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.wordfrequency.info/free.asp?s=y\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCOCA\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, and after setting aside\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://world-english.org/english500.htm\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eanother list\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e you finally choose the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://ucrel.lancs.ac.uk/bncfreq/samples/120.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003elist based on the BNC\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e as the most reliable, and will use the first 100 words on that list. This list will be available for you to access as an input variable,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebncWordlist\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, to your function. Note: (i) some entries on the list are morphemes (e.g. \\\" n't \\\" and \\\" 's \\\") rather than words; (ii) some entries appear more than once (representing different grammatical word classes). Of course, in the original messages any capitalisation might be used.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou have been instructed that your 'certitude' (degree of confidence) in the decoding must be reported for each batch, and shall depend proportionally upon the sum of the characters for the words/morphemes in the decoded message that match words/morphemes in\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebncWordlist\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Being able to match words/morphemes accounting for three-tenths of the characters (excluding spaces) shall correspond to 100% certitude; matching three-twentieths would be 50% certitude, and so on. Certitude shall be reported as a percentage, rounded\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eup\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to the nearest integer, not greater than 100. You need to maximise your certitude for each batch by appropriate choice of the shifting parameter. If there are multiple shifts of equal certitude, report both options on different rows (arranged in order of ascending shift parameter).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour task is to crack the codes and report back in a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/struct.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003estructure array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e: (1) the shifting parameter that had been used in the encoding [as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/uint8.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003euint8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e] (usually scalar, but may be column vector); (2) the decoded messages [as a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/cell.html?s_tid=doc_ta\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecell array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (containing\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/char.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003echaracter arrays\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e)] (usually an array with a single row, but occasionally with multiple rows); (3) your 'certitude' in the decoding [as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/uint8.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003euint8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e] (always scalar). The name of the structure array shall be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, with respective fields\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eshift\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emessage\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecertitude\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEXAMPLE 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSuppose the batch contained two encoded messages —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Vomftt qvstvfe, pqfo op eppst.\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Ffmt dbo ljmm, opu pomz xpvoe.\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (provided as character arrays within a cell array) — and a (right-shifting) ROT1 cipher had been applied. In that case A→B, B→C, ..., Y→Z, and Z→A; similarly, a→b, b→c, ..., y→z, and z→a. Thus the original messages would have been: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Unless pursued, open no doors.\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Eels can kill, not only wound.\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e . Twelve of the 51 characters have been matched: \\\"no\\\", \\\"can\\\", \\\"not\\\", and \\\"only\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe correct answer would therefore comprise:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s.shift = uint8(1)  \\ns.message = {'Unless pursued, open no doors.', 'Eels can kill, not only wound.'}\\ns.certitude = uint8(79)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEXAMPLE 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSuppose the batch contained one encoded message —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Oa oqvvq'u cnycau dggp: \\\"Ctu itcvkc ctvku\\\".\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (provided as a character array within a cell array) — and a (right-shifting) ROT2 cipher had been applied. In that case A→C, B→D, ..., Y→A, and Z→B; similarly, a→c, b→d, ..., y→a, and z→b. Thus the original message would have been: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"My motto's always been: \\\"Ars gratia artis\\\".\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e . Eight of the 37 characters have been matched: \\\"My\\\", \\\" 's \\\", and \\\"been\\\". Note carefully that: \\\" 's \\\" should only be matched once; \\\"to\\\" (in motto), \\\"be\\\" (in been), \\\"at\\\" (in gratia), \\\"is\\\" (in artis) and \\\"a\\\" (passim) should\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e be matched at all; and \\\" ' \\\" will only ever be used as an apostrophe (never as a quotation mark).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe correct answer would therefore comprise:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s.shift = uint8(2)  \\ns.message = {'My motto''s always been: \\\"Ars gratia artis\\\".'}\\ns.certitude = uint8(73)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the batch of messages cannot be decoded when following the above assumptions, then return the original encoded message(s) unchanged, with a shift of zero and a certitude of zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote: Many Cody solutions are hard to read and not necessarily computationally efficient. To direct attention toward efficient runtime speed of execution, timings are measured in the Test Suite, and reported back (if the submission is runnable). Although the timings are not perfectly reproducible, they do provide an indication of computational resource demand. (Try resubmitting on another day if your code runs slowly, in case it is caused by a server issue.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo provide some extra motivation for you to get your code to run efficiently, it will fail the Test Suite if it is deemed \\\"too slow\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e----------\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrevious problem: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44356\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOperation Orthos\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Next problem: TBA.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47473,"title":"Slitherlink IV: Recursive (medium size)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 615.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 307.833px; transform-origin: 407px 307.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 144.667px 7.91667px; transform-origin: 144.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink IV: Recursive (medium size)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 239.35px 7.91667px; transform-origin: 239.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving but is solveable using Recursion with limited Guessing.  Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking Recursion.  When Evolve is used within a recursive routine that asserts incorrect content the Evolve may produce an invalid output for the invalid input. The two medium test cases are from Games World of Puzzles October 2020. I was unable to manually solve these puzzles on my first attempt prior to making an error thus I decided to program this simple pencil puzzle. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 324.633px 7.91667px; transform-origin: 324.633px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv init solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n') \r\n  return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n%  show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv evolve solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\n \r\n % Check if solved\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n \r\n % Start recursive processing\r\n if ~valid\r\n  [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap);\r\n  [sv,valid]=pcheck(s,p,bsegs);\r\n end\r\n%\r\n if valid\r\n  fprintf('sv recursion solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n else\r\n  fprintf('No solution found\\n')\r\n end\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\nfunction [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n solved=0;\r\n \r\n %work thru options of first end found with minimum options (2 or 3)  \r\n %(first 2 then 3 if any found)\r\n % extend a segment\r\n ps=sum(p);\r\n ptr=find(ps==7,1,'first'); % First Segment with 2 options\r\n if isempty(ptr)\r\n  ptr=find(ps==8,1,'first'); % First Segment with 3 options\r\n end\r\n pc=find(p(ptr,:)==1);\r\n \r\n for i=pc\r\n  pn=p;\r\n  %insertion of code required here\r\n  \r\n  %This modified pn may be invalid and create an invalid evolve result\r\n  [pn,evalid]=evolve(pn,bsegs,s,c,emap,pmap);\r\n  if ~evalid,continue;end\r\n  \r\n  [v,valid]=pcheck(s,pn,bsegs); % check if segment add and evolve solved\r\n  if valid\r\n   solved=1;\r\n   p=pn;\r\n   return;\r\n  end\r\n  \r\n  %Invoke the next level of recursion build with the recursion assert and Evolve\r\n  [pn,solved]=slither_recur(pn,bsegs,s,c,emap,pmap);\r\n  if solved\r\n   p=pn;\r\n   return\r\n  end\r\n end %i\r\n % Loop through options\r\n % Perform evolve\r\n %  if invalid try next option\r\n %  call next level recur\r\n %  if solved return\r\nend %[p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10  0,10  1,11  2,12  3,13 to avoid reprocess\r\n while ~isequal(p,pb)\r\n  pb=p;\r\n  s1=find(sp==1)';\r\n  for i=s1 %1 \r\n   v=bsegs(i,:);\r\n   %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==1 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e5\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end % i s1 1\r\n  \r\n  \r\n  s2=find(sp==2)';\r\n  for i=s2 %2\r\n   v=bsegs(i,:);\r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e10 % 0 non-5 segments, have 2 links\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==6 || sum(wv)==2 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e10\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end %i s2 2\r\n  \r\n  s3=find(sp==3)';\r\n  for i=s3 %3\r\n   v=bsegs(i,:);\r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e15 % 0 non-5 segments, have 3 links\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==11 || sum(wv)==3 || sum(wv)==7 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e10\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n  end %i s3 3\r\n  if ~isequal(p,pb) % s update created new walls\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n   continue;\r\n  end\r\n  %show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n  %Process links for new walls\r\n  % RR straight blocks perp, Binto corner makes B outcorner\r\n  % RR corner blocks to corner\r\n  % R into corner extends R\r\n  % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n  % R node with one option extends R\r\n  [nrc,ncc]=size(c);\r\n  % Bcorners if either corner edge B then both B\r\n  if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n   p(1,2)=0; p(2,1)=0;\r\n   p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n  end\r\n  if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n   p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n   p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n   p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=0; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=0;\r\n   p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=0;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=0;\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n   p(nrc*ncc,nrc*ncc-1)=0; p(nrc*ncc-1,nrc*ncc)=0;\r\n   p(nrc*ncc,nrc*ncc-nrc)=0;p(nrc*ncc-nrc,nrc*ncc)=0;\r\n  end\r\n  \r\n  % Rcorners if either corner edge R then both R\r\n  if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n   p(1,2)=5; p(2,1)=5;\r\n   p(1,nrc+1)=5;p(nrc+1,1)=5;\r\n  end\r\n  if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n   p(nrc-1,nrc)=5; p(nrc,nrc-1)=5;\r\n   p(nrc,2*nrc)=5;p(2*nrc,nrc)=5;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n   p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=5; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=5;\r\n   p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=5;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=5;\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n   p(nrc*ncc,nrc*ncc-1)=5; p(nrc*ncc-1,nrc*ncc)=5;\r\n   p(nrc*ncc,nrc*ncc-nrc)=5;p(nrc*ncc-nrc,nrc*ncc)=5;\r\n  end\r\n  \r\n  % BB edges\r\n  %Top Row\r\n  for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n   cv=c(1,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n   end\r\n  end % j Top row\r\n  %Bottom Row\r\n  for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n   cv=c(nrc,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up, virtual cv(2)+1==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up\r\n   end\r\n  end % j Bot row\r\n  \r\n  %Left Col edge\r\n  for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n   cv=c(i:i+2,1);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor, virt cv(2)-nrc==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n    p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor rt\r\n   end\r\n  end % j L col\r\n  %Right Col edge\r\n  for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n   cv=c(i:i+2,ncc);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor, virt cv(2)+nrc==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n    p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor lt\r\n   end\r\n  end % j L col\r\n  \r\n  %Hor segs not on an edge\r\n  for i=2:nrc-1\r\n   for j=1:ncc-2\r\n    cv=c(i,j:j+2);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+1)==0\r\n     p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n    end\r\n    if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B  vud\r\n     p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  %Ver segs not on an edge\r\n  for i=1:nrc-2\r\n   for j=2:ncc-1\r\n    cv=c(i:i+2,j);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+nrc)==0\r\n     p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n    end\r\n    if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B  hLR\r\n     p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  \r\n  % RR corner blocks to corner\r\n  %[rr;xr]  [rr;rx]  [xr;rr]  [rx;rr]\r\n  %RR;xR or RR;Rx\r\n  for i=1:nrc-1\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab top pair\r\n    if p(cv(1),cv(2))==5 % Top Red\r\n     if p(cv(2),cv(2)+1)==5 % rr;xr\r\n      if i\u003e1\r\n       p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0;\r\n      end\r\n      if j\u003cncc-1\r\n       p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n      end\r\n     end\r\n     \r\n     if p(cv(1),cv(1)+1)==5 % rr;rx\r\n      if i\u003e1\r\n       p(cv(1),cv(1)-1)=0;p(cv(1)-1,cv(1))=0;\r\n      end\r\n      if j\u003e1\r\n       p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n      end\r\n     end\r\n    end % Top RR\r\n   end %j\r\n  end %i\r\n  \r\n  for i=2:nrc % Rx;RR  xR;RR\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab lower pair\r\n    if p(cv(1),cv(2))==5 % Bot Red\r\n     if p(cv(2),cv(2)-1)==5 % xr;rr\r\n      if i\u003cnrc\r\n       p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0;\r\n      end\r\n      if j\u003cncc-1\r\n       p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n      end\r\n     end\r\n     \r\n     if p(cv(1),cv(1)-1)==5 % rx;rr\r\n      if i\u003cnrc\r\n       p(cv(1),cv(1)+1)=0;p(cv(1)+1,cv(1))=0;\r\n      end\r\n      if j\u003e1\r\n       p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n      end\r\n     end\r\n     \r\n    end %Bot RR\r\n   end %j\r\n  end %i\r\n  \r\n  % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n  i=1; % Top\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    if p(cv(1),cv(1)+1)==0 % down dead end left side\r\n     if j\u003e1\r\n      p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)+1)==0 % down dead end, rt side\r\n     if j\u003cncc-1\r\n      p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n     end\r\n    end\r\n   end\r\n  end % j\r\n  \r\n  i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    if p(cv(1),cv(1)-1)==0 % up dead end left side\r\n     if j\u003e1\r\n      p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)-1)==0 % up dead end rt side\r\n     if j\u003cncc-1\r\n      p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n     end\r\n    end\r\n   end\r\n  end % j\r\n  \r\n  j=ncc; % Right\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    if p(cv(1),cv(1)-nrc)==0 % rt dead end up side\r\n     if i\u003e1\r\n      p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)-nrc)==0 % rt dead end down side\r\n     if i\u003cnrc-1\r\n      p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n     end\r\n    end\r\n   end\r\n  end % i\r\n  \r\n  j=1; % Left\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    if p(cv(1),cv(1)+nrc)==0 % left dead end up side\r\n     if i\u003e1\r\n      p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)+nrc)==0 % left dead end down side\r\n     if i\u003cnrc-1\r\n      p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n     end\r\n    end\r\n   end\r\n  end % i\r\n  \r\n  if ~isequal(p,pb),continue;end\r\n  \r\n  % R node with one option extends R \r\n%   [pr5,pc5]=find(p==5);\r\n%   for i=1:length(pr5)\r\n%    if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n%     new_node=find(p(pr5(i),:)==1);\r\n%     p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n%    end\r\n%   end\r\n  \r\n  [pr5,pc5]=find(p==5);\r\n  pr5=unique(pr5); % could sort then remove dupes which are mids\r\n  while ~isempty(pr5)\r\n   if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n    new_node=find(p(pr5(1),:)==1);\r\n    p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n    pr5(1)=new_node;\r\n   else\r\n    pr5(1)=[];\r\n   end\r\n  end\r\n  \r\n  %need an isequal(p,pb)\r\n  %check if red seg closes a loop of less than X thus seg must be black\r\n  if isequal(p,pb) % check for bad R bars\r\n   ps=sum(p);\r\n   pv= ps\u003e4  \u0026 ~(ps==10);\r\n   pidx=find(pv);\r\n   for i=pidx\r\n    v=[i find(p(i,:)==5)];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n%    v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n     \r\n%      v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n%      if nnz(v(1:end-2)==v(end))\r\n%       v(end)=[];\r\n%      elseif nnz(v(1:end-2)==v(end))\r\n%       v(end-1)=[];\r\n%      end\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     \r\n    end\r\n    if Lv\u003c4,continue;end % Need at least 3 segments to make a loop\r\n    if p(v(1),v(end)) % path ends are currently adjacent, likely sb 0 but may be final solve\r\n     if Lv\u003cnnz(p==5)/2\r\n      p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n     else % Possible solve\r\n      pchk=p;\r\n      pchk(v(1),v(end))=5;pchk(v(end),v(1))=5;\r\n      [sv,valid]=pcheck(s,pchk,bsegs); % check if solved\r\n      if valid\r\n       p=pchk;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n      end\r\n     end % Lv\r\n    end % p( v 1 end)\r\n   end % pidx\r\n  end % isequal p pb  after cells, ends make no change\r\n  \r\n  %possible evolve is try seg to see if evolve base leads to a fail thus must be black\r\n  \r\n%   isequal(p,pb)\r\n%   show_pfig(s,p,c,emap,pmap,3)\r\n%   show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n\r\n end % while p~=pb\r\n \r\n % Valid checks\r\n   for sptr=1:nr*nc %invalid set/clear segment count\r\n    %if s(sptr)==5,continue;end % what if a 4 seg circle occurs around a 5?\r\n    vsptr=bsegs(sptr,:);\r\n    psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n    if s(sptr)==5\r\n     if nnz(psegs==5)==4\r\n      evalid=0;\r\n      return\r\n     else\r\n      continue\r\n     end\r\n    end % s 5\r\n    \r\n    if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    ps=sum(p); % .48  17K\r\n    %if nnz(sum(p)==5) % Node with no escape %.48\r\n    if nnz(ps==5) % Node with no escape\r\n     evalid=0;\r\n     return\r\n    end\r\n    %if nnz(sum(p)\u003e14) % Node with too many segments % .47\r\n    if nnz(ps\u003e14) % Node with too many segments\r\n     evalid=0;\r\n     return\r\n    end\r\n   end % sptr\r\n   \r\n   %check for any loops created                  **********************************\r\n   %show_pfig(s,p,c,emap,pmap,3)\r\n   ps=sum(p);\r\n   pidx=find(ps==10);\r\n   pchecked=[];\r\n   %pidx=[];\r\n   for i=pidx\r\n    if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n    vn=find(p(i,:)==5); % Guaranteed 2 points\r\n    if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n     pchecked=[pchecked i];\r\n     continue;\r\n    end\r\n    v=[i find(p(i,:)==5,1,'first')];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n%    v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n     \r\n%      v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n%      if nnz(v(1:end-2)==v(end))\r\n%       v(end)=[];\r\n%      elseif nnz(v(1:end-2)==v(end))\r\n%       v(end-1)=[];\r\n%      end\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end % No loop\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     if v(1)==v(end),break;end % Loop created\r\n    end % while extending\r\n    pchecked=[pchecked v];\r\n    \r\n    if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n    if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n     if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop   [1 2 4 3 1] loop\r\n      evalid=0;\r\n      return\r\n     else % Possible solve\r\n      [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n      if valid\r\n       evalid=1;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       evalid=0;\r\n       return\r\n      end\r\n     end % Lv-1 compare to total current segments\r\n    end %  v 1 end)\r\n   end % pidx\r\n   \r\n   evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  bidx=idx1(i);\r\n  if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n  end\r\n  \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n  end\r\n  \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n  \r\n  if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  end % if TL/TR/BL/BR\r\n  \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % Top edge\r\n   if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   \r\n  elseif nr3(i)==nr % Bot Edge\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   \r\n  elseif nc3(i)==1 %Left Edge\r\n   if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   \r\n  elseif nc3(i)==nc % Rt edge\r\n   if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   end\r\n   \r\n   \r\n  else %non-edge 3\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n     \r\n   elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   end\r\n  end % Edges/Mid 3\r\n    \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1\r\n   if nc3(i)==1 % TL  only one R or D possible\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % TR only one L or D possible. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Top Row  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  \r\n  if nr3(i)\u003cnr  % Mid section 33\r\n   if nc3(i)==1 % check only one R and D p\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % check only D. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Mid Row (not col 1 or nc)  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  if nr3(i)==nr  % Bot row 33\r\n    if nc3(i)==nc,continue;end % No process BR corner\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    continue\r\n  end\r\n \r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx+1-nr)==0 %BL\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %BR\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==1\r\n  \r\n  if nr3(i)==nr % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %TL\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %TR\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==nr\r\n  \r\n  if nc3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==1\r\n  \r\n  if nc3(i)==nc % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==nc\r\n  \r\n  %mid : check 4 courners\r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr  down diag, RB set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003c=nc-2 % Not near right edge\r\n     vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n  \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    \r\n    vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr  down diag, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not top edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003cnc % Not Right edge\r\n     vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e=3 % Not near Left edge\r\n     vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; \r\n   %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[3 3 2 3 5 5 3 3 5 1;\r\n    5 5 5 2 5 5 5 5 5 5;\r\n    1 5 5 5 5 1 1 5 5 2;\r\n    0 5 5 5 5 2 5 5 3 3;\r\n    0 5 5 5 1 3 5 5 5 5;\r\n    5 5 5 5 2 3 5 5 5 0;\r\n    3 2 5 5 1 5 5 5 5 2;\r\n    3 5 5 2 0 5 5 5 5 2;\r\n    5 5 5 5 5 5 2 5 5 5;\r\n    3 5 1 3 5 5 3 3 2 3]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['252';\r\n   '151';\r\n   '212']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n    '1551';\r\n    '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n   '1521';\r\n   '0532';\r\n   '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['33353';\r\n   '15551';\r\n   '25055';\r\n   '55253';\r\n   '13511']-'0';% evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[5 1 5 5 3 5 5 5 0 1;\r\n    5 0 5 5 5 3 3 5 5 5;\r\n    5 5 5 1 2 5 5 5 3 5;\r\n    2 5 5 5 5 5 2 0 5 2;\r\n    0 5 5 5 5 5 5 5 5 5;\r\n    5 5 5 5 5 5 5 5 5 3;\r\n    3 5 1 2 5 5 5 5 5 1;\r\n    5 3 5 5 5 3 0 5 5 5;\r\n    5 5 5 0 0 5 5 5 3 5;\r\n    2 1 5 5 5 1 5 5 3 5]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T20:32:38.000Z","updated_at":"2026-04-21T01:40:28.000Z","published_at":"2020-11-12T23:28:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink IV: Recursive (medium size)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving but is solveable using Recursion with limited Guessing.  Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking Recursion.  When Evolve is used within a recursive routine that asserts incorrect content the Evolve may produce an invalid output for the invalid input. The two medium test cases are from Games World of Puzzles October 2020. I was unable to manually solve these puzzles on my first attempt prior to making an error thus I decided to program this simple pencil puzzle. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink V: Assert/Evolve/Check 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lationshipId\":\"rId1\"}]}"},{"id":47478,"title":"Slitherlink V: Assert/Evolve/Check (large)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 678.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 339.333px; transform-origin: 407px 339.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 210px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 105px; text-align: left; transform-origin: 384px 105px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 168.4px 7.91667px; transform-origin: 168.4px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink V:  Assert/Evolve/Check(large size)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 207.3px 7.91667px; transform-origin: 207.3px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving and Recursion due to time and depth issues.  Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking the Assert/Evolve/Check/Update method.  The advanced solving techniques on the web are weak and complicated. The simple method is not to immediately invoke recursion due to the sparseness of data leading to too many false options. Ther actual simple method is to use Try/Catch by asserting segments as Black/Red and then checking if the layout using a robust Evolve creates an invalid state. If the state became invalid when asserting a single segment as Black then it must be Red with the same being true of Red assertion being invalid must mean the segment is Black. If an Evolve is invalid then Assert the right Bar type and perform an evolve to update the board.  The two large test cases are from Games World of Puzzles October 2020. I was completely hopeless for the large puzzles. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 314.917px 7.91667px; transform-origin: 314.917px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive(medium size)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n') \r\n  return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\n \r\n %Implement Assert/Check/Evolve\r\n [p]=assert(p,bsegs,s,c,emap,pmap); \r\n \r\n % Check if solved\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n if valid\r\n  fprintf('sv Assert solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n end\r\n \r\n % Start recursive processing\r\n if ~valid\r\n  [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap);\r\n  [sv,valid]=pcheck(s,p,bsegs);\r\n end\r\n%\r\n if valid\r\n  fprintf('sv recursion solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n else\r\n  fprintf('No solution found\\n')\r\n end\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\nfunction [p]=assert(p,bsegs,s,c,emap,pmap)\r\n %Insert code here to assert a segment as Red/Black\r\n %Check if evolve of is valid\r\n %If not valid then Assert segment as Black/Red depending on case and then evolve\r\n %Keep asserting until no more p updates and/or s is solved\r\n %Asserting ends of red segments first may reduce total time\r\n pb=p*0;\r\n valid=0;\r\n while ~isequal(p,pb) \u0026\u0026 ~valid\r\n  pb=p;\r\n  [pr,pc]=find(p==1);\r\n  % insert code here\r\n end % while\r\nend\r\n\r\nfunction [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n solved=0;\r\n \r\n %work thru options of first end found with minimum options (2 or 3)  \r\n %(first 2 then 3 if any found)\r\n % extend a segment\r\n ps=sum(p);\r\n ptr=find(ps==7,1,'first'); % First Segment with 2 options\r\n if isempty(ptr)\r\n  ptr=find(ps==8,1,'first'); % First Segment with 3 options\r\n end\r\n pc=find(p(ptr,:)==1);\r\n \r\n for i=pc\r\n  pn=p;\r\n  pn(ptr,i)=5;pn(i,ptr)=5; % make linkage\r\n  \r\n  %This modified pn may be invalid and create an invalid evolve result\r\n  [pn,evalid]=evolve(pn,bsegs,s,c,emap,pmap);\r\n  if ~evalid,continue;end\r\n  \r\n  [v,valid]=pcheck(s,pn,bsegs); % check if segment add and evolve solved\r\n  if valid\r\n   solved=1;\r\n   p=pn;\r\n   return;\r\n  end\r\n  \r\n  %Invoke the next level of recursion build with the recursion assert and Evolve\r\n  [pn,solved]=slither_recur(pn,bsegs,s,c,emap,pmap);\r\n  if solved\r\n   p=pn;\r\n   return\r\n  end\r\n end %i\r\n % Loop through options\r\n % Perform evolve\r\n %  if invalid try next option\r\n %  call next level recur\r\n %  if solved return\r\nend %[p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10  0,10  1,11  2,12  3,13 to avoid reprocess\r\n while ~isequal(p,pb)\r\n  pb=p;\r\n  s1=find(sp==1)';\r\n  for i=s1 %1 \r\n   v=bsegs(i,:);\r\n   %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==1 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e5\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end % i s1 1\r\n  \r\n  \r\n  s2=find(sp==2)';\r\n  for i=s2 %2\r\n   v=bsegs(i,:);\r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e10 % 0 non-5 segments, have 2 links\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==6 || sum(wv)==2 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e10\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end %i s2 2\r\n  \r\n  s3=find(sp==3)';\r\n  for i=s3 %3\r\n   v=bsegs(i,:);\r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e15 % 0 non-5 segments, have 3 links\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==11 || sum(wv)==3 || sum(wv)==7 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e10\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n  end %i s3 3\r\n  if ~isequal(p,pb) % s update created new walls\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n   continue;\r\n  end\r\n  %show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n  %Process links for new walls\r\n  % RR straight blocks perp, Binto corner makes B outcorner\r\n  % RR corner blocks to corner\r\n  % R into corner extends R\r\n  % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n  % R node with one option extends R\r\n  [nrc,ncc]=size(c);\r\n  % Bcorners if either corner edge B then both B\r\n  if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n   p(1,2)=0; p(2,1)=0;\r\n   p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n  end\r\n  if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n   p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n   p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n   p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=0; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=0;\r\n   p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=0;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=0;\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n   p(nrc*ncc,nrc*ncc-1)=0; p(nrc*ncc-1,nrc*ncc)=0;\r\n   p(nrc*ncc,nrc*ncc-nrc)=0;p(nrc*ncc-nrc,nrc*ncc)=0;\r\n  end\r\n  \r\n  % Rcorners if either corner edge R then both R\r\n  if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n   p(1,2)=5; p(2,1)=5;\r\n   p(1,nrc+1)=5;p(nrc+1,1)=5;\r\n  end\r\n  if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n   p(nrc-1,nrc)=5; p(nrc,nrc-1)=5;\r\n   p(nrc,2*nrc)=5;p(2*nrc,nrc)=5;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n   p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=5; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=5;\r\n   p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=5;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=5;\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n   p(nrc*ncc,nrc*ncc-1)=5; p(nrc*ncc-1,nrc*ncc)=5;\r\n   p(nrc*ncc,nrc*ncc-nrc)=5;p(nrc*ncc-nrc,nrc*ncc)=5;\r\n  end\r\n  \r\n  % BB edges\r\n  %Top Row\r\n  for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n   cv=c(1,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n   end\r\n  end % j Top row\r\n  %Bottom Row\r\n  for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n   cv=c(nrc,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up, virtual cv(2)+1==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up\r\n   end\r\n  end % j Bot row\r\n  \r\n  %Left Col edge\r\n  for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n   cv=c(i:i+2,1);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor, virt cv(2)-nrc==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n    p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor rt\r\n   end\r\n  end % j L col\r\n  %Right Col edge\r\n  for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n   cv=c(i:i+2,ncc);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor, virt cv(2)+nrc==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n    p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor lt\r\n   end\r\n  end % j L col\r\n  \r\n  %Hor segs not on an edge\r\n  for i=2:nrc-1\r\n   for j=1:ncc-2\r\n    cv=c(i,j:j+2);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+1)==0\r\n     p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n    end\r\n    if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B  vud\r\n     p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  %Ver segs not on an edge\r\n  for i=1:nrc-2\r\n   for j=2:ncc-1\r\n    cv=c(i:i+2,j);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+nrc)==0\r\n     p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n    end\r\n    if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B  hLR\r\n     p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  \r\n  % RR corner blocks to corner\r\n  %[rr;xr]  [rr;rx]  [xr;rr]  [rx;rr]\r\n  %RR;xR or RR;Rx\r\n  for i=1:nrc-1\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab top pair\r\n    if p(cv(1),cv(2))==5 % Top Red\r\n     if p(cv(2),cv(2)+1)==5 % rr;xr\r\n      if i\u003e1\r\n       p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0;\r\n      end\r\n      if j\u003cncc-1\r\n       p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n      end\r\n     end\r\n     \r\n     if p(cv(1),cv(1)+1)==5 % rr;rx\r\n      if i\u003e1\r\n       p(cv(1),cv(1)-1)=0;p(cv(1)-1,cv(1))=0;\r\n      end\r\n      if j\u003e1\r\n       p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n      end\r\n     end\r\n    end % Top RR\r\n   end %j\r\n  end %i\r\n  \r\n  for i=2:nrc % Rx;RR  xR;RR\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab lower pair\r\n    if p(cv(1),cv(2))==5 % Bot Red\r\n     if p(cv(2),cv(2)-1)==5 % xr;rr\r\n      if i\u003cnrc\r\n       p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0;\r\n      end\r\n      if j\u003cncc-1\r\n       p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n      end\r\n     end\r\n     \r\n     if p(cv(1),cv(1)-1)==5 % rx;rr\r\n      if i\u003cnrc\r\n       p(cv(1),cv(1)+1)=0;p(cv(1)+1,cv(1))=0;\r\n      end\r\n      if j\u003e1\r\n       p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n      end\r\n     end\r\n     \r\n    end %Bot RR\r\n   end %j\r\n  end %i\r\n  \r\n  % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n  i=1; % Top\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    if p(cv(1),cv(1)+1)==0 % down dead end left side\r\n     if j\u003e1\r\n      p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)+1)==0 % down dead end, rt side\r\n     if j\u003cncc-1\r\n      p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n     end\r\n    end\r\n   end\r\n  end % j\r\n  \r\n  i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    if p(cv(1),cv(1)-1)==0 % up dead end left side\r\n     if j\u003e1\r\n      p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)-1)==0 % up dead end rt side\r\n     if j\u003cncc-1\r\n      p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n     end\r\n    end\r\n   end\r\n  end % j\r\n  \r\n  j=ncc; % Right\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    if p(cv(1),cv(1)-nrc)==0 % rt dead end up side\r\n     if i\u003e1\r\n      p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)-nrc)==0 % rt dead end down side\r\n     if i\u003cnrc-1\r\n      p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n     end\r\n    end\r\n   end\r\n  end % i\r\n  \r\n  j=1; % Left\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    if p(cv(1),cv(1)+nrc)==0 % left dead end up side\r\n     if i\u003e1\r\n      p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)+nrc)==0 % left dead end down side\r\n     if i\u003cnrc-1\r\n      p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n     end\r\n    end\r\n   end\r\n  end % i\r\n  \r\n  if ~isequal(p,pb),continue;end\r\n  \r\n  % R node with one option extends R \r\n%   [pr5,pc5]=find(p==5);\r\n%   for i=1:length(pr5)\r\n%    if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n%     new_node=find(p(pr5(i),:)==1);\r\n%     p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n%    end\r\n%   end\r\n  \r\n  [pr5,pc5]=find(p==5);\r\n  pr5=unique(pr5); % could sort then remove dupes which are mids\r\n  while ~isempty(pr5)\r\n   if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n    new_node=find(p(pr5(1),:)==1);\r\n    p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n    pr5(1)=new_node;\r\n   else\r\n    pr5(1)=[];\r\n   end\r\n  end\r\n  \r\n  %need an isequal(p,pb)\r\n  %check if red seg closes a loop of less than X thus seg must be black\r\n  if isequal(p,pb) % check for bad R bars\r\n   ps=sum(p);\r\n   pv= ps\u003e4  \u0026 ~(ps==10);\r\n   pidx=find(pv);\r\n   for i=pidx\r\n    v=[i find(p(i,:)==5)];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n%    v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n     \r\n%      v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n%      if nnz(v(1:end-2)==v(end))\r\n%       v(end)=[];\r\n%      elseif nnz(v(1:end-2)==v(end))\r\n%       v(end-1)=[];\r\n%      end\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     \r\n    end\r\n    if Lv\u003c4,continue;end % Need at least 3 segments to make a loop\r\n    if p(v(1),v(end)) % path ends are currently adjacent, likely sb 0 but may be final solve\r\n     if Lv\u003cnnz(p==5)/2\r\n      p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n     else % Possible solve\r\n      pchk=p;\r\n      pchk(v(1),v(end))=5;pchk(v(end),v(1))=5;\r\n      [sv,valid]=pcheck(s,pchk,bsegs); % check if solved\r\n      if valid\r\n       p=pchk;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n      end\r\n     end % Lv\r\n    end % p( v 1 end)\r\n   end % pidx\r\n  end % isequal p pb  after cells, ends make no change\r\n  \r\n  %possible evolve is try seg to see if evolve base leads to a fail thus must be black\r\n  \r\n%   isequal(p,pb)\r\n%   show_pfig(s,p,c,emap,pmap,3)\r\n%   show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n\r\n end % while p~=pb\r\n \r\n % Valid checks\r\n   for sptr=1:nr*nc %invalid set/clear segment count\r\n    %if s(sptr)==5,continue;end % what if a 4 seg circle occurs around a 5?\r\n    vsptr=bsegs(sptr,:);\r\n    psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n    if s(sptr)==5\r\n     if nnz(psegs==5)==4\r\n      evalid=0;\r\n      return\r\n     else\r\n      continue\r\n     end\r\n    end % s 5\r\n    \r\n    if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    ps=sum(p); % .48  17K\r\n    %if nnz(sum(p)==5) % Node with no escape %.48\r\n    if nnz(ps==5) % Node with no escape\r\n     evalid=0;\r\n     return\r\n    end\r\n    %if nnz(sum(p)\u003e14) % Node with too many segments % .47\r\n    if nnz(ps\u003e14) % Node with too many segments\r\n     evalid=0;\r\n     return\r\n    end\r\n   end % sptr\r\n   \r\n   %check for any loops created                  **********************************\r\n   %show_pfig(s,p,c,emap,pmap,3)\r\n   ps=sum(p);\r\n   pidx=find(ps==10);\r\n   pchecked=[];\r\n   %pidx=[];\r\n   for i=pidx\r\n    if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n    vn=find(p(i,:)==5); % Guaranteed 2 points\r\n    if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n     pchecked=[pchecked i];\r\n     continue;\r\n    end\r\n    v=[i find(p(i,:)==5,1,'first')];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n%    v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n     \r\n%      v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n%      if nnz(v(1:end-2)==v(end))\r\n%       v(end)=[];\r\n%      elseif nnz(v(1:end-2)==v(end))\r\n%       v(end-1)=[];\r\n%      end\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end % No loop\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     if v(1)==v(end),break;end % Loop created\r\n    end % while extending\r\n    pchecked=[pchecked v];\r\n    \r\n    if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n    if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n     if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop   [1 2 4 3 1] loop\r\n      evalid=0;\r\n      return\r\n     else % Possible solve\r\n      [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n      if valid\r\n       evalid=1;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       evalid=0;\r\n       return\r\n      end\r\n     end % Lv-1 compare to total current segments\r\n    end %  v 1 end)\r\n   end % pidx\r\n   \r\n   evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  bidx=idx1(i);\r\n  if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n  end\r\n  \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n  end\r\n  \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n  \r\n  if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  end % if TL/TR/BL/BR\r\n  \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % Top edge\r\n   if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   \r\n  elseif nr3(i)==nr % Bot Edge\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   \r\n  elseif nc3(i)==1 %Left Edge\r\n   if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   \r\n  elseif nc3(i)==nc % Rt edge\r\n   if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   end\r\n   \r\n   \r\n  else %non-edge 3\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n     \r\n   elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   end\r\n  end % Edges/Mid 3\r\n    \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1\r\n   if nc3(i)==1 % TL  only one R or D possible\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % TR only one L or D possible. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Top Row  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  \r\n  if nr3(i)\u003cnr  % Mid section 33\r\n   if nc3(i)==1 % check only one R and D p\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % check only D. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Mid Row (not col 1 or nc)  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  if nr3(i)==nr  % Bot row 33\r\n    if nc3(i)==nc,continue;end % No process BR corner\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    continue\r\n  end\r\n \r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx+1-nr)==0 %BL\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %BR\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==1\r\n  \r\n  if nr3(i)==nr % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %TL\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %TR\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==nr\r\n  \r\n  if nc3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==1\r\n  \r\n  if nc3(i)==nc % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==nc\r\n  \r\n  %mid : check 4 courners\r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr  down diag, RB set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003c=nc-2 % Not near right edge\r\n     vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n  \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    \r\n    vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr  down diag, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not top edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003cnc % Not Right edge\r\n     vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e=3 % Not near Left edge\r\n     vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; \r\n   %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[3 3 2 3 5 5 3 3 5 1;\r\n    5 5 5 2 5 5 5 5 5 5;\r\n    1 5 5 5 5 1 1 5 5 2;\r\n    0 5 5 5 5 2 5 5 3 3;\r\n    0 5 5 5 1 3 5 5 5 5;\r\n    5 5 5 5 2 3 5 5 5 0;\r\n    3 2 5 5 1 5 5 5 5 2;\r\n    3 5 5 2 0 5 5 5 5 2;\r\n    5 5 5 5 5 5 2 5 5 5;\r\n    3 5 1 3 5 5 3 3 2 3]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\ns=['053552235013';\r\n   '505555535555';\r\n   '355135525552';\r\n   '521552155535';\r\n   '555305555553';\r\n   '535555335551';\r\n   '525050255352';\r\n   '325255555505';\r\n   '525555552521';\r\n   '152552253525';\r\n   '255533555535';\r\n   '255555522555';\r\n   '535551355315';\r\n   '355535512553';\r\n   '555525555515';\r\n   '132523255153']-'0'; % Solves with Assert\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n    '1551';\r\n    '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n   '1521';\r\n   '0532';\r\n   '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=['225355223525';\r\n    '555235535535';\r\n    '555255555555';\r\n    '232535355512';\r\n    '355555535515';\r\n    '255035555502';\r\n    '555555522555';\r\n    '055515555315';\r\n    '513555535550';\r\n    '555025555555';\r\n    '015555522552';\r\n    '505535555553';\r\n    '315553525223';\r\n    '555555553555';\r\n    '525515531555';\r\n    '535312551533']-'0'; % solves with Assert, Dies in Recursion\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[5 1 5 5 3 5 5 5 0 1;\r\n    5 0 5 5 5 3 3 5 5 5;\r\n    5 5 5 1 2 5 5 5 3 5;\r\n    2 5 5 5 5 5 2 0 5 2;\r\n    0 5 5 5 5 5 5 5 5 5;\r\n    5 5 5 5 5 5 5 5 5 3;\r\n    3 5 1 2 5 5 5 5 5 1;\r\n    5 3 5 5 5 3 0 5 5 5;\r\n    5 5 5 0 0 5 5 5 3 5;\r\n    2 1 5 5 5 1 5 5 3 5]; % solves with recursive/assert\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T21:28:57.000Z","updated_at":"2026-04-16T16:18:26.000Z","published_at":"2020-11-12T23:19:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink V:  Assert/Evolve/Check(large size)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving and Recursion due to time and depth issues.  Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking the Assert/Evolve/Check/Update method.  The advanced solving techniques on the web are weak and complicated. The simple method is not to immediately invoke recursion due to the sparseness of data leading to too many false options. Ther actual simple method is to use Try/Catch by asserting segments as Black/Red and then checking if the layout using a robust Evolve creates an invalid state. If the state became invalid when asserting a single segment as Black then it must be Red with the same being true of Red assertion being invalid must mean the segment is Black. If an Evolve is invalid then Assert the right Bar type and perform an evolve to update the board.  The two large test cases are from Games World of Puzzles October 2020. I was completely hopeless for the large puzzles. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive(medium size)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"rela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III: Evolve","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 615.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 307.833px; transform-origin: 407px 307.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 84.75px 7.91667px; transform-origin: 84.75px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink III: Evolve\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 265.65px 7.91667px; transform-origin: 265.65px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques but requires additional Evolving that is always valid for a valid input. Evolve examples are a Red bar into a corner must continue that Red bar out of the corner, an s=1 cell with a Red bar must have Black bars on its other 3 edges.  Cases of Trivial and Gimmes should be solved prior to invoking Evolve. The Evolve subroutine is the most critical routine and must be very comprehensive. A general Evolve routine should check if the output State is valid. When Evolve is used within a recursive routine that asserts possibly incorrect content the Evolve may produce an invalid output for the invalid input.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 366.783px 7.91667px; transform-origin: 366.783px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv init solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n') \r\n  return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n%  show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv evolve solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\n\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10  0,10  1,11  2,12  3,13 to avoid reprocess\r\n while ~isequal(p,pb) %Keep evolving while there is any update to p\r\n  pb=p;\r\n  s1=find(sp==1)';\r\n  for i=s1 %1 \r\n   v=bsegs(i,:);\r\n   %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==1 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e5\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end % i s1 1\r\n  \r\n  \r\n  s2=find(sp==2)';\r\n  for i=s2 %2\r\n   v=bsegs(i,:);\r\n   %insert code\r\n  end %i s2 2\r\n  \r\n  s3=find(sp==3)';\r\n  for i=s3 %3\r\n   v=bsegs(i,:);\r\n   %insert code\r\n  end %i s3 3\r\n  \r\n  if ~isequal(p,pb) % s update created new walls\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n   continue;\r\n  end\r\n  %show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n  %Process links for new walls\r\n  % RR straight blocks perp, Binto corner makes B outcorner\r\n  % RR corner blocks to corner\r\n  % R into corner extends R\r\n  % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n  % R node with one option extends R\r\n  [nrc,ncc]=size(c);\r\n  % Bcorners if either corner edge B then both B\r\n  if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n   p(1,2)=0; p(2,1)=0;\r\n   p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n  end\r\n  if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n   p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n   p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n  %insert code\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n   %insert code\r\n  end\r\n  \r\n  % Rcorners if either corner edge R then both R\r\n  if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n   %insert code\r\n  end\r\n  if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n   %insert code\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n   %insert code\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n   %insert code\r\n  end\r\n  \r\n  % BB edges\r\n  %Top Row\r\n  for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n   cv=c(1,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n   end\r\n  end % j Top row\r\n  %Bottom Row\r\n  for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n   cv=c(nrc,j:j+2);\r\n   %insert code\r\n  end % j Bot row\r\n  \r\n  %Left Col edge\r\n  for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n   cv=c(i:i+2,1);\r\n   %insert code\r\n  end % j L col\r\n  %Right Col edge\r\n  for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n   cv=c(i:i+2,ncc);\r\n   %insert code\r\n  end % \r\n  \r\n  %Hor segs not on an edge\r\n  for i=2:nrc-1\r\n   for j=1:ncc-2\r\n    cv=c(i,j:j+2);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n    %insert code\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  %Ver segs not on an edge\r\n  for i=1:nrc-2\r\n   for j=2:ncc-1\r\n    cv=c(i:i+2,j);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n    %insert code\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  \r\n  % RR corner blocks to corner\r\n  %[rr;xr]  [rr;rx]  [xr;rr]  [rx;rr]\r\n  %RR;xR or RR;Rx\r\n  for i=1:nrc-1\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab top pair\r\n    if p(cv(1),cv(2))==5 % Top Red\r\n     %insert code\r\n    end % Top RR\r\n   end %j\r\n  end %i\r\n  \r\n  for i=2:nrc % Rx;RR  xR;RR\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab lower pair\r\n    if p(cv(1),cv(2))==5 % Bot Red\r\n     %insert code\r\n    end %Bot RR\r\n   end %j\r\n  end %i\r\n  \r\n  % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n  i=1; % Top\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    %insert code\r\n   end\r\n  end % j\r\n  \r\n  i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    %insert code\r\n   end\r\n  end % j\r\n  \r\n  j=ncc; % Right\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    %insert code\r\n   end\r\n  end % i\r\n  \r\n  j=1; % Left\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    %insert code\r\n   end\r\n  end % i\r\n  \r\n  if ~isequal(p,pb),continue;end\r\n  \r\n  % R node with one option extends R \r\n%   [pr5,pc5]=find(p==5);\r\n%   for i=1:length(pr5)\r\n%    if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n%     new_node=find(p(pr5(i),:)==1);\r\n%     p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n%    end\r\n%   end\r\n  \r\n  [pr5,pc5]=find(p==5);\r\n  pr5=unique(pr5); % could sort then remove dupes which are mids\r\n  while ~isempty(pr5) %Extend Red Bars where there is only 1 option\r\n   if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n    new_node=find(p(pr5(1),:)==1);\r\n    p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n    pr5(1)=new_node;\r\n   else\r\n    pr5(1)=[];\r\n   end\r\n  end\r\n  \r\n  %check if red seg closes a loop of less than X thus seg must be black\r\n  if isequal(p,pb) % check for bad R bars only if no prior evolves have updated p\r\n   % insert code\r\n  end % isequal p pb  after cells, ends make no change\r\n  \r\n end % while p~=pb\r\n \r\n % Valid checks\r\n   for sptr=1:nr*nc %invalid set/clear segment count\r\n    vsptr=bsegs(sptr,:);\r\n    psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n    if s(sptr)==5\r\n     if nnz(psegs==5)==4\r\n      evalid=0;\r\n      return\r\n     else\r\n      continue\r\n     end\r\n    end % s 5\r\n    \r\n    if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    ps=sum(p); %\r\n    if nnz(ps==5) % Node with no escape\r\n     evalid=0;\r\n     return\r\n    end\r\n    if nnz(ps\u003e14) % Node with too many segments\r\n     evalid=0;\r\n     return\r\n    end\r\n   end % sptr\r\n   \r\n   %check for any loops created\r\n   %show_pfig(s,p,c,emap,pmap,3)\r\n   ps=sum(p);\r\n   pidx=find(ps==10);\r\n   pchecked=[];\r\n   for i=pidx\r\n    if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n    vn=find(p(i,:)==5); % Guaranteed 2 points\r\n    if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n     pchecked=[pchecked i];\r\n     continue;\r\n    end\r\n    v=[i find(p(i,:)==5,1,'first')];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end % No loop\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     if v(1)==v(end),break;end % Loop created\r\n    end % while extending\r\n    pchecked=[pchecked v];\r\n    \r\n    if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n    if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n     if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop   [1 2 4 3 1] loop\r\n      evalid=0;\r\n      return\r\n     else % Possible solve\r\n      [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n      if valid\r\n       evalid=1;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       evalid=0;\r\n       return\r\n      end\r\n     end % Lv-1 compare to total current segments\r\n    end %  v 1 end)\r\n   end % pidx\r\n   \r\n   evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  bidx=idx1(i);\r\n  if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n  end\r\n  \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n  end\r\n  \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n  \r\n  if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  end % if TL/TR/BL/BR\r\n  \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % Top edge\r\n   if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   \r\n  elseif nr3(i)==nr % Bot Edge\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   \r\n  elseif nc3(i)==1 %Left Edge\r\n   if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   \r\n  elseif nc3(i)==nc % Rt edge\r\n   if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   end\r\n   \r\n   \r\n  else %non-edge 3\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n     \r\n   elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   end\r\n  end % Edges/Mid 3\r\n    \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1\r\n   if nc3(i)==1 % TL  only one R or D possible\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % TR only one L or D possible. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Top Row  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  \r\n  if nr3(i)\u003cnr  % Mid section 33\r\n   if nc3(i)==1 % check only one R and D p\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % check only D. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Mid Row (not col 1 or nc)  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  if nr3(i)==nr  % Bot row 33\r\n    if nc3(i)==nc,continue;end % No process BR corner\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    continue\r\n  end\r\n \r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx+1-nr)==0 %BL\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %BR\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==1\r\n  \r\n  if nr3(i)==nr % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %TL\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %TR\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==nr\r\n  \r\n  if nc3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==1\r\n  \r\n  if nc3(i)==nc % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==nc\r\n  \r\n  %mid : check 4 courners\r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr  down diag, RB set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003c=nc-2 % Not near right edge\r\n     vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n  \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    \r\n    vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr  down diag, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not top edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003cnc % Not Right edge\r\n     vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e=3 % Not near Left edge\r\n     vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['252';\r\n   '151';\r\n   '212']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n    '1551';\r\n    '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n   '1521';\r\n   '0532';\r\n   '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['33353';\r\n   '15551';\r\n   '25055';\r\n   '55253';\r\n   '13511']-'0';% evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5]; % Trivial 33\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T19:13:03.000Z","updated_at":"2026-04-29T19:28:00.000Z","published_at":"2020-11-12T23:28:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink III: Evolve\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques but requires additional Evolving that is always valid for a valid input. Evolve examples are a Red bar into a corner must continue that Red bar out of the corner, an s=1 cell with a Red bar must have Black bars on its other 3 edges.  Cases of Trivial and Gimmes should be solved prior to invoking Evolve. The Evolve subroutine is the most critical routine and must be very comprehensive. A general Evolve routine should check if the output State is valid. When Evolve is used within a recursive routine that asserts possibly incorrect content the Evolve may produce an invalid output for the invalid input.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check 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=(tan(2.*x.^2+7.*x-30.25)+log(x.^3-2.25))./(nthroot(sin(x.^3).^2+1/5*log(x.^4-2.5),3));\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":3,"created_by":90467,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":91,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-10-11T09:42:37.000Z","updated_at":"2026-04-07T19:08:21.000Z","published_at":"2016-10-11T09:42:37.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolve expression for given vector x. Expression = (tan(2*x^2+7*x-30.25)+log(x^3-2.25))/(nthroot(sin(x^3)^2+1/5*log(x^4-2.5),3))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":49850,"title":"Simple Circuit of Resistors","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 345px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 172.5px; transform-origin: 407px 172.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe configuration of a group of resistors is described in a matrix with two rows. 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data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe circuit_def (i.e., the matrix defining the problem) for this configuration is [1 3 2; R1 R2 R3]. Find the resultant resistance for any given configuration.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = tot_res(circuit_def)\r\n  y = circuit_def(1,:)./circuit_def(2,:);\r\nend","test_suite":"%%\r\ncircuit_def=[3 3 3 3;1 2 3 4];\r\ntot_corr=3.3333;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n%%\r\ncircuit_def=[1 2 3 4;1 2 3 4];\r\ntot_corr=4;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n%%\r\ncircuit_def=[11 12 3 7;4 5 3 8];\r\ntot_corr=2.92316;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n%%\r\ncircuit_def=[3 3 4 4 6 7 8;3 1 1 5 9 10 11];\r\ntot_corr=7.136905;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n%%\r\ncircuit_def=[2 5 3;11 2 20];\r\ntot_corr=12.56667;\r\nassert(abs(tot_res(circuit_def)-tot_corr)\u003c1e-3)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":"2021-01-17T21:52:11.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-17T21:37:24.000Z","updated_at":"2026-02-26T11:54:37.000Z","published_at":"2021-01-17T21:52:11.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe configuration of a group of resistors is described in a matrix with two rows. The first row provides the information regarding the number of resistors at each junction and the second row provides the resistance of each detector at each junction. Consider the following configuration:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe circuit_def (i.e., the matrix defining the problem) for this configuration is [1 3 2; R1 R2 R3]. Find the resultant resistance for any given 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of nine?","description":"Given a positive number n, return true if n is a multiple of 9 and false if not. Do not make the division and do not use functions like mod or rem.","description_html":"\u003cp\u003eGiven a positive number n, return true if n is a multiple of 9 and false if not. Do not make the division and do not use functions like mod or rem.\u003c/p\u003e","function_template":"function [ y ] = multipleof9( x )\r\n  y = false;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = false;\r\nassert(isequal(multipleof9(x),y_correct))\r\n\r\n%%\r\nx = 9;\r\ny_correct = true;\r\nassert(isequal(multipleof9(x),y_correct))\r\n\r\n%%\r\nx = 10;\r\ny_correct = false;\r\nassert(isequal(multipleof9(x),y_correct))\r\n\r\n%%\r\nx = 111222;\r\ny_correct = true;\r\nassert(isequal(multipleof9(x),y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":61785,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":112,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-01-04T12:46:33.000Z","updated_at":"2026-02-18T14:18:36.000Z","published_at":"2016-01-04T12:46:33.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a positive number n, return true if n is a multiple of 9 and false if not. Do not make the division and do not use functions like mod or rem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60809,"title":"Inductor Energy Storage Calculation","description":"The energy (EEE) stored in an inductor is given by the formula:\r\n\r\nWhere:\r\nE is the energy in joules (J)\r\nL is the inductance in henrys (H)\r\nI is the current in amperes (A)\r\nWrite a function that takes inductance LLL and current III as inputs and returns the stored energy EEE.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 228.181px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 114.083px; transform-origin: 407px 114.09px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe energy (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) stored in an inductor is given by the formula:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 55.8889px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 27.9444px; text-align: left; transform-origin: 384px 27.9444px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.6389px; transform-origin: 391px 30.6458px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the energy in joules (J)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the inductance in henrys (H)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the current in amperes (A)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes inductance \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and current \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as inputs and returns the stored energy \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function E = inductor_energy(L, I)\r\n% inductor_energy calculates the energy stored in an inductor.\r\n% Inputs: L (Inductance in Henrys), I (Current in Amperes)\r\n% Output: E (Energy in Joules)\r\n\r\n% Your code here\r\n\r\nend\r\n","test_suite":"%% Basic Test Case\r\nassert(isequal(inductor_energy(2, 3), 9))\r\n\r\n%% Edge Case: Zero Inductance\r\nassert(isequal(inductor_energy(0, 5), 0))\r\n\r\n%% Edge Case: Zero Current\r\nassert(isequal(inductor_energy(5, 0), 0))\r\n\r\n%% Larger Values\r\nassert(isequal(inductor_energy(10, 4), 80))\r\n\r\n%% Fractional Inputs\r\nassert(isequal(inductor_energy(0.5, 2.5), 1.5625))\r\n","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":383919,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":408,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-02-17T16:08:16.000Z","updated_at":"2026-05-17T20:44:47.000Z","published_at":"2025-02-17T16:08:16.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe energy (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) stored in an inductor is given by the formula:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"50\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"113\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhere:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the energy in joules (J)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the inductance in henrys (H)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the current in amperes (A)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that takes inductance \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and current \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as inputs and returns the stored energy \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eE\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60810,"title":"LED Current Calculation","description":"In an electrical circuit, the current (III) flowing through an LED is determined using Ohm’s Law:\r\n​​\r\nWhere:\r\nI is the current in amperes (A)\r\nV is the supply voltage in volts (V)\r\nVf​ is the forward voltage of the LED in volts (V)\r\nR is the series resistor in ohms (Ω)\r\nWrite a function that takes supply voltage V, LED forward voltage Vf​, and resistor R as inputs and returns the current III.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 275.611px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 137.806px; transform-origin: 407px 137.806px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn an electrical circuit, the current (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) flowing through an LED is determined using Ohm’s Law:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 82.8889px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 41.4444px; text-align: left; transform-origin: 384px 41.4444px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e​\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e​\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWhere:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 81.7222px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 40.8611px; transform-origin: 391px 40.8611px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the current in amperes (A)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the supply voltage in volts (V)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ef​\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the forward voltage of the LED in volts (V)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4306px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2083px; text-align: left; transform-origin: 363px 10.2153px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eR\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the series resistor in ohms (Ω)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes supply voltage \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, LED forward voltage \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ef​\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and resistor \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eR\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as inputs and returns the current \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function I = led_current(V, V_f, R)\r\n% led_current calculates the current flowing through an LED.\r\n% Inputs: V (Supply Voltage in Volts), V_f (LED Forward Voltage in Volts), R (Resistance in Ohms)\r\n% Output: I (Current in Amperes)\r\n\r\n% Your code here\r\n\r\nend\r\n","test_suite":"%% Basic Test Case\r\nassert(isequal(led_current(9, 2, 1000), 0.007))\r\n\r\n%% Edge Case: Zero Supply Voltage\r\nassert(isequal(led_current(0, 2, 500), -0.004)) % Negative means insufficient voltage\r\n\r\n%% Edge Case: Zero Resistance (Short Circuit)\r\nassert(isequal(led_current(5, 2, 0), Inf)) % Infinite current (theoretically)\r\n\r\n%% Larger Values\r\nassert(isequal(led_current(12, 3, 1500), 0.006))\r\n\r\n%% Fractional Inputs\r\nassert(isequal(led_current(5.5, 2.2, 330), 0.01))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":383919,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":406,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-02-17T16:26:01.000Z","updated_at":"2026-05-17T20:46:47.000Z","published_at":"2025-02-17T16:26:01.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn an electrical circuit, the current (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) flowing through an LED is determined using Ohm’s Law:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"77\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"137\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e​\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e​\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhere:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the current in amperes (A)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the supply voltage in volts (V)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ef​\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the forward voltage of the LED in volts (V)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the series resistor in ohms (Ω)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that takes supply voltage \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, LED forward voltage \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ef​\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and resistor \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as inputs and returns the current 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co-occurrence matrix","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1287.17px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 643.578px; transform-origin: 407px 643.586px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eConsider following transaction dataset;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 391px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 195.5px; text-align: left; transform-origin: 384px 195.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"911\" height=\"385\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWe can transform this dataset to a binary format and then create co-occurence matrix as follow;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 695.172px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 347.578px; text-align: left; transform-origin: 384px 347.586px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCo-occurence matrix shows how many times product pairs are bought together in market baskets (transactions). For example apple and tea bought together in two market baskets, milk and lemon never bought together and so on. It is a  symmetric matrix.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = cooccurrence(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [1\t1\t1\t0\t0\t0\t0\r\n0\t1\t1\t1\t0\t0\t0\r\n0\t1\t1\t1\t1\t0\t0\r\n0\t1\t0\t0\t1\t1\t1\r\n1\t1\t0\t1\t1\t1\t0];\r\ny_correct = [2\t2\t1\t1\t1\t1\t0\r\n2\t5\t3\t3\t3\t2\t1\r\n1\t3\t3\t2\t1\t0\t0\r\n1\t3\t2\t3\t2\t1\t0\r\n1\t3\t1\t2\t3\t2\t1\r\n1\t2\t0\t1\t2\t2\t1\r\n0\t1\t0\t0\t1\t1\t1];\r\nassert(isequal(cooccurrence(x),y_correct))\r\n\r\n%%\r\nx = [1\t1\t1\t0\t0\t0\t0\t0\r\n0\t0\t1\t1\t1\t1\t0\t0\r\n1\t0\t1\t1\t0\t1\t1\t0\r\n0\t1\t1\t1\t0\t1\t0\t0\r\n1\t0\t0\t1\t0\t1\t0\t1];\r\ny_correct = [3\t1\t2\t2\t0\t2\t1\t1\r\n1\t2\t2\t1\t0\t1\t0\t0\r\n2\t2\t4\t3\t1\t3\t1\t0\r\n2\t1\t3\t4\t1\t4\t1\t1\r\n0\t0\t1\t1\t1\t1\t0\t0\r\n2\t1\t3\t4\t1\t4\t1\t1\r\n1\t0\t1\t1\t0\t1\t1\t0\r\n1\t0\t0\t1\t0\t1\t0\t1];\r\nassert(isequal(cooccurrence(x),y_correct))\r\n\r\n%%\r\nx = randi([0 1],150,9);\r\ny = cooccurrence(x);\r\nassert(isequal(y(logical(eye(size(y)))), sum(x)'))\r\nassert(issymmetric(y))\r\n\r\n\r\n%%\r\nx = [1 0 1 0 0\r\n    0 0 1 0 0\r\n    1 1 1 1 1\r\n    0 0 0 0 0\r\n    1 0 0 1 1\r\n    0 1 1 0 1\r\n    0 0 1 1 0];\r\ny_correct = [3 1 2 2 2\r\n    1 2 2 1 2\r\n    2 2 5 2 2 \r\n    2 1 2 3 2\r\n    2 2 2 2 3];\r\nassert(isequal(cooccurrence(x),y_correct))\r\n\r\n%%\r\nx = [0 1 1 0\r\n    0 1 0 1\r\n    0 0 1 1\r\n    1 1 0 0 \r\n    1 0 1 0\r\n    0 1 0 1\r\n    0 1 0 1\r\n    0 1 0 1];\r\ny_correct = [2 1 1 0\r\n    1 6 1 4\r\n    1 1 3 1\r\n    0 4 1 5];\r\nassert(isequal(cooccurrence(x),y_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":"2020-12-10T08:46:02.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-12-10T07:43:57.000Z","updated_at":"2025-09-17T22:29:41.000Z","published_at":"2020-12-10T08:42:18.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider following transaction dataset;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"385\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"911\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe can transform this dataset to a binary format and then create co-occurence matrix as follow;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCo-occurence matrix shows how many times product pairs are bought together in market baskets (transactions). For example apple and tea bought together in two market baskets, milk and lemon never bought together and so on. It is a  symmetric matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc 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\",\"relationship\":null},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44132,"title":"Find my secret function III","description":"only write a function gives you an outputs as\r\nexpls:\r\n\r\ninput: x=2 -------\u003e\u003e\u003e Output: Y=1\r\n\r\ninput: x=100 -------\u003e\u003e\u003e Output: Y=[ 1 2 4 6 10 12 16 18 22 28 30 36 40 42 46     52 58 60 66 70 72 78 82 88 96]\r\n\r\nHint: here we use the same function used in the series of \"Find my secret function\".","description_html":"\u003cp\u003eonly write a function gives you an outputs as\r\nexpls:\u003c/p\u003e\u003cp\u003einput: x=2 -------\u0026gt;\u0026gt;\u0026gt; Output: Y=1\u003c/p\u003e\u003cp\u003einput: x=100 -------\u0026gt;\u0026gt;\u0026gt; Output: Y=[ 1 2 4 6 10 12 16 18 22 28 30 36 40 42 46     52 58 60 66 70 72 78 82 88 96]\u003c/p\u003e\u003cp\u003eHint: here we use the same function used in the series of \"Find my secret function\".\u003c/p\u003e","function_template":"function y = your_fcn_name(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 2;\r\ny_correct = 1;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n%%\r\nx = 23;\r\ny_correct =[1 2 4 6 10 12 16 18 22];\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n%%\r\nx = 100;\r\ny_correct =[1 2 4 6 10 12 16 18 22 28 30 36 40 42 46 52 58 60 66 70 72 78 82 88 96];\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n%%\r\nx = 1;\r\ny_correct =[];\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":37163,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":"2017-04-26T16:02:09.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-04-25T14:50:07.000Z","updated_at":"2025-04-25T12:27:08.000Z","published_at":"2017-04-25T14:50:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eonly write a function gives you an outputs as expls:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003einput: x=2 -------\u0026gt;\u0026gt;\u0026gt; Output: Y=1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003einput: x=100 -------\u0026gt;\u0026gt;\u0026gt; Output: Y=[ 1 2 4 6 10 12 16 18 22 28 30 36 40 42 46 52 58 60 66 70 72 78 82 88 96]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint: here we use the same function used in the series of \\\"Find my secret function\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42643,"title":"MATLAB Basic: rounding III","description":"Do rounding towards minus infinity.\r\n\r\nExample: -8.8, answer -9\r\n\r\n+8.1 answer 8\r\n\r\n+8.50 answer 8","description_html":"\u003cp\u003eDo rounding towards minus infinity.\u003c/p\u003e\u003cp\u003eExample: -8.8, answer -9\u003c/p\u003e\u003cp\u003e+8.1 answer 8\u003c/p\u003e\u003cp\u003e+8.50 answer 8\u003c/p\u003e","function_template":"function y = round_x(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = -8.8;\r\ny_correct = -9;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx = -8.4;\r\ny_correct = -9;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx =  8.8;\r\ny_correct =  8;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx =  8.4;\r\ny_correct =  8;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx =  8.49;\r\ny_correct =  8;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n%%\r\nx =  128.52;\r\ny_correct =  128;\r\nassert(isequal(round_x(x),y_correct))\r\n\r\n\r\n%%\r\nx =  pi;\r\ny_correct =  3;\r\nassert(isequal(round_x(x),y_correct))","published":true,"deleted":false,"likes_count":18,"comments_count":2,"created_by":27760,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6259,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-10-01T05:28:46.000Z","updated_at":"2026-05-22T10:59:01.000Z","published_at":"2015-10-01T05:28:46.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDo rounding towards minus infinity.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample: -8.8, answer -9\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e+8.1 answer 8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e+8.50 answer 8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":223,"title":"Which quadrant?","description":"Given a complex number, output quadrant 'I' 'II' 'III' or 'IV'\r\n\r\n        |              \r\n   II   |   I          \r\n        |              \r\n -------+------- real\r\n        |              \r\n   III  |   IV          \r\n        |\r\n      imag\r\n              \r\n","description_html":"\u003cp\u003eGiven a complex number, output quadrant 'I' 'II' 'III' or 'IV'\u003c/p\u003e\u003cpre\u003e        |              \r\n   II   |   I          \r\n        |              \r\n -------+------- real\r\n        |              \r\n   III  |   IV          \r\n        |\r\n      imag\u003c/pre\u003e","function_template":"function y = your_fcn_name(x)\r\n y='IV';\r\nend\r\nend","test_suite":"%%\r\nx = 1+1i;\r\ny_correct = 'I';\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = -1-1i;\r\ny_correct = 'III';\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 1-1i;\r\ny_correct = 'IV';\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = -1+1i;\r\ny_correct = 'II';\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":166,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":369,"test_suite_updated_at":"2012-02-02T03:13:00.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-02-02T03:13:00.000Z","updated_at":"2026-05-13T14:28:28.000Z","published_at":"2012-02-02T03:13:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a complex number, output quadrant 'I' 'II' 'III' or 'IV'\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[        |              \\n   II   |   I          \\n        |              \\n -------+------- real\\n        |              \\n   III  |   IV          \\n        |\\n      imag]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":54350,"title":"String Manipulator","description":"Write a script that takes a string as an input and returns a cell array containing  –\r\nI. the count of vowels.\r\nII. Find the index of ‘o’\r\nIII. The string removing all the vowels.\r\nIV. The string removing all the letters from a to j.\r\nV. The string removing all the consonants.\r\nVI. The string replacing all the vowels with an asterisk (*)\r\nVII. The string removing all the digits.\r\nHint: Use Regular Expression\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 291px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.5px 145.5px; transform-origin: 406.5px 145.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eWrite a script that takes a string as an input and returns a cell array containing\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e  –\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eI. the count of vowels.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eII. Find the index of ‘o’\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIII. The string removing all the vowels.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIV. The string removing all the letters from a to j.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eV. The string removing all the consonants.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eVI. The string replacing all the vowels with an asterisk (*)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eVII. The string removing all the digits.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; text-decoration: underline; text-decoration-line: underline; \"\u003eHint: Use Regular Expression\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.5px; text-align: left; transform-origin: 383.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function k=strman(x)\r\n  k = regexprep(x);\r\nend","test_suite":"%%\r\nx = 'david attenborough';\r\ny_correct = {[7] [13 15] 'dvd ttnbrgh' 'v ttnorou' 'ai aeoou' 'd*v*d *tt*nb*r**gh' 'david attenborough'};\r\nassert(isequal(strman(x),y_correct))\r\n%%\r\nx = 'ilove bayernmunichhhn';\r\ny_correct = {[7] [3] 'lv byrnmnchhhn' 'lov yrnmunn' 'ioe aeui' '*l*v* b*y*rnm*n*chhhn' 'ilove bayernmunichhhn'};\r\nassert(isequal(strman(x),y_correct))\r\n%%\r\nx = 'n';\r\ny_correct = {[0] [] 'n' 'n' '' 'n' 'n'};\r\nassert(isequal(strman(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":5,"created_by":1274403,"edited_by":1274403,"edited_at":"2022-04-26T04:59:21.000Z","deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":"2022-04-26T04:59:22.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-04-23T20:36:19.000Z","updated_at":"2026-04-07T08:26:15.000Z","published_at":"2022-04-23T20:37:11.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eWrite a script that takes a string as an input and returns a cell array containing\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e  –\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI. the count of vowels.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eII. Find the index of ‘o’\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIII. The string removing all the vowels.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIV. The string removing all the letters from a to j.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eV. The string removing all the consonants.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVI. The string replacing all the vowels with an asterisk (*)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVII. The string removing all the digits.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHint: Use Regular Expression\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":46783,"title":"PIN code III","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 40.7273px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 20.3636px; transform-origin: 407px 20.3636px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.3636px; text-align: left; transform-origin: 384px 20.3636px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA cell phones requires a PIN code to open which only includes numbers; how long (in terms of days) does it take to enter all possible inputs with X input digits. (entering each digit takes 1 sec- Round up the output)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = your_fcn_name(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 4;\r\ny_correct = 1;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 5;\r\ny_correct = 6;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 6;\r\ny_correct = 70;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":430136,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":48,"test_suite_updated_at":"2020-10-16T19:08:35.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-16T19:06:20.000Z","updated_at":"2026-03-16T11:52:29.000Z","published_at":"2020-10-16T19:08:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA cell phones requires a PIN code to open which only includes numbers; how long (in terms of days) does it take to enter all possible inputs with X input digits. (entering each digit takes 1 sec- Round up the output)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":46973,"title":"How many bytes an audio requires from RAM?","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 670.4px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 335.2px; transform-origin: 407px 335.2px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 62.4px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.2px; text-align: left; transform-origin: 384px 31.2px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eAudio files, as many other files are stored compressed in our computers: MP3, OGG, or M4A, are all different types of compressed audio files. Audio must be first decompressed to its total size before it can be played: hearing compressed audio is utterly incomprehensible to humans. Your task is to compute its full length.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 104px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52px; text-align: left; transform-origin: 384px 52px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWe may calculate an audio size in bits with the following formula: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"97.5\" height=\"17.5\" style=\"width: 97.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, in which \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003et\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is its duration in minutes/seconds, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003es\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is its sample rate in hertz (Hz), \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is its number of channels, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eδ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is its bit depth, the number of bits used to store audio intensity. Usually, an audio file's duration is easily obtained from any software, but the other parameters are not always available. Sometimes the bitrate is presented instead, which is defined as \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJ0AAAAjCAYAAACO5gjTAAAHj0lEQVR4Xu2bBahtRRSGv2e3qJiY2IiFHYhgd3d3dyB2FwYmJnZ3gF3Yhd2CiorYjYHFB2se+56399n7xDtPrzNw4Z579p5Y86+1/vXP3BHkli0wYAuMGPB4ebhsATLoMggGboEMuoGbPA+YQZcxMHALZNAN3OR5wKag87nJgF+A37PZsgV6sUA70K0BrA7MDywE/ASsBbzYy4D53WFnATE0G7AdIGYWBj4CngUuAB4D/iquui7STQCcBuwB3AtsBXw17MyWF9StBcTPJsBqwGXAW8DiwHHAgsCPwHrAQ52AbiLgLGBH4CTgCODPbmeY3xt2FpgP2Bs4GPi+sLqVgZvjc8egmwW4FlgaWAe4c9iZLS+oFwtsAWwA7AB8W+jICDgtMC7waafpdSXgfuCVCKPv9DLD/O6ws8AKwG3AecCJkU5rF9mO0/ndYZGfrwpeZ47OLVsgWWBi4FRgd+DuoF8v15mnHegmBy4BNgT2B86s6+x/+v1GwOnAAcBN/2IbzAFcDjwPHBryVz+maxo9B9AO3wBHBm5+q+q8HegWCDI4XVQnLwArAnsCy8QAtwYYP+7H7AfQx9TAXoBp4VFAx/oMGC/WY9HUabO4OjaMbdXWjzZWyFQ6/MzA+/H5c+B4oBt7u2dPAPcBcrGv+zHR6GPKcLxt4/P5EfUE4SitHeic2NWxOTtHep0GuAaYC9gHsNAwp+8CfNlgEXMDN0Q53eDxykf0pk43eArgQmBZYF3gOcDN3Sn0pCaFUplIrmRwPbBprM1JTxhA/gH4u8OFzhTAmh3YN3RR+0j82miqvYvEvWwI5zB26Kt+b6RznupnRuVfgXGASYOL/dHhPH1cJ7aI0DkeiYMDCwv3WeBZ1f7c2m8V6PT8k4H9giTK5fS2KwAn53u7Rsf2aYn8QINJj0nQLQLcFTrjxsDbMd8ZwrlMOc/UrCER56cj4r8HmBEuAnTMV4F5Q7MyQwjuWo5TGNM5Ssptiq3qXjarwBOAgwKEAtz9qGrTA1cCcwY1MjBMEn0/Gc5X5GNG/3Mb7F96RGddP5zjzcCJgrDNv99SpdH5QBXo3Agj2vJRuSqbnBGASwOncO3nLeP5DuY98EcT6NwQASJfNYIYEY4GLq7ZSCfsmi8NT3bzjDhfROUmaE0zfu9zRhTFdIHZpCWwmk6158MtLzn/rYF7IkW2i6BGIHmWUdgUJ6jMMFIB+/VUSRCbrQwo2sMo2KQZHQ04Vqv2pQ1M+6kVZbZjAH+GzLUKdMmjDb1V4Xy5OOJwsKaRrsmiRtczplcBoVgpIIqRpJMxjRCLhUcLBPmcdONd4MCI+Crwr3VwTi3wrQLly2dHWqok4g0nKzic15qxZgOHKVkOK6hNe+quFhZ1qbo4pFHWyO6xaJlzTBUBaJWIqGZLz+xHtjLQFaWS0mOMeFuEe7b2X9Lw5G3KPzrTHSVe2nA/Rz4mWIw+Ak/+pYN2w410YCONKXAUBb/TSZU8bzpcKqKfZ6FGn++66HfWoCJG8irnMMoKcItOFY9DWp2vDHRFqcQSW+9rJYNumtzD9NEvz+zCBh2/ovdrhFSEyGMku0M8saZXbWYa1fDbRKTwQFvPl7PKn4weoxDoin6LTv4UsHkcmHe8uJIXdAovbBidvCVkgLC4sZiS+JseTY1Nix2LBmlJu+xWpDGlUlsZ6JJUIgm1ElEWaW0p/coXrFyUU5q0MVlIpPmpK1nFGvXaRfKy9fiumpyVvWv3IsSDQagPB4xYcl9BqbcrrtdJE0UH7pcIr3N5Jur4zkUpyN8FgUCTh+pw0qLH4+91e+jlD9euENzOOVIGNI0nlWCILctAl6QSec9mwAct1k/Sg2KgUcOJNE0pYwJ0S8bGFwl9SmcWFZ1cZEjFk5WaG2hUc01yHDOCPE6bSTuaXgWr5UBNvLnlmWKfEn5/jGaCT3DpdKlwWaKh+F/ss8o5lNQsyNYOIbosS45SvRalkrKO9SA1HuWUSh2mCyONzles2ASJEkJqxejiOpJuVTeP8SNdmZI+iY3UwPLDpPPpyGps3tB5vfWwu2SAIp1pF+nU74yuTdJ2moNFzxsRFFJVaTBJPEsgmdF0lrp+DTbOzztzgra1QHBMA5bPaO/KDNga6YpSSWu567PJizs64K3bydH4fUoJAqDodXIdSa6pwGtbVrXdtl5PJBRwjwo5Q13ODCJQUtPuq4Y47Fh14KhaR68nEsV5em1JuxWvM6XIKYilHUo0pVyxFXTeEL49ThqKoNNjdgM85lDf6bZK63Zju30vRTS5haTftWkIDXQd8GEfKlhJumlLzaup1tW6nmKqc17yQ6OF0Um7+y8CZpdeLlwIBqOQRU6nxVOab1FLtGpXGtKeztNANGNkDXljZXHSCjo/W0hsD8wT1Y4bZ2Tw5rCKfree1i1wennP8v2UuAGhwbxWrSa1KHBjpNx+rEda0uv/jphltLvpSx76UpD8dHQ15Mp3l0aRHtlPL305TyOZxaSnLeqJ2lnNrxE+6q6rd7m2/Fq2QLUFMugyOgZugQy6gZs8D5hBlzEwcAtk0A3c5HnADLqMgYFbIINu4CbPA2bQZQwM3AL/ALG9wjMB4nimAAAAAElFTkSuQmCC\" width=\"78.5\" height=\"17.5\" style=\"width: 78.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and is given as kbps \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e(kilobits per second)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. Bit rate is used as a measure of quality for digital streamming:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 100px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 50px; transform-origin: 404px 50px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eDigital \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eWalkie-talkies have a bitrate of 16kbps.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eOn-line AM \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eradio has a bitrate of 32kbps.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eOn-line FM \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eradio has a bitrate of 96kbps.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eCD \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eaudio has a bitrate of 1\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e,411 kbps.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eHD \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003eaudio may have a bitrate up to 9\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e,216 kbps. (over 24 bit-depth, and 44 kHz sample rate)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 104px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 52px; text-align: left; transform-origin: 384px 52px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eCompute how many bytes an image requires from RAM, given its duration (d) and bitrate (b) or sample rate (s), number of channels (c) and  bit depth \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eδ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eMoreover, make the results more human-readable by showing them with units: bytes (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"15.5\" height=\"18.5\" style=\"width: 15.5px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e), KB (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"20.5\" height=\"18.5\" style=\"width: 20.5px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e), MB (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"20.5\" height=\"18.5\" style=\"width: 20.5px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e), GB (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"20.5\" height=\"18.5\" style=\"width: 20.5px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e) rounded to 2 decimal places. There must be at least a non-zero integer value to display it in some units, you should always use the greatest unit possible, and data cannot be stored in less than 1 byte (8 bits).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.8px; text-align: left; transform-origin: 384px 20.8px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote-1\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e: The range of \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Hearing_range\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehuman hearing\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is 20 to 20,000 Hz, and unsurprisingly, audio sampling rate is usally greater than 40,000 Hz (\u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Nyquist_rate\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eNyquist rate\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 62.4px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.2px; text-align: left; transform-origin: 384px 31.2px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote-2\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e: Audio files do not need to be necessarily wholly loaded into the RAM; they only require that a certain amount of bytes per second be always available in it (their bitrate). However, loading full audio files into RAM for playing is the best option to guarantee a smooth sound (without lag). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; font-weight: 700; \"\u003eNext:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/46913-how-many-bytes-an-image-requires-from-ram\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eHow many bytes an image requires from RAM? (Problem 46913)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eHow many bytes a video requires from RAM?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = audio_size(t,s,c,d,b)\r\n  y = t/s/c/d; % or t/b?\r\nend","test_suite":"%% anti-hacking\r\n\r\nfiletext = fileread('audio_size.m');\r\nassert(~contains(filetext, 'eval'),       'eval is forbidden.');\r\nassert(~contains(filetext, 'fopen'),      'fopen is forbidden.');\r\nassert(~contains(filetext, 'regexp'),     'regexp is forbidden.');\r\nassert(~contains(filetext, '!'),          'Shell commands are forbidden.');\r\nassert(~contains(filetext, 'mlock'),      'mlock is forbidden.');\r\nassert(~contains(filetext, 'munlock'),    'munlock is forbidden.');\r\n\r\n%%case 1. The Song of the White Wolf - Sonya Belousova - The Witcher (55)\r\nt = '3m45s'; \r\ns = 44100;\r\nc = 5;\r\nd = 32;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '189.26 MB'));\r\n\r\n%%case 2. Overture - Gerard Marino - God of War III Original V(001)\r\nt = '3m36s';\r\nb = 9216;\r\nassert(strcmp(audio_size(t,[],[],[],b), '237.30 MB'));\r\n\r\n%%case 3. Rip \u0026 Tear - Mick Gordon - Doom (Original Game Soundtra(002)\r\nt = '4m17s'; \r\ns = 384000;\r\nc = 7;\r\nd = 24;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '1.93 GB'));\r\n\r\n%%case 4. Live Free or Die - Volturnus - Audiomachine\r\nt = '3m13s';\r\nb = 1411;\r\nassert(strcmp(audio_size(t,[],[],[],b), '32.46 MB'));\r\n\r\n%%case 5. Bury the Light - Vergil's battle theme from Devil May Cry 5 Special Edition\r\nt = '9m42s';\r\nb = 132;\r\nassert(strcmp(audio_size(t,[],[],[],b), '9.16 MB'));\r\n\r\n%%case 6. Rob The Frontier - UVERworld - Nanatsu no Taizai Season 3 Opening V2\r\nt = '1m30s'; \r\ns = 22050;\r\nc = 2;\r\nd = 16;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '7.57 MB'));\r\n\r\n%%case 7. Ich Will - Rammstein\r\nt = '3m37s'; \r\ns = 384000;\r\nc = 2;\r\nd = 16;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '317.87 MB'));\r\n\r\n%%case 8. Ratamahatta - Sepultura \r\nt = '4m30s'; \r\ns = 92000;\r\nc = 2;\r\nd = 24;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '142.14 MB'));\r\n\r\n%%case 9. Dark Crow - Man with a Mission\r\nt = '4m38s';\r\nb = 9216;\r\nassert(strcmp(audio_size(t,[],[],[],b), '305.42 MB'));\r\n\r\n%%case 10. On Our Own - Bobby Brown\r\nt = '4m54s';\r\ns = 22050;\r\nc = 1;\r\nd = 8;\r\nassert(strcmp(audio_size(t,s,c,d,[]), '6.18 MB'));\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":443343,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":"2020-10-26T22:59:29.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-20T20:01:52.000Z","updated_at":"2026-04-14T13:03:30.000Z","published_at":"2020-10-26T20:08:12.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAudio files, as many other files are stored compressed in our computers: MP3, OGG, or M4A, are all different types of compressed audio files. Audio must be first decompressed to its total size before it can be played: hearing compressed audio is utterly incomprehensible to humans. Your task is to compute its full length.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe may calculate an audio size in bits with the following formula: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey = t*s*c*\\\\delta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, in which \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003et\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is its duration in minutes/seconds, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is its sample rate in hertz (Hz), \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is its number of channels, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\delta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is its bit depth, the number of bits used to store audio intensity. Usually, an audio file's duration is easily obtained from any software, but the other parameters are not always available. Sometimes the bitrate is presented instead, which is defined as \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eb = s*c*\\\\delta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and is given as kbps \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(kilobits per second)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Bit rate is used as a measure of quality for digital streamming:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Digital Walkie-talkies have a bitrate of 16kbps.\\nOn-line AM radio has a bitrate of 32kbps.\\nOn-line FM radio has a bitrate of 96kbps.\\nCD audio has a bitrate of 1,411 kbps.\\nHD audio may have a bitrate up to 9,216 kbps. (over 24 bit-depth, and 44 kHz sample rate)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCompute how many bytes an image requires from RAM, given its duration (d) and bitrate (b) or sample rate (s), number of channels (c) and  bit depth \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\delta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eMoreover, make the results more human-readable by showing them with units: bytes (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2^{0}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e), KB (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2^{10}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e), MB (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2^{20}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e), GB (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2^{30}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) rounded to 2 decimal places. There must be at least a non-zero integer value to display it in some units, you should always use the greatest unit possible, and data cannot be stored in less than 1 byte (8 bits).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote-1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: The range of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Hearing_range\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehuman hearing\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is 20 to 20,000 Hz, and unsurprisingly, audio sampling rate is usally greater than 40,000 Hz (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Nyquist_rate\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eNyquist rate\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote-2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Audio files do not need to be necessarily wholly loaded into the RAM; they only require that a certain amount of bytes per second be always available in it (their bitrate). However, loading full audio files into RAM for playing is the best option to guarantee a smooth sound (without lag). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNext:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/46913-how-many-bytes-an-image-requires-from-ram\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eHow many bytes an image requires from RAM? (Problem 46913)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHow many bytes a video requires from RAM?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2577,"title":"Sum of series III","description":"What is the sum of the following sequence:\r\n\r\n Σ(2k-1)^3 for k=1...n\r\n\r\nfor different n?","description_html":"\u003cp\u003eWhat is the sum of the following sequence:\u003c/p\u003e\u003cpre\u003e Σ(2k-1)^3 for k=1...n\u003c/pre\u003e\u003cp\u003efor different n?\u003c/p\u003e","function_template":"function s = sumOfSeriesIII(n)\r\ns = n;\r\nend","test_suite":"%%\r\nn = 1;\r\ns_correct = 1;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 2;\r\ns_correct = 28;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 5;\r\ns_correct = 1225;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 10;\r\ns_correct = 19900;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 42;\r\ns_correct = 6221628;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 99;\r\ns_correct = 192109401;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 124;\r\ns_correct = 472827376;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))\r\n\r\n%%\r\nn = 222;\r\ns_correct = 4857776028;\r\nassert(isequal(sumOfSeriesIII(n),s_correct))","published":true,"deleted":false,"likes_count":4,"comments_count":2,"created_by":3062,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1848,"test_suite_updated_at":"2017-06-13T18:03:10.000Z","rescore_all_solutions":false,"group_id":29,"created_at":"2014-09-10T09:51:33.000Z","updated_at":"2026-05-22T14:02:20.000Z","published_at":"2014-09-10T09:52:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhat is the sum of the following sequence:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Σ(2k-1)^3 for k=1...n]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efor different n?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":43078,"title":"How many bottles can you drink?","description":"Sometimes if you buy a drink in a glass bottle you can return that bottle and get some money back.\r\n\r\nLet's assume we have \"x\" amount of money, the drink cost \"p\" per bottle, and we get \"c\" back money for each bottle.\r\n\r\nIf we have enough time, how much can we buy drinks for \"x\" money?\r\n\r\nExample\r\n\r\nWe have $10, drink costs $1, for each bottle we return we get $0.35\r\n\r\n Step I: \r\n We buy 10 drinks\r\n We get $3.50 back for bottles\r\n \r\n Step II: \r\n We buy 3 drinks and we are left with $0.50\r\n We get back $1.05 back so we have $1.55\r\n\r\n Step III:\r\n We buy 1 drink and we have $0.55\r\n We get $0.35 for bottle, so we have $0.90 and we can't buy another drink for that.\r\n\r\nSummary for $10 we were able to drink 14 bottles.","description_html":"\u003cp\u003eSometimes if you buy a drink in a glass bottle you can return that bottle and get some money back.\u003c/p\u003e\u003cp\u003eLet's assume we have \"x\" amount of money, the drink cost \"p\" per bottle, and we get \"c\" back money for each bottle.\u003c/p\u003e\u003cp\u003eIf we have enough time, how much can we buy drinks for \"x\" money?\u003c/p\u003e\u003cp\u003eExample\u003c/p\u003e\u003cp\u003eWe have $10, drink costs $1, for each bottle we return we get $0.35\u003c/p\u003e\u003cpre\u003e Step I: \r\n We buy 10 drinks\r\n We get $3.50 back for bottles\u003c/pre\u003e\u003cpre\u003e Step II: \r\n We buy 3 drinks and we are left with $0.50\r\n We get back $1.05 back so we have $1.55\u003c/pre\u003e\u003cpre\u003e Step III:\r\n We buy 1 drink and we have $0.55\r\n We get $0.35 for bottle, so we have $0.90 and we can't buy another drink for that.\u003c/pre\u003e\u003cp\u003eSummary for $10 we were able to drink 14 bottles.\u003c/p\u003e","function_template":"function y = bottle(x,p,c)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 10;\r\np=1;\r\nc=0.35;\r\ny_correct = 14;\r\nassert(isequal(bottle(x,p,c),y_correct))\r\n\r\n%%\r\nx = 20;\r\np=1.5;\r\nc=0.4;\r\ny_correct = 17;\r\nassert(isequal(bottle(x,p,c),y_correct))","published":true,"deleted":false,"likes_count":13,"comments_count":1,"created_by":90955,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":80,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-10-05T21:45:06.000Z","updated_at":"2026-02-06T20:05:06.000Z","published_at":"2016-10-05T21:45:06.000Z","restored_at":"2017-09-28T06:15:07.000Z","restored_by":null,"spam":false,"simulink":false,"admin_reviewed":true,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSometimes if you buy a drink in a glass bottle you can return that bottle and get some money back.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eLet's assume we have \\\"x\\\" amount of money, the drink cost \\\"p\\\" per bottle, and we get \\\"c\\\" back money for each bottle.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf we have enough time, how much can we buy drinks for \\\"x\\\" money?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe have $10, drink costs $1, for each bottle we return we get $0.35\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Step I: \\n We buy 10 drinks\\n We get $3.50 back for bottles\\n\\n Step II: \\n We buy 3 drinks and we are left with $0.50\\n We get back $1.05 back so we have $1.55\\n\\n Step III:\\n We buy 1 drink and we have $0.55\\n We get $0.35 for bottle, so we have $0.90 and we can't buy another drink for that.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSummary for $10 we were able to drink 14 bottles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":467,"title":"Mr. Pyramidad \u0026 Ms. Whissy Bolower","description":"Businessman Mr. B. M. Pyramidad was drinking with his new girlfriend Ms. Whissy Bolower. He disclosed to her that he has an illegal business plan:\r\n \r\n# He, as Boss-I, will recruit x individuals as Operator-II, \r\n# Each of these operators will recruit x individuals at Helper-III, \r\n# Every helper must recruit x individuals as Peddler-IV. \r\n# All of them will be promoted to next higher level as soon as each of those peddlers deposit $1,000 cash to their acting Boss-I.\r\n# Then, whoever was the acting Boss-I, he or she must retire, but the business will continue to expand as usual. \r\n\r\nMs. Whissy Bolower has decided to blow the whistle. She is calculating that Mr. Pyramidad may come back at the Peddler-IV level after retiring. What could be the minimum x so he collects minimum y dollars net after retiring z times? Please try a general solution, the test suite may expand. For more information, see \u003chttp://en.wikipedia.org/wiki/Pyramid_scheme Pyramid Scheme\u003e.   ","description_html":"\u003cp\u003eBusinessman Mr. B. M. Pyramidad was drinking with his new girlfriend Ms. Whissy Bolower. He disclosed to her that he has an illegal business plan:\u003c/p\u003e\u003col\u003e\u003cli\u003eHe, as Boss-I, will recruit x individuals as Operator-II,\u003c/li\u003e\u003cli\u003eEach of these operators will recruit x individuals at Helper-III,\u003c/li\u003e\u003cli\u003eEvery helper must recruit x individuals as Peddler-IV.\u003c/li\u003e\u003cli\u003eAll of them will be promoted to next higher level as soon as each of those peddlers deposit $1,000 cash to their acting Boss-I.\u003c/li\u003e\u003cli\u003eThen, whoever was the acting Boss-I, he or she must retire, but the business will continue to expand as usual.\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eMs. Whissy Bolower has decided to blow the whistle. She is calculating that Mr. Pyramidad may come back at the Peddler-IV level after retiring. What could be the minimum x so he collects minimum y dollars net after retiring z times? Please try a general solution, the test suite may expand. For more information, see \u003ca href=\"http://en.wikipedia.org/wiki/Pyramid_scheme\"\u003ePyramid Scheme\u003c/a\u003e.\u003c/p\u003e","function_template":"function x = Pyramidad(y,z)\r\n   % mimimum y dollars net for Boss-I\r\n   % retire z times\r\n   x=12; % peddlers-IV under every helper-III\r\nend","test_suite":"%%\r\nx = 10; % peddlers-IV under every helper-III\r\ny = 1000000; % net dollars minimum\r\nz = 1; % retire counter minimum\r\nassert(10==Pyramidad(y,z))\r\n%%\r\nx = 10; % peddlers-IV under every helper-III\r\ny = 1999000; % net dollars minimum\r\nz = 2; % retire counter minimum\r\nassert(10==Pyramidad(y,z))\r\n%%\r\nx = 5; % peddlers-IV under every helper-III\r\ny = 373000; % net dollars minimum\r\nz = 3; % retire counter minimum\r\nassert(5==Pyramidad(y,z))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":166,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":"2012-03-10T05:26:55.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-07T19:11:12.000Z","updated_at":"2026-04-17T11:00:57.000Z","published_at":"2012-03-13T19:35:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBusinessman Mr. B. M. Pyramidad was drinking with his new girlfriend Ms. Whissy Bolower. He disclosed to her that he has an illegal business plan:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHe, as Boss-I, will recruit x individuals as Operator-II,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach of these operators will recruit x individuals at Helper-III,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEvery helper must recruit x individuals as Peddler-IV.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll of them will be promoted to next higher level as soon as each of those peddlers deposit $1,000 cash to their acting Boss-I.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThen, whoever was the acting Boss-I, he or she must retire, but the business will continue to expand as usual.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMs. Whissy Bolower has decided to blow the whistle. She is calculating that Mr. Pyramidad may come back at the Peddler-IV level after retiring. What could be the minimum x so he collects minimum y dollars net after retiring z times? Please try a general solution, the test suite may expand. For more information, see\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Pyramid_scheme\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePyramid Scheme\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2867,"title":"Matlab Basics - Rounding III","description":"Write a script to round a large number to the nearest 10,000\r\n\r\ne.g. x = 12,358,466,243 --\u003e y = 12,358,470,000","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 51px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 25.5px; transform-origin: 407px 25.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 190px 8px; transform-origin: 190px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a script to round a large number to the nearest 10,000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 149.5px 8px; transform-origin: 149.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ee.g. x = 12,358,466,243 --\u0026gt; y = 12,358,470,000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = round_ten_thou(x)\r\n  y = ;\r\nend","test_suite":"%%\r\nx = 12358466243;\r\ny_correct = 12358470000;\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = -1235846.325;\r\ny_correct = -1240000;\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = 1266243.325;\r\ny_correct = 1270000;\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = 987654321;\r\ny_correct = 987650000;\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = [1:4999];\r\ny_correct = zeros(1,4999);\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = [];\r\nassert(isempty(round_ten_thou(x)))\r\n%%\r\nx = [-5001:-1:-6000];\r\ny_correct = -1e4*ones(1,1e3);\r\nassert(isequal(round_ten_thou(x),y_correct))\r\n%%\r\nx = -54321.67890;\r\ny_correct = -5e4;\r\nassert(isequal(round_ten_thou(x),y_correct))","published":true,"deleted":false,"likes_count":29,"comments_count":7,"created_by":34237,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5374,"test_suite_updated_at":"2021-05-09T15:34:34.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-01-21T17:19:47.000Z","updated_at":"2026-05-22T21:40:31.000Z","published_at":"2015-01-22T19:27:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a script to round a large number to the nearest 10,000\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ee.g. x = 12,358,466,243 --\u0026gt; y = 12,358,470,000\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":52188,"title":"Roman numbers: how old is that building?","description":"The front of old buildings sometimes show the year when they were built in roman numbers. These number are rather confusing and not easy to understand, Can you make a program that can translate any string with any roman number between 0 to 3999 back a decimal number? (an empty string should return zero).\r\nI found this even more challenging than the reverse problem (problem 63 Encode Roman numbers) which I liked a lot and was obviously the inspiration of this problem.\r\nExamples:\r\n'XXIII' should return 23 (XX=20 III=3)\r\n'MMXXI' should return 2021 (MM=2000 XX=20 I=1)\r\n'MDCLXVI' should return 1666 (MDC =1600 LX=60 VI=6)\r\n'CMXCIX' should return 999 (CM=900,XC=90,IX=9)\r\n'' (empty string) should return 0\r\nOnly integer numbers between 0 and 3999 can be handled.\r\n\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 355px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 177.5px; transform-origin: 407px 177.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe front of old buildings sometimes show the year when they were built in roman numbers. These number are rather confusing and not easy to understand, Can you make a program that can translate any string with any roman number between 0 to 3999 back a decimal number? (an empty string should return zero).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eI found this even more challenging than the reverse problem (\u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/63-encode-roman-numerals?s_tid=ta_cody_results\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eproblem 63 Encode Roman numbers\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e) which I liked a lot and was obviously the inspiration of this problem.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eExamples:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 100px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 50px; transform-origin: 391px 50px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'XXIII' should return 23 (XX=20 III=3)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'MMXXI' should return 2021 (MM=2000 XX=20 I=1)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'MDCLXVI' should return 1666 (MDC =1600 LX=60 VI=6)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'CMXCIX' should return 999 (CM=900,XC=90,IX=9)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10px; text-align: left; transform-origin: 363px 10px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e'' (empty string) should return 0\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eOnly integer numbers between 0 and 3999 can be handled.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = rom2dec(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 'XLIX';\r\ny_correct = 49;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n%%\r\nx =  'MCMXCIX';\r\ny_correct = 1999;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n%%\r\nx = 'MDCCLXXVI';\r\ny_correct = 1776;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n%%\r\nx = '';\r\ny_correct = 0;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n%%\r\nx = 'MMMDCCCLXXXVIII';\r\ny_correct = 3888;\r\nassert(isequal(rom2dec(x),y_correct))\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":1260358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-07-04T07:36:30.000Z","updated_at":"2026-04-14T14:01:58.000Z","published_at":"2021-07-04T08:42:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe front of old buildings sometimes show the year when they were built in roman numbers. These number are rather confusing and not easy to understand, Can you make a program that can translate any string with any roman number between 0 to 3999 back a decimal number? (an empty string should return zero).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI found this even more challenging than the reverse problem (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/63-encode-roman-numerals?s_tid=ta_cody_results\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eproblem 63 Encode Roman numbers\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) which I liked a lot and was obviously the inspiration of this problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'XXIII' should return 23 (XX=20 III=3)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'MMXXI' should return 2021 (MM=2000 XX=20 I=1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'MDCLXVI' should return 1666 (MDC =1600 LX=60 VI=6)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'CMXCIX' should return 999 (CM=900,XC=90,IX=9)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'' (empty string) should return 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOnly integer numbers between 0 and 3999 can be handled.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":43664,"title":"Use a timetable to analyze a train timetable (Part 1)","description":"Oh no, you missed your train to Boston! Find the departure time of the next available train by analyzing the train timetable. The trip should not take longer than 5 hours.\r\nExample:\r\nDepTime = datetime(2016,11,1,[6 8 9],[22 51 05],0)';\r\nArrTime = datetime(2016,11,1,[9 10 12],[17 32 09],0)';\r\nDestination = categorical({'BOS';'NYC';'BOS'});\r\ntt = timetable(DepTime,Destination,ArrTime)\r\ntt = \r\n         DepTime          Destination          ArrTime       \r\n  ____________________    ___________    ____________________\r\n  01-Nov-2016 06:22:00    BOS            01-Nov-2016 09:17:00\r\n  01-Nov-2016 08:51:00    NYC            01-Nov-2016 10:32:00\r\n  01-Nov-2016 09:05:00    BOS            01-Nov-2016 12:09:00\r\nFeature Tip: R2016b introduces timetables with related functions which may be helpful. To learn more see MATLAB Timetables.\r\nRelated Problems:\r\nUse a timetable to analyze a train timetable (Part 1)\r\nUse a timetable to analyze a train timetable (Part 2)\r\nUse a timetable to analyze a train timetable (Part 3)\r\nUse a timetable to analyze a train timetable (Part 4)\r\nUse a timetable to analyze a train timetable (Part 5)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 518.333px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 259.167px; transform-origin: 407px 259.167px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOh no, you missed your train to Boston! Find the departure time of the next available train by analyzing the train timetable. The trip should not take longer than 5 hours.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.5px 8px; transform-origin: 31.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 204.333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 102.167px; transform-origin: 404px 102.167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 208px 8.5px; tab-size: 4; transform-origin: 208px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eDepTime = datetime(2016,11,1,[6 8 9],[22 51 05],0)';\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 216px 8.5px; tab-size: 4; transform-origin: 216px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eArrTime = datetime(2016,11,1,[9 10 12],[17 32 09],0)';\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 188px 8.5px; tab-size: 4; transform-origin: 188px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 108px 8.5px; transform-origin: 108px 8.5px; \"\u003eDestination = categorical({\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'BOS'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'NYC'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'BOS'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003e});\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 172px 8.5px; tab-size: 4; transform-origin: 172px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ett = timetable(DepTime,Destination,ArrTime)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ett = \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 104px 8.5px; transform-origin: 104px 8.5px; \"\u003e         DepTime          \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 44px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 44px 8.5px; \"\u003eDestination\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 40px 8.5px; transform-origin: 40px 8.5px; \"\u003e          \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003eArrTime\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 28px 8.5px; transform-origin: 28px 8.5px; \"\u003e       \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(255, 0, 0); border-block-start-color: rgb(255, 0, 0); border-bottom-color: rgb(255, 0, 0); border-inline-end-color: rgb(255, 0, 0); border-inline-start-color: rgb(255, 0, 0); border-left-color: rgb(255, 0, 0); border-right-color: rgb(255, 0, 0); border-top-color: rgb(255, 0, 0); caret-color: rgb(255, 0, 0); color: rgb(255, 0, 0); column-rule-color: rgb(255, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(255, 0, 0); perspective-origin: 80px 8.5px; text-decoration-color: rgb(255, 0, 0); text-emphasis-color: rgb(255, 0, 0); transform-origin: 80px 8.5px; \"\u003e____________________\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 16px 8.5px; transform-origin: 16px 8.5px; \"\u003e    \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(255, 0, 0); border-block-start-color: rgb(255, 0, 0); border-bottom-color: rgb(255, 0, 0); border-inline-end-color: rgb(255, 0, 0); border-inline-start-color: rgb(255, 0, 0); border-left-color: rgb(255, 0, 0); border-right-color: rgb(255, 0, 0); border-top-color: rgb(255, 0, 0); caret-color: rgb(255, 0, 0); color: rgb(255, 0, 0); column-rule-color: rgb(255, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(255, 0, 0); perspective-origin: 44px 8.5px; text-decoration-color: rgb(255, 0, 0); text-emphasis-color: rgb(255, 0, 0); transform-origin: 44px 8.5px; \"\u003e___________\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 16px 8.5px; transform-origin: 16px 8.5px; \"\u003e    \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(255, 0, 0); border-block-start-color: rgb(255, 0, 0); border-bottom-color: rgb(255, 0, 0); border-inline-end-color: rgb(255, 0, 0); border-inline-start-color: rgb(255, 0, 0); border-left-color: rgb(255, 0, 0); border-right-color: rgb(255, 0, 0); border-top-color: rgb(255, 0, 0); caret-color: rgb(255, 0, 0); color: rgb(255, 0, 0); column-rule-color: rgb(255, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(255, 0, 0); perspective-origin: 80px 8.5px; text-decoration-color: rgb(255, 0, 0); text-emphasis-color: rgb(255, 0, 0); transform-origin: 80px 8.5px; \"\u003e____________________\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 164px 8.5px; transform-origin: 164px 8.5px; \"\u003e  01-Nov-2016 06:22:00    BOS            \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 80px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 80px 8.5px; \"\u003e01-Nov-2016 09:17:00\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 164px 8.5px; transform-origin: 164px 8.5px; \"\u003e  01-Nov-2016 08:51:00    NYC            \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 80px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 80px 8.5px; \"\u003e01-Nov-2016 10:32:00\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 244px 8.5px; tab-size: 4; transform-origin: 244px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 164px 8.5px; transform-origin: 164px 8.5px; \"\u003e  01-Nov-2016 09:05:00    BOS            \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 80px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 80px 8.5px; \"\u003e01-Nov-2016 12:09:00\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 42px 8px; transform-origin: 42px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eFeature Tip:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/help/matlab/release-notes.html?rntext=\u0026amp;startrelease=R2016b\u0026amp;endrelease=R2016b\u0026amp;groupby=release\u0026amp;sortby=descending\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eR2016b\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 266px 8px; transform-origin: 266px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e introduces timetables with related functions which may be helpful. To learn more see\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMATLAB Timetables\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 64px 8px; transform-origin: 64px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Problems:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 161px 8px; transform-origin: 161px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 1)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 2)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 3)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 4)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eUse a timetable to analyze a train timetable (Part 5)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function nextTime = myFun(tt)\r\n  nextTime = tt;\r\nend","test_suite":"%%\r\nDepTime = datetime(2016,11,[1;1;1;1;1;1;2;2;2;2;3;3;3;3;3],...\r\n[6;7;7;8;8;9;4;6;8;3;10;5;8;4;2],[22;27;39;43;46;17;41;40;10;8;30;58;21;36;14],0);\r\nArrTime = datetime(2016,11,[1;1;1;1;1;1;2;2;2;2;3;3;3;3;3],...\r\n[9;10;10;13;11;12;7;9;11;6;13;8;11;7;5],[17;32;09;03;26;46;13;20;19;28;40;38;27;32;24],0);\r\nDestination = categorical([1;2;1;1;1;1;2;1;3;2;1;3;3;1;2],1:3,{'BOS';'NYC';'DC'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,1,6,22,0);\r\nassert(isequal(myFun(tt),nextTime))\r\n\r\n%%\r\nDepTime = datetime(2016,11,[1;1;1;1;1;1], [6;6;7;7;8;8],[0;10;0;20;0;30],0);\r\nArrTime = datetime(2016,11,[1;1;1;1;1;1], [13;13;12;12;11;11],[0;30;0;20;0;10],0);\r\nDestination = categorical([1;1;1;1;1;1],1,{'BOS'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,1,8,0,0);\r\nassert(isequal(myFun(tt),nextTime))\r\n\r\n%%\r\nDepTime = datetime(2016,11,[1;2;3;4], [3;5;7;9],0,0);\r\nArrTime = datetime(2016,11,[1;2;3;4], [13;12;11;10],0,0);\r\nDestination = categorical([1;2;1;2],1:2,{'BOS';'NYC'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,3,7,0,0);\r\nassert(isequal(myFun(tt),nextTime))\r\n\r\n%%\r\nDepTime = datetime(2016,11,[1;1;1;1], [7;7;8;8],0,0);\r\nArrTime = datetime(2016,11,[1;1;1;1], [12;11;10;9],0,0);\r\nDestination = categorical([1;2;1;2],1:2,{'BOS';'NYC'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,1,8,0,0);\r\nassert(isequal(myFun(tt),nextTime))\r\n\r\n%%\r\nDepTime = datetime(2016,11,[1;1;1;1], [8;8;7;8],10,30);\r\nArrTime = datetime(2016,11,[1;1;1;1], [12;13;10;9],0,0);\r\nDestination = categorical([1;2;1;2],1:2,{'BOS';'DC'});\r\ntt = timetable(DepTime,Destination,ArrTime);\r\nnextTime = datetime(2016,11,1,7,10,30);\r\nassert(isequal(myFun(tt),nextTime))","published":true,"deleted":false,"likes_count":2,"comments_count":4,"created_by":102743,"edited_by":223089,"edited_at":"2022-04-27T08:40:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":38,"test_suite_updated_at":"2022-04-27T08:40:00.000Z","rescore_all_solutions":false,"group_id":16,"created_at":"2016-11-18T20:20:09.000Z","updated_at":"2026-04-24T15:06:39.000Z","published_at":"2016-11-18T20:26:18.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOh no, you missed your train to Boston! Find the departure time of the next available train by analyzing the train timetable. The trip should not take longer than 5 hours.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[DepTime = datetime(2016,11,1,[6 8 9],[22 51 05],0)';\\nArrTime = datetime(2016,11,1,[9 10 12],[17 32 09],0)';\\nDestination = categorical({'BOS';'NYC';'BOS'});\\ntt = timetable(DepTime,Destination,ArrTime)\\ntt = \\n         DepTime          Destination          ArrTime       \\n  ____________________    ___________    ____________________\\n  01-Nov-2016 06:22:00    BOS            01-Nov-2016 09:17:00\\n  01-Nov-2016 08:51:00    NYC            01-Nov-2016 10:32:00\\n  01-Nov-2016 09:05:00    BOS            01-Nov-2016 12:09:00]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFeature Tip:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/help/matlab/release-notes.html?rntext=\u0026amp;startrelease=R2016b\u0026amp;endrelease=R2016b\u0026amp;groupby=release\u0026amp;sortby=descending\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eR2016b\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e introduces timetables with related functions which may be helpful. To learn more see\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMATLAB Timetables\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Problems:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 3)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 4)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUse a timetable to analyze a train timetable (Part 5)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42841,"title":"Return the sequence element III","description":"This problem is related to \u003chttp://www.mathworks.com/matlabcentral/cody/problems/42832-segmented-number-sequence Problem 42832\u003e.\r\n\r\nGiven positive integers x1, x2 and n, return a positive integer, y, which is the nth term in the \u003chttps://en.wikipedia.org/wiki/Ulam_number Ulam sequence\u003e beginning with [x1 x2].\r\n\r\nAn Ulam sequence is an increasing sequence of positive integers, beginning with two arbitrary integers, x1 and x2. Every subsequent element is the smallest integer that can be expressed uniquely by the sum of any two distinct preceding elements. In other words, integers that can be expressed as sums of two distinct preceding elements in more than one way are excluded.\r\n\r\nAssume n \u003e 2.\r\n\r\nExample:\r\n\r\nx1 = 1\r\n\r\nx2 = 2\r\n\r\nn = 5\r\n\r\ny = 6","description_html":"\u003cp\u003eThis problem is related to \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/42832-segmented-number-sequence\"\u003eProblem 42832\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eGiven positive integers x1, x2 and n, return a positive integer, y, which is the nth term in the \u003ca href = \"https://en.wikipedia.org/wiki/Ulam_number\"\u003eUlam sequence\u003c/a\u003e beginning with [x1 x2].\u003c/p\u003e\u003cp\u003eAn Ulam sequence is an increasing sequence of positive integers, beginning with two arbitrary integers, x1 and x2. Every subsequent element is the smallest integer that can be expressed uniquely by the sum of any two distinct preceding elements. In other words, integers that can be expressed as sums of two distinct preceding elements in more than one way are excluded.\u003c/p\u003e\u003cp\u003eAssume n \u0026gt; 2.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cp\u003ex1 = 1\u003c/p\u003e\u003cp\u003ex2 = 2\u003c/p\u003e\u003cp\u003en = 5\u003c/p\u003e\u003cp\u003ey = 6\u003c/p\u003e","function_template":"function y = ulam(x1,x2,n)\r\n  y = -1;\r\nend","test_suite":"%%\r\nx1 = 1;\r\nx2 = 2;\r\nn = 5;\r\ny_correct = 6;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 1;\r\nx2 = 2;\r\nn = 22;\r\ny_correct = 77;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 1;\r\nx2 = 3;\r\nn = 49;\r\ny_correct = 212;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 1;\r\nx2 = 5;\r\nn = 10;\r\ny_correct = 22;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 2;\r\nx2 = 9;\r\nn = 8;\r\ny_correct = 20;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 2;\r\nx2 = 9;\r\nn = 26;\r\ny_correct = 77;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 4;\r\nx2 = 5;\r\nn = 29;\r\ny_correct = 123;\r\nassert(isequal(ulam(x1,x2,n),y_correct))\r\n\r\n%%\r\nx1 = 6;\r\nx2 = 7;\r\nn = 29;\r\ny_correct = 123;\r\nassert(isequal(ulam(x1,x2,n),y_correct))","published":true,"deleted":false,"likes_count":5,"comments_count":1,"created_by":15521,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-04-29T20:39:06.000Z","updated_at":"2026-01-19T17:42:01.000Z","published_at":"2016-04-29T20:39:06.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is related to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/42832-segmented-number-sequence\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 42832\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven positive integers x1, x2 and n, return a positive integer, y, which is the nth term in the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Ulam_number\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUlam sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e beginning with [x1 x2].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn Ulam sequence is an increasing sequence of positive integers, beginning with two arbitrary integers, x1 and x2. Every subsequent element is the smallest integer that can be expressed uniquely by the sum of any two distinct preceding elements. In other words, integers that can be expressed as sums of two distinct preceding elements in more than one way are excluded.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAssume n \u0026gt; 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex1 = 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex2 = 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en = 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ey = 6\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44338,"title":"Recaman Sequence - I","description":"Recaman Sequence (A005132 - \u003chttp://oeis.org/A005132 - OEIS Link\u003e) is defined as follow;\r\n\r\n  seq(0) = 0; \r\n  for n \u003e 0, seq(n) = seq(n-1) - n if positive and not already in the sequence, \r\n  otherwise seq(n) = seq(n-1) + n. \r\n\r\n  seq = 0, 1, 3, 6, 2, 7, 13, 20, 12, 21, 11, 22, 10, 23, 9 ...\r\nindex = 1, 2, 3 ,...\r\n\r\nTo avoid zero index, start indexing from 1.\r\nreturn the first n elements in Recaman Sequence\r\n\r\n*Related Challenges :*\r\n\r\n# Recaman Sequence - I\r\n# \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44339 Recaman Sequence - II\u003e\r\n# \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44340 Recaman Sequence - III\u003e\r\n","description_html":"\u003cp\u003eRecaman Sequence (A005132 - \u003ca href = \"http://oeis.org/A005132\"\u003e- OEIS Link\u003c/a\u003e) is defined as follow;\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eseq(0) = 0; \r\nfor n \u0026gt; 0, seq(n) = seq(n-1) - n if positive and not already in the sequence, \r\notherwise seq(n) = seq(n-1) + n. \r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eseq = 0, 1, 3, 6, 2, 7, 13, 20, 12, 21, 11, 22, 10, 23, 9 ...\r\nindex = 1, 2, 3 ,...\r\n\u003c/pre\u003e\u003cp\u003eTo avoid zero index, start indexing from 1.\r\nreturn the first n elements in Recaman Sequence\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelated Challenges :\u003c/b\u003e\u003c/p\u003e\u003col\u003e\u003cli\u003eRecaman Sequence - I\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44339\"\u003eRecaman Sequence - II\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44340\"\u003eRecaman Sequence - III\u003c/a\u003e\u003c/li\u003e\u003c/ol\u003e","function_template":"function y = Recaman(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 0;\r\nassert(isequal(Recaman(x),y_correct))\r\n\r\n%%\r\nx = 5;\r\ny_correct = [0 1 3 6 2];\r\nassert(isequal(Recaman(x),y_correct))\r\n\r\n%%\r\nx = 8;\r\ny_correct = [0 1 3 6 2 7 13 20];\r\nassert(isequal(Recaman(x),y_correct))\r\n\r\n%%\r\nx = 10;\r\ny_correct = [0 1 3 6 2 7 13 20 12 21];\r\nassert(isequal(Recaman(x),y_correct))\r\n\r\n%%\r\nx = 5e4;\r\ny = Recaman(x);\r\nassert(isequal(length(Recaman(x)),x))\r\nassert(isequal(y(954),739))\r\nassert(isequal(y(7589),17654))\r\nassert(isequal(y(12345),18554))\r\n\r\n%%\r\nx = 1e5;\r\ny = Recaman(x);\r\nassert(isequal(length(Recaman(x)),x))\r\nassert(isequal(y(1e4),8658))\r\nassert(isequal(y(2e4),34358))\r\nassert(isequal(y(3e4),92474))\r\nassert(isequal(y(4e4),102344))","published":true,"deleted":false,"likes_count":3,"comments_count":8,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":321,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":34,"created_at":"2017-09-19T06:55:43.000Z","updated_at":"2026-03-22T11:16:16.000Z","published_at":"2017-10-16T01:45:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence (A005132 -\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://oeis.org/A005132\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e- OEIS Link\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) is defined as follow;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq(0) = 0; \\nfor n \u003e 0, seq(n) = seq(n-1) - n if positive and not already in the sequence, \\notherwise seq(n) = seq(n-1) + n. \\n\\nseq = 0, 1, 3, 6, 2, 7, 13, 20, 12, 21, 11, 22, 10, 23, 9 ...\\nindex = 1, 2, 3 ,...]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo avoid zero index, start indexing from 1. return the first n elements in Recaman Sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44339\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44340\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - III\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":56503,"title":"Cricket - Career Bowling Statistics","description":"Given a vector s of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \"O-M-R-W\", such as \"31.4-8-123-4\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\r\nNote that overs can be given as integers (\"31\" meaning 31 complete overs) or as pseudo decimals (\"31.4\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\r\nCareer statistics are defined as:\r\nAverage = (total runs conceded) / (total wickets taken)\r\nSR = (total balls delivered) / (total wickets taken)\r\nEcon = (total runs conceded) / (total overs delivered)\r\n(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\r\nFor example:\r\ns = [\"23.1-8-55-5\";\"24-5-84-0\"];\r\nstats = bowlingstats(s)\r\nstats =\r\n   27.8000   56.6000    2.9470\r\nHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 472.533px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 236.267px; transform-origin: 407px 236.267px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48px 8px; transform-origin: 48px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003es\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 330px 8px; transform-origin: 330px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \"O-M-R-W\", such as \"31.4-8-123-4\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374px 8px; transform-origin: 374px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNote that overs can be given as integers (\"31\" meaning 31 complete overs) or as pseudo decimals (\"31.4\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 100.5px 8px; transform-origin: 100.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCareer statistics are defined as:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 170.5px 8px; transform-origin: 170.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAverage = (total runs conceded) / (total wickets taken)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 152px 8px; transform-origin: 152px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSR = (total balls delivered) / (total wickets taken)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 166.5px 8px; transform-origin: 166.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEcon = (total runs conceded) / (total overs delivered)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 355.5px 8px; transform-origin: 355.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 41px 8px; transform-origin: 41px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 128px 8.5px; tab-size: 4; transform-origin: 128px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es = [\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 52px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 52px 8.5px; \"\u003e\"23.1-8-55-5\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 44px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 44px 8.5px; \"\u003e\"24-5-84-0\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats = bowlingstats(s)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 120px 8.5px; tab-size: 4; transform-origin: 120px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e   27.8000   56.6000    2.9470\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function s = bowlingstats(bf)\r\n\r\ns = mean(bf);\r\n\r\nend","test_suite":"%% Random figures (of various levels of realism) part I\r\ns = [\"47-5-6-9\";\"4.4-43-167-1\";\"35-3-73-1\";\"37-8-158-1\";\"43-11-176-4\";\"28.3-39-136-2\";\"43-18-110-10\"];\r\nassert( max(abs(bowlingstats(s) - [29.5 51.0357142857143 3.46815955213436])) \u003c 0.001 )\r\ns = [\"38-8-148-4\";\"35-42-147-6\";\"39-45-164-1\";\"46.3-4-107-8\";\"16.4-24-113-9\"];\r\nassert( max(abs(bowlingstats(s) - [24.25 37.5357142857143 3.87630827783064])) \u003c 0.001 )\r\ns = [\"2-41-199-5\";\"46.1-48-41-4\";\"5-50-76-1\";\"43-22-142-6\";\"27-22-33-3\";\"1.3-17-65-6\";\"7.5-23-162-1\";\"30-39-44-7\";\"37-38-66-5\"];\r\nassert( max(abs(bowlingstats(s) - [21.7894736842105 31.5 4.15037593984962])) \u003c 0.001 )\r\ns = [\"16-4-85-5\";\"15.1-46-74-4\";\"22-33-171-2\";\"28-48-197-6\";\"7.3-28-11-2\";\"7.3-16-72-4\";\"12.5-5-200-10\"];\r\nassert( max(abs(bowlingstats(s) - [24.5454545454545 19.8181818181818 7.43119266055046])) \u003c 0.001 )\r\ns = [\"13-9-186-6\";\"38-1-177-3\";\"17.5-44-124-8\";\"19.5-43-179-7\";\"25-17-42-2\";\"11.5-5-52-3\";\"15-32-1-2\";\"15.5-21-19-6\"];\r\nassert( max(abs(bowlingstats(s) - [21.0810810810811 25.3513513513514 4.98933901918977])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part II\r\ns = [\"43.5-36-199-6\";\"2-10-104-3\"];\r\nassert( max(abs(bowlingstats(s) - [33.6666666666667 30.5555555555556 6.61090909090909])) \u003c 0.001 )\r\ns = [\"26.2-48-119-8\";\"26-31-180-7\";\"24.4-39-102-5\";\"8-4-107-6\"];\r\nassert( max(abs(bowlingstats(s) - [19.5384615384615 19.6153846153846 5.97647058823529])) \u003c 0.001 )\r\ns = [\"26-32-62-4\";\"7.3-49-89-2\";\"14-37-134-9\";\"47-49-165-7\";\"30-39-64-9\";\"27-27-132-10\";\"47.3-23-102-7\";\"13-32-32-6\"];\r\nassert( max(abs(bowlingstats(s) - [14.4444444444444 23.5555555555556 3.67924528301887])) \u003c 0.001 )\r\ns = [\"50-45-186-2\";\"32.1-27-22-8\";\"43.3-14-167-8\";\"25.1-9-31-1\";\"1.2-3-172-2\";\"13-9-120-7\"];\r\nassert( max(abs(bowlingstats(s) - [24.9285714285714 35.3928571428571 4.22603430877901])) \u003c 0.001 )\r\ns = [\"14-13-8-7\";\"40-10-88-7\";\"31-44-14-4\";\"35.4-2-196-5\"];\r\nassert( max(abs(bowlingstats(s) - [13.304347826087 31.4782608695652 2.53591160220994])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part III\r\ns = \"11-16-166-7\";\r\nassert( max(abs(bowlingstats(s) - [23.7142857142857 9.42857142857143 15.0909090909091])) \u003c 0.001 )\r\ns = [\"45-43-122-9\";\"13.3-6-153-3\";\"48.1-36-33-1\";\"23-28-99-7\";\"45-27-153-6\";\"24.2-16-61-3\";\"30-9-168-10\";\"1.4-16-136-10\";\"37.2-34-66-5\";\"21.3-27-108-7\"];\r\nassert( max(abs(bowlingstats(s) - [18.016393442623 28.4754098360656 3.79620034542314])) \u003c 0.001 )\r\ns = [\"15-37-129-8\";\"1-20-101-10\";\"15-35-107-2\";\"34-4-148-4\";\"8.2-38-55-1\";\"12-3-48-10\"];\r\nassert( max(abs(bowlingstats(s) - [16.8 14.6285714285714 6.890625])) \u003c 0.001 )\r\ns = [\"37-7-85-7\";\"40-33-108-10\";\"29.2-2-93-10\";\"19.1-12-126-8\";\"9-49-101-2\";\"45-48-159-9\";\"46.1-50-143-2\";\"38-4-114-8\";\"19-10-32-1\"];\r\nassert( max(abs(bowlingstats(s) - [16.859649122807 29.7543859649123 3.3997641509434])) \u003c 0.001 )\r\ns = [\"3-22-47-3\";\"46-33-148-4\";\"9-4-176-9\";\"5.4-20-155-4\";\"16.3-22-8-5\";\"34.1-21-111-8\";\"11.4-14-118-5\"];\r\nassert( max(abs(bowlingstats(s) - [20.0789473684211 19.8947368421053 6.05555555555556])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part IV\r\ns = [\"34-25-192-3\";\"5-32-164-3\";\"50.4-38-85-1\";\"29-35-164-6\";\"11-7-89-10\";\"5-46-77-8\";\"4-19-142-3\";\"2.2-27-48-7\";\"17-50-36-1\"];\r\nassert( max(abs(bowlingstats(s) - [23.7380952380952 22.5714285714286 6.31012658227848])) \u003c 0.001 )\r\ns = [\"17-27-112-5\";\"17-1-158-3\";\"39-14-83-4\";\"20-50-17-7\";\"11.1-36-118-6\";\"29-11-128-5\";\"12.2-33-26-4\";\"9.4-7-174-6\"];\r\nassert( max(abs(bowlingstats(s) - [20.4 23.275 5.25886143931257])) \u003c 0.001 )\r\ns = \"28-17-161-2\";\r\nassert( max(abs(bowlingstats(s) - [80.5 84 5.75])) \u003c 0.001 )\r\ns = \"23.3-35-107-3\";\r\nassert( max(abs(bowlingstats(s) - [35.6666666666667 47 4.5531914893617])) \u003c 0.001 )\r\ns = [\"21-29-169-6\";\"22.5-28-158-1\"];\r\nassert( max(abs(bowlingstats(s) - [46.7142857142857 37.5714285714286 7.46007604562738])) \u003c 0.001 )\r\n%% Tim Southee's stats\r\nts = [\"23.1-8-55-5\";\"24-5-84-0\";\"16-2-59-0\";\"18-3-63-4\";\"16.2-5-62-1\";\"27-1-100-0\";\"18-1-94-2\";\"12-2-58-0\";\"17-4-62-1\";\"31-8-64-2\";\"16-2-62-1\";\"11-4-41-3\";\"19-4-68-0\";\"19-3-61-4\";\"23-4-89-2\";\"33-6-119-3\";\"4-0-11-0\";\"29-5-94-1\";\"32-10-82-2\";\"1.4-0-10-0\";\"28-7-102-2\";\"15-2-49-2\";\"28.2-5-103-2\";\"1-0-11-0\";\"12-2-32-1\";\"19-3-77-2\";\"3.5-0-8-2\";\"8-2-20-0\";\"10-1-40-0\";\"26-4-100-0\";\"19-5-70-2\";\"14-4-30-1\";\"24-6-64-7\";\"18-3-68-1\";\"18-4-46-4\";\"22-4-62-5\";\"20-5-58-3\";\"15-3-45-1\";\"36-8-94-0\";\"32-9-77-0\";\"23.2-9-44-3\";\"30-6-77-2\";\"28.2-8-58-4\";\"19-4-50-6\";\"26-6-76-2\";\"15-4-51-0\";\"16-1-52-4\";\"29.1-4-101-2\";\"15.5-2-58-2\";\"11-2-24-3\";\"28-3-79-4\";\"8.5-5-12-3\";\"19-6-38-3\";\"23-4-81-3\";\"20-0-93-3\";\"16-3-50-2\";\"16.2-9-19-4\";\"9-2-32-2\";\"30-9-69-1\";\"4-1-21-0\";\"21-8-63-1\";\"16-4-28-3\";\"23-5-62-0\";\"9-0-33-0\";\"30-5-67-3\";\"11-3-21-1\";\"24-4-54-1\";\"11-3-20-0\";\"12-4-17-2\";\"37-8-91-4\";\"26-3-87-1\";\"17.4-6-41-1\";\"24-1-104-1\";\"34-4-162-2\";\"30-5-83-4\";\"18-7-43-1\";\"24-8-70-1\";\"29-6-88-0\";\"25-4-97-4\";\"17-1-50-1\";\"16-1-58-0\";\"27-4-71-3\";\"21-6-52-3\";\"21-5-63-3\";\"12.3-2-26-4\";\"31-5-87-2\";\"25-4-85-0\";\"7-2-30-1\";\"17-8-28-2\";\"15-3-68-2\";\"28-14-73-1\";\"14-7-35-1\";\"23-3-80-1\";\"35-5-114-1\";\"16-6-46-3\";\"19-11-20-2\";\"23.4-10-53-3\";\"21-4-80-6\";\"24-6-60-2\";\"34-5-158-2\";\"13-5-34-1\";\"28.3-7-94-5\";\"12.5-2-48-3\";\"27-7-98-2\";\"6-2-17-1\";\"19-9-34-2\";\"19-3-71-2\";\"10-3-25-4\";\"26-4-86-1\";\"26-7-62-6\";\"19-4-65-1\";\"25-5-56-1\";\"12-3-42-3\";\"27-7-68-6\";\"25-8-52-2\";\"15-5-35-3\";\"27-13-61-2\";\"14-2-76-3\";\"24-4-98-3\";\"15-2-52-1\";\"12-1-57-0\";\"7-3-17-0\";\"12-2-33-1\";\"29-7-63-4\";\"12-6-15-2\";\"32-7-88-4\";\"20-4-60-1\";\"37-4-90-2\";\"30.2-7-93-4\";\"21.1-6-69-5\";\"33.1-6-103-3\";\"15-3-44-0\";\"20.1-5-49-4\";\"21-6-61-5\";\"13-5-38-2\";\"11-2-36-3\";\"19-7-35-4\";\"15-2-62-1\";\"17.4-6-32-5\";\"22-4-96-2\";\"26-7-69-2\";\"23-8-33-2\";\"23-7-61-2\";\"20-8-45-0\";\"25.1-8-43-6\";\"17-1-37-1\";\"22-6-64-1\";\"19-4-48-4\";\"27.4-6-69-5\";\"22-2-75-3\";\"22-6-43-0\";\"13-2-31-0\";\"38-4-114-2\";\"5-2-21-1\";\"12-4-28-3\";\"17-6-54-1\";\"12-2-33-1\";\"17.4-6-35-5\";\"32-11-75-1\";\"26-5-90-2\";\"14-3-55-4\";\"23.5-5-87-0\";\"32-1-154-0\";\"11-0-67-1\";\"23-2-100-3\";\"19-5-68-1\"];\r\nassert( max(abs(bowlingstats(ts) - [28.9942363112392 57.8933717579251 3.00492807008811])) \u003c 0.001 )\r\n%% No wickets\r\nfor k = 1:4\r\n    n = randi(10);\r\n    o = randi([1 50],n,1);\r\n    m = randi(50,n,1);\r\n    r = randi([1 200],n,1);\r\n    w = zeros(n,1);\r\n    bf = o + \"-\" + m + \"-\" + r + \"-\" + w;\r\n    s = bowlingstats(bf);\r\n    assert( isinf(s(1)) )\r\n    assert( isinf(s(2)) )\r\nend\r\n%% No runs or wickets\r\nfor k = 1:4\r\n    n = randi(10);\r\n    o = randi([1 50],n,1);\r\n    m = randi(50,n,1);\r\n    r = zeros(n,1);\r\n    w = zeros(n,1);\r\n    bf = o + \"-\" + m + \"-\" + r + \"-\" + w;\r\n    s = bowlingstats(bf);\r\n    assert( isnan(s(1)) )\r\n    assert( isinf(s(2)) )\r\nend","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-11-08T15:13:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":29,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:38:20.000Z","updated_at":"2026-05-14T20:49:15.000Z","published_at":"2022-11-08T15:13:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \\\"O-M-R-W\\\", such as \\\"31.4-8-123-4\\\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that overs can be given as integers (\\\"31\\\" meaning 31 complete overs) or as pseudo decimals (\\\"31.4\\\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCareer statistics are defined as:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAverage = (total runs conceded) / (total wickets taken)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSR = (total balls delivered) / (total wickets taken)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEcon = (total runs conceded) / (total overs delivered)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s = [\\\"23.1-8-55-5\\\";\\\"24-5-84-0\\\"];\\nstats = bowlingstats(s)\\nstats =\\n   27.8000   56.6000    2.9470]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1642,"title":"BULLSEYE Part 2:   Reference Problem 18 BULLSEYE ","description":"Given n (always odd), return output a that has concentric rings of the 1s and 0s  around the center point. Examples:\r\n\r\n Input  n = 3\r\n Output a is [ 1 1 1 \r\n               1 0 1\r\n               1 1 1 ]\r\n\r\n Input  n = 5\r\n Output a is [    1     1     1     1     1\r\n                  1     0     0     0     1\r\n                  1     0     1     0     1\r\n                  1     0     0     0     1\r\n                  1     1     1     1     1]\r\n\r\n","description_html":"\u003cp\u003eGiven n (always odd), return output a that has concentric rings of the 1s and 0s  around the center point. Examples:\u003c/p\u003e\u003cpre\u003e Input  n = 3\r\n Output a is [ 1 1 1 \r\n               1 0 1\r\n               1 1 1 ]\u003c/pre\u003e\u003cpre\u003e Input  n = 5\r\n Output a is [    1     1     1     1     1\r\n                  1     0     0     0     1\r\n                  1     0     1     0     1\r\n                  1     0     0     0     1\r\n                  1     1     1     1     1]\u003c/pre\u003e","function_template":"function y = bullseye2(n)\r\n  y = n;\r\nend","test_suite":"%%\r\nn = 3;\r\na = [1     1     1;\r\n     1     0     1;\r\n     1     1     1;]\r\nassert(isequal(bullseye2(n),a));\r\n\r\n%%\r\nn = 5;\r\na = [1     1     1     1     1;\r\n     1     0     0     0     1;\r\n     1     0     1     0     1;\r\n     1     0     0     0     1;\r\n     1     1     1     1     1;]\r\nassert(isequal(bullseye2(n),a));\r\n\r\n%%\r\nn = 7;\r\na = [1     1     1     1     1     1     1;\r\n     1     0     0     0     0     0     1;\r\n     1     0     1     1     1     0     1;\r\n     1     0     1     0     1     0     1;\r\n     1     0     1     1     1     0     1;\r\n     1     0     0     0     0     0     1;\r\n     1     1     1     1     1     1     1];\r\nassert(isequal(bullseye2(n),a));\r\n\r\n%%\r\nn = 9;\r\na = [1     1     1     1     1     1     1     1     1;\r\n     1     0     0     0     0     0     0     0     1;\r\n     1     0     1     1     1     1     1     0     1;\r\n     1     0     1     0     0     0     1     0     1;\r\n     1     0     1     0     1     0     1     0     1;\r\n     1     0     1     0     0     0     1     0     1;\r\n     1     0     1     1     1     1     1     0     1;\r\n     1     0     0     0     0     0     0     0     1;\r\n     1     1     1     1     1     1     1     1     1];\r\nassert(isequal(bullseye2(n),a));","published":true,"deleted":false,"likes_count":2,"comments_count":2,"created_by":13514,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":161,"test_suite_updated_at":"2017-07-12T16:03:39.000Z","rescore_all_solutions":false,"group_id":31,"created_at":"2013-06-11T06:57:35.000Z","updated_at":"2026-05-07T19:38:11.000Z","published_at":"2013-06-11T06:57:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven n (always odd), return output a that has concentric rings of the 1s and 0s around the center point. Examples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input  n = 3\\n Output a is [ 1 1 1 \\n               1 0 1\\n               1 1 1 ]\\n\\n Input  n = 5\\n Output a is [    1     1     1     1     1\\n                  1     0     0     0     1\\n                  1     0     1     0     1\\n                  1     0     0     0     1\\n                  1     1     1     1     1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44339,"title":"Recaman Sequence - II","description":"Take an arbitrary starting point as input and create Recaman Sequence. Then find the 1, return its index.\r\n\r\nFor example: if n = 0 (default Recaman sequence)\r\n  \r\n  seq = [0 1 3 6 2];\r\n\r\n1 is in the second place. \r\n\r\nif n = 10;\r\n\r\n  seq = [10 9 7 4 8 3 9 2 10 1 11 22 34 21];\r\n\r\n1 is in the 10th place\r\n\r\n*Related Challenges :*\r\n\r\n# \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44338 Recaman Sequence - I\u003e\r\n# Recaman Sequence - II\r\n# \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44340 Recaman Sequence - III\u003e","description_html":"\u003cp\u003eTake an arbitrary starting point as input and create Recaman Sequence. Then find the 1, return its index.\u003c/p\u003e\u003cp\u003eFor example: if n = 0 (default Recaman sequence)\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eseq = [0 1 3 6 2];\r\n\u003c/pre\u003e\u003cp\u003e1 is in the second place.\u003c/p\u003e\u003cp\u003eif n = 10;\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eseq = [10 9 7 4 8 3 9 2 10 1 11 22 34 21];\r\n\u003c/pre\u003e\u003cp\u003e1 is in the 10th place\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelated Challenges :\u003c/b\u003e\u003c/p\u003e\u003col\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44338\"\u003eRecaman Sequence - I\u003c/a\u003e\u003c/li\u003e\u003cli\u003eRecaman Sequence - II\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44340\"\u003eRecaman Sequence - III\u003c/a\u003e\u003c/li\u003e\u003c/ol\u003e","function_template":"function y = RecamanII(startPoint)\r\n\r\nend","test_suite":"%%\r\nx = 0;\r\ny_correct = 2;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 1;\r\ny_correct = 1;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 3;\r\ny_correct = 4;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 90;\r\ny_correct = 35;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 123456;\r\ny_correct = 895;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n%%\r\nx = 123456789;\r\ny_correct = 46633;\r\nassert(isequal(RecamanII(x),y_correct))\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":281,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":34,"created_at":"2017-09-19T07:08:59.000Z","updated_at":"2026-04-07T13:57:31.000Z","published_at":"2017-10-16T01:45:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTake an arbitrary starting point as input and create Recaman Sequence. Then find the 1, return its index.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example: if n = 0 (default Recaman sequence)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq = [0 1 3 6 2];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 is in the second place.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eif n = 10;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq = [10 9 7 4 8 3 9 2 10 1 11 22 34 21];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 is in the 10th place\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44338\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44340\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - III\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44819,"title":"Relative pose in 2D: problem 1","description":"We consider a world reference frame denoted by {0} which has its x-axis pointing east and its y-axis pointing north.  There is a robot with an attached body-fixed coordinate frame {B} whose origin is in the centre of the robot, and whose x-axis points in the robot's forward direction.\r\n\r\n*With respect to the world frame origin* , the robot's centre is at a distance of 123m in the x-direction and -74.6m in the y-direction.  The car's heading angle is exactly SSW.\r\n\r\nWrite a 3x3 matrix homogeneous transformation matrix that expresses the pose of the robot frame {B} with respect to the world frame {O}.*","description_html":"\u003cp\u003eWe consider a world reference frame denoted by {0} which has its x-axis pointing east and its y-axis pointing north.  There is a robot with an attached body-fixed coordinate frame {B} whose origin is in the centre of the robot, and whose x-axis points in the robot's forward direction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWith respect to the world frame origin\u003c/b\u003e , the robot's centre is at a distance of 123m in the x-direction and -74.6m in the y-direction.  The car's heading angle is exactly SSW.\u003c/p\u003e\u003cp\u003eWrite a 3x3 matrix homogeneous transformation matrix that expresses the pose of the robot frame {B} with respect to the world frame {O}.*\u003c/p\u003e","function_template":"function T = user_function()\r\n  % return a 3x3 matrix describing the pose\r\n  T = [];\r\nend","test_suite":"%%\r\nT = user_function\r\n\r\nassert(all(size(T)==3), 'The matrix must be 3x3');\r\nassert(isreal(T), 'The matrix must be real, not complex');\r\n\r\n%% bottom row\r\nT = user_function\r\nassert(isequal(T(3,:), [0 0 1]), 'The bottom row of the homogeneous transformation matrix is not correct')\r\n\r\n%% x coordinate\r\nT = user_function\r\nassert(isequal(T(1,3),123), 'The representation of the x-coordinate is not correct')\r\n\r\n%% y coordinate\r\nT = user_function\r\nassert(isequal(T(2,3),-74.6), 'The representation of the y-coordinate is not correct')\r\n\r\n%% valid rotation matrix\r\nT = user_function\r\nR = T(1:2,1:2);\r\nassert( abs(det(R)-1) \u003c 0.01, 'The determinant of the rotation submatrix is not correct')\r\n\r\n%% correct rotation matrix\r\nT = user_function\r\nR = T(1:2,1:2);\r\nassert( abs(atan2d(R(2,1), R(1,1)) + 112.5) \u003c 0.1, 'The rotation matrix is not correct, check your calculation of the heading SSW and whether you are using radians or degrees')","published":true,"deleted":false,"likes_count":3,"comments_count":7,"created_by":13332,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":175,"test_suite_updated_at":"2019-01-20T07:53:15.000Z","rescore_all_solutions":false,"group_id":77,"created_at":"2019-01-06T07:56:12.000Z","updated_at":"2026-05-17T21:46:18.000Z","published_at":"2019-01-06T08:55:49.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe consider a world reference frame denoted by {0} which has its x-axis pointing east and its y-axis pointing north. There is a robot with an attached body-fixed coordinate frame {B} whose origin is in the centre of the robot, and whose x-axis points in the robot's forward direction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eWith respect to the world frame origin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e , the robot's centre is at a distance of 123m in the x-direction and -74.6m in the y-direction. The car's heading angle is exactly SSW.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a 3x3 matrix homogeneous transformation matrix that expresses the pose of the robot frame {B} with respect to the world frame {O}.*\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44340,"title":"Recaman Sequence - III","description":"I want to create a Recaman sequence where there is a \"1\" in the n-th position. So from which integer should I start the Recaman sequence? If there are more than one starting integer that generates a sequence with a 1 in the n-th position, return the lowest one.\r\nFor example if I want to place the digit 1 in the 7th place in the sequence then I should start the sequence from six as follow;\r\nseq = [6 5 3 6 2 7 1 8 16]\r\nYou can also start the sequence with 12 and obtain a series where there is a 1 in 7th position;\r\nseq = [12 11 9 6 2 7 1]\r\nRelated Challenges :\r\nRecaman Sequence - I\r\nRecaman Sequence - II\r\nRecaman Sequence - III","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 308.167px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 154.083px; transform-origin: 407px 154.083px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371.5px 8px; transform-origin: 371.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI want to create a Recaman sequence where there is a \"1\" in the n-th position. So from which integer should I start the Recaman sequence? If there are more than one starting integer that generates a sequence with a 1 in the n-th position, return the lowest one.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.5px 8px; transform-origin: 368.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example if I want to place the digit 1 in the 7th place in the sequence then I should start the sequence from six as follow;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 104px 8.5px; tab-size: 4; transform-origin: 104px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eseq = [6 5 3 6 2 7 1 8 16]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 294.5px 8px; transform-origin: 294.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can also start the sequence with 12 and obtain a series where there is a 1 in 7th position;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eseq = [12 11 9 6 2 7 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 72.5px 8px; transform-origin: 72.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges :\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44338\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRecaman Sequence - I\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44339\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRecaman Sequence - II\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 75px 8px; transform-origin: 75px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRecaman Sequence - III\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function startPoint = RecamanIII(onesplace)\r\n\r\nend","test_suite":"%%\r\nfiletext = fileread('RecamanIII.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'str2num'); \r\nassert(~illegal)\r\n\r\n%%\r\nx = 1;\r\ny_correct = 1;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 2;\r\ny_correct = 0;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 3;\r\ny_correct = 4;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 9;\r\ny_correct = 13;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 13;\r\ny_correct = 15;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 15;\r\ny_correct = 26;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 26;\r\ny_correct = 54;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 54;\r\ny_correct = 208;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n%%\r\nx = 208;\r\ny_correct = 2485;\r\nassert(isequal(RecamanIII(x),y_correct))\r\nassert(~any(cellfun(@(x)ismember(max([0,str2num(x)]),[13 15 26 54 208 2485]),regexp(fileread('RecamanIII.m'),'[\\d\\.\\+\\-\\*\\/]+','match'))))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":12,"created_by":8703,"edited_by":223089,"edited_at":"2022-10-11T07:22:46.000Z","deleted_by":null,"deleted_at":null,"solvers_count":104,"test_suite_updated_at":"2022-10-11T07:22:46.000Z","rescore_all_solutions":false,"group_id":35,"created_at":"2017-09-19T07:36:19.000Z","updated_at":"2026-05-15T02:34:25.000Z","published_at":"2017-10-16T01:50:59.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI want to create a Recaman sequence where there is a \\\"1\\\" in the n-th position. So from which integer should I start the Recaman sequence? If there are more than one starting integer that generates a sequence with a 1 in the n-th position, return the lowest one.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example if I want to place the digit 1 in the 7th place in the sequence then I should start the sequence from six as follow;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq = [6 5 3 6 2 7 1 8 16]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can also start the sequence with 12 and obtain a series where there is a 1 in 7th position;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[seq = [12 11 9 6 2 7 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44338\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44339\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRecaman Sequence - III\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44238,"title":"Mastermind III: Solve in 1","description":"\u003chttps://en.wikipedia.org/wiki/Mastermind_(board_game) Mastermind\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\r\n\r\n  Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\r\n[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v) \r\n\r\nwhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\r\n\r\nChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\r\n\r\nTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\r\n\r\nFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Mastermind_(board_game)\"\u003eMastermind\u003c/a\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eAnswer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\u003c/pre\u003e\u003cp\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/p\u003e\u003cp\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\u003c/p\u003e\u003cp\u003eChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\u003c/p\u003e\u003cp\u003eTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\u003c/p\u003e\u003cp\u003eFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]\u003c/p\u003e","function_template":"function [guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\r\n% mguess and mpegs are kx4 and kx2 and will not be empty\r\n% The player gets only one guess\r\n guess=[1 1 1 2];\r\nend","test_suite":"%%\r\nglobal m mpc mc mpc5c v\r\ntic\r\nv=1111:6666;\r\nvL=length(v);\r\nm=zeros(vL,4);\r\nfor i=1:vL\r\n  vp=v(i);\r\n  for k=4:-1:1\r\n   m(i,k)=mod(vp,10);\r\n   vp=floor(vp/10);\r\n  end\r\nend\r\nmdel=sum((m==0)+(m\u003e6),2)\u003e0;\r\nm(mdel,:)=[];\r\nv=m*[1000;100;10;1];\r\nmL=size(m,1);\r\n\r\nmpc=zeros(mL); % 0.030\r\nfor j=1:mL\r\nmpc(:,j)=sum(m==repmat(m(j,:),mL,1),2);\r\nend\r\n\r\nmch=zeros(mL,6); % 0.038\r\nfor i=1:mL\r\n  for k=1:6\r\n   mch(i,k)=nnz(m(i,:)==k);\r\n  end\r\nend\r\n\r\nmc=zeros(mL); % 0.06\r\nfor j=1:mL\r\n  mc(:,j)=sum(min(mch,repmat(mch(j,:),mL,1)),2);\r\nend\r\nmc=mc-mpc; % remove mpc part\r\nmpc5c=5*mpc+mc;\r\nfprintf('Initialization %.3f\\n',toc)\r\n% finished initilaiztion calculation in less than 0.2 sec\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  1 0\r\n3 6 5 6  2 1\r\n6 6 5 5  4 0]; % case 940\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  2 0\r\n3 6 3 6  0 1\r\n6 4 4 5  3 0\r\n6 5 4 5  4 0]; % case 900\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 2\r\n4 4 1 5  3 0\r\n1 4 5 6  1 3\r\n6 4 1 5  4 0]; % case 850\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 2\r\n2 3 4 3  1 1\r\n2 1 3 5  0 3\r\n6 3 2 1  4 0]; % case 816\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 2\r\n2 3 4 3  1 1\r\n2 1 3 5  1 2\r\n2 4 5 1  0 2\r\n6 1 2 3  4 0]; % case 750\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 1\r\n4 2 5 6  0 2\r\n6 5 1 5  1 1\r\n5 5 3 2  4 0]; % case 700\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  2 0\r\n3 6 3 6  0 1\r\n6 4 4 5  1 0\r\n5 3 5 5  4 0]; % case 650\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 0\r\n1 1 3 4  0 2\r\n3 2 1 5  2 2\r\n3 5 1 2  1 3\r\n5 2 1 3  4 0]; % case 600\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  1 2\r\n4 3 2 5  2 1\r\n4 4 2 3  3 0\r\n4 6 2 3  4 0]; % case 550\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  1 3\r\n3 4 5 3  2 2\r\n4 3 5 3  4 0]; % case 500\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  0 1\r\n4 1 5 6  2 1\r\n4 5 5 1  1 1\r\n4 1 6 4  4 0]; % case 450\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 3\r\n1 4 4 5  0 2\r\n3 6 1 4  4 0]; % case 400\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 1\r\n1 3 1 4  1 3\r\n1 1 4 3  0 4\r\n3 4 1 1  4 0]; % case 350\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 2\r\n1 1 2 3  2 1\r\n1 4 1 5  0 1\r\n3 1 2 2  4 0]; % case 300\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  2 0\r\n1 3 5 5  1 1\r\n2 6 5 3  1 1\r\n2 5 4 5  4 0]; % case 250\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 1\r\n1 3 1 4  1 0\r\n1 5 2 6  0 1\r\n2 2 4 4  1 1\r\n2 3 3 2  4 0]; % case 200\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 2\r\n1 1 2 3  1 2\r\n1 1 1 4  3 0\r\n2 1 1 4  4 0]; % case 150\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  1 1\r\n3 2 5 3  0 2\r\n1 5 3 6  3 0\r\n1 5 3 5  4 0]; % case 100\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 1\r\n1 1 3 2  2 1\r\n4 1 1 5  0 1\r\n1 3 2 2  4 0]; % case 50\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 0\r\n1 1 3 4  2 1\r\n1 4 2 5  1 0\r\n1 1 1 3  4 0]; % case 1\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-06-18T19:30:49.000Z","updated_at":"2025-12-12T14:19:44.000Z","published_at":"2017-06-18T19:58:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Mastermind_(board_game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47073,"title":"Find the nth Fibbinary number","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 308.75px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 154.375px; transform-origin: 407px 154.375px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63.25px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.625px; text-align: left; transform-origin: 384px 31.625px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 255.117px 7.91667px; transform-origin: 255.117px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe numbers 0, 1, 2, 4, 5, 8, 9, and 10 form the first eight elements (i.e., elements \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABOUlEQVRYhe2VXbGDMBBGj4c4wEANREEV1AEO4qAW0ICEeLgWqgEL3Idkh21uEmiB2+lMzkweYMl+2T8CjUaj0Wh8iAvQAw6w8Z2Nz5czRT3wiOIWGIAfYI6rO0P4Fp0/AJPYZmX7V2Etfj9a+Kqc24y9W7G/jQGm6HgsfCNZmfibFUPoCVmeFxrSsURV2uSj3RcOPqpD9Su+npCoU8eCVYdziW0gX4qJMB1Vao4FTz4zRr1PSyF7qv2hU37N2HuWzKT1loPnIhS/1cmoid8IYzfyXJYh7pO9NfFSKYGQxll9aOJyLPMukUs3y0RsEV+tu0Sml+7eKXm/RWCzOISUO0KN0/926TKRkdot/g67G24vUo4UGbXcBB1G7SdTuqAOQ6ZBj5T0wqlRCx3LhXInTMShN1+j0fgefgFsKIIPBf1eagAAAABJRU5ErkJggg==\" alt=\"a_0\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.71667px 7.91667px; transform-origin: 9.71667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABJ0lEQVRYhe2WUY3DMAyGPw5lMAIlEARFMAZhUAajEAyFEA6lUAylsHuYc3Ozutmm5aaT8kl+aGz5d+y0KTQajUaj8SV6wAMj4GTNyXNfUzQCi4g7IAAzcBU71RA+S/IF6DLfVfn+VFiLXz4tPKjkbsd/KvjfpgNWSTwZMakrK9uuBFXUnhULHVWwdYqj+GNW9CKFucxm7PFtSLuOht+p4ka17kXYyhlKwlZiTaTcGU06P0MpULd8L9hz70w+b4vwbOyR+Jnb3Ca2YwnYXUKErYO7oVfiUartJHk6MGnnQewoccpnnYUH0s60TdzbtmbrR1wk7pnx/DJw263n8bv9ymWyYL81VUlfQf8NcU/FG6/ELPbSvD9BavnRK1iNdPFU+8NpNBr/gx+twmvaJTM4bAAAAABJRU5ErkJggg==\" alt=\"a_7\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 102.683px 7.91667px; transform-origin: 102.683px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) of the Fibbinary sequence. The name is a portmanteau that arose because of connections to Fibonacci numbers and binary numbers. The connection to Fibonacci numbers \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"F(k)\" style=\"width: 32.5px; height: 19px;\" width=\"32.5\" height=\"19\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 37.7167px 7.91667px; transform-origin: 37.7167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis that if the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/47058-determine-the-zeckendorf-expansion-of-a-number\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eZeckendorf expansion\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.71667px 7.91667px; transform-origin: 9.71667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.94167px 7.91667px; transform-origin: 8.94167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.25px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.625px; text-align: left; transform-origin: 384px 10.625px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.70833px 7.91667px; transform-origin: 9.70833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e     \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"n = F(k_1) + F(k_2) + \\ldots + F(k_m)\" style=\"width: 199px; height: 20px;\" width=\"199\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 7.91667px; transform-origin: 1.94167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 13.6167px 7.91667px; transform-origin: 13.6167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ethen\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 26px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 13px; text-align: left; transform-origin: 384px 13px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.70833px 7.91667px; transform-origin: 9.70833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e     \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"a_n = 2^{k_1-2}+2^{k_2-2}+\\ldots+2^{k_m-2}\" style=\"width: 192px; height: 26px;\" width=\"192\" height=\"26\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 7.91667px; transform-origin: 1.94167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 357.083px 7.91667px; transform-origin: 357.083px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe connection to binary numbers is that the binary representations of the Fibbinary numbers have no adjacent 1s. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.25px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.625px; text-align: left; transform-origin: 384px 10.625px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 42.7833px 7.91667px; transform-origin: 42.7833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB8AAAAoCAYAAAAG0SEsAAABJ0lEQVRYhe2WUY3DMAyGPw5lMAIlEARFMAZhUAajEAyFEA6lUAylsHuYc3Ozutmm5aaT8kl+aGz5d+y0KTQajUaj8SV6wAMj4GTNyXNfUzQCi4g7IAAzcBU71RA+S/IF6DLfVfn+VFiLXz4tPKjkbsd/KvjfpgNWSTwZMakrK9uuBFXUnhULHVWwdYqj+GNW9CKFucxm7PFtSLuOht+p4ka17kXYyhlKwlZiTaTcGU06P0MpULd8L9hz70w+b4vwbOyR+Jnb3Ca2YwnYXUKErYO7oVfiUartJHk6MGnnQewoccpnnYUH0s60TdzbtmbrR1wk7pnx/DJw263n8bv9ymWyYL81VUlfQf8NcU/FG6/ELPbSvD9BavnRK1iNdPFU+8NpNBr/gx+twmvaJTM4bAAAAABJRU5ErkJggg==\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 148.975px 7.91667px; transform-origin: 148.975px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 10. The Zeckendorf expansion of 7 is 2+5, or \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJ8AAAAmCAYAAADa3kldAAAE+klEQVR4nO1bbbGrMBA9HnCAAQxUQRXgoA7qoBbQgAQ81AIanoX3fsB5bNOE3YTlTnsnZyZz51ImIcnZr+wGqKioqKioqKioqKio+MVoAAzrXw+0AB6O/f1W9GsrRRv8fwdwOdDfGy5rpyXtaui/ATAB6JT3OgC3td8e7xOPffcT30vA0jW/wzbnHovA76HdGeOxNokGwIhjhH5Dsw74V7Rn4oOe4p2bod8n9qWlAzAHY7Pdlf77tf9vRTj3P1gIE677uP7GdzT0WARew4D4urPFFAD31KJ4zLgYBiZIVE0Fj3iXHokOy2JOWCZzwUJouSEawQdljE/HhG2ue5qqwbZWe+CaapajxaZkpkjbE/zWOIYZUvPNyrsk6p7677F84N47M+IqvMFGQE2zcVNcfZEV7dqv5jIcgRR4zZxN0AVthm4xgIXoR6zGHTbtasKIbRG0CdLf2oO2CL3y+wN2M/OALjAloECOJ/QNLKSW5NN8OVqIFCwCj/V3i1Wx9OHi/8lFsNjzPW3QQ1/MTvmdG2+RLpoQVz8E55NPWhuLFmqhWxKLC8JxBywELNXsR7UngFd/LyU5lgiUoB9xBNTEVkI94U+Ss8kn/b2YFWhh1yxX2PxwuilhcDFjIWLO6QHHPOSWSAmMkYYOpuXDqIUsfkcKNEc5gQTNtCfOJp/c/BhpGPlawMhVwxXp0wVqYCuZaHqP7PXL8UnYEUNr6wbQ5JYGABcsRB+RJ4VHx43hTPJRa6SsDedjJcKMfGvDs9WQjDPsVq5k3P8ge2W4f1//TthUtFX9c8NyVfEF7+dOf2A3u3QdjjjRIc4kHzU1N5tnehM2ZZATRB39zhtezbG1L3KkCKEEhoebe35g6mNyzZ/Mskg/aM8khaCpPmQCApxJPmltRrwe5FMTWedCwTs6d54Rcs8t2o8cKYLUNrFFHpGnVkvIF6LFKwmtEVUuUfbSS1IQYtke2XJNPf1ittBKUJCs/XqRD1g0YM6pR6mlA6BnE6bE8xQ8yAe8HjRbpdB6NEOEWZ3Slmvq6c+lzjEviefaPDzIJ90wy7ys2a43hBIY2+Ax8TwFL/IBr1Jokaxc8lk136y8lyv10trEUmpd4nkKnuQDtnmfSj65uV4ZAk/ySc1kzVV+g88nHXuPDMFZ5LOYXesRzxtkSi1H0vbAKM6j1ImLahEM7w0AziFfmFLzSM7TVHoVWNDdsexhsbKREuiVmqI29ThvY18WQllP+HNwBvmktfEsB7NUvFhAIbbOuSiblZvUzu3XUut3QVryGXBYC0ZJFM/i0jPIl1PAkduvFqQ02Ap2U+tOTWbVyEXzkIec3gWZM3QzLjchrM5lViXnpP0J/3l4ky880PcshGAEbanDjEWzvOqQU55Ga5M1D4byMtjwNFcP6FIYLgTNxoT8JLdreU/kGz3Ix82Vc/a8h2Ipk2oRT6UxSzEgzwfNLmVjHV2qeYCRp0aGLhj7irLDSqaGvO9zeJFPO9LxEvwBNjJcI+OXBD7MiH0cHnCsdFVATemNn6hk9gSrj86o6g7RY1n3j7zAxYDB2xSGuOO7LxF5g1UqZ4I1gd7Fu67g7Sy3iyaR/l0vsvwSDDj3UpV2MexjcMVifr3VMyPibzGJP40J51idG/wSEj+CDr4E5OXlSrx9DPAlYI8v0XghPDVf49zfb4bnOlX3pqKioqKioqKioqLic/APujg5G2axcCgAAAAASUVORK5CYII=\" alt=\"F(3)+F(5)\" style=\"width: 79.5px; height: 19px;\" width=\"79.5\" height=\"19\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 17.5px 7.91667px; transform-origin: 17.5px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.70833px 7.91667px; transform-origin: 9.70833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e     \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"10=2^{3-2}+2^{5-2} = 2+8\" style=\"width: 155px; height: 19.5px;\" width=\"155\" height=\"19.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 210.442px 7.91667px; transform-origin: 210.442px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAlso, the binary expansion of 10 is 1010, which has no adjacent 1s. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 83.1px 7.91667px; transform-origin: 83.1px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to find the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 65.7333px 7.91667px; transform-origin: 65.7333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth Fibbinary number. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Fibbinary(n)\r\n  y = f(n);\r\nend","test_suite":"%%\r\nn = 0;\r\ny_correct = 0;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 4;\r\ny_correct = 5;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 7;\r\ny_correct = 10;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 77;\r\ny_correct = 321;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 777;\r\ny_correct = 9282;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 5000;\r\ny_correct = 140562;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 7777;\r\ny_correct = 278597;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 77777;\r\ny_correct = 8455236;\r\nassert(isequal(Fibbinary(n),y_correct))\r\n\r\n%%\r\nn = 100000;\r\ny_correct = 9704021;\r\nassert(isequal(Fibbinary(n),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":2,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-25T02:19:28.000Z","updated_at":"2020-10-27T04:39:32.000Z","published_at":"2020-10-25T03:48:13.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe numbers 0, 1, 2, 4, 5, 8, 9, and 10 form the first eight elements (i.e., elements \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"a_0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"a_7\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_7\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) of the Fibbinary sequence. The name is a portmanteau that arose because of connections to Fibonacci numbers and binary numbers. The connection to Fibonacci numbers \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"F(k)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eF(k)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis that if the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/47058-determine-the-zeckendorf-expansion-of-a-number\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eZeckendorf expansion\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e     \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n = F(k_1) + F(k_2) + \\\\ldots + F(k_m)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en = F(k_1) + F(k_2) + \\\\ldots + F(k_m)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethen\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e     \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"a_n = 2^{k_1-2}+2^{k_2-2}+\\\\ldots+2^{k_m-2}\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_n = 2^{k_1-2}+2^{k_2-2}+\\\\ldots+2^{k_m-2}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe connection to binary numbers is that the binary representations of the Fibbinary numbers have no adjacent 1s. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea_7\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e = 10. The Zeckendorf expansion of 7 is 2+5, or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"F(3)+F(5)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eF(3)+F(5)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e     \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"10=2^{3-2}+2^{5-2} = 2+8\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e10=2^{3-2}+2^{5-2} = 2+8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlso, the binary expansion of 10 is 1010, which has no adjacent 1s. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to find the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eth Fibbinary number. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":50629,"title":"Solve the snake puzzle","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 752px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 376px; transform-origin: 407px 376px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eMy son received this puzzle toy from grandma for his 7th birthday, but he's having a bit of trouble solving it. Can you help?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe toy is a \"twisting snake\" puzzle made of 27 blocks connected by hidden strings into sixteen segments, each either two or three blocks long. The snake's path must make a 90-degree turn at the end of each segment.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe object is to arrange the puzzle's blocks into a 3-by-3-by-3 cube.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThese pictures show the puzzle's configuration when laid out two-dimensionally and its final state when solved.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 518px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 259px; text-align: left; transform-origin: 384px 259px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eYour code should return a 27x3 array containing the path traversed by the snake when the puzzle is solved. Each row corresponds to one position, with [1 1 1] representing the back left lower corner of the cube and [3 3 3] representing the front right upper corner. For example, if the path begins in the front left lower corner and the first segment proceeds to the right, the first three rows of the array would be [3 1 1; 3 2 1; 3 3 1].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function moves = solve_snake\r\n    moves = ones(27,3);\r\nend","test_suite":"%%\r\n    result = solve_snake;\r\n    assert(isequal(sortrows(result), ...\r\n        [1 1 1;1 1 2;1 1 3;1 2 1;1 2 2;1 2 3;1 3 1;1 3 2;1 3 3; ...\r\n         2 1 1;2 1 2;2 1 3;2 2 1;2 2 2;2 2 3;2 3 1;2 3 2;2 3 3; ...\r\n         3 1 1;3 1 2;3 1 3;3 2 1;3 2 2;3 2 3;3 3 1;3 3 2;3 3 3]));\r\n    d = diff(result);\r\n    dd = diff(d);\r\n    assert(all(abs(sum(d,2))==1));\r\n    assert(~any(dd([1 5 8 12 14 19 21 23 25],:),'all') || ...\r\n        ~any(dd([1 3 5 7 12 14 18 21 25],:),'all'));\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":877713,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-03-01T15:12:16.000Z","updated_at":"2021-03-01T15:12:16.000Z","published_at":"2021-03-01T15:12:16.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMy son received this puzzle toy from grandma for his 7th birthday, but he's having a bit of trouble solving it. Can you help?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe toy is a \\\"twisting snake\\\" puzzle made of 27 blocks connected by hidden strings into sixteen segments, each either two or three blocks long. The snake's path must make a 90-degree turn at the end of each segment.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe object is to arrange the puzzle's blocks into a 3-by-3-by-3 cube.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThese pictures show the puzzle's configuration when laid out two-dimensionally and its final state when solved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"540\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"810\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour code should return a 27x3 array containing the path traversed by the snake when the puzzle is solved. Each row corresponds to one position, with [1 1 1] representing the back left lower corner of the cube and [3 3 3] representing the front right upper corner. For example, if the path begins in the front left lower corner and the first segment proceeds to the right, the first three rows of the array would be [3 1 1; 3 2 1; 3 3 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tionship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47463,"title":"Slitherlink II: Gimmes","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 531.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 265.833px; transform-origin: 407px 265.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87.8667px 7.91667px; transform-origin: 87.8667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink II: Gimmes\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 293.283px 7.91667px; transform-origin: 293.283px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.683px 7.91667px; transform-origin: 363.683px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\n\r\n[sv,valid]=pcheck(s,p,bsegs); \r\nfprintf('sv  init solution\\n')\r\nfprintf('%i ',sv);fprintf('\\n') \r\n\r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n%Author Note: I found creating the complete set was time consuming\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge  add by raz as a Gimme\r\n\r\n [nr,nc]=size(s);\r\n %Example Zero processing\r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  %enter setting of p for 1s in corners\r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  %enter setting of p for 1s in corners\r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n %enter setting of p for 1s in corners \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n %setting p for 03 adjacent cases\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n %setting p for 33 adjacent\r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  %setting p for 03 diagonal\r\n \r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); \r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n  %setting p for 33 diagonal\r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); \r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i); \r\n  %3-0 adjacent set segs to 0/5\r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n %Slithering Starting Techniques misses the 13 edge Gimme     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 2;2 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5;5 0 5;5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 3 2;5 0 5 0 5;5 3 5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 5 3 5;5 0 5 5 0 5;5 3 5 5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T17:23:06.000Z","updated_at":"2025-05-02T19:04:22.000Z","published_at":"2020-11-12T23:27:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink II: Gimmes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check 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your own Test Suite (part 3)","description":"In this task you need to imagine that you — _yes, YOU_ — have developed a problem on Cody for _me_ to solve, and now *you need to implement a robust Test Suite* to check whether _my_ submitted solutions meet _your_ requirements or not, and to be fairly assured that I am not trying to 'game' the system.  \r\n\r\nSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!  \r\n\r\nThe problem you've set me is to:\r\n\r\n* output the *sine* of an angle, when the angle is specified in degrees as a (scalar) \u003chttps://au.mathworks.com/help/matlab/ref/double.html |double|\u003e, with no restriction in the domain,   \r\n* *without* using |regexp| or |regexpi| or |ans|, \r\n* *within* less than 0.01 seconds (10 milliseconds).\r\n\r\nYou provide me with the following example for the function defined as |s = SINE(a)|:\r\n\r\n % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\r\n\r\nNow I have responded by submitting a large number of purported 'solutions', some of which are _reasonably accurate_, and others which are either too imprecise or else logically flawed.  \r\n\r\nYour Test Suite (contained within _your_ function |testSuite|) must address each of the elements of your problem specification:  \r\n\r\n# Check that my submitted code reliably returns *sufficiently accurate* values for sine of many different angles.  Use the \u003chttps://au.mathworks.com/help/matlab/ref/assert.html |assert|\u003e function.  \r\n# Ensure that the returned *data type* is suitable.  \r\n# You *cannot* use (or mention) the functions |sind|, |sin|, |cscd| or |cosd| in your Test Suite;  any other functions are allowed.  [ _MOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!_ ]\r\n# You must check that I *haven't used* \"regexp\" or \"regexpi\" or \"ans\".  You could try using the \u003chttps://au.mathworks.com/help/coursework/ug/assessfunctionabsence.html |assessFunctionAbsence|\u003e function in the form |assessFunctionAbsence(..., 'FileName','SINE.m')|, or else you could try 'manually' opening the file |SINE.m| that corresponds to my submitted solution and then parsing it for occurrence of the prohibited function/variable.  \r\n# You must check that my code returns a result within *less than 0.01 seconds* (for a single input of an arbitrary angle).  You could use \u003chttps://au.mathworks.com/help/matlab/ref/tic.html |tic|\u003e \u0026 \u003chttps://au.mathworks.com/help/matlab/ref/toc.html |toc|\u003e or \u003chttps://au.mathworks.com/help/matlab/ref/timeit.html |timeit|\u003e, or a variety of other MATLAB functions (but not |cputime| in this case).  If your Test Suite claims that my submission runs slowly, then you must be confident that it is truly caused only by inefficiency in my submitted code, *not* just general/erratic overheads in 'queuing' or suchlike on the Cody servers.  \r\n# Your |assert| (or other) function must throw \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44357 errors/exceptions\u003e with the following *|error message| text* contents.\r\n\r\n* 'Incorrect value' if the output is too inaccurate.\r\n* 'Incorrect data type' if the output is not appropriate.\r\n* 'Too slow' if my submitted code is too slow.\r\n* 'Banned word' if my submitted code contains the |regexp| or |regexpi| functions or the \u003chttps://au.mathworks.com/help/matlab/ref/ans.html |ans|\u003e variable.  For _this_ infringement, _additional_ text can _also_ (optionally!) be present in the message — for example, 'You cannot do that!  Banned word (regexp/regexpi)' would also be acceptable.\r\n\r\nWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!  \r\n\r\nSee also:\r\n\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44631 Problem 44631\u003e (part *0*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44617 Problem 44617\u003e (part *1*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44616 Problem 44616\u003e (part *2*)","description_html":"\u003cp\u003eIn this task you need to imagine that you — \u003ci\u003eyes, YOU\u003c/i\u003e — have developed a problem on Cody for \u003ci\u003eme\u003c/i\u003e to solve, and now \u003cb\u003eyou need to implement a robust Test Suite\u003c/b\u003e to check whether \u003ci\u003emy\u003c/i\u003e submitted solutions meet \u003ci\u003eyour\u003c/i\u003e requirements or not, and to be fairly assured that I am not trying to 'game' the system.\u003c/p\u003e\u003cp\u003eSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!\u003c/p\u003e\u003cp\u003eThe problem you've set me is to:\u003c/p\u003e\u003cul\u003e\u003cli\u003eoutput the \u003cb\u003esine\u003c/b\u003e of an angle, when the angle is specified in degrees as a (scalar) \u003ca href = \"https://au.mathworks.com/help/matlab/ref/double.html\"\u003e\u003ctt\u003edouble\u003c/tt\u003e\u003c/a\u003e, with no restriction in the domain,\u003c/li\u003e\u003cli\u003e\u003cb\u003ewithout\u003c/b\u003e using \u003ctt\u003eregexp\u003c/tt\u003e or \u003ctt\u003eregexpi\u003c/tt\u003e or \u003ctt\u003eans\u003c/tt\u003e,\u003c/li\u003e\u003cli\u003e\u003cb\u003ewithin\u003c/b\u003e less than 0.01 seconds (10 milliseconds).\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou provide me with the following example for the function defined as \u003ctt\u003es = SINE(a)\u003c/tt\u003e:\u003c/p\u003e\u003cpre\u003e % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\u003c/pre\u003e\u003cp\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are \u003ci\u003ereasonably accurate\u003c/i\u003e, and others which are either too imprecise or else logically flawed.\u003c/p\u003e\u003cp\u003eYour Test Suite (contained within \u003ci\u003eyour\u003c/i\u003e function \u003ctt\u003etestSuite\u003c/tt\u003e) must address each of the elements of your problem specification:\u003c/p\u003e\u003col\u003e\u003cli\u003eCheck that my submitted code reliably returns \u003cb\u003esufficiently accurate\u003c/b\u003e values for sine of many different angles.  Use the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/assert.html\"\u003e\u003ctt\u003eassert\u003c/tt\u003e\u003c/a\u003e function.\u003c/li\u003e\u003cli\u003eEnsure that the returned \u003cb\u003edata type\u003c/b\u003e is suitable.\u003c/li\u003e\u003cli\u003eYou \u003cb\u003ecannot\u003c/b\u003e use (or mention) the functions \u003ctt\u003esind\u003c/tt\u003e, \u003ctt\u003esin\u003c/tt\u003e, \u003ctt\u003ecscd\u003c/tt\u003e or \u003ctt\u003ecosd\u003c/tt\u003e in your Test Suite;  any other functions are allowed.  [ \u003ci\u003eMOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/i\u003e ]\u003c/li\u003e\u003cli\u003eYou must check that I \u003cb\u003ehaven't used\u003c/b\u003e \"regexp\" or \"regexpi\" or \"ans\".  You could try using the \u003ca href = \"https://au.mathworks.com/help/coursework/ug/assessfunctionabsence.html\"\u003e\u003ctt\u003eassessFunctionAbsence\u003c/tt\u003e\u003c/a\u003e function in the form \u003ctt\u003eassessFunctionAbsence(..., 'FileName','SINE.m')\u003c/tt\u003e, or else you could try 'manually' opening the file \u003ctt\u003eSINE.m\u003c/tt\u003e that corresponds to my submitted solution and then parsing it for occurrence of the prohibited function/variable.\u003c/li\u003e\u003cli\u003eYou must check that my code returns a result within \u003cb\u003eless than 0.01 seconds\u003c/b\u003e (for a single input of an arbitrary angle).  You could use \u003ca href = \"https://au.mathworks.com/help/matlab/ref/tic.html\"\u003e\u003ctt\u003etic\u003c/tt\u003e\u003c/a\u003e \u0026 \u003ca href = \"https://au.mathworks.com/help/matlab/ref/toc.html\"\u003e\u003ctt\u003etoc\u003c/tt\u003e\u003c/a\u003e or \u003ca href = \"https://au.mathworks.com/help/matlab/ref/timeit.html\"\u003e\u003ctt\u003etimeit\u003c/tt\u003e\u003c/a\u003e, or a variety of other MATLAB functions (but not \u003ctt\u003ecputime\u003c/tt\u003e in this case).  If your Test Suite claims that my submission runs slowly, then you must be confident that it is truly caused only by inefficiency in my submitted code, \u003cb\u003enot\u003c/b\u003e just general/erratic overheads in 'queuing' or suchlike on the Cody servers.\u003c/li\u003e\u003cli\u003eYour \u003ctt\u003eassert\u003c/tt\u003e (or other) function must throw \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44357\"\u003eerrors/exceptions\u003c/a\u003e with the following \u003cb\u003e\u003ctt\u003eerror message\u003c/tt\u003e text\u003c/b\u003e contents.\u003c/li\u003e\u003c/ol\u003e\u003cul\u003e\u003cli\u003e'Incorrect value' if the output is too inaccurate.\u003c/li\u003e\u003cli\u003e'Incorrect data type' if the output is not appropriate.\u003c/li\u003e\u003cli\u003e'Too slow' if my submitted code is too slow.\u003c/li\u003e\u003cli\u003e'Banned word' if my submitted code contains the \u003ctt\u003eregexp\u003c/tt\u003e or \u003ctt\u003eregexpi\u003c/tt\u003e functions or the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/ans.html\"\u003e\u003ctt\u003eans\u003c/tt\u003e\u003c/a\u003e variable.  For \u003ci\u003ethis\u003c/i\u003e infringement, \u003ci\u003eadditional\u003c/i\u003e text can \u003ci\u003ealso\u003c/i\u003e (optionally!) be present in the message — for example, 'You cannot do that!  Banned word (regexp/regexpi)' would also be acceptable.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!\u003c/p\u003e\u003cp\u003eSee also:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44631\"\u003eProblem 44631\u003c/a\u003e (part \u003cb\u003e0\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44617\"\u003eProblem 44617\u003c/a\u003e (part \u003cb\u003e1\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44616\"\u003eProblem 44616\u003c/a\u003e (part \u003cb\u003e2\u003c/b\u003e)\u003c/li\u003e\u003c/ul\u003e","function_template":"function dummy = testSuite()\r\n    \r\n    %% Test 1 — test outputs of submitted SINE function for various input values\r\n    % RATIONALE:  Computing SINE requires relatively simple code, \r\n    %             as the focus in the present task is on implementing a robust Test Suite.  \r\n    % MOTIVATION:  I could be submitting lazy code that only works for \r\n    %              a small number of angles, if that's all you test.\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    \r\n    \r\n    %% Test 2 — test for use/presence of  \"regexp\" or \"regexpi\" or \"ans\" in the submission\r\n    % MOTIVATION:  The functions regexp and regexpi are sometimes pertinent to efficient solution of a problem, \r\n    %              but at other times are used to artificially decrease the size of a Cody submission.\r\n    %              If I'm using regexp or regexpi for *this* problem of yours (where neither is relevant), \r\n    %              then it's probably because I'm trying to 'cheat' to get a smaller Cody score.\r\n    %              Use of the ans variable is also generally employed purely to decrease\r\n    %              Cody size, at the expense of clarity in the code.  \r\n    assessFunctionAbsence( '' , 'FileName','SINE.m' )\r\n\r\n    \r\n    %% Test 3 — check the speed of execution of the submission \r\n    % MOTIVATION:  Some code that is rated as \"small\" on Cody is actually quite computationally inefficient, \r\n    %              but it is possible to 'manually' enforce a requirement for Cody submissions to be at least \r\n    %              moderately efficient.  See e.g. Cody Problems 44351, 44356 \u0026 44383.  \r\n    %              So this will balance the Cody focus on small code 'size' with \r\n    %              the desire to ensure code is also computationally efficient.\r\n    toc\r\n    timeit( SINE(45) )\r\n    duration = tic\r\n    assert( duration , '' )\r\n\r\nend\r\n\r\n%{\r\nNOTE:  \r\nThe text \"dummy = \" was added to this Function Template, \r\nbecause Cody was generating a spurious error, namely\r\n        Your problem cannot be published until you:\r\n        •Edit the function name in the test suite to match the function name in the function template\r\nHowever, in the Reference Solution it was confirmed to be unnecessary.  \r\n%}\r\n\r\n%{\r\nFOOTNOTE:  \r\nAlthough 10 milliseconds may not seem like a slow execution time, \r\ncomputing the sine of an angle is a very common task, and a general program \r\nmight need to compute it hundreds, thousands or even millions of times.  \r\nIn the latter case, if each computation of sine were to take more than 10 \r\nmilliseconds, then the hypothetical program would run for several hours.  \r\nBy way of comparison, for a 1×1000000 vector input MATLAB's built-in sin \r\nfunction is currently taking a total of about 10 to 25 milliseconds on Cody, \r\nwhile sind is taking about 20 to 50 milliseconds._  \r\n%}","test_suite":"%% Timing\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfprintf(fileID,'   pause(0.012);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Too slow') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Use of ans\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function ans = SINE(angle)\\n');\r\nfprintf(fileID,'   sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    e.message\r\n    assert( contains(e.message, 'Banned word') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Possible false alarm for use of ans\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   % This is a simple problem:  the answer only takes one line!\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Use of regexp/regexpi (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfprintf(fileID,'   dummy = regexp(''Test text'', ''t'');\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    e.message\r\n    assert( contains(e.message, 'Banned word') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Use of regexp/regexpi (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfprintf(fileID,'   dummy = regexpi(''Test text'', ''t'');\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    e.message\r\n    assert( contains(e.message, 'Banned word') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Ensure no \"sind\" or \"sin\" or \"cosd\" in _your_ Test Suite\r\n% NOTE:  You may notice that the user function has been named \"SINE\", \r\n%        in uppercase.  That is an extra precaution to avoid accidentally \r\n%        triggering an error due to a banned 'word' (sequence of characters).  \r\n%        Careful choice of code to check for banned _functions_ is better!  \r\nassessFunctionAbsence('sind', 'FileName','testSuite.m')\r\nassessFunctionAbsence('sin',  'FileName','testSuite.m')\r\nassessFunctionAbsence('cscd', 'FileName','testSuite.m')\r\nassessFunctionAbsence('cosd', 'FileName','testSuite.m')\r\n\r\n\r\n%% Exactly right\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\n                    testSuite()\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * pi / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = 1 ./ cscd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * 3.14 / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * (22/7) / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + 10000*eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Completely wrong\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = cosd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = -sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle)*sign(sind(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Fixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(fix(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Unfixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   inc=0;\\n');\r\nfprintf(fileID,'   if mod(angle,1)==0, inc=1; end;\\n');\r\nfprintf(fileID,'   s = sind(angle + inc);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int8(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int16(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') | ...\r\n            isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int32(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int64(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') | ...\r\n            isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(double(int32(angle)));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = \"ratio\";\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = \"?\";\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = {1:1E4};\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Wrong data type\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = {sind(angle)};\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2018-04-18T14:04:26.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-02-13T12:05:07.000Z","updated_at":"2018-05-11T13:20:58.000Z","published_at":"2018-02-20T13:11:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task you need to imagine that you —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyes, YOU\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — have developed a problem on Cody for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eme\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to solve, and now\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyou need to implement a robust Test Suite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to check whether\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e submitted solutions meet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e requirements or not, and to be fairly assured that I am not trying to 'game' the system.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo the tables are turned! You are now in the role of Tester! I am in the role of Player!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe problem you've set me is to:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of an angle, when the angle is specified in degrees as a (scalar)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/double.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edouble\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, with no restriction in the domain,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewithout\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e using\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eregexp\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eregexpi\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eans\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewithin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e less than 0.01 seconds (10 milliseconds).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou provide me with the following example for the function defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es = SINE(a)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % INPUT\\n a = 45 % degrees\\n % OUTPUT\\n s = 1/sqrt(2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ereasonably accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and others which are either too imprecise or else logically flawed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour Test Suite (contained within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etestSuite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) must address each of the elements of your problem specification:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck that my submitted code reliably returns\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esufficiently accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values for sine of many different angles. Use the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/assert.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEnsure that the returned\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edata type\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is suitable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecannot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e use (or mention) the functions\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esind\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecscd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecosd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in your Test Suite; any other functions are allowed. [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eMOTIVATION: You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou must check that I\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ehaven't used\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \\\"regexp\\\" or \\\"regexpi\\\" or \\\"ans\\\". You could try using the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/coursework/ug/assessfunctionabsence.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassessFunctionAbsence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function in the form\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassessFunctionAbsence(..., 'FileName','SINE.m')\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, or else you could try 'manually' opening the file\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSINE.m\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that corresponds to my submitted solution and then parsing it for occurrence of the prohibited function/variable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou must check that my code returns a result within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eless than 0.01 seconds\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (for a single input of an arbitrary angle). You could use\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/tic.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etic\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e \u0026amp;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/toc.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etoc\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/timeit.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etimeit\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, or a variety of other MATLAB functions (but not\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecputime\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in this case). If your Test Suite claims that my submission runs slowly, then you must be confident that it is truly caused only by inefficiency in my submitted code,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e just general/erratic overheads in 'queuing' or suchlike on the Cody servers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (or other) function must throw\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44357\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eerrors/exceptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e with the following\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eerror message\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e text\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e contents.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Incorrect value' if the output is too inaccurate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Incorrect data type' if the output is not appropriate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Too slow' if my submitted code is too slow.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Banned word' if my submitted code contains the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eregexp\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eregexpi\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e functions or the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/ans.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eans\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e variable. For\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ethis\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e infringement,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eadditional\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e text can\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ealso\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (optionally!) be present in the message — for example, 'You cannot do that! Banned word (regexp/regexpi)' would also be acceptable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee also:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44631\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44631\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44617\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44617\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44616\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44616\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44616,"title":"Make your own Test Suite (part 2)","description":"In this task you need to imagine that you — _yes, YOU_ — have developed a problem on Cody for _me_ to solve, and now *you need to develop a Test Suite* to check whether _my_ submitted solutions meet _your_ requirements or not.  \r\n\r\nSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!  \r\n\r\nThe problem you've set me is to:\r\n\r\n* output the *sine* of an angle, when the angle is specified in degrees as a (scalar) \u003chttps://au.mathworks.com/help/matlab/ref/double.html |double|\u003e, with no restriction in the domain.\r\n\r\nYou provide me with the following example for the function defined as |s = SINE(a)|:\r\n\r\n % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\r\n\r\nNow I have responded by submitting a large number of purported 'solutions', some of which are _reasonably accurate_, and others which are either too imprecise or else logically flawed.  \r\n\r\nYour Test Suite (contained within _your_ function |testSuite|) must address each of the elements of your problem specification:  \r\n\r\n# Check that my submitted code reliably returns *sufficiently accurate* values for sine of many different angles.  Use the \u003chttps://au.mathworks.com/help/matlab/ref/assert.html |assert|\u003e function.  \r\n# Ensure that the returned *data type* is suitable.  \r\n# You *cannot* use (or mention) the functions |sind|, |sin|, |cscd| or |cosd| in your Test Suite;  any other functions are allowed.  [ _MOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!_ ]\r\n# Your |assert| (or other) function must throw \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44357 errors/exceptions\u003e with the following *|error message| text* contents.\r\n\r\n* 'Incorrect value' if the output is too inaccurate.\r\n* 'Incorrect data type' if the output is not appropriate.\r\n\r\nWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!  \r\n\r\nSee also:\r\n\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44631 Problem 44631\u003e (part *0*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44617 Problem 44617\u003e (part *1*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44521 Problem 44521\u003e (part *3*)","description_html":"\u003cp\u003eIn this task you need to imagine that you — \u003ci\u003eyes, YOU\u003c/i\u003e — have developed a problem on Cody for \u003ci\u003eme\u003c/i\u003e to solve, and now \u003cb\u003eyou need to develop a Test Suite\u003c/b\u003e to check whether \u003ci\u003emy\u003c/i\u003e submitted solutions meet \u003ci\u003eyour\u003c/i\u003e requirements or not.\u003c/p\u003e\u003cp\u003eSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!\u003c/p\u003e\u003cp\u003eThe problem you've set me is to:\u003c/p\u003e\u003cul\u003e\u003cli\u003eoutput the \u003cb\u003esine\u003c/b\u003e of an angle, when the angle is specified in degrees as a (scalar) \u003ca href = \"https://au.mathworks.com/help/matlab/ref/double.html\"\u003e\u003ctt\u003edouble\u003c/tt\u003e\u003c/a\u003e, with no restriction in the domain.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou provide me with the following example for the function defined as \u003ctt\u003es = SINE(a)\u003c/tt\u003e:\u003c/p\u003e\u003cpre\u003e % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\u003c/pre\u003e\u003cp\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are \u003ci\u003ereasonably accurate\u003c/i\u003e, and others which are either too imprecise or else logically flawed.\u003c/p\u003e\u003cp\u003eYour Test Suite (contained within \u003ci\u003eyour\u003c/i\u003e function \u003ctt\u003etestSuite\u003c/tt\u003e) must address each of the elements of your problem specification:\u003c/p\u003e\u003col\u003e\u003cli\u003eCheck that my submitted code reliably returns \u003cb\u003esufficiently accurate\u003c/b\u003e values for sine of many different angles.  Use the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/assert.html\"\u003e\u003ctt\u003eassert\u003c/tt\u003e\u003c/a\u003e function.\u003c/li\u003e\u003cli\u003eEnsure that the returned \u003cb\u003edata type\u003c/b\u003e is suitable.\u003c/li\u003e\u003cli\u003eYou \u003cb\u003ecannot\u003c/b\u003e use (or mention) the functions \u003ctt\u003esind\u003c/tt\u003e, \u003ctt\u003esin\u003c/tt\u003e, \u003ctt\u003ecscd\u003c/tt\u003e or \u003ctt\u003ecosd\u003c/tt\u003e in your Test Suite;  any other functions are allowed.  [ \u003ci\u003eMOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/i\u003e ]\u003c/li\u003e\u003cli\u003eYour \u003ctt\u003eassert\u003c/tt\u003e (or other) function must throw \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44357\"\u003eerrors/exceptions\u003c/a\u003e with the following \u003cb\u003e\u003ctt\u003eerror message\u003c/tt\u003e text\u003c/b\u003e contents.\u003c/li\u003e\u003c/ol\u003e\u003cul\u003e\u003cli\u003e'Incorrect value' if the output is too inaccurate.\u003c/li\u003e\u003cli\u003e'Incorrect data type' if the output is not appropriate.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!\u003c/p\u003e\u003cp\u003eSee also:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44631\"\u003eProblem 44631\u003c/a\u003e (part \u003cb\u003e0\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44617\"\u003eProblem 44617\u003c/a\u003e (part \u003cb\u003e1\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44521\"\u003eProblem 44521\u003c/a\u003e (part \u003cb\u003e3\u003c/b\u003e)\u003c/li\u003e\u003c/ul\u003e","function_template":"function dummy = testSuite()\r\n    \r\n    % Test A — test outputs of submitted SINE function for various input values\r\n    % RATIONALE:  Computing SINE requires relatively simple code, \r\n    %             as the focus in the present task is on implementing a robust Test Suite.  \r\n    % MOTIVATION:  I could be submitting lazy code that only works for \r\n    %              a small number of angles, if that's all you test.\r\n    \r\n    %% Part 1\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    \r\n    %% Part 2\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    \r\n    %% Part 3\r\n    assert( isequal( SINE(45), 1/sqrt(2) ) , '' )\r\n    \r\n\r\n    %% Test B — test outputs of submitted SINE function to ensure the data type is correct.\r\n    \r\n\r\nend\r\n\r\n%{\r\nNOTE:  \r\nThe text \"dummy = \" was added to this Function Template, \r\nbecause Cody was generating a spurious error, namely\r\n        Your problem cannot be published until you:\r\n        •Edit the function name in the test suite to match the function name in the function template\r\nHowever, in the Reference Solution it was confirmed to be unnecessary.  \r\n%}\r\n","test_suite":"%% Ensure no \"sind\" or \"sin\" or \"cosd\" in _your_ Test Suite\r\n% NOTE:  You may notice that the user function has been named \"SINE\", \r\n%        in uppercase.  That is an extra precaution to avoid accidentally \r\n%        triggering an error due to a banned 'word' (sequence of characters).  \r\n%        Careful choice of code to check for banned _functions_ is better!  \r\nassessFunctionAbsence('sind', 'FileName','testSuite.m')\r\nassessFunctionAbsence('sin',  'FileName','testSuite.m')\r\nassessFunctionAbsence('cscd', 'FileName','testSuite.m')\r\nassessFunctionAbsence('cosd', 'FileName','testSuite.m')\r\n\r\n\r\n%% Exactly right\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\n                    testSuite()\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * pi / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = 1 ./ cscd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * 3.14 / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * (22/7) / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + 10000*eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Completely wrong\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = cosd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = -sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle)*sign(sind(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Fixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(fix(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Unfixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   inc=0;\\n');\r\nfprintf(fileID,'   if mod(angle,1)==0, inc=1; end;\\n');\r\nfprintf(fileID,'   s = sind(angle + inc);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int8(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int16(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') | ...\r\n            isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int32(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int64(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') | ...\r\n            isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(double(int32(angle)));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect value') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = \"ratio\";\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = \"?\";\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Total nonsense (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = {1:1E4};\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Wrong data type\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = {sind(angle)};\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\n    assert( isequal(e.message, 'Incorrect data type') , 'Wrong message' )\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-04-18T13:09:12.000Z","updated_at":"2018-05-11T13:19:31.000Z","published_at":"2018-04-18T13:51:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task you need to imagine that you —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyes, YOU\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — have developed a problem on Cody for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eme\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to solve, and now\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyou need to develop a Test Suite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to check whether\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e submitted solutions meet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e requirements or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo the tables are turned! You are now in the role of Tester! I am in the role of Player!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe problem you've set me is to:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of an angle, when the angle is specified in degrees as a (scalar)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/double.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edouble\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, with no restriction in the domain.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou provide me with the following example for the function defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es = SINE(a)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % INPUT\\n a = 45 % degrees\\n % OUTPUT\\n s = 1/sqrt(2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ereasonably accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and others which are either too imprecise or else logically flawed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour Test Suite (contained within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etestSuite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) must address each of the elements of your problem specification:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck that my submitted code reliably returns\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esufficiently accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values for sine of many different angles. Use the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/assert.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEnsure that the returned\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edata type\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is suitable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecannot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e use (or mention) the functions\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esind\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecscd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecosd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in your Test Suite; any other functions are allowed. [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eMOTIVATION: You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (or other) function must throw\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44357\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eerrors/exceptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e with the following\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eerror message\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e text\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e contents.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Incorrect value' if the output is too inaccurate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'Incorrect data type' if the output is not appropriate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee also:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44631\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44631\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44617\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44617\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44521\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44521\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44617,"title":"Make your own Test Suite (part 1)","description":"In this task you need to imagine that you — _yes, YOU_ — have developed a problem on Cody for _me_ to solve, and now *you need to prepare a simple Test Suite* to check whether _my_ submitted solutions meet _your_ requirements or not.  \r\n\r\nSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!  \r\n\r\nThe problem you've set me is to:\r\n\r\n* output the *sine* of an angle, when the angle is specified in degrees as a (scalar) \u003chttps://au.mathworks.com/help/matlab/ref/double.html |double|\u003e, with no restriction in the domain.\r\n\r\nYou provide me with the following example for the function defined as |s = SINE(a)|:\r\n\r\n % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\r\n\r\nNow I have responded by submitting a large number of purported 'solutions', some of which are _reasonably accurate_, and others which are either too imprecise or else logically flawed.  \r\n\r\nYour Test Suite (contained within _your_ function |testSuite|) must address each of the elements of your problem specification:  \r\n\r\n# Check that my submitted code reliably returns *sufficiently accurate* values for sine of many different angles.  Use the \u003chttps://au.mathworks.com/help/matlab/ref/assert.html |assert|\u003e function to generate \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44357 errors/exceptions\u003e if the values are not sufficiently accurate.  \r\n# You *cannot* use (or mention) the functions |sind|, |sin|, |cscd| or |cosd| in your Test Suite;  any other functions are allowed.  [ _MOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!_ ]\r\n\r\nWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!  Therefore the above specification is merely a _starting point_.  You will develop more robust Test Suites in:\r\n\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44616 Problem 44616\u003e (part *2*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44521 Problem 44521\u003e (part *3*)\r\n\r\nAlternatively, if even this Problem seems daunting, you may want to start with \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44631 Problem 44631\u003e (part *0*).","description_html":"\u003cp\u003eIn this task you need to imagine that you — \u003ci\u003eyes, YOU\u003c/i\u003e — have developed a problem on Cody for \u003ci\u003eme\u003c/i\u003e to solve, and now \u003cb\u003eyou need to prepare a simple Test Suite\u003c/b\u003e to check whether \u003ci\u003emy\u003c/i\u003e submitted solutions meet \u003ci\u003eyour\u003c/i\u003e requirements or not.\u003c/p\u003e\u003cp\u003eSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!\u003c/p\u003e\u003cp\u003eThe problem you've set me is to:\u003c/p\u003e\u003cul\u003e\u003cli\u003eoutput the \u003cb\u003esine\u003c/b\u003e of an angle, when the angle is specified in degrees as a (scalar) \u003ca href = \"https://au.mathworks.com/help/matlab/ref/double.html\"\u003e\u003ctt\u003edouble\u003c/tt\u003e\u003c/a\u003e, with no restriction in the domain.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou provide me with the following example for the function defined as \u003ctt\u003es = SINE(a)\u003c/tt\u003e:\u003c/p\u003e\u003cpre\u003e % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\u003c/pre\u003e\u003cp\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are \u003ci\u003ereasonably accurate\u003c/i\u003e, and others which are either too imprecise or else logically flawed.\u003c/p\u003e\u003cp\u003eYour Test Suite (contained within \u003ci\u003eyour\u003c/i\u003e function \u003ctt\u003etestSuite\u003c/tt\u003e) must address each of the elements of your problem specification:\u003c/p\u003e\u003col\u003e\u003cli\u003eCheck that my submitted code reliably returns \u003cb\u003esufficiently accurate\u003c/b\u003e values for sine of many different angles.  Use the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/assert.html\"\u003e\u003ctt\u003eassert\u003c/tt\u003e\u003c/a\u003e function to generate \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44357\"\u003eerrors/exceptions\u003c/a\u003e if the values are not sufficiently accurate.\u003c/li\u003e\u003cli\u003eYou \u003cb\u003ecannot\u003c/b\u003e use (or mention) the functions \u003ctt\u003esind\u003c/tt\u003e, \u003ctt\u003esin\u003c/tt\u003e, \u003ctt\u003ecscd\u003c/tt\u003e or \u003ctt\u003ecosd\u003c/tt\u003e in your Test Suite;  any other functions are allowed.  [ \u003ci\u003eMOTIVATION:  You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/i\u003e ]\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ!  Therefore the above specification is merely a \u003ci\u003estarting point\u003c/i\u003e.  You will develop more robust Test Suites in:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44616\"\u003eProblem 44616\u003c/a\u003e (part \u003cb\u003e2\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44521\"\u003eProblem 44521\u003c/a\u003e (part \u003cb\u003e3\u003c/b\u003e)\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eAlternatively, if even this Problem seems daunting, you may want to start with \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44631\"\u003eProblem 44631\u003c/a\u003e (part \u003cb\u003e0\u003c/b\u003e).\u003c/p\u003e","function_template":"function dummy = testSuite()\r\n    \r\n    % Check outputs of submitted SINE function for various input values\r\n    % RATIONALE:  Computing SINE requires relatively simple code, \r\n    %             as the focus in the present task is on implementing a robust Test Suite.  \r\n    % MOTIVATION:  I could be submitting lazy code that only works for \r\n    %              a small number of angles, if that's all you test.\r\n    \r\n    %% Test 1\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 2\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 3\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 4\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n\r\n    %% Test 5\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 6\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 7\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n\r\nend\r\n\r\n%{\r\nNOTE:  \r\nThe text \"dummy = \" was added to this Function Template, \r\nbecause Cody was generating a spurious error, namely\r\n        Your problem cannot be published until you:\r\n        •Edit the function name in the test suite to match the function name in the function template\r\nHowever, in the Reference Solution it was confirmed to be unnecessary.  \r\n%}","test_suite":"%% Ensure no \"sind\" or \"sin\" or \"cosd\" in _your_ Test Suite\r\n% NOTE:  You may notice that the user function has been named \"SINE\", \r\n%        in uppercase.  That is an extra precaution to avoid accidentally \r\n%        triggering an error due to a banned 'word' (sequence of characters).  \r\n%        Careful choice of code to check for banned _functions_ is better!  \r\nassessFunctionAbsence('sind', 'FileName','testSuite.m')\r\nassessFunctionAbsence('sin',  'FileName','testSuite.m')\r\nassessFunctionAbsence('cscd', 'FileName','testSuite.m')\r\nassessFunctionAbsence('cosd', 'FileName','testSuite.m')\r\n\r\n\r\n%% Exactly right\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\n                    testSuite()\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * pi / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = 1 ./ cscd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * 3.14 / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * (22/7) / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + 10000*eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Completely wrong\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = cosd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = -sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle)*sign(sind(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Fixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(fix(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Unfixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   inc=0;\\n');\r\nfprintf(fileID,'   if mod(angle,1)==0, inc=1; end;\\n');\r\nfprintf(fileID,'   s = sind(angle + inc);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int8(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int16(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nif rand() \u003c 0.5,\r\n    fprintf(fileID,'   s = int32(sind(angle));\\n');\r\nelse\r\n    fprintf(fileID,'   s = int64(sind(angle));\\n');\r\nend;\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Altered data type (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(double(int32(angle)));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-04-18T13:35:30.000Z","updated_at":"2018-05-11T13:20:07.000Z","published_at":"2018-04-18T13:49:32.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task you need to imagine that you —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyes, YOU\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — have developed a problem on Cody for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eme\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to solve, and now\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyou need to prepare a simple Test Suite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to check whether\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e submitted solutions meet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e requirements or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo the tables are turned! You are now in the role of Tester! I am in the role of Player!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe problem you've set me is to:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of an angle, when the angle is specified in degrees as a (scalar)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/double.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edouble\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, with no restriction in the domain.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou provide me with the following example for the function defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es = SINE(a)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % INPUT\\n a = 45 % degrees\\n % OUTPUT\\n s = 1/sqrt(2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ereasonably accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and others which are either too imprecise or else logically flawed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour Test Suite (contained within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etestSuite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) must address each of the elements of your problem specification:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck that my submitted code reliably returns\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esufficiently accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values for sine of many different angles. Use the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/assert.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function to generate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44357\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eerrors/exceptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e if the values are not sufficiently accurate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecannot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e use (or mention) the functions\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esind\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecscd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecosd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in your Test Suite; any other functions are allowed. [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eMOTIVATION: You shouldn't be exposing an efficient way of solving the problem within your Test Suite, otherwise I can just copy and paste your solution and submit it as my own!\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhen creating a Test Suite for a Cody problem it is a good habit to try to anticipate the tactics that diverse Players might employ! Therefore the above specification is merely a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003estarting point\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. You will develop more robust Test Suites in:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44616\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44616\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44521\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44521\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlternatively, if even this Problem seems daunting, you may want to start with\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44631\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44631\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44631,"title":"Make your own Test Suite (part 0)","description":"_Have no knowledge of \u003chttps://au.mathworks.com/help/matlab/matlab_prog/floating-point-numbers.html \"floating-point numbers\"\u003e?  Read the documentation, and/or try \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44690 Problem 44690\u003e first._\r\n\r\n——————————————————————————————————————————\r\n\r\nIn this task you need to imagine that you — _yes, YOU_ — have developed a problem on Cody for _me_ to solve, and now *you need to draft a naïve Test Suite* as the first step in developing a more robust Test Suite that would check whether _my_ submitted solutions meet _your_ requirements or not.  \r\n\r\nSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!  \r\n\r\nThe problem you've set me is to:\r\n\r\n* output the *sine* of an angle, when the angle is specified in degrees as a (scalar) \u003chttps://au.mathworks.com/help/matlab/ref/double.html |double|\u003e, with no restriction in the domain.\r\n\r\nYou provide me with the following example for the function defined as |s = SINE(a)|:\r\n\r\n % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\r\n\r\nNow I have responded by submitting a large number of purported 'solutions', some of which are _reasonably accurate_, and others which are either too imprecise or else logically flawed.  \r\n\r\nYour final Test Suite (contained within _your_ function |testSuite|) will have to address several aspects arising in your problem specification.  However, for this 'first draft' you are creating a naïve Test Suite with only one requirement:  \r\n\r\n# Check that my submitted code reliably returns *sufficiently accurate* values for sine of many different angles.  Use the \u003chttps://au.mathworks.com/help/matlab/ref/assert.html |assert|\u003e function to generate \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44357 errors/exceptions\u003e if the values are not sufficiently accurate.  \r\n\r\nYou *can* use the functions |sind|, |sin|, _etc._ in this draft Test Suite, if you want — even though this would be bad practice in your final Test Suite! \r\n\r\nThus, the above specification is merely a _starting point_.  You will develop more robust Test Suites in:\r\n\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44617 Problem 44617\u003e (part *1*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44616 Problem 44616\u003e (part *2*)\r\n* \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44521 Problem 44521\u003e (part *3*)","description_html":"\u003cp\u003e\u003ci\u003eHave no knowledge of \u003ca href = \"https://au.mathworks.com/help/matlab/matlab_prog/floating-point-numbers.html\"\u003e\"floating-point numbers\"\u003c/a\u003e?  Read the documentation, and/or try \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44690\"\u003eProblem 44690\u003c/a\u003e first.\u003c/i\u003e\u003c/p\u003e\u003cp\u003e——————————————————————————————————————————\u003c/p\u003e\u003cp\u003eIn this task you need to imagine that you — \u003ci\u003eyes, YOU\u003c/i\u003e — have developed a problem on Cody for \u003ci\u003eme\u003c/i\u003e to solve, and now \u003cb\u003eyou need to draft a naïve Test Suite\u003c/b\u003e as the first step in developing a more robust Test Suite that would check whether \u003ci\u003emy\u003c/i\u003e submitted solutions meet \u003ci\u003eyour\u003c/i\u003e requirements or not.\u003c/p\u003e\u003cp\u003eSo the tables are turned!  You are now in the role of Tester!  I am in the role of Player!\u003c/p\u003e\u003cp\u003eThe problem you've set me is to:\u003c/p\u003e\u003cul\u003e\u003cli\u003eoutput the \u003cb\u003esine\u003c/b\u003e of an angle, when the angle is specified in degrees as a (scalar) \u003ca href = \"https://au.mathworks.com/help/matlab/ref/double.html\"\u003e\u003ctt\u003edouble\u003c/tt\u003e\u003c/a\u003e, with no restriction in the domain.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou provide me with the following example for the function defined as \u003ctt\u003es = SINE(a)\u003c/tt\u003e:\u003c/p\u003e\u003cpre\u003e % INPUT\r\n a = 45 % degrees\r\n % OUTPUT\r\n s = 1/sqrt(2)\u003c/pre\u003e\u003cp\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are \u003ci\u003ereasonably accurate\u003c/i\u003e, and others which are either too imprecise or else logically flawed.\u003c/p\u003e\u003cp\u003eYour final Test Suite (contained within \u003ci\u003eyour\u003c/i\u003e function \u003ctt\u003etestSuite\u003c/tt\u003e) will have to address several aspects arising in your problem specification.  However, for this 'first draft' you are creating a naïve Test Suite with only one requirement:\u003c/p\u003e\u003col\u003e\u003cli\u003eCheck that my submitted code reliably returns \u003cb\u003esufficiently accurate\u003c/b\u003e values for sine of many different angles.  Use the \u003ca href = \"https://au.mathworks.com/help/matlab/ref/assert.html\"\u003e\u003ctt\u003eassert\u003c/tt\u003e\u003c/a\u003e function to generate \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44357\"\u003eerrors/exceptions\u003c/a\u003e if the values are not sufficiently accurate.\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eYou \u003cb\u003ecan\u003c/b\u003e use the functions \u003ctt\u003esind\u003c/tt\u003e, \u003ctt\u003esin\u003c/tt\u003e, \u003ci\u003eetc.\u003c/i\u003e in this draft Test Suite, if you want — even though this would be bad practice in your final Test Suite!\u003c/p\u003e\u003cp\u003eThus, the above specification is merely a \u003ci\u003estarting point\u003c/i\u003e.  You will develop more robust Test Suites in:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44617\"\u003eProblem 44617\u003c/a\u003e (part \u003cb\u003e1\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44616\"\u003eProblem 44616\u003c/a\u003e (part \u003cb\u003e2\u003c/b\u003e)\u003c/li\u003e\u003cli\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44521\"\u003eProblem 44521\u003c/a\u003e (part \u003cb\u003e3\u003c/b\u003e)\u003c/li\u003e\u003c/ul\u003e","function_template":"function dummy = testSuite()\r\n    \r\n    % Check outputs of submitted SINE function for various input values\r\n    % RATIONALE:  Computing SINE requires relatively simple code, \r\n    %             as the focus in the present task is on implementing a robust Test Suite.  \r\n    % MOTIVATION:  I could be submitting lazy code that only works for \r\n    %              a small number of angles, if that's all you test.\r\n    \r\n    %% Test 1\r\n    assert( isequal( SINE(45), 1/sqrt(2) )  )\r\n    \r\n    %% Test 2\r\n    assert( isequal( SINE(45), sine(45) )  )\r\n    \r\n    %% Test 3\r\n    assert( isequal( SINE(45), sin(45) )  )\r\n    \r\n    %% Test 4\r\n    assert( isequal( SINE(45), sind(45) )  )\r\n\r\n    %% Test 5\r\n    assert( isequal( SINE(45), sine(1/sqrt(2)) )  )\r\n    \r\n    %% Test 6\r\n    assert( isequal( SINE(45), sin(1/sqrt(2)) )  )\r\n    \r\n    %% Test 7\r\n    assert( isequal( SINE(45), sind(1/sqrt(2)) )  )\r\n    \r\n\r\nend\r\n\r\n%{\r\nNOTE:  \r\nThe text \"dummy = \" was added to this Function Template, \r\nbecause Cody was generating a spurious error, namely\r\n        Your problem cannot be published until you:\r\n        •Edit the function name in the test suite to match the function name in the function template\r\nHowever, in the Reference Solution it was confirmed to be unnecessary.  \r\n%}","test_suite":"%% Placeholder\r\n%assessFunctionAbsence('sind', 'FileName','testSuite.m')\r\n\r\n\r\n%% Exactly right\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\n                    testSuite()\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * pi / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = 1 ./ cscd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'pass';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * 3.14 / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Insufficiently close (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sin(angle * (22/7) / 180);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Sufficiently close (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle + 10000*eps(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Completely wrong\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = cosd(angle);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (I)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (II)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = -sind(angle*sign(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% Partly wrong (III)\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(angle)*sign(sind(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Fixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   s = sind(fix(angle));\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n\r\n\r\n%% 'Unfixed' angles\r\nfileID = fopen('SINE.m','w');\r\nfprintf(fileID,'function s = SINE(angle)\\n');\r\nfprintf(fileID,'   inc=0;\\n');\r\nfprintf(fileID,'   if mod(angle,1)==0, inc=1; end;\\n');\r\nfprintf(fileID,'   s = sind(angle + inc);\\n');\r\nfclose(fileID);\r\nstatus_correct = 'fail';\r\nstatus = 'pass';\r\ntry\r\n    testSuite()\r\ncatch e\r\n    status = 'fail'\r\nend\r\nassert( isequal(status, status_correct) , 'Wrong status' )\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":6,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-05-05T12:36:47.000Z","updated_at":"2018-06-17T02:29:04.000Z","published_at":"2018-05-05T13:35:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHave no knowledge of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/matlab_prog/floating-point-numbers.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"floating-point numbers\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e? Read the documentation, and/or try\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44690\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eProblem 44690\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e first.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e——————————————————————————————————————————\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task you need to imagine that you —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyes, YOU\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — have developed a problem on Cody for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eme\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to solve, and now\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyou need to draft a naïve Test Suite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as the first step in developing a more robust Test Suite that would check whether\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e submitted solutions meet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e requirements or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo the tables are turned! You are now in the role of Tester! I am in the role of Player!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe problem you've set me is to:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of an angle, when the angle is specified in degrees as a (scalar)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/double.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edouble\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, with no restriction in the domain.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou provide me with the following example for the function defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es = SINE(a)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % INPUT\\n a = 45 % degrees\\n % OUTPUT\\n s = 1/sqrt(2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow I have responded by submitting a large number of purported 'solutions', some of which are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ereasonably accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and others which are either too imprecise or else logically flawed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour final Test Suite (contained within\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eyour\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etestSuite\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) will have to address several aspects arising in your problem specification. However, for this 'first draft' you are creating a naïve Test Suite with only one requirement:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck that my submitted code reliably returns\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esufficiently accurate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values for sine of many different angles. Use the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://au.mathworks.com/help/matlab/ref/assert.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eassert\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e function to generate\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44357\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eerrors/exceptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e if the values are not sufficiently accurate.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecan\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e use the functions\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esind\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eetc.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in this draft Test Suite, if you want — even though this would be bad practice in your final Test Suite!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThus, the above specification is merely a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003estarting point\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. You will develop more robust Test Suites in:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44617\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44617\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44616\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44616\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44521\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44521\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (part\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":49743,"title":"Determine aquifer properties: unsteady pump test in a confined aquifer","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 714.15px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 357.075px; transform-origin: 407px 357.075px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 382.358px 7.79167px; transform-origin: 382.358px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAn important task in characterizing the flow of groundwater is to determine the properties of the aquifer, or the underground water-bearing formation. One approach is to disturb the aquifer, observe its response, and fit a theoretical formula to the observations. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 106.633px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 53.3167px; text-align: left; transform-origin: 384px 53.3167px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 297.567px 7.79167px; transform-origin: 297.567px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, suppose a confined aquifer initially has no flow. In that case, the piezometric head \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eh\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 67.675px 7.79167px; transform-origin: 67.675px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, or the level to which water would rise in an observation well, would be a uniform value \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"h0\" style=\"width: 15.5px; height: 20px;\" width=\"15.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 124.467px 7.79167px; transform-origin: 124.467px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. A well turned on and pumped at a rate \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"Q0\" style=\"width: 19px; height: 20px;\" width=\"19\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.9083px 7.79167px; transform-origin: 38.9083px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e will create a cone of depression; that is, it will draw down the piezometric head to a level \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"h(r)\" style=\"width: 29px; height: 18.5px;\" width=\"29\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 24.8917px 7.79167px; transform-origin: 24.8917px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003er\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 95.2917px 7.79167px; transform-origin: 95.2917px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the radial distance from the well. Applying conservation of mass and Darcy’s law to this situation leads to a diffusion equation whose solution for the drawdown \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"s = h0 - h\" style=\"width: 64.5px; height: 20px;\" width=\"64.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 79.3417px 7.79167px; transform-origin: 79.3417px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as a function of distance \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003er\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 30.725px 7.79167px; transform-origin: 30.725px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and time \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003et\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7px 7.79167px; transform-origin: 7px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 44px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 22px; text-align: left; transform-origin: 384px 22px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-17px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"s = (Q0/(4 pi T)) integral(exp(-x)/x, u, infinity)\" style=\"width: 117px; height: 44px;\" width=\"117\" height=\"44\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21.0083px 7.79167px; transform-origin: 21.0083px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ewhere \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eT\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 65.9833px 7.79167px; transform-origin: 65.9833px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the transmissivity, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eS\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 67.15px 7.79167px; transform-origin: 67.15px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the storativity, and \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 36.05px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 18.025px; text-align: left; transform-origin: 384px 18.025px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"u = Sr^2/(4Tt)\" style=\"width: 49.5px; height: 36px;\" width=\"49.5\" height=\"36\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 349.917px 7.79167px; transform-origin: 349.917px 7.79167px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that achieves the objective of a pumping test: to determine the transmissivity and storativity from measurements of drawdown in time. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 347.467px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 173.733px; text-align: left; transform-origin: 384px 173.733px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 497px;height: 342px\" 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data-image-state=\"image-loaded\" width=\"497\" height=\"342\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [T,S] = confinedPumpTest(t,s,Q0,r)\r\n  % t = time, s = drawdown, Q0 = pumping rate, r = distance from well\r\n  T = f1(t,s,Q0,r);\r\n  S = f2(t,s,Q0,r);\r\nend","test_suite":"%%\r\nQ0 = 0.15;                                %  Pumping rate (m3/s)\r\nr  = 100;                                 %  Distance from well (m)\r\nt  = [100 1000 10000 1e5 2e5];            %  Time (s)\r\ns  = [0.0402 1.236 3.664 6.276 7.05];     %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nT_correct = 1.042e-2;                     %  Transmissivity (m2/s)\r\nS_correct = 9.864e-4;                     %  Storativity\r\nassert(abs(T-T_correct)/T_correct \u003c 1e-3 \u0026\u0026 abs(S-S_correct)/S_correct \u003c 1e-3)\r\n\r\n%%\r\nQ0 = 0.30;                                %  Pumping rate (m3/s)\r\nr  = 100;                                 %  Distance from well (m)\r\nt  = [100 1000 10000 1e5 2e5];            %  Time (s)\r\ns  = [0.0804 2.472 7.328 12.552 14.1];    %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nT_correct = 1.042e-2;                     %  Transmissivity (m2/s)\r\nS_correct = 9.864e-4;                     %  Storativity\r\nassert(abs(T-T_correct)/T_correct \u003c 1e-3 \u0026\u0026 abs(S-S_correct)/S_correct \u003c 1e-3)\r\n\r\n%%\r\nQ0 = 0.1;                                      %  Pumping rate (m3/s)\r\nr  = 40;                                       %  Distance from well (m)\r\nt  = [300 2000 8000 12000 24000 40000];        %  Time (s)\r\ns  = [0.494 1.749 2.830 3.153 3.709 4.120];    %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nT_correct = 9.838e-3;                          %  Transmissivity (m2/s)\r\nS_correct = 3.4e-3;                            %  Storativity\r\nassert(abs(T-T_correct)/T_correct \u003c 1e-3 \u0026\u0026 abs(S-S_correct)/S_correct \u003c 1e-3)\r\n\r\n%%\r\nQ0 = 0.1;                                      %  Pumping rate (m3/s)\r\nr  = 65;                                       %  Distance from well (m)\r\nt  = [300 2000 8000 12000 24000 40000];        %  Time (s)\r\ns  = [0.125 1.050 2.067 2.383 2.931 3.339];    %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nT_correct = 9.838e-3;                          %  Transmissivity (m2/s)\r\nS_correct = 3.4e-3;                            %  Storativity\r\nassert(abs(T-T_correct)/T_correct \u003c 2e-3 \u0026\u0026 abs(S-S_correct)/S_correct \u003c 1e-3)\r\n\r\n%%\r\nQ0 = 0.05;                                     %  Pumping rate (m3/s)\r\nr  = 5+10*rand;                                %  Distance from well (m)\r\nt  = [4e5 9e5 14e5 19e5 24e5];                 %  Time (s)\r\ns  = [0.859 0.918 0.951 0.973 0.991];          %  Drawdown (m)\r\n[T,S] = confinedPumpTest(t,s,Q0,r);\r\nlogfit = polyfit(log(t),s,1);                  \r\nTapprox = Q0/(4*pi*logfit(1));                       \r\nSapprox = 2.25*Tapprox*exp(-logfit(2)/logfit(1))/r^2;      \r\nassert(abs(T-Tapprox)/Tapprox \u003c 1e-3 \u0026\u0026 abs(S-Sapprox)/Sapprox \u003c 2e-3)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-04T00:26:53.000Z","updated_at":"2026-01-09T18:01:40.000Z","published_at":"2021-01-04T05:23:32.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn important task in characterizing the flow of groundwater is to determine the properties of the aquifer, or the underground water-bearing formation. One approach is to disturb the aquifer, observe its response, and fit a theoretical formula to the observations. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, suppose a confined aquifer initially has no flow. In that case, the piezometric head \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"h\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eh\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, or the level to which water would rise in an observation well, would be a uniform value \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"h0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eh_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. A well turned on and pumped at a rate \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"Q0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eQ_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e will create a cone of depression; that is, it will draw down the piezometric head to a level \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"h(r)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eh(r)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, where \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"r\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the radial distance from the well. Applying conservation of mass and Darcy’s law to this situation leads to a diffusion equation whose solution for the drawdown \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"s = h0 - h\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003es = h_0-h\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e as a function of distance \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"r\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and time \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"t\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003et\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"s = (Q0/(4 pi T)) integral(exp(-x)/x, u, infinity)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003es = {Q_0 \\\\over 4 \\\\pi T} \\\\int_u^\\\\infty {e^{-x} \\\\over x} dx\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"T\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the transmissivity, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"S\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the storativity, and \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"u = Sr^2/(4Tt)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eu = {S r^2 \\\\over 4 T t}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that achieves the objective of a pumping test: to determine the transmissivity and storativity from measurements of drawdown in time. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"342\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"497\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45434,"title":"Weighted Names","description":"A cell is given which contains several names. You've to return those names sorted by their weight. \r\n\r\nWeight is to be calculated by the following rules - \r\n \r\n 1.each letter's weight is the ASCII value. \r\n\r\n 2.the 1st name weights 2 times its original value but if the name contains any middle name \r\n   then the middle name would weight 2x while 1st name would weight 3x.\r\n   The family name will always weight 1x.\r\n\r\n\r\n 3. Some of the names might contain some prefix like Col, Prof, Hon etc. They would weight\r\n   10 times their 1st letter. [each letter is not to be weighted for this case]\r\n 4. Again some of the names might also contain some suffix.\r\n\r\nFor example,\r\n\r\n  Ser Arthur Dayne\r\n\r\n Here, 'Ser' is a prefix. so value = 83*10 = 830.  [ASCII value of 'S' is 83]\r\n       'Dayne' is the family name. so value = 497*1 = 497\r\n       'Arthur' is the 1st name.   so value = 630*2 = 1260\r\n So total value= 830 + 497 + 1260 = 2587.\r\n","description_html":"\u003cp\u003eA cell is given which contains several names. You've to return those names sorted by their weight.\u003c/p\u003e\u003cp\u003eWeight is to be calculated by the following rules -\u003c/p\u003e\u003cpre\u003e 1.each letter's weight is the ASCII value. \u003c/pre\u003e\u003cpre\u003e 2.the 1st name weights 2 times its original value but if the name contains any middle name \r\n   then the middle name would weight 2x while 1st name would weight 3x.\r\n   The family name will always weight 1x.\u003c/pre\u003e\u003cpre\u003e 3. Some of the names might contain some prefix like Col, Prof, Hon etc. They would weight\r\n   10 times their 1st letter. [each letter is not to be weighted for this case]\r\n 4. Again some of the names might also contain some suffix.\u003c/pre\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eSer Arthur Dayne\r\n\u003c/pre\u003e\u003cpre\u003e Here, 'Ser' is a prefix. so value = 83*10 = 830.  [ASCII value of 'S' is 83]\r\n       'Dayne' is the family name. so value = 497*1 = 497\r\n       'Arthur' is the 1st name.   so value = 630*2 = 1260\r\n So total value= 830 + 497 + 1260 = 2587.\u003c/pre\u003e","function_template":"function out = weighted_names(x)","test_suite":"%%\r\nx={'Col Theodore Roosevelt', 'Gen Ulysses S. Grant','Cap Abraham Lincoln'};\r\ny_correct ={'Gen Ulysses S. Grant','Col Theodore Roosevelt','Cap Abraham Lincoln'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx={'Jean-Marie Sainthillier','Asif Newaz','Binbin Qi','Alfonso Nieto-Castanon','J. S. Kowontan'} ;\r\ny_correct ={'Jean-Marie Sainthillier','Alfonso Nieto-Castanon','J. S. Kowontan','Binbin Qi','Asif Newaz'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx={'Ser Gerold Hightower','Prince Lewyn Martell','Ser Barristan Selmy','Ser Arthur Dayne','Ser Jaime Lannister'};\r\ny_correct ={'Ser Barristan Selmy','Ser Gerold Hightower','Ser Jaime Lannister','Ser Arthur Dayne','Prince Lewyn Martell'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx={'Eddard Ned Stark','Rob Stark','Brandon Stark','Aegon Targaryen VI','George R. R. Martin'};\r\ny_correct ={'George R. R. Martin','Eddard Ned Stark','Aegon Targaryen VI','Brandon Stark','Rob Stark'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx = {'william','Ser Arthur','William II','William III'};\r\ny_correct ={'Ser Arthur','William II','William III','william'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n\r\n%%\r\nx={'Harry','Harry Potter','Harry James Potter','Ms. Hermione Granzer','Albus Percival Wulfric Brian Dumbledore'} ;\r\ny_correct ={'Albus Percival Wulfric Brian Dumbledore','Harry James Potter','Ms. Hermione Granzer','Harry Potter','Harry'};\r\nassert(isequal(weighted_names(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":9,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-10T07:52:03.000Z","updated_at":"2020-04-10T09:59:56.000Z","published_at":"2020-04-10T09:57:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA cell is given which contains several names. You've to return those names sorted by their weight.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWeight is to be calculated by the following rules -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 1.each letter's weight is the ASCII value. \\n\\n 2.the 1st name weights 2 times its original value but if the name contains any middle name \\n   then the middle name would weight 2x while 1st name would weight 3x.\\n   The family name will always weight 1x.\\n\\n 3. Some of the names might contain some prefix like Col, Prof, Hon etc. They would weight\\n   10 times their 1st letter. [each letter is not to be weighted for this case]\\n 4. Again some of the names might also contain some suffix.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Ser Arthur Dayne\\n\\n Here, 'Ser' is a prefix. so value = 83*10 = 830.  [ASCII value of 'S' is 83]\\n       'Dayne' is the family name. so value = 497*1 = 497\\n       'Arthur' is the 1st name.   so value = 630*2 = 1260\\n So total value= 830 + 497 + 1260 = 2587.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46833,"title":"Roots, Bloody Roots: part 1/2","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1239.6px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 619.8px; transform-origin: 407px 619.8px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 83.2px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 41.6px; text-align: left; transform-origin: 384px 41.6px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eUsing curves or lines, we can easily spot roots from n-degree polynomials in 2D: wherever they cross the real line, we know there is a root. However, as we are well aware, roots can also be complex numbers. In this case, plotting curves no longer seem adequate since they won't be found at the real line. That's when coloring comes into place; by painting the 2D plane, we can visualize where complex or real roots are and get a sense of what's happening in a function.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 163.2px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 81.6px; text-align: left; transform-origin: 384px 81.6px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn this problem, we will color the complex plane using the color space HSV. Imagine that there is a vector spawning from the origin to each point in the complex plane; horizontal axis imaginary and vertical axis real. Each vector has an angle \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eα\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e (from the real-line) and a norm \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eβ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e that defines a hue (H), and a brightness (V), respectively; for saturation (S), we assume a fixed value of 1, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"37.5\" height=\"17.5\" style=\"width: 37.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. H and V must range from 0 to 1, and to fit them into it, we will have to divide the angle by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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width=\"51.5\" height=\"34\" style=\"width: 51.5px; height: 34px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, and apply the cube root to the\u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.codecademy.com/articles/normalization#:~:text=Min%2Dmax%20normalization%20is%20one,decimal%20between%200%20and%201.\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e Min-max normalization\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e of the distance, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg 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Xgb1l3RO2M9KtRl3/4gT7BCPK/Y7Ngpy7q9xkb+fkwQ2pVMO7VrjW8+lDfAEJYGKXs9d6aUJCaMcIvbD7rjkXmDDMwo5l9i4I2fF9Y4SuDzGwJ4aoyMkllxpDhabYVMkDIjYUswYa163SJ55GPndk37NF4i2XiQBIhbgcjIOREYsiULKxUo8OmuREC8oLs2rF9iaDR3oBugsDIxx9qa0sKMkltxq+EgVf99kulgb1OydIZTi+Alw22oU2+0CasS/UjRmbVCz3plLRF10gVsA28VBsyIm/9wRc3eUxJLtqTt+h8U2knDz+2bu4LTB0YYkplxEuJDOOj964ui5acqoYXVDx984npgrAeQPtxXgmcOU4LgJj5IjJ2sKrexnU01W/04QkemLtPuT/QSwXRzKvPr54yZGm8SScN5st5o7MY74HBOQ2g8cIUYnOxMdbhZHG2hAZL3AmUMGDDK5knnD23AAQDKSkGNsTW7lOY4LoY8UYMbOReadtWxdNVlQzAcz1efNNDXgmETbxnlHSUBCxiC7E9rIODOmdsFAICcdFeknA/QD2C4Olw8tasXdkViSXFdnVcFnh9VKuA9cR3KS9XiW/StZih52ZVJhGa3Ij0vJsUMhISI/IJBfZea954NOldZbYxoANPdJGvRU7YRaX5gRSBcFEPv08hazXOKT3kQS2YTZMKvpygPYyiK3nGStsnJHSCy5xgTP2yAyBQ2L6vGtknz5sxw3yB2GFpDvyrZoSwWQzaMND5GFclWvPTW2m0t6RIq4GeW14lRiHDEdoJn7mqWWnbiUjMF4ozwHG6FYiCaArTz90cTw/pZSLNsTFkML+I1qJUdedJbTDedLXj2+9d1WbAR7S14WznusayFvLBAyVOTZcFv9xu/zJMDGTvLL0Q2MsYQ2Bijm2Yd5EwsIqFWyZ30+YKp0YABbeUCNkEy2A9IzlSpOHSGx5LzpPP0UzhdoQdjYRit1kQOOjYLjSZ7f33MMa7niyImHV47FMpKdZQs5HL1N28DI8MNGM3KxrrCn4U0t1TW12g21SvZWFvD+koo28AC28nAQTcACI79/LdMH6jKkXCr4jB63RibBud3LpCS2OC8m0voOX6u0ZBuyJJgkWKSPEg3H0mOjKY5kZ2m9W/x+UQI2HveogcuE0NCosalNmRTQ2rC1lVKTG6hiYyvWOg5gK0vf8quhak8de+YmlsQQji0ErQbuz8MkoSWinhPORTwp3j36Jj8W42S/4xLHc8RRD28Sv+cXXkW8SmguGPJJmsmRAfIk6j92KMu5ReD/bRNI3zk1BFCTvokqRbSFsZ7MJTwPdQOvVYmXZvYPPGU9BX79e9tC4d/y1Nbs7ExgJvpU2Tib8MgEe1ytEPBlAhXfcqNUKIUxYY4hYzSXO6V6DLDs4WtRu4BxolocvzM+zBu0JAjk9nv+PcwX+qDdnNDKpk0UyN2Scwf6DPZQxpdiysiOcaei1VREB8dB7qH+w4h27DVvvJ65ucJq1pKwlXVRou/Y+iTBZvEYHMBWn+KmDpc8M3MTSwIiLFp2IyYqg0zFJ8CN4h6o1Xj0GHCoEuxW7G5MIo5Zj5d0/TSYFCQpFb0FXKCfQIyloAiGeFjgVvsVMKX9nFvGhGeS8R553nwmPJ4oPI6AYu0yuyOAMuopzuXNouKdoYqQrhrNgOPp1PHWPKcmA2gBp3Sh5fMtyBg7IrV6Cdzm20i585Q01oAMmwsgzqYElQJ705+kzZC5w+/MJR8JRNwzNkbGCfDymi+gTwQIYwmgcvEuzEU2FfrGswy4crViwtHK2fB6CjmXNjDSLQGKgDjvRqgdEQZsqAA2XvUpTiIbF+/OyeCC9h7AVp/2AAmT5tmS7polkFyaWNIXTWGCcpylwpENJBPssUnbQjOiWA6/m5ZlRXRwl+fHNp8wMg/kN6cIE4ckod4ThSR8u/dKOzoGfQAPkG0V97A6tGgUIxqT38V5jn7QSNDQuNA6AEqAfSplNfGj7PIU+eXZU80pZ153vo0Ng7J6Nha+7B8cNDR3xsLMHb6auz/KoSWTU8+0L9ouHemNgI4mb0c5bKLMLdv8AMZW0krGCU0fObMx9162gcFPYw4CzsjALjZ6wK01dvdMoFoE1gC2+nD4cmZ5Ve3exJK11j0nC1sDGpbXLjzwtX6Hu4WHyC4/cfNyiVNudJ73WYQBEzQLjrQAYu6SL32bARsg6N+pNek9GJuskRFAxU6OkRithgnPIp86IrHg+M5TBjbjaZFiJ980kZUBX+t3NL78O30mnhKweVlzHOU46eM6rdoVJ4epY+YcOft5byYe5hwbG/P/9omoy/whTO5ZExOH70aTDWBrra7s96lMH72JJXuArWRAbdUfmPodGw557zn2opl5EGgBG+9r/CIKDcMj4sJe1lOwZy6w2WKqufcNCNBaWjn95yy4wamx+PZW0Zwlv3vgagFb6fdWhXv7+DlynlIWaNe/e4scH8A2cxrWMn2YgZxF74tHj3SzBLjop/W8fxe+gwnFRMD+gu2lVYWKIwbueI4rHA1gl/cU+JhzFPWOmpp732dwnmLTm1wwnJeO2rUxMiAZGcP8XmIle4sLLwEu+p16fk9gGz2K2gZW44d6bbO04XuZA3zQfooJYeMoOj2VS5k+sIGQggevzSjb2nprAdPS3+kHjxgeVmq+cqSBjU+g+JM7gA2tj+/DWI/XDJAoeV9z6c1xHvjY3BJo+ZqZ9JebBfw7mNzsONebpy2A7f+l2KuxjToP/DjWQMtswLxNq+7IpMYYwDYNbGav4i6zN6yRWHIpcLWev0ECJkJecIebO77nKMq3kiKGyceOyIVBHgN3q9bkHLqHlY4jeWmpgrmvmQnIlgidNoqmMeI5LfKblqhkKz57BI1tlO7hx7FE87AUYGabLUUk5IpB1fsdwDY9W31NVTPEr5FYsgVMS343UMKzigZE+he7eoANbY0AY7hyVNxCcytRC6aOddBMegi6fjLXCuhAT6BqFjZP89TW+GnGSKcu7FwzwYr4VW3qCMBmR16cV1MZcPzce4Ik0oGVnBKYPSBV40CgPTyztaLlRqDn6F+Meghgm57GRh9goaIB4J1jccGbGY2DLB2Z8DSu7TwwVncpUWYPsEEXgANHVAWAhleWCAwcET1p4Y0ywsRs2Zy897dkLPbUhhJnLx89tAiIrXhzq5kf9kCuRh9HADZzrv1R2vxaGXPN02s0j+c7GfnY0Z5N1OYYZpYnlmQdwNae5T7TB6l02CHg3YzGQe4BbB6IS8BmNoya88C0PbQnI/YaE5yJjM2NvGdTjH4LjcKuhzNgasJbtAEcrNx2xtwEAAhtg3oyFXGAbE37g8MGL3A0B1x7Jqx3xxGADWmwibLptZIOeIZByXYGHxPCODV9WWO1iAMbAeQHHaRaHDqArT1ZfaYP02SWJpZcctTkjWvPew8jOx+DzzEOcMFxAPGViWi0CdjuOBkgBtsR9GbJ4fCCJBpoIxxHOZ4CMNjBWtQPkkRyDG5NeCNrlrxkaI5wrLhg4rPTT4EkRxlCgSD2nnqyyaMAG7nRLGEBkQu1y29geQIDb89lfZHXbcrpYxvn06cI4wFsbWDzmT6IsyQUZDRcKO/Fs8tLOd/8Dgcw5QAx9bxlyKANAIwJYBcaFNQNdkbqqGLDgPjKpARIiAHl2J2nYfITE9AAkJ43ITreD62WXbgGMh6cychBm4ApQIvhGDsZWh9HVP8NtW4BU76d44mBcnt097+DNcf8QSvJQ9fsbQzwIckSH+pBo/W8j1phM8q1Zv97yYhvpgzeZcqAb1oyWXpft+FcMiAnJIw2bTxZW5gOoCNh6nl0I4yKPnl/7sUsQpbg4hXA1p7YPtMHd/eW5Su1jPYD3wmth8E28OHYxx8psIkB5Tf7nXbYFRl04jQJ9WJCYkfiArzseTQwxpT2ifWjXgCeRDhpACjHMxYVYPEMSQ9OzgVUeo5vUC8IrMfuZzYQvFksNPLZ875cMN4BQUKcasdSgJJFgnabh27RhidrYr/DK8ofdkA0Q74JgG15YmmLGEliLlkkLbtee8S3u4OqWmjMMP4xvpss0UxJjY2zinH1v0NaBvgJe6KgN78x/jh3/PNkPcHRgtOGOcRF0gJspJgWoCbh+GLjIg6Zi82NUD3+GG/GC9uovRvzkZhN+q9lsEHjQu7XVTR5f4qgPaIJsMsRzI/phI2Pb2uFUPG+lgKedcA7Va8AtvYkznlUvYWU2y0f+w7kxibAVbLLGVmzprX0SseO0IRetWJZe9uM+8YkwGYErYhNLA9I9xtYSUPs7QmsgupDAD/B/JMbXgBbn1iN+T6VUbevpat1FzssmiNaI15Ns5H5zaJG8+iRFPOXDBXYBdHYerS7nnbjnjEJTI2D2ahbsaetHuGUYm7glNHMfxjA1hLnC3+3TB9k3BhNg9zXw3HvAtywe3EU4ggLuHmyZismsCYZ5i4hYnjUcBgEqF3uHLLxwB778OQA8DzFEs2j940BNTR8jrtNUKPRALY+0Zo6jR1kNA1yXw/HvguHAOmgn5OMwxZtwFe3gtprksFeSZscZXviWI8t4dP5OqJe8GpC7l5jA0O7x6PP3OnevALY+iaEFUaGGV0kBPY1E3c5vhmhM62cXyGw85aA38DIFrPb2glgO++Jc45vzy6OnQSKwGNSqvIWa/0cv/OqvzNaOiaIO0r64yyZ5uayCWDbXMTRQUggJLC3BALY9pZ49BcSCAlsLoEAts1FHB2EBEICe0sggG1viUd/IYGQwOYSCGDbXMTRQUggJLC3BP4Pmr7gxkLfggMAAAAASUVORK5CYII=\" width=\"155\" height=\"46\" style=\"width: 155px; height: 46px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e (the cube root allows a faster transition between low and high brightness).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 62.4px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.2px; text-align: left; transform-origin: 384px 31.2px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eOnce we have the HSV value for a point at the complex plane, we can use the function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ehsv2rgb \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eto find the RGB corresponding value implement it if it becomes unavailable in the future (read about it \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.rapidtables.com/convert/color/hsv-to-rgb.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehere\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e). \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; text-decoration: underline; text-decoration-line: underline; \"\u003eAll RGB values must be rounded to 4 decimal places\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e to obtain the following result:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 608px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 304px; text-align: left; transform-origin: 384px 304px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: middle\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 75.4px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 37.7px; text-align: left; transform-origin: 384px 37.7px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eAll you have to do in this problem is to reproduce the above result for any matrix size, m x n x 3, given m and n as input. Moreover, the complex plane origin is always in the middle of the image \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"126\" height=\"34\" alt=\"origin\" style=\"width: 126px; height: 34px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. Notice that since the plane origin has the vector null, it will generate a black dot that brightens while going to the figure's edge.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.8px; text-align: left; transform-origin: 384px 20.8px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote-1:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eThis problem is inspired by a 3Blue1Brown's video: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.youtube.com/watch?v=b7FxPsqfkOY\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"font-style: italic; \"\u003ehttps://www.youtube.com/watch?v=b7FxPsqfkOY\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e, and I recommend that you watch his video if you haven't already. However, it is not required that you do to solve this problem.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.8px; text-align: left; transform-origin: 384px 20.8px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eThis problem has two parts, since it may not be easy to do everything at once. And you are encouraged to use the code for this 1st part of the problem into the next: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/46963-roots-bloody-roots-part-2-2\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblem 46963\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.4px; text-align: left; transform-origin: 384px 10.4px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 20.8px; text-align: left; transform-origin: 384px 20.8px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote-2\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e: This problem was last updated 21/08/2020; thanks to Alex's and Svyatoslav's\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003efeedback. All solutions were rescored. Please, let me know if anyone run into issues. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function cp = visComplexPlane(m,n)\r\n   cp = zeros(m,n,3);\r\nend","test_suite":"%% 1\r\nfiletext = fileread('visComplexPlane.m');\r\nassert(isempty(strfind(filetext, 'eval')),       'eval is forbidden.');\r\nassert(isempty(strfind(filetext, 'regexp')),     'regexp is forbidden.');\r\nassert(isempty(strfind(filetext, 'str2num')),    'str2num is forbidden.');\r\nassert(isempty(strfind(filetext, '!')),          'Shell commands are forbidden.');\r\nassert(isempty(strfind(filetext, 'mlock')),      'mlock is forbidden.');\r\nassert(isempty(strfind(filetext, 'munlock')),    'munlock is forbidden.');\r\nassert(isempty(strfind(filetext, 'fopen')),      'fopen is forbidden.');\r\n\r\n%% 2\r\nm = 500;\r\nn = 500;\r\nJ = visComplexPlane(m,n);\r\ny_correct = [25 166 212 32 32 242 196 9 240 14 196 74 21 239 79 166 192 155 87 14 90 203 196 123 106 103 228 231 133 176 133 16 121 220 125 188 164 128 53 118 3 38 18 99 144 255 171 204 104 9 125 233 196 129 18 113 142 72 7 251 36 178 60 100];\r\nassert(all(uint8(py.hashlib.sha512(py.str(mat2str(J(:))).encode('utf-8')).digest()) == y_correct));\r\n\r\n%% 3\r\nm = 251;\r\nn = 501;\r\nJ = visComplexPlane(m,n);\r\ny_correct = [187 126 253 159 247 242 249 25 213 223 27 57 35 39 110 223 166 247 82 101 71 12 193 15 251 152 215 87 39 71 245 94 66 73 135 243 163 56 168 217 198 201 20 19 255 247 100 139 72 201 30 85 234 182 238 233 137 161 206 156 86 45 71 171];\r\nassert(all(uint8(py.hashlib.sha512(py.str(mat2str(J(:))).encode('utf-8')).digest()) == y_correct));\r\n\r\n%% 4\r\nm = 750;\r\nn = 503;\r\nJ = visComplexPlane(m,n);\r\ny_correct = [83 99 239 135 129 205 10 212 2 78 245 228 122 186 117 122 39 246 53 200 68 153 227 131 222 205 146 174 216 106 200 116 100 39 179 255 244 175 226 38 246 215 98 181 228 63 118 196 176 151 192 27 150 223 123 183 2 89 219 78 215 214 125 145];\r\nassert(all(uint8(py.hashlib.sha512(py.str(mat2str(J(:))).encode('utf-8')).digest()) == y_correct));\r\n\r\n%% 5\r\nm = 501;\r\nn = 250;\r\nJ = visComplexPlane(m,n);\r\ny_correct = [82 133 222 186 239 236 118 153 188 94 116 95 105 194 37 121 52 33 230 224 181 136 182 185 124 112 24 77 148 212 113 178 164 22 80 252 117 188 26 178 66 195 83 52 159 22 205 207 210 220 12 199 110 145 121 39 101 110 2 51 117 40 93 113];\r\nassert(all(uint8(py.hashlib.sha512(py.str(mat2str(J(:))).encode('utf-8')).digest()) == y_correct));\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":20,"created_by":443343,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":"2020-10-20T14:25:24.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-17T03:50:33.000Z","updated_at":"2020-10-22T02:43:12.000Z","published_at":"2020-10-18T02:00:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUsing curves or lines, we can easily spot roots from n-degree polynomials in 2D: wherever they cross the real line, we know there is a root. However, as we are well aware, roots can also be complex numbers. In this case, plotting curves no longer seem adequate since they won't be found at the real line. That's when coloring comes into place; by painting the 2D plane, we can visualize where complex or real roots are and get a sense of what's happening in a function.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem, we will color the complex plane using the color space HSV. Imagine that there is a vector spawning from the origin to each point in the complex plane; horizontal axis imaginary and vertical axis real. Each vector has an angle \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\alpha\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (from the real-line) and a norm \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\beta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e that defines a hue (H), and a brightness (V), respectively; for saturation (S), we assume a fixed value of 1, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eS=1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. H and V must range from 0 to 1, and to fit them into it, we will have to divide the angle by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e2\\\\pi\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eH = \\\\frac{\\\\alpha}{2\\\\pi}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and apply the cube root to the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.codecademy.com/articles/normalization#:~:text=Min%2Dmax%20normalization%20is%20one,decimal%20between%200%20and%201.\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e Min-max normalization\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e of the distance, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eV = \\\\sqrt[3]{\\\\frac{\\\\beta-\\\\min(\\\\beta)}{\\\\max(\\\\beta)-\\\\min(\\\\beta)}}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (the cube root allows a faster transition between low and high brightness).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOnce we have the HSV value for a point at the complex plane, we can use the function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ehsv2rgb \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eto find the RGB corresponding value implement it if it becomes unavailable in the future (read about it \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.rapidtables.com/convert/color/hsv-to-rgb.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehere\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e). \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAll RGB values must be rounded to 4 decimal places\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to obtain the following result:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"middle\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll you have to do in this problem is to reproduce the above result for any matrix size, m x n x 3, given m and n as input. Moreover, the complex plane origin is always in the middle of the image \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"origin\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\left ( \\\\left \\\\lfloor\\\\frac{m+1} {2} \\\\right \\\\rfloor, \\\\left \\\\lfloor\\\\frac{n+1} {2} \\\\right \\\\rfloor \\\\right )\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. Notice that since the plane origin has the vector null, it will generate a black dot that brightens while going to the figure's edge.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote-1:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis problem is inspired by a 3Blue1Brown's video: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.youtube.com/watch?v=b7FxPsqfkOY\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ehttps://www.youtube.com/watch?v=b7FxPsqfkOY\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, and I recommend that you watch his video if you haven't already. However, it is not required that you do to solve this problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis problem has two parts, since it may not be easy to do everything at once. And you are encouraged to use the code for this 1st part of the problem into the next: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/46963-roots-bloody-roots-part-2-2\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 46963\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote-2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e: This problem was last updated 21/08/2020; thanks to Alex's and Svyatoslav's\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efeedback. All solutions were rescored. Please, let me know if anyone run into issues. 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2450,"title":"Back to the Future III","description":"Marty McFly and Doc Brown are \"back\" again. See the test suite to see what they are up to.","description_html":"\u003cp\u003eMarty McFly and Doc Brown are \"back\" again. See the test suite to see what they are up to.\u003c/p\u003e","function_template":"function y = your_solution()\r\n  y = 'use the flux capacitor';\r\nend","test_suite":"%%\r\n% Create necessary files\r\nfid = fopen('myfunction.m', 'w');fprintf(fid, 'function [a, b]=myfunction(), a=now; b=@()1;');fclose(fid);\r\nrehash;\r\n\r\n% Execute my function 10 times\r\ny=cell(10,1);\r\nfor iter=1:10\r\n[x y{iter}]=myfunction();\r\nend\r\n\r\n% Ouch! I have overwritten the value of \"x\"\r\n% Your task is to recover the value of the 3rd \"x\"\r\n\r\nx3=your_solution();\r\n\r\n% Clean the working directory (just in case)\r\n!/bin/rm checksolution.*\r\n\r\n% Build the solution checker checksolution.p\r\n!/bin/echo \"7630302e30307630302e30300005001cafeb3fb50000002c000000b7000000d0ef156cd482f6727568c9cb0434da4ffad9a709bcec12d21f8ccf6a6c736d54e232a2d8ed0f6c4af6e41cc8816d0521bc25f8f51890e51c1b4b151fd6b9731f0a501974f4c3b7fc8831644a60d74f49599303f43d51c9ce82b407b10a500ddd2e02933868a8815ce645d02a48c2c7fb5de99f2bc5fa313693cfc79482545e1aba66aa34776b8e2a46a370f472bfb3e2332bbf0ccc4043646014651579160c1ef8694310327422bee0059f02d33326f7b77b41cf2f273e4e\" | /usr/bin/xxd -ps -r \u003e checksolution.p\r\n\r\n% Check how you have done\r\nif checksolution(x3) else 'WRONG'+'ANSWER', end\r\n\r\n%{  \r\n\r\n \"I would prefer even to fail with honor than win by cheating.\"\r\n\r\n                                                                Sophocles\r\n\r\n%}\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":3,"created_by":5925,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2014-07-19T20:22:59.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-19T20:11:02.000Z","updated_at":"2025-11-22T20:19:29.000Z","published_at":"2014-07-19T20:16:20.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMarty McFly and Doc Brown are \\\"back\\\" again. See the test suite to see what they are up to.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":302,"title":"Convert Roman to Arabic Numerals","description":"Based upon what I see on tv and at the movies, the use of Roman numerals indicates something is important or sophisticated (e.g. the Superbowl, Olympics, movie credits). But who wants to bother with trying to translate them into decimal notation? I sure don't.. thus the problem is to take an input string array with an arbitrary number of Roman numerals, separated by spaces, and return an array of the resulting Arabic versions. Note since there is no standardization regarding some of the more 'modern' rules of Roman numeral notation, there may be multiple ways of representing the same Arabic number, as shown in the example below:\r\n'XXXIX CCXLVI' -\u003e [39 246]\r\n'DLV MCCXXXIV MDCCLXXVI' -\u003e [555 1234 1776]","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 176.867px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 88.4333px; transform-origin: 407px 88.4333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 351.5px 8px; transform-origin: 351.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBased upon what I see on tv and at the movies, the use of Roman numerals indicates something is important or sophisticated (e.g. the Superbowl, Olympics, movie credits). But who wants to bother with trying to translate them into decimal notation? I sure don't.. thus the problem is to take an input string array with an arbitrary number of Roman numerals, separated by spaces, and return an array of the resulting Arabic versions. Note since there is no standardization regarding some of the more 'modern' rules of Roman numeral notation, there may be multiple ways of representing the same Arabic number, as shown in the example below:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 40.8667px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 20.4333px; transform-origin: 404px 20.4333px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 104px 8.5px; tab-size: 4; transform-origin: 104px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e'XXXIX CCXLVI' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 44px 8.5px; transform-origin: 44px 8.5px; \"\u003e-\u0026gt; [39 246]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 172px 8.5px; tab-size: 4; transform-origin: 172px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 100px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 100px 8.5px; \"\u003e'DLV MCCXXXIV MDCCLXXVI' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 72px 8.5px; transform-origin: 72px 8.5px; \"\u003e-\u0026gt; [555 1234 1776]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function arabic = roman(x)\r\n  arabic = x;\r\nend","test_suite":"%%\r\nx = 'XIX';\r\ny_correct = 19;\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'I II III IV V VI VII VIII';\r\ny_correct = [1 2 3 4 5 6 7 8];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'IV MMXII LIV';\r\ny_correct = [4 2012 54];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'M D C L X V I';\r\ny_correct = [1000 500 100 50 10 5 1];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'IX XLIX XCIX CDXCIX CMXCIX';\r\ny_correct = [9 49 99 499 999];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'MDCCCCX MCMIII MCMX';\r\ny_correct = [1910 1903 1910];\r\nassert(isequal(roman(x),y_correct))\r\n\r\n%%\r\nx = 'MDCCCCLXXXXVIIII MCMXCIX MIM';\r\ny_correct = [1999 1999 1999];\r\nassert(isequal(roman(x),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":1022,"edited_by":223089,"edited_at":"2023-05-04T07:35:37.000Z","deleted_by":null,"deleted_at":null,"solvers_count":124,"test_suite_updated_at":"2023-05-04T07:35:37.000Z","rescore_all_solutions":false,"group_id":38,"created_at":"2012-02-10T04:20:30.000Z","updated_at":"2026-05-12T11:23:31.000Z","published_at":"2012-02-10T04:21:00.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBased upon what I see on tv and at the movies, the use of Roman numerals indicates something is important or sophisticated (e.g. the Superbowl, Olympics, movie credits). But who wants to bother with trying to translate them into decimal notation? I sure don't.. thus the problem is to take an input string array with an arbitrary number of Roman numerals, separated by spaces, and return an array of the resulting Arabic versions. Note since there is no standardization regarding some of the more 'modern' rules of Roman numeral notation, there may be multiple ways of representing the same Arabic number, as shown in the example below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA['XXXIX CCXLVI' -\u003e [39 246]\\n'DLV MCCXXXIV MDCCLXXVI' -\u003e [555 1234 1776]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42633,"title":"Cumulative maximum of an array","description":"Find the cumulative maximum of an array without using the built-in function cummax (and a few others). Your function should act identically to cummax, allowing the same inputs.\r\nExamples\r\nIf X = [0 4 3\r\n        6 5 2]\r\n\r\ncumax(X,1) is [0 4 3  and cumax(X,2) is [0 4 4\r\n               6 5 3]                    6 6 6]\r\n\r\ncumax(X,1,'reverse') is [6 5 3  and cumax(X,2,'reverse') is [4 4 3\r\n                         6 5 2]                              6 5 2]\r\n\r\nAlso,\r\ncumax([8 9 1 10 6 1 3 6 10 10]) returns [8 9 9 10 10 10 10 10 10 10]\r\n\r\ncumax([8 9 1 10 6 1 3 6 10 10]') returns [8 9 9 10 10 10 10 10 10 10]'\r\nSee also cumin.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 378.633px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 189.317px; transform-origin: 407px 189.317px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 234px 8px; transform-origin: 234px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFind the cumulative maximum of an array without using the built-in function\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ecummax\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 107px 8px; transform-origin: 107px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (and a few others). Your function should act identically to cummax, allowing the same inputs.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 33.5px 8px; transform-origin: 33.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExamples\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 265.633px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 132.817px; transform-origin: 404px 132.817px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eIf \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 40px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 40px 8.5px; \"\u003eX = [0 4 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; tab-size: 4; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e        6 5 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 184px 8.5px; tab-size: 4; transform-origin: 184px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 56px 8.5px; transform-origin: 56px 8.5px; \"\u003ecumax(X,1) is \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 128px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 128px 8.5px; \"\u003e[0 4 3  and cumax(X,2) is [0 4 4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 188px 8.5px; tab-size: 4; transform-origin: 188px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e               6 5 3]                    6 6 6]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 264px 8.5px; tab-size: 4; transform-origin: 264px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 40px 8.5px; transform-origin: 40px 8.5px; \"\u003ecumax(X,1,\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 36px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 36px 8.5px; \"\u003e'reverse'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003e) is \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 168px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 168px 8.5px; \"\u003e[6 5 3  and cumax(X,2,'reverse') is [4 4 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 268px 8.5px; tab-size: 4; transform-origin: 268px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e                         6 5 2]                              6 5 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eAlso,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 272px 8.5px; tab-size: 4; transform-origin: 272px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 160px 8.5px; transform-origin: 160px 8.5px; \"\u003ecumax([8 9 1 10 6 1 3 6 10 10]) returns \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 112px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 112px 8.5px; \"\u003e[8 9 9 10 10 10 10 10 10 10]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 280px 8.5px; tab-size: 4; transform-origin: 280px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 164px 8.5px; transform-origin: 164px 8.5px; \"\u003ecumax([8 9 1 10 6 1 3 6 10 10]') returns \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(196, 0, 0); border-block-start-color: rgb(196, 0, 0); border-bottom-color: rgb(196, 0, 0); border-inline-end-color: rgb(196, 0, 0); border-inline-start-color: rgb(196, 0, 0); border-left-color: rgb(196, 0, 0); border-right-color: rgb(196, 0, 0); border-top-color: rgb(196, 0, 0); caret-color: rgb(196, 0, 0); color: rgb(196, 0, 0); column-rule-color: rgb(196, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(196, 0, 0); perspective-origin: 116px 8.5px; text-decoration-color: rgb(196, 0, 0); text-emphasis-color: rgb(196, 0, 0); transform-origin: 116px 8.5px; \"\u003e[8 9 9 10 10 10 10 10 10 10]'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27.5px 8px; transform-origin: 27.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSee also\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ecumin\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = cumaX(x,varargin)\r\n  y = x;\r\nend","test_suite":"%%\r\nfiletext = fileread('cumaX.m');\r\nassert(isempty(strfind(filetext,'max')))\r\nassert(isempty(strfind(filetext,'cummin')))\r\nassert(isempty(strfind(filetext,'cummax')))\r\nassert(isempty(strfind(filetext,'feval')))\r\n\r\n%%\r\nx = randi(100);\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n\r\n%%\r\nx = randi(100,randi([2 100]),1);\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n\r\n%%\r\nx = randi(100,1,randi([2 100]));\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n\r\n%%\r\nx = magic(10);\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n\r\n%%\r\nx = [];\r\nassert(isequal(cumaX(x),cummax(x)))\r\nassert(isequal(cumaX(x,1),cummax(x,1)))\r\nassert(isequal(cumaX(x,2),cummax(x,2)))\r\nassert(isequal(cumaX(x,1,'reverse'),cummax(x,1,'reverse')))\r\nassert(isequal(cumaX(x,2,'reverse'),cummax(x,2,'reverse')))\r\nassert(isequal(cumaX(x,'reverse'),cummax(x,'reverse')))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":5,"created_by":4793,"edited_by":223089,"edited_at":"2022-10-09T10:09:17.000Z","deleted_by":null,"deleted_at":null,"solvers_count":44,"test_suite_updated_at":"2022-10-09T10:09:17.000Z","rescore_all_solutions":false,"group_id":24,"created_at":"2015-09-23T19:36:49.000Z","updated_at":"2026-05-06T01:37:59.000Z","published_at":"2015-09-23T19:38:39.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the cumulative maximum of an array without using the built-in function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecummax\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (and a few others). Your function should act identically to cummax, allowing the same inputs.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[If X = [0 4 3\\n        6 5 2]\\n\\ncumax(X,1) is [0 4 3  and cumax(X,2) is [0 4 4\\n               6 5 3]                    6 6 6]\\n\\ncumax(X,1,'reverse') is [6 5 3  and cumax(X,2,'reverse') is [4 4 3\\n                         6 5 2]                              6 5 2]\\n\\nAlso,\\ncumax([8 9 1 10 6 1 3 6 10 10]) returns [8 9 9 10 10 10 10 10 10 10]\\n\\ncumax([8 9 1 10 6 1 3 6 10 10]') returns [8 9 9 10 10 10 10 10 10 10]']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee also\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecumin\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44248,"title":"Mastermind V: Optimal Solver - average number of guesses","description":"The following description contains a copy of Richard Zapor's \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses Mastermind IV: Optimal Solver - max of 5 guesses\u003e\r\n\r\n\u003chttps://en.wikipedia.org/wiki/Mastermind_(board_game) Mastermind\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\r\n\r\n  Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\r\n[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\r\n\r\nwhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.  will be provided. \r\n\r\n*Scoring:* the average number of guesses of all 1296 cases.\r\n\r\n*See Also:*\r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/44236-mastermind-i-solve-all-1296-cases Mastermind I: Solve all 1296 cases\u003e\r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/44237-mastermind-ii-solve-in-8-or-less Mastermind II: Solve in 8 or less\u003e\r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/44238-mastermind-iii-solve-in-1 Mastermind III: Solve in 1\u003e\r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses Mastermind IV: Optimal Solver - max of 5 guesses\u003e\r\n","description_html":"\u003cp\u003eThe following description contains a copy of Richard Zapor's \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses\"\u003eMastermind IV: Optimal Solver - max of 5 guesses\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Mastermind_(board_game)\"\u003eMastermind\u003c/a\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eAnswer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\u003c/pre\u003e\u003cp\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/p\u003e\u003cp\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.  will be provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e the average number of guesses of all 1296 cases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSee Also:\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44236-mastermind-i-solve-all-1296-cases\"\u003eMastermind I: Solve all 1296 cases\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44237-mastermind-ii-solve-in-8-or-less\"\u003eMastermind II: Solve in 8 or less\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44238-mastermind-iii-solve-in-1\"\u003eMastermind III: Solve in 1\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses\"\u003eMastermind IV: Optimal Solver - max of 5 guesses\u003c/a\u003e\u003c/p\u003e","function_template":"function guess = solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\r\n  \r\nend","test_suite":"assessFunctionAbsence({'regexp','regexpi','regexprep','str2num','!','system','unix','persistent'},'File','solve_mastermind.m')\r\n\r\n%%\r\nv = 10 * (1:6) + (1:6)';\r\nv = 10 * v(:)' + (1:6)';\r\nv = reshape(10*v(:)' + (1:6)', [], 1);\r\nm = dec2base(v, 10) - 48;\r\nmpc = sum(permute(m, [1 3 2]) == permute(m, [3 1 2]), 3);\r\nmc = histc(m, 1:6, 2);\r\nmc = sum(min(permute(mc, [1 3 2]), permute(mc, [3 1 2])), 3) - mpc;\r\nmpc5c = 5 * mpc + mc;\r\nL = zeros(1296, 1);\r\nP = [1:1296 randi(1296, 1, 6000)];\r\nt = accumarray(P', 1);\r\nfor p = P(randperm(end))\r\n  mguess = []; mpegs = [];\r\n  while 1\r\n    mguessn = solve_mastermind(mguess, mpegs, m, mpc, mc, mpc5c, v);\r\n    k = v == polyval(mguessn, 10);\r\n    assert(any(k), 'invalid guess.')\r\n    mguess(end+1, :) = mguessn;\r\n    mpegs(end+1, :) = [mpc(p, k) mc(p, k)];\r\n    if mpegs(end, 1) == 4, L(p) = L(p) + size(mguess, 1); break, end\r\n  end\r\nend\r\nL = sum(L ./ t);\r\nfid = fopen('score.p', 'Wb');\r\nfwrite(fid, uint8(sscanf([...\r\n  '7630312E30307630302E3030000D201C07E23FB1000000340000015B000001C36FCD46'...\r\n  '4336A6F9BD2918A670F86BF0AC534F616971CE31A66833ECF344BF30BF9F163C19F925'...\r\n  '0AC8280807727BFFC8E8FF46AA63FA75C2890672D48C5599920C846EB7E0E230FDC14E'...\r\n  '1EA1768A5F84951A22717883064D6B9AD895493F53F6443E63783C5BFDD36A65EE33DB'...\r\n  'DF0C38A4238848AF99B21B91FC5F8E0E31A8D33533721B36C2AB371D7B4D59A1036FBF'...\r\n  'E2A4A0233812C46AA22FE134B0DE0FCFDCDCD4A49106161DDD64A97EC0B1F14A8E1FB4'...\r\n  '81CE94D11A50006D5AA9536055488421EC1682EF9945B98FB175A9CE96624DE8C575C2'...\r\n  'B442A7EAD282A0A90E89D3F25C7B22AF0D1FA61A2F76ED95E8C6EF78A197B13C3B570E'...\r\n  'D2F8EA301AAA8795CB4B2DEF21140589C0FC80A0B75F896CCEB1B560C07273FC4BCBD5'...\r\n  '5D98ED8516915D9A344A91D913E1E106F8CB3C87EBDBB7C0501AE307F5B1807555F8D2'...\r\n  '65EE53842038231C5E6C0BFDC5EB1A33CA999C68E612C9DC17050B9606'], '%2X')));\r\nfclose(fid);\r\nfprintf('Average number of guesses: %.2f', L / 1296)\r\nscore(floor((L / 1296 - 4) * 100))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":14,"created_by":1434,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2017-07-04T07:08:13.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2017-07-02T23:16:44.000Z","updated_at":"2025-12-12T16:02:19.000Z","published_at":"2017-07-02T23:16:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe following description contains a copy of Richard Zapor's\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind IV: Optimal Solver - max of 5 guesses\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Mastermind_(board_game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666. will be provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e the average number of guesses of all 1296 cases.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSee Also:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44236-mastermind-i-solve-all-1296-cases\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind I: Solve all 1296 cases\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44237-mastermind-ii-solve-in-8-or-less\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind II: Solve in 8 or less\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44238-mastermind-iii-solve-in-1\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind III: Solve in 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44239-mastermind-iv-optimal-solver-max-of-5-guesses\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind IV: Optimal Solver - max of 5 guesses\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42829,"title":"Number construction III","description":"Given a positive integer, n, return a, b and c, such that\r\n\r\n1. n = a^1.5+b^2.5+c^3.5\r\n\r\n2. a, b and c are all positive integers greater than 1\r\n\r\nIf a solution does not exist, set all three output variables to zero.\r\n\r\nExample 1:\r\n\r\nn = 168\r\n\r\na = 4\r\n\r\nb = 4\r\n\r\nc = 4\r\n\r\nExample 2:\r\n\r\nn = 100\r\n\r\na = 0\r\n\r\nb = 0\r\n\r\nc = 0","description_html":"\u003cp\u003eGiven a positive integer, n, return a, b and c, such that\u003c/p\u003e\u003cp\u003e1. n = a^1.5+b^2.5+c^3.5\u003c/p\u003e\u003cp\u003e2. a, b and c are all positive integers greater than 1\u003c/p\u003e\u003cp\u003eIf a solution does not exist, set all three output variables to zero.\u003c/p\u003e\u003cp\u003eExample 1:\u003c/p\u003e\u003cp\u003en = 168\u003c/p\u003e\u003cp\u003ea = 4\u003c/p\u003e\u003cp\u003eb = 4\u003c/p\u003e\u003cp\u003ec = 4\u003c/p\u003e\u003cp\u003eExample 2:\u003c/p\u003e\u003cp\u003en = 100\u003c/p\u003e\u003cp\u003ea = 0\u003c/p\u003e\u003cp\u003eb = 0\u003c/p\u003e\u003cp\u003ec = 0\u003c/p\u003e","function_template":"function [a b c] = numcons(n)\r\n  a = n;\r\n  b = n;\r\n  c = n;\r\nend","test_suite":"%%\r\nn = 100;\r\n[a b c] = numcons(n);\r\nassert(all([a b c]==0))\r\n\r\n%%\r\nn = 888;\r\n[a b c] = numcons(n);\r\nassert(all([a b c]==0))\r\n\r\n%%\r\nn = 19666;\r\n[a b c] = numcons(n);\r\nassert(all([a b c]==0))\r\n\r\n%%\r\nn = 314159;\r\n[a b c] = numcons(n);\r\nassert(all([a b c]==0))\r\n\r\n%%\r\nn = 1100;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 116600;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 16999;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 10000040;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 94940;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)\r\n\r\n%%\r\nn = 9990;\r\n[a b c] = numcons(n);\r\nassert(all(mod([a b c],1)==0))\r\nassert(all([a b c]\u003e1))\r\nassert(a^1.5+b^2.5+c^3.5==n)","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":15521,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":34,"test_suite_updated_at":"2016-04-28T18:19:03.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2016-04-25T11:29:04.000Z","updated_at":"2026-04-09T07:25:23.000Z","published_at":"2016-04-25T11:29:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a positive integer, n, return a, b and c, such that\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1. n = a^1.5+b^2.5+c^3.5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2. a, b and c are all positive integers greater than 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf a solution does not exist, set all three output variables to zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en = 168\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ea = 4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eb = 4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en = 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ea = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eb = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47453,"title":"Slitherlink I: Trivial","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 540.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 270.333px; transform-origin: 407px 270.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 78.5333px 7.91667px; transform-origin: 78.5333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink I: Trivial\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 258.667px 7.91667px; transform-origin: 258.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.367px 7.91667px; transform-origin: 368.367px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s  yields R 3 R 3 R board values if trivial did not already solve. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.267px 7.91667px; transform-origin: 373.267px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np=trivial_solve(p,bsegs,s);\r\n\r\n[sv,valid]=pcheck(s,p,bsegs);\r\n\r\n  %show_pfig(s,p,c,emap,pmap,1)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns = [5 5 5;5 4 5;5 5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [5 5;4 5;5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3 3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3;3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[0 5 5;5 3 5;5 3 5;5 5 0];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T00:38:37.000Z","updated_at":"2020-11-12T23:27:09.000Z","published_at":"2020-11-12T23:27:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink I: Trivial\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s  yields R 3 R 3 R board values if trivial did not already solve. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAPEAAAB/CAIAAACxE7P2AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAB3RJTUUH5AsMABUVJm9/YgAAACR0RVh0U29mdHdhcmUATUFUTEFCLCBUaGUgTWF0aFdvcmtzLCBJbmMuPFjdGAAAACJ0RVh0Q3JlYXRpb24gVGltZQAxMS1Ob3YtMjAyMCAxNjoyMToyMaHVoXMAAAxGSURBVHic7Z1tSFPvG8evpotJLQslF0WeIn492hShZ2q/JJOKNd/ki4JmikGFQlFRkS7oRUKQPYA9UP4L60UPZCRJ0cM07WFRMdIyk5qVYcNSi9LM6f/FqbHfzmY2z+772tn1YS/sPtt9vl77enef+5z7uob09fUBQSgIFW8BBCEz5GlCaZCnCaVBniaUBnmaUBqRwe3eag1u/4TCMBgG38eQ4K7lDRkSxM4JxVFptep0uri4uJEjRwbcSZDHaYL4S1paWrq6ugbjaZpPE+hob2/v6uoK+ONBmHtYrfDvvzL3SYQbJSVgNgf2URqnCaVBniaUBqtrRLMZFi1idC4P/nf6dKVkPbHAYhHi49mL2bNnj8Ph8GosKSlhrwQAMjMzvVoWGQzmtWs5SGlqAotFzg77ZOfOnT4A71dJifwnGgAWX8F68+YNFzEGyeKrIAhclPT5uogym818pMhtGJp7EEqDPE0oDfI0oTRC4z5ibW3tixcvenp6oqOjU1NTIyP5y3748GFDQ8Pq1atVKv7jAgYxDQ0Nz58/7+zsjIyMTEhImDJlCi8l/M3RP48fP87KyrLb7e6W6Ojo7du379ixg6Oqjx8/Go1Gp9OZkZExdOhQjkowiKmpqdm4caPndwQAer2+uLh47ty57PWg9vTdu3eXLl3a2dkZGxublpam0Wjq6+urq6t37txZW1t79uxZLqra2tqWL1/udDq5nN0L7mKuXbtmNBpdLtewYcOWLVsWHR3d0dFRXl5ut9sXLVpUXl6emprKWpN8SzK/kWlpxuVyTZw4EQCSk5M/f/7sbj927Jio/PLly3/sRPa1vLdv3yYkJLi7+vHjx8A/K/ta3mDESMMSwFret2/fYmNjAWDevHmtra3u9g8fPkyfPh0AdDpdZ2fnH3oJn7W8srKy169fA0BpaemoUaPc7Tk5OQsWLBDbGUs6efJkQkLCs2fPGJ/XJxjEXLhwobW1NSIi4vz58zExMe72MWPGnD9/HgBaWlquXLnCWBVeT7e1tc2ZMycpKUl6tTFp0iQAqK2tZSamp6dn5syZ2dnZHR0dycnJhw8fZnZqzGJu3LgBAPPnzx87dqzXoWnTpmm1WgB4+PAhY1V4PZ2VlXX//v0nT55ID3369AkA/vnnH2Zient7nz17ptVq9+3bZ7PZxo0bx+zUmMVs3br1ypUre/fu9Xm0t7cXANhftqK+RvRJZWXl1atXASA9PZ3ZSVUqlcViyc3N9ZwF8QKPmMTExMTERJ+Hampqvn37BgBz5sxhKyp0PF1ZWdnR0XH16lXxoR+z2Sx9Cid4REZGFhQUMDtd/6AS44/NmzcDgE6nW7FiBeNTh4ane3t7U1JSXC6X+M/09PTi4mK+koh+WL9+vc1mA4CioiL2N8jwzqc9+f79u8lkMpvN6enparX68uXLEyZMuHv3Lm9dhA+2bdt2/PhxAMjLy8vIyGAvIDTG6eHDh1+8eFH8uampaenSpS9fvly5cmVdXd2YMWP4aiM8ycrKOnXqFACYzeaioiIuGkJjnPYkPj6+rKwMANra2o4ePcpbDvGLL1++LFmyRDT0li1beO11gFD0NABMmTIlOTkZALyeMSB40dzcPGvWrJs3bwLAwYMH9+/fz1EM3rnH69ev6+vrY2JiZs+eLT2q0+kAoKenh7kuwpva2tqUlBSn06nVai9evMjhAY//gtfTmzZtqqioWLx48a1bt6RHxRE6OjqauS7iPzQ2NoqG1ul0FRUV/parWYJ37iE+pnj79u3nz597HSorK3v//j0ApKWlcVBG/Ka7u3vFihVOp3P06NEPHjzAYGjAPE5v2LDh0KFDra2tq1atun79uvuJghs3bqxbtw4AEhISVq9ezVVjuFNYWPjy5UsAyMzMrKurq6urk75n/PjxM2bMYKkKr6djYmLOnj1rMpnq6uomTJhgNBqjoqIaGhrExXxBECoqKjDsMQlnjhw5Iv5QWFhYWFjo8z3Z2dknTpxgKArx3AMAUlNTbTbbggULfv78eenSpdLSUpvNplar8/Lynjx5In0WjGBJY2Mjko0RXuAdp0VmzJiB8H6hyWTqQ1OvjJeYSZMm4QmCJ6jHaYIIAPI0oTgC3vXlF5/by9C8BN4BJ7ww+PymFLkfMUgYyNZKJ+w8ze3JGoIVYedpQvGQpwmlIb+nrQBDfr8Igj3Bvedi/f2DIAiCIAT1XL5xOECSmt9gMHg3McHqqwKqNDkTG6RieH1HBpC7Nqx8a3i/uHPnjvQsJZzqBPRZLD7WiahOANUJIIgQgjxNKA3yNKE0sD+XJ4InB70bDKn5m5ubHz161NnZGRUVtXjx4hEjRvBS4gn/yMg31f+FvNeI1dXVer3eqze9Xn/v3r0BfT4414gtLS2jR48GfvmnOzo61qxZ49mVWq3evn27y+UaYA/S70iWa8RAIiP3NSLqcRpjDnoEqfm7u7uXLFlis9kiIiKMRmNMTIxYPqGwsLCxsdGd3Ic93CPzi4D/Gvwh1zgtTw56ucdpDHUC8vPzAUCr1T548MDdKCaLAYBz584NpBPpdzTIcTrwyITPWh7CHPQYUvP39PQcOHAAAPLz8z0zn2RmZmZnZwOAv32BQQVDZNzg9TSqHPR4UvOXl5d//foVfBUMz8nJAQC73V5fX89MD57IuMHraVQ56PGk5hfvaQuC4Pl/l0hycrJarQa25SbwRMYN3mtEVDno8aTmf/v2LQAkJSVJD6lUKkEQXr16VV1dvXbtWjZ68ETGDd5xuh/Y56AXU/Nj+Nq+f/8O/pOqiTVuxPewAU9k3ISep/nmoOfO06dPAcDfLy62//jxg6kmZISYp7nnoOeOeCHhD/HWXf/vUTyhNM5hyEHPHY1G089R0c1hnnItNH55PDnouSPeb+ru7vZ5VGyPiopiqgkZITBONzc3p6SkiPkzDx48mJuby1sRT4YPHw4A4hK1lBcvXgCAdJkvrMDuaWw56LkzefJkAGhoaJAe6u3tfffuHQD4rKwQPqCee3jmoK+qqiJDA8D8+fMBoL6+XixQ7UlNTY1YQtLn6nX4gNfTOHPQcyctLS02NtblconrP56ILUlJSRieL+cI3rkHzhz03FGpVLm5ufn5+bt37546darJZBLbi4qKSktLAUB8ai+cwetpnDnoMbBr166bN29WVVWlp6cvXLhQEAS73S4WbcrOzna7PGxBOvdAm4MeAyqV6vr163l5eWq1uqqq6syZM3a7XavV7t27Nwz/wqUgHafR5qAX4V4nQKPRFBUVIbzxxD0ygHacJoiAIU8TSoPR3OP06dOVlZVszuXJWofDIGncs2ePg7kSAHBIMveBr+0qvLBarVzECA5Hgbw9BryT0R8+99jywkLlL9BjoNoXBNE/5GlCaZCnCaVBniaUBnmaUBqMPM2rTkCBxSIV84bqBGCqEyD7QhmN04TSIE8TSoM8TSgN8jShNJA+a+oP7nUVEFbhAARhAUyRCSVPf/z40Wg0Op3OjIwMZhlN3dTU1GzcuFHcTuJGr9cXFxfPnTuXsRhP+IYF8EUmZDzNt64CziocwDssgDMybJYbB7k+HXhdBTlqX8hThSMI69ODKcQh/Y4CWJ+WJzLhU/vCDfe6CgircACCsADWyKD2NJK6CqiqcACasAC+yIig9jSSugqoqnAAmrAAvsiIoL5GRFJXAVUVDkATFsAXGRHUnhbrKvBW0R/sq3BAKIQFOEVGBPXcAzlhXoWjH/hGhjwdIFSFwx/cI0OjSyBQFQ5/YIgMjdN/B1Xh8AeeyNA4/RdQFQ5/oIoMeXqgUBUOf2CLDHl6QHhW4aioqKCiBW4QRoY8/We8qnDEx8fzVoQFnJEhT/8ZqsLhD5yRIU//GarC4Q+ckaG1vD9AVTj8gTYyoTROc6mrgLwKB/ArN4E2MjROE0qD0Tjd1NRktVrZnMsTweEQpK1WKwg+moONQdIiAACPsIBPMQ4HHzGyF5AIeNeXP/DXCaBXCLyUvR+RIP4K8jShNIb0yX3p2tXV5d5WKd39zx4LQAFvDcQAqfw9odfr9SNHjgysE/nHaY1Go9PpZO+WCB90Ol3AhoYgzT3i4+PJ1kRgaDSauLi4wfQg/9zDTXt7e1lZWZA6HziCr3UrAiftJtNgRmiRIHqaILhA6x6E0iBPE0qDPE0oDfI0oTTI04TSIE8TSoM8TSgN8jShNMjThNL4P3ncDDWW+w8KAAAAAElFTkSuQmCC\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"re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whether a player solved a Cody problem","description":"Write a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are  players and  problems, the first output should be an  matrix. The second output should be a cell array of the problem titles.\r\nI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through my profile page. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 156px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 78px; transform-origin: 407px 78px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003em\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e players and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e problems, the first output should be an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"37.5\" height=\"18\" alt=\"mxn\" style=\"width: 37.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix. The second output should be a cell array of the problem titles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/profile/authors/1887879\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003emy profile page\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [y,CPtitle] = isSolved(CPnum,players)\r\n% CPnum    1xn vector of problem numbers\r\n% players  either a character vector or a 1xm cell array of player names\r\n% y        mxn matrix of results (1 = solved, 0 = not solved, -1 = problem does not exist)\r\n% CPtitle  cell array of problem titles\r\n\r\ny = randi(3)-2;\r\nCPtitle = 'Verb the adjective noun';\r\nend","test_suite":"%% \r\nn  = 44340:44345;\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 1 1 0 1 1];\r\nt_correct = {'Recaman Sequence - III','Hexagonal numbers on a spiral matrix','Spot the First Occurrence of 5','Pair Primes','The 5th Root','MATLAB Counter'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t,t_correct))\r\n\r\n%%\r\nn  = 2100:2105;\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 -1 -1 0 1 -1];\r\nt_correct = {'distance to a straight line (2D) given any 2 distinct points on this straight line','Simple Robotics 1: On track?','construct matrix with identical rows'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t(y~=-1),t_correct))\r\n\r\n%% Grab bag\r\nn  = [46031 46053 46114 46573 47415 51451 51675 51803];\r\np  = {'William','Tim','David Hill','Nikolaos Nikolaou','Dyuman Joshi','Ramon Villamangca'};\r\nID = [173294 496166 10491973 7310613 2873 13897958];\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 0 1 1 1 1 1 1; 1 1 1 1 1 0 1 0; 0 0 0 1 1 1 1 1; 0 0 0 0 0 0 1 0; 0 0 1 1 1 0 1 0; 0 0 0 0 0 0 1 0];\r\nt_correct = {'Construct dimensionless parameters','Construct finite difference approximations of derivatives','Compute the date of a special marriage milestone','Determine the winner of a goofy golf tournament','List ways to reach a target sum',['Guess the card in Fitch Cheney’s five-card trick'],'Add 100','Eliminate redundant numbers in text'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t,t_correct))\r\n\r\n%% My targets (2022/01/29)\r\nn  = [193 375 782 2523 42834 45272 45274 47623];\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,~] = isSolved(n,p,ID);   \r\n%assert(all(y==0));\r\ny_correct = [1 0 0 1 1 1 1 0];   %  2022/12/29\r\nassert(isequal(y,y_correct))\r\n\r\n%% Problems with \u003e9 likes that I haven't solved as of 2022/01/29 \r\nn = [283 3075 42888 42996 43670 44376 44630];      % CP 375 is in the target list\r\np = {'William','Tim','David Hill','Nikolaos Nikolaou','Mehmet OZC','minnolina','Dyuman Joshi','Ramon Villamangca','ChrisR'};\r\nID = [173294 496166 10491973 7310613 3877541 9646924 12862873 13897958 1887879];\r\n[y,~] = isSolved(n,p,ID);\r\ny_correct = [1 1 1 1 1 1 1; 1 1 1 0 0 1 1; 0 0 0 1 1 0 0; 0 1 0 1 1 0 1; 0 1 0 1 0 0 0; 0 0 0 0 0 0 0; 0 1 0 0 1 0 0; 0 0 1 0 0 0 0; 0 1 1 1 1 0 1];  %  Updated 2022/04/24\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nfiletext = fileread('isSolved.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":2,"comments_count":6,"created_by":46909,"edited_by":46909,"edited_at":"2024-11-02T15:08:42.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2024-11-02T15:08:42.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-01-29T16:08:07.000Z","updated_at":"2025-10-12T06:40:33.000Z","published_at":"2022-01-29T16:17:43.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"m\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e players and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e problems, the first output should be an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"mxn\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003em\\\\times n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e matrix. The second output should be a cell array of the problem titles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/profile/authors/1887879\\\"\u003e\u003cw:r\u003e\u003cw:t\u003emy profile page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61285,"title":"The Genesis Sypnapse Protocol","description":"Ⅰ.Context\r\nIn a synthetic bio-neural network, information is transmitted between  neurons through chemical and electrical signals. Your objective is to find the minimum Metabolic Cost to trnasmit a signal from a Source_Neuron to a Target_Neuron.\r\nⅡ.Neuron Structure and Genetic Coding\r\nEach neuron  is identified by a unique Protein Code (a string consisting of bases A, C, G, T).\r\nGenetic Distance (): The base cost to jump from neuron  to neuron  is defined as the Levenshtein Distance (minimum number of edits: insertions, deletions, or substitutions) between their Protein Code strings.\r\nⅢ.Polarization States and Transitions\r\nAt any time step , a neuron exists in one of three Polarization States: P ∈ {0,1,2}. When a signal jumps from u to v, the state of the signal evolves based on a 3x3 Transition Matrix M:\r\nIf  is even: \r\nIf  is odd: \r\n(Note: Matrix M is 1-indexed in logic, e.g., ).\r\nⅣ.Sypnatic Barriers (Constraints)\r\nA connection from neuron u to v is only valid if all the following conditions are met:\r\nChemotaxis Condition: The Protein Code of  must contain at least one sub-sequence of length 2 that exists within the Protein Code of . (e.g., if u is AGCT,  must contain AG, GC, or CT).\r\nEnergy Limit: The total accumulated cost from the source must not exceed .\r\nTemporal Gating: A neuron  enters a \"refractory period\" (disabled) if the current time step T is a multiple of its Stability Index . (i.e., if mod() == 0, the neuron cannot be entered).\r\nⅤ.Metabolic Cost Calculation\r\nThe total cost to transition from  to  at time  with the current signal state  is:\r\n                \r\nWhere: \r\n is a weight vector correspoding to the 3 polarization states.\r\nInterference() =  (simulating temporal electromagnetic noise).\r\nⅥ.Input / Output Requirements\r\nInput: * neurons: A struct array containing .code(string) and .S(integer).\r\nM: A 3x3 state transition matrix.\r\nW_state: A 1x3 weight vector.\r\nE_max: Maximum energy allowed (scalar).\r\nstart_id, target_id: Indices of the start and neurons.\r\nOutput: * The minimum metabolic cost (scalar). Return -1 if the target is unreachable.\r\n(Optional) The path taken (sequence of neuron IDs).","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1013.33px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 333.5px 506.667px; transform-origin: 333.5px 506.667px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅠ.Context\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 31.5px; text-align: left; transform-origin: 309.5px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn a synthetic bio-neural network, information is transmitted between \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eN\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e neurons through chemical and electrical signals. Your objective is to find the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eminimum Metabolic Cost\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to trnasmit a signal from a \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eSource_Neuron\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to a \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eTarget_Neuron\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅡ.Neuron Structure and Genetic Coding\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach neuron \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ei\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is identified by a unique \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eProtein Code\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (a string consisting of bases \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eA, C, G, T\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 30.65px; transform-origin: 316.5px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 30.65px; text-align: left; transform-origin: 288.5px 30.65px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eGenetic Distance\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eG\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e): The base cost to jump from neuron \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eu\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to neuron \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is defined as the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eLevenshtein Distance\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (minimum number of edits: insertions, deletions, or substitutions) between their Protein Code strings.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅢ.Polarization States and Transitions\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 21px; text-align: left; transform-origin: 309.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAt any time step \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eT\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, a neuron exists in one of three \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ePolarization States\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: P ∈ {0,1,2}. When a signal jumps from u to v, the state of the signal evolves based on a 3x3 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eTransition Matrix M\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 63.3667px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 31.6833px; transform-origin: 316.5px 31.6833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 21.4667px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.7333px; text-align: left; transform-origin: 288.5px 10.7333px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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jJWp0GwvcPclTazb5eA8RdoNgYOJc6H8KPFe/tmDiVmLha8BbDfj1q1QrRiOrbBVNWnSJMe/1veAx79jqEN/tij3wQKvpZygBrltwB6E/NmAOp9M3S6Hbi7QVTjcjvUzRW6HgZZROe2LZ2tAuVDVwj3KOdMADZ5XFi5cqMLzKvTz0pYW1R7059MoPNtTV2WO8s0zWTtNnTo1WEKlH312Anjeth1OHjvQNi8NSXc/eXIyvPZ38ghWTPryZeIHg691MSil9NH+hKn8rm48dY+uA+m+Q/rx4BxEmr2J/yZPlcpanheoxItM+GOMo92ou5ZRg96lX725rz9D/Teh/w4n/hxAfulGno/X1tbGz3McRrj96k1/rYNJ5Tzk0ZB/WfnvHDAYa23Jb042JL9C3IEo1OTrxTbrvT60cNyGeaQQN/lOGyMvfU3FOBnXnvzwoQtdLz+sCh84xzaoeRUVOJjJQ+tRw/fA+gIEy1oqDwyG3YD7Ya4/U6IHBP2A5m4Mes9hEZS6hlIgS2+3NZ9W+EJbV75m0x2GXlVPpOM2DwAAEABJREFUFnJbp7yfBs0/It6BEbe6S14wAFdHtjVb6iHN1BkzZszXXwkYhJbtxBHRxsAHawJruVqED05jkGr2dnU+G6STEVJaQ5pi4SCb1DWEAnSsE43CfSATeFx4JbOo2i9fyB/ZBE+S3+WeT8m+Z3h3MWefG/QS/e82q4q7725VjsajFVmFRGWG1+BqyPdHgGFuTb0SQhP/mOS8WKBcMfQALHVakfRnpk2bpmK4L219FGuI1r4QHv+heJpnyTO4jC9pojybS3ppFZM16AnfDybBWbTh2dmzZ/8Df8516dLFydhP6hj2FcocoUfIlmn/+Hox7dRiudiX7Bi+RT+wNunyrIiszlVWHOPI+cXTwck5AlQG3EI3bCOUuvdUGnlRseXRIPcE+R2L/HFiCQl51zpon9iXG6KM+MmEEIelUVq4aDMuyqsQV+4fbWvSvGLdUKQmQU+tNBYzDqXHYyGX0Cf7EPA7wPo8vNpqq01FuVoFHnLbzkPjzlOHYWSINPTTFn8F337xExc5axlhGdquAqf8K7lboWJYW1urMv4n8P1+454sKkJfUBd5jeDMx/Pnzw/97As8445KtEq/glKToxtpn0J5PoW0gYd4vkY7ZwI3AI/Q7k14xs92ed4810/m3VBIjHOVqgYrEOXKY05QP3ievn4VHJVk+2Nt2uMN/iulG+FlHW0MNOG5ngp/WcQ2HoEMcP4NxhDauhW8VOqoVNlWVlLgNGEnO3R1BsnTMhtMLYMqYAdT6J8I2xLG2hIh1GaVNwh5IW1wddVUKNqWKEv9/IhI77g9qfC1cwMWdI4DPsPgCFunRPySOj/vCoWBEQYJYa6EH+ZZUnkCV2UwWGfI8y2FIbgVHZOz1r1hWaRFpDuOPn8LeBeeeJmyjwDeBI5lRTGYOv0yOdln06WPFqYA/eIiyzOHKvKfba7i6NfF06dPlx9zWSYEv/xWX/wA+C5YkOFdtwlV9M3rVcZdjscNyAJF1mmh01ql1UdrV43WLHguboE+ySRcdNsNpW4xiUveBoNXwxgifjqKR5ggsuU16AGddySBsu+ZwvEzZswYFzBBcQWnF7glt0Wog1akPJqCG+uk9chFMVkscdRdRUmreQdokLOAGfvxxx9/SH7hchnPIZHWxjUUSP8J+X2STJftozDJEN99wYIFnWI8736sVYXOX62I9Y/RpZ5RzsW4xs4r5hN5/H1odxoZXkhdH0X+/ABQjnsIXv68kDgtym9Q38OZp/Ju3JNWPloIjlat5JyX/KTTZNJ6TAW0JU4lhAXFr0iv1XUaffSTmDd8Pohwtz6DgpjdFgwJ4T37Mii95PkEdc4bA127dnWMhNuu5PEv8E8l4UPk7aLY/AIfE/Yh8UULIMKrdvCvi5nYb+tV+tpB1ZmCCP3vBjZDGZU3tNC60IlK7KHQzYUYmKUd7ZLWyjJ5Ps8aXTpFmw6NetMw2h0swNW2pqwCxyqrL5nlTKr4j6VDegEODEH/cJjqEFcNFBg7B28ZlwaXpUCC3q+yeo7CMghlEzHQw1aA1k/e3Tp9nj5xpVnnCgW/1gWiMqW2JAwPgJm/O7gNup6Nwh5XoeYxivf14IOOgHzQEQG0DvBl4NfvvPNOqLuIbRUQvtcDJbfToZ8/0+QX1G1e8kB4If50rJO58SNyS0NSeYFfWqXQg/ecuAL/Uce8iTRJHyZDhZoKqSv3IcOHD3ebbC3SbJjF+0/2WfWDssOkWHWC0ohks8SCTfQppfiEcA+E88hYdy2Nmeb8owKeN8tlySQ8hzAX1Za3GAUvTzHMIbaAh3KjMlFV7gk5F/BJ36h5pSCfIeQzBav/X0KmiX8oCn4jUVCxvxQZFZXrHBY8pbLsosewnHLKgsEjKfGzPEXn+1jE7E65bilrvfrdpEmTPMvoWbU+ZPR78h0OXMOi1qMlBC1x8LYWSBUbb427Bb4kIvsfq5U87iJbK7E3X9dkbF9BdB2gBdv0KqavzZo1q6g94DTEmWecu9dYZZVVpEVD0lfElTeZJ54AdoIW3wVZZVlF2bNteQsR4nIOuroIs27iKjNycaU8ylrG4nSgUA6Hd9J4ZtbxsZ0yvBwe4deDu0wddInGlp7whrxTdfllFTi0fm8TRjN+SfNx1aUsB0RXoYMGDfJqblkmWQ7VKltkpDed6baQjKt53K2LmMZPi8StU03RJ6M8hwmQAe+Ajub6Nxjs42OiwicMshJlKHAKo8q9J1eh4hQJMgNTaHUUaNBKblnVHt6LFpuKRWKF8FxO4GPShO8Jsj3qpNYgAVdQSG5yLgiv+hW5krNgU6+fMDG5iKkEx1adeQVEJjQnc7f5nLi1thddcsgmH/3BBx8EvOx7iz6QJw2iaZRz2Uo1aF7JpgmPgnw0NhRZ/ZmM14BufvVfeTcavxdJQvqCf142CZ9fAidn0Vp55ZW1lClXVXDc1cglQ2HwA7x+OkWF50OU5r9gkevIFtie7Fg8D2+sR14HoDCOLLAUJ+Vp3G3J5Zv1aAW0zlpw14fGx8U8VFzBCVZdynxCBYn3ZnHUOc+62iyZLs2krm/fvn4fMChSBA+nLSqxeOt1rVKW1VvrhiN0QcbFT61UlbqsAgfTbEEOganp2IrfFAOvVTkGUie07IthcA9DO/haVf1KVSZL74UoY54tCihYulytxPr3HzJkiDf0PPB8IYIhnEkRkf7JnRHC36yf7ihYhVqXPEFm+e0NmJQPBkpOymwJ9ITGHsq12ckbfYX4fcaNGzdKpBRKU4AJzkVJyUi3bKGz/JaL592zSEEmkbZBikMukyZ6evbs6cQazqlQh2U2sWDFkZf85IAt2B0FIi6uM9kzqi643cb8owitFbJyLvQh/dnoeSWZD239M2PN25R4lzry98xg3Jm4izEdLLpLMYLPrVgVJl9mIXOf0SMkysi7aGAceFrhtZSpUM+mrLPhWS8RuVV7JMrKYBbY3sLP42GUSi99eGvYbMaw2+I2of4A2b6M540XU4ezkPU5K1tCcVVJj/wQ0rb2fx6rYczcbT2hV7Mqi/T/KPq3D1Aoh8M7ZYbLHpT7iDK8HB7hWlRBbxuupALHKqLwo61FZz1asHk1AwYM6A+jewhyR1a84exMLI+69RYyS/86IMz6ERaEKv4BmLY9lHoozOKtrmDNWope2kf65XYLlbIjvfOsZ2jj3vyala2x5njPQLyyYMGCawgL7coO+M/yHhxKqwpWiAsBBf+6dOnidku83VoQW/zKKlQBGE3YRYKsOEXlEJVrtoE3oH+/Rbu3BUK/ZVP53aV+KKpboji6sg3B8gDW1LWSuOYD3kDgq+KSZy/ylGdcTWfqiw8ZN+hfm0MueZB/ebcC/vS8jdYkqzIk2acGlALG8fj58+d7K9UFzUJxEMQqLHobAm7LNgS/CHf27NlOyPHMkluyy8TCrwWGNvvtRn8ez+92XcOYGCzvIyf8WTDP5B3IRFakyBQ1ohkD6M/kLkHFnO1r+jL5MfBGzSsF+cxCyfFzLEUyj7JUzFS4vZnt7dSi+kHDWvDiAvhfKGZhG7SgjLyLBmZCmpi3/HAhfHEAk/9AlK39eP5TZUW8QiCdPBMWLvSnsjpunwVUrHe588bgPozCFi1WIZ62RsNKkfWOOvdGBm5Om5TZAV/+IGwHIMjFEFjmH+VNpU6Rt8tgNS2Y/MN8RllhTFeTGzzWKmVZNXVvIM4cds+ibKwqaUkFDiZxqyKuEsoyf6USYJitgStgqhd4jkex2hC/38D5D+/uS98qcyXzQBnxGz6PwMQeVPb2yjlY0m4mXRdgX9J5E8er2m/yPgJYk7D7qe8HMMTV+DfHfzZM4upoLs89wflJYTnJMluDnzpHeudtT9ImV4xxhaYS5dmXk5LnzKBVbsDTlnq3JMaMGeMkqGIIeuWf6BIBGrqKXVk/UCTICKvWdaAvDmaV+grtrZ03b979JDycNj4Db/Sj753MLyHuXco8G8UxlGnfYfX4K4PYT1oEnkRp24V8LgPP7zSdAg02RfB6a+suwv5A/DcrxasQUna7c7S9K/QMdMPv1fRW10YElNuiUZH6HP0t7xfVs1u3bv6Gbpx0nndbkD5WkIczlrQzT8kvyqBEAGkmGczTW4Ceb/K1QcCW1XzShzqQcBt4t6wi6SICnj8DnAadEyPfkg7l4H3o5zf+HqQOzzImTob3L6CvX8CqsC4WH7/bV6TIlMys+QKVJVqDei1atMgD52VzzvZ1GMMgNWpeIV3eR8Whw1PwRVC6jIvgGIcuykaDpoEj7+jPA2i4MwFulapYXKyizHteGeTzBLTPKTbZg/6RN2exazIKvqj2NqgfkdYCWfKsMvWM5409s3d1rI91gpe60N6gAFOn5Kec3Lrdlji/Kfg0T7d2VVwzKPfeXr4J/HtZ5K5uPgXgAiRu202BHs4PBSjN90o9wninjk87psvlDF5X4pRlLticB3ltw65C1aFFtOTPwMAiH1bAzo8qqcDBkJqGA6FBr/hNMeJLOlYhjxPhx0A3ooL/o0M+x9Mtzf0JnwCMAHLnWZh0v0G5Mt8/EERe93bAeANtIeboxQygW8jTw8EeCF0NRtsCOJ98z0MwfB7/6cQ/x/tl5KtweAphd/jEiRN/kRwExJV04C23W6jUfXMq5a9ZPMYz5xjM3gYTYtivaaNn5OJ78pq7pvxqtiT84Gvcpl25d+/eJXnAArJC0PM2vpp/niALgVX8cyJDub6A/r+AvjqM/r0Xxbw77VaYe8ak3+TJk8fTd/76hDm+Bk5g5M6dO/uBVOswFqXOLYoMcfeSl9sTTqZT8B9PWs+6PEae9xJ/B2Fl4xG2LSqkbMDyACbzVSlXi7WHo1V4eV3iamtru7AyX8vnkpDl859+nkFfeRbGCgxgzOd9tsFAgXA/Cr0mfrcFAz5K/1uk9fMk8uKXaa+THSjVOXjCz564wu0InzR2C9bxY320vPgLKJcMGTIkCuBcReR5dgJOpcy1sYrltsByCI3wqAjS5uugwTHw+MnIgh8AXgK4G56uVoFoRMnlk7C1qyx/Dwy6rENXnmUdCE2eV8y8IJ8/l5Lv9G8dtLeP5JV5pIsLYbxLHGOhFt8xgHjnQMucbE2U4TcDX7SPkWEXOYa6dg2/4+ktYpJmvFRQUoaaP2mS3wX1UyeeyzZd2G0hv/XA8ZapClfyfJx8Hj+TIn6eUkn7tN75RYK9qdsQ5q9Hme+8beuHtt2ZUA5kCH8FOlwMzCNNkXKPjO8CP/UNBWQyr4LvvJt9zcTdrWrPqsV0JZ8uxqmDH55+l6c/FVkSz0DqpCxT0f2AhYGLPoPbKwSLLI0r2lL3o9Xw0Tb0U+hPcPJcScYDI5qH8WYqfVPM+LJAJ7iy6cSkPRP/7jDYT3l6+LYnz4cYeF4Vlgk3513FTCvKVQg/6zWSjD0oenHSHE1eDjJXJ5cS/zwM99ikSZNeQkiGW1gwxgaErwY8Tv5JZiSoVTpXQA5yt4iChSDWklW1VroFZPcAABAASURBVMS43fkMwvIy4pKDMDngMwzSZ6oR5NDIm0+umod8/PHHpVZlFJPJYAVJfshZpcB0Ia4h/7CG+W2m40hzDX3lb6hm6K8P6Usn4Q3wB2WDem0Jju3IbX3DF9LGwTz6jTfecPIVRWFmPzvAtyKfWyZOnPgAAnhbJrZLQPDsSMV4cNqdQ9D1o1G9gAnQLXeDmQnCD+eOgr7vwiMfIvC/Ac5yc9TjtxT+IGBf+1NC0ZphkOBE52LN7fXz4Y+wLShvU/9fgaAC7gHvq1VqeI+uholXS8QAA8Dt56ShX0D+OBFo/XNCip/FMapBQBmegfR7b6bbAVo/QD38eKyTsMcAhsLzNxPupxLOAylvm4z3BjvyV1n8G22aSz/nyYn6MqupqXHcON5FdTHkswY++Gq5iUGEagAZq3VEua2iVN+t62aZV6hXzOctaBHkCWF5LmvdUQmSx4o+y0LbtcJ6i39d+ul3ffr0uYwMkrLVc2j252Tky1jgAuJfQX69N3HiRBeOnkkkKNOb9J6vcl7zXfDDvvvRV/cBKreGeVZRq7I7Kb5rQe1M/Dm8OBYWw6taheOtVz+HonWT6CUOvosK8HuMoZeZ2D1vt+Wqq66qEp2Bv1VSHfsqpsoCE1K9Oj9t8w/kZ7QcGx7AhTR18Fb4QhD9ZZQQzhzcCblxJe12d2sqZdX37Tb56UjSvAKciLVPORTyyv6rYQG+J2VsQXnHjB07Nsj8bFzRA5yhBvJ8jTrkaGhYe4KsNTcayzTgLIrtU7mHZi9Bg0fpp7vl2RgXnypK0R+edJSd//XwsuSfKw1XKEveqv+fUy5gNq9Cn8pgn47fibUXT7/ztFgBQqeeT7auAq7F6tRH4cf7jlR8VwZM3gAF1y9pOxG9Rh5q8clBFw+kziUfVy/JOLJsfQ56+2FGrRBFQoYJK36jxwnrjOTWqS1BqPt9na/pz0IU0tnXso//QEdvW62BiT1uMxQiOxm6WtJCZpyWQHlBf9XAYFYo+TMq0+gvr/nn+gRBOBOhMpXM6rKM7PbCNPrdPs7AwIOpp9vpTg6eYYl8mOMt0r44d+5ceQlvztUXn0NsbR4FJ3Vy8uCR6Q4fl1WwRUgC9FX4O+kUCv+NoGP8nIVX1X/ouEumLfQjMPzcTJgE6I/uWB06JXF8N9wwnv3E1x/Bd8N955mXHiXbD0/brw9m63U9vByULtvPmHDxptL/s8KJlXc/Hq6lwUWcN/SeR7BdB58dCtyCsNeaEcYBee+ExcqfM9I6m5k8efIM6vKAdSJOq7fekkA+rorDJEQa65aboFEoXXy4lalCaPovgv8k5S8C8H6q8rwxHn9lIqdsZfvSRYdKRZHVjnKSClCc6M1fS7v95y9XeMtxNjR6m7LGZcGfk/otdPgFdDSPXF1NTFutT5jkeXeSPYt0fyR8Y2VMQV8NxOqdVzd5BVxpYL17IjOU1WQVXNIiuRH1UukOEcl/hDfLvDJw4EAXc3F81Hek4xJo6hf9+6P8qEwHuqAo9aE9v6d+KrPHIYeOThoJErJIuUMWNe4eLIb3lF8kyxh+HRHm7ftPoef90P+7gAuSUeR/BLAzvB53O6Sd1i6NC6Zxa/M28vDjvM8bwGTt2TX7XTmnUpeTlcaTn9v1ju8u8JI/d7Y/6U+Pdc8qrc6NPYkP5cAPGkF2Jq3fbc3LzzxJb3mOc39FKSjihvsLFqRRUfbVT3nsO3Nm+Z96gmbdwN8LZG/IXohi/SJ9fjCK3DrQZBj+3zAeTgVnD2hyG3iVnFbNz4sA/r3oDc5/vrZm8Ocp3TGwn/MWjpUq3bNnT/lZZdWbzCpwSfRaXqJytyH00yBG0FKXU+AcpDChP8nyENHJ1em+EP8hwJ/1IKo6xyBx4nZCcQ/7HISe219BwaJT3oJxVCAyNGAEfgekeIcxCX2PirodugUdLWPnMR1M7ne4poCzBuBKOlehYcOGeZ1cATaBfOLgysW3Jo/0hqbn0fa7qZed6OcJ/k4fXI7iEpUm2z4fev0W65L9AmpGi6W/A3g77ffMS5hkQkQmcxqD5XbydfLTspcNzn9AVz8/4pfD/U1ILVx5CNRha+AOyvU3GWNcb8q7nfBk/WJc2SdpPp/JZNYjr4rfLGLSkF8UUK+D+xYZQpoatzdk2g8RwDlBmOCtWfTzpUxCedtH9cWTd6tz1HltaHsui5dXaXi0kK2M/4/06dXwxF7glN2ikp+gmwLXLUe/ZSTvxHZ6ntSxFN8HYV3VGhDfc0/LgH8c6wp8JxTjNoX+txK+RzZ+D9+JiFuYWgZuI/7g4cOH9+Qp//nhTsNBy+TS8xL4kknzfXh6J9r3LcI6wyf/pf2TaP//eHcS2Iz4c+LkRFhwvsO/nvXxZ4Q8R7mAdn8TOJK8HiMfrSFuG/0C//rkMRDwVyJCevjFRZ8XKbZFXqwWAhP/qPsI6nEDQTcCTpY8MkcQ/jfC/YCv7xkWli9Bg40p04tF0UruxOuh/pOME0fkLM2OBVeLj+d6DD568ODBN5DvCCa51QcPHnwFbTjeiCycSdjZpO3jO4uUF4j3lxoswwlVBdPFl+DPSfnzSyfR5mfJ8zcqwqYTaP8HpFUpVuaqQEojP7R6KWXsCk6yr4ai9N4Lz32P8I7E74aCdw919ye2CMr0Qzm4g/Dv8xL6krZGi+QmlON4JWqJky/BbbZ5hbrZ3jA/UdY98FHZXRba7a81fJ2a/AE4iXo8R7tuJg/b+xw7Gn50PPdrGOAEhyIkT2kh8qPlPWn7cyhvx8l7AYF/MW/q4AUKt2i/hv8aYB/wTyX+K9Qtb+scfphCnDzr4sOx4ZGYK8kujFXSxvPGE7CmRQsf0TnnERN5VyW5AzgHMRaU5TkEPFqYe9EnziHOtwfA8zdSlzHEFTn4xTK1Iv6JOmtNDTirrbba1Gxdo0VoVXCjMhFwkv+gmcaGkaT5F+Ef8/QIxO9R5J4h3c+ow9+pw+eAQiUF9HwHz1uOyvVo0uVd4sjHXO5vHRlre8BXt1JPjQiOLZX7HeGx+wk/EbkdZWjJyjKWVFRVou9CR1L25fDow6fhCXlVPry2b9++8mQuXk9OgXMSpAPPgMBDeYart9lnJ8K+Cig8TFMVoGhZcSvmttt9JKpDYEYF65XevXuH7VPCtbqovNxBeV8FTofRNX0rqIjOcx1p8Jk0am0YRAGWu30pFsyiAqBJ/D/ko2XH4FYJ0huankI9+wGR3vqPpv3vxUoT933w3H4Mg9xwwu5m4O7FsxsQ0/rsZjj4rhTjwDNJETD4ncic1HdglTYgiUCejwN7AJ0A8w1Avuvwnle/ZLpSfvrJPnLCeZc2B8tIGTxXpA7ccP4N5t+etFodPAs3hvrmDiEneOtFeKFI0NUXX6r8asMYqPaDgs7LNM22MmSV+Q60VfD7keRAb9599qNPj4AnbgenbHlMshtDr02hxw0IgrDlGNtEPpOAzQFQarRETJo3b57bKhEl97QM+tnzptuDn/tYM/7tCb8zG3+n70CMt55bEn/9mDFjZvP8C3EjAMMF8UJ6Ckry5WLadht4WwB+GHwQz3VJ/z2eY8At5+qIf5y0O/NcE+hFmk15vxIr23jev4L/ZGhWJEfYunFh54T+GeTF1oUFkI+/gXkQeVgf6y4oA79JmBNoLgl5zbIcwgcA4tnO/8N/kXERMUuzXxGuMimeYP4HWR64U4gbCRhmnKAsOJ204SefGDtzEOg30r/esHfB+206U8uiSmEA4vzOmRPvoTNmzIgKV6gG5TxG/vZJ5+zTfBbjV5Ez3DIjjKBd0mhRNn5bnrYtxJPXUN69CR/6En5zq8/fafUXBPIWhNYb3GabVygr97kI6uFWfGhfuX/gv0X5hwOrA5vRru/4BC4q3NFI5LGIvI8DR/m3BmkuSSpvEc+8wfsueLHflMfbgO9kXmr+kp5Hgx8/fq6cVp6ELAk/FZDG/0e/5+RdiOQfZcmba4NjOQeAE3iDqJyDB0KfGICy6kJwMIrqHbznysEfnOer4CGtZq/AWy76Qrj/bC/lWNdOxDk/vITsU3k0uiTQ7jeA7Uhn/WxHbOd+hGuAKEWTorxQflQqB9GWX0vjIoRGBNBOZed4kn5AO4poQXhjnHyiLPx2os2h3bw7Zi7KyqCSeWcXWd4L+AAaXwZSru/wZ6DZRxMnTtyFvDrhP9k+MTwJOQUuGdgcfohvJ7ja/BuFh1UCAjMoWBDzEVba66G9KohKFocW3nXw4MF7Y5oNWq1IMKRbemq4fhzTVYxaeke03L2yeEEZJP+nCNsa+JLpUiimQFZ4nUFMLSskfwqmBn+zOwaLW6/2mdaMHL/JvPRnUO4sFLzc+Tf6XcX/QHjoKeJ6AQqYgeDvit+8Am8Rfzu8pYJncA4IrxifQ2yEx5Umg2o/BpW3/hRKjcileZM4Vsjxx8AEBIHHEUoKKGkObfwZtuucWMFfEV0dvKbVZTS0ONFJrA0QoQZZeTDj9Bq2L8+F954DlKtX8vQiQwD48htYigbQrj+D68p+mTWNBZbbYlrqfsiC0C2hZVZ2WtBSCsDbWoCVkd+HD/amX85A0VN5WYqU9aEofRuvMvhEFCWVcF6LHfO25zv/i9FFS34xQjOGwDtu555AlvdRrpZwvE13zBMPMVZcHO6nDG96jk3PAR1oW3LZlX46m0VckXWNuHpdbkKtF7MBCNlzCm7n5O3rUlEZ5hOec4Aj6aA3saj54UQP8H+diVtG6cBzI5jL1eT8rl27fpb3iwEn9MCQpJWRZLivMKmfxqQ1mE7xLIYKmz/825mwr5N30fXyBjSj3aPC0CogZ9JQt4g259nsDmXa28geot4cvgiH1VU4pk2b9nP6UYX+UyYntwTcNptLmFtGv2MCuoq08ayNFq+jWUk+7ZYMOCru5ll0zrG++GZv4PLPsIaxMhKabAq9DqsgiGsQGD8C7xMmeVf+y7/my6kGWRq5aBnC1r1bgRUWL8upkolikX1b0rde3ngaQa8FMRGb73WVDq6H3IssOPmYzfuWVRKOpew5yN1TXCw0bwlpbtVQwPGdxeuCMncC/TI9+573gKc2p69OB87IzgN58fEli3cU76fJWzxbzMkzyP2zswUcS91LKp7Z+Db9gK7OhZfQX39ka/TaxjamRRQ4JpRwTgHmGLVgwYJxsXK8e6C3J09/m87Pe0zHxPgMjfD8hucIHqNhTswj6cgjYay7YEK3Yj0LNRJ/uAhBmjdJo1l4BM9uTOxXoQQohLU89CDtJoSZf7tlgEjTJj7divKgr9fMr8diKa2bmGV+cibLF+lvJ8n5KNX/pn9vhD8012t6/hvYdVgOVL4996a17Rj69Ez6+N+kk180u3tm5yIG9PRu3boNINx6eoA578wAeSVvzpaMF6c9AQuYb0CPg6HZbqwyc+cEC9rYgfHht6FWZtF0fEsL4oKyW+UrsuU55MRe0O0oaHMQlVR+8GiVznOCjg0PhPssW0nGlwuxL8ATD5ZPUF91AAADnElEQVRFaqEIaOqZsz0oezO2cE91Qm6hotJsK1PgCfj6EGSvMrUIEx4ZRB9dRcR5yIxLeTpv8sh3zAfuZPyYeXd/+zY/ttnfwgKTXDdinth1GZRHUcvHIbO9JOSxhwdQ3o5tijxuEQUOxgnnFGCOnZl0c0oU75fQMZ6l2JpJOx7wrMP/F8DzVe4fe57gcCbwcGuKNNeRxrMIm5NvMA9Ddveej8qGn2QZpJ/P+z6A51W+S1jJlQdp25RbBpX1HJIHsc9n4NzIoFU5as5i6+jD2+ifDembNQCFwT68J61nsT/t/88Sp1XWdJFfPL81xkrBF6PJoz/gOaewNW94hPriI147eIZtNQT1ESgiu9Du3A2yEm3zO4pnQbNzGRe58VgCb4UKgmZPQztvcY9s5UqcB9Ptm23p7weYgLeivlqtDctodWbcbkH49cRfSZuORla6QxHil+U/yh1LHXYAvpQqccuS8kvKYox7pjE5vy6JyP6HT5Tvnmc/E9zcBYpsdN6D8fEvcPahT0sqgnnITXhR0YefT4dnNmbLdxeszPFSUBNybZ1JaeeajE8vvtwDbUuea2tIzVtEgWtIBVLcVkEBLXF/ZFW2J6BptzVbI1oFwZZ3JVjFaYmZgcDbHYU3LoaWd7XaXPlMUqOxSvp1+xlZmra6NtDHXigI3zekch4feJxx+jEKW53Qo0eP2VhJ/DzKhx07dtzONoG33Bz86A3jnZmonvnoo4+8Xb7c6rKCF1zYfNimxm9C7s2i2i8YlLS8FSZq6fcsjzwH35S8mNHS5S/L/BmnQxkXJ6K8eQ7X3aUmFZ8qcE0iX/tK7EqLQeRE0CoGdvuibvO2BgHsDaVwK7R5c17xctMqCd/fKU1bY+utHwLfrSw/0eHFI7dH/eUSb2D/BsvFToC3Vk/CelF0qWc5tUmr7wPUp91aU5YTXZtSrLtdDyjnm5JJc6eVRxh/zjtNVmiau27NnR8y5snJkydXPMfakDJTBa4h1EpxUwqkFKieAilms1LAiRdF7mxgR8AbdZvyHOnkB5T9HlqzViLNLKVASoFWQ4FUgWs1XZFWJKVASoGUAikFUgqkFEgpUB0F2rMCVx0FUqyUAikFUgqkFEgpkFIgpUAbo0CqwLWxDkurm1IgpUBKgZQCLU2BNP+UAq2fAqkC1/r7KK1hSoGUAikFUgqkFEgpkFIgjwKpApdHjvQlpUDroEBai5QCKQVSCqQUSClQiQL/DwAA//9nx9dbAAAABklEQVQDADlTONdak7cgAAAAAElFTkSuQmCC\" width=\"312\" height=\"20\" style=\"width: 312px; height: 20px;\"\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Note: Matrix M is 1-indexed in logic, e.g., \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAAAkCAYAAABrA8OcAAAPoUlEQVR4AeyaC3BWxRXHvy8JSSAxAQJEeYUAEmmttlKxrY+ptWqrYyvtSNWZqpTq2LHWQpEyFGJLBQYEpWWGjliEAWdE7KAgtS2+cKTIIxShLUgaSAgBCiGE0hBCHsTff3Pv5d6b+90vxAny+DJ7vn2dPXt295yzZ89NUiTxl9iBDtiBhGB1wKYmSEYiCcFKSEGH7EBCsDpkWxNEE4KVkIEO2YGEYHXItiaIJgTrYpGBM1hnz549MwcNGnRlfn7+aGDZgAEDvnsGww1qXMGC6HUQXwJUA80uuMtQiP+TAo15rnHN1CuAaVpA2PBhw4Z1Ytw4YCp4KUAidfAOsNejMzMzF5w6dWodU/0RuBU4SPuUgQMHjtWZUI+b4gpWWVnZhtLS0h9C6QHASdFodLBTCSnA0B3gPuRCKaZ8A3QnVlZW1lAOTH379u1+5MiRl5qbmwd279791yA1AonUwTvAWS8A7mWaFYBSeWpqagln8FsqWToTnQ3l0BRXsOzRHHBvu6ycemflYTB48OC+9MvauHFXI1R7aI+ZNK5Tp05vgnA0Jyfnic2bNzdQTqSztANDhgzJYaqrgAjnvLm4uLhKZ9CtWzed5VHOZinWK1v9saCtgpWC1bnFR2SIr+6vpjQ2Nk6ksQBoApSUv0WhGQhMYripqelFOk8w53gtiHIincUd4NyuYLpBQCQpKekdcnNeOgudCXUJ3Mywa7FNgoXpy4XYl4D/ABIOskjv3NzcDBWCwLoCdX2+Tn8doHSIu1s0VA6CKP269q6js3D37t3/I0+ks78D1zKlzrYaQfqYspOsM9G1OLK6uvpxp8NXaJNgpaamDmVcH+BV4BiglJWWlpaqgh90ldEms7mWXD6VmJSU76itrd1LW2BCGG9gIT/C/K7inv8wECnR2NE7kML+f9WapLihoWG3VXYynQ04bwJP8HoM9LXbJFgQuBmqh8lXkx8FJCQ9EIJ0lX1gX4G9sD5T6LsaMAn89SEOuxb0KIhZmF/5Vxe4s85Kz8Hkup3E3Yby8vJqFXzQaJ1Rf874QfqigCfFFSyFBBCorzBqK4JRRm4cacrZgK5Imk4nrI5egQ/QN4Nx/6XHFqx62t6jHpiQ/Cvo/yadB/Cx/kEemMQPc9wGPEnIYjlQASwAOQqNfrQvBU4CK+Sv0e6kvLy8y2h/mvbN5K8Cm4Byxk8XXSGCM5Q2xW8UYiml/BHObA/1+SCFcfOgNcbdrvG0lzDuGH3fc/d9mnKfPn1yoDkZ+BD6fyZfAZQyR1gIIJn+O8FbBawFtOb95O/TfkMQP9bt1F99nMf7yoPAOqMD9N2LMHoedrTF/wjdpUuXfkwwFCH5oKampoq8QgOBzkhtJrmTXFegYiALUlJSBtJpC195fX39DuqBCUavp6MXUJmWllZJHpgyMjIak5OTt8NHN/gaAfSBj3c50CvQnrcZ9ANAV/SN9BkzLSeTzRwH3k76CuDjVsz5PYAU5nXwJmRmZi5ns7P37Nmzg6f1Ytr2gTsAyMOZzSf3JOhJgR6CD/eLV87uKcamgXwJfY+G+aHgxE027xz4LpAHsfbbeVXfyX4phHOQOWZWVVWNps+TWMsQeFxL/xI65rPWG4F7qN9BfTD5anC+T9mT2MNraND+ScFjnpd1RjqnfF6JwxjjSXEtFgvRRLqe1nGNHWfT9lsUMmCum1VW5lyBVCbJyaNfV6iYpCmypaKi4qAKAQDZqNEgxhzGKbSd/VaobGpdSUlJBXgf0KmHxAE2Yx9Cs5C2QmCC1V5EuYTNy1bshbZngPVM9GP4OEJZqYn6MgrHgW9A59vkEb1+yFcBau9K3g9wkkuBJFSe1zGHtxOa94FczfwVBw8erKXcriQrCu+yxuL9FQT+YdZ+TMRYr24ChQSSmc9zsKz5TuZeD97V9D0MTyspm5cd+/cvyn8HOrPeseA6YQMsYTr4N9InVyfUH9YZMcdhcJMBf8QgrsXSK02DdmF99DoQc86hw4SxCBCOoB3SYL0CZ7GQTdoUJpZFULcY1bM10G8CV0JqzCk09yLANWZQyA94X6Bbm3qA8k8ov8WmLQNmMH8KcBtxl1p4mEnfSKCY8iMSeMpOYuwJKvWAaGmtFCOyPLr2D6nCATjrpJ6CtZDwKoxCNdKFH89XASyclK8K2i/Tpz0jO7MkS4UVnc0oBafXQMsTekGwtrCe52lfhPL/BjyTOIfhtCtcI4s+hfUuNx2nf3QGRtgZO4S1OdaYss5AAhuhL8wfjnBGbiMz1G+ZQy0WvkUOE0gbtilIJt5g2gkXUO6hNpcGbwJ/Pm3N9hVKWamaxRepEARsYAbjFEwN6m7V5tMs8dCL620OiJ5DtK4IXRNN8PpcGX/geBLtsjq2Ve1kd8KvQh3lqsPb55QLODhzlVBeCkhhclAMzyOGQ7+NMUVYmDXCaQ9gqWQ9JVT18DgDARE/DinVWc4Y8lFYMeOe4GPKldD+K5dPLAFrdga1FKQEUgbVWGayLI7KEiYpi4RLN8EG0xj7R3SFJ4zc7Oxs7aPKBkIFC80zgTIWprCBCEmTS8zIlh+FIOwrsA94E1io2QA41hV6WQtaJPDZavVF0JQkxjoLtNtj5eBr8UazwLmUQ5y7b9++KspOQgByaX+MBtHdQfk1ykEpj0YTDgFnK2WTTp48qUfK/00l0hKzg6Zwf0XbVPjVlaLD6CvFoM0khH4AdB7Aqs20rlTTfiY/CGom9J9gTGfyIgS1TaEX9uUextj78sKuXbuMxaXNSf3797+EinHOySsZY4SSss5W7oj2az/8m/WpPR7AYwb75Sil8EMFi0m/BlI9QuJYGybU1WFLam/u6JFs5EPATDRoI/gmMdnXTYEfytt5tpowBdVPneDnSohIuHSw72Ct/kLdk5hT/p3ib2pfWVpaGuTf2Ve9cI6xNvklKkdYi658W1hTLY2cCN110FrLYRsFo57MPpl91PVF+3TaFvMI2GIIteMnKyurgP1UQFrrK7KVNYwU55DNvPdbOHtZS6s9UR+Oth5Uxi8Ev4i1mDVawmy7LltZh65zDWkXmA0JGonm2Y6cx9rga0kLjAPJuAKYm0G+FYl9gdxYNbRCTr18IJqMvyL/yvSZBt9Penp6DRvpaI6vO6gqX0ia1cShLsYZl7B78KBn48QMc+Tl5cm62ArwHofxkYdIJFKsOmvsi/Dq0K5h/fJ7aGqWRdPBd6dNQh7l+vo5/Oj7phxuDW0XQFw3hfZQ9B1lDSPGvPms2QgMeDEFA9r6rxTR1iezReDK54r4XJdtlmLRHT8xr2KcUkQHOaZgwag2S2bVEySDiDTftj4yqVlQG+++itxaQZ+erTHjUvRHdu7cKcGQwKoa+qnIEtrrhAjoimsVG7O0T5oJSqQKgVHoQGUPoJXyl+S8SlFm+wWUQzCbzpp7A7JWs2x/hrHSaAUPo+AlYTEUs7qF9gntvQJt5th7uRimCm3xZsphP/Cnl6v8TaHtDhIMrvJc6NkhhpVuHxC+L2egfLOYiki/O7l9tUrG6wyd/piCBeJwsLShniAZhyRNFdBt0u8xp/LBTEU/4HyZXFoh5zb02QqeUiMLtq/b1K5du8bkyye0gVccvDez0ea6hq5eQLYiaC4DWGTFqH5GRXhP+9dAuzvJMV2Pz6lnu2mnLLrS0q4IgoRKXxkKY11bzJeOhbxMuSEQ/mP8VKGwjizlfpDyQEuWLao+eJASaC3a8yBFglR0FLhXRqPR7eS/9CmAbeFNvJGgZ2eUZSHg3DyMcRLzp7O3+i8ItbWycLEOUNIok3kMJjz/4kKQ9DgE7WtrPVfEHCi7rznPf0IwPvTZyliTEAYFVRU3GnjixImepjHgxyW0eu4G+hHEjkTHBPeYPxV+O7tJsVn6aqDX0yD65qO5/jUYdMYaGlRkNfwW7ShjJVy6rvRImIxwBl5bWIo81rcZ2A/NQ8wvQYRscILuNno0px42sqo6D5paEgJ1KQ+GP0Hv87TYe++ER5jDiU3Rb5LmhG4hFVnaB+HVOdeCggI59Lqd6I4orHQEBX4M/H+jKIFOPPPbL3m9WhVT1FgHAgULxofBnJ67rQ7FFb+Q6St0X4GiypP3JvLbATvpkO1yWP5PFqJXWS4+i32N+fHdQmvH1vw4dv1Z1iDN7I2zL/NvNBtN7M48+s9IOapjysrKHvdprj3eycF/kYPwWGWXXyhfZRQHsNwZ0LpwFTTskMUlWJfH0HjPVwv3kB49esh6m3AGaxiBUIwTPnk2QjqaNh3kPPecPBYkEM9BR0Hfu8HLo6wUpfwd5pcPrKDx9YwTffUZqKur0yvUhHvAS0KofkFHfxTud+S24FI8neBBbpCuzmLKrR4qHsES8wjVNBDfgEQ3QIHL5TA2V2acupImqoOB59lsfUJRmwKkd7Hw19g0Razd5nuS2qGrTy0ezTMDrR8WK/Ovzw/J0Ja1tHpOZwiFPg+Z1xKta4itKfJLsXWCtz2s41v0KJYznjVshI+X2bS/0bYRS5sPzlzK5vogb5UYL+VRPGgOnVo3WUvisdJAv/6tRF8ZwoRKA2SBNqlgQR7OsqyEVfVmEnQO9ae0KuD8MftRiIVQJF+v1nz2+CZ417Xs5qmZtmcZcz/4clWKWLO+Db4Ln6OwbnfRf3MZf+B4EkJZCY6CuVISRfPrGhoa/FelZww86NXdlbmWQFd+t6ffI1hYoxrmnQhiLyBqgcqPM7k+OJrBtD8Mnj6+Oguj7Q2EYwR5F8Aeq7yL2sF/hcHyA8iCE4vRv+XoAG7F8vXzY+Fc74P25YDoKs7jR/HUiePsBfcRoCdwLXzcpxx4xm9pPQOtCvhvg/tFwLk2rC4TjqB/BKDDdPbB7nfnGg8MBzi/qKznntraWjtG5kZ1yhIu8HVo15BrTzuRDwUmuc/CGdBSaKJ/GSCetWZ9D70ZHkewF/qME0uJGhmjq1zz9KM8i72WUrVQ9f3qgzgLGUHzVm4DvSwpepNHsLxdZ79mHbb8gAFoxCNwEAUumKQ4FxquT18LpcTn68Kw+grEyql/EoG1X/Oe5ZxTgiXO0BZdr09RfpTrUy9TihdEMnEuBKuBa07W+7xcFNfrcKzVZKDQOqvAdZxzggWXxleA8dnAInw7O3pO13mbklGSyXCfSphirK45yuddQqjyOJM/wPg0rlc9FGK6AOeiYMF3pAnGFdGfzh3+0gUgXE34mFPQ8KlhvosWfq6CdQZ6qT7FOubBZ0yhoi/uv80I57MCWS79w93daImezheUv/VZbWo75+UIoorFjUTh9eoPFSrNca5aLPFmAOdwL9r+VypxFwNOInXMDjTrDHQWbSV/ZoLVVqoJvIt+BxKCddGLQMdswCcAAAD//4GA9DwAAAAGSURBVAMAKgIjsuGvVZsAAAAASUVORK5CYII=\" width=\"75\" height=\"18\" style=\"width: 75px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅣ.Sypnatic Barriers (Constraints)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA connection from neuron u to v is only valid if all the following conditions are met:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 124.667px; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 62.3333px; transform-origin: 316.5px 62.3333px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 61.3px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 30.65px; text-align: left; transform-origin: 288.5px 30.65px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eChemotaxis Condition\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: The Protein Code of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e must contain at least one sub-sequence of length 2 that exists within the Protein Code of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eu\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. (e.g., if u is \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAGCT\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e must contain \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAG\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eGC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, or \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eCT\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 21.4667px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.7333px; text-align: left; transform-origin: 288.5px 10.7333px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eEnergy Limit\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: The total accumulated cost from the source must not exceed \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADsAAAAoCAYAAABAZ4KGAAAGfElEQVR4AeyYe2zTVRTHf13bvZisZWydY692Y3MKiMwQXDQyX+EfooZEJFNRTIgxi4aYgI8JfxhnQkhM9A9fCT6ixkiijD/ATEUFNZk4CWOA21j3zLKtsmmw6+ja1c+56a92rw6jq79Al/PdOffc1zn3nnvu/TVJu4r+Es5eqZud2NnEzl4BK5AI4/AmWlwu1zan09ngdDonQOgf4BJ97wqPYxgWa2cDbrd7f1dX171Y+zbQqTcpKWk5elM0rFZrNg22g1EwFAgE3HBDUSxnow1dFFVo6uzs7I4qK7G9vf03nH+HwhvAnZaW5oEbiuZ1try8/BosLgaKQqHQ1wgBMCtR/wcVHW1tbRfhhqJ5nR0fH8/H4jIgNGo2m38WIQbaTSbTjzHq/7eqeZ3FuTVYdy0Qap+YmIh5Fru7uw+63e53pbHRMJ+zpsnJyTujjG7q7e2VBBRRlZSUrCBLN5B9MyNKgwoxnS0rK8siJCvDtgfhcl5hETIFg8HHKbWym3JWEY1LMZ3l+rgO00uA0ACOtYogyM7OzmBHa1mMx8A3ojM6YjpLCFfhgH7tFHB+O3FQPS4yMjIk275Gvcfv95+DG57mdLa4uDiVHbtN94ArZRf3qP6QSGIhKtF1app2sr+/f0hvZ2Q+p7M4k4fhNwIhL6+m6OskNDY21k5FDw7LOZ7z3qWNYWhOZy0WiwsrHUCok/KvIujghWRl5304e0zXGZ3P6SxOVGN8MtCQm3kOXhBZh1xBycnJW3p6emSHdbWh+azOSqbFwXW65YSwhGpIL+s8/CSUK0mp8vLy0hHM4D8lucNJjPeAjcAB7i4qKpLHTmQecoxNoP39Z+YNkIMuVVfN6mx6enoBIVoRbjSKPCWEw/opTAZOSUlpxIi1VJiZZAP4CJzHuK8KCwvtyJtBP+UA/Anameh3B+XTIITuVdEBRfn5+Wno68gfh1EMcvWdgcuxacSmVcgafR6kzfuU3aCD8mqQi+4I/YbQvSntBLM6yxUjq3bZT8TKykorA79ENFwicZ1m4CDPxi/gp5isBDQzpjw+ypGfQ2+Gr2THduBADX13ovsdrCKq1FVHXSafje/Rbgt9t3ITtPh8vmHm6KfdMDr1RmeeT6jbik6+tpYShVXgFdrVM+4a5BepUzSbs9OfiB2cTzFEdZj+r7S0dPHIyMh+9Nsx7DOPx/MnsiLKyxH8IAd5EsNexghZxCA8F7h4uNRi+BLa2DDsBP29snjU7UW3EYOf4ZPyPLJmt9tTGWcpcg99BuERot33FGRciY4TzPUd+eQkffvQK5rhLGElOxH9Hi4gnOyqdfgfq59BuFYRKm+xM7LSD1HVh3wErkjCFmElSMbwRTw8ZOWx1XQzOtnZxXxU7OaOloy+Hp0fg7+Fh1g8KT+MfMzr9YoTiJrG+EUIBaBpesJkjjb0A0zQyqIdQJ6RYyLOigPEej0DyoSFNNbpdsLpAo6FdMjriQF/oIH8MiHfu5KxjxMBvegU0UeuLvk0HMCQenGKRZOr7CYajKKrQzeiLwrlPnZLzqQF+VHaJIMP2Wk9UiTi7kMnC38cPsUZboaLOOphwRzARv0Mijgrg7L1z4N8zoD+UrpsTr8aRo88LphQsrkdww9RJ+dYwyBJerKQjVlZWb/QXotalFM2m22QY5GL0bfQbxDeJG0E6K+HbwPDLPRZeDRZ2KQ99FlGn2VUSETBplLE2anqf1ciQiTdb2AULwZ8ClfXE7Lc3RLCB5qbmyfQS2hKWNsx8ijhW0EbOUI2yn6c8kkbl8uViTP7kCUyejgSPnSbKKtrDll+J5PoehqdnFtZaAtH7X6Hw6ESHnptQZxlV/Wn5lmuI7WrGJSJI+tBN478JJMDRNOtcEmASRR2cI4ly8oDJgcHK8ghBfR5nTYSuqIf5jUnWdzOsdoLHqH+AfrthrfQbhhUM18dx8I5NDQ0TlnRgjjLJCsYPc9kMh3VEwlyKZAwVuFKvUYEpMDlbGZg6Drqd3Huz8D3oPeCwyzcB/B98M/hktlvgDcwxyT8KVBLBLwg55/s28E4B9GthqcTAZIUVVShW5id5cwfAhY+6J9lEpVIuAKa0S3h/G7SQxh5HN1mYEWuoY3sika/j9E5gBVUgxYcOQcvAE7wJW3kZ16pX0s/dTUxV4BxnqRe9DtZAHUM0CtakJ1VIxvwX8LZuG9KnCZM7GycFjru0yR2Nu5LHqcJr6qd/QsAAP//HwESLwAAAAZJREFUAwAyGDR+8uIGQAAAAABJRU5ErkJggg==\" width=\"29.5\" height=\"20\" style=\"width: 29.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 41.9px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 20.95px; text-align: left; transform-origin: 288.5px 20.95px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eTemporal Gating\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: A neuron \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ei\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e enters a \"refractory period\" (disabled) if the current time step T is a multiple of its \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eStability Index\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABsAAAAoCAYAAAAPOoFWAAADnklEQVR4AeyVW0iTYRjHv7k5KqPVbKY10805qcBOUDcFKh2gw42ESReRdBGF0IHK6KbrgqIurEgiqJu6iaibIMiL6MKLWRQUzebmGCJipEHadPPr94ydPue3zVpehOP/93m+533f5/++z3uwSJnH34JYQYq9UMZ5K6PR4XC0wF44DlU4Bd9XV1efgJvwu+HlXDPKumdOp9NCsuckeQKL4V6/32+C5qKiokMGg6EVviO+c3p6WiyuPrKJGVRVPU+yfQz/TvJjiLzBj0LV5/N9JXYU/zMchgGYFbpilKWMkS1Q8MFkMnnFSafP5wvx7YFBJjaEzQpdMWZtZ6QNZgMa6kc69AWDwVFsVuiKRSKRcUb+goL6cDi8XpxZ+APFt8RVmBW6YkajcZAk/vjoFdhOl8slq8VNIRAI3BWmIvqerlh/f/8Yh+MqQyeggr81Go0+4nSWy/efUFdMknH6XmCvQDmBGKUB0WezrVDJ45dVjPGq1Wq9STnv4ScEt7PCB3a73UpsTsglpng8nqnS0tLTZL0PE9hVXFz8WC59IpBuibdxdcLYc+nxnGLSWQRZYTu+rBATQxN/j8MM8JrIlTFja2k0whjyEpOeIsh+XcR/BQVGynuScq6Rj3RyOq9zT1cROwsT5VcyxDhtrVVVVZvplAE5oSQ5RUMQCirMZrNDnBmM8roMI5q4p7HmmWImZt9Mwoz7FOvNH5LIG9iDKyhhdXIHxRca3G73SibcwH61uVyuZRJMUCNWV1e3mIYyBF1YPURokNcFo4zSd0iJ/xDZxsvTRayb/TpgsVhidzTerC0jR9pCw1q4m70QYVwtbDbbUlZTKVHsJ2zygaZsPfGYPAKvZZ9pT0KzMmZTQcsKBuzhlT+Mb4AalJSU7GfmOwhOUO4bso/4MbCyRTj18Ce5ZCK4KWjEEJGnaDnNRhLepu4dUFariCVZB/Eu2uWEXUDoKX4SCKzmYyP0M5GcYutIdosLa0O4mcH12C9cUIwq+yN37SHxDTxlnSRVYRJUw8mHHPle2kfwNdCsjJpfY7ZnvF7vCP5LeIRB5dAQZyW2fWBgIPHfQJOMGTUSMGO7sbJ6TAoasVR47h4lTuzXMNXppRoHa2pq5L99MlnBxBCQvXWTuY+VNfFdy538xncShRST12KQzFs4HOWTk5N38DWlLJgYez3GfjbCJfiXQqGQ5kIjrL3UEviXLNjK8pnkglg+VcrZ5y/LmDO/psP/K/YbAAD//1+XrKEAAAAGSURBVAMAGZ5gYESx7H4AAAAASUVORK5CYII=\" width=\"13.5\" height=\"20\" style=\"width: 13.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. (i.e., if mod(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"29.5\" height=\"20\" style=\"width: 29.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) == 0, the neuron cannot be entered).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅤ.Metabolic Cost Calculation\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.4667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.7333px; text-align: left; transform-origin: 309.5px 10.7333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe total cost to transition from \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eu\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e at time \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eT\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e with the current signal state \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"31\" height=\"20\" style=\"width: 31px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.4667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.7333px; text-align: left; transform-origin: 309.5px 10.7333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e                \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg 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\" width=\"351\" height=\"20\" style=\"width: 351px; height: 20px;\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWhere: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 41.9px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 20.95px; transform-origin: 316.5px 20.95px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 21.4667px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.7333px; text-align: left; transform-origin: 288.5px 10.7333px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"35\" height=\"20\" style=\"width: 35px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a weight vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJAAAAAoCAYAAAAR33OgAAAMxklEQVR4AeybC3CU1RXHv82LQHglMWZ4hBAIINqCioBYBMFKedQBgdZhbKm2Vp3aSovYKrUjMnVw7ENKHRUHBZkO9UEtKA6OFgaECgqp0PIQJLCBDRIgBAiQTB5sf//P7/v4Nmw2+0iCg1/mnL3n3nMf55577rnn3t0kGd6fp4EENOAZUALK85oahmdAnhUkpAHPgBJSn9fYMyDPBhLSgGdACanPa+wZkGcDCWkgrAEVFBTcDgaFPXv2DJAuARdCzwM7a0QPL08N5Ofnj9NaC1nrNaS1YFB0Tk5O+4azDmtAdqVgMFialJQ06sCBA3eD9/v9/sfAkzbfSy8/DZSUlKzWWgtZ628zwxfBRiGiAdGqur6+/gypB19PDeBDgociTb0pA4rU1uN5GvAeEj0bSEwDngdKTH9f+9bNbUDJeXl5vYnYJxG5T+3evXs3l4ZTKL9F5eCYXr16dXLxLgmpW4Utk1KESAFNkOzIOVXIzaQ/hcngJYW+fftegTxjwKnIO9QtjGS0yifB6wnPB7Y4NJcBJWMQ0xC8JCUl5W8+n6+c6GtVIBAodc2gjqh+nWEYGyj7IfzjtHl60KBBqeRbFXr37p2Hsl9r3769AsQRdXV1HyHbeoSoA00IBAKlWVlZK5lLFTfRddTfwSJdZzJb+aNHjx5XM/7G2tranQydl5qaug55P4Z2gNvTbnS6ioJcZN5F/fWFhYXdybcoJGxAGEEnhF2G8MsQfDWTHM0VcAMTrA4nObwybnZPwPuCNrPKy8t/At1qgLwTzp8/v50BR5FOQM65hw4dOkw+CIZAUVFR7f79+9+lcBF4FYa0GOO7Erq1wIduH0xOTt6KbjMZ/1vo7+W9e/ceDycAc6kGX4YnQ7oZPT+HJ21LvsUgIQNiMTphBG8i3ffBzTU1NY8GAoEqaEOeBY/0PAqoI32WMselVlVVHaXd55Qlo5gJ8NOhWxyQdwrjSt6ODPYwu/YjUhPg3YCspUJ27jVm4YWPzRbZn/YDLLqlExnPLAb5C1jLuA8UFxfvgzYBeScj62lwWwOjrqPuVrOSYQzB+HScWdnmTxIxIB+CaoK3IVYN9JOlpaXl0CbIs2Ac95FR7DAgJycnA/oioJ0WJdNi+FDGaJSyBQX9E8NaQ7oW7Gvx407ot5Cx/kwHbZHrfTzlP6BNgHclPHmZrhR0Zef2Ig0HaXit6xsyMLiOyPwM+A7zvOi1tmH9aPL0NZx6j4PS31I8z0ZoExjvGuT9K5kOYA8wDwwHXfBahTYDfY6k3y2gXpfrSGV8o+E7mxs6JojbgFC6dqkMxGBB1hBHKIYwB7eU+D0ymrz4h48dO3aWfETAUCazQG9T6Q2OjsnZ2dljUdTn4AYmO4TyeMFHvzNoLGXXQC+wPSVlBvkbSb8BCk4ynyMimkIWsiMyP4rBbaPuI8jZjjRh0LGDDI/RkTzlUYxgIbRzxKLrCeRl7CSG5C0zmvhDzin0+Q7VdFy/SqoTYCBzX4VubycfF8RtQAw8jRHNeAB6pXtB2rVrl4ewurlQxTBQrHaPowD42jn5YlIvkJ6efoYAtYB6f6DsALgUDCoGQXnaaepjnmWYsGIDgtACWkwEBZ+lpaXZLl554a18mMZOepCLgGSA/BKQy5kL8jjHCP1UZ2Zm/ok53EnNZvuKh34HMeYI+hR8goE6Y+JF0hnvZjEs3IlBHbdoM4F/tUkYxknoI2x2edhHoO/Bk00E7+WCoKP4JcMw2jLWjHh1G5cBsSA6cqR0xjcUDH8owoU6d03jouwo5/C/SR0g353JZFsF2/fs2VPJwownr4XexgSPQpvAYpZA7Kb+8A4dOshTkI0N6GMYLWw3v84dhObm5upodQyEeh/Cd45i8gzt+yap4AsWc7cI4a5du2pk5FQ4T97ZINAJAQs6jg4klzbOKgJj50LCWHr+cB/p77n5/fr10+aU/unCMDcD/clYivDqK1UolNzoXJtTus63NrVYMWFcBsQIMiDbhR5EwBCXj2Ap1LF39NvFxcWfkXcAj6U4Qt/qK3ZaDUP1R5JKYf8jdRZDxkXebxhGGuOMgo4ZaGfvSB2nn7g7QBbFcrasFci+BL4zPm8vMnQtgNpuw4D88FsSpIs+1gBnkUdXdytr6LhNYj62vLvh68h3+NXV1bq62wZmbobKysrNtHmYSs4zBbSB59JzSyVGeRy+Y6TiRYtxGRDvEDIgO1g8yF9lIwMextvMh+cWPAWBTe+F0FtRwCY8mnaN4hODvPvtiKYm2GVSrBRsFsbw0c2qqzhMHs3KhibI8yrG/h93KUq+inxvsB5jW+o+qilrduAoSUcOGa36PsH4illEN8R66i1AXr1lOTz0rc3ZhYLTyLuMNEj8ecbv94czkM6sheK2LaxhXEdwXAbEpM4hmC2QaLeBwDJMY4BYvm/fvpAdxHmsBZG3qUL4ObjVU6RS2hXUlwc6rdSNKOqU8qTZUrBoxQLETV2UKt8Emu2pU8NY5jMDtAk8JmZQpuNWR7FiAsf7UEHeSfGNjpNVzNs5AuDFBMjdnmA1V88bkRpmZGTUIY90Kl1cJC/HcTZ8bbidxErL3X0p+CZ/F6i2r/BMsVl0Y0g/A+GlsmlDvC5lUUNcBoSVH2Yx7UBTFux4BRZU5+9DlgTiWaRhvg2xK3Q11Q57gljnXw4zBoKFyGfSReBhlHCUG8bkSM2RdRP8elDHYFtSG2juu4eMbmCpPp+vDbQDjKOr9HTKd1E4I17vQz9DMNRi+jhy4sQJP/LeAB0W5CkYr0hM0hB5ZXwcoTPhyeDTeYqwjzKKDIOTQe9cY8h8gG7mkAbBsNCtWzetwS9gPoMXC/G6lEUNSVHXdFWU10BAvaloNw/Fq8hoDNJCJr2CqnK7i0hHWAZlaHfwNrSAsjvAOdwC5pOaE2SBq2ln3iRIdXWFdQEoMw2UtBx3LM83gDZ2XNMBo3xQO/xCi1AKWRVnraU0g3bfIdW7RzILO4v8bPJPgeeoN5ZUPCM/P1+B92vkD7JoUzH2Ro8+6kQExriFClp0EqMr8v4MwhyH9CKg/mLmKqPtyma9SRVs/VE+HpxLWRb11C+koUdHPZs8T2Yt5dO1RtCNga9NmzY/pZ8d7nVorHKk8rgMSB0i4Fso/LvQFShkNYuxglSPc4vYGePBh5iIvt54kx3399TU1O0IrEe661iM3+sWQFsTOH8VQx1Uhj7seEVZG+2gUG8XOi7/C2MLaEPEWwSynmIhpiKPjPbnyPMu8m6k8XCM40bkeZxxJ5HXl7zifcDcXiE/j3kM4Shwbl6UxQz0JeN1B9/5GLyOxbB9IU8JuhoL6tX8WWRdgf42kU+mr8HMZw7yyjPOhLcclIfVu9G9tB3n9/tDLjUNBvGxqX9E+8H092v3OjSoF1U2bgOi9yCuby0CXwv2ASeBA8Hn5OqFTORJ8prwNNK+4ESMRTuL5iGg5/c1KmFStmdR1nBdS3Vj0xexBv2UgENAqvumULHk3LlzMkLI8EAsdhp5fkWbTBZgPOkw0JEHI/mU8tGgeLfB6w+acwnfY/Sl9LmVvgpAeZ3f0rIET6qAHjI8oNtDtJPecmgn3UrP96mcFkHk1U9Ph8KTd9QmuB76DXg6qknCA5tnMkqbgiH+mP7t2DB85ShKEzGgKLqPvgre4X1qK64axI7KhTaBwFUPjv3hmzc2s9D6UExAuVz6YhaksZ/eWrUvfcLi6Q3nJhZwMdKYxzdpqwHjK0a6m/F/4DYeyjvhlV7gycK8yMQi0FfGgNhRMp55CK/jajqpTwaCq1Wgp5vCb9yTFp+A9JcYUC3n+OvkLxkgg/Qo79KoDNZcnqbCejyFjk/I1gOMZAJyvsCIuv7re7uFbFThIso345HKeEA141DqRA2aeKOVsdQ8YgcFahGV02gHMTJQ7CIMRrHIXUz4LYLu95ChD3HKcIzHrfRkdszv6D4NDzUz0XOcfuICAtss5LiTBVDw2hlZh3ENn02ZdGYG/upYr90VFRV69f2YOery0areB3lGIqNiuhzk0Vc6+g7TRv2cJgvZQ54EqGdefChv9MaoOkn6aIgsYoAyvYksYWD9ivBFWSuCzAP1ggy7RcA+26/FYO4gZrmVdHSYuKke3lwW4ynFWi0iSRSdMvYJ5HgdOQaDPrAd+dngOpor2CcxjLKysrPM4wHK9d4SMUYxGzTzB+PK6+Uin2QMh7nEVjs0LLdP5//CCNznU1YBvoQhrWZzOHOizISwBsRx8imD3d8Q/X7v/8JMrV3GH6y9gvOL1h5b+CPrryeUkNmHNaCQGl7G00AEDXgGFEE5HqtpDXgGFEFHHqtpDXgG1LSOvBoRNOAZUATleKymNeAZUNM68mpE0IBnQBGU47Ga1sD/AQAA///6+jFCAAAABklEQVQDAPji1pxxDeweAAAAAElFTkSuQmCC\" width=\"72\" height=\"20\" style=\"width: 72px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ecorrespoding to the 3 polarization states.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInterference(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eT\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAO8AAAAkCAYAAABhX23OAAAOcklEQVR4AeycC3BU1RnH7yaBJIRXQiGCMbuEDErE0Yq1OqUoPhhnqlVnrJTOaC1OHRzq0Fa0LSOhygjTVmwVq9MHlkFqtXVGkEcdxEdbLVILlEKxhGd4U4TwkpCQkP7+t/csu5t93E3vZtnkZr4v5/mdx3e+75zvPO7mWf6fzwGfAznJAV95c3LY/Eb7HLAsX3l9KfA5kKMc8JU3RwfOb7bPAV95z8mA7/M5kFMcSKm8VVVVNw0dOnRbKBR6uby8vCSneuc3Nuc4gJxNkryBc3Ku8Z3c4JTK29bWVkybqsDys2fPBnB98DmQSQ70pXDJ2wBcH5JwIKXyJqH1JKmioqKY2fZFZtom3FoK7ZQJYtSoUT2ocyr4JHUWgDYQfgZs6yjShxcpSGU/gdXyXdVDOOdA40L7J8KHvwWDwcFuOlBZWVlD/1+G5l3wVXAt+NywYcMuckPfFfJ0Jt+yrryFhYU1gUDgDgauJ3gvgnIBbkYBBpcdOXJkIVZFVVlZ2Q+prAW0EL5S3M+DLaS9QLuGk95zx44deANfJP5TUNCQl5d3leJBKf44IreDgtX8OwPdTNy+qkf14c8JoK3FVVVVEwsKCrbAg3lgWaqGa4JCSR/Lz89fB1/q6Ps4+DIepo2FfggW23omgi+lKieX07PBt6wrb1NT0yYGeBED1wwuqK+vP4CbMaiurq7o0aPHcio4OmDAgClr1qw5g98G4mWuDSMwbefOnZO3b9++xaQjgJcSb/b8dWfOnDHK2oqgvkUfpKwNCPDfyWeJrrS0VKv6Ucp9BeHtp/jzGZk471RbaePFKF4vXFfQ0NDwEBk1Ca5lPOeq74Qt+HcMfjyK/wT8kXV1Nf4uB9niW9aVd8+ePY0oykQUoBD3CUa2DcwISIFaW1tl1jYinI8aITOVkXYV/g9QzOdwI9tRQP4biTOweteuXQ0mIJf0w7iRSm0rMPESXgvh/bFWKPKcrxAoKSlZxjjcjtJ9n0YuA1MCk+Gl9O1hMuaDC/fu3Ss+4P0fbOMP3xvgIPLNHjhwYG/8WQHG/zIsBJny72HKD/KoEVnjW9aV1yMGuikmwOqp1UFmcS0CeiyWCEUbgNn3rCaUyDRMonLCnwUFrfx7G4wCBFMr+AexSu3Uo1X5bmeFiqJLFqDeMvaQzzOzj0iWLzYNAQ2KDkxnC9K2adMmWT8qThOX+il/UoSnXyHDEFBbiQ9xY0FlvUVkK/wdzQQxGn9WgPq1NQsxVhUtLS2abLxoR9b4ludF63OhDAR6NIM3kYFbyuqyKl6biZ/FQvFObFrPnj2lPJVO/D5W6I2OP+xgNbwJvVagcJzxEL+KepeDU5jxq018CpdtZ8FTtPlBJpRFbhWYfgahWQ4+SPmzQO3JcbyHysrKUvp0i1Pybkzkescf5aDgW4j4DyjluRs3AOYKeN5Or/iWEeVF0AYjRHPBVeACcDW4lJVgNmlj43FD5hRmzWhWG11NhbNAUwTeAv0S8BdKoPM1+BeDx8FT4BJohystARYgZJNI64siaL9rH1ARdgUI35VklODhWOspY588aWALNKq3krK+Dp0b4VUbp5H3PXA49CkVWBNDIBDQJFEDjehEr3IIeg/UdQGlmknt4LFjxxoJx4Oj8P+UEqCpYfz6y99dER54wjfPlZc9UAWCtoLBKuHUcQyrzr3gNYRfo9FTSAvvebQHRPHGoZzLevfu3YBgz2f2Dg8sadOh+RhcykDfChYRN5k8K4k7RZkfEdcDvBX/7xHeuPsY4i8h/03k28+quRbXNdC2Imh10mxo/skqe9oE3LpOvfvJ/1UmKJmZeJMD9RygXxPIJUVMqsD0sZq8i0FbcXEniB7aTEKIwg3P9x08eNCcxhN9Dk6dOnWCkFmVK+GnhJeobgue8M1z5WUv8TWGZCQD9HrEgVAbJ7u/Jf4NhGoEriWlOHz48Dfwy5y9FredeYfSz+T083Ok/QsUSJDHcKA0kr3kBITzJuqZrgRwJApyA247IP4LRErIDnE1dQi/a2BCkaJd7hA0U9+7jj8tx6lXdQ+lT6PcEtPHlAqcJcW1mERlJaW7dyxiP+D6JNstn3Ipn1d881x5EW7N/OKlrgXC5qEUGcVdRPpZJSKUp8FfoqAzCCc82eSQ5zR0n5BH8CGKOIkDpSMKgG2EF+NqRcunbE0CBKOA6IB9SKJyVF5UaooAxBeTRQqMY+1qbm7+WJ50UfWqfugk7JEn10QlB/iUUIFjFHc1gnGP8icv0ZtUJja3+/fICkuhM/yMjO82fvrvCd8yobw7nVF4GBN3In4JK45lsSqvyMvL052uHXb+6TTS1ckmwn+msbFRp7oOqe2E91OE+oFRwF66BDpbWFDE3YcOHToZlSFFADNfim/6sI6J42AKkrjJ1Psp9Zu98oh034lLIemHLI+wCQ1/v0ycMZWluHdt3bp1T9wGZCay3Ym9i2pkvSTaG7sg7xJZPOFbntesQEAXUuYuUCbVrxGw9zmgsFdjBP8Ip7lbSes0YC9dQpsqOlIhit8b5bjG0OLXFVFHD4AiJ6nyfv36iT+maFdurAJDlE3FteCrmYxoimvQJBZ1R+6aMkXGYDA4mO3YVmQu7vNWVjw9oCml3cOYlPclykcZb2vsU1TX4WTq94RvniuvlJPG3U/PdDWAY12DKbceRj3LYZYenSuu05ABy0PpzMqZVr29evW6iL6McIjCr6eccIcd2lPS1NSkg7a0y5ACQ/Q90DyGOEYbp3byikv1lpRXShj3kMpK/MdOp9WVpZW4iNxOYbw84Zvnyiu2cpi0EgHVYwiZeBooHUY9xKitY3Y0jx2U9bxGJh0prnmUH/V6KlsN1x6Xwf8N9ZuvbvrB63nwVW0luvOA8dQEctypcUiirUCM9bOPe/OMmPb19fX7mdyqOUcJxENWW72ga4Bf25jUh8TLozjKuJFtTlrbK4cHrhyv+JYR5VUPYMBOGHEDDNOj/X8rDqwivIAVuENmLPRpQ1FR0UmEvUPCwiBfF1FhuyeREWlpeWnPJ5R92rLSIrOkuNCFTWXK0aGgJsik10jp1eI+N1dAu2mPOcArT7QVQFH0cUMflUz+jXV1dUfl767oFd88V17M4+kRDybaMKPf4WpHjxyeZrC0Co9kMCOVgujMwebNm3U4Ykz4hKtDbAvYp5svjOwkFOVPtqfj/2R9mCuSQ0xiapfr0mIVF6vgLiycj1CGqEOsYDDYaSuwVif68RenEwPZCgx0/LGOudfU+C8hsaPnBpDmPnjFN8+VF2EainJKWcNc5qCqkbvN2USYBxK6cyXYKaDP+3RQocp69u/f31Wfaa++MDKvtvS4w6wwKidt5ACkCN4YUzethx7xFNfscbFwEl4jpd3IDhAwqZkDysFMKFHjbopDwfWEUucOG8nT7vmpydedXC/45kqQ02EqjWKs8u6tqKgojqQjvomwnsgdx+aP94Cd5MwADforJetgpYqrpkSrA1nOAW3U/qjUianD/O6Q6e3QWxH7vmaU2KxWJjmhiyUTJH/YVEb4210HZVOBsay2MrbP0IFWJu2JTFLhF3TE2aY+rr7XlqVRS35jBRHdfQE+/N9881x5NRwI2zgOJabg12yLY1mYztr7SiF+wsGCnjXa8VJyBt9eiXH7FBQUmNXJTneE3qSXFBcXx57ShqCzFRJ3kMqzCaP/baBN64kqp3ytqHgTA3vygZSl11+JM6WZQnl6A6x+1OFf54Zcikte81Y56T2ulwo8fPjwz9A++3qP+tuNCWlRUFpaOpd8T4Gj+/Tp8y0S7XGXIqPQtYQ1CU7iDEQmM8F4kPtxnc03z5UXJdnHID6JeynCt5I7s3m42i9OJu4uBlAfqLdIyUi7D/P0j8RLsTV6g1jxFhE/g/Qy6BT/CgkjQcFVrDyvkz6e9GLSv0ldL5HQF9Q3szdT3nzSr1DYIHtDXYorXz513WbiY13KG8N+/Xe0oY40nZbj2DC6ublZH0b8PBQKRcbbiW7+IcT6IKM/9b8ED9w89JACTCW/lCip4pr64yjwd2pqaswHFSZbQpe+Xwb+jL7+g0x6lopj2WMCb+aQpvfh2rsrPox6PQePf0Bbx4DXkncV+BoT7/tkOk3fL6fPC/DrrhunawF8yQrfPFdeBGgaAzmDwboHHEv4ftzriLsB/5sMmz2A2gcTnk/a9WABaB/vE1cNPk76EeJWgGNBOw1X+a4n/VXSGwn/Cn/k1UAP4sYTJ+GjqnPAyv8HQlrxb2YPGfc3laD9M+2cgFsKmjrl2vUSN5my9TM3FOUeLrzwwgFMMndCsZ7JZz6uG2g9efKkFELfII83e9xUhLTP7IFnUucjEd/opiLVL19soP/fpowK+qp+20hYPH6YtJWWZSU6bGoj32robgevBjVRX4H7AJbWDuiyDkws+l55J3zZgwWmwzNP2gRfssI3z5XXE25koBDnFx5qKTrESvAAbvjdNf6MAtaAPli/jEoeYa/jes+nU0kU4nEUwHyRQxGpAZoD0NQiVLI4UhN0kxzwYwN8uRK8Pp1xOF/Z022UVwPAoGnl0IcQk0KhkO5IFZ1RxHy8mplenzbWOvVntD6/8K7AAXd9cK28CGBlYWFhu4f/7qo5b3K1oUBP05c54PxgMLN3oiiuftXiBXo/i1n/p7j2lgHXh/gckDV0SfwkPzaWA66VF0J9hzpJJ4j4cxlaUaQf0YHZ7D8XZkqBnXJ12DaDCeN56vMVFyYkgfxgMHgLk2qX/onYJP1POykd5dXp52OcIJ5gRYn8asPtAUzajcsggVbgBQjKHWCQejTj43gGFBsIcr98NxOFfgXEV9wY1iJDt4GRcmR+KkjXaTG5/WA8DqRUXlaNJaB96pjAvS9ewbkQx6HFbg53wifgHra5TeWqfA/L7FJFIUtdVq46a6BSKm9nNSSj9fiF+xzoghzwlbcLDqrfpe7Bgf8CAAD//4TOyrMAAAAGSURBVAMAeqS1stBMVncAAAAASUVORK5CYII=\" width=\"119.5\" height=\"18\" style=\"width: 119.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (simulating temporal electromagnetic noise).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eⅥ.Input / Output Requirements\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: * \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eneurons\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: A struct array containing \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e.code\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(string) and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e.S\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(integer).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 81.7333px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 40.8667px; transform-origin: 316.5px 40.8667px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eM\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: A 3x3 state transition matrix.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eW_state\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: A 1x3 weight vector.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eE_max\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Maximum energy allowed (scalar).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003estart_id, target_id\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Indices of the start and neurons.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: * The minimum metabolic cost (scalar). Return \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e-1 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eif the target is unreachable.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4333px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 316.5px 10.2167px; transform-origin: 316.5px 10.2167px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 288.5px 10.2167px; text-align: left; transform-origin: 288.5px 10.2167px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Optional) The path taken (sequence of neuron IDs).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [min_cost, best_path = solve_genesis_protocol(neurons,M,W,E_max,start_id,target_id)\r\n  [min_cost,best_path] = size(neurons);\r\nend","test_suite":"%% \r\nn(1).code = 'AGCT'; n(1).S = 10;\r\nn(2).code = 'GCT';  n(2).S = 10;\r\nM = [0 1 2; 2 0 1; 1 2 0]; W = [1, 1.5, 2];\r\ncost1 = solve_genesis_protocol(n, M, W, 100, 1, 2);\r\nassert(cost1 \u003e 0, 'Target should be reachable');\r\n\r\n%% \r\nn(1).code = 'AGCT'; n(1).S = 10;\r\nn(2).code = 'GCT';  n(2).S = 10;\r\nn(3).code = 'AAAA'; n(3).S = 2; % Will sleep at T=2\r\nM = [0 1 2; 2 0 1; 1 2 0];W = [1, 1.5, 2];\r\ncost2 = solve_genesis_protocol(n, M, W, 100, 1, 3);\r\nassert(cost2 == -1, 'Neuron 3 should be gated at T=2');\r\n\r\n\r\n%% \r\nneurons = struct('code', {'AAAA', 'AATT'}, 'S', {10, 10});\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nE_max = 100;\r\nres1 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 2);\r\nassert(abs(res1 - 21) \u003c 1e-6, 'Test Case 1 Failed: Basic cost calculation');\r\n\r\n\r\n\r\n%% \r\nneurons = struct('code', {'AAAA', 'CCCC'}, 'S', {10, 10});\r\nE_max = 100;\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nres3 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 2);\r\nassert(res3 == -1, 'Test Case 3 Failed: Chemotaxis should block this path');\r\n\r\n%%\r\nneurons = struct('code', {'AAAA', 'AATT', 'TTTT'}, 'S', {10, 10, 10});\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nE_max = 15;\r\nres4 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 2);\r\nassert(res4 == -1, 'Test Case 4 Failed: Should return -1 due to Energy Limit');\r\n\r\n%% \r\nneurons = struct('code', {'AAAA', 'AAAT'}, 'S', {10, 10});\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nE_max = 100;\r\nres5 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 2);\r\nassert(abs(res5 - 20) \u003c 1e-6, 'Test Case 5 Failed: Matrix M transition logic error');\r\n\r\n%% \r\nc1 = repmat('AGCT', 1, 10); \r\nc2 = repmat('TCGA', 1, 10);\r\nneurons = struct('code', {c1, [c1(1:38) 'AA'], c2}, 'S', {100, 100, 100});\r\nE_max = 5000;\r\nM_test = [0, 1, 2; \r\n          1, 2, 0; \r\n          2, 0, 1]; \r\nW_test = [1.0, 1.5, 2.0];\r\nres6 = solve_genesis_protocol(neurons, M_test, W_test, E_max, 1, 3);\r\nassert(res6 \u003e 0, 'Test Case 6 Failed: Performance issue or logic error with long strings');\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":4945722,"edited_by":4945722,"edited_at":"2026-03-20T16:01:53.000Z","deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2026-03-20T07:26:50.000Z","updated_at":"2026-04-30T09:14:03.000Z","published_at":"2026-03-20T07:26:50.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅠ.Context\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn a synthetic bio-neural network, information is transmitted between \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e neurons through chemical and electrical signals. Your objective is to find the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eminimum Metabolic Cost\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to trnasmit a signal from a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSource_Neuron\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTarget_Neuron\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅡ.Neuron Structure and Genetic Coding\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach neuron \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ei\\n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is identified by a unique \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eProtein Code\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (a string consisting of bases \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eA, C, G, T\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGenetic Distance\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e): The base cost to jump from neuron \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eu\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to neuron \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\\n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is defined as the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eLevenshtein Distance\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (minimum number of edits: insertions, deletions, or substitutions) between their Protein Code strings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅢ.Polarization States and Transitions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAt any time step \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, a neuron exists in one of three \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePolarization States\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: P ∈ {0,1,2}. When a signal jumps from u to v, the state of the signal evolves based on a 3x3 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTransition Matrix M\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(u,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is even: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP_{next} = (P_{curr} + 1) \\\\text{ (mod 3).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(u,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is odd: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP_{next} = M(P_{curr}+1), \\\\text{mod(length(} Protein_{v}),3)+1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(Note: Matrix M is 1-indexed in logic, e.g., \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eM(row,col)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅣ.Sypnatic Barriers (Constraints)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA connection from neuron u to v is only valid if all the following conditions are met:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eChemotaxis Condition\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: The Protein Code of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e must contain at least one sub-sequence of length 2 that exists within the Protein Code of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eu\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. (e.g., if u is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAGCT\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e must contain \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAG\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCT\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEnergy Limit\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: The total accumulated cost from the source must not exceed \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eE_{max}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTemporal Gating\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: A neuron \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ei\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e enters a \\\"refractory period\\\" (disabled) if the current time step T is a multiple of its \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eStability Index\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eS_{i\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. (i.e., if mod(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT,S_{i\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) == 0, the neuron cannot be entered).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅤ.Metabolic Cost Calculation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe total cost to transition from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eu\\n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e at time \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e with the current signal state \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP_{curr\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e                \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eTotalCost = (G(u,v) \\\\times W_{state}(P_{curr}+1)) + \\\\text{Interference}(T)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhere: \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eW_{state\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is a weight vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\left[\\\\omega_{0},\\\\omega_{1},\\\\omega_{2}\\\\right]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003ecorrespoding to the 3 polarization states.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInterference(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) = \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\lfloor \\\\sin(T) \\\\times 10 \\\\rfloor + 10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (simulating temporal electromagnetic noise).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eⅥ.Input / Output Requirements\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: * \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eneurons\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: A struct array containing \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.code\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(string) and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.S\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(integer).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: A 3x3 state transition matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eW_state\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: A 1x3 weight vector.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eE_max\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Maximum energy allowed (scalar).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003estart_id, target_id\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Indices of the start and neurons.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: * The minimum metabolic cost (scalar). Return \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e-1 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eif the target is unreachable.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(Optional) The path taken (sequence of neuron IDs).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44383,"title":"Code breaker, Part III:  Operation Xiangliu","description":"You have been tasked with decoding several batches of coded messages that have been intercepted.  \r\n\r\nBased on previous intelligence that has been gathered, you can be confident that the messages were all encoded using a simple \u003chttps://en.wikipedia.org/wiki/Caesar_cipher Caesar cipher\u003e (a type of \u003chttps://en.wikipedia.org/wiki/Substitution_cipher substitution cipher\u003e), an example of which is the \u003chttps://en.wikipedia.org/wiki/ROT13 ROT13 cipher\u003e (discussed in \u003chttps://www.mathworks.com/matlabcentral/cody/problems/78-implement-a-rot13-cipher Cody Challenge Problem 78\u003e).  As in the Cody Challenge Problem, uppercase and lowercase letters are handled independently of one another, and all other characters (e.g. punctuation, numbers, accented letters) are unchanged.  Unlike the Cody Challenge Problem, here the number of letters to shift is not known in advance, and will vary _between_ (not within) batches — also, here you need to decode, not encode.  \r\n\r\nThese latest activities that you are investigating have been nicknamed 'Operation Xiangliu' by your own organisation.  A few decoding options are at your organisation's disposal:  \r\n\r\n# Test the candidate decodings against all words in a large dictionary — this could work, but it is very slow.\r\n# Test the candidate decodings for the appearance of a name or phrase that is certain to appear regularly (e.g. \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44351 \"Operation Phoenix\"\u003e) — unfortunately as yet there is not enough knowledge of the activities to be sure of a suitable name or phrase to use.  \r\n# Test the candidate decodings against all words in a short list comprising, say, the hundred most frequently used English words.\r\n\r\nThe *third option* will be faster than the first option, and more reliable than the second option, so this is the approach you will be taking.  \r\n\r\nYou have been careful to avoid using 'lemmatised' lists (which contain e.g. the verb \"to be\", rather than the various inflected forms such as \"am\", \"is\", \"are\") like those based on the \u003chttps://web.archive.org/web/20111226085859/http://oxforddictionaries.com/words/the-oec-facts-about-the-language OEC\u003e or \u003chttps://www.wordfrequency.info/free.asp?s=y COCA\u003e, and after setting aside \u003chttp://world-english.org/english500.htm another list\u003e you finally choose the \u003chttp://ucrel.lancs.ac.uk/bncfreq/samples/120.pdf list based on the BNC\u003e as the most reliable, and will use the first 100 words on that list.  \r\nThis list will be available for you to access as an input variable, |bncWordlist|, to your function. Note: (i) some entries on the list are morphemes (e.g. \"\u0026#x200A;n't\u0026#x200A;\" and \"\u0026#x200A;'s\u0026#x200A;\") rather than words;  (ii) some entries appear more than once (representing different grammatical word classes).  Of course, in the original messages any capitalisation might be used.  \r\n\r\nYou have been instructed that your 'certitude' (degree of confidence) in the decoding must be reported for each batch, and shall depend proportionally upon the sum of the characters for the words/morphemes in the decoded message that match words/morphemes in |bncWordlist|.  Being able to match words/morphemes accounting for three-tenths of the characters (excluding spaces) shall correspond to 100% certitude;  matching three-twentieths would be 50% certitude, and so on.  Certitude shall be reported as a percentage, rounded _up_ to the nearest integer, not greater than 100.  You need to maximise your certitude for each batch by appropriate choice of the shifting parameter.   If there are multiple shifts of equal certitude, report both options on different rows (arranged in order of ascending shift parameter).\r\n\r\nYour task is to crack the codes and report back in a \u003chttp://au.mathworks.com/help/matlab/ref/struct.html structure array\u003e: \u0026nbsp; (1) the shifting parameter that had been used in the encoding [as \u003chttp://au.mathworks.com/help/matlab/ref/uint8.html uint8\u003e] (usually scalar, but may be column vector); \u0026nbsp; (2) the decoded messages [as a \u003chttp://au.mathworks.com/help/matlab/ref/cell.html?s_tid=doc_ta cell array\u003e (containing \u003chttp://au.mathworks.com/help/matlab/ref/char.html character arrays\u003e)] (usually an array with a single row, but occasionally with multiple rows); \u0026nbsp; (3) your 'certitude' in the decoding [as \u003chttp://au.mathworks.com/help/matlab/ref/uint8.html uint8\u003e] (always scalar).  \r\nThe name of the structure array shall be |s|, with respective fields |shift|, |message|, and |certitude|.  \r\n\r\n*EXAMPLE 1*\r\n\r\nSuppose the batch contained two encoded messages — _\"Vomftt qvstvfe, pqfo op eppst.\"_ and _\"Ffmt dbo ljmm, opu pomz xpvoe.\"_ (provided as character arrays within a cell array) — and a (right-shifting) ROT1 cipher had been applied.  In that case A→B, B→C, ..., Y→Z, and Z→A;  similarly, a→b, b→c, ..., y→z, and z→a.  \r\nThus the original messages would have been:  _\"Unless pursued, open no doors.\"_ and _\"Eels can kill, not only wound.\"_ .  Twelve of the 51 characters have been matched:  \"no\", \"can\", \"not\", and \"only\".  \r\n\r\nThe correct answer would therefore comprise:  \r\n\r\n  s.shift = uint8(1)  \r\n  s.message = {'Unless pursued, open no doors.', 'Eels can kill, not only wound.'}\r\n  s.certitude = uint8(79)\r\n\r\n*EXAMPLE 2*\r\n\r\nSuppose the batch contained one encoded message — _\"Oa oqvvq'u cnycau dggp: \"Ctu itcvkc ctvku\".\"_ (provided as a character array within a cell array) — and a (right-shifting) ROT2 cipher had been applied.  In that case A→C, B→D, ..., Y→A, and Z→B;  similarly, a→c, b→d, ..., y→a, and z→b.  \r\nThus the original message would have been:  _\"My motto's always been: \"Ars gratia artis\".\"_ .  Eight of the 37 characters have been matched: \"My\", \"\u0026#x200A;'s\u0026#x200A;\", and \"been\".  Note carefully that:  \"\u0026#x200A;'s\u0026#x200A;\" should only be matched once;  \"to\" (in motto), \"be\" (in been), \"at\" (in gratia), \"is\" (in artis) and \"a\" (passim) should _not_ be matched at all;  and \"\u0026#x200A;'\u0026#x200A;\" will only ever be used as an apostrophe (never as a quotation mark).  \r\n\r\nThe correct answer would therefore comprise:  \r\n\r\n  s.shift = uint8(2)  \r\n  s.message = {'My motto''s always been: \"Ars gratia artis\".'}\r\n  s.certitude = uint8(73)\r\n\r\nIf the batch of messages cannot be decoded when following the above assumptions, then return the original encoded message(s) unchanged, with a shift of zero and a certitude of zero.  \r\n\r\n|Note:  Many Cody solutions are hard to read and not necessarily computationally efficient.  To direct attention toward efficient runtime speed of execution, timings are measured in the Test Suite, and reported back (if the submission is runnable).  Although the timings are not perfectly reproducible, they do provide an indication of computational resource demand. (Try resubmitting on another day if your code runs slowly, in case it is caused by a server issue.)|  \r\n\r\nTo provide some extra motivation for you to get your code to run efficiently, it will fail the Test Suite if it is deemed \"too slow\".  \r\n\r\n----------\r\n\r\nPrevious problem:  \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44356 Operation Orthos\u003e.  Next problem:  TBA.","description_html":"\u003cp\u003eYou have been tasked with decoding several batches of coded messages that have been intercepted.\u003c/p\u003e\u003cp\u003eBased on previous intelligence that has been gathered, you can be confident that the messages were all encoded using a simple \u003ca href = \"https://en.wikipedia.org/wiki/Caesar_cipher\"\u003eCaesar cipher\u003c/a\u003e (a type of \u003ca href = \"https://en.wikipedia.org/wiki/Substitution_cipher\"\u003esubstitution cipher\u003c/a\u003e), an example of which is the \u003ca href = \"https://en.wikipedia.org/wiki/ROT13\"\u003eROT13 cipher\u003c/a\u003e (discussed in \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/78-implement-a-rot13-cipher\"\u003eCody Challenge Problem 78\u003c/a\u003e).  As in the Cody Challenge Problem, uppercase and lowercase letters are handled independently of one another, and all other characters (e.g. punctuation, numbers, accented letters) are unchanged.  Unlike the Cody Challenge Problem, here the number of letters to shift is not known in advance, and will vary \u003ci\u003ebetween\u003c/i\u003e (not within) batches — also, here you need to decode, not encode.\u003c/p\u003e\u003cp\u003eThese latest activities that you are investigating have been nicknamed 'Operation Xiangliu' by your own organisation.  A few decoding options are at your organisation's disposal:\u003c/p\u003e\u003col\u003e\u003cli\u003eTest the candidate decodings against all words in a large dictionary — this could work, but it is very slow.\u003c/li\u003e\u003cli\u003eTest the candidate decodings for the appearance of a name or phrase that is certain to appear regularly (e.g. \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44351\"\u003e\"Operation Phoenix\"\u003c/a\u003e) — unfortunately as yet there is not enough knowledge of the activities to be sure of a suitable name or phrase to use.\u003c/li\u003e\u003cli\u003eTest the candidate decodings against all words in a short list comprising, say, the hundred most frequently used English words.\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eThe \u003cb\u003ethird option\u003c/b\u003e will be faster than the first option, and more reliable than the second option, so this is the approach you will be taking.\u003c/p\u003e\u003cp\u003eYou have been careful to avoid using 'lemmatised' lists (which contain e.g. the verb \"to be\", rather than the various inflected forms such as \"am\", \"is\", \"are\") like those based on the \u003ca href = \"https://web.archive.org/web/20111226085859/http://oxforddictionaries.com/words/the-oec-facts-about-the-language\"\u003eOEC\u003c/a\u003e or \u003ca href = \"https://www.wordfrequency.info/free.asp?s=y\"\u003eCOCA\u003c/a\u003e, and after setting aside \u003ca href = \"http://world-english.org/english500.htm\"\u003eanother list\u003c/a\u003e you finally choose the \u003ca href = \"http://ucrel.lancs.ac.uk/bncfreq/samples/120.pdf\"\u003elist based on the BNC\u003c/a\u003e as the most reliable, and will use the first 100 words on that list.  \r\nThis list will be available for you to access as an input variable, \u003ctt\u003ebncWordlist\u003c/tt\u003e, to your function. Note: (i) some entries on the list are morphemes (e.g. \"\u0026#x200A;n't\u0026#x200A;\" and \"\u0026#x200A;'s\u0026#x200A;\") rather than words;  (ii) some entries appear more than once (representing different grammatical word classes).  Of course, in the original messages any capitalisation might be used.\u003c/p\u003e\u003cp\u003eYou have been instructed that your 'certitude' (degree of confidence) in the decoding must be reported for each batch, and shall depend proportionally upon the sum of the characters for the words/morphemes in the decoded message that match words/morphemes in \u003ctt\u003ebncWordlist\u003c/tt\u003e.  Being able to match words/morphemes accounting for three-tenths of the characters (excluding spaces) shall correspond to 100% certitude;  matching three-twentieths would be 50% certitude, and so on.  Certitude shall be reported as a percentage, rounded \u003ci\u003eup\u003c/i\u003e to the nearest integer, not greater than 100.  You need to maximise your certitude for each batch by appropriate choice of the shifting parameter.   If there are multiple shifts of equal certitude, report both options on different rows (arranged in order of ascending shift parameter).\u003c/p\u003e\u003cp\u003eYour task is to crack the codes and report back in a \u003ca href = \"http://au.mathworks.com/help/matlab/ref/struct.html\"\u003estructure array\u003c/a\u003e: \u0026nbsp; (1) the shifting parameter that had been used in the encoding [as \u003ca href = \"http://au.mathworks.com/help/matlab/ref/uint8.html\"\u003euint8\u003c/a\u003e] (usually scalar, but may be column vector); \u0026nbsp; (2) the decoded messages [as a \u003ca href = \"http://au.mathworks.com/help/matlab/ref/cell.html?s_tid=doc_ta\"\u003ecell array\u003c/a\u003e (containing \u003ca href = \"http://au.mathworks.com/help/matlab/ref/char.html\"\u003echaracter arrays\u003c/a\u003e)] (usually an array with a single row, but occasionally with multiple rows); \u0026nbsp; (3) your 'certitude' in the decoding [as \u003ca href = \"http://au.mathworks.com/help/matlab/ref/uint8.html\"\u003euint8\u003c/a\u003e] (always scalar).  \r\nThe name of the structure array shall be \u003ctt\u003es\u003c/tt\u003e, with respective fields \u003ctt\u003eshift\u003c/tt\u003e, \u003ctt\u003emessage\u003c/tt\u003e, and \u003ctt\u003ecertitude\u003c/tt\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEXAMPLE 1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSuppose the batch contained two encoded messages — \u003ci\u003e\"Vomftt qvstvfe, pqfo op eppst.\"\u003c/i\u003e and \u003ci\u003e\"Ffmt dbo ljmm, opu pomz xpvoe.\"\u003c/i\u003e (provided as character arrays within a cell array) — and a (right-shifting) ROT1 cipher had been applied.  In that case A→B, B→C, ..., Y→Z, and Z→A;  similarly, a→b, b→c, ..., y→z, and z→a.  \r\nThus the original messages would have been:  \u003ci\u003e\"Unless pursued, open no doors.\"\u003c/i\u003e and \u003ci\u003e\"Eels can kill, not only wound.\"\u003c/i\u003e .  Twelve of the 51 characters have been matched:  \"no\", \"can\", \"not\", and \"only\".\u003c/p\u003e\u003cp\u003eThe correct answer would therefore comprise:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003es.shift = uint8(1)  \r\ns.message = {'Unless pursued, open no doors.', 'Eels can kill, not only wound.'}\r\ns.certitude = uint8(79)\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eEXAMPLE 2\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSuppose the batch contained one encoded message — \u003ci\u003e\"Oa oqvvq'u cnycau dggp: \"Ctu itcvkc ctvku\".\"\u003c/i\u003e (provided as a character array within a cell array) — and a (right-shifting) ROT2 cipher had been applied.  In that case A→C, B→D, ..., Y→A, and Z→B;  similarly, a→c, b→d, ..., y→a, and z→b.  \r\nThus the original message would have been:  \u003ci\u003e\"My motto's always been: \"Ars gratia artis\".\"\u003c/i\u003e .  Eight of the 37 characters have been matched: \"My\", \"\u0026#x200A;'s\u0026#x200A;\", and \"been\".  Note carefully that:  \"\u0026#x200A;'s\u0026#x200A;\" should only be matched once;  \"to\" (in motto), \"be\" (in been), \"at\" (in gratia), \"is\" (in artis) and \"a\" (passim) should \u003ci\u003enot\u003c/i\u003e be matched at all;  and \"\u0026#x200A;'\u0026#x200A;\" will only ever be used as an apostrophe (never as a quotation mark).\u003c/p\u003e\u003cp\u003eThe correct answer would therefore comprise:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003es.shift = uint8(2)  \r\ns.message = {'My motto''s always been: \"Ars gratia artis\".'}\r\ns.certitude = uint8(73)\r\n\u003c/pre\u003e\u003cp\u003eIf the batch of messages cannot be decoded when following the above assumptions, then return the original encoded message(s) unchanged, with a shift of zero and a certitude of zero.\u003c/p\u003e\u003cp\u003e\u003ctt\u003eNote:  Many Cody solutions are hard to read and not necessarily computationally efficient.  To direct attention toward efficient runtime speed of execution, timings are measured in the Test Suite, and reported back (if the submission is runnable).  Although the timings are not perfectly reproducible, they do provide an indication of computational resource demand. (Try resubmitting on another day if your code runs slowly, in case it is caused by a server issue.)\u003c/tt\u003e\u003c/p\u003e\u003cp\u003eTo provide some extra motivation for you to get your code to run efficiently, it will fail the Test Suite if it is deemed \"too slow\".\u003c/p\u003e\u003cp\u003e----------\u003c/p\u003e\u003cp\u003ePrevious problem:  \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44356\"\u003eOperation Orthos\u003c/a\u003e.  Next problem:  TBA.\u003c/p\u003e","function_template":"% Comments are nice, aren't they....\r\nfunction s = decode(x, bncWordlist)\r\nend","test_suite":"%% NOTE:  \r\n% This test suite can be updated if inappropriate 'hacks' \r\n% are discovered in any submitted solutions.\r\n% The assessment of your submission may therefore change over time.  \r\n\r\nglobal bncWordlist\r\nbncWordlist = {'the', 'of', 'and', 'a', 'in', 'to', 'it', 'is', 'to', 'was', ...\r\n    'I', 'for', 'that', 'you', 'he', 'be', 'with', 'on', 'by', 'at', ...\r\n    'have', 'are', 'not', 'this', '''s', 'but', 'had', 'they', 'his', ...\r\n    'from', 'she', 'that', 'which', 'or', 'we', '''s', 'an', 'n''t', 'were', ...\r\n    'as', 'do', 'been', 'their', 'has', 'would', 'there', 'what', 'will', 'all', ...\r\n    'if', 'can', 'her', 'said', 'who', 'one', 'so', 'up', 'as', 'them', 'some', ...\r\n    'when', 'could', 'him', 'into', 'its', 'then', 'two', 'out', 'time', ...\r\n    'my', 'about', 'did', 'your', 'now', 'me', 'no', 'other', 'only', 'just', ...\r\n    'more', 'these', 'also', 'people', 'know', 'any', 'first', 'see', 'very', 'new', ...\r\n    'may', 'well', 'should', 'her', 'like', 'than', 'how', 'get', 'way', 'one', 'our'};\r\n\r\n\r\n%% Anti-hacking\r\n% EDIT (2019-07-02). Anti-hacking provision\r\n% Ensure only builtin functions will be called.\r\n! rm -v fileread.m\r\n! rm -v assert.m\r\n% END EDIT (2019-07-02)\r\n% EDIT (2018-06-18).  Anti-hacking provision\r\nRE = regexp(fileread('decode.m'), '\\w+', 'match');\r\ntabooWords = {'ans', 'assert', 'freepass', 'tic'};\r\ntestResult = cellfun( @(z) ismember(z, tabooWords), RE );\r\nmsg = ['Please do not do that in your code!' char([10 13]) ...\r\n    'Found: ' strjoin(RE(testResult)) '.' char([10 13]) ...\r\n    'Banned word.' char([10 13])];\r\nassert(~any(  cellfun( @(z) ismember(z, tabooWords), RE )  ), msg)\r\n\r\nfor j = 1 : randi(10)\r\n    decode({'Pizza for dinner.'}, {'mozzarella', 'mushrooms'});\r\nend;\r\n% END EDIT (2018-06-18)\r\n\r\n\r\n%% Test 1\r\nglobal bncWordlist\r\nx =                 {'Vomftt qvstvfe, pqfo op eppst.', 'Ffmt dbo ljmm, opu pomz xpvoe.'};\r\ns_correct.shift = uint8(1);\r\ns_correct.message = {'Unless pursued, open no doors.', 'Eels can kill, not only wound.'};\r\ns_correct.certitude = uint8(79);\r\ns = decode(x, bncWordlist);\r\n%disp(' *** ');  disp(s.message);  disp(' *** ')\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.' )\r\nassert( isequal(s.message{1}, s_correct.message{1}), 'Wrong message{1}.' )\r\nassert( isequal(s.message{2}, s_correct.message{2}), 'Wrong message{2}.' )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.' )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.' )\r\nassert( isequal(s, s_correct), 'Wrong s.' )\r\nassert( isequal(class(s.shift), 'uint8'), 'Wrong class.' )\r\nassert( isequal(class(s.message), 'cell'), 'Wrong class.'  )\r\nassert( isequal(class(s.message{1}), 'char'), 'Wrong class.'  )\r\nassert( isequal(class(s.certitude), 'uint8'), 'Wrong class.'  )\r\n\r\n\r\n%% Test 2\r\nglobal bncWordlist\r\nx =                 {'Doo zduiduh lv edvhg rq ghfhswlrq.', ...\r\n    'Khqfh, zkhq deoh wr dwwdfn, zh pxvw vhhp xqdeoh;  zkhq xvlqj rxu irufhv, zh pxvw vhhp lqdfwlyh;  zkhq zh duh qhdu, zh pxvw pdnh wkh hqhpb eholhyh zh duh idu dzdb; zkhq idu dzdb, zh pxvw pdnh klp eholhyh zh duh qhdu.'};\r\ns_correct.shift = uint8(3);\r\ns_correct.message = {'All warfare is based on deception.', ...\r\n    'Hence, when able to attack, we must seem unable;  when using our forces, we must seem inactive;  when we are near, we must make the enemy believe we are far away; when far away, we must make him believe we are near.'};\r\ns_correct.certitude = uint8(95);\r\ns = decode(x, bncWordlist);\r\n%disp(' *** ');  disp(s.message);  disp(' *** ')\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 3\r\nglobal bncWordlist\r\nx =                 {'Elia lrq yxfqp ql bkqfzb qeb bkbjv.  Cbfdk afploabo, xka zorpe efj.', ...\r\n    'Fc eb fp pbzrob xq xii mlfkqp, yb mobmxoba clo efj.  Fc eb fp fk prmboflo pqobkdqe, bsxab efj.'};\r\ns_correct.shift = uint8(23);\r\ns_correct.message = {'Hold out baits to entice the enemy.  Feign disorder, and crush him.', ...\r\n    'If he is secure at all points, be prepared for him.  If he is in superior strength, evade him.'};\r\ns_correct.certitude = uint8(100);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 4\r\nglobal bncWordlist\r\nx =                 {'Ax qgmj ghhgfwfl ak gx uzgdwjau lwehwj, kwwc lg ajjalslw zae.  Hjwlwfv lg tw owsc, lzsl zw esq yjgo sjjgysfl.', ...\r\n    'Ax zw ak lscafy zak wskw, yanw zae fg jwkl.', ...\r\n    'Ax zak xgjuwk sjw mfalwv, kwhsjslw lzwe.', ...\r\n    'Sllsuc zae ozwjw zw ak mfhjwhsjwv, shhwsj ozwjw qgm sjw fgl wphwulwv.'};\r\ns_correct.shift = uint8(18);\r\ns_correct.message = {'If your opponent is of choleric temper, seek to irritate him.  Pretend to be weak, that he may grow arrogant.', ...\r\n    'If he is taking his ease, give him no rest.', ...\r\n    'If his forces are united, separate them.', ...\r\n    'Attack him where he is unprepared, appear where you are not expected.'};\r\ns_correct.certitude = uint8(100);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 5\r\nglobal bncWordlist\r\nx =                 {'Aes cèwo vo foxd bémyvdo vo dowzy', ...\r\n    'Yx wo dbksdo no dbkîdbo aekxn to dbksdo no vk népksdo ne csvoxmo / Vo csvoxmo ocd n''yb, wksc t''ks mryscs vk mknoxmo', ... \r\n    'Mrkaeo wyd, mrkaeo zrbkco nsdc kfom owzrkco / Pksd no Mvkeno WM, vo mywwkxny no vk zrbkco', ...\r\n    'Mkb t''cesc ex WM n''kddkaeo, ckxc dsmc, kedroxdsaeo zkc ox dym / Zbêd à pbkzzob ceb vo lokd zyeb vo wyefowoxd rsz-ryz', ...\r\n    'Ne bkz n''kddkaeo aes pbkzzo, ézkdo, wkdbkaeo od zkdkdbkaeo / Zvec no ckxq de mvkaeoc, t''cesc WM noc Mkbzkdoc', ...\r\n    'Vo zénkqyqeo ox fyqeo ke xyw no myno Cyvkkb ye Mvkeno WM / Do zbyzyco n''émyedob moms / Ae''yx ézovvo voc fyiovvoc, nèc ae''yx cyxxo voc myxcyxxoc'};\r\ns_correct.shift = uint8([0 21]');\r\n% TIP:  The original message was not English!\r\ns_correct.message(1, :) = {'Aes cèwo vo foxd bémyvdo vo dowzy', ... \r\n    'Yx wo dbksdo no dbkîdbo aekxn to dbksdo no vk népksdo ne csvoxmo / Vo csvoxmo ocd n''yb, wksc t''ks mryscs vk mknoxmo', ... \r\n    'Mrkaeo wyd, mrkaeo zrbkco nsdc kfom owzrkco / Pksd no Mvkeno WM, vo mywwkxny no vk zrbkco', ... \r\n    'Mkb t''cesc ex WM n''kddkaeo, ckxc dsmc, kedroxdsaeo zkc ox dym / Zbêd à pbkzzob ceb vo lokd zyeb vo wyefowoxd rsz-ryz', ... \r\n    'Ne bkz n''kddkaeo aes pbkzzo, ézkdo, wkdbkaeo od zkdkdbkaeo / Zvec no ckxq de mvkaeoc, t''cesc WM noc Mkbzkdoc', ... \r\n    'Vo zénkqyqeo ox fyqeo ke xyw no myno Cyvkkb ye Mvkeno WM / Do zbyzyco n''émyedob moms / Ae''yx ézovvo voc fyiovvoc, nèc ae''yx cyxxo voc myxcyxxoc'}\r\ns_correct.message(2, :) = {'Fjx hèbt at ktci gérdait at itbed', ... \r\n    'Dc bt igpxit st igpîigt fjpcs yt igpxit st ap séupxit sj hxatcrt / At hxatcrt thi s''dg, bpxh y''px rwdxhx ap rpstcrt', ... \r\n    'Rwpfjt bdi, rwpfjt ewgpht sxih pktr tbewpht / Upxi st Rapjst BR, at rdbbpcsd st ap ewgpht', ... \r\n    'Rpg y''hjxh jc BR s''piipfjt, hpch ixrh, pjiwtcixfjt eph tc idr / Egêi à ugpeetg hjg at qtpi edjg at bdjktbtci wxe-wde', ... \r\n    'Sj gpe s''piipfjt fjx ugpeet, éepit, bpigpfjt ti epipigpfjt / Eajh st hpcv ij rapfjth, y''hjxh BR sth Rpgepith', ... \r\n    'At eéspvdvjt tc kdvjt pj cdb st rdst Hdappg dj Rapjst BR / It egdedht s''érdjitg rtrx / Fj''dc éetaat ath kdntaath, sèh fj''dc hdcct ath rdchdccth'}\r\ns_correct.certitude = uint8(11);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 6\r\nglobal bncWordlist\r\nx =                 {'Brvdujcnm mrbxamna yxbcdujcnb ynaonlc mrblryurwn;  brvdujcnm onja yxbcdujcnb lxdajpn;  brvdujcnm fnjtwnbb yxbcdujcnb bcanwpcq.' ...\r\n    'X mrerwn jac xo bdkcunch jwm bnlanlh!'};\r\ns_correct.shift = uint8(9);\r\ns_correct.message = {'Simulated disorder postulates perfect discipline;  simulated fear postulates courage;  simulated weakness postulates strength.' ...\r\n    'O divine art of subtlety and secrecy!'};\r\ns_correct.certitude = uint8(12);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 7\r\nglobal bncWordlist\r\nx =                 {'Nv treefk vekvi zekf rcczretv nzky evzxysfizex gizetvj lekzc nv riv rthlrzekvu nzky kyvzi uvjzxej.', ... \r\n    'Nv riv efk wzk kf cvru re ridp fe kyv drity lecvjj nv riv wrdzczri nzky kyv wrtv fw kyv tflekip — zkj dflekrzej reu wfivjkj, zkj gzkwrccj reu givtzgztvj, zkj drijyvj reu jnrdgj.', ... \r\n    'Nv jyrcc sv lerscv kf klie erklirc rumrekrxvj kf rttflek lecvjj nv drbv ljv fw cftrc xlzuvj.'};\r\ns_correct.shift = uint8(17);\r\ns_correct.message = {'We cannot enter into alliance with neighboring princes until we are acquainted with their designs.', ...  \r\n    'We are not fit to lead an army on the march unless we are familiar with the face of the country — its mountains and forests, its pitfalls and precipices, its marshes and swamps.', ...  \r\n    'We shall be unable to turn natural advantages to account unless we make use of local guides.'};\r\ns_correct.certitude = uint8(97);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 8\r\nglobal bncWordlist\r\nx =                 {'Oa oqvvq''u cnycau dggp: \"Ctu itcvkc ctvku\".'};\r\ns_correct.shift = uint8(2);\r\ns_correct.message = {'My motto''s always been: \"Ars gratia artis\".'};\r\ns_correct.certitude = uint8(73);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% Test 9\r\nglobal bncWordlist\r\nx =                 {'Bestow rewards without regard 2 rule;  issue orders without regard 2 previous arrangements', ...\r\n    '\"Let advance bee richly rewarded \u0026 retreat bee heavily punished.\"', ...\r\n    ' Qwertyuiop''asdfghjkl.   Zxcvbnm-0123456789 = pass. ', ...\r\n    'αβγδ — persimon–apricot hybrid.', ...\r\n    'aIanasatbebydoheifinisitmemynoofonor''ssotoupweallandanyarebutcandidforgethadhasherhimhishowitsmaynewnotnown''toneouroutseeshethetwowaswaywhoyoualsobeenfromhaveintojustknowlikemoreonlysaidsomethanthatthemthentheythistimeverywellwerewhatwhenwillwithyouraboutcouldfirstothertheirtherethesewhichwouldpeopleshould'};\r\ns_correct.shift = uint8(0);\r\ns_correct.message = x;\r\ns_correct.certitude = uint8(0);\r\ns = decode(x, bncWordlist);\r\nassert( isequal(s.shift, s_correct.shift), 'Wrong shift.'  )\r\nassert( isequal(s.message, s_correct.message), 'Wrong message.'  )\r\nassert( isequal(s.certitude, s_correct.certitude), 'Wrong certitude.'  )\r\n\r\n\r\n%% TIMING \r\n% Note:  The Time Trial section does not check accuracy; that is done above.\r\n\r\n% Acknowledgements  \r\n% Portions of this timing test code were inspired by:\r\n% (1) Problem 937. \"Rubik's Mini Cube: Solve Randomized Cube in 11 Moves or Less; Score is by Time (msec)\" by Richard Zapor.\r\n% (2) Problem 2733. \"Evil Number\" by Jan Orwat.\r\n% (3) Feedback in comments from Peng Liu.\r\n% (4) Problem Problem 1237. \"It's race time! Write a faster function than the test suite call of unique().\" by Jeremy.\r\n\r\n% INITIALISE\r\nglobal bncWordlist\r\nx =                 {'Vomftt qvstvfe, pqfo op eppst.', 'Ffmt dbo ljmm, opu pomz xpvoe.'};\r\nqSmall = 50;\r\nqBig = 1000;\r\n%cutoffTimeBig = 10;    % Maximum allowable walltime (in seconds) to run function in a loop with qBig iterations.\r\n\r\n% EDIT (2018-06-17).  Reduced time [slightly] to pose reasonable challenge.\r\n% Accounted for improving Cody server speed per Problem 44655.\r\ncutoffTimeBig = 8;    % Maximum allowable walltime (in seconds) to run function in a loop with qBig iterations.\r\ntRef = datetime('2018-06-17', 'InputFormat','yyyy-MM-dd');\r\ntNow = datetime('now');\r\nyearsElapsed = (datenum(tNow) - datenum(tRef)) / 365.24;\r\ndisp(' . ');\r\nfprintf('\\r\\n\\r\\n\\r\\nSubmission evaluated for speed on %s.\\r\\n', datestr(tNow, 'dd mmmm yyyy'))\r\nrInf = 0.2;   delta = cutoffTimeBig - rInf;  tau = 3.6;  % Data from Problem 44655, based on Problem 963.\r\nqBig = ceil( qBig * (cutoffTimeBig - rInf) * exp(yearsElapsed/tau) / delta );\r\nfprintf('\\r\\n\\r\\n\\r\\nTo account for computational power increases over time, number of iterations increased to %u.\\r\\n', qBig)\r\n% END EDIT (2018-06-17)\r\n\r\n\r\n% *** PRELIMINARY TIMING WITH timeit ***\r\nfDecode = @()   decode(x, bncWordlist);\r\ndt_timeit = timeit( fDecode );\r\nfor dummy = 1 : 10,  disp(' . ');  end;\r\nfprintf('APPROXIMATE time to decode %u message batches ~ %2.2f seconds, according to ''timeit''.\\n\\r', qBig, dt_timeit * qBig)\r\n\r\n% *** PRELIMINARY TIMING WITH SHORT LOOP ***\r\n% In case the submitted function has a lot of text output, \r\n% get an estimate based on just a few iterations\r\n% Initialise\r\nt0 = clock;\r\n\r\n% Loop\r\nfor i = 1 : qSmall\r\n    solution = decode(x, bncWordlist);\r\nend;\r\n\r\n% Compute and display elapsed time.\r\ndt = etime(clock, t0);\r\nfor dummy = 1 : 10,  disp(' . ');  end;\r\n%fprintf('Your wall time to decode %u message batches = %i seconds.\\n\\r', qSmall, floor(dt))\r\nfprintf('APPROXIMATE wall time to decode %u message batches ~ %2.2f seconds, by extrapolating from %u batches.\\n\\r', qBig, dt * qBig / qSmall, qSmall)\r\n\r\n% *** 'OFFICIAL' TIMING ***\r\n% Re-initialise timer\r\nt0 = clock;\r\nt0_cpu = cputime;\r\n\r\n% Loop\r\nfor i = 1 : qBig\r\n    % EDIT (2018-06-17).   Ensure each case is unique.\r\n    characters = ['  ,   .' char(randi([97,122], [1,23]))];\r\n    x{2} = characters( randperm(30) );\r\n    % END EDIT (2018-06-17)\r\n    solution = decode(x, bncWordlist);\r\nend;\r\n\r\n% Compute and display elapsed time.\r\ndt = etime(clock, t0);\r\nfor dummy = 1 : 10,  disp(' . ');  end;\r\nfprintf('Your wall time to decode %u message batches = %2.2f seconds.\\n\\r', qBig, dt)\r\n\r\ndt_cpu = (cputime - t0_cpu);\r\nfprintf(' ( Your CPU time for this = %2.2f seconds. ) \\n\\r', dt_cpu)\r\n\r\n% Display (default) Cody size-based score.\r\nall_nodes = mtree('decode.m', '-file');\r\nsize_score = count(all_nodes);\r\nfprintf('Your Cody-standard size-based score = %i.\\n\\r', size_score)\r\n\r\n% Report revised performance score\r\ncombinedScore = size_score + round(dt * 10);\r\nfprintf('Your combined score = %i.\\n\\r', combinedScore)\r\ndisp('     -----=====|||||=====-----     ')\r\n\r\n% Now disallow any candidate solutions that are TOO SLOW!\r\nif dt \u003e cutoffTimeBig, \r\n    fprintf('Sorry, your submission is TOO SLOW.  It must be able to finish within %u seconds.\\n\\r', cutoffTimeBig)\r\nend;\r\n\r\nassert( dt \u003c= cutoffTimeBig, 'Exceeded time limit specified in Test Suite.' )","published":true,"deleted":false,"likes_count":2,"comments_count":16,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2019-07-02T13:23:18.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2017-10-12T23:17:24.000Z","updated_at":"2026-04-02T20:05:58.000Z","published_at":"2017-10-15T06:52:40.000Z","restored_at":"2017-10-25T07:03:11.000Z","restored_by":null,"spam":false,"simulink":false,"admin_reviewed":true,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou have been tasked with decoding several batches of coded messages that have been intercepted.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBased on previous intelligence that has been gathered, you can be confident that the messages were all encoded using a simple\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Caesar_cipher\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCaesar cipher\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (a type of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Substitution_cipher\\\"\u003e\u003cw:r\u003e\u003cw:t\u003esubstitution cipher\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e), an example of which is the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/ROT13\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eROT13 cipher\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (discussed in\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/78-implement-a-rot13-cipher\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCody Challenge Problem 78\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e). As in the Cody Challenge Problem, uppercase and lowercase letters are handled independently of one another, and all other characters (e.g. punctuation, numbers, accented letters) are unchanged. Unlike the Cody Challenge Problem, here the number of letters to shift is not known in advance, and will vary\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebetween\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (not within) batches — also, here you need to decode, not encode.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThese latest activities that you are investigating have been nicknamed 'Operation Xiangliu' by your own organisation. A few decoding options are at your organisation's disposal:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest the candidate decodings against all words in a large dictionary — this could work, but it is very slow.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest the candidate decodings for the appearance of a name or phrase that is certain to appear regularly (e.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44351\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"Operation Phoenix\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) — unfortunately as yet there is not enough knowledge of the activities to be sure of a suitable name or phrase to use.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest the candidate decodings against all words in a short list comprising, say, the hundred most frequently used English words.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ethird option\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e will be faster than the first option, and more reliable than the second option, so this is the approach you will be taking.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou have been careful to avoid using 'lemmatised' lists (which contain e.g. the verb \\\"to be\\\", rather than the various inflected forms such as \\\"am\\\", \\\"is\\\", \\\"are\\\") like those based on the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://web.archive.org/web/20111226085859/http://oxforddictionaries.com/words/the-oec-facts-about-the-language\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOEC\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.wordfrequency.info/free.asp?s=y\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCOCA\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, and after setting aside\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://world-english.org/english500.htm\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eanother list\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e you finally choose the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://ucrel.lancs.ac.uk/bncfreq/samples/120.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003elist based on the BNC\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e as the most reliable, and will use the first 100 words on that list. This list will be available for you to access as an input variable,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebncWordlist\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, to your function. Note: (i) some entries on the list are morphemes (e.g. \\\" n't \\\" and \\\" 's \\\") rather than words; (ii) some entries appear more than once (representing different grammatical word classes). Of course, in the original messages any capitalisation might be used.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou have been instructed that your 'certitude' (degree of confidence) in the decoding must be reported for each batch, and shall depend proportionally upon the sum of the characters for the words/morphemes in the decoded message that match words/morphemes in\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebncWordlist\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Being able to match words/morphemes accounting for three-tenths of the characters (excluding spaces) shall correspond to 100% certitude; matching three-twentieths would be 50% certitude, and so on. Certitude shall be reported as a percentage, rounded\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eup\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to the nearest integer, not greater than 100. You need to maximise your certitude for each batch by appropriate choice of the shifting parameter. If there are multiple shifts of equal certitude, report both options on different rows (arranged in order of ascending shift parameter).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour task is to crack the codes and report back in a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/struct.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003estructure array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e: (1) the shifting parameter that had been used in the encoding [as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/uint8.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003euint8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e] (usually scalar, but may be column vector); (2) the decoded messages [as a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/cell.html?s_tid=doc_ta\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecell array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (containing\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/char.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003echaracter arrays\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e)] (usually an array with a single row, but occasionally with multiple rows); (3) your 'certitude' in the decoding [as\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://au.mathworks.com/help/matlab/ref/uint8.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003euint8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e] (always scalar). The name of the structure array shall be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, with respective fields\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eshift\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emessage\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecertitude\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEXAMPLE 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSuppose the batch contained two encoded messages —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Vomftt qvstvfe, pqfo op eppst.\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Ffmt dbo ljmm, opu pomz xpvoe.\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (provided as character arrays within a cell array) — and a (right-shifting) ROT1 cipher had been applied. In that case A→B, B→C, ..., Y→Z, and Z→A; similarly, a→b, b→c, ..., y→z, and z→a. Thus the original messages would have been: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Unless pursued, open no doors.\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Eels can kill, not only wound.\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e . Twelve of the 51 characters have been matched: \\\"no\\\", \\\"can\\\", \\\"not\\\", and \\\"only\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe correct answer would therefore comprise:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s.shift = uint8(1)  \\ns.message = {'Unless pursued, open no doors.', 'Eels can kill, not only wound.'}\\ns.certitude = uint8(79)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEXAMPLE 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSuppose the batch contained one encoded message —\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Oa oqvvq'u cnycau dggp: \\\"Ctu itcvkc ctvku\\\".\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (provided as a character array within a cell array) — and a (right-shifting) ROT2 cipher had been applied. In that case A→C, B→D, ..., Y→A, and Z→B; similarly, a→c, b→d, ..., y→a, and z→b. Thus the original message would have been: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"My motto's always been: \\\"Ars gratia artis\\\".\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e . Eight of the 37 characters have been matched: \\\"My\\\", \\\" 's \\\", and \\\"been\\\". Note carefully that: \\\" 's \\\" should only be matched once; \\\"to\\\" (in motto), \\\"be\\\" (in been), \\\"at\\\" (in gratia), \\\"is\\\" (in artis) and \\\"a\\\" (passim) should\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e be matched at all; and \\\" ' \\\" will only ever be used as an apostrophe (never as a quotation mark).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe correct answer would therefore comprise:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s.shift = uint8(2)  \\ns.message = {'My motto''s always been: \\\"Ars gratia artis\\\".'}\\ns.certitude = uint8(73)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the batch of messages cannot be decoded when following the above assumptions, then return the original encoded message(s) unchanged, with a shift of zero and a certitude of zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote: Many Cody solutions are hard to read and not necessarily computationally efficient. To direct attention toward efficient runtime speed of execution, timings are measured in the Test Suite, and reported back (if the submission is runnable). Although the timings are not perfectly reproducible, they do provide an indication of computational resource demand. (Try resubmitting on another day if your code runs slowly, in case it is caused by a server issue.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo provide some extra motivation for you to get your code to run efficiently, it will fail the Test Suite if it is deemed \\\"too slow\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e----------\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrevious problem: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44356\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOperation Orthos\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Next problem: TBA.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47473,"title":"Slitherlink IV: Recursive (medium size)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 615.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 307.833px; transform-origin: 407px 307.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 144.667px 7.91667px; transform-origin: 144.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink IV: Recursive (medium size)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 239.35px 7.91667px; transform-origin: 239.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving but is solveable using Recursion with limited Guessing.  Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking Recursion.  When Evolve is used within a recursive routine that asserts incorrect content the Evolve may produce an invalid output for the invalid input. The two medium test cases are from Games World of Puzzles October 2020. I was unable to manually solve these puzzles on my first attempt prior to making an error thus I decided to program this simple pencil puzzle. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 324.633px 7.91667px; transform-origin: 324.633px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv init solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n') \r\n  return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n%  show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv evolve solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\n \r\n % Check if solved\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n \r\n % Start recursive processing\r\n if ~valid\r\n  [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap);\r\n  [sv,valid]=pcheck(s,p,bsegs);\r\n end\r\n%\r\n if valid\r\n  fprintf('sv recursion solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n else\r\n  fprintf('No solution found\\n')\r\n end\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\nfunction [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n solved=0;\r\n \r\n %work thru options of first end found with minimum options (2 or 3)  \r\n %(first 2 then 3 if any found)\r\n % extend a segment\r\n ps=sum(p);\r\n ptr=find(ps==7,1,'first'); % First Segment with 2 options\r\n if isempty(ptr)\r\n  ptr=find(ps==8,1,'first'); % First Segment with 3 options\r\n end\r\n pc=find(p(ptr,:)==1);\r\n \r\n for i=pc\r\n  pn=p;\r\n  %insertion of code required here\r\n  \r\n  %This modified pn may be invalid and create an invalid evolve result\r\n  [pn,evalid]=evolve(pn,bsegs,s,c,emap,pmap);\r\n  if ~evalid,continue;end\r\n  \r\n  [v,valid]=pcheck(s,pn,bsegs); % check if segment add and evolve solved\r\n  if valid\r\n   solved=1;\r\n   p=pn;\r\n   return;\r\n  end\r\n  \r\n  %Invoke the next level of recursion build with the recursion assert and Evolve\r\n  [pn,solved]=slither_recur(pn,bsegs,s,c,emap,pmap);\r\n  if solved\r\n   p=pn;\r\n   return\r\n  end\r\n end %i\r\n % Loop through options\r\n % Perform evolve\r\n %  if invalid try next option\r\n %  call next level recur\r\n %  if solved return\r\nend %[p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10  0,10  1,11  2,12  3,13 to avoid reprocess\r\n while ~isequal(p,pb)\r\n  pb=p;\r\n  s1=find(sp==1)';\r\n  for i=s1 %1 \r\n   v=bsegs(i,:);\r\n   %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==1 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e5\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end % i s1 1\r\n  \r\n  \r\n  s2=find(sp==2)';\r\n  for i=s2 %2\r\n   v=bsegs(i,:);\r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e10 % 0 non-5 segments, have 2 links\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==6 || sum(wv)==2 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e10\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end %i s2 2\r\n  \r\n  s3=find(sp==3)';\r\n  for i=s3 %3\r\n   v=bsegs(i,:);\r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e15 % 0 non-5 segments, have 3 links\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==11 || sum(wv)==3 || sum(wv)==7 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e10\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n  end %i s3 3\r\n  if ~isequal(p,pb) % s update created new walls\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n   continue;\r\n  end\r\n  %show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n  %Process links for new walls\r\n  % RR straight blocks perp, Binto corner makes B outcorner\r\n  % RR corner blocks to corner\r\n  % R into corner extends R\r\n  % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n  % R node with one option extends R\r\n  [nrc,ncc]=size(c);\r\n  % Bcorners if either corner edge B then both B\r\n  if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n   p(1,2)=0; p(2,1)=0;\r\n   p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n  end\r\n  if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n   p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n   p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n   p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=0; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=0;\r\n   p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=0;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=0;\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n   p(nrc*ncc,nrc*ncc-1)=0; p(nrc*ncc-1,nrc*ncc)=0;\r\n   p(nrc*ncc,nrc*ncc-nrc)=0;p(nrc*ncc-nrc,nrc*ncc)=0;\r\n  end\r\n  \r\n  % Rcorners if either corner edge R then both R\r\n  if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n   p(1,2)=5; p(2,1)=5;\r\n   p(1,nrc+1)=5;p(nrc+1,1)=5;\r\n  end\r\n  if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n   p(nrc-1,nrc)=5; p(nrc,nrc-1)=5;\r\n   p(nrc,2*nrc)=5;p(2*nrc,nrc)=5;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n   p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=5; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=5;\r\n   p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=5;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=5;\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n   p(nrc*ncc,nrc*ncc-1)=5; p(nrc*ncc-1,nrc*ncc)=5;\r\n   p(nrc*ncc,nrc*ncc-nrc)=5;p(nrc*ncc-nrc,nrc*ncc)=5;\r\n  end\r\n  \r\n  % BB edges\r\n  %Top Row\r\n  for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n   cv=c(1,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n   end\r\n  end % j Top row\r\n  %Bottom Row\r\n  for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n   cv=c(nrc,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up, virtual cv(2)+1==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up\r\n   end\r\n  end % j Bot row\r\n  \r\n  %Left Col edge\r\n  for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n   cv=c(i:i+2,1);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor, virt cv(2)-nrc==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n    p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor rt\r\n   end\r\n  end % j L col\r\n  %Right Col edge\r\n  for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n   cv=c(i:i+2,ncc);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor, virt cv(2)+nrc==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n    p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor lt\r\n   end\r\n  end % j L col\r\n  \r\n  %Hor segs not on an edge\r\n  for i=2:nrc-1\r\n   for j=1:ncc-2\r\n    cv=c(i,j:j+2);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+1)==0\r\n     p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n    end\r\n    if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B  vud\r\n     p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  %Ver segs not on an edge\r\n  for i=1:nrc-2\r\n   for j=2:ncc-1\r\n    cv=c(i:i+2,j);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+nrc)==0\r\n     p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n    end\r\n    if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B  hLR\r\n     p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  \r\n  % RR corner blocks to corner\r\n  %[rr;xr]  [rr;rx]  [xr;rr]  [rx;rr]\r\n  %RR;xR or RR;Rx\r\n  for i=1:nrc-1\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab top pair\r\n    if p(cv(1),cv(2))==5 % Top Red\r\n     if p(cv(2),cv(2)+1)==5 % rr;xr\r\n      if i\u003e1\r\n       p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0;\r\n      end\r\n      if j\u003cncc-1\r\n       p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n      end\r\n     end\r\n     \r\n     if p(cv(1),cv(1)+1)==5 % rr;rx\r\n      if i\u003e1\r\n       p(cv(1),cv(1)-1)=0;p(cv(1)-1,cv(1))=0;\r\n      end\r\n      if j\u003e1\r\n       p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n      end\r\n     end\r\n    end % Top RR\r\n   end %j\r\n  end %i\r\n  \r\n  for i=2:nrc % Rx;RR  xR;RR\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab lower pair\r\n    if p(cv(1),cv(2))==5 % Bot Red\r\n     if p(cv(2),cv(2)-1)==5 % xr;rr\r\n      if i\u003cnrc\r\n       p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0;\r\n      end\r\n      if j\u003cncc-1\r\n       p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n      end\r\n     end\r\n     \r\n     if p(cv(1),cv(1)-1)==5 % rx;rr\r\n      if i\u003cnrc\r\n       p(cv(1),cv(1)+1)=0;p(cv(1)+1,cv(1))=0;\r\n      end\r\n      if j\u003e1\r\n       p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n      end\r\n     end\r\n     \r\n    end %Bot RR\r\n   end %j\r\n  end %i\r\n  \r\n  % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n  i=1; % Top\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    if p(cv(1),cv(1)+1)==0 % down dead end left side\r\n     if j\u003e1\r\n      p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)+1)==0 % down dead end, rt side\r\n     if j\u003cncc-1\r\n      p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n     end\r\n    end\r\n   end\r\n  end % j\r\n  \r\n  i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    if p(cv(1),cv(1)-1)==0 % up dead end left side\r\n     if j\u003e1\r\n      p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)-1)==0 % up dead end rt side\r\n     if j\u003cncc-1\r\n      p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n     end\r\n    end\r\n   end\r\n  end % j\r\n  \r\n  j=ncc; % Right\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    if p(cv(1),cv(1)-nrc)==0 % rt dead end up side\r\n     if i\u003e1\r\n      p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)-nrc)==0 % rt dead end down side\r\n     if i\u003cnrc-1\r\n      p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n     end\r\n    end\r\n   end\r\n  end % i\r\n  \r\n  j=1; % Left\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    if p(cv(1),cv(1)+nrc)==0 % left dead end up side\r\n     if i\u003e1\r\n      p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)+nrc)==0 % left dead end down side\r\n     if i\u003cnrc-1\r\n      p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n     end\r\n    end\r\n   end\r\n  end % i\r\n  \r\n  if ~isequal(p,pb),continue;end\r\n  \r\n  % R node with one option extends R \r\n%   [pr5,pc5]=find(p==5);\r\n%   for i=1:length(pr5)\r\n%    if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n%     new_node=find(p(pr5(i),:)==1);\r\n%     p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n%    end\r\n%   end\r\n  \r\n  [pr5,pc5]=find(p==5);\r\n  pr5=unique(pr5); % could sort then remove dupes which are mids\r\n  while ~isempty(pr5)\r\n   if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n    new_node=find(p(pr5(1),:)==1);\r\n    p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n    pr5(1)=new_node;\r\n   else\r\n    pr5(1)=[];\r\n   end\r\n  end\r\n  \r\n  %need an isequal(p,pb)\r\n  %check if red seg closes a loop of less than X thus seg must be black\r\n  if isequal(p,pb) % check for bad R bars\r\n   ps=sum(p);\r\n   pv= ps\u003e4  \u0026 ~(ps==10);\r\n   pidx=find(pv);\r\n   for i=pidx\r\n    v=[i find(p(i,:)==5)];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n%    v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n     \r\n%      v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n%      if nnz(v(1:end-2)==v(end))\r\n%       v(end)=[];\r\n%      elseif nnz(v(1:end-2)==v(end))\r\n%       v(end-1)=[];\r\n%      end\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     \r\n    end\r\n    if Lv\u003c4,continue;end % Need at least 3 segments to make a loop\r\n    if p(v(1),v(end)) % path ends are currently adjacent, likely sb 0 but may be final solve\r\n     if Lv\u003cnnz(p==5)/2\r\n      p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n     else % Possible solve\r\n      pchk=p;\r\n      pchk(v(1),v(end))=5;pchk(v(end),v(1))=5;\r\n      [sv,valid]=pcheck(s,pchk,bsegs); % check if solved\r\n      if valid\r\n       p=pchk;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n      end\r\n     end % Lv\r\n    end % p( v 1 end)\r\n   end % pidx\r\n  end % isequal p pb  after cells, ends make no change\r\n  \r\n  %possible evolve is try seg to see if evolve base leads to a fail thus must be black\r\n  \r\n%   isequal(p,pb)\r\n%   show_pfig(s,p,c,emap,pmap,3)\r\n%   show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n\r\n end % while p~=pb\r\n \r\n % Valid checks\r\n   for sptr=1:nr*nc %invalid set/clear segment count\r\n    %if s(sptr)==5,continue;end % what if a 4 seg circle occurs around a 5?\r\n    vsptr=bsegs(sptr,:);\r\n    psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n    if s(sptr)==5\r\n     if nnz(psegs==5)==4\r\n      evalid=0;\r\n      return\r\n     else\r\n      continue\r\n     end\r\n    end % s 5\r\n    \r\n    if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    ps=sum(p); % .48  17K\r\n    %if nnz(sum(p)==5) % Node with no escape %.48\r\n    if nnz(ps==5) % Node with no escape\r\n     evalid=0;\r\n     return\r\n    end\r\n    %if nnz(sum(p)\u003e14) % Node with too many segments % .47\r\n    if nnz(ps\u003e14) % Node with too many segments\r\n     evalid=0;\r\n     return\r\n    end\r\n   end % sptr\r\n   \r\n   %check for any loops created                  **********************************\r\n   %show_pfig(s,p,c,emap,pmap,3)\r\n   ps=sum(p);\r\n   pidx=find(ps==10);\r\n   pchecked=[];\r\n   %pidx=[];\r\n   for i=pidx\r\n    if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n    vn=find(p(i,:)==5); % Guaranteed 2 points\r\n    if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n     pchecked=[pchecked i];\r\n     continue;\r\n    end\r\n    v=[i find(p(i,:)==5,1,'first')];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n%    v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n     \r\n%      v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n%      if nnz(v(1:end-2)==v(end))\r\n%       v(end)=[];\r\n%      elseif nnz(v(1:end-2)==v(end))\r\n%       v(end-1)=[];\r\n%      end\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end % No loop\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     if v(1)==v(end),break;end % Loop created\r\n    end % while extending\r\n    pchecked=[pchecked v];\r\n    \r\n    if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n    if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n     if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop   [1 2 4 3 1] loop\r\n      evalid=0;\r\n      return\r\n     else % Possible solve\r\n      [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n      if valid\r\n       evalid=1;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       evalid=0;\r\n       return\r\n      end\r\n     end % Lv-1 compare to total current segments\r\n    end %  v 1 end)\r\n   end % pidx\r\n   \r\n   evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  bidx=idx1(i);\r\n  if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n  end\r\n  \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n  end\r\n  \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n  \r\n  if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  end % if TL/TR/BL/BR\r\n  \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % Top edge\r\n   if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   \r\n  elseif nr3(i)==nr % Bot Edge\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   \r\n  elseif nc3(i)==1 %Left Edge\r\n   if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   \r\n  elseif nc3(i)==nc % Rt edge\r\n   if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   end\r\n   \r\n   \r\n  else %non-edge 3\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n     \r\n   elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   end\r\n  end % Edges/Mid 3\r\n    \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1\r\n   if nc3(i)==1 % TL  only one R or D possible\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % TR only one L or D possible. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Top Row  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  \r\n  if nr3(i)\u003cnr  % Mid section 33\r\n   if nc3(i)==1 % check only one R and D p\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % check only D. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Mid Row (not col 1 or nc)  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  if nr3(i)==nr  % Bot row 33\r\n    if nc3(i)==nc,continue;end % No process BR corner\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    continue\r\n  end\r\n \r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx+1-nr)==0 %BL\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %BR\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==1\r\n  \r\n  if nr3(i)==nr % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %TL\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %TR\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==nr\r\n  \r\n  if nc3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==1\r\n  \r\n  if nc3(i)==nc % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==nc\r\n  \r\n  %mid : check 4 courners\r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr  down diag, RB set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003c=nc-2 % Not near right edge\r\n     vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n  \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    \r\n    vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr  down diag, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not top edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003cnc % Not Right edge\r\n     vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e=3 % Not near Left edge\r\n     vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; \r\n   %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[3 3 2 3 5 5 3 3 5 1;\r\n    5 5 5 2 5 5 5 5 5 5;\r\n    1 5 5 5 5 1 1 5 5 2;\r\n    0 5 5 5 5 2 5 5 3 3;\r\n    0 5 5 5 1 3 5 5 5 5;\r\n    5 5 5 5 2 3 5 5 5 0;\r\n    3 2 5 5 1 5 5 5 5 2;\r\n    3 5 5 2 0 5 5 5 5 2;\r\n    5 5 5 5 5 5 2 5 5 5;\r\n    3 5 1 3 5 5 3 3 2 3]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['252';\r\n   '151';\r\n   '212']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n    '1551';\r\n    '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n   '1521';\r\n   '0532';\r\n   '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['33353';\r\n   '15551';\r\n   '25055';\r\n   '55253';\r\n   '13511']-'0';% evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[5 1 5 5 3 5 5 5 0 1;\r\n    5 0 5 5 5 3 3 5 5 5;\r\n    5 5 5 1 2 5 5 5 3 5;\r\n    2 5 5 5 5 5 2 0 5 2;\r\n    0 5 5 5 5 5 5 5 5 5;\r\n    5 5 5 5 5 5 5 5 5 3;\r\n    3 5 1 2 5 5 5 5 5 1;\r\n    5 3 5 5 5 3 0 5 5 5;\r\n    5 5 5 0 0 5 5 5 3 5;\r\n    2 1 5 5 5 1 5 5 3 5]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T20:32:38.000Z","updated_at":"2026-04-21T01:40:28.000Z","published_at":"2020-11-12T23:28:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink IV: Recursive (medium size)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving but is solveable using Recursion with limited Guessing.  Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking Recursion.  When Evolve is used within a recursive routine that asserts incorrect content the Evolve may produce an invalid output for the invalid input. The two medium test cases are from Games World of Puzzles October 2020. I was unable to manually solve these puzzles on my first attempt prior to making an error thus I decided to program this simple pencil puzzle. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink V: Assert/Evolve/Check 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lationshipId\":\"rId1\"}]}"},{"id":47478,"title":"Slitherlink V: Assert/Evolve/Check (large)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 678.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 339.333px; transform-origin: 407px 339.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 210px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 105px; text-align: left; transform-origin: 384px 105px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 168.4px 7.91667px; transform-origin: 168.4px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink V:  Assert/Evolve/Check(large size)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 207.3px 7.91667px; transform-origin: 207.3px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving and Recursion due to time and depth issues.  Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking the Assert/Evolve/Check/Update method.  The advanced solving techniques on the web are weak and complicated. The simple method is not to immediately invoke recursion due to the sparseness of data leading to too many false options. Ther actual simple method is to use Try/Catch by asserting segments as Black/Red and then checking if the layout using a robust Evolve creates an invalid state. If the state became invalid when asserting a single segment as Black then it must be Red with the same being true of Red assertion being invalid must mean the segment is Black. If an Evolve is invalid then Assert the right Bar type and perform an evolve to update the board.  The two large test cases are from Games World of Puzzles October 2020. I was completely hopeless for the large puzzles. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 314.917px 7.91667px; transform-origin: 314.917px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive(medium size)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n') \r\n  return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\n \r\n %Implement Assert/Check/Evolve\r\n [p]=assert(p,bsegs,s,c,emap,pmap); \r\n \r\n % Check if solved\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n if valid\r\n  fprintf('sv Assert solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n end\r\n \r\n % Start recursive processing\r\n if ~valid\r\n  [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap);\r\n  [sv,valid]=pcheck(s,p,bsegs);\r\n end\r\n%\r\n if valid\r\n  fprintf('sv recursion solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n else\r\n  fprintf('No solution found\\n')\r\n end\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\nfunction [p]=assert(p,bsegs,s,c,emap,pmap)\r\n %Insert code here to assert a segment as Red/Black\r\n %Check if evolve of is valid\r\n %If not valid then Assert segment as Black/Red depending on case and then evolve\r\n %Keep asserting until no more p updates and/or s is solved\r\n %Asserting ends of red segments first may reduce total time\r\n pb=p*0;\r\n valid=0;\r\n while ~isequal(p,pb) \u0026\u0026 ~valid\r\n  pb=p;\r\n  [pr,pc]=find(p==1);\r\n  % insert code here\r\n end % while\r\nend\r\n\r\nfunction [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n solved=0;\r\n \r\n %work thru options of first end found with minimum options (2 or 3)  \r\n %(first 2 then 3 if any found)\r\n % extend a segment\r\n ps=sum(p);\r\n ptr=find(ps==7,1,'first'); % First Segment with 2 options\r\n if isempty(ptr)\r\n  ptr=find(ps==8,1,'first'); % First Segment with 3 options\r\n end\r\n pc=find(p(ptr,:)==1);\r\n \r\n for i=pc\r\n  pn=p;\r\n  pn(ptr,i)=5;pn(i,ptr)=5; % make linkage\r\n  \r\n  %This modified pn may be invalid and create an invalid evolve result\r\n  [pn,evalid]=evolve(pn,bsegs,s,c,emap,pmap);\r\n  if ~evalid,continue;end\r\n  \r\n  [v,valid]=pcheck(s,pn,bsegs); % check if segment add and evolve solved\r\n  if valid\r\n   solved=1;\r\n   p=pn;\r\n   return;\r\n  end\r\n  \r\n  %Invoke the next level of recursion build with the recursion assert and Evolve\r\n  [pn,solved]=slither_recur(pn,bsegs,s,c,emap,pmap);\r\n  if solved\r\n   p=pn;\r\n   return\r\n  end\r\n end %i\r\n % Loop through options\r\n % Perform evolve\r\n %  if invalid try next option\r\n %  call next level recur\r\n %  if solved return\r\nend %[p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10  0,10  1,11  2,12  3,13 to avoid reprocess\r\n while ~isequal(p,pb)\r\n  pb=p;\r\n  s1=find(sp==1)';\r\n  for i=s1 %1 \r\n   v=bsegs(i,:);\r\n   %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==1 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e5\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end % i s1 1\r\n  \r\n  \r\n  s2=find(sp==2)';\r\n  for i=s2 %2\r\n   v=bsegs(i,:);\r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e10 % 0 non-5 segments, have 2 links\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==6 || sum(wv)==2 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e10\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end %i s2 2\r\n  \r\n  s3=find(sp==3)';\r\n  for i=s3 %3\r\n   v=bsegs(i,:);\r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e15 % 0 non-5 segments, have 3 links\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==11 || sum(wv)==3 || sum(wv)==7 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e10\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n  end %i s3 3\r\n  if ~isequal(p,pb) % s update created new walls\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n   continue;\r\n  end\r\n  %show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n  %Process links for new walls\r\n  % RR straight blocks perp, Binto corner makes B outcorner\r\n  % RR corner blocks to corner\r\n  % R into corner extends R\r\n  % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n  % R node with one option extends R\r\n  [nrc,ncc]=size(c);\r\n  % Bcorners if either corner edge B then both B\r\n  if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n   p(1,2)=0; p(2,1)=0;\r\n   p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n  end\r\n  if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n   p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n   p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n   p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=0; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=0;\r\n   p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=0;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=0;\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n   p(nrc*ncc,nrc*ncc-1)=0; p(nrc*ncc-1,nrc*ncc)=0;\r\n   p(nrc*ncc,nrc*ncc-nrc)=0;p(nrc*ncc-nrc,nrc*ncc)=0;\r\n  end\r\n  \r\n  % Rcorners if either corner edge R then both R\r\n  if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n   p(1,2)=5; p(2,1)=5;\r\n   p(1,nrc+1)=5;p(nrc+1,1)=5;\r\n  end\r\n  if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n   p(nrc-1,nrc)=5; p(nrc,nrc-1)=5;\r\n   p(nrc,2*nrc)=5;p(2*nrc,nrc)=5;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n   p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=5; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=5;\r\n   p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=5;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=5;\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n   p(nrc*ncc,nrc*ncc-1)=5; p(nrc*ncc-1,nrc*ncc)=5;\r\n   p(nrc*ncc,nrc*ncc-nrc)=5;p(nrc*ncc-nrc,nrc*ncc)=5;\r\n  end\r\n  \r\n  % BB edges\r\n  %Top Row\r\n  for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n   cv=c(1,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n   end\r\n  end % j Top row\r\n  %Bottom Row\r\n  for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n   cv=c(nrc,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up, virtual cv(2)+1==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up\r\n   end\r\n  end % j Bot row\r\n  \r\n  %Left Col edge\r\n  for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n   cv=c(i:i+2,1);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor, virt cv(2)-nrc==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n    p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor rt\r\n   end\r\n  end % j L col\r\n  %Right Col edge\r\n  for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n   cv=c(i:i+2,ncc);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor, virt cv(2)+nrc==0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n    p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor lt\r\n   end\r\n  end % j L col\r\n  \r\n  %Hor segs not on an edge\r\n  for i=2:nrc-1\r\n   for j=1:ncc-2\r\n    cv=c(i,j:j+2);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+1)==0\r\n     p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n    end\r\n    if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B  vud\r\n     p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  %Ver segs not on an edge\r\n  for i=1:nrc-2\r\n   for j=2:ncc-1\r\n    cv=c(i:i+2,j);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+nrc)==0\r\n     p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n    end\r\n    if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B  hLR\r\n     p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  \r\n  % RR corner blocks to corner\r\n  %[rr;xr]  [rr;rx]  [xr;rr]  [rx;rr]\r\n  %RR;xR or RR;Rx\r\n  for i=1:nrc-1\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab top pair\r\n    if p(cv(1),cv(2))==5 % Top Red\r\n     if p(cv(2),cv(2)+1)==5 % rr;xr\r\n      if i\u003e1\r\n       p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0;\r\n      end\r\n      if j\u003cncc-1\r\n       p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n      end\r\n     end\r\n     \r\n     if p(cv(1),cv(1)+1)==5 % rr;rx\r\n      if i\u003e1\r\n       p(cv(1),cv(1)-1)=0;p(cv(1)-1,cv(1))=0;\r\n      end\r\n      if j\u003e1\r\n       p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n      end\r\n     end\r\n    end % Top RR\r\n   end %j\r\n  end %i\r\n  \r\n  for i=2:nrc % Rx;RR  xR;RR\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab lower pair\r\n    if p(cv(1),cv(2))==5 % Bot Red\r\n     if p(cv(2),cv(2)-1)==5 % xr;rr\r\n      if i\u003cnrc\r\n       p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0;\r\n      end\r\n      if j\u003cncc-1\r\n       p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n      end\r\n     end\r\n     \r\n     if p(cv(1),cv(1)-1)==5 % rx;rr\r\n      if i\u003cnrc\r\n       p(cv(1),cv(1)+1)=0;p(cv(1)+1,cv(1))=0;\r\n      end\r\n      if j\u003e1\r\n       p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n      end\r\n     end\r\n     \r\n    end %Bot RR\r\n   end %j\r\n  end %i\r\n  \r\n  % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n  i=1; % Top\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    if p(cv(1),cv(1)+1)==0 % down dead end left side\r\n     if j\u003e1\r\n      p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)+1)==0 % down dead end, rt side\r\n     if j\u003cncc-1\r\n      p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n     end\r\n    end\r\n   end\r\n  end % j\r\n  \r\n  i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    if p(cv(1),cv(1)-1)==0 % up dead end left side\r\n     if j\u003e1\r\n      p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)-1)==0 % up dead end rt side\r\n     if j\u003cncc-1\r\n      p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n     end\r\n    end\r\n   end\r\n  end % j\r\n  \r\n  j=ncc; % Right\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    if p(cv(1),cv(1)-nrc)==0 % rt dead end up side\r\n     if i\u003e1\r\n      p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)-nrc)==0 % rt dead end down side\r\n     if i\u003cnrc-1\r\n      p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n     end\r\n    end\r\n   end\r\n  end % i\r\n  \r\n  j=1; % Left\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    if p(cv(1),cv(1)+nrc)==0 % left dead end up side\r\n     if i\u003e1\r\n      p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n     end\r\n    end\r\n    if p(cv(2),cv(2)+nrc)==0 % left dead end down side\r\n     if i\u003cnrc-1\r\n      p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n     end\r\n    end\r\n   end\r\n  end % i\r\n  \r\n  if ~isequal(p,pb),continue;end\r\n  \r\n  % R node with one option extends R \r\n%   [pr5,pc5]=find(p==5);\r\n%   for i=1:length(pr5)\r\n%    if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n%     new_node=find(p(pr5(i),:)==1);\r\n%     p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n%    end\r\n%   end\r\n  \r\n  [pr5,pc5]=find(p==5);\r\n  pr5=unique(pr5); % could sort then remove dupes which are mids\r\n  while ~isempty(pr5)\r\n   if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n    new_node=find(p(pr5(1),:)==1);\r\n    p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n    pr5(1)=new_node;\r\n   else\r\n    pr5(1)=[];\r\n   end\r\n  end\r\n  \r\n  %need an isequal(p,pb)\r\n  %check if red seg closes a loop of less than X thus seg must be black\r\n  if isequal(p,pb) % check for bad R bars\r\n   ps=sum(p);\r\n   pv= ps\u003e4  \u0026 ~(ps==10);\r\n   pidx=find(pv);\r\n   for i=pidx\r\n    v=[i find(p(i,:)==5)];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n%    v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n     \r\n%      v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n%      if nnz(v(1:end-2)==v(end))\r\n%       v(end)=[];\r\n%      elseif nnz(v(1:end-2)==v(end))\r\n%       v(end-1)=[];\r\n%      end\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     \r\n    end\r\n    if Lv\u003c4,continue;end % Need at least 3 segments to make a loop\r\n    if p(v(1),v(end)) % path ends are currently adjacent, likely sb 0 but may be final solve\r\n     if Lv\u003cnnz(p==5)/2\r\n      p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n     else % Possible solve\r\n      pchk=p;\r\n      pchk(v(1),v(end))=5;pchk(v(end),v(1))=5;\r\n      [sv,valid]=pcheck(s,pchk,bsegs); % check if solved\r\n      if valid\r\n       p=pchk;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n      end\r\n     end % Lv\r\n    end % p( v 1 end)\r\n   end % pidx\r\n  end % isequal p pb  after cells, ends make no change\r\n  \r\n  %possible evolve is try seg to see if evolve base leads to a fail thus must be black\r\n  \r\n%   isequal(p,pb)\r\n%   show_pfig(s,p,c,emap,pmap,3)\r\n%   show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n\r\n end % while p~=pb\r\n \r\n % Valid checks\r\n   for sptr=1:nr*nc %invalid set/clear segment count\r\n    %if s(sptr)==5,continue;end % what if a 4 seg circle occurs around a 5?\r\n    vsptr=bsegs(sptr,:);\r\n    psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n    if s(sptr)==5\r\n     if nnz(psegs==5)==4\r\n      evalid=0;\r\n      return\r\n     else\r\n      continue\r\n     end\r\n    end % s 5\r\n    \r\n    if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    ps=sum(p); % .48  17K\r\n    %if nnz(sum(p)==5) % Node with no escape %.48\r\n    if nnz(ps==5) % Node with no escape\r\n     evalid=0;\r\n     return\r\n    end\r\n    %if nnz(sum(p)\u003e14) % Node with too many segments % .47\r\n    if nnz(ps\u003e14) % Node with too many segments\r\n     evalid=0;\r\n     return\r\n    end\r\n   end % sptr\r\n   \r\n   %check for any loops created                  **********************************\r\n   %show_pfig(s,p,c,emap,pmap,3)\r\n   ps=sum(p);\r\n   pidx=find(ps==10);\r\n   pchecked=[];\r\n   %pidx=[];\r\n   for i=pidx\r\n    if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n    vn=find(p(i,:)==5); % Guaranteed 2 points\r\n    if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n     pchecked=[pchecked i];\r\n     continue;\r\n    end\r\n    v=[i find(p(i,:)==5,1,'first')];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n%    v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n     \r\n%      v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n%      if nnz(v(1:end-2)==v(end))\r\n%       v(end)=[];\r\n%      elseif nnz(v(1:end-2)==v(end))\r\n%       v(end-1)=[];\r\n%      end\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end % No loop\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     if v(1)==v(end),break;end % Loop created\r\n    end % while extending\r\n    pchecked=[pchecked v];\r\n    \r\n    if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n    if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n     if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop   [1 2 4 3 1] loop\r\n      evalid=0;\r\n      return\r\n     else % Possible solve\r\n      [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n      if valid\r\n       evalid=1;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       evalid=0;\r\n       return\r\n      end\r\n     end % Lv-1 compare to total current segments\r\n    end %  v 1 end)\r\n   end % pidx\r\n   \r\n   evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  bidx=idx1(i);\r\n  if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n  end\r\n  \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n  end\r\n  \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n  \r\n  if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  end % if TL/TR/BL/BR\r\n  \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % Top edge\r\n   if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   \r\n  elseif nr3(i)==nr % Bot Edge\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   \r\n  elseif nc3(i)==1 %Left Edge\r\n   if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   \r\n  elseif nc3(i)==nc % Rt edge\r\n   if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   end\r\n   \r\n   \r\n  else %non-edge 3\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n     \r\n   elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   end\r\n  end % Edges/Mid 3\r\n    \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1\r\n   if nc3(i)==1 % TL  only one R or D possible\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % TR only one L or D possible. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Top Row  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  \r\n  if nr3(i)\u003cnr  % Mid section 33\r\n   if nc3(i)==1 % check only one R and D p\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % check only D. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Mid Row (not col 1 or nc)  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  if nr3(i)==nr  % Bot row 33\r\n    if nc3(i)==nc,continue;end % No process BR corner\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    continue\r\n  end\r\n \r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx+1-nr)==0 %BL\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %BR\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==1\r\n  \r\n  if nr3(i)==nr % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %TL\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %TR\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==nr\r\n  \r\n  if nc3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==1\r\n  \r\n  if nc3(i)==nc % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==nc\r\n  \r\n  %mid : check 4 courners\r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr  down diag, RB set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003c=nc-2 % Not near right edge\r\n     vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n  \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    \r\n    vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr  down diag, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not top edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003cnc % Not Right edge\r\n     vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e=3 % Not near Left edge\r\n     vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; \r\n   %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[3 3 2 3 5 5 3 3 5 1;\r\n    5 5 5 2 5 5 5 5 5 5;\r\n    1 5 5 5 5 1 1 5 5 2;\r\n    0 5 5 5 5 2 5 5 3 3;\r\n    0 5 5 5 1 3 5 5 5 5;\r\n    5 5 5 5 2 3 5 5 5 0;\r\n    3 2 5 5 1 5 5 5 5 2;\r\n    3 5 5 2 0 5 5 5 5 2;\r\n    5 5 5 5 5 5 2 5 5 5;\r\n    3 5 1 3 5 5 3 3 2 3]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\ns=['053552235013';\r\n   '505555535555';\r\n   '355135525552';\r\n   '521552155535';\r\n   '555305555553';\r\n   '535555335551';\r\n   '525050255352';\r\n   '325255555505';\r\n   '525555552521';\r\n   '152552253525';\r\n   '255533555535';\r\n   '255555522555';\r\n   '535551355315';\r\n   '355535512553';\r\n   '555525555515';\r\n   '132523255153']-'0'; % Solves with Assert\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n    '1551';\r\n    '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n   '1521';\r\n   '0532';\r\n   '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=['225355223525';\r\n    '555235535535';\r\n    '555255555555';\r\n    '232535355512';\r\n    '355555535515';\r\n    '255035555502';\r\n    '555555522555';\r\n    '055515555315';\r\n    '513555535550';\r\n    '555025555555';\r\n    '015555522552';\r\n    '505535555553';\r\n    '315553525223';\r\n    '555555553555';\r\n    '525515531555';\r\n    '535312551533']-'0'; % solves with Assert, Dies in Recursion\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[5 1 5 5 3 5 5 5 0 1;\r\n    5 0 5 5 5 3 3 5 5 5;\r\n    5 5 5 1 2 5 5 5 3 5;\r\n    2 5 5 5 5 5 2 0 5 2;\r\n    0 5 5 5 5 5 5 5 5 5;\r\n    5 5 5 5 5 5 5 5 5 3;\r\n    3 5 1 2 5 5 5 5 5 1;\r\n    5 3 5 5 5 3 0 5 5 5;\r\n    5 5 5 0 0 5 5 5 3 5;\r\n    2 1 5 5 5 1 5 5 3 5]; % solves with recursive/assert\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T21:28:57.000Z","updated_at":"2026-04-16T16:18:26.000Z","published_at":"2020-11-12T23:19:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink V:  Assert/Evolve/Check(large size)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving and Recursion due to time and depth issues.  Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking the Assert/Evolve/Check/Update method.  The advanced solving techniques on the web are weak and complicated. The simple method is not to immediately invoke recursion due to the sparseness of data leading to too many false options. Ther actual simple method is to use Try/Catch by asserting segments as Black/Red and then checking if the layout using a robust Evolve creates an invalid state. If the state became invalid when asserting a single segment as Black then it must be Red with the same being true of Red assertion being invalid must mean the segment is Black. If an Evolve is invalid then Assert the right Bar type and perform an evolve to update the board.  The two large test cases are from Games World of Puzzles October 2020. I was completely hopeless for the large puzzles. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive(medium size)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"rela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III: Evolve","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 615.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 307.833px; transform-origin: 407px 307.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 84.75px 7.91667px; transform-origin: 84.75px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink III: Evolve\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 265.65px 7.91667px; transform-origin: 265.65px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques but requires additional Evolving that is always valid for a valid input. Evolve examples are a Red bar into a corner must continue that Red bar out of the corner, an s=1 cell with a Red bar must have Black bars on its other 3 edges.  Cases of Trivial and Gimmes should be solved prior to invoking Evolve. The Evolve subroutine is the most critical routine and must be very comprehensive. A general Evolve routine should check if the output State is valid. When Evolve is used within a recursive routine that asserts possibly incorrect content the Evolve may produce an invalid output for the invalid input.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 366.783px 7.91667px; transform-origin: 366.783px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n  %show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv init solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n') \r\n  return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n%  show_pfig(s,p,c,emap,pmap,4)\r\n  fprintf('sv evolve solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\n\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10  0,10  1,11  2,12  3,13 to avoid reprocess\r\n while ~isequal(p,pb) %Keep evolving while there is any update to p\r\n  pb=p;\r\n  s1=find(sp==1)';\r\n  for i=s1 %1 \r\n   v=bsegs(i,:);\r\n   %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n   wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n   if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   elseif sum(wv)==1 % set 1 to 5\r\n    for j=1:4\r\n     if wv(j)==1\r\n      vz=v(2*j-1:2*j);\r\n      p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n     end\r\n    end\r\n    sp(i)=sp(i)+10;\r\n   end % if sum \u003e5\r\n   %show_pfig(s,p,c,emap,pmap,2)\r\n  end % i s1 1\r\n  \r\n  \r\n  s2=find(sp==2)';\r\n  for i=s2 %2\r\n   v=bsegs(i,:);\r\n   %insert code\r\n  end %i s2 2\r\n  \r\n  s3=find(sp==3)';\r\n  for i=s3 %3\r\n   v=bsegs(i,:);\r\n   %insert code\r\n  end %i s3 3\r\n  \r\n  if ~isequal(p,pb) % s update created new walls\r\n   %show_pfig(s,p,c,emap,pmap,2);\r\n   continue;\r\n  end\r\n  %show_pfig(s,p,c,emap,pmap,2)\r\n  \r\n  %Process links for new walls\r\n  % RR straight blocks perp, Binto corner makes B outcorner\r\n  % RR corner blocks to corner\r\n  % R into corner extends R\r\n  % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n  % R node with one option extends R\r\n  [nrc,ncc]=size(c);\r\n  % Bcorners if either corner edge B then both B\r\n  if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n   p(1,2)=0; p(2,1)=0;\r\n   p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n  end\r\n  if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n   p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n   p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n  %insert code\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n   %insert code\r\n  end\r\n  \r\n  % Rcorners if either corner edge R then both R\r\n  if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n   %insert code\r\n  end\r\n  if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n   %insert code\r\n  end\r\n  if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n   %insert code\r\n  end\r\n  if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n   %insert code\r\n  end\r\n  \r\n  % BB edges\r\n  %Top Row\r\n  for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n   cv=c(1,j:j+2);\r\n   if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n   end\r\n   if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n    p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n   end\r\n  end % j Top row\r\n  %Bottom Row\r\n  for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n   cv=c(nrc,j:j+2);\r\n   %insert code\r\n  end % j Bot row\r\n  \r\n  %Left Col edge\r\n  for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n   cv=c(i:i+2,1);\r\n   %insert code\r\n  end % j L col\r\n  %Right Col edge\r\n  for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n   cv=c(i:i+2,ncc);\r\n   %insert code\r\n  end % \r\n  \r\n  %Hor segs not on an edge\r\n  for i=2:nrc-1\r\n   for j=1:ncc-2\r\n    cv=c(i,j:j+2);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n     p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n    end\r\n    %insert code\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  %Ver segs not on an edge\r\n  for i=1:nrc-2\r\n   for j=2:ncc-1\r\n    cv=c(i:i+2,j);\r\n    if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n     p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n    end\r\n    %insert code\r\n   end % j 1:ncc-2\r\n  end % i 2:nrc-1\r\n  \r\n  \r\n  % RR corner blocks to corner\r\n  %[rr;xr]  [rr;rx]  [xr;rr]  [rx;rr]\r\n  %RR;xR or RR;Rx\r\n  for i=1:nrc-1\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab top pair\r\n    if p(cv(1),cv(2))==5 % Top Red\r\n     %insert code\r\n    end % Top RR\r\n   end %j\r\n  end %i\r\n  \r\n  for i=2:nrc % Rx;RR  xR;RR\r\n   for j=1:ncc-1\r\n    cv=c(i,j:j+1); % grab lower pair\r\n    if p(cv(1),cv(2))==5 % Bot Red\r\n     %insert code\r\n    end %Bot RR\r\n   end %j\r\n  end %i\r\n  \r\n  % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n  i=1; % Top\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    %insert code\r\n   end\r\n  end % j\r\n  \r\n  i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n  for j=1:ncc-1\r\n   cv=c(i,j:j+1);\r\n   if p(cv(1),cv(2))==0 % BB Top\r\n    %insert code\r\n   end\r\n  end % j\r\n  \r\n  j=ncc; % Right\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    %insert code\r\n   end\r\n  end % i\r\n  \r\n  j=1; % Left\r\n  for i=1:nrc-1\r\n   cv=c(i:i+1,j);\r\n   if p(cv(1),cv(2))==0 % BB Right\r\n    %insert code\r\n   end\r\n  end % i\r\n  \r\n  if ~isequal(p,pb),continue;end\r\n  \r\n  % R node with one option extends R \r\n%   [pr5,pc5]=find(p==5);\r\n%   for i=1:length(pr5)\r\n%    if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n%     new_node=find(p(pr5(i),:)==1);\r\n%     p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n%    end\r\n%   end\r\n  \r\n  [pr5,pc5]=find(p==5);\r\n  pr5=unique(pr5); % could sort then remove dupes which are mids\r\n  while ~isempty(pr5) %Extend Red Bars where there is only 1 option\r\n   if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n    new_node=find(p(pr5(1),:)==1);\r\n    p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n    pr5(1)=new_node;\r\n   else\r\n    pr5(1)=[];\r\n   end\r\n  end\r\n  \r\n  %check if red seg closes a loop of less than X thus seg must be black\r\n  if isequal(p,pb) % check for bad R bars only if no prior evolves have updated p\r\n   % insert code\r\n  end % isequal p pb  after cells, ends make no change\r\n  \r\n end % while p~=pb\r\n \r\n % Valid checks\r\n   for sptr=1:nr*nc %invalid set/clear segment count\r\n    vsptr=bsegs(sptr,:);\r\n    psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n    if s(sptr)==5\r\n     if nnz(psegs==5)==4\r\n      evalid=0;\r\n      return\r\n     else\r\n      continue\r\n     end\r\n    end % s 5\r\n    \r\n    if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n     evalid=0;\r\n     return\r\n    end\r\n    ps=sum(p); %\r\n    if nnz(ps==5) % Node with no escape\r\n     evalid=0;\r\n     return\r\n    end\r\n    if nnz(ps\u003e14) % Node with too many segments\r\n     evalid=0;\r\n     return\r\n    end\r\n   end % sptr\r\n   \r\n   %check for any loops created\r\n   %show_pfig(s,p,c,emap,pmap,3)\r\n   ps=sum(p);\r\n   pidx=find(ps==10);\r\n   pchecked=[];\r\n   for i=pidx\r\n    if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n    vn=find(p(i,:)==5); % Guaranteed 2 points\r\n    if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n     pchecked=[pchecked i];\r\n     continue;\r\n    end\r\n    v=[i find(p(i,:)==5,1,'first')];\r\n    Lv=0;\r\n    while length(v)\u003eLv\r\n     Lv=length(v);\r\n     vn=find(p(v(end),:)==5);\r\n     if length(vn)==1,break;end % No loop\r\n     if vn(1)==v(end-1)\r\n      v=[v vn(2)];\r\n     else\r\n      v=[v vn(1)];\r\n     end\r\n     if v(1)==v(end),break;end % Loop created\r\n    end % while extending\r\n    pchecked=[pchecked v];\r\n    \r\n    if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n    if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n     if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop   [1 2 4 3 1] loop\r\n      evalid=0;\r\n      return\r\n     else % Possible solve\r\n      [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n      if valid\r\n       evalid=1;\r\n       return\r\n      else % invalid loop connect thus must be 0\r\n       evalid=0;\r\n       return\r\n      end\r\n     end % Lv-1 compare to total current segments\r\n    end %  v 1 end)\r\n   end % pidx\r\n   \r\n   evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  bidx=idx1(i);\r\n  if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n   \r\n  elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   p(vbsegs(3),vbsegs(4))=0;\r\n   p(vbsegs(4),vbsegs(3))=0;\r\n  end\r\n  \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n   vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n   vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n   vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n   \r\n  elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n   vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n   p(vbsegs(1),vbsegs(2))=5;\r\n   p(vbsegs(2),vbsegs(1))=5;\r\n   p(vbsegs(3),vbsegs(4))=5;\r\n   p(vbsegs(4),vbsegs(3))=5;\r\n  end\r\n  \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n  \r\n  if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n    \r\n  end % if TL/TR/BL/BR\r\n  \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % Top edge\r\n   if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   \r\n  elseif nr3(i)==nr % Bot Edge\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   \r\n  elseif nc3(i)==1 %Left Edge\r\n   if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   \r\n  elseif nc3(i)==nc % Rt edge\r\n   if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   end\r\n   \r\n   \r\n  else %non-edge 3\r\n   if s(nr3(i)-1,nc3(i))==0 % Top 0  3below0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n    vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n     \r\n   elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n    vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    \r\n   end\r\n  end % Edges/Mid 3\r\n    \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n  if nr3(i)==1\r\n   if nc3(i)==1 % TL  only one R or D possible\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % TR only one L or D possible. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Top Row  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  \r\n  if nr3(i)\u003cnr  % Mid section 33\r\n   if nc3(i)==1 % check only one R and D p\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    elseif s(bidx+1)==3 %D\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   if nc3(i)==nc % check only D. Process only D\r\n    if s(bidx+1)==3\r\n     vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(3),vbsegs(4))=5;\r\n     p(vbsegs(4),vbsegs(3))=5;\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n     vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n     vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n     p(vbsegs(5),vbsegs(6))=5;\r\n     p(vbsegs(6),vbsegs(5))=5;\r\n    end\r\n    continue\r\n   end\r\n   % Mid Row (not col 1 or nc)  L or R or D possible, check only R/D\r\n   if s(bidx+nr)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n   elseif s(bidx+1)==3\r\n    vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n   end\r\n   continue \r\n  end\r\n  \r\n  if nr3(i)==nr  % Bot row 33\r\n    if nc3(i)==nc,continue;end % No process BR corner\r\n    if s(bidx+nr)==3 %R\r\n     vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(1),vbsegs(2))=5;\r\n     p(vbsegs(2),vbsegs(1))=5;\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=5;\r\n     p(vbsegs(8),vbsegs(7))=5;\r\n     vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    continue\r\n  end\r\n \r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  if nr3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx+1-nr)==0 %BL\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %BR\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==1\r\n  \r\n  if nr3(i)==nr % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %TL\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %TR\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n   end\r\n   continue\r\n  end % nr3==nr\r\n  \r\n  if nc3(i)==1 % double diagonal zeros possible  \r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==1\r\n  \r\n  if nc3(i)==nc % double diagonal zeros possible  \r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   continue\r\n  end % nc3==nc\r\n  \r\n  %mid : check 4 courners\r\n   if s(bidx-1-nr)==0 %LT\r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1-nr)==0 %LB\r\n    vbsegs=bsegs(bidx,:); %bidx, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n   if s(bidx-1+nr)==0 %RT\r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n   end\r\n   if s(bidx+1+nr)==0 %RB\r\n    vbsegs=bsegs(bidx,:); %bidx, RB set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n   end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr  down diag, RB set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e1 % Not left edge\r\n     vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003c=nc-2 % Not near right edge\r\n     vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n  \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    \r\n    vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr  down diag, LB set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    \r\n    if nr3(i)\u003e1 % Not top edge\r\n     vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n     p(vbsegs(7),vbsegs(8))=0;\r\n     p(vbsegs(8),vbsegs(7))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003cnc % Not Right edge\r\n     vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n     p(vbsegs(3),vbsegs(4))=0;\r\n     p(vbsegs(4),vbsegs(3))=0;\r\n    end\r\n    \r\n    if nc3(i)\u003e=3 % Not near Left edge\r\n     vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n     p(vbsegs(5),vbsegs(6))=0;\r\n     p(vbsegs(6),vbsegs(5))=0;\r\n    end\r\n    if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n     vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n     p(vbsegs(1),vbsegs(2))=0;\r\n     p(vbsegs(2),vbsegs(1))=0;\r\n    end\r\n    \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['252';\r\n   '151';\r\n   '212']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n    '1551';\r\n    '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n   '1521';\r\n   '0532';\r\n   '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['33353';\r\n   '15551';\r\n   '25055';\r\n   '55253';\r\n   '13511']-'0';% evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5]; % Trivial 33\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T19:13:03.000Z","updated_at":"2026-04-29T19:28:00.000Z","published_at":"2020-11-12T23:28:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink III: Evolve\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques but requires additional Evolving that is always valid for a valid input. Evolve examples are a Red bar into a corner must continue that Red bar out of the corner, an s=1 cell with a Red bar must have Black bars on its other 3 edges.  Cases of Trivial and Gimmes should be solved prior to invoking Evolve. The Evolve subroutine is the most critical routine and must be very comprehensive. A general Evolve routine should check if the output State is valid. When Evolve is used within a recursive routine that asserts possibly incorrect content the Evolve may produce an invalid output for the invalid input.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check 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