{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-16T00:12:35.000Z","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":"2026-04-16T00:00:00.000Z","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":2050,"title":"remove nans fast","description":"There are several ways to locate and remove nans in a matrix, and return an 1d row vector. \r\n\r\nIn this problem the challenge is to do it within a set time as measured by tic-toc.\r\n\r\nThe problem will be run numerous times and an average time tested.\r\n\r\nNote: I noticed a rare failure depending on when I ran the testsuite, so you _might_ have to resubmit your code if it runs faster on your machine than the limit set here.","description_html":"\u003cp\u003eThere are several ways to locate and remove nans in a matrix, and return an 1d row vector.\u003c/p\u003e\u003cp\u003eIn this problem the challenge is to do it within a set time as measured by tic-toc.\u003c/p\u003e\u003cp\u003eThe problem will be run numerous times and an average time tested.\u003c/p\u003e\u003cp\u003eNote: I noticed a rare failure depending on when I ran the testsuite, so you \u003ci\u003emight\u003c/i\u003e have to resubmit your code if it runs faster on your machine than the limit set here.\u003c/p\u003e","function_template":"function nanless = removenan(m)\r\nloc=find(nan);\r\nnanless= removenan(m,loc);\r\nend","test_suite":"%% T1\r\ntm=[];\r\nfor t=1:100\r\n rng('default');\r\n m=rand(100,100,100);\r\n m(m\u003e0.7)=nan;\r\n tic\r\n  o=removenan(m);\r\n tm(t)=toc;\r\nend\r\nrng('default');\r\nm=rand(100,100,100);\r\nm(m\u003e0.7)=nan;\r\nm(isnan(m))=[];\r\nt_correct=0.019;\r\nassert(mean(tm)\u003ct_correct)\r\nassert(isequal(o,m'))\r\n\r\n%% T2\r\ntm=[];\r\nfor t=1:100\r\n rng('default');\r\n m=rand(100,10000);\r\n m(m\u003e0.71)=nan;\r\n tic\r\n  o=removenan(m);\r\n tm(t)=toc;\r\nend\r\nrng('default');\r\nm=rand(100,10000);\r\nm(m\u003e0.71)=nan;\r\nm(isnan(m))=[];\r\nt_correct=0.019;\r\nassert(mean(tm)\u003ct_correct)\r\nassert(isequal(o,m'))\r\n\r\n%% T3\r\ntm=[];\r\nfor t=1:100\r\n rng('default');\r\n m=rand(2,500000);\r\n m(m\u003e0.69)=nan;\r\n tic\r\n  o=removenan(m);\r\n tm(t)=toc;\r\nend\r\nrng('default');\r\nm=rand(2,500000);\r\nm(m\u003e0.69)=nan;\r\nm(isnan(m))=[];\r\nt_correct=0.019;\r\nassert(mean(tm)\u003ct_correct)\r\nassert(isequal(o,m'))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":17471,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":44,"test_suite_updated_at":"2013-12-15T04:17:20.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-12-15T03:36:48.000Z","updated_at":"2026-02-13T18:32:27.000Z","published_at":"2013-12-15T04:15:46.000Z","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\",\"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\u003eThere are several ways to locate and remove nans in a matrix, and return an 1d row vector.\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 problem the challenge is to do it within a set time as measured by tic-toc.\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 will be run numerous times and an average time tested.\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\u003eNote: I noticed a rare failure depending on when I ran the testsuite, so 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\u003emight\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e have to resubmit your code if it runs faster on your machine than the limit set here.\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":42256,"title":"Speed of car","description":"Calculate the Speed of car given its Distance travelled and time taken in x and y respectively","description_html":"\u003cp\u003eCalculate the Speed of car given its Distance travelled and time taken in x and y respectively\u003c/p\u003e","function_template":"function z_correct= Speed(x,y)\r\n  z_correct= Speed;\r\nend","test_suite":"%%\r\nx = 1;\r\ny = 1;\r\nz_correct=1;\r\nassert(isequal(Speed(x,y),z_correct))\r\n\r\n%%\r\nx=4;\r\ny=2;\r\nz_correct=2;\r\nassert(isequal(Speed(x,y),z_correct))","published":true,"deleted":false,"likes_count":5,"comments_count":1,"created_by":38003,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":424,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-04-24T05:48:08.000Z","updated_at":"2026-04-15T03:33:00.000Z","published_at":"2015-04-24T05:48:11.000Z","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\u003eCalculate the Speed of car given its Distance travelled and time taken in x and y respectively\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":44241,"title":"NCHOOSEK - Time Optimization","description":"Input\r\nV —— Set of all choices, a vector of N, 1 \u003c N \u003c 100\r\nK —— Number of selected choices, a scalar, 0 \u003c= K \u003c= N\r\nOutput\r\nC —— All combinations of V, equivalent to nchoosek(V, K)\r\nScoring\r\nTime consumed","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: 214.292px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 107.139px; transform-origin: 407px 107.146px; 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; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 40.8611px; 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 20.4306px; transform-origin: 391px 20.4306px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"background-position: 0px 50%; 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=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e —— Set of all choices, a vector of N, 1 \u0026lt; N \u0026lt; 100\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"background-position: 0px 50%; 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=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eK\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e —— Number of selected choices, a scalar, 0 \u0026lt;= K \u0026lt;= N\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; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4306px; 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 10.2083px; transform-origin: 391px 10.2153px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"background-position: 0px 50%; 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=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e —— All combinations of\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, equivalent to\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003enchoosek(V, K)\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; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eScoring\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; \"\u003e\u003cspan style=\"\"\u003eTime consumed\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function c = nchoosekFast(v, k)\r\n  c = nchoosek(v, k);\r\nend","test_suite":"%%\r\nassessFunctionAbsence({'regexp','regexpi','regexprep','str2num','!','system','unix'},'File','nchoosekFast.m')\r\n\r\n%%\r\nv = rand(1,10);\r\nk = randi(10);\r\nassert(isequal(nchoosek(v,k),nchoosekFast(v,k)));\r\n\r\n%%\r\nv = num2cell(randn(5,30),1);\r\nk = randi(4);\r\nassert(isequal(nchoosek(v,k),nchoosekFast(v,k)));\r\n\r\n%%\r\nv = rand(1,100);\r\nassert(isequal(nchoosek(v,1),nchoosekFast(v,1),nchoosekFast(v,100)',v') \u0026\u0026 isempty(nchoosekFast(v,0)))\r\n\r\n%%\r\nv = rand(1,29);\r\nt = tic;\r\nc = nchoosekFast(v,9);\r\nt = toc(t);\r\nassert(isequal(size(c),[10015005 9]) \u0026\u0026 ...\r\n    all(ismember(c(:),v)) \u0026\u0026 ...\r\n    size(unique(sort(c(randperm(end,1e6),:),2),'rows'),1) == 1e6)\r\nfid = fopen('score.p','Wb');\r\nfwrite(fid,uint8(sscanf([...\r\n     '7630312E30307630302E3030000B901C454EFFB100000031000001330000018D483A60'...\r\n     '366BC9545F84AE26323B67424D4E8A7A2E5B7D8ACAA45A1C3C5C8B33E245C95243E3CB'...\r\n     'AF5D0D993BDA70B7AB5DA365A83E8CA87FFC45265E23EF80943784C5F48E6E53D5DA34'...\r\n     'F1F2ECD34683EABE3B7461DC9E8004CC50B2A79D73495F6F625B5365602B2E6C6093D2'...\r\n     '997D371DA457CE82327E686AF512A507B2CB62A375BFD1B283DDD2C01EDEF2771EDAA3'...\r\n     '6ABB4852BA4061E20149688E812EB41A9AF8627EF35755492D2830EB8718BCFE88027E'...\r\n     '6EA960B63A3B3E26E0451B1DCF14F3C20E70D9D93B08E7FF4AE8D82E7CC38042FD38F7'...\r\n     'A14D312EF5652823FEB7E8B52AF5C69F5E7D16B116B5F979EDA77459D6BB61B7971A51'...\r\n     '041227DD601319D667DF62E8DA5E381FDD07A2806FE835BD2569E5315CDFC19C6B6A2B'...\r\n     '4F0FF6BA803F1759ACAB133CCFAB6D5A5D002FC2C5F381F0'],'%2X')));\r\nfclose(fid);\r\nscore(round(3*t))","published":true,"deleted":false,"likes_count":2,"comments_count":8,"created_by":1434,"edited_by":427930,"edited_at":"2023-07-18T13:35:29.000Z","deleted_by":null,"deleted_at":null,"solvers_count":91,"test_suite_updated_at":"2017-12-21T05:50:57.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-06-27T01:24:44.000Z","updated_at":"2026-03-30T19:11:03.000Z","published_at":"2017-06-27T01:38:46.000Z","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\u003eInput\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\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e —— Set of all choices, a vector of N, 1 \u0026lt; N \u0026lt; 100\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\u003eK\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e —— Number of selected choices, a scalar, 0 \u0026lt;= K \u0026lt;= N\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\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\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e —— All combinations of\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\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, equivalent to\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\u003enchoosek(V, K)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eScoring\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eTime consumed\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":61036,"title":"The MATLAB Treasure Hunt – Cross the River of Ratios by Finding Successive Proportions in the Data Stream","description":"Following the glowing script, you arrive at the River of Ratios — a flowing stream of numbers.\r\nA carved message on the rocks reads: “Only those who know the balance between every pair shall cross.”\r\nGiven a numeric vector a, calculate the ratio of each element to the previous one.\r\nReturn a vector r such that r(i) = a(i+1)/a(i) for all valid i.\r\nThis will reveal the rhythm of the river and guide your next step!","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; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 141px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 70.5px; transform-origin: 408px 70.5px; 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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eFollowing the glowing script, you arrive at 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; \"\u003eRiver of Ratios\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 flowing stream of numbers.\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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eA carved message on the rocks reads: \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-style: italic; \"\u003e“Only those who know the balance between every pair shall cross.”\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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eGiven a numeric 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003ea\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, calculate 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; \"\u003eratio of each element to the previous one\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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eReturn 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\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 such that \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-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003er(i) = a(i+1)/a(i)\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 for all valid \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-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\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\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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eThis will reveal the rhythm of the river and guide your next step!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = riverRatios(a)\r\n  y = x;\r\nend","test_suite":"%%\r\na = [2 4 8 16];\r\ny_correct = [2 2 2];\r\nassert(isequal(riverRatios(a),y_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":4953963,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":69,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-10-21T09:38:42.000Z","updated_at":"2026-03-10T14:17:27.000Z","published_at":"2025-10-21T09:38:42.000Z","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\u003eFollowing the glowing script, you arrive at the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRiver of Ratios\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — a flowing stream of numbers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 carved message on the rocks reads: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e“Only those who know the balance between every pair shall cross.”\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 numeric 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\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, calculate the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eratio of each element to the previous one\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\u003eReturn 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\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e such that \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\u003er(i) = a(i+1)/a(i)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for all valid \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\u003ei\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\u003eThis will reveal the rhythm of the river and guide your next step!\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":958,"title":"ismember: Enhanced Performance for 'rows'  and width - Speed Scoring (66% savings)","description":"The Challenge is to perform very fast the 'ismember' function for a long and wide array.  The width of the array is expanded from 16 to 48.\r\n\r\nFast methods can reduce time by 66%.\r\n\r\nThe data is small integer representing data permutations of items like DNA and Rubik's cube faces and orientations.\r\n\r\n*Input:* Array of uint8 of dimensions (m, 48) with values 0:3\r\n\r\n*Output:* Array Equivalent to ismember(A,B,'rows')\r\n\r\n*Hints:*\r\n\r\n1) Columns can be merged to form a reduced number of columns\r\n2) Unique has the option to provide an Array and a sorting Index\r\n\r\nNote: Enhancements to speed usually improve memory allocation issues.","description_html":"\u003cp\u003eThe Challenge is to perform very fast the 'ismember' function for a long and wide array.  The width of the array is expanded from 16 to 48.\u003c/p\u003e\u003cp\u003eFast methods can reduce time by 66%.\u003c/p\u003e\u003cp\u003eThe data is small integer representing data permutations of items like DNA and Rubik's cube faces and orientations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Array of uint8 of dimensions (m, 48) with values 0:3\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Array Equivalent to ismember(A,B,'rows')\u003c/p\u003e\u003cp\u003e\u003cb\u003eHints:\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1) Columns can be merged to form a reduced number of columns\r\n2) Unique has the option to provide an Array and a sorting Index\u003c/p\u003e\u003cp\u003eNote: Enhancements to speed usually improve memory allocation issues.\u003c/p\u003e","function_template":"function idx = ismember_fast_rows(a,b)\r\n idx=ismember(a,b,'rows');\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',20000);\r\n%%\r\n% Functionality Tests\r\nL=128;\r\na=randi(4,L,48,'uint8')-1;\r\nb=randi(4,L,48,'uint8')-1;\r\nassert(isequal(ismember(a,b,'rows'),ismember_fast_rows(a,b)))\r\n\r\nb=a;\r\nassert(isequal(ismember(a,b,'rows'),ismember_fast_rows(a,b)))\r\n\r\nL=256;\r\na=randi(4,L,48,'uint8')-1;\r\nb=randi(4,L,48,'uint8')-1;\r\na(16:32,:)=b(32:48,:);\r\nassert(isequal(ismember(a,b,'rows'),ismember_fast_rows(a,b)))\r\n\r\n%%\r\n% 2M has a crash for 2x ismember\r\nL=1900000;  % ismember 19.6    fast  C 8.3 2M\r\ntic\r\na=randi(4,L,48,'uint8')-1;\r\nb=randi(4,L,48,'uint8')-1;\r\na(100:200,:)=b(400:500,:); % Put in some matching data\r\ntoc\r\n\r\n\r\nta=clock;\r\nidx = ismember_fast_rows(a,b);\r\nt1=etime(clock,ta)*1000;\r\n\r\nfprintf('Elapsed time = %.0f msec\\n',t1)\r\n\r\nassert(isequal(ismember(a,b,'rows'),idx))\r\n\r\nt2=min(20000,t1); % ismember 1.9M x 48 scores 19000 msec\r\nfeval(@assignin,'caller','score',floor(t2));\r\n\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":21,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-09-24T05:10:17.000Z","updated_at":"2026-01-21T12:28:29.000Z","published_at":"2012-09-24T05:37:58.000Z","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\u003eThe Challenge is to perform very fast the 'ismember' function for a long and wide array. The width of the array is expanded from 16 to 48.\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\u003eFast methods can reduce time by 66%.\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 data is small integer representing data permutations of items like DNA and Rubik's cube faces and orientations.\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\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Array of uint8 of dimensions (m, 48) with values 0:3\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\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Array Equivalent to ismember(A,B,'rows')\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\u003eHints:\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) Columns can be merged to form a reduced number of columns 2) Unique has the option to provide an Array and a sorting 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\u003eNote: Enhancements to speed usually improve memory allocation issues.\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":934,"title":"Find: Faster Alternatives for Large Sorted/Unique Vectors","description":"The Challenge is to create faster Find methods for large unique ascending vectors.\r\n\r\nMethods exist that are 1000 times faster than Find.\r\n\r\nMethods have applications where data files are loaded for multiple searches(Rubik's Mini Cube, DNA).\r\n\r\n*Input:* [a,val]\r\n\r\n*   a: vector [N large,1] of type uint32 that is unique and sorted ascending\r\n*   val: The value to find in the \"a\" vector. Val will exist in \"a\".\r\n\r\n*Output:* [ptr]\r\n\r\n*   ptr: The index in array \"a\" where a(ptr) is val\r\n\r\n*Score:* Time in msec to find 200 random values in a 12,000,000 long vector\r\n\r\n\r\nThe basic find(a==val,1,'first') takes approximately 14 seconds (12M, 200 cases).\r\n\r\nHigh performance find_fast can find 200 values in 4 to 12 milli-seconds for a 12M vector.\r\n\r\nFind time increases linearly with the array size. High performance methods don't appear to incur any increased processing time.\r\n\r\nHints:\r\n\r\n* There are  multiple methods that significantly outperform find.\r\n* Bisect searches\r\n* Predictive searches\r\n* Combining Bisect/Predictive with a Linear Chaser\r\n* \r\n\r\nA static goal vector find time could be enhanced by a Pre-Index array. Not applicable to this challenge.\r\n","description_html":"\u003cp\u003eThe Challenge is to create faster Find methods for large unique ascending vectors.\u003c/p\u003e\u003cp\u003eMethods exist that are 1000 times faster than Find.\u003c/p\u003e\u003cp\u003eMethods have applications where data files are loaded for multiple searches(Rubik's Mini Cube, DNA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [a,val]\u003c/p\u003e\u003cul\u003e\u003cli\u003ea: vector [N large,1] of type uint32 that is unique and sorted ascending\u003c/li\u003e\u003cli\u003eval: The value to find in the \"a\" vector. Val will exist in \"a\".\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e [ptr]\u003c/p\u003e\u003cul\u003e\u003cli\u003eptr: The index in array \"a\" where a(ptr) is val\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eScore:\u003c/b\u003e Time in msec to find 200 random values in a 12,000,000 long vector\u003c/p\u003e\u003cp\u003eThe basic find(a==val,1,'first') takes approximately 14 seconds (12M, 200 cases).\u003c/p\u003e\u003cp\u003eHigh performance find_fast can find 200 values in 4 to 12 milli-seconds for a 12M vector.\u003c/p\u003e\u003cp\u003eFind time increases linearly with the array size. High performance methods don't appear to incur any increased processing time.\u003c/p\u003e\u003cp\u003eHints:\u003c/p\u003e\u003cul\u003e\u003cli\u003eThere are  multiple methods that significantly outperform find.\u003c/li\u003e\u003cli\u003eBisect searches\u003c/li\u003e\u003cli\u003ePredictive searches\u003c/li\u003e\u003cli\u003eCombining Bisect/Predictive with a Linear Chaser\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eA static goal vector find time could be enhanced by a Pre-Index array. Not applicable to this challenge.\u003c/p\u003e","function_template":"function ptr = find_fast(a,val)\r\n  ptr = find(a==val,1,'first');\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',0);\r\n%%\r\nL=1000;\r\na=randi(2^32-1,L*1.2,1,'uint32');\r\na=unique(a,'R2012a');\r\na=a(1:L);\r\n\r\n% Warm-Up Test cases\r\nfor i=1:5:1000\r\n assert(isequal(find_fast(a,a(i)),i))\r\nend\r\n%%\r\n% Timing Performance Case\r\nL=12000000;\r\na=randi(2^32-1,L*1.2,1,'uint32');\r\na=unique(a,'R2012a');\r\na=a(1:L);\r\n\r\nq=200;\r\n\r\nval=zeros(q,1);\r\nfor i=1:q\r\n val(i)=a(randi(L));\r\nend\r\n\r\nt0=clock;\r\nfor i=1:q\r\n ptr=find_fast(a,val(i));\r\nend\r\ndt=etime(clock,t0)*1000;\r\n\r\n\r\nassert(isequal(find_fast(a,val(1)),find(a==val(1),1,'first')))\r\n\r\nfprintf('Your Time = %i msec\\n',floor(dt))\r\n\r\nfeval(@assignin,'caller','score',min(200,floor(dt)));\r\n%   Performance Score\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":37,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-09-07T01:12:34.000Z","updated_at":"2025-12-08T15:39:06.000Z","published_at":"2012-09-07T03:26:59.000Z","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 Challenge is to create faster Find methods for large unique ascending vectors.\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\u003eMethods exist that are 1000 times faster than Find.\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\u003eMethods have applications where data files are loaded for multiple searches(Rubik's Mini Cube, DNA).\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\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [a,val]\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\u003ea: vector [N large,1] of type uint32 that is unique and sorted ascending\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\u003eval: The value to find in the \\\"a\\\" vector. Val will exist in \\\"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\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 [ptr]\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\u003eptr: The index in array \\\"a\\\" where a(ptr) is val\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\u003eScore:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Time in msec to find 200 random values in a 12,000,000 long vector\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 basic find(a==val,1,'first') takes approximately 14 seconds (12M, 200 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:t\u003eHigh performance find_fast can find 200 values in 4 to 12 milli-seconds for a 12M vector.\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\u003eFind time increases linearly with the array size. High performance methods don't appear to incur any increased processing 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHints:\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\u003eThere are multiple methods that significantly outperform find.\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\u003eBisect searches\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\u003ePredictive searches\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\u003eCombining Bisect/Predictive with a Linear Chaser\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\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\u003eA static goal vector find time could be enhanced by a Pre-Index array. Not applicable to this challenge.\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":1888,"title":"Get ranking of a combination","description":"I have the numbers pulled without replacement from the set [1 2 3 4 5 6 7 8 9 10 11 12 13]; They are then ordered from least to greatest.\r\nSo a selection of [3 2 9], [9 2 3] are both considered to be [2 3 9].\r\nThere are 286 unique selections possible. These can be ordered in lexicographic order:\r\nElement   1 = [ 1  2  3]\r\nElement   2 = [ 1  2  4]\r\nElement   3 = [ 1  2  5]\r\nElement   4 = [ 1  2  6]\r\nElement   5 = [ 1  2  7]\r\nElement   6 = [ 1  2  8]\r\nElement   7 = [ 1  2  9]\r\nElement   8 = [ 1  2 10]\r\nElement   9 = [ 1  2 11]\r\nElement  10 = [ 1  2 12]\r\nElement  11 = [ 1  2 13]\r\nElement  12 = [ 1  3  4]\r\nElement  13 = [ 1  3  5]\r\nElement  14 = [ 1  3  6]\r\nElement  15 = [ 1  3  7]\r\n...\r\nElement 285 = [10 12 13]\r\nElement 286 = [11 12 13]\r\nGiven the three ordered values as a row vector, return the element number.\r\nDo this with an eye for speed, though it is not tested for here.\r\nLooking for a way to do this WITHOUT generating the nchoosek matrix.","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: 570.8px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 285.4px; transform-origin: 407px 285.4px; 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: 377px 8px; transform-origin: 377px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI have the numbers pulled without replacement from the set [1 2 3 4 5 6 7 8 9 10 11 12 13]; They are then ordered from least to greatest.\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: 206px 8px; transform-origin: 206px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSo a selection of [3 2 9], [9 2 3] are both considered to be [2 3 9].\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: 276.5px 8px; transform-origin: 276.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThere are 286 unique selections possible. These can be ordered in lexicographic order:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 367.8px; 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 183.9px; transform-origin: 404px 183.9px; 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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e1 = [ 1  2  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e2 = [ 1  2  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e3 = [ 1  2  5]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e4 = [ 1  2  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e5 = [ 1  2  7]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e6 = [ 1  2  8]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e7 = [ 1  2  9]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e8 = [ 1  2 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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e9 = [ 1  2 11]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e10 = [ 1  2 12]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e11 = [ 1  2 13]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e12 = [ 1  3  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e13 = [ 1  3  5]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e14 = [ 1  3  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e15 = [ 1  3  7]\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: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003eElement \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: 64px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 64px 8.5px; \"\u003e285 = [10 12 13]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003eElement \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: 64px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 64px 8.5px; \"\u003e286 = [11 12 13]\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: 237.5px 8px; transform-origin: 237.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven the three ordered values as a row vector, return the element number.\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: 192.5px 8px; transform-origin: 192.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDo this with an eye for speed, though it is not tested for here.\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: 223px 8px; transform-origin: 223px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eLooking for a way to do this WITHOUT generating the nchoosek matrix.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function value = get3HCRank(vec)\r\n  value = vec;\r\nend","test_suite":"%%\r\nfiletext = fileread('get3HCRank.m');\r\nillegal = contains(filetext, 'regexp') || contains(filetext, 'nchoosek') || ...\r\n          contains(filetext, 'elseif') || contains(filetext, 'switch'); \r\nassert(~illegal)\r\n\r\n%%\r\nvec = [1 2 3];\r\ny_correct = 1;\r\nassert(isequal(get3HCRank(vec),y_correct))\r\n\r\n%%\r\nvec = [1 2 4];\r\ny_correct = 2;\r\nassert(isequal(get3HCRank(vec),y_correct))\r\n\r\n%%\r\nvec = [3 5 8];\r\ny_correct = 133;\r\nassert(isequal(get3HCRank(vec),y_correct))\r\n\r\n%%\r\nvec = [5 9 11];\r\ny_correct = 222;\r\nassert(isequal(get3HCRank(vec),y_correct))\r\n\r\n%%\r\nvec = [11 12 13];\r\ny_correct = 286;\r\nassert(isequal(get3HCRank(vec),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":240,"edited_by":223089,"edited_at":"2022-10-30T10:50:12.000Z","deleted_by":null,"deleted_at":null,"solvers_count":53,"test_suite_updated_at":"2022-10-30T10:50:12.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-09-23T15:25:55.000Z","updated_at":"2026-02-08T19:54:25.000Z","published_at":"2013-09-23T15:25:55.000Z","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\u003eI have the numbers pulled without replacement from the set [1 2 3 4 5 6 7 8 9 10 11 12 13]; They are then ordered from least to greatest.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eSo a selection of [3 2 9], [9 2 3] are both considered to be [2 3 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\u003eThere are 286 unique selections possible. These can be ordered in lexicographic order:\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[Element   1 = [ 1  2  3]\\nElement   2 = [ 1  2  4]\\nElement   3 = [ 1  2  5]\\nElement   4 = [ 1  2  6]\\nElement   5 = [ 1  2  7]\\nElement   6 = [ 1  2  8]\\nElement   7 = [ 1  2  9]\\nElement   8 = [ 1  2 10]\\nElement   9 = [ 1  2 11]\\nElement  10 = [ 1  2 12]\\nElement  11 = [ 1  2 13]\\nElement  12 = [ 1  3  4]\\nElement  13 = [ 1  3  5]\\nElement  14 = [ 1  3  6]\\nElement  15 = [ 1  3  7]\\n...\\nElement 285 = [10 12 13]\\nElement 286 = [11 12 13]]]\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\u003eGiven the three ordered values as a row vector, return the element number.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eDo this with an eye for speed, though it is not tested for here.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eLooking for a way to do this WITHOUT generating the nchoosek matrix.\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":52427,"title":"ICFP2021 Hole-In-Wall: Solve Problem 4, Score=0, Bonus GLOBALIST assumed","description":"The ICFP held its annual 3-day contest in July 2021 with Hole-In-Wall. Contest Specification.\r\nThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \r\nThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the Specification when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\r\nValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u003c= Edges*epsilon/1000000.  Lsqr is length squared.\r\nScore is sum of minimum square distances to the figure from each unique hole vertex. \r\nnpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)  \r\nThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\r\nThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\r\nThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use Register Team. Anyone can select Problems Page and then click problem numbers to see the puzzles and to download problem files.\r\n","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: 922px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 461px; transform-origin: 407px 461px; 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: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.icfpconference.org/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP\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: 148.5px 8px; transform-origin: 148.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e held its annual 3-day contest in July 2021 with \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eHole-In-Wall\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: 30.5px 8px; transform-origin: 30.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Contest \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\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: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 168px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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 84px; text-align: left; transform-origin: 384px 84px; 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: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\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: 332px 8px; transform-origin: 332px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\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: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*epsilon/1000000.  Lsqr is length squared.\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 = \"https://www.mathworks.com/matlabcentral/cody/problems/52308\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eScore\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: 253px 8px; transform-origin: 253px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is sum of minimum square distances to the figure from each unique hole vertex. \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: 157.5px 8px; transform-origin: 157.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)\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=\"\"\u003e  \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: 377.5px 8px; transform-origin: 377.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\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: 368.5px 8px; transform-origin: 368.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\u003c/span\u003e\u003c/span\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: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/register\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRegister Team\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: 43.5px 8px; transform-origin: 43.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Anyone can select \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/problems\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblems 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: 259px 8px; transform-origin: 259px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 358px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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 179px; text-align: left; transform-origin: 384px 179px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: top;width: 776px;height: 358px\" 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\" data-image-state=\"image-loaded\" width=\"776\" height=\"358\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function npxy=Solve_ICFP004(hxy,pxy,mseg,epsilon)\r\n% Annealing Solver with Gloabalist Bonus tweak\r\n% Create list of nodes that are placed on hole nodes\r\n% Adjust Segment Max lengths for select segments\r\n% Place hole vertex values into npxy array\r\n% npxy values outside hole are randomly placed inside hole\r\n% as all jiggles are checked for staying in hole\r\n% This routine lacks edge crossing check\r\n% Infinite jiggle traps exist so iterations are limited followed\r\n% by node being randomly placed\r\n% Anneal by moving selected node to/away from goal node with\r\n% directional randomness eg to Bottom Right [1 1;1 0;0 1\r\n% to Top Left the move options would be [-1 -1;-1 0;0 -1]\r\n\r\n npxy=pxy;\r\n np=size(npxy,1); % figure/pose vertices\r\n nhp1=size(hxy,1); % hole vertices\r\n nh=nhp1-1;\r\n nseg=size(mseg,1);\r\n \r\n msegMM=calc_msegMM(pxy,mseg,epsilon,nseg); %Create Min and Max segment integer values\r\n\r\n%By inspection assign these nodes to the hole vertices. See Challenge page Figure \r\n%Revise msegMM for Bonus Stretch of Problem 4\r\n  msegMM([9 46],2)=20*20; % Fit segs 9 and 46 to hole\r\n  msegMM(49,2)=2*msegMM(49,2); %Allow Extend seg 49\r\n  msegMM(50,2)=2*msegMM(50,2); %Allow extend seg 50\r\n%starting at top left\r\n  p2hEdge=[7 27 16 35 42 41 38 23 12 6 5]'; %Assign nodes to hole vertices\r\n  npxy(p2hEdge,:)=hxy(1:nh,:);\r\n \r\n %hplot(hxy,pxy,mseg,nseg,1);\r\n %hplot3(hxy,npxy,mseg,nseg,3,msegMM);\r\n %pause(0.01)\r\n \r\n %Create a simple matrix for indexing in hole positions\r\n %Simple vector calculations determines good/bad node\r\n %May need a -1 check for x or y\r\n xhmax=max(hxy(:,1)); %hole node 0:79 thus 80 47 mod by xchmax\r\n xhmax1=xhmax+1;\r\n yhmax=max(hxy(:,2)); %[x,y]*[1;80]+1 to make in-index\r\n %[x,y]*[1;xhmax+1]+1\r\n dmap=ones(xhmax+1,yhmax+1); % x=column, y=row  hxy(col=x,row=y)\r\n [dx,dy]=find(dmap); %in vector TL2BR across figures L2R,T2B\r\n dxy=[dx dy]-1;\r\n in=inpolygon(dxy(:,1),dxy(:,2),hxy(:,1),hxy(:,2));\r\n hdxy=dxy(:,1)\u003c0; % make logical hdxy of entire board\r\n hdxy(in,:)=1; % [0,0] maps to 1, [1,0] is 2, [79,0] is 80,\r\n %hdxy holds oversized hole map\r\n phdxy=find(hdxy); % Pointer to all in-hole nodes\r\n Lphdxy=nnz(phdxy); % used for outside hole and infinite loops\r\n % new_xy=dxy(phdxy(randi(Lphdxy)),:);\r\n \r\n fprintf('IN-Hole Nodes:%i  HoleV:%i FigV:%i\\n',nnz(hdxy),nh,np);\r\n  \r\n %Problem 4 Solution format in JSON using Bonus from Problem 57\r\n %{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n %\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n %...,[73,45]]}\r\n\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\n% epsilon=200000;\r\n% hxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;\r\n%      5 50;5 5];\r\n% pxy=[10 10;10 25;10 35;15  5;15 30;20 50;30  5;30 30;30 35;30 50;\r\n%      30 55;30 65;35 45;35 60;40  5;40 20;40 30;40 45;40 60;40 80;\r\n%      45 50;45 55;50 95;55 20;55 50;55 55;60  5;60 30;60 35;60 45;\r\n%      60 60;60 80;65 45;65 60;70  5;70 50;70 55;70 65;80 30;80 35;\r\n%      80 50;90 5;90 35];\r\n% mseg=[23 32;32 20;20 23;32 38;38 12;12 20;12 6;38 41;11 10;10 13;13 18;18 21;21 22;22 19;19 14;14 11;34 37;37 36;36 33;33 30;30 25;25 26;26 31;31 34;6 3;43 41;41 36;25 21;10 6;7 4;4 1;1 2;2 5;5 8;15 16;16 17;17 28;28 24;24 27;27 15;16 24;42 39;39 35;8 9;28 29;39 40;43 40;40 29;29 9;9 3];\r\n%Cody mseg cleaned and sorted\r\n%mseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8 9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\n  \r\n%  hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%  pause(0.1);\r\n  \r\n  pstatus=zeros(np,1); %Status 0/1/2/3=Final, nseg attached, \r\n  pstatus(p2hEdge,1)=3; % Assign as Fixed node status\r\n  \r\n  %Find all nodes outside hole and randomly place inside\r\n  % should prioitize these but not implemented.\r\n  \r\n  %Find all segments with issues\r\n  %Grab all nodes of problem segments\r\n  %remove nodes that are pstatus==3\r\n  nodechk=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\n  %Find all nodes that are outside hole and randomly place in-hole\r\n  for i=find(~nodechk)'\r\n   npxy(i,:)=[0 0]; %    *****  Line changed from working program  *****\r\n  end\r\n  \r\n  ztic=tic; % while timer\r\n  iter=0; % purely informational\r\n  badnodes=zeros(1,np);\r\n  %Hole intersect check not required for Problem 4 with given Starts\r\n  while toc(ztic)\u003c10 % repeat until anneal solves, should be quick usually \u003c 1.2s\r\n   iter=iter+1; %Tracking number of anneal iterations for info only\r\n   segchk=ones(nseg,1);\r\n   for i=1:nseg % Find bad segments to identify nodes to jiggle\r\n    segchk(i)=prod(msegMM(i,:)-dist2(npxy(mseg(i,1),:),npxy(mseg(i,2),:)) )\u003e0;\r\n   end\r\n   segnodes=mseg(find(segchk),:);\r\n   badnodes(segnodes(:))=1;\r\n   badnodes(pstatus(:,1)==3)=0; %Remove placed nodes from bad list\r\n  \r\n   if nnz(badnodes)==0  % If no badnodes then problem solved\r\n%    hplot3(hxy,npxy,mseg,nseg,4,msegMM); % hplot3 exists below for out of cody usage\r\n   \r\n    vLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); %Method to evaluate GLOBALIST function\r\n    vLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2);\r\n    ET=epsilon*nseg/1e6;\r\n    ETseg=[[1:nseg]' mseg vLB2 vLN2 vLN2./vLB2 abs(vLN2./vLB2-1)]; % Info table\r\n%    fprintf('%2i %2i %2i  %4i  %4i   %.3f   %.3f\\n',ETseg')\r\n    fprintf('Cody ET Performance: ET Lim:%.3f  Current ET:%.3f\\n',ET,sum(ETseg(:,end)));\r\n   \r\n    fprintf('Solution found in %i iterations,  %.1fs\\n',iter,toc(ztic));\r\n   \r\n%    pause(0.1);\r\n    return; % No bad nodes , return the solution has been found\r\n   end\r\n  \r\n   setbn=find(badnodes);\r\n   %Perfom jiggle on randomized bad nodes\r\n   for jptr= 0                          % *****  Line changed from working program  *****\r\n    jxy=npxy(jptr,:);\r\n   \r\n    jsegs=[find(mseg(:,1)==jptr);find(mseg(:,2)==jptr)];\r\n    Ljsegs=size(jsegs,1);\r\n    jsegnodes=sum(mseg(jsegs,:)-jptr,2)+jptr;\r\n    jsegxy=npxy(jsegnodes,:);\r\n    \r\n   vjsegs=jsegs*0; %1 is valid\r\n   for i=1:Ljsegs\r\n    vjsegs(i)=prod(msegMM(jsegs(i),:)-sum((jxy-jsegxy(i,:)).^2))\u003c=0;\r\n   end\r\n   if nnz(vjsegs==0)==0\r\n    continue; % can this be reached?  if outside hole placement occurs to good/good\r\n   end % ALL Valid\r\n   \r\n   for i=1:Ljsegs\r\n    if vjsegs(i),continue;end % original length okay \r\n    \r\n     subiter=0; %Break out of jiggle inf loop\r\n    if sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2) %too long\r\n     %Perform rand pick of 3 moves until no longer too long\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2)\r\n    % Create quadrant directed approach jiggle\r\n      deltaxy=-sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0];\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop\r\n       deltaxy(:,1)=[0 0 0];  % *****  Line changed from working program  *****\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 0 0];  % *****  Line changed from working program  *****\r\n      end\r\n      \r\n      % add a random directed deltaxy\r\n      tjxy=jxy+[0 0];          % *****  Line changed from working program  *****\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %Banging thru wall\r\n       subiter=subiter+1; % Break out of infinite loop       \r\n       if subiter\u003e10\r\n        subiter=0;\r\n        %Place node at random in-hole\r\n        jxy=[0 0];           %  *****  Line changed from working program  *****\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1); \r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);\r\n     end % while too long\r\n     \r\n    else % must be too short, Push away\r\n     %Perform rand pick of 3 moves until no longer too short\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003cmsegMM(jsegs(i),1)\r\n      deltaxy=sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0]; %mov deltas\r\n    % Create quadrant directed push away jiggle\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop, no 0 0 move\r\n       deltaxy(:,1)=[0 -1 1];\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 -1 1];\r\n      end\r\n      \r\n      tjxy=jxy+deltaxy(randi(3),:); % Randomize selection\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %check jiggle goes outside hole\r\n       subiter=subiter+1; % Break out of locked position\r\n       %Pushing a node into a corner can create inf loop when\r\n       %using directed quadrant push\r\n       if subiter\u003e10\r\n        subiter=0;\r\n        jxy=dxy(phdxy(randi(Lphdxy)),:);  %Place node at random in-hole\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1);\r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);    \r\n     end % while too short\r\n    \r\n    end %  if Long or Short msegMM\r\n    break; %perform jiggle on only first Lseg  (need to randomize?)\r\n   end % i Ljsegs\r\n  \r\n  end % jptr\r\n   badnodes=badnodes*0; % reset badnodes   \r\n%      hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%      pause(0.1);\r\n  end % while 1\r\n  \r\nend %Solve_ICFP004\r\n\r\nfunction d2=dist2(a,b)\r\n% distance squared a-matrix to b-vector \r\n d2=sum((a-b).^2,2);\r\nend %dist2\r\n\r\nfunction msegMM=calc_msegMM(pxy,mseg,epsilon,nseg)\r\n%determine Min and Max integer value of allowed length squared for each segment\r\n%abs(Lsqr(npxy,seg(i))/Lsqr(pxy,seg(i))-1)\u003c= epsilon/1000000.\r\n%mseg has indices of connected vertices [nseg,2].  The nseg may exceed number of vertices.\r\n msegMM=zeros(nseg,2);\r\n for i=1:nseg\r\n  Lseg=sum((pxy(mseg(i,1),:)-pxy(mseg(i,2),:)).^2);\r\n  delta=floor(epsilon*Lseg/1000000);\r\n  msegMM(i,:)=[-delta delta]+Lseg;\r\n end\r\nend % calc_msegMM\r\n\r\n% function [epsilon,hxy,pxy,mseg]=read_problem(pid)\r\n%  path='D:\\Users\\oglraz\\Documents\\MATLAB\\ICFP\\2021_Hole\\all_problems';\r\n%  fid=fopen([path '\\' num2str(pid) '.problem'],'r');\r\n%   pstr=fgetl(fid);\r\n%  fclose(fid)\r\n%  \r\n%  Lpstr=length(pstr);\r\n%  holidx=findstr('\"hole\":[[',pstr); %starting location match\r\n%  epsidx=findstr('\"epsilon\":',pstr);\r\n%  figidx=findstr(',\"figure\"',pstr);\r\n%  edgidx=findstr('\"edges\":[[',pstr);\r\n%  veridx=findstr('\"vertices\":[[',pstr);\r\n%  epsilon=str2num(pstr(epsidx+10:figidx-1));\r\n%  \r\n%  hxy=reshape(str2num(pstr(holidx+8:epsidx-3)),2,[])';\r\n%  hxy=[hxy;hxy(1,:)]; %repeat row1 to close path\r\n%  \r\n%  pxy=reshape(str2num(pstr(veridx+12:Lpstr-3)),2,[])';\r\n%  \r\n%  mseg=reshape(str2num(pstr(edgidx+9:veridx-3)),2,[])'+1;\r\n% end % read_problem\r\n\r\n%Problem 4 JSON\r\n %{\"bonuses\":[{\"bonus\":\"BREAK_A_LEG\",\"problem\":63,\"position\":[95,26]},\r\n %{\"bonus\":\"BREAK_A_LEG\",\"problem\":92,\"position\":[5,32]}],\r\n %\"hole\":[[5,5],[35,15],[65,15],[95,5],[95,50],[70,70],[70,90],[50,95],\r\n %[30,90],[30,70],[5,50]],\"epsilon\":200000,\"figure\":{\r\n %\"edges\":[[22,31],[31,19],[19,22],[31,37],[37,11],[11,19],[11,5],\r\n %[37,40],[10,9],[9,12],[12,17],[17,20],[20,21],[21,18],[18,13],\r\n %[13,10],[33,36],[36,35],[35,32],[32,29],[29,24],[24,25],[25,30],\r\n %[30,33],[5,2],[42,40],[40,35],[24,20],[9,5],[6,3],[3,0],[0,1],[1,4],\r\n %[4,7],[14,15],[15,16],[16,27],[27,23],[23,26],[26,14],[15,23],[41,38],\r\n %[38,34],[7,8],[27,28],[38,39],[42,39],[39,28],[28,8],[8,2]],\r\n %\"vertices\":[[10,10],[10,25],[10,35],[15,5],[15,30],[20,50],[30,5],\r\n %[30,30],[30,35],[30,50],[30,55],[30,65],[35,45],[35,60],[40,5],[40,20],\r\n %[40,30],[40,45],[40,60],[40,80],[45,50],[45,55],[50,95],[55,20],\r\n %[55,50],[55,55],[60,5],[60,30],[60,35],[60,45],[60,60],[60,80],\r\n %[65,45],[65,60],[70,5],[70,50],[70,55],[70,65],[80,30],[80,35],\r\n %[80,50],[90,5],[90,35]]}}\r\n\r\n% function write_bonus_submission(npxy,pid,bonus_type,bonus_prob)\r\n%  fname=['Solution_' num2str(pid) '_' datestr(now,'yyyymmdd_HHMMSS') '.txt'];\r\n%  %fn=['zH' datestr(now,'yyyymmdd_HHMMSS') '.html'];\r\n%  fid=fopen(fname,'wt'); % t for notepad editing\r\n%  \r\n%  %Create ICFP submission with a bonus\r\n%  fprintf('{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf(fid,'{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf('\"vertices\": [');\r\n%  fprintf(fid,'\"vertices\": [');\r\n%  for i=1:size(npxy,1)-1 \r\n%   fprintf('[%i,%i],',npxy(i,:));\r\n%   fprintf(fid,'[%i,%i],',npxy(i,:));\r\n%  end \r\n%  fprintf('[%i,%i]]}\\n',npxy(end,:));\r\n%  fprintf(fid,'[%i,%i]]}\\n',npxy(end,:));\r\n%  fclose(fid);\r\n% end %write_submission_bonus\r\n% \r\n% \r\n% \r\n% function hplot3(vxy,qxy,mseg,Lmseg,id,segMM)\r\n%  segMNM=[segMM(:,1) segMM(:,1)+segMM(:,2) segMM(:,2)];\r\n%  [in] = inpolygon(qxy(:,1),qxy(:,2),vxy(:,1),vxy(:,2)); % inside or on edge\r\n%  figure(id)\r\n%   plot(vxy(:,1),vxy(:,2),'k.-') % hole polygon\r\n%   hold on\r\n%   plot(qxy(in,1),qxy(in,2),'b*') % points inside\r\n%   plot(qxy(~in,1),qxy(~in,2),'ro') % points outside\r\n%   for i=1:size(qxy,1) %length(xq)\r\n%    text(qxy(i,1)+.75,qxy(i,2)-1.5,num2str(i));\r\n%   end\r\n%   \r\n%   for i=1:Lmseg\r\n%    d2seg=(qxy(mseg(i,1),1)-qxy(mseg(i,2),1))^2+(qxy(mseg(i,1),2)-qxy(mseg(i,2),2))^2;\r\n%    if d2seg\u003csegMNM(i,1)\r\n%      plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'b-') % segment too short\r\n%    elseif d2seg\u003esegMNM(i,3)\r\n%      plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'r-') % segment too long\r\n%    else\r\n%     plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'g-') \r\n%    end\r\n%    text(sum(qxy(mseg(i,:),1))/2,sum(qxy(mseg(i,:),2))/2,num2str(i),'Color','b');\r\n%   end\r\n%   \r\n%   %o+*.x_|sd^v\u003e\u003cph\r\n%   %colors ymcrgbwk\r\n%   \r\n%   axis tight\r\n%   axis ij\r\n%   hold off\r\n%end % hplot3\r\n\r\n","test_suite":"%%\r\n% ICFP Problem  4  \r\n% Assume that globalist bonus is enabled so the strict edge lengths are not required\r\n%\r\n%Problem 4 Solution format in JSON using Bonus from Problem 57\r\n%{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n%\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n%...,[73,45]]}\r\ntic\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\nepsilon=200000;\r\nhxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;5 50;5 5];\r\npxy=[10 10;10 25;10 35;15 5;15 30;20 50;30 5;30 30;30 35;30 50;30 55;30 65;35 45;35 60;40 5;40 20;40 30;40 45;40 60;40 80;45 50;45 55;50 95;55 20;55 50;55 55;60 5;60 30;60 35;60 45;60 60;60 80;65 45;65 60;70 5;70 50;70 55;70 65;80 30;80 35;80 50;90 5;90 35];\r\nmseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8  9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\nnseg=size(mseg,1);\r\nnpxy=Solve_ICFP004(hxy,pxy,mseg,epsilon);\r\nvalid=isequal(npxy,round(npxy));\r\nvalid=valid*isequal(size(npxy),size(pxy));\r\nfor i=1:size(hxy,1) % verify all holes covered\r\n valid=valid*(min(sum(abs(npxy-hxy(i,:)),2))==0);\r\nend\r\nin=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\nvalid=valid*(nnz(in==0)==0);\r\n\r\nvLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); % Base seg d2\r\nvLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2); % New seg d2\r\nET=epsilon*nseg/1e6; %Total allowed stretch  10.00\r\nETseg=abs(vLN2./vLB2-1);\r\nETP=sum(ETseg);\r\nvalid=valid*(ETP\u003c=ET);\r\n\r\nfprintf('ET Lim:%.3f  Current ET:%.3f\\n',ET,ETP);\r\nfprintf('%i %i\\n',npxy');\r\ntoc\r\nassert(isequal(valid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2022-09-13T15:37:03.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-08-05T03:23:08.000Z","updated_at":"2022-09-13T15:37:03.000Z","published_at":"2021-08-05T04:53:23.000Z","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 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.icfpconference.org/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e held its annual 3-day contest in July 2021 with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eHole-In-Wall\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Contest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\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:t\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 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\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*epsilon/1000000.  Lsqr is length squared.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\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/52308\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eScore\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003enpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)\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\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/register\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRegister Team\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Anyone can select \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/problems\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblems Page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\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=\\\"358\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"776\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"top\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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With Spaces - Convert Some Messy Data Into A Clean Array","description":"I have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\r\nI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\r\n'- 6.496' =\u003e -6.496\r\n'-10.430' =\u003e -10.430\r\n'+++++++' =\u003e NaN\r\n'+11.664' =\u003e 11.664\r\nOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!","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: 215.795px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.989px 107.898px; transform-origin: 406.996px 107.898px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; 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; \"\u003e\u003cspan style=\"\"\u003eI have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; 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; \"\u003e\u003cspan style=\"\"\u003eI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7614px; 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: 403.991px 40.8807px; transform-origin: 403.999px 40.8807px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; 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: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; 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: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\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-collapse: preserve; 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); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'- 6.496' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; -6.496\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; 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: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; 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: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\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-collapse: preserve; 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); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-10.430' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; -10.430\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; 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: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; 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: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\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-collapse: preserve; 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); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'+++++++' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; NaN\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; 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: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; 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: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\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-collapse: preserve; 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); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'+11.664' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; 11.664\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; 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: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space-collapse: preserve; 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=\"\"\u003eOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = str2numBetter(x)\r\n  y = str2num(cell2mat(x));\r\nend","test_suite":"%%\r\n% This first test should cover every possible case.\r\nx = {'- 6.496';'-10.430';'- 0.493';'+++++++';'+ 6.949';'- 5.368';'+13.214';'+11.664'};\r\nfinalData = [-6.496;-10.430;-0.493';NaN;+6.949;-5.368;+13.214;+11.664];\r\n\r\nassert(isequaln(str2numBetter(x),finalData))\r\n\r\n%%\r\nrng shuffle\r\nn = 30000; % Something small to start you off with.\r\nrNums=10*rand([n 1]);\r\nrNums(rNums\u003e9.99)=NaN;\r\nstr=char(sprintfc('%.3f', rNums, false));\r\nneg=2*randi([0 1],[n 1]);\r\ntens=randi([0 1],[n 1]);\r\nstr = [char(neg+'+') char(17*tens+' ') str];\r\nplus=('+');\r\nstr(isnan(rNums),:)=plus(ones(nnz(isnan(rNums)),7));\r\nx=mat2cell(str,ones(1,n),7);\r\ntic; y=str2numBetter(x); toc % The code I wrote took 0.005872 seconds to run\r\nfinalData = round(-1*(neg-1).*(rNums+10*tens),3);\r\n\r\nassert(isequaln(y,finalData))\r\n\r\n%%\r\nrng shuffle\r\nn = 12000000; % Yes! I have this much data!!!\r\nrNums=10*rand([n 1]);\r\nrNums(rNums\u003e9.99)=NaN; % Real world values overflow\r\nstr=char(sprintfc('%.3f', rNums, false)); % This is what num2str uses\r\nneg=2*randi([0 1],[n 1]); % Some numbers will be random\r\ntens=randi([0 1],[n 1]); % Some numbers will be \u003e10, but in an odd format \r\nstr = [char(neg+'+') char(17*tens+' ') str]; % Add the weird 10's place and multiply by negatives\r\nplus=('+');\r\nstr(isnan(rNums),:)=plus(ones(nnz(isnan(rNums)),7)); % Replace NaN with something more annoying\r\nx=mat2cell(str,ones(1,n),7); % The data is in cells, not an array!\r\ntic; y=str2numBetter(x); toc % The code I wrote takes about 3.5 seconds to run, with a size of 130.\r\nfinalData = round(-1*(neg-1).*(rNums+10*tens),3);\r\n\r\nassert(isequaln(y,finalData))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3115675,"edited_by":3115675,"edited_at":"2023-08-08T14:27:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2023-08-08T14:27:45.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-07T21:52:56.000Z","updated_at":"2023-08-08T14:27:45.000Z","published_at":"2023-08-07T22:00:31.000Z","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 have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still 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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\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['- 6.496' =\u003e -6.496\\n'-10.430' =\u003e -10.430\\n'+++++++' =\u003e NaN\\n'+11.664' =\u003e 11.664]]\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\u003eOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!\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":1237,"title":"It's race time! Write a faster function than the test suite call of unique().","description":"Write a function to get unique elements of a vector faster than unique()! Input will be a vector (of integers or floating point numbers) of any size. The order of the returned vector is unimportant. \r\n\r\nExample:\r\nInput: x = [1 1 2 2 3 3];\r\nOutput: [1 2 3];\r\n\r\nInput: x = [0.1 3.1 2.1 2.0 3.1];\r\nOutput: [0.1 3.1 2.1 2.0]; % or any order","description_html":"\u003cp\u003eWrite a function to get unique elements of a vector faster than unique()! Input will be a vector (of integers or floating point numbers) of any size. The order of the returned vector is unimportant.\u003c/p\u003e\u003cp\u003eExample:\r\nInput: x = [1 1 2 2 3 3];\r\nOutput: [1 2 3];\u003c/p\u003e\u003cp\u003eInput: x = [0.1 3.1 2.1 2.0 3.1];\r\nOutput: [0.1 3.1 2.1 2.0]; % or any order\u003c/p\u003e","function_template":"function y = my_unique(x)\r\n   y = x;\r\nend","test_suite":"%%\r\nx = rand(10000, 1);\r\nz = rand(10000, 1);\r\nx = vertcat(x, z);\r\ntic\r\ny_correct = unique(x);\r\nt_unique = toc\r\n\r\ntic\r\ny_myunique = my_unique(x);\r\nt_myunique = toc\r\n\r\nassert(isequal(sort(my_unique(x)),y_correct) \u0026\u0026 t_unique \u003e t_myunique)\r\n\r\n%%\r\nx = rand(50000, 1);\r\nz = rand(50000, 1);\r\nx = vertcat(x, z);\r\n\r\ntic\r\ny_correct = unique(x);\r\nt_unique = toc\r\n\r\ntic\r\ny_my_unique = my_unique(x);\r\nt_my_unique = toc\r\n\r\nassert(isequal(sort(my_unique(x)),y_correct) \u0026\u0026 t_unique \u003e t_my_unique)\r\n\r\n%%\r\nx = [1; 2; 3; 4; 2; 3; 4; 5;];\r\n\r\ntic\r\ny_correct = unique(x);\r\nt_unique = toc\r\n\r\ntic\r\ny_my_unique = my_unique(x);\r\nt_my_unique = toc\r\n\r\nassert(isequal(sort(my_unique(x)),y_correct) \u0026\u0026 t_unique \u003e t_my_unique)\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":9,"created_by":10338,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":31,"test_suite_updated_at":"2013-02-03T20:33:40.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-01T03:36:13.000Z","updated_at":"2025-09-07T01:43:50.000Z","published_at":"2013-02-01T03:36:13.000Z","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\u003eWrite a function to get unique elements of a vector faster than unique()! Input will be a vector (of integers or floating point numbers) of any size. The order of the returned vector is unimportant.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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: Input: x = [1 1 2 2 3 3]; Output: [1 2 3];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 = [0.1 3.1 2.1 2.0 3.1]; Output: [0.1 3.1 2.1 2.0]; % or any order\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":1940,"title":"Decimation - Optimized for speed","description":"This problem is similar to http://www.mathworks.com/matlabcentral/cody/problems/1092-decimation, only this time the score will be based on how quickly you can determine which person will survive.\r\n\r\nThe sample sizes (num_prisoners) and number of prisoners killed (num_killed) will be larger than in the original problem, but other than that the problem sets are identical.","description_html":"\u003cp\u003eThis problem is similar to \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\"\u003ehttp://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\u003c/a\u003e, only this time the score will be based on how quickly you can determine which person will survive.\u003c/p\u003e\u003cp\u003eThe sample sizes (num_prisoners) and number of prisoners killed (num_killed) will be larger than in the original problem, but other than that the problem sets are identical.\u003c/p\u003e","function_template":"function y = speed_decimation(num_prisoners, num_killed)\r\n  y = num_prisoners+num_killed;\r\nend","test_suite":"%%\r\nassert(isequal(speed_decimation(10,3),4))\r\n%%\r\nassert(isequal(speed_decimation(1024,3),676))\r\n%%\r\nassert(isequal(speed_decimation(2012,50),543))\r\n%%\r\nassert(isequal(speed_decimation(30,5),3))\r\n%%\r\nassert(isequal(speed_decimation(10,10),8))\r\n%%\r\nassert(isequal(speed_decimation(2048,2),1))\r\n%%\r\nassert(isequal(speed_decimation(2048,1024),1773))\r\n%%\r\nt_in=clock;\r\nj=1:50;\r\nv=arrayfun(@(x) speed_decimation(100000,x),j);\r\ncorrect=[100000 68929 92620 32942 40333 54212 27152 67341 42610 77328 82991 13252 91717 6850 45758 71249 38339 86953 63331 66903 72606 83990 87828 46101 99979 47141 16871 60389 51549 76409 42868 78390 79590 27573 95835 53636 36954 39891 45943 63811 71589 70886 49313 4069 93694 96031 20739 41403 93714 60023];\r\nassert(all(isequal(v,correct)));\r\nt_out=etime(clock,t_in)*1000;\r\nfprintf('Actual Time = %.0f msec\\n',t_out)\r\nv=[100000:100000:1000000];\r\nv=arrayfun(@(x) speed_decimation(x,17),v);\r\ncorrect=[38339 162859 151602 99465 462955 559860 337009 546467 563784 364193];\r\nassert(all(isequal(v,correct)));\r\nt_out=etime(clock,t_in)*1000;\r\nfprintf('Actual Time = %.0f msec\\n',t_out)\r\nassert(isequal(speed_decimation(2^20,2^9),210856));\r\nt_out=etime(clock,t_in)*1000;\r\nt2=min(100000,t_out);\r\nfprintf('Actual Time = %.0f msec\\n',t_out)\r\nfeval(@assignin,'caller','score',floor(t2));","published":true,"deleted":false,"likes_count":9,"comments_count":2,"created_by":1615,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":224,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":13,"created_at":"2013-10-16T19:58:38.000Z","updated_at":"2026-04-08T14:54:11.000Z","published_at":"2013-10-16T19:58:38.000Z","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 similar 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/1092-decimation\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, only this time the score will be based on how quickly you can determine which person will survive.\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 sample sizes (num_prisoners) and number of prisoners killed (num_killed) will be larger than in the original problem, but other than that the problem sets are identical.\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":44448,"title":"Project Euler: Problem 14 Longest Collatz sequence","description":"This problem is a hard version of \"Problem 42673. Longest Collatz Sequence\", because of time limits.\r\n\u003chttps://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\u003e\r\n\r\nThe following iterative sequence is defined for the set of positive integers:\r\n\r\nn → n/2 (n is even)\r\nn → 3n + 1 (n is odd)\r\n\r\nUsing the rule above and starting with 13, we generate the following sequence:\r\n\r\n13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1\r\nIt can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.\r\n\r\nWhich starting number, no more than N, produces the longest chain, and how long?\r\nDon't cheat!","description_html":"\u003cp\u003eThis problem is a hard version of \"Problem 42673. Longest Collatz Sequence\", because of time limits. \u003ca href = \"https://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\"\u003ehttps://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\u003c/a\u003e\u003c/p\u003e\u003cp\u003eThe following iterative sequence is defined for the set of positive integers:\u003c/p\u003e\u003cp\u003en → n/2 (n is even)\r\nn → 3n + 1 (n is odd)\u003c/p\u003e\u003cp\u003eUsing the rule above and starting with 13, we generate the following sequence:\u003c/p\u003e\u003cp\u003e13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1\r\nIt can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.\u003c/p\u003e\u003cp\u003eWhich starting number, no more than N, produces the longest chain, and how long?\r\nDon't cheat!\u003c/p\u003e","function_template":"function [num, len] = euler014(N)\r\n  \r\nend","test_suite":"%%\r\nfiletext = fileread('euler014.m');\r\nassert(isempty(strfind(filetext, 'tic')),'tic forbidden');\r\nassert(isempty(strfind(filetext, 'toc')),'toc forbidden');\r\nassert(isempty(strfind(filetext, 'pause')),'pause forbidden');\r\n\r\n%%\r\nN = 1234321;\r\nnum_correct = 1117065;\r\nlen_correct = 528;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 10;\r\nnum_correct = 9;\r\nlen_correct = 20;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 100;\r\nnum_correct = 97;\r\nlen_correct = 119;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1000;\r\nnum_correct = 871;\r\nlen_correct = 179;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1e4;\r\nnum_correct = 6171;\r\nlen_correct = 262;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1e5;\r\nnum_correct = 77031;\r\nlen_correct = 351;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1e6;\r\nnum_correct = 837799;\r\nlen_correct = 525;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1e7;\r\nnum_correct = 8400511;\r\nlen_correct = 686;\r\ntic;\r\n[num, len] = euler014(N);\r\nt = toc;\r\nt\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\nassert(t \u003c 1.5);\r\nassert(t \u003e 0.001);\r\n\r\n%%\r\nN = 1e8;\r\nnum_correct = 63728127;\r\nlen_correct = 950;\r\ntic;\r\n[num, len] = euler014(N);\r\nt = toc;\r\nt\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\nassert(t \u003c 15);\r\nassert(t \u003e 1);\r\n\r\n%%\r\nN = randi([7e7, 1e8]);\r\nnum_correct = 63728127;\r\nlen_correct = 950;\r\ntic;\r\n[num, len] = euler014(N);\r\nt = toc;\r\nt\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\nassert(t \u003c 15);\r\nassert(t \u003e 1);\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":9,"created_by":8269,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":"2018-10-10T02:15:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-12-12T01:06:38.000Z","updated_at":"2018-10-26T04:19:54.000Z","published_at":"2017-12-12T01:44:33.000Z","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\",\"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 a hard version of \\\"Problem 42673. Longest Collatz Sequence\\\", because of time limits.\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://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\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 following iterative sequence is defined for the set of positive 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en → n/2 (n is even) n → 3n + 1 (n is odd)\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\u003eUsing the rule above and starting with 13, we generate 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=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 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\u003eWhich starting number, no more than N, produces the longest chain, and how long? Don't cheat!\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":937,"title":"Rubik's Mini Cube: Solve Randomized Cube in 11 Moves or Less; Score is by Time (msec)","description":"The Challenge is to Near or Optimally Solve a thoroughly scrambled Mini-Rubik's Cube(2x2x2).\r\n\r\nAny Mini-Cube can be solved in 11 or fewer Face Moves. There are only 3,674,160 unique cube positions, with only 2344 requiring the fulll 11 moves. Cube moves do not count.\r\n\r\nThe Performance metric is cumulative Time to Solve 500 cubes (msec).\r\n\r\nA standard Mini-Rubik's Cube is shown in 3-D and also unfolded to identify the specific Tile-face/Vector numbering.\r\n\r\nThe faces are White-Up / Red-Left / Blue-Front / Orange-Right / Yellow-Down / Green-Back (ULFRDB)\r\n\r\nMoves are denoted as F for clockwise rotation of the Front face. F'(or FP) is CCW and F2 is F twice.  The provided function r_new=rubick_rot_mini(mov,r) implements moves 1-18: UFDLBRU'F'D'L'B'R'U2F2D2L2B2R2.\r\n\r\n\r\n\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/minicube2.png\u003e\u003e\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/miniCube_Map24_200.png\u003e\u003e\r\n\r\n\u003c\u003chttp://mathworks.com/matlabcentral/images/surf.gif\u003e\u003e\r\n\r\n\r\n  \r\n  \r\n  Input: (rubik)\r\n  \r\n  rubik: row vector of size 24\r\n  (The cube started [L=0,U=1,F=2,D=3,B=4,R=5] and then receives forty face moves.\r\n\r\n  Output: move_vec (Numeric of moves to solve)\r\n   move_vec:values 1:27 for UFDLBR U'F'D'L'B'R' U2F2D2L2B2R2 XYZX'Y'Z'X2Y2Z2\r\n\r\n* Example:\r\n* If the cube was randomized by [1 9] UD', one solution is [3 7]  which are the complements in reverse order. \r\n* Scoring is Time in msec to solve 500 cubes\r\n* Cube Moves X, Y, and Z do not constitute a move but are needed in the vector \r\n* A string to numeric value function is provided in the template\r\n* Verifications will be by executing your move vector against the provided Rubik and counting number of face moves.\r\n\r\nThe function rubik_rot_mini(mov,r) is provided in the template. Other functions are provided to re-orient the cube in Cody Challenge 931, Rubik's Mini-Cube.\r\n\r\n\r\nThe Challenge \u003chttp://www.mathworks.com/matlabcentral/cody/problems/931-rubik-s-mini-cube-solve-randomized-mini-cube-score-moves.html Challenge 931 Rubik's Mini-Cube\u003e contains a 3D Mini-Cube Viewer for program development.\r\n\r\n\r\n* \r\n* \u003chttp://kociemba.org/cube.htm Cube Theory: 20-moves Any Cube\u003e \r\n* \u003chttp://peter.stillhq.com/jasmine/rubikscubesolution.html General Cube Info and Middle Layer\u003e\r\n* \u003chttp://www.speedcubing.com/final_layer_print.html SpeedCube Bottom Sequences\u003e\r\n* The site \u003chttp://www.speedcubing.com/CubeSolver/MiniCubeSolver.html MiniCube Solver\u003e claims an outstanding 10 minutes to solve a randomized Mini-Cube. Matlab can achieve any Mini-Cube solution in \u003c 497 usec, independent of moves on an i5/16GB machine.\r\n\r\n\r\n(Note: Mini-Cube can use the full cube moves and ignore edge effects)\r\n\r\nComing Soon: Matlab Tetris\r\n","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: 1316.98px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 658.5px; transform-origin: 407px 658.5px; 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: 294.65px 7.91667px; transform-origin: 294.65px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Challenge is to Near or Optimally Solve a thoroughly scrambled Mini-Rubik's Cube(2x2x2).\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: 377.2px 7.91667px; transform-origin: 377.2px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAny Mini-Cube can be solved in 11 or fewer Face Moves. There are only 3,674,160 unique cube positions, with only 2344 requiring the fulll 11 moves. Cube moves do not count.\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: 221.45px 7.91667px; transform-origin: 221.45px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Performance metric is cumulative Time to Solve 500 cubes (msec).\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: 355.467px 7.91667px; transform-origin: 355.467px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA standard Mini-Rubik's Cube is shown in 3-D and also unfolded to identify the specific Tile-face/Vector numbering.\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: 316.733px 7.91667px; transform-origin: 316.733px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe faces are White-Up / Red-Left / Blue-Front / Orange-Right / Yellow-Down / Green-Back (ULFRDB)\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: 380.533px 7.91667px; transform-origin: 380.533px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMoves are denoted as F for clockwise rotation of the Front face. F'(or FP) is CCW and F2 is F twice. The provided function r_new=rubick_rot_mini(mov,r) implements moves 1-18: UFDLBRU'F'D'L'B'R'U2F2D2L2B2R2.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 138.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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 69.4667px; text-align: center; transform-origin: 384px 69.4667px; 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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ArrgDLogC7IgCLVgAY3gCT4ADYIAlSBIWB3BARmgBZIgAGNglRCAGGjZBGpJHYpgAWPQCGPwliugBV2wYM9hA2yJHpJgWu4BH3EgCUkAl2QQB03ACgrwHKCVGK7ZBmMQBPqQmj6pAGfABWnpBuAQmZZnVMcVAIepAG0gAQIgC1xwBpiwAuaxCbLQGFzAlt//xQZHoAhu4JFBsJKPEAtmUB02QFtjkATsMZv0RR/2gV8CkALeIAH34Q1moAAMsGKyEAdcIAwCIAAS4A1xgpXmJZkdsYXokA7lcAwZkAeLcCUQAAjSlQJ/gAVvkgIwEmXZlQF1YF4pkgJ5kBeKUZUSsAJ7sAg8BgjyQA9YIgt5YAaplQf0IZf08Qf96QS5qWD6sCBOkAH3kQKS4AdsAAjW8CYvsggOyhGvEEjToCyucip5gF2LQAZtQCZkACcxMiNHQGVgkJsp4AWY4CoCkFoNwAmKsAkMYGFKwmNMMANgAAZt+iqxUmQ7wCd0OgP6BAZVsgMXtgeBwARDIABeYA2L/5ArM7BhUboR2MAAJ8YzcuAqgwIrG5YoizJnqNBpgJACYypk+4AtbbBfgYAKvOAHYLAFTOAHizAMZEAG+8BjW3AEicIEnCAHKLZhhXJl8nAFDPAII1INKOAKnCAEVMAGbdpopjBnJgAGPSaZHKALBFAE9WA3c7YH8iAHM2AlJsAJezAKhEJoc0ZodiYEJCAKTGApnABoD3OnO8AJCbAPooICJnAKF/orRTAvJsADGUACKFAPVIACa1YNWWcre6AEVKAM0SAHQkANAksy1fBonIAtSoAsKABwJEAEPGMC9WAs1YBpe3BpDFsPQmAK5OItoZAAPFANn2pnk1Y1QzAEPP9AsQmQADRWaNhGBkUQMHvwro2WAURQBe5CLFVABSTgJzywB/WgbKh2bSQQDUBwA7z2A2eTCS3QAt9SD5wALgCHAsogayiAAolAADzAA37yA1VQBcqQBYiAbexiLpgCMrJ2ZzxwAilzAomwLfbid1RQZ9d2ti0gDkXgbwWDAplADaFAN2k7L8AWDQgHMi57AiwgcyoDBcx2A2WjuCKXLkAQDQCHM4SgD6ZbugSAM/pwB9FwAndQN9GQCIkAAiDQbkaQMvditYkAL8SQBSEwBZmgBAkAArRDbnHLAxEgAwnwtuxWbSlzNiIABPJGOxFwNiNgO9MmAlCQCyxQdycgOn//R7tdkziXUAYABzJAcwkigGwn8G6pmwFAcwcnkAWyVjWZQADfCwIk0HEnEDcRsGt3YL8+I29lIHPqFg1GYATIZgQSlwVGcMDytjSYc29VYwQEp8Bec2w5GwGuWwbz224/57oAtzojcAJLEAHVAzIhUAYyIAKt4AilMDQDRzr3dgMRkAWmkztedwkvIGu4I3LECwJRAwOpcwdpVwZi0zZbAwJG3AoR8DyCozg2/AKm0wrhaztlkA270woEcAlEHDVWVwoA1wqfAAUhkDswAAXLUzzZ8AnGQD/h8AlRsHCtIMStwAJLUAaq0zriEwKlUArhIAKpwzxLoMbiozNerG1W/9wK2QA8I6DHUwACraC+h2M8ZBwDUUDCMDAFk3MJh6MJUBAO21MKVucH33B0gzMCITB7ufB0DiQDU/AAtqAJE2AL48AA59ABj1AGIQADLAAF2cA8XqcBfjc9ABQFFxAFuVAGs6M73QsDIeB0dXd3I3AJVlcGMdB7kLDGLPDHuaAGuTAF4XABsxAC48wNynx9uuAWJAFw35NBFdAMVNcDFdAPNdB5uVAD4yAFBwBUHUEKmiACEkRAmqAJkIAHnwBFDBQFIsR7vgd8wkd8F3QByFcB3ADRsBcOkMANoNcP/sANfvcAqXABxjABNaABX5AKLFQDNHAO3QAPuxRP0ZIKn//nCAFoAA+gARhQRqmAAAXwBh1wDsvIgebAAeUAG45gQsBXAY4ABcUXRVNkz82HP25c0sTXDOADCZAwBZrgCM5nQ6D0fxXQAwUAejRE0zgkBc6HBNlgCZPwC2BIEkEFcB/gD6kQA5MERh+gSW9AevJ3C+rwDSrtCJd0Aa3Q0zkU1dUngAZAgDttgKFURzWkRlwER6EkSmWkRT3kAgxwSxyhCiLIgYBFWC8oBWJkAOPwBh7wlJgYAB0Qf5GgD9jEQyq4AT1gSc/3BjWwQ5rk2KBURmRUQ+NQ2pOkAmDk0y/oSQXwTaTkPkn4iOVwfkPEkJuoDh1ggrogBR8gBVIgg2//8HwxSIHT5IPgRE6nhErNNISuhAHedN5vEEtE6ALf9xu5tIQybYkcIYqaWIIhMQ3Q9IMGEEt8MEenJN40eINDKOA56Eqy9Abg5ACuJIcFEISVkFcRBt0P1htCJFj4TVH0BxYU4AFIMIQO7kzb5IMe0ErUlN4D7oNByIff5AAvnofmJA15hYG/wQFrMSfmAC0C8VCbGORCPuQjuFy6UAwLhRH/YA7qcCHL8OQUEOVSPuVUXuVWfuVYnuVavuVZ/uQfURFKvg1DZYJEXuZmrolM3olKbg4SeeZu/uYUZQ5qLhCEFVEbsYWXqAugwFGp0RJuwAgfZQVZVVIn1RPxOI8s/1WPL/UEnrACMkULNAUANoUDWBEJXrENFkkWGxhRimgR0ULm5SCKoA4WBwALYZUZAGBZMKEDeqAFJdCOswiPKkWPaHWPjHAPhQDpMtWPcEALGFVTdHAL5jDUuNSM5RAhx/AMFlEh25iQHcCQH5FLUqiQC7ALHDANoFALmPEEndASscgIK1AC7HgTsW7oKIWLurjo+MhWTZAVOMDrT/AU7z4IdOAMC3CQwLAA9JTvC9kWwREh07AMETlEC9DhHYENK2AICiALOdAJwsAKdiCLtiUByFAT5F7oeuALOSCP6K7oafUETPEXgjAGgkALVuCPRwEAv4gDoJABimQJwhEhxv81RBSpSM6wAnbQBPhwD50wBlwQVXYAALBQCFagDYUgC47pCQDQCT2vAGMgDPgAVvdgVjqw8eq+Vp2RWTYgCCsQlHQV72ugB7BABhsOUckwhRsRqaEuHHAREgdQF1pgASsQCwUKC4wAGmkwAJ6QBkv/BI3ABhAADpHxkutoHutYAj9ZCL7w8Vh/l6DRBUEgAT5RCJ5QUisgBjqQAXbuDJHAACovCIzQB6Cg9sCAhOXQU9OAmPhQ8QqgBWngBrOhioIQi3ZABp2ABtoAAImRlo5xHlrQBuSxCrLQCDbJFFn/F6LxkXWwAp2QA2jACMTfB36gCqHOAKAgBmnAF2IgBvf/AABqP1i/YQ66wAqKoADcsQcr4AY57x90kAYrcPeXFQRbfw90IAtagAyguZa+f5rzCRDeJORbAcCTjQFpfLip1cTCwyZu8HVqVALcvUb6/MRqosBGnTSwGjVJA0BduXIHKBz69wzlS5gL3MGEqSuWtwxB6pARsGKABS5NhNngMsaoHTtp3HDZtMKGLEFjSlg4E+SnDS1tHkqAYMZbkjh5zLTJIEuLmxIOIdYB2sTsGVmwgpRQtGJMnTOsliLDdzLlypY0YarzS5OGLEVx2tCRpSBfEHAQhVmwEWTTGQkKPH4k47HRIzeUWW2irAXC1iBewbb52iaIN0wKZEEYIywi/xdksSbLehSrxBhFY850CbKijWtZllCqZOlSMMpn7szR5MdgU5wUefwI45KvCTiirCbnJX1GUWcbaRUpOANghQVkVkuPQa1acZIkkuJ8xSRAwplVFAgiDsbGaAwCWSRgyo82zJAgDraY8ou5wJ4rJ4AADJOFDTJSwEIWCPERIIMjjoAgAwUUOWOMy87Y5Ags5mJFghLSaIIMBhBUQBhwFMHnDAskSC2F1fDTD7/+zEhilTjMSOGMOB4RAAIyNuniiDb2SyIFBbqoow5WJgTMueficWmbcn7RRYAt9mjjiDziyMAJb8IQwIk//hBgGC/yyICVM1wU5Y8UyHjvjDpkAf+TxCG2YEKUPUSRJB8IvioyPy0VEcCrFPY7Ax994jgijDhkScHDKQU4QoAwnHACAjGbewlNc7axtQNgLLFVFzKwEIUTV45YhJ4jnEihPyzyFACQLbw4woQ4zBMFz1Uy2MSGMxTYY8VHFnF0CHmY2AKQUVyZUgFJvDEDgibHIqMNAUakRxQ/UEzh2UqxcIUML7bYQgBMCIVAub9kRUmdbTjgwFZgdpmEklEyAGQGMPw4wosUJDkiBW+cAJjQPZs9ghcwjggCgjyxEIATfLiIQ55oFchgByYWYeMUJhqguIEdOJEnEJ05ASMQejg5BWc24mBgBldEEYWJIQRgA4wrWHX/wotg44Bgj1grLDMAfkaZYQ8BZhCAnjzymIGMSGcwIQNR8ug0D5Fn2AGVtY21EwxONgliGGKDyIAMJgC5+WiKwehZDlMakGcGnulx5RRHM3jEhB32YMMaQPbghOchRBnGFU5GAQMVVHjpmkzBApiGDDmuyIANQOjxvIHUOdlhBzBMuMIUKjJYBOMjqGgAahJEScFjoDkEYwswhBeFDHqGOIXmGQKhmZMhdmiAF4p3CEQAcEfRhxM5qDABkH2KKGKGnE3YwxRThDBB+x1mWN3CZfbw4wpO7GEPJhgCKuTBi93tQ2i88x39TKCMGTiLZN9DQRaOsAoBXMF7ewAEE6xB/7otHCED8MPZDK7QgAagb3fgM8Xa9oAKVzxiDzOYgRASMYorCEEZnOAENYjgu9+ZAAyA4MUeCEYh1qHkFwegAgpM4AdXyOFzTBiGKLIHhhlwggkMDET9TPCDUBRhD937Hg9C8YN4KY4JrtjDKbwwgzLswxoC8AMPpzeDIQCCdLwI4B5ySAT1EUAJeyiCElBAgh8oQQgkSEQozmgCVFwBBRkoQjWAt79Z+cEPVBDCKPzQADmYgABUIEIVqFBIcRTBBChAHyd+ZwoTGCEUQuBBBhS3DxQQQRxG2AMYUMiJLJjgUUU4wRFOMUBO6NCRoeBBEapQhR0KQQhUIAQhqUEFZf/wABE8qAcxs4ACJSiBCHvggRGIwYNwKoEKmEwJCXjACSqMwgQkqIc0eVANRZ5SmkUwYxWIQIBkNuCLoagCCk7AAzAswoyhUAJAt2iCFkSAhtQwZRRVyQkTMJSZRQiFOLJJhUTsgQSkrAIJlBGKbZIgAkA4AQqq8IMEJKAFMzWBEkxBBBKwUxmJIIAfUEANE4zCfTwY5Q/qMYpEKFKVuiRCOVFQDU6AsaAnuAEVRIECR1YjGiHgBCpiiYgfZIAaSihoMpUgPx78oApp/QEVeMADZaCAByaYqkxbkAUSZAEIQFAGCU4AAiPI1JF7SMAJWMrORNQjESaoBxH6CsYfEsH/kcr4gRFQQE6GEiERIABCC/bwA3GIgwd6BcJiO0qEBMhgBDT8QQuIkYhGKuEHJEhmJ5VRhUTogwF7CCkBqIGIFpAgATdYQgJgqtosALcMCRDBDSJgRhRkYrg3uMEJ2MkDaiTCuD+QK3B/wFsqhIKyLQArCbBahQQY4QZAyEQi1MoDwwIhC34ALWpBIIJJShYR0ShDNKpQhCxEIBE6NEI0qMED1xIjrj+IBgnuQF0gkCATSxBBerMwgktQFQgRIMAIiCvTzuZ0OWN6iR+iQYAynIC7ichCNhNxA2IAIQGJQAQxfpAJEYAgESRowV4TAIITKEMZGr7wCxL5YwrfAYzM/4zAJXJ5giXcYMYtgHFMs3CCMtyBwjAYbhZeUAriyrgMMGBBCEYg3xe8YAlrTkAmoHwJdmaiDGVgwCMIcec76+POj/BDnv3AAH3wmc5leCsB7hCBE/ghvXd9AQwyQYRo3FcEpeAxMVBwCRYImLSTlikxohEBEYjgyqEGwR0aLYNSgOAGl3hECGDQijmTAApl8MMdCKCPQGvkEexcr5t/DGQjkIAAjygDCRos3GjwNxPLVql0CZCALBghE9HIAk8JsGz+nuAEEbjDCDKB18JmIQF+1fYdTuBm4QY4AXe48onbnAg/GFvHnz7BHpabBbxKNwEE0HYCysDO5i6XwhEQ+P8SbO2HYoc7E3dw8wn8Om2/qvoOyx3BuiMAAm4DNgspPjeKM/GCiafZ3wl4wQgMHYEIxJS6Ao7AlZ8N7URsm9spRvTDHa7tE9zhiCRGyY8vMQIKg5y4NyhDBFggAj/w++cSvvgd2gyCH0dgBFmOgHOhDIJMvDzrhy51Ji5+ggSgPAKlCDAISkHyEdh6qyC/Lwug/XNDG4EFFR9BKeoOgxG8oAw/Tzs7wzH3E1wiBFPwsNkfDIJcXCACDChD2kcAAhCU4QU3EMEdStHoEYAaBHonubjbnom2Y1gGMvg55Dcv6RcYPdMhGL3JL9+KO5CZBaXIxgtaEYJLLKEVUNAyDF7/AIVLXKIVpQD4pKEgAxa0AsMjCEcuLhEFY4SAAOGAwSMIEAEZ6H0JeC+DpEtRBtZLncJmv0SaCfCCS3B5BMiHQe7XPAIYwEAG2chGLnIx61YsYfPIZ0EU6h6CRmMBGZg1EGABGCCAENC9MWuFKPADdsoFGQiBEPg7GCiDKWCBcCgDTVADNdCE+NOACSCAGDCzUpBAERiBvCO8VhDAF4iBF4C8F5CBv4NADLQ7yGuFXJA/35M/GYiCC2SBnwuHcAABTciFF/iEbEC+cPg9CLy9VtCAKXiBELiAKYCCEIgBbCgHNCkYr/k7MwuHT5iFGGSBKYgBSMCDKJiFT7iACYgB/32YBl2IAitMPgIgs3CAAigowFZ4gXBQLQLIhfx7wb9DPyGEAdYTwkNcwhWcu/qLglyIAjUQwhj8hFwohQu8QyhQAzzQACiAhAu4gEeIBlrxu7mLgQv4ggvIhSUIAQ2AQm74AFuAggkYhxqgAZSYhkfAQdWKggkMgU/wRP8Twh6EBDUoQ03wRSgIwPb7u3AYgVZohU+AgmzQgAvghlzgwCmARBlohRi4RjwAxE+8gCmEwln4xBpghnKYjnJgJw3oAW44xS+YAE3ghh7oB01whHqMAQ0Yh0c4BpjoBgsEATM0hjL8BE2cAkfghikYRg6MgjPcRDacgGqcAIrkhgmAhP8voMYvQEVvhAEoeMd+4IYznIIa0IAvqABNmIUKgIRUmIVUiIFXoIR0eImdM5hy2Mhc0ICVhIJZ6IcKmIAa6IEeeAAMkIIY6AbB6IAYoERHmIJvdIQLUINP4IYKqAANMMVwQMMp4MDEq8oJ0MmTBMGqpMYKQEUJTAUM4IZ+6Ad/0AAY4AYEeIBffIBxuAANwIAH8ABdKAdVAIZJMId2KIea9BpIsMoYwAAE+ACLJMoHeAADQAAXyIBXeAVgOARgWABgAIZXIIdsiAGJ1IBc+IQKuABH3MgJoEd3HEEZ+EZ4lMiqtMqybAZqvIAHqIFU2IAN+IBUWEtbqAFb8IcNeAD/RzCADbCFWUAAA/CAA7CEZegACsCVzBxMMvkABBiHB8DND6iBcUAABHiDB/gAFyCFc7AQdkiTMqiBKTCGGuAGY0DF9YxHtfTJVPgAA3iAGPBNu7wASFhDTXiAVKDFBwBODNjOxOQGW8CADxiHD/CHD5ACW9gAA3ABDIjQYiiH8aQE1tlCJHoJ3LSF7yzKVODOAqgBo0RKC3kJddgFUigAW7gAvNzPB0DOB9CEteyH+cRNx+ROAxiHx0QAxESADZhQf/CHxgROA6gB6qzOHphQBcUADEiFGvCASICJAAAGmNBQwDiElxgHKK2Bx9zRCTWASjiAE6WJW1gAXIiB3nzM/wfIhhjdAH+ogQLogQqYTyBNhR41gB+tziQdB1vgTlvgBgwogEHlTgSQggJ4TAd4Az11gUqYhnR8iQBYAJhQRwrRUpRQAT3lUQx4gxrAABfwgyQq0wCwh3XYhRqozQ8I0hrATgXV06AEznH4UT01VAXlTiE1gDfoBQNQgQEVUUalT2lwUiTwgAqlCWew0lkZMZbA1HJwhAJwASQogALwgH4czzIVjL5UjkPQB0KlT2o1AH/gUepEVELlUSD9gD390QZ9gwkN116dVQdQASlQVweogV6I1mOliUkdDGb9B2dFCUrQ0nOghG0wB3XMVpjggAVQR3J4BCcd1E2dzw/4AP9CVQGLndYCkAI/MNc3KIB5dQAH4AMVcIAC0NROJVkVeAMkGNk3kAJpINPnUAXW6ZoMsZAFcAaFfQ4OAIaExYVHgFVNjdACqNiLVQGQJVk+kAI+4IOibFp6LQCSNdmi7AUkwACkdYGmRQJpeNQTndSEtdkTjYeA3VmU6NmELQdc+FQkqFcDQAI+uNqLNYCkVYGlbVoH6AWVlYJ5LVkXENm89Vu9LQAkqIQMQIevVdaD+debfY4FKFuzRVua6AAPaFkpMAC+5QMH8AAHMACL7Vs+6IWRDV2ozVwkcAHRzdtpRYJekFau1QVstZB+XVYuLAzBuEyz5dcOSFuU0IVKcNn/N2ja0EUC4n0D0GVapz1Zu3UBaWBd1BXZXmheq21eadCFSfjL50CTKrWVdcRSltjCSA0Ad3gGzFwGzHSHbXiGAFDf9T0Ed1jf6LDMZ7AHfsDMBVgA8u0AS9AFaRhZ1G3aXkjd05XWXnhZ1n0D1yXeAH7e1kXdAZaGR7jfzNwGd7Dfnr3fDmCG+23cZaCQfzjY3A1hER5hEjbbZcgAGlgGdfgHFk7fDtgFyC1hGZ7hGQYGGsgABsiAaaCAZ9gGFm5hdQCGZSgGGpiGSDiAIz4AJV5iJm5iJ35iKI5iKZ5iKm7iSIiEaaCBYqAAYAgAH/7hH0ZYwsAQDDkEMj5jNE5jETVeYzZuYzd+YzdGmIMF44AAADs=\" 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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: 0px 7.91667px; transform-origin: 0px 7.91667px; 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: 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: 107.8px 7.91667px; transform-origin: 107.8px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 69.3px 7.91667px; transform-origin: 69.3px 7.91667px; \"\u003erubik: row vector \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: 38.5px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 38.5px 7.91667px; \"\u003eof size 24\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: 304.15px 7.91667px; transform-origin: 304.15px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e(The cube started [L=0,U=1,F=2,D=3,B=4,R=5] and then receives forty face moves.\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: 0px 7.91667px; transform-origin: 0px 7.91667px; 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: 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: 169.4px 7.91667px; transform-origin: 169.4px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eOutput: move_vec (Numeric of moves to solve)\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: 284.9px 7.91667px; transform-origin: 284.9px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 84.7px 7.91667px; transform-origin: 84.7px 7.91667px; \"\u003e move_vec:values 1:27 \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 15.4px 7.91667px; text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 15.4px 7.91667px; \"\u003efor \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 127.05px 7.91667px; transform-origin: 127.05px 7.91667px; \"\u003eUFDLBR U'F'D'L'B'R' U2F2D2L2B2R2 \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: 57.75px 7.91667px; text-decoration: none; text-decoration-color: rgb(196, 0, 0); text-emphasis-color: rgb(196, 0, 0); transform-origin: 57.75px 7.91667px; \"\u003eXYZX'Y'Z'X2Y2Z2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cul style=\"block-size: 122.6px; 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 61.3px; transform-origin: 391px 61.3px; 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: 29.1833px 7.91667px; transform-origin: 29.1833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eExample:\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: 327.667px 7.91667px; transform-origin: 327.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the cube was randomized by [1 9] UD', one solution is [3 7] which are the complements in reverse order.\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: 134.333px 7.91667px; transform-origin: 134.333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eScoring is Time in msec to solve 500 cubes\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: 242.583px 7.91667px; transform-origin: 242.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCube Moves X, Y, and Z do not constitute a move but are needed in the vector\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: 187.1px 7.91667px; transform-origin: 187.1px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA string to numeric value function is provided in the template\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: 355.933px 7.91667px; transform-origin: 355.933px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eVerifications will be by executing your move vector against the provided Rubik and counting number of face moves.\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: 374.067px 7.91667px; transform-origin: 374.067px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function rubik_rot_mini(mov,r) is provided in the template. Other functions are provided to re-orient the cube in Cody Challenge 931, Rubik's Mini-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: 45.5333px 7.91667px; transform-origin: 45.5333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Challenge\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\u003ca target='_blank' href = \"http://www.mathworks.com/matlabcentral/cody/problems/931-rubik-s-mini-cube-solve-randomized-mini-cube-score-moves.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eChallenge 931 Rubik's Mini-Cube\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: 183.883px 7.91667px; transform-origin: 183.883px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e contains a 3D Mini-Cube Viewer for program development.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 122.6px; 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 61.3px; transform-origin: 391px 61.3px; 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\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 = \"http://kociemba.org/cube.htm\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eCube Theory: 20-moves Any Cube\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 = \"http://peter.stillhq.com/jasmine/rubikscubesolution.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eGeneral Cube Info and Middle Layer\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 = \"http://www.speedcubing.com/final_layer_print.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpeedCube Bottom Sequences\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; 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 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 24.9px 7.91667px; transform-origin: 24.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe site\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 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\u003ca target='_blank' href = \"http://www.speedcubing.com/CubeSolver/MiniCubeSolver.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMiniCube Solver\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 276.2px 7.91667px; transform-origin: 276.2px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e claims an outstanding 10 minutes to solve a randomized Mini-Cube. Matlab can achieve any Mini-Cube solution in \u0026lt; 497 usec, independent of moves on an i5/16GB machine.\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: 218.883px 7.91667px; transform-origin: 218.883px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Note: Mini-Cube can use the full cube moves and ignore edge effects)\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: 85.9667px 7.91667px; transform-origin: 85.9667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eComing Soon: Matlab Tetris\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function solve_vec = rubik_solve_mini(r)\r\n% Expect Numeric representation of moves (1:27):\r\n% 1:27 are ufdlbr upfpdplpbprp u2f2d2l2b2r2 xyzxpypzpx2y2z2\r\n solve_vec=[]; \r\n\r\n% One path is to use Challenge 931's, Rubik's Mini Cube, initial Cube re-orientation provided in the template, followed by a solving algorithm that needs only RDB type moves\r\n% Loading an external data file is one method. First solve is not timed.\r\nend\r\n\r\nfunction r=rubik_rot_mini(mov,r)\r\n%mov is 1:27;  1-6 UFDLBR 7-12 U'F;D'L'B'R' 13-18 U2F2D2L2B2R2 \r\n%             19-27 XYZ X'Y'Z' X2Y2Z2  \r\n% X cube-R, Y cube-U,  Z cube-F\r\n%\r\n% r is a 24 element row vector\r\n% r output is a single row vector \r\n%\r\n% vector mov\r\n% r output is array of length(mov) x 24\r\n%\r\n% Perform Rubik Cube face rotations and cube rotations\r\n% L 1:4 U 5:8 F 9:12 D 13:16 B 17:20 R 21:24 \r\n% \r\npersistent vf\r\nif isempty(vf) %\r\nvf=[9 2 11 4 6 8 5 7 21 10 23 12 13 14 15 16 17 3 19 1 20 22 18 24 ; \r\n    1 2 13 15 5 4 7 3 10 12 9 11 22 14 21 16 17 18 19 20 6 8 23 24 ;\r\n    1 19 3 17 5 6 7 8 9 2 11 4 14 16 13 15 24 18 22 20 21 10 23 12 ;\r\n    2 4 1 3 17 18 7 8 5 6 11 12 9 10 15 16 13 14 19 20 21 22 23 24 ;\r\n    7 5 3 4 23 6 24 8 9 10 11 12 13 1 15 2 18 20 17 19 21 22 16 14 ;\r\n    1 2 3 4 5 6 11 12 9 10 15 16 13 14 19 20 17 18 7 8 22 24 21 23 ;\r\n    20 2 18 4 7 5 8 6 1 10 3 12 13 14 15 16 17 23 19 21 9 22 11 24 ;\r\n    1 2 8 6 5 21 7 22 11 9 12 10 3 14 4 16 17 18 19 20 15 13 23 24 ;\r\n    1 10 3 12 5 6 7 8 9 22 11 24 15 13 16 14 4 18 2 20 21 19 23 17 ;\r\n    3 1 4 2 9 10 7 8 13 14 11 12 17 18 15 16 5 6 19 20 21 22 23 24 ;\r\n    14 16 3 4 2 6 1 8 9 10 11 12 13 24 15 23 19 17 20 18 21 22 5 7 ;\r\n    1 2 3 4 5 6 19 20 9 10 7 8 13 14 11 12 17 18 15 16 23 21 24 22 ;\r\n    21 2 23 4 8 7 6 5 20 10 18 12 13 14 15 16 17 11 19 9 1 22 3 24 ;\r\n    1 2 22 21 5 15 7 13 12 11 10 9 8 14 6 16 17 18 19 20 4 3 23 24 ;\r\n    1 22 3 24 5 6 7 8 9 19 11 17 16 15 14 13 12 18 10 20 21 2 23 4 ;\r\n    4 3 2 1 13 14 7 8 17 18 11 12 5 6 15 16 9 10 19 20 21 22 23 24 ;\r\n    24 23 3 4 16 6 14 8 9 10 11 12 13 7 15 5 20 19 18 17 21 22 2 1 ;\r\n    1 2 3 4 5 6 15 16 9 10 19 20 13 14 7 8 17 18 11 12 24 23 22 21 ;\r\n    3 1 4 2 9 10 11 12 13 14 15 16 17 18 19 20 5 6 7 8 22 24 21 23 ;\r\n    9 10 11 12 6 8 5 7 21 22 23 24 15 13 16 14 4 3 2 1 20 19 18 17 ;\r\n    14 16 13 15 2 4 1 3 10 12 9 11 22 24 21 23 19 17 20 18 6 8 5 7 ;\r\n    2 4 1 3 17 18 19 20 5 6 7 8 9 10 11 12 13 14 15 16 23 21 24 22 ;\r\n    20 19 18 17 7 5 8 6 1 2 3 4 14 16 13 15 24 23 22 21 9 10 11 12 ;\r\n    7 5 8 6 23 21 24 22 11 9 12 10 3 1 4 2 18 20 17 19 15 13 16 14 ;\r\n    4 3 2 1 13 14 15 16 17 18 19 20 5 6 7 8 9 10 11 12 24 23 22 21 ;\r\n    21 22 23 24 8 7 6 5 20 19 18 17 16 15 14 13 12 11 10 9 1 2 3 4 ; \r\n    24 23 22 21 16 15 14 13 12 11 10 9 8 7 6 5 20 19 18 17 4 3 2 1 ];\r\nend\r\n\r\nr=r(vf(mov,:));\r\nend\r\n\r\n\r\nfunction move_vec=decode27_movestr_rev001(movestr)\r\n% Active character Inputs: UFDLBRXYZ, GQ are pre-processed\r\n% 1-6 UFDLBR 7-12 U'F;D'L'B'R' 13-18 U2F2D2L2B2R2 19-27 XYZX'Y'Z'X2Y2Z2\r\nmovestr=upper(movestr);\r\nmovestr=strrep(movestr,'''','P'); % simplify further searches\r\n\r\nmovestr=strrep(movestr,'UP',' 7 ');\r\nmovestr=strrep(movestr,'FP',' 8 ');\r\nmovestr=strrep(movestr,'DP',' 9 ');\r\nmovestr=strrep(movestr,'LP',' 10 ');\r\nmovestr=strrep(movestr,'BP',' 11 ');\r\nmovestr=strrep(movestr,'RP',' 12 ');\r\nmovestr=strrep(movestr,'U2',' 13 ');\r\nmovestr=strrep(movestr,'F2',' 14 ');\r\nmovestr=strrep(movestr,'D2',' 15 ');\r\nmovestr=strrep(movestr,'L2',' 16 ');\r\nmovestr=strrep(movestr,'B2',' 17 ');\r\nmovestr=strrep(movestr,'R2',' 18 ');\r\nmovestr=strrep(movestr,'U',' 1 ');\r\nmovestr=strrep(movestr,'F',' 2 ');\r\nmovestr=strrep(movestr,'D',' 3 ');\r\nmovestr=strrep(movestr,'L',' 4 ');\r\nmovestr=strrep(movestr,'B',' 5 ');\r\nmovestr=strrep(movestr,'R',' 6 ');\r\nmovestr=strrep(movestr,'XP',' 22 ');\r\nmovestr=strrep(movestr,'YP',' 23 ');\r\nmovestr=strrep(movestr,'ZP',' 24 ');\r\nmovestr=strrep(movestr,'X2',' 25 ');\r\nmovestr=strrep(movestr,'Y2',' 26 ');\r\nmovestr=strrep(movestr,'Z2',' 27 ');\r\nmovestr=strrep(movestr,'X',' 19 ');\r\nmovestr=strrep(movestr,'Y',' 20 ');\r\nmovestr=strrep(movestr,'Z',' 21 ');\r\n\r\nmove_vec=str2num(movestr);\r\n\r\nend % move_vec","test_suite":"%%\r\nfeval(@assignin,'caller','score',0);\r\n%%\r\nvf=[9 2 11 4 6 8 5 7 21 10 23 12 13 14 15 16 17 3 19 1 20 22 18 24 ; \r\n    1 2 13 15 5 4 7 3 10 12 9 11 22 14 21 16 17 18 19 20 6 8 23 24 ;\r\n    1 19 3 17 5 6 7 8 9 2 11 4 14 16 13 15 24 18 22 20 21 10 23 12 ;\r\n    2 4 1 3 17 18 7 8 5 6 11 12 9 10 15 16 13 14 19 20 21 22 23 24 ;\r\n    7 5 3 4 23 6 24 8 9 10 11 12 13 1 15 2 18 20 17 19 21 22 16 14 ;\r\n    1 2 3 4 5 6 11 12 9 10 15 16 13 14 19 20 17 18 7 8 22 24 21 23 ;\r\n    20 2 18 4 7 5 8 6 1 10 3 12 13 14 15 16 17 23 19 21 9 22 11 24 ;\r\n    1 2 8 6 5 21 7 22 11 9 12 10 3 14 4 16 17 18 19 20 15 13 23 24 ;\r\n    1 10 3 12 5 6 7 8 9 22 11 24 15 13 16 14 4 18 2 20 21 19 23 17 ;\r\n    3 1 4 2 9 10 7 8 13 14 11 12 17 18 15 16 5 6 19 20 21 22 23 24 ;\r\n    14 16 3 4 2 6 1 8 9 10 11 12 13 24 15 23 19 17 20 18 21 22 5 7 ;\r\n    1 2 3 4 5 6 19 20 9 10 7 8 13 14 11 12 17 18 15 16 23 21 24 22 ;\r\n    21 2 23 4 8 7 6 5 20 10 18 12 13 14 15 16 17 11 19 9 1 22 3 24 ;\r\n    1 2 22 21 5 15 7 13 12 11 10 9 8 14 6 16 17 18 19 20 4 3 23 24 ;\r\n    1 22 3 24 5 6 7 8 9 19 11 17 16 15 14 13 12 18 10 20 21 2 23 4 ;\r\n    4 3 2 1 13 14 7 8 17 18 11 12 5 6 15 16 9 10 19 20 21 22 23 24 ;\r\n    24 23 3 4 16 6 14 8 9 10 11 12 13 7 15 5 20 19 18 17 21 22 2 1 ;\r\n    1 2 3 4 5 6 15 16 9 10 19 20 13 14 7 8 17 18 11 12 24 23 22 21 ;\r\n    3 1 4 2 9 10 11 12 13 14 15 16 17 18 19 20 5 6 7 8 22 24 21 23 ;\r\n    9 10 11 12 6 8 5 7 21 22 23 24 15 13 16 14 4 3 2 1 20 19 18 17 ;\r\n    14 16 13 15 2 4 1 3 10 12 9 11 22 24 21 23 19 17 20 18 6 8 5 7 ;\r\n    2 4 1 3 17 18 19 20 5 6 7 8 9 10 11 12 13 14 15 16 23 21 24 22 ;\r\n    20 19 18 17 7 5 8 6 1 2 3 4 14 16 13 15 24 23 22 21 9 10 11 12 ;\r\n    7 5 8 6 23 21 24 22 11 9 12 10 3 1 4 2 18 20 17 19 15 13 16 14 ;\r\n    4 3 2 1 13 14 15 16 17 18 19 20 5 6 7 8 9 10 11 12 24 23 22 21 ;\r\n    21 22 23 24 8 7 6 5 20 19 18 17 16 15 14 13 12 11 10 9 1 2 3 4 ; \r\n    24 23 22 21 16 15 14 13 12 11 10 9 8 7 6 5 20 19 18 17 4 3 2 1 ];\r\n\r\n%r=r(vf(mov,:));  % Standard move, r=rubik_rot_min(mov,r);\r\n\r\nPass=1;\r\nr_fail=0;\r\n% Execute 500 cubes and verify completeness and count moves\r\n % initialize cube\r\n r(1:4)=0;     %Left   0 R\r\n r(5:8)=1;     %Up     1 W\r\n r(9:12)=2;    %Front  2 B\r\n r(13:16)=3;   %Down   3 Y\r\n r(17:20)=4;   %Back   4 G\r\n r(21:24)=5;   %Right  5 O\r\n rnorm=r;\r\n\r\nzcnt=500;\r\nsum_solve=0;\r\nmin_solve=1000;\r\nmax_solve=0;\r\nasolve=199;\r\nmix=40;\r\n\r\ntic\r\n\r\nfor cube_check=1:zcnt %zcnt\u003c100 %500\r\n %zcnt=zcnt+1;\r\n  r=rnorm;\r\n % Initial mix\r\n mov=randi(18,[mix,1]);\r\n for i=1:length(mov)  % Ignoring Move Undos since mix=40\r\n   r=r(vf(mov(i),:));\r\n end\r\n\r\n r_reset=r; % Used in assert\r\n\r\n solve_vec=rubik_solve_mini(r);\r\n\r\n for i=1:length(solve_vec) \r\n  r=r(vf(solve_vec(i),:));\r\n end\r\n\r\n  if all(r(1:4)==r(4)) \u0026\u0026 all(r(5:8)==r(8))  \u0026\u0026 all(r(9:12)==r(9)) \u0026\u0026 ...\r\n     all(r(13:16)==r(13))  \u0026\u0026 all(r(17:20)==r(17)) \u0026\u0026 all(r(21:24)==r(21))\r\n   solve_vec(solve_vec\u003e18)=[]; %   \r\n   lsolve=length(solve_vec);\r\n   if lsolve\u003e11, Pass=0;end % Length Rqmt\r\n   sum_solve=sum_solve+lsolve;\r\n   min_solve=min(min_solve,lsolve);\r\n   max_solve=max(max_solve,lsolve);\r\n   asolve=floor(sum_solve/zcnt);\r\n %  fprintf('Cube Solved Moves=%i  Avg Moves=%i min=%i  max=%i\\n',lsolve,asolve,min_solve,max_solve)\r\n  else % Deug info\r\n   Pass=0;\r\n   r_fail=r_reset;\r\n  % fprintf('\\n\\nCube NOT Solved???\\n\\n') \r\n  % fprintf('%i ',r); % Current ending data\r\n  % fprintf('\\n')\r\n  % fprintf('%i ',r_reset); % Starting Cube\r\n  end\r\n\r\nend % while of cubes\r\ntoc\r\n\r\nassert(isequal(Pass,1),sprintf('Max Len=%i \\n',max_solve)); % Length Exception\r\nassert(isequal(Pass,1),sprintf('%i ',r_fail)); % Output Non-Solved Cube Start\r\n\r\n%if Pass\r\n% feval(@assignin,'caller','score',min(100,floor(asolve)));\r\n%end\r\n\r\nfprintf('Moves: Avg %i   Min %i   Max %i\\n',asolve,min_solve,max_solve)\r\n\r\n%%\r\nvf=[9 2 11 4 6 8 5 7 21 10 23 12 13 14 15 16 17 3 19 1 20 22 18 24 ; \r\n    1 2 13 15 5 4 7 3 10 12 9 11 22 14 21 16 17 18 19 20 6 8 23 24 ;\r\n    1 19 3 17 5 6 7 8 9 2 11 4 14 16 13 15 24 18 22 20 21 10 23 12 ;\r\n    2 4 1 3 17 18 7 8 5 6 11 12 9 10 15 16 13 14 19 20 21 22 23 24 ;\r\n    7 5 3 4 23 6 24 8 9 10 11 12 13 1 15 2 18 20 17 19 21 22 16 14 ;\r\n    1 2 3 4 5 6 11 12 9 10 15 16 13 14 19 20 17 18 7 8 22 24 21 23 ;\r\n    20 2 18 4 7 5 8 6 1 10 3 12 13 14 15 16 17 23 19 21 9 22 11 24 ;\r\n    1 2 8 6 5 21 7 22 11 9 12 10 3 14 4 16 17 18 19 20 15 13 23 24 ;\r\n    1 10 3 12 5 6 7 8 9 22 11 24 15 13 16 14 4 18 2 20 21 19 23 17 ;\r\n    3 1 4 2 9 10 7 8 13 14 11 12 17 18 15 16 5 6 19 20 21 22 23 24 ;\r\n    14 16 3 4 2 6 1 8 9 10 11 12 13 24 15 23 19 17 20 18 21 22 5 7 ;\r\n    1 2 3 4 5 6 19 20 9 10 7 8 13 14 11 12 17 18 15 16 23 21 24 22 ;\r\n    21 2 23 4 8 7 6 5 20 10 18 12 13 14 15 16 17 11 19 9 1 22 3 24 ;\r\n    1 2 22 21 5 15 7 13 12 11 10 9 8 14 6 16 17 18 19 20 4 3 23 24 ;\r\n    1 22 3 24 5 6 7 8 9 19 11 17 16 15 14 13 12 18 10 20 21 2 23 4 ;\r\n    4 3 2 1 13 14 7 8 17 18 11 12 5 6 15 16 9 10 19 20 21 22 23 24 ;\r\n    24 23 3 4 16 6 14 8 9 10 11 12 13 7 15 5 20 19 18 17 21 22 2 1 ;\r\n    1 2 3 4 5 6 15 16 9 10 19 20 13 14 7 8 17 18 11 12 24 23 22 21 ;\r\n    3 1 4 2 9 10 11 12 13 14 15 16 17 18 19 20 5 6 7 8 22 24 21 23 ;\r\n    9 10 11 12 6 8 5 7 21 22 23 24 15 13 16 14 4 3 2 1 20 19 18 17 ;\r\n    14 16 13 15 2 4 1 3 10 12 9 11 22 24 21 23 19 17 20 18 6 8 5 7 ;\r\n    2 4 1 3 17 18 19 20 5 6 7 8 9 10 11 12 13 14 15 16 23 21 24 22 ;\r\n    20 19 18 17 7 5 8 6 1 2 3 4 14 16 13 15 24 23 22 21 9 10 11 12 ;\r\n    7 5 8 6 23 21 24 22 11 9 12 10 3 1 4 2 18 20 17 19 15 13 16 14 ;\r\n    4 3 2 1 13 14 15 16 17 18 19 20 5 6 7 8 9 10 11 12 24 23 22 21 ;\r\n    21 22 23 24 8 7 6 5 20 19 18 17 16 15 14 13 12 11 10 9 1 2 3 4 ; \r\n    24 23 22 21 16 15 14 13 12 11 10 9 8 7 6 5 20 19 18 17 4 3 2 1 ];\r\n\r\n%r=r(vf(mov,:));  % Standard move, r=rubik_rot_min(mov,r);\r\n\r\nPass=1;\r\nr_fail=0;\r\n% Execute 500 cubes and verify completeness and count moves\r\n % initialize cube\r\n r(1:4)=0;     %Left   0 R\r\n r(5:8)=1;     %Up     1 W\r\n r(9:12)=2;    %Front  2 B\r\n r(13:16)=3;   %Down   3 Y\r\n r(17:20)=4;   %Back   4 G\r\n r(21:24)=5;   %Right  5 O\r\n rnorm=r;\r\n\r\n for jrand=1:40  % Ignoring Move Undos since mix=40\r\n   r=r(vf(randi(18),:));\r\n end\r\n\r\n q=500;\r\n ra=zeros(q,24);\r\n for i=1:q\r\n  for jrand=1:10  % Ignoring Move Undos since base mix=40\r\n    r=r(vf(randi(18),:));\r\n  end\r\n % add 10 new moves to prior vector\r\n  ra(i,:)=r;\r\n end\r\n\r\n% The Time Trail section does not check accuracy, that is done above\r\nt0=clock;\r\nfor i=1:q\r\n solve_vec=rubik_solve_mini(ra(q,:));\r\nend\r\ndt=etime(clock,t0)*1000;\r\n\r\n%assert(isequal(find_fast(a,val(1)),find(a==val(1),1,'first')))\r\nfprintf('Your Time = %i msec\\n',floor(dt))\r\nfeval(@assignin,'caller','score',min(2000,floor(dt)));\r\n%   Performance Score","published":true,"deleted":false,"likes_count":5,"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":"2012-09-09T17:48:25.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-09-09T06:26:08.000Z","updated_at":"2025-11-17T16:25:58.000Z","published_at":"2012-09-09T16:33:58.000Z","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 Challenge is to Near or Optimally Solve a thoroughly scrambled Mini-Rubik's Cube(2x2x2).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eAny Mini-Cube can be solved in 11 or fewer Face Moves. There are only 3,674,160 unique cube positions, with only 2344 requiring the fulll 11 moves. Cube moves do not count.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 Performance metric is cumulative Time to Solve 500 cubes (msec).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 standard Mini-Rubik's Cube is shown in 3-D and also unfolded to identify the specific Tile-face/Vector numbering.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 faces are White-Up / Red-Left / Blue-Front / Orange-Right / Yellow-Down / Green-Back (ULFRDB)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eMoves are denoted as F for clockwise rotation of the Front face. F'(or FP) is CCW and F2 is F twice. The provided function r_new=rubick_rot_mini(mov,r) implements moves 1-18: UFDLBRU'F'D'L'B'R'U2F2D2L2B2R2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"center\\\"/\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=\\\"center\\\"/\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=\\\"center\\\"/\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=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\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: (rubik)\\n\\nrubik: row vector of size 24\\n(The cube started [L=0,U=1,F=2,D=3,B=4,R=5] and then receives forty face moves.\\n\\nOutput: move_vec (Numeric of moves to solve)\\n move_vec:values 1:27 for UFDLBR U'F'D'L'B'R' U2F2D2L2B2R2 XYZX'Y'Z'X2Y2Z2]]\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\u003eExample:\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 the cube was randomized by [1 9] UD', one solution is [3 7] which are the complements in reverse order.\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\u003eScoring is Time in msec to solve 500 cubes\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\u003eCube Moves X, Y, and Z do not constitute a move but are needed in the 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:t\u003eA string to numeric value function is provided in the template\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\u003eVerifications will be by executing your move vector against the provided Rubik and counting number of face moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 function rubik_rot_mini(mov,r) is provided in the template. Other functions are provided to re-orient the cube in Cody Challenge 931, Rubik's Mini-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\u003eThe Challenge\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/931-rubik-s-mini-cube-solve-randomized-mini-cube-score-moves.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eChallenge 931 Rubik's Mini-Cube\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contains a 3D Mini-Cube Viewer for program development.\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\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:hyperlink w:docLocation=\\\"http://kociemba.org/cube.htm\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCube Theory: 20-moves Any Cube\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=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://peter.stillhq.com/jasmine/rubikscubesolution.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGeneral Cube Info and Middle Layer\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=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.speedcubing.com/final_layer_print.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpeedCube Bottom Sequences\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=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe site\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.speedcubing.com/CubeSolver/MiniCubeSolver.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMiniCube Solver\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e claims an outstanding 10 minutes to solve a randomized Mini-Cube. Matlab can achieve any Mini-Cube solution in \u0026lt; 497 usec, independent of moves on an i5/16GB machine.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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: Mini-Cube can use the full cube moves and ignore edge effects)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eComing Soon: Matlab 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Euler: Problem 14 Longest Collatz sequence(harder version)","description":"This problem is a harder version of \"Problem 44448. Project Euler: Problem 14 Longest Collatz sequence\", because of time limits. \u003chttps://ww2.mathworks.cn/matlabcentral/cody/problems/44448\u003e \r\n\r\nThe following iterative sequence is defined for the set of positive integers:\r\nn → n/2 (n is even) n → 3n + 1 (n is odd)\r\nUsing the rule above and starting with 13, we generate the following sequence:\r\n13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.\r\nWhich starting number, no more than N, produces the longest chain, and how long? Don't cheat!","description_html":"\u003cp\u003eThis problem is a harder version of \"Problem 44448. Project Euler: Problem 14 Longest Collatz sequence\", because of time limits. \u003ca href = \"https://ww2.mathworks.cn/matlabcentral/cody/problems/44448\"\u003ehttps://ww2.mathworks.cn/matlabcentral/cody/problems/44448\u003c/a\u003e\u003c/p\u003e\u003cp\u003eThe following iterative sequence is defined for the set of positive integers:\r\nn → n/2 (n is even) n → 3n + 1 (n is odd)\r\nUsing the rule above and starting with 13, we generate the following sequence:\r\n13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.\r\nWhich starting number, no more than N, produces the longest chain, and how long? Don't cheat!\u003c/p\u003e","function_template":"function y = euler014_v2(x)\r\n  y = x;\r\nend","test_suite":"1\r\n%%\r\nassessFunctionAbsence({'tic','toc','pause','etime','clock','now','str2num','timer'},'FileName','euler014_v2.m')\r\n\r\n2\r\n%%\r\nN = 2e8;\r\nnum_correct = 169941673;\r\nlen_correct = 954;\r\ntic\r\n[num, len] = euler014_v2(N);\r\nt=toc\r\nassert(t\u003e1);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n3\r\n%%\r\nN = 4e8;\r\nnum_correct = 268549803;\r\nlen_correct = 965;\r\ntic\r\n[num, len] = euler014_v2(N);\r\nt=toc\r\nassert(t\u003e1);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":3668,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2018-11-11T06:11:05.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-11-09T16:59:43.000Z","updated_at":"2018-11-11T06:11:05.000Z","published_at":"2018-11-09T16:59:43.000Z","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 a harder version of \\\"Problem 44448. Project Euler: Problem 14 Longest Collatz sequence\\\", because of time limits.\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://ww2.mathworks.cn/matlabcentral/cody/problems/44448\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://ww2.mathworks.cn/matlabcentral/cody/problems/44448\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\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 following iterative sequence is defined for the set of positive integers: n → n/2 (n is even) n → 3n + 1 (n is odd) Using the rule above and starting with 13, we generate the following sequence: 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1. Which starting number, no more than N, produces the longest chain, and how long? Don't cheat!\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":908,"title":"AVIRIS Inscribed Rectangle Bit Mask - Speed Test","description":"The AVIRIS data sometimes is provided uncropped. This creates edge regions with values of \"-50\". Shown is AVIRIS Moffett Field image Layer 1 (400nm) and a zoom of the top-right corner. The dark blue on the edges is non-imaged ground.\r\n\r\nTo expedite compression the unused mask can be created with an interior excluded rectangle of no Zeros and maximum pixels. The challenge is to quickly and accurately determine the maximum non-zero inscribed rectangle for this image and some other test cases.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/AVIRIS_L001.jpg\u003e\u003e \r\n\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/AVIRIS_L001_TR.jpg\u003e\u003e\r\n\r\n\r\n*Input:* m ( 2-D array with zero and non-zero values )\r\n\r\n*Output:* [idxtlc rmnr rmnc]\r\n\r\n* idxtlc : array index of top left corner\r\n* rmnr : numer of rows of the non-zero rectangle mask\r\n* rmnc : number of columns of the non-zero rectangle mask\r\n\r\n*Score:* Time ( msec ) Based upon time to process the 1924x753 AVIRIS image.\r\n\r\n(If time-out occurs I suggest downloading the mat file using the test suite code and then optimize with profiler.)\r\n\r\n*Passing:* Minimum number of pixels in rectangle\r\n\r\n\r\n\r\n*Example:* \r\n\r\n* m=[ 1 1 0 1 1......idxtlc = 3  ( index of TLC row 3, col 1) \r\n* ........1 0 1 1 1......rmnr = 3 ( rectangle mask number rows )\r\n* ........1 1 1 1 1......rmnc = 5 ( rectangle mask number columns )\r\n* ........1 1 1 1 1......Maximum rectangle pixels of 15\r\n* ........1 1 1 1 1 ]\r\n \r\n \r\nFor the AVIRIS image the zero edge bit mask encoding can be reduced 89% using an inscribed rectangle.\r\n\r\nNote: Additional test cases may be invoked if hard coded solution achieves best score. ","description_html":"\u003cp\u003eThe AVIRIS data sometimes is provided uncropped. This creates edge regions with values of \"-50\". Shown is AVIRIS Moffett Field image Layer 1 (400nm) and a zoom of the top-right corner. The dark blue on the edges is non-imaged ground.\u003c/p\u003e\u003cp\u003eTo expedite compression the unused mask can be created with an interior excluded rectangle of no Zeros and maximum pixels. The challenge is to quickly and accurately determine the maximum non-zero inscribed rectangle for this image and some other test cases.\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/AVIRIS_L001.jpg\"\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/AVIRIS_L001_TR.jpg\"\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m ( 2-D array with zero and non-zero values )\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e [idxtlc rmnr rmnc]\u003c/p\u003e\u003cul\u003e\u003cli\u003eidxtlc : array index of top left corner\u003c/li\u003e\u003cli\u003ermnr : numer of rows of the non-zero rectangle mask\u003c/li\u003e\u003cli\u003ermnc : number of columns of the non-zero rectangle mask\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eScore:\u003c/b\u003e Time ( msec ) Based upon time to process the 1924x753 AVIRIS image.\u003c/p\u003e\u003cp\u003e(If time-out occurs I suggest downloading the mat file using the test suite code and then optimize with profiler.)\u003c/p\u003e\u003cp\u003e\u003cb\u003ePassing:\u003c/b\u003e Minimum number of pixels in rectangle\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003em=[ 1 1 0 1 1......idxtlc = 3  ( index of TLC row 3, col 1)\u003c/li\u003e\u003cli\u003e........1 0 1 1 1......rmnr = 3 ( rectangle mask number rows )\u003c/li\u003e\u003cli\u003e........1 1 1 1 1......rmnc = 5 ( rectangle mask number columns )\u003c/li\u003e\u003cli\u003e........1 1 1 1 1......Maximum rectangle pixels of 15\u003c/li\u003e\u003cli\u003e........1 1 1 1 1 ]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eFor the AVIRIS image the zero edge bit mask encoding can be reduced 89% using an inscribed rectangle.\u003c/p\u003e\u003cp\u003eNote: Additional test cases may be invoked if hard coded solution achieves best score.\u003c/p\u003e","function_template":"function [idxtlc rmnr rmnc]=rect_mask(m)\r\n  idxtlc=1; rmnr=0; rmnc=0;\r\nend","test_suite":"global dt\r\ndt=0;\r\nm=ones(5); m(7)=0;\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(m);\r\ndt=etime(clock,t0)*1000; % ms\r\ndt\r\n\r\n[x y]=ind2sub(size(m),idxtlc);\r\npass=~any(any(m(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=15,sprintf('Expected 15 pixels, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n%%\r\nm=ones(5); m(7)=0; m(11)=0;\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(m);\r\ndt=etime(clock,t0)*1000; % ms\r\ndt\r\n\r\n[x y]=ind2sub(size(m),idxtlc);\r\npass=~any(any(m(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=15,sprintf('Expected 15 pixels, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n%%\r\n m=zeros(6);m(15)=1;\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(m);\r\ndt=etime(clock,t0)*1000; % ms\r\ndt\r\n\r\n[x y]=ind2sub(size(m),idxtlc);\r\npass=~any(any(m(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=1,sprintf('Expected 1 pixel, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n%%\r\nm=ones(8); m(2,:)=0;  m(7,:)=0;  m(4,2)=0;  m(5,7)=0;\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(m);\r\ndt=etime(clock,t0)*1000; % ms\r\ndt\r\n\r\n[x y]=ind2sub(size(m),idxtlc);\r\npass=~any(any(m(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=16,sprintf('Expected 16 pixels, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n%%\r\n% Load aviris file Layer 1 ; 1.8MB mat file\r\nglobal dt\r\ntic % approx 2.5 sec to write and load\r\nurlwrite('http://tinyurl.com/matlab-avmofL001','aviris_moffett_L001.mat');\r\ntoc\r\nload('aviris_moffett_L001.mat');\r\ntoc\r\n\r\n% Array variable is L001\r\n%Files also posted are L010,L023,L157, and L158 with same tinyurl format\r\n% L010 and L023 have high contrast L157 and L158 are in atmospheric notch\r\n\r\n% Time Trial File 1924 x 753\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(L001);\r\ndt=etime(clock,t0)*1000; % ms\r\n\r\n[x y]=ind2sub(size(L001),idxtlc);\r\npass=~any(any(L001(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=1282281,sprintf('Expected 1282281 pixels, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n\r\n%%\r\nglobal dt\r\n%Write file based on time in test 1\r\nnet_time=uint32(dt);\r\n% net_time in ms\r\n% Create graph data\r\nnet_time=min(4000,net_time); % Limit graph y-axis\r\nfh=fopen('rect_mask.m','wt');\r\nfeval(@assignin,'caller','score',net_time)\r\nfprintf(fh,'%s\\n',repmat('1;',[1,round(net_time/2)]));\r\nfclose(fh);","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2012-08-15T00:11:30.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-08-13T00:01:09.000Z","updated_at":"2012-08-15T00:22:27.000Z","published_at":"2012-08-13T05:16:56.000Z","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\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.JPEG\"}],\"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 AVIRIS data sometimes is provided uncropped. This creates edge regions with values of \\\"-50\\\". Shown is AVIRIS Moffett Field image Layer 1 (400nm) and a zoom of the top-right corner. The dark blue on the edges is non-imaged ground.\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 expedite compression the unused mask can be created with an interior excluded rectangle of no Zeros and maximum pixels. The challenge is to quickly and accurately determine the maximum non-zero inscribed rectangle for this image and some other test 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: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=\\\"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\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=\\\"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\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 m ( 2-D array with zero and non-zero values )\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\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [idxtlc rmnr rmnc]\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\u003eidxtlc : array index of top left corner\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\u003ermnr : numer of rows of the non-zero rectangle mask\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\u003ermnc : number of columns of the non-zero rectangle mask\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\u003eScore:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Time ( msec ) Based upon time to process the 1924x753 AVIRIS image.\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(If time-out occurs I suggest downloading the mat file using the test suite code and then optimize with profiler.)\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\u003ePassing:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Minimum number of pixels in rectangle\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:\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\u003em=[ 1 1 0 1 1......idxtlc = 3 ( index of TLC row 3, col 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e........1 0 1 1 1......rmnr = 3 ( rectangle mask number rows )\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........1 1 1 1 1......rmnc = 5 ( rectangle mask number columns )\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........1 1 1 1 1......Maximum rectangle pixels of 15\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........1 1 1 1 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\u003eFor the AVIRIS image the zero edge bit mask encoding can be reduced 89% using an inscribed rectangle.\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\u003eNote: Additional test cases may be invoked if hard coded solution achieves best 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oxs1vB9plNzbLJkcGM7MEntgYrZsoWtbBZI3RDgkBAQRj3AxWVNPEi7y34d60hd2V1bQrG7+ZHwcDC475981pVjKyXQdRznFLoWW1P7VDCdjTyR5BEhBz+HJ/SqshWWZ5CixDOfkzjNQW27zpnHKZGASCMnvipbl2T5Y+XIwGwDjB9MVEaag7Ix5UpcqGzJv2u8jHJKn1GKvLbrauoiR8soKh8jPbn/61U0m3TLLJ5QOQCEAQcD06D8BS6nqat5PlqqJkbyMDJFJxm2lYUoylJQWxBfTJ5yZZD33JkYNaNreWFuiI6pKWzgL1X6k1lllm2Ou4jHI5bbmoY41a542x/Lgg8E+/PFaypKUbN7GjpqUeV9DpFtm/11re2cAA5Ejg7QeMnOcV434jtvs+tSELxL+8GMY564x2zmu6uRKn71ZWOOvz8fiOlcH4guWuNRJPAQBVHt1/Hkmojh3T95u9z2cng4SdnofTPwZ/5JRov/bf/wBHyUUfBj/klGi/9t//AEfJRXw+I/iy9WfVLY4X9o3/AFnhr/t5/wDaVct4Xu/tWjxZ3B0JjY4GDjkfoRXU/tF/f8NZ/wCnn/2lXK+E4Fi0KB0GDIWZj1yc4/kBX1OUf7svmeJndvYanSxH7uOp+8evNW4z90D0qlF6H8fpVyM967ZHw1Umj9+Rjvx0qx/cxxj/AD/OqqjtVpXX5cNiueRzyJlPYc88d6em7t3/AJ1GPfk9sVIPmRQPXn61iznZOPTc3r7U8bep59fWox/D/Kn+gC/ietZsxYpPcdafjsPw9qbs7fnT/wDgNSQw/wAmszUreSWHKHGeCPUVpj3WmSx74cbecHHHtRCXLJGlKXJJM5HyNky4bDDr6ZrorabyreLO3qN2OnNZ3lqr42/dPOKbPefI6FcjsRXZV/eKx6VS9WyOp9qGqKCXzbaKT1H8qSSTyuX4FeKqbcmjllpp1JKRjWPPr9lb7kMjE9ABg5zT7fXdPmwn2lAf9v5f58V0/VqiV7B7Cra/KabvsQ+vb61WkdbeH5++ahbULSWZYklidz3Dg/lg1BdIz4B569OgranTel9CVSkmuZWKN69p0MWD6jvVYz74WTzG2D+Eng/hTb9FRN7vwOADXP3V7Jv2Q8DHXu1erRo8yPXw9D2iWpql43fCMgfPA4Gac2lakjs0cDOPvfdzk9vwrnInfzlRG3nf/Dya9gsILt9EiQSLBOUXLuudox/X/OK8zPMz/sukppXv0PVw2ClUrKmnoeZXemahbus93byJG/Cll4yapiHytzjegJzk9K9FvLiDU01CB9WikSK3ZFiQKuGBUhuGOTkY6V5hdTKiMgiUyA4LdT09K6MjzKrmFO9SHK+x2YjCqjU9nGXMgubj5AHZihPY1UgupLeZ/LbEZP6Gq3mSb/3jZ56EninyOvGEwOw5xmvpVTS0fUFTUVyvU620kaWx3xrk+r//AK6WH97K7ll2gEDB7/rUGhSb7Nw3Vf4k6/zFThPK4i5LjJIwcc15c9JtHkzXLJosbdyJvZj2754/Gq8tus64f7oPUDJbmmE+qtg4646+1Ss0aokglcyk/MhU4XHvmod1a3UhJrZkrWsEFuDA7lySGSRAvB7jBqhNHseNxtwDggKB1/nV0HzXw7oM98YqvdJ5qbAuHA7AnvVU7p6u5UJO+vUhuk34Oxjgn16fTmuN8V2ccUsM8a4MoYMB04x/jXc+RvRd7McdcnqAfzrlvG8bJBYgx7E/eEHOf7tOcvdsenltR+3UU9z3r4Mf8kp0X/tv/wCj5KKPgz/ySjRvrP8A+jpKK+Cr/wAWXqz669jhf2jPv+Gv+3n/ANpVxHgq6aWwktnXIib5fx7fnXb/ALRv3/DX/bz/AO0q47wckP8AY++NlMhkPmkqCVI6D6Yr6fKf92XzPJze3sHdHUxFv7zc1cU/dG7PvVSMfPj07/jVtPm244HQkV3zPhahKD86ncpI6Gp17EMpHUj3qJfTr7+1Sxj7pDfnzxXPI5ZE6HqBxnvUgPpu68VGvp09c1LGPXr/AJ/wrFnOyb0J75/DFODfnjAqNf8AebIPIp27p3AP86yZi0SA+vfg/hTwfk+7k9qj+ZsAc04HelS0RYePTvS//qpoPzjHWl9x1qJAu5gX8flXLjapySQTWXJIyP8AM+Oc565rf1FPnb5unIrBkj+ckKxB9OvAr0qDTiexhpJx1NSLWvstnsf53OdoHJXB71hXWoXN6+DIwBGMZwKkZPndPqMc+tXrXT1i/wBeuWIyqAdD7+lXGNKleVrtm6VOn71tTLGnNs8yR8J6E/ezTBZ2/wDfbdnHUD+p/lXUJpiy5eVcn36VO2lWkqNmLHHBAANS8XYj6/FaM4mWzntX8yNmKdQQav23iOVITHPHvkXIB6Y+vrWo1k1lncvmRHkr3FYt/YfP5sS5Qg4PAFbxnCrpNHTCpTraSVyneXktxNvkZuvAAIANT2+n/ufPljcoTtyBTLSHzblEZWD5H3+e9dP4hla1s4bRFQIemxSBx2NOtW9nKNOCNKlTkahAxtEsoLfW186RBHw3zuFHJ9TxXY6jfQancjSrGZnEm37RMrcKo42gj1rnJNPgl06HUI3eKWIbOSAOOQfzqtFd2llh0lQE5IQOOuf8mvHxuAp4+rGvLVx27XNoYqpGMlT3ZsP4URXmC2kcacqjozbif5dK5y68K3MULzybvkB4KkdPcVq2Hjy5028a1uo/OgOdpbO9ST9eRXbK8epWaSGNSkseSMDBBGa4J5jj8tqL28Vyvqa+z5UpQk7vp/wTxExKzs7RKcdjn+mKt6Xpf9pXJjk80bM42c4/Ouz8S+GNPsNNlvY/kORiP1JrlNGu/sUwnl34J2gjHUdM596+qoY+OLw7q0maSqz9m+XdGudIi0+FmSeY4IGN45+tVVna3cFGwwIKkDIBxz/kVe1OZpbhF3k5HAyccmqh3ImDuLjnHUYqKd3G8tbnnRlJq89WRNNcXU2+5nDkHA2IMkn+f405tqIoX+I8K4znFL5flJ/rFzxgdKcLdbhw8nJBBJx1/lV6LbZGjmr3ZH83RF5/vcfL9MVIsnyAyNkjPBA/KpLtPs+BsVAf4QQ2MU20m3o6O7vGfmC9BkDjmi943SIveN+hSnuJHd0i3BB907PU+vasvxlawPokNxuXzUl2g84ORyPrwDzXRQFfOQFXHODjpxXP+Po2/s23MMbJbrKRIc8EkfLkfQNUVJdDuwM74mEVoe1fBr/klejD/rv/AOjpKKX4Nf8AJK9H9f33/o6SivhsR/Fl6s+2jscL+0b9/wANf9vP/tKvP/AZl3XeNvl4Xd65OcY/Wu//AGjf9Z4a/wC3n/2lXn/ge8jR5rN1UO3zq3GW9Rn26j8a+myn/do/M8vNE3QlZXO6iHycrgf4CrMTfIoLdDzVWI7OjLnHUetWo/v8c8V6Ez4Op1LCnvtzUoPccDgkVHnueD2+tSr/ABA8c8VhI5GTRleM/XFSr/CA2QO9QAdcNg/pTw7dN3HTFYtGLRb+rfXikA6/njpUX3ueg64p+euVUgdKzsY2JAy8U4fPTAf9nn1HFP8A++vWpZDH7PRulKP97p70A73x83HIpWHbcw9hisnvqTYz7qaF92/huq49KyHXfwOh6445rQuC3zIjsMHuaryI2wHzXZj7nFdtK8UelS91EcaR+ciJuLk5Y55Az2rQW3WJ8Dcc5+bvisyHdFeJI/8Af5A9q3JBs3fe5pVW015iryasu4/zY1TLtzio5Ltdm8K2OmcVFMn7kENiqYjZHy/GMHOeuazjBM54U4vU1Niyw87cHvWRe2jW75RPNhb7y46GrEN19nflsp1I61bkkV4WdNpGO9Nc0H5FwcqUttDm4zFDc/aI4lKKeVftW/a3tjqEyfbYokkAOC+Cv61ktp1zcK0loqsyjlB39arxyL8yTReWc4YYxtHsK0rU414uKdmekrO092joddey/s1vs00DuhwYUdeVzjIArzC9srlb95ltJwmTtAjPTP0rrp0VPuKz+mOfwrKkljl3MFUYyCO/NaZVQnhKfI5c1+56NKvHmcoR0JrPdPcwyPplyGhxg+RnODnmu8t9TVU2+RPaRqp5kQKoA9xwPzrzhvl+4zfhUdrqrafOTJGjrnkSIGB/wpY7LfraV9bBJSndw0Os8U3Ud1YRwRXayEnIQuM5I9R9awLTQ7mLEjXKBAP+WbhhwehIIwabqeuWl7aJHGjB1OTsQLgY6delGjagz74C/wC75ZQT3OOuOa2oYadCgoxVkiVGrCk31LV6WSYyfLnPqf8AGqzStvDv0JyCOf8AIqxPN++JDMSeg45PvULn5H3rsyMhsHP0Fbw0SMoLRXE+b5Xfp1J9RT0lbeCi4I5yPeo1j83f83y4yM9c/wBKnjVpXxHGpx90DHp+RptqwSsNd5JUcb8g5BXnJ71GU+Qv1z1PA/pW5HoUn2ZnZF3YyqHPWsWVZ4tz3LfvBwcgEZz+VZ06kZO0SKc41LqL2Hwrv3OOQvsDgVneL7qOLwtLbru2yMq5xxnIOM/gaDqLxPsjjWQtzjhskew6U7W7CfWtBu5ZLNoBFH5yN6bRzx05ANKro/eO3DU+SvCcnpc9a+DX/JKdF/7b/wDo6Sij4N/8kp0f/tv/AOjnor4fEfxZerPuI7HC/tHff8Nf9vP/ALSrzbwRaLLfT3TdYQAo92/+sDXpH7Rv3/DX/bz/AO0q8r8HytFrqBGwGR1YfhkfqK+nyn/donDmCbozSfQ9LT0DNkfpVlD+Z6ZqtGfk5ZgR+Oasod38OcgfhXoyPgKhcUfgen409T/vc1HEfkJK/j71Oh7Hn27VzSOORKf4s8HHelz3G6k/4Dx0NKfvn5fm9M1mYkg9u/FSfNznk9cVEPQN3qUey4J/Ws2ZMCNj/e59qmH+92xxUO3v0OO/NSL8vTdn2qGTImQdy2XoP8XqOlN9vm+tOz97fxiszMw7+Nt5xx0wKZahvORB+OecVp6hBuTf1x2qjbld+8r0612RleB306l6ZDdxMj7w2fnyTj1rXlf51IZqz7mXfbuB9xTk1fibzbeJwv8AD0PWs5vZsVVtxTaInPyDPPNVpjuTD9/SrFyNmE7Hvmqiv975eff6Vcdrk01pcpGKR85bjOc/WprNJEdU8z5DyQTxxS3E/ccHHeobWRpblonbHXj1xWzu46nX7zidJC6W9sp3YIG41iXxttQmdYlV5TkhhwD+Pf8AGq89vK/HmNk8kdazLa7lt7kBVw4OVIFZ0sNZucXqVRpbyTuxpkkTKCRgRwOSAfyqrsaL5Hj+Q8574+tXtWuZLqYSyKofHvVVJvtCeXJ8hByG9K9CnzcqbR6FO/LcpSS+a/7qLYo4wMkk/jWfc+Yz7DjHvnmtKZZonbPY9cc8+9UXCxOSjK7H0Gf1rrpNLY7qbW6IFs/nbYrc/dFTRRNF993R0IIwme9acNizWyELn+LjPWiBPKm8uVvLfspByc0SrXuhOve/WxbtLj7WifKwkQDJLckflT4odu5nbnJ4OB+OfpUb6Q1xdgpeQxknnfx/KrRZosRu6u65wyEDgehrhlKN/dOKbj9l7lS4Efnfu0nJAwxfBA/KrdhJ9nm5ZeO7jp9CaayLsc7kyMYAIx+RJrPmuPKTAbknscA4PtTUeePKJr2i5TsLvUYIrMyMvbOPcVxe6S8uQNpEZPQHk5p6efcOI5ZHfsBy2K1DZfZ7Zi68ZztTI+nUVEKcaGnVk0qccPdbtmrpFxYWsJPkW8WzguiAsT7nrWF441OS60W9+ybkjYDdj7zAHnOOgxT7Sxv7iaXy4EMcXzckg4x2qG4aBLaZ7tWMQVt4dSePpx2rD2FP2jle5pQjy14zbvqj074On/i1Gj/Wfn/ts9FHwdGfhXo495z/AORnor4/EfxZerP0Kn8KOG/aO+/4a/7ev/aVeYeCI1fWmZ/vLEzJz3JA/kTXp/7Rv+s8Nf8Abz/7Srynwcf+Kjtl9Qw/Q19NlX+6x+ZwY5XozS7HpyL94hePXr0qyg2bex4+Wq+Op2/Tmpo/4fbAJr0JH59MsKdufvAYIqynYs2PXNVk/hx6frVpD8nP4dq55HLMkX769+Kft+6S2e3vSZ+6R1pyfwnrzWTOdsdj7vapPr26GnLt6lW46fjTh9xf51m2YtifL24b0POaf7dAPTimD7/PPvQT0G3OBjJqbCJR7fWlzv5qMDv+eKdnr3PT2pWIsDr58LoeQQQD+FYjfI+wswI7Vux/cx83P6Vl6hGyOxf+L074rSi7SsdNCWvKR23zxSxnuP5VatpP9Ei+bLrlazra48q4A3Zz+XIq9b/6l0KqCrcHp1q6kdTeomlqEh83O9v06VWdfkYvwT179anY/exu9qrMPnwW49TVQQoGfdN5SeYrY7k46GmaTc+bqSZ6gH8qTVE2od/U8Djrj3rO0H97q5AZsYI7+hrsSTpNnpwpqVCUmdNdvs3Oi889PSuce9jluVjVdvz/AHe1bt0/yLnjIIJ6VykEX/E0wPUn8qeGgrNsWCguV3NUQ70YdM9Ac1kXSeVNs5Gec+lawkaKVUO7B6HNR61YNFCkr7XR1DZHvW1OdpJPqb0ZWmk9mZRuZlznkDBxkYxULGPfnvn7tMMbS7inIA5qJ42V8vuBI44NdsYR7noqKOjXVVisXj2FC6EK2cgismOSOWZvMl2Z7ck49OBms9biRHKCRgOmB3H06U8mR3ARVwTjBwvP4YrNYdQb8xQw8YXt1NW31SeJ9glZ4/4Vf0Ptwavtcq2BPKseVzuBJwPw5rnUgnZ9kahH6Nsk44qVoLn5d3lbRzx2/SolRi3o7EzoQbuXUuJLd8B3HB2t0yPxqpLOu/J5B7n1/GrMCebD5BbkZK8dKrRxr5xjKs5PXmqikr9xRUU2zSsPndHC4HPbvWjI+5G3ovIHQKD+gqgkH2eF5B16YTO4Z6cZ6U6K6VOJHU5zhsZP9TXLUSk7rocs480uZFyOaf7O6I8qREfNg9j9B/Sq+pQLcadcoFUubd/LVIxubKkdgCeajkuIYkGz5yf4wxA4+oBqKC483e7so6ZHDDP4/wBazdO/vJWLoxcZKXZnq/wb/wCSV6P9Z/8A0fJRS/B9f+LXaOP9qf8A9HSUV8PiP4svVn3tN3ijhP2jf9Z4a/7ef/aVeTeEmVfEdpllGSwyfUg16z+0d9/w1/29f+0q8Y0lpE1a1MCh5fNXYp7nPH619NlX+6L5nJio80ZLuj2NfXbkDuKeo+6PmzTI93y54xUyj8D/ACrvbPzqejJVGzo3FSEdH+hqNP4Rt4HOKlDfdHT1HWspHPLctw/wfN74zU23sVx3qrG/b8qsh/vAfoK55LU5Zp3JFHyNlvpT1T8qRD/vY9+Kkxt2/wCetZNmLYzC/KduKNnb1H60/wDl/u0e44Pb1pXFciPYHv17CpIxt/UgfSgjtSgem4ZzQ3oFxwHQbuaralHuh3lc47fWrA+/nv608jemx161N7STHTdpJnMGRYrlW8tT3q7Ffqkzh1wJME+1RT2/ztleRwaqXQZNinjPJA5PFd/LGdj00o1LI2WXzU3pyPbjoKrMn545FaGkqsulKG5OTUjWq/KDzg1xqtFScexyy/duzMSUK6ESbSD1UjP+eadpywROyRRICwySMgg1curPfvQfJ6Ejj86rQ2UiTK5RinTA5x+VdHPFxtc3jUvBq9ipeR+buMbZOT8pAA4rItrZYryWR2Uegzk5rXeL98X2rs7Z5xVGY7JvuqQfwIzXVSbS5Uzsoz93lXUp3csPV25zxSvdedbIjvgLwMDOR6ZqZ4o7hGDp+fFUZ7dU+SNVGO/PeumPK/U6ocrSWzRdhstqYLYUjOAOcn9aytS2phPP83HfZg/T3rb0z7S6bJdp9CGGR9aytaeSJ9j/ADxnOfkBH+IpUpP2m5pQm/auLdzmZNyTYRmwP5Vdsf8AXIXfkHjPWo4rdrh8Rx5J655FTx20kMwI2kA/wcivUnNNW6np1JJxtfU1rh96EO2D17dabKFfALbDjJFRu3mupTaGU8EpkU+KOWV98qu53ZUAYGB6f/rrh2OG1ldvUfZWkss28LgkHknGeK0LjTGtYVO1S5GGbJzjFMim+yv5m18NuIB4OM+/X8KtyXO/f82UI4PXArnnOblpsctSdTm02M6JG+583PHTij7O3dmcDpsP/wBalluFifYNuQe4/wAmpY9SW1hfeqkEH5cAbvT361b5t0i1zaWW5TnH3CVcqOhzwKgium34klYp/d3HH480RxfaMAI3BJ+RDnFQyXH2d8SSRbEP8R9PWteVWt1OunB35ep7V8Io2i+Gmlxuu1le4DD0InkopvwhmaX4Y6VM7biz3BY+pM8lFfnuI/iy9WfZwuoo4P8AaN+/4a/7ev8A2lXlHg9FfxJaeYFIBZsEZ5AJH6ivV/2jfv8Ahr/t5/8AaVeN6Netp+sW90GYBH+bb12ngj8s19Jln+6R+ZzYqLlCUV2PZo0+Tjt2qwq7UHzdulQx/J169/rU6jbjHJrskfm1V62GfRcY4pwPb261IY+w7Ugj/wBr8/rU3Mrocn6cn8qswv8AdJ+8TVYHt823P44p4P4+lRJXMpK5eX0De351L7nr6VDC/Yr+Yqf+XXmuZ7nLLQVB8gHzcjpmne+33z9KYBu53c98U8ffUGoZDGEdvxPrTx/s/hQT2HX+lJ7buaYDsd/0qUVEp7GpRWFXuOBk6gjec2F4PNY17J9wnaM8H3FdHfJuRd/QZ6e9YGojZ5Z+UHOfoK9DDSukejh37yRtaCd9mybskEfyrRrL8PPvtpQemRWo3yV5ddfvpE10RTRq6Z79jVVo9n8NXX9O9Rsm/OfwFbUpe7Y45NpmbJBv528jO3AwBmqFzp6yo2G6fdD9q3JB/unPSqc0qxJj5c56DuK7Kc5aWOilVldWOWCqjsJH2EZDeuRW3dwaMmlRSQTJJcFFGA+W57lfSqeoQK6ec65YcMevFYrp86EyMUzwdvb6V0ypOvKMlK1j2qTjUVzoEjWJN8crOSAeOD09jUV8kD6dNJIrSEHO13PFYv8AaEtuijZlDhWMg7ioLi/luI/L8zGf4dmMjHsK3jh5cydxww01JSvoZlrdMlzgNwXyRk5zWo4idi3QEdBVOLT/AJPMdWPcAUqxSS7gqgD6Hj9a9CfK3dHfU5ZPR2sadpPBEhLxb+yjkDJ6VYktZns/tUEaSKPvOJOBjtgj+VGnWcH2djPcshjzhY0znj1AxUl8NLSX/RY5iAMsHJwT78151Sp7/unLeKl3MeOZfnd1eQrgDuPyq4l03+rCbF4wpI6U0yx7FCWKRtn7/NMlhmR0LxNHkdSc8fSuhSjLfQuVnuRTGT7SH8tsdCRjHP1zUF7FsRj9plRD/Dzt6+3FWZ4PukbDu6dCOKjdPNtj5rJvHQbRz7dK0i1ozWnNKxWs4bLYSsalx1Y57fjRc6R9rhcpKqR4we3UVasoFlTynfymPZ+OKlEezciLhR1BfqcdxRKdm7bl+1andHrXwcX/AItZpPrun/8ARz0Vb+GUaxeBLKNVQBZ7kAL0H7+Tiivz3EX9rL1Z9lT96CZ5z+0d/rPDX/b1/wC0q8QtlaWeNQuWLAADqea9v/aO+/4a/wC3r/2lXjmg7f7e0/PH+kxkn/gQr6TK/wDdF8zCs7XZ7Qn/AAInH6VMo9V5PT8KSJPwIGBn6VOib34bgdMV0yZ+ZVXqxQOvy4PU0u3d03ZBp6j5P4sjr3qRR93PFZuRzOViqw77WAzTc/7PBPT0NXTHu3D6Hnk1GYepH5Y70KaBTQkb7Od2c8gVcif5PqTVNPk/u54yMdOKlQ+jc+lRJXIqK5ez6dzQW7Dbk5x+FMjbeid/UfSnMN+PSsOpzPR6i577sjt0oUfdNAHfr9FxSgUhMQfM4z0HSpUptPWsKz0ZpT3RBe/6njrXN6jH5qJ8vHQ84rpb3/jzc7cnFczdO0roi7Rz6V2YJ3in2O+gmpXLOi3vlTeS+3Y+Af8AZxXRkbK52LTGTYzJgn7xTn9DW956v1XntWOLjefNTW5NeUW7pj2NRMfvYanb9+4HqO1Mb5Xz6c4qKem5wy3IZB2PU9D6VmzjfMSnetCaVVTO5azWKvtPc8V20+50UU9xlwi/Y5SeyEdOawo7Ke4R/JjZyvzcZ6AVq6kGW2XZwcnOKy4b+9sn8yGXZldpJAPBrspKfK3Dc9bCp8ujMqWddnlnnnNTw2s/kx3ZiXyy4QN15qBrG5lm/cOoHbOBn+lWbazuXhe3LsSTuxGxxn6d/rXZOVoqz1PSbio7lvYvTb+B7fhURVWeIIuCD24NR3Oj38X+sDHjIBzTLWJkl5THHB55z25qU4tXTuYJRs2pGgWgS7QpvICkuDjOR6E8GrVs8nEiSbHAC7+gGf5msy2lk84ky7P4VPH5VsW+o6bZOnmM19KeM7CURcdPmwc/pXHXvDRK9yJQbskZd/qPn3bSHd5jENkDA+ox9BUjalaSo53XTuoHDkFQc96bcQrqrtPPJHaRoTsTncqnsB3/ADqewtLa12SSeVLtfGyQAggew9fpVtw5FpZouXs1HXcpSzNeuz+SqR5yflAH4HjNXI4raBECO0oIJJ8s9fp1OKW5uYpd0iRuXL5BJ4PoPbFI17LFjdbRvHL2kQkNg5wfXBoblyq2hF27K1kZszrvSXawJAPKdh/SgTsiB9uM9nUjj1/Cpru/WW5b9wkfAG1PlHTn/Go7u/8AtGzzZWkI4RXbOwDtya3Sm0ro6oK7Wh6z8Jp2uPhvps7bd8klw5/GeSil+E8X2f4cabFuyI5Lhd3rieTmivg8T/Gl6s+3p25FY4D9o37/AIa/7ef/AGlXiFv/AK+M9w1e3/tG/f8ADX/bz/7SrxvREWXW9PjdQ4e5jBU9CCw4r6XLP90XzMa2lz2yEbOi8nt3xVtQvI69Of51BHH3LZ9MduKsInYc84rokz8urv32SIfn+nr1qVE6fdIz1psY7buh44zUyp1/u/lWDZySYgTv82O9L655FOVPugN3qTZu53cf1qHIzbKrRbN2PpnFMB7bef8AGrzJv+/9MCqksTK69+aqMr7lxlfRj4/lwQ3fpVr3FV0/h+bGOpp8R7Hv0zUSM5K5Ljt174NOH36P4Pekz/KsmyRw+/Ui1GP92pFrkrNm9Ihufktnz0xg1l6TBG945MeQAcZrRv8A/jzl7e/Sq+iR7d7+taU6rp4WUludlNe9buXWj/yaayfnU7j5/WmVnSruaTOWrTSbRFIPkYjqO461C0rInz847jrVlju47kdKpXDbM4ZgD2+ldlPXoZxWttyjdy+b853bB049qo/Tjn86nmuVi6Nv/rVR75eflcP/ALuf616NOLS2PQpwdrJEtxJvhweM/wAxWPcJ23fJ2Jx0q292ro33hgj5SuDzUSRNcOyJsOB3bA/DNdFP3dzrpRcdykl1JasNjYBGPk46/jWrbzR7MhVEmwgngZGPbvzWTJYea/liRQB6uMf4UhWOHHlvceaDhgG4JB+laVIxkrLdnXKnGa31O6tZItQswl18ka4Ks+fm4/WsWWL57tDFFJHnG+E4wB39BXM3OtX+wRorCMcbTmrlrrd7Lb/Z3dvLIwcMcYx3HI/SuVYGpT95PQyWFnCNySysV1LUlht1ljjJ2sXIOBnvWtf6KulQxPEUm8yXYvmRFZV4+vQY9OtQw3y2tgiJsR2OGbAY4I6/UcUx9TtrdnaJZLq7zgPON4H05yKxqxxEqsZRfurp3NY1fdlFx16DjPaWVzKNQt7iWRh22yZ/HjHFR3dxaXsMMNrCsaRfM7cKZD+H+NUnkld2lk/eM3GdmVOeDTLWP7PC+WyoyeO1dUcOvib1J5Va/Utx28abhImxD3TPapVuG+xtbmJZYy/yNJnK/Q5wDSafdW11bt5m/wA1TwvBVh3980jReVDk7AoPDHPGfSk1dtSRk207S3Mu+sZPKDpGuSMZC4xjvxVZdM2bZDveTqVDfex6EitWWZYvnK5Tkbucf4VHDNHcbfL3cc567cda6lOSj5HXSqzVrrQ9X+Fk63Hw70+cDykkluWCZ+6DcScZopnwmga3+HGnQO3zxy3Knb0yJ5OlFfn+Iv7aXqz7unbkRwH7Rv3/AA1/28/+0q8Pt3aKeN0Zg4cEEdQc17h+0b9/w1/28/8AtKvDoP8AXr9a+nyz/dF8zCr1Pf0/uHjNWYk+8d2MHIzVeB1l+dJM/T3q9AnouOx961qM/LMS7TY5R6d6mVdvv25p6r6KvPQ05E+965rmlI4W7jRHv3duelLjf7CpAvyUmz8qyc0LlYAfxHrTJY9+fXHBqYUEVCq2ZfK9yl/d9j+tPz8n1PU05oe696ao7+nGK6FJNEsl9PmwfzpwHfvUanb9MVIDUSRI8CngUwVID3rgrStuddFdSlqR/wBG2bsBzg/hU+mw7LNccZ5qhqEm+5ijTaSMnFa8a7IVB9M8VOLap4eMO510Y3k30Q2Q/wCzUMr+VC0jdBzxSXVytujSP27VzN3qM9w6GRvKjLYGUz09ulbYLDynG+xDpe1m7bGld6xBFC5VegzyR3/nWJfXUkqMxkdARyoG0n9eKq3dyrQv5fM3ZzjGB9eR+NYyXkibgWdyeuegz/WvocPhVY9DD4NJXW502m/vt5uOAjr8qcgD2/KtWbRdySsJVSNclQ6Dp7np+XNc3plysCN9oZkkfAOTjaRXTRarE9t9nu490RH8BBORjGDnFcmMjWhJOnsRUi4VH2Ofkj3O6FdrHOF78cDHc1Tlt57f59zOT2wc4/Gtt9KnuofOSJJAG3cNhgPqev4VrWNv5v8AokkCQuMMsrbZAoB9Dgg9qjE5lHD0nNLma6G+HtUqRi3ZM4z+yr5LZ7p42jiPG9+Oo7VXa6uYrN4UkcKT8wzgNjvXo2o6T9tubjy51MT22za5+XzA2Vwo4wOR+QrzjVNOnsrhlnjZJB9fmz0rPI87p5nzRmrSXQ9nF4NYeUbO6fUzZH7/ADd81dsLxUR0dEIORygzyMfWqMqdwrenQ9aWJlVHJXoMfn0r6iUYyiYOKasbdi0M6MjqyOM4ZHwD7c0PF9ldkPAPJ9SBVG11GO1hYPGxfHGAMfjUS6jJcTb3ZQCOOoxiub2UuZvoc3sZuTfQ0kfcnG/B9BgH69jTkDb0Hzc84Ixjn1qrbTRvd7J4p/LHXYeSce9XvlaZyibMdA45NZydnaxnUXLoWIoo7dMllyTyQckEn37UpZXhUSo5Q8EIx6e/GKdHB8+HVjuHQjuKetwtrulSBJNmBtk5BrnlfpqzlTvLzJ4ZdJitHjFsoJyGD4kwc8HO7H6VlQwL52Y4lDsOV2YU89scUyK4ubq+uLh49jStkKmQOfp/9atOxhuWuH2wM4ALZQ/49/xqVH2Sbb3NneLte9z0r4Zjb4FtV4yLm7/9KJKKj+GU63fgCzuEBVZZ7l1VhyAbiQ80V8XW/iy9WffUm1BHnX7R3+s8Nf8Ab1/7SrxOyjWW8gjfhGkUMR6E17Z+0d/rPDX/AG9f+0q8Pj/1i/nX1GU/7qiau7seq2OqyW+z5scc10Vl4gjd/n5yeMY9K4uKTzf3iL8hGQRzxnt+FX7KSOHdKIt7g8Keg4PNetWo05xvY+HxOEpzbutT0eJt6IR+VSj0/lXM22rNcQsZIo0QD5QAck/nSh7l3BjbnBLDfxj6HBrxZYd3d9Dwfqji2mzpwv8AvUu3sPxFYjPKqO32uQSgZxuwCfoRTbRrmW4Tz535J+VpDhv1/pXO8PKzd9AVGNr31N35fmpDTI7Zokbc34BjwKV5Vi271bYeNx7fnXLdJ6O4nTfUdVZhsdh261bYfjzUMqM/TitKNW8mjGdOyEHzopDYPcUqntupPm6UJ7L+NbSuluY6N6E6ilJ2IxPbrSqNlZ2rXnlQ+XHy7/yrhhGVWqonoRjZLuxtnH9ovGmbueMVo3d4tum0fPIR8qjvWPYXP2ezaWf5P7u9Dk+/bNVmvI05Enmu4yxBB5PHJ6V2VcI61VN7I1hzQi0luTOd7mSZsk9h/IVauLGC4hQeWvGffk/1qlbM3nJI21yMjCZwuf61dik2PLEvII3Jzzk11SUotculjnldPR6nOXFj5T7E5BGOev41m3tjJFYPMi43Ahh3OD/Kunlt9/zhsZ49aztcX/iVNEi4cZ2/Lgk9f5V30qzckjvw2Ik5JXOQSaPYPn3OemSRxWxpQuZd0pVjG2FRBzyTVPSdIlNzvmTAHAQ8ngelehaFon2d0nnijIPIUdV59x/Wlm2ZUMJSberPVnapP2UOpJpO63hSGTaekhQqRsUnjr/OrkRsYrl445I/PkBJU4D5Jz9R9Ks6ltt7abZH5hCYBdidoHXr6cmuQvXaKbe8SB3HzErvJz2y2T+VfF5dz5tGVWS5Lk4unDBVVTi+br8zrmvrS3RFur2zjKR4kiLLneRzwTzx+Nef+KNZg1W5Ro4tsSR8EoAcD/ParsU9zysd3KMZC7HIFIdB+26a9/cSS7w4BxySD1+uB6V6eUZLQyqu8RKTlKWhvWzSWJjGnKNlHscjIY3xsLhxwwOMdPan+XB5Lgpsk6jHINdNDZ6X5qLZRCSQAtI86cYPfjng1PJpclxZhJI1t0bBkeZMPyf4T6YFfTPHw2OeVeKdtkcBcq0XBbr17Gm26s77E3bjnIGMVtanof8AZt2kcrK+7mOSNgy7c8ZqVdLtg+QbgEcg5XH6Zr0FioOCcdbnU60FEdabRCo25zjJwOT+PerCpNvf5GX1CA/r2p0cS2/ybmKDo2zcOfwqysDSoZEiYxDqQM9PfpXHOornmzlq2NB2cOykgZGzjkUzyujbeON2Senap1smf540Tg8b5Bnj8aldbiLZhlJIwWjORk9Ae2eKx9otkY8yT0ZXh2JguilumOuPpzT7nVILVN8CuZBx1Heo5YFROdxJBBzzg4/M1lPGvnAMjEAA47fiKtQjPVmtKEZS5n0PWvhGGi+Gmlo68q9wCP8AtvJRWh8PEVPBNiEVUH7zgD/po1FfE4iyrS9WfoNGSlTTseZftG/f8Nf9vP8A7Srw1fy969y/aO/1nhr/ALev/aVeQeHbeC78Q6Za3Me+3mu4o5FyRuUuARkdODX0OAm4YHmXS5NR2ep9A6V4PtF0KxS7jaG+FpFFLhg2xwoBAzkdfSsfWfCn9mobqyl3RquW3kfL7+9doYblP34uWCHkQhBtx7nG48+9cZq+tz6xN9kMXkwgneAwO8g+px0xXy+RYrMcRim41eamnr5HzeYOjDeNpMrWI810Ty1zyAOK14NPZ+EbYCPmYdSfSq1t9mtUDlPMIGAvX/IrUgnWVFdGVAeq4H86+sxNWWvKfI1qjvdbFd9N+6CzEnOSeg/M0iafJFhzKwCj+AcnP1rSVt/Rsjjpg4prfJvd5cE8dOK5fbTsrmCqPUfbSM6Zftwc1FqM8f2aXO3hCce9LH+92nynx/eGMGobqNZXSPYp5+bPPFc8aVP23MXGpJJLoO0i7+1WIzuDJxz3q6U/WsO9jW1vIo4/kVxkqOADmtmym86H724jqazxVPktWgatKb5WRY2PtHbtTwP/ANVPkT5/X2pP5VTqc8E4nH7PklZiTzxWsJkmlVFHPJrnJdStJblvKXziW5eTGPwFM8Ubt8JLMUAPH41W0SwaabezZRc5ORnmvRwmDhSpe1k7tnpxjB0vaMg1HUoZZvIKM4GPmf0x7deKLOGWWZozwp52p0xT5NMWV7nO1PKOdp7f41LpJZLnKvtB4J7dP0r0LxVN8hrKUVS9w1rqFrezIjb5xyuzqCKy4b2ZrbeOHhbDZGDjPH863rkxJDvl2x47H1rlrqdZZj5aqO47CsKK9ommjlw3vppo121mBM7IGdB/EXANZd5cLe3CSCNgVHR3DCq6+g5I7dsVJHOqIyNErg843bdprdUVDVas6Y04wd4rUu2t79ifzfLikcHKlyDz+BrWh8SSO7TTweecfKscRwPzY81zxngdBsiUD+LDn9ccUNHt27OAwxtJrlxGBo4j+LHU1hOVPbQ2j4k83zQkbQMScSSPkj34T36Vn3d7bPCu+5Z5D87Mc/ePpx0rNki+fJZOOyYbNQLHvdcNgj+HtV4fBUKH8PRFuHtHzM0TcxxTJ5c6nPJxkDP4gVtyy6XdpaJHexH7M/7ze2BtIBbPHzfga5q3nW3mxPbyXMac7PmUVbu9V0h0KR6EiSYwrm45z+Vc2KpqtUjGLb9Dpp4aSXNymxpTwJqtxDNtjglleWOTYsgdC2QC4B2jHbIrQ8QvB9phLrujjKlhC+cx+uBz0Jrhmu7PZzpsQB5GZCR07Y6frV+wsYNQtml8q678RnCHjrubgfSvNo5C8PiPrE6kmuz8zvzHHRxFNRdNRtb8DQls49Vv5ZIJXSBBtQPnO0fXmkudDZbeI2geVwcMoXbgf8CP8qrtay2VuBbysMdUyemO5UgGoLy3aJA7pG5Yfej3nH5nivaipJpRloeAn73xaDXiVNu9/wB4MgKOSD784pY7hre0ltw37qUqcGMYHf3p9lBsdXvFxGRhTkdf681oXMMESI4gbY4Bzk7fz7YzWlSrBNRnqNza91akFvNvhCp6DvwBipmdd6TpCSMZKuuBRGItigKqOepKs27t0xzTLeNHwkMih88ru5J/z7VDabOZ2u3Yr3ksNw6ZjUN1IAC8Z9MYqq0GzOJVHc4PYH0qU2k+9iV4zuZnGMZ65q/HbQbEmnZpNnIhhiOWJ9yME47Vo6sacVY6qckpJX0O4+FVw118OtOuJcb5ZLl229MmeTpRS/C63ay+H2n2siurxyTjDLtbHnSYOPcUV8dW/iS9T9FpNOC5Tz39o37/AIa/7ev/AGlXhqe27I5GDivcv2jfv+Gv+3n/ANpV4jDBLcOkcMbPI7hURASzEnoB3r6LAW+pe9tqTU+I+mLbxTbX+m2U/wC7hmmiSQQ7/u5UEjJ64JrjLiX7PqryOqyrJkk5GGOc/wAORXpVjZxWszLBCsaIgXYgyuB0C+gHtXPar4Si1CaW6tpFgkPLRMABuPuOK+TyPOMvw2Lnh0rX6+Z4GOwGIqx9vJ3Rl2Op229D5mwnAIPtXSRDem9OU9h61zWk+EWu4WkmllhTHy4Rhk/RgKdPp+oeH38xJWeAnlvavoq+JwuJqexo1PePnJ4CUV7SzsdIdvzZX2OVqPy4/JZ4/lHcY/pVXzFuLZH3MhPzZjyTmrIEFxb+Z9+Vfu5zziuSbdG0m3Y4YwVS8drEfzL8hbKgDkLUSDypnnmb5EGF29PyAzmtK1sZ7hMybU7t1P8AkVpxWcdvyOQRjsM15eM4hwuGk6e8mejgcjxOIXNa0Ti728XznKx7ynKs3T8qoWmsXct+odsYflRwpFWb5FfUrop8gMhUj6VV0i3+0ak5/gDce+K+voqlLD8zXQ5eWMOaPY7FfmTJ5NQ1P9xMCoX+X+GvAoTvOUehzYiNop9TK1uya4s96dUOfwqp4cVt8ydRjr71ut8/ynoawT5ui3hkRVdGyVzkD6V7NKpKVJ0luKjJSi6ZPqG23mYOrKkvBxjvWEbqO33Ig57sDijUtTnvf9ZtCf3U4Az+tZvlb+TuJ7c/dr0sPRagufc7aNBKPvMsT3k9w5V3bIwPXimY7D9aVI9n8Pfp0NPI37T39a6UorRI20WiWgLH8jOfoPSpVj/2dwPAXGff+VRg/rxWlYhUf7Q67xGMhenNZVZOMWzNytuTafoyyuhmkWGNydgPVsf7PU1WuBEmsS2kMcl0kf3iimM/Qda1Igst/wD2jJbNIQ+YC7MojXH3VVTz9TU8mrS2iM8awQTP/wAtB5jFf/HSAfxr4zFTzevWfsnyxPpMHWyfDx/fJ1J/gZ1pYXdxM4t9Jih7xmTcz4+jEr+lcxrB1u0v5du62kTJ+WIJ0PsAK6eLxPPvSN5t7k53YIb/AArbttV0/U4fL1NovMTkM6glq83HZdm2Gj7aUnNdj6DKuJMuhV9nUw6jFniWoavrN3uhu9Ru5I+8ZlYr+XSrosb638FP58Sw2s15FLExPX5JAeB68da9hfSvB926wmC2kkB58tv4vwNO8S6JbaroKaWGe2hQqUaMFtpHtnngmscrx+IpYyk6kHFXu7ndmec5e8PKNFang9uH+YFlJGcY5NdV4T+02ly2pCZUghwHUEHdntiuu0/4c6fa4d55J8gny3wARnjpz+Fcdrvh+D+1Xg0y7ZyTl15QKc8j3+tfp8czw+PvSp7eh8ZUq06rdNvRm7dTf2hdtd2cDeQOQEJIAHWtOy1Ge9szbC2llK8uEOSVHbg5rK0cWVvpH2XfEk3d/M3bsj+7n1FRST3dlNLHG2wOoDqQCMHn+dczp8y9nFbbHjTpRlJxttsX9Vu9rpBHB5IUhtrkhgPeumhMlxM+oLPsLIqt8v8AD1xnOe+cVyluLSW0RhtadAQqiPIbJ/z/AJJro9Pv4kttkkLg/wAOyA4cVw4qn7qVtUc9VOMVGBba3spd/wB0l8/MBH8rY7gjNU/sGl6ejTx3qyXaDEaB1xgn+6CTx70xY5rh5Xktoo+6iQZJA+hGKydVmu9IS0BtLUpc/daNDuwMZzn61xrDVHNRVRpP+rG1CbcZR5dbGi0VzqFy3mSRJGuN7Z79qtRTr88brhxw0WQM4H9OvFVoI7+4iZmnaISYBHQEe3pWPrN5HpVxb2iRNvPzCZGPXPfjmu9Q55clzkpUfazUU9j0/wCHt2uoeDba6R9yST3GxsY+UTyBf0oqj8Ht3/Cr9J3Z+9P/AOjnor5avpVl6n6dRShTUUcH+0d/rPDX/b1/7SrxS0uGtLmKcKrmKQOFJYA45xlSGH4EGvbP2jvv+Gv+3r/2lXhi+nc19Jl8OfBcve5E37x9UJqNjLYW6afJGkd1B5tsiblLI3IIGMjI9a43XdTuZdY/0GZZLWA4jHJDEDqezZIrW0XWYLLSrK0/eE21ulup+55m1ducE4ycetYNvdxWs1w1xAwuXcsQQTtzyBzXzOQ5A8NiqtSvC/Y8LN81jVpxhh+m50Fr45ni3Je2G0dmVumfUU7WPEltqGl+VbNI7ycBUA+XB796529kkv4nmkPkW44YjBLYH69KTw/PbTXK2lpA0YPJlPLkj+X4V7csjwdGf1uMbSj9x5EsVXqUeRs6nQrG9SzCPDsYkfMee9bMVmtu7h9oyewyWJ9qnYtCiRjam0cEEZP5+9Vr66Zd8ZtpMEDbJHLGFz+JB4r4PF5tmGOrShRjaJ62Hy3BYdKpWld9TQysWwDjcQAHQk//AFqp3kEr3P2t5WRIUJWJSQPxAOCenWom1SDyWE+1yg3HY+7bgY9c1zms+Jp5ne0h8uCEIMlxk/TFcWWcNY/EYtTqK0VuelVzvC0qDp0NW/wKPmea8r9X+Z/u9yat6KqpMPqQAOtUIN32RinTGOeuOa0NASTzsHjH6iv1jEJUsPJdEj8/neTdtzos9vSoWO7Py1aMexOW4qFjXzGFrJu8UGJptK0mVW8zoir9SaJYI7hHjm+dG9f4alb74Ipp+5Xr80pWa0secpezZxV9p0trctG/A6BscYqtvVPkz179q71lWVGjmVXHvWdcaZEqb4Ild+wOK9SljdEpnfTxiaSaOWWJW2jcpz3FSiD5xhPXgZx0rTktbm3f5LFiPXqKkC6lLy6+SmPvY5rd4i+xq63W+hlfYp/m3xOO+cVZlb7PZ+UFzIeX/wBnH6VfEcnZmJ9SSW/+t0qhdQv/AHcDseu3JqVNzauyVV5nZk9lrn2e28me285B90h9v9OlPF3banuxC0Eh5VXcMrH0Bxwax5AwwX28dDmo3LPt9O560/qkeZzi9Tfli9kJqdhs37OHQ429xzVaz1P7PxO8qOpAWSPhgB+I/Or4vfNRY7vc6AbVYYyv+NZeoWckWJHXfG3KMOQa6qfvLkqHVR1XJM0Fbe6XEMrF3+8+euf/AK9dfpmqebbeTNJyRgMevSvM7a+ksnbHKE/MvQA10Vpe+aiSRyYTGT7YFc2Py+FSNpEYilOOsXodLr121vYSvDLg4xjGc5rhdKt5JrueYbhtjdzjkgjv+dWtb1tpoYoy7BRjcx6U+w8vTdO87zVlluRkAEMqoDnnHqR0ow9FYajype8x4enOnRbe7M6yZ4t4RcSHoSelXnt1t8pcMxmz8pQgjFSR6nBcII7iNYwZThxztB44HsKnvLf7O7x7t4PzI2B0PQ10Ob57SVmVOTUtUU4JZIuEkcDvg4z+VPMskvzvKxOMcueh/GmAdn+/nFSonccnpg1cox3sZtrcntBHE6OY94HUHkN+dbRe21C82WttbRpsG5PJjLOR3JxkAVgp/Fg4XrmtXS4FimS7uPkjjONx6E+nHWvPxVGD9/qtjJykr67ly+v/ALJqsGnm0kMDx+aWDjzHPJxHGvLKMZJ6D5qr68IH2/u1mk/glRyQGHXHNVtV1GJ9SeaOwgS4CBBcOuZFABxtOcLwT061WhK7Gjk/1bn7wHCn1Fedl2FxML1cRLfod+KeFapfV48rS182epfDnyP+EHshD/q/Mm9fvea+7r75oqj8Iv8AkmWldc7p/wD0fJRXiV9asvVn29FWpo4L9o77/hr/ALev/aVeGR/6wV7n+0d9/wANf9vX/tKvDUG4/wDAq+myp2wifqZ1T2mG5gaFdjt2wQQwPHuMUr6a2oTbEgvBcsMs8b/IuPY47e9PXTbK1itra3SW4kjhUGYcKxGBkpjIGB2NaNjcx26RJHe+XKf4Sdy+uCP4a2qVXy3p6M+DqT9lNuGpx+qaXPYTKBvMbdDIAjE+4zVvQJ1srzzpFbYm4kp+PpXS36SXcE1hcQwCRMOtyrEbcdMgAnvWDomh3EU0lxdLmFOCRg7c8cg1tGtGrQlGp/w5qq6lSfPujpdV8ZxylYbDzCmBmUKQT7DP86zk1O58lTLNFBFzvCEkkdOeadJGu/EcS89tiD9QKwdYeNlWMKofPIX+ED/GuXBZfh6cVCnEz9osTLU1n1FZ4j598oKgqijcSoxxjHA/OshXm2+WjPsY527iuce3SoobZnb92jvgemec1cWP50Hl7HHXgjFejClCndLUdo072Nix+SwyeM8Y6da6TSLdYLMP/E38q5RJfNeKBNuwcZPGSa7KH5IUQdgB+leLmsZOnyrqebKXs58z6k0ku7io6Md6K8mnSjTSUUc9Scqj5pMj9w3FIf8AeqTFJiuuEkc0okWd/tTh2z1oYd6aP92t2oyRhqmP/T3qnqEM0qb42zjqtXM9qM0oPkdzWM7NMxLeXf8AI7YI7nipZ4t+EDLg9eKvzWscvO3B9RVaSLykw/5jNdcaik7o2503dGNcW+3n5inrwCKpMi/N8vPUfhW9KN6MPmxjnvWRPbMrsUXC+ldtKd9zto1bqzM6Q/8A6x6mo0vPs+9DGrxH7ynHNWJE34+XGO30qo8X8fTnk12JRaszvptFHULDfCbu03GPkunVkH9R71n22r3+nwtFE7IkgwcZXj9M1tiZonV04I5Uj2qB9LttSfejrbzkjC87W+mOhrWM1GNqiuj0aVaPLaa0G6DFqGq3gTavlAhncovyrn1qxqvk28zQQcxg4z0yfwrrn0iTR9B+x6dF5s74MjqfmPHOO5/CvOrySfznE28OpGQ+c5HX6Vy4arHEVZSi7JGMJ+3qNx0SFupVR1RP4QMjPc9a2dL1hZbRLSZfnUnypM42n0+hrl3kZ9zvyc5IPFWY5YVT95G4PQOhGDzzkf8A1xXoV8PGUFpdnZPDqUbM6iYsyJnbx04HHNOjT7Qi4b5h0GMbqba3MF6ibLlZZMhf3nyk445zx+ta09u2hWyXDKgu5QxjBcfKBjJGD83WvJrYiNK0Xo2eT7OV3BLVFeILZJ5l3Gp3DKQ55fn8wKrvd/akUSSNlAAnGMVTLtcbneRnLEsx6gmtG1soLdFnv2YIeUiQfM5/oKckormm7slwS33HJZz3ti8hXBhwqM5A3ZPTJ61BYJ9rf7NuUSPkJubA6dPxpLm6kuMCRseWPkQdFB5xTm1KR7by1WKEkYLoPmfA9Tkjp2xUtVOWyW/4FQjd2Z6p8PljXwbbiCUyx/aLr59uzcftEmePTPTnp+VFU/hCrL8MtKRgVYPcAj0/fyUV8XWTVSXqfoFNWgjgv2jfv+Gv+3n/ANpV4hDI0UiukjAhsgg4II6V7f8AtG/f8Nf9vP8A7SrxG1ia4uYo125dwoz7mvqcq/3aPzMqltbnsWl6qrpHIW2CRAVbvgjuM46VclEdxmSNWkQEgtvUDn0BrCSL7qwRbCABl0IAA7Vqi42aasBkQOeWRF3hQO5OSR19K7qtNJpx6nwtaklNyh1LFvEXT/RR5QPOADg4HXB4qWKW682SN5U2vjcdnOBzg44FVILjf8jrsypPHQ/gcVLGyS25FvuDesjdaxlG26OWV1e5a+yXdxbGO1iDhOuGHv8Ama5a8hX7Sw8vDj7w5HIPPWu5dLaKwt0lZQzH/lnyCQO9TmLT4vNeeKxZ8cLIqgj6Z5Fc8Mb7J6RuhYev7OVmtzjNPkk3t8yA9gCP/wBVX1Rtksjsu/OMAg/yqpI0Wn3kyImUYMEbOeDVqKRZUQBVDY+YBD2rvk23zWtc1ra+8tmRWcjRXiuPv549q7eIbUXfye5/CuUtbX/iZRb15z19a6/+pryczqK6SOKu1KSsKPSjFGO9OBrxm+xkl3GmkxSj79B+XrWl2RZbjMUxvuVNmmNW9Ko9jCcVuRE9vTvTge1DDfTM9j+ddVuZGOzJajkXemHXing0jffqI3iyr6XRlywsmfkYoPu9s1Xyr7k/n1rQ1Kf7PYPIm3eOnAPWuQGuXKHJZSM9CABXpUITqRuj0MPRnWjzI0b2JuqrwM81jPK3zZ5ANSjXWl/1kSbO+zg81Ey+b88bfVXxn8K9CnGUdJI9KlTlTVpkPyy/X3qsSyPkNjHPBwetPk3J13DFMJ81MfNv7FvSupLTyOuK+46S18SS3FmbS6RZHx8rHj8D61Tlu/OQJKqzIOiyAH/64/CsJG2OTuxzmtCNml56noDxXK8JCm24q1zJ0lTd49SrrNhbLbLcW8DwuWAI3lwcjsDz+prAz8mSuOcDHNdxbRTXFtKolAiP397EKwz3HemOLKJ3KQWpkzjckI249geB+VaUcTKn7lr2Omli+VWlqzI0CxuUf7c0cqRKflfDYJ9M12ltql3bp5ZDIHGMlQB+IPBzWCl3LsSMf6tMhR6Drx6fhR5kpk3szHnq/wDFj61y4ml7eV6iWhyVqkqk3JOxt6ZZWMVyZZm2ls+WhUlR+JqpqVnPFeSpJJl+Sr5696huSyJCBuBaPnnjkmljv2XbHJ+9AyAsgDAfT0rCNOfNzp3RzLm+K9zMZWT5H+RhyO3T+dWre3TY0k8qwx5yWfuPb1/CjUbu2g2PHFvcgna/3eKwrm9ubqbz55GJJyMg4H+HFdqjOotNDto05VLPY9v+Gjxv4DtGhkEiNcXRR8Ebh9olwfXnrRVf4RpIvwz0tHHzK9wCM5/5byUV8LXVqsvVn3tJJQSucH+0b9/w1/28/wDtKvDY2ZZFIOMHIr3L9o37/hr/ALev/aVeJ2MSz3sEbswV5FUkehPNfTZV/usfmY1erZ6R53mxIUZwjKGw4zwRxnmnhGdMurAg8EkAc0pRf+WaEkngR8jn39K0ns2gSHJQbsnbvVicD2+tevKpFJLufF1JK+iLNufK/dRrKZCORtLEHPTrgVbt5oPlQxbCr7WfjaxH0zx+dZahUdEk2OepVwSMf0/GpVCoi7Im8wv1HIH5c/pXHOF3ucU4JrzZuzTwNNEkflTkANzh8D9DnirGoJaWVm13cRfaCuFA3ZrmoL9X4dV39dzp2/HNGp6hcy2awu37tMBT2/D1rneEk5RSehlGh76RlXF211fO6R7Iy/CgYwCfatazi+7L8zn157VhW5ztyzY9K3ozssfl64zXpVVZKKOrEaJRRf0q5WXVUQseC3X6cV1QrgdL837emNwL8d8jJrvoU/cphmOB1NfP5rFRlE4K1K0tB1IBTsU6vGcr7Gaj3IjSfWn1Gw/2q3g76MwmraodSGgH1oJrSzTIumiP61FL98H1qfFRMnr+ddlN+ZzyQL86feyakNQqvT731zUhPyVclqiUZOs/NZugbHf8jXEXEfT5uTXZ6kWlhdNyjsCfWuSuo9m75lfGemR2r2ME+VWPcy7SNipCF3rleK0oNvQqvrWanqNnPA7/AOeKnifZz3zXfNXPRqxuaTxqyfdVz6Pg1QksW4I4PfuP8amN1s/i/CoJrqRUA8xsE9BWUVJPQypqa0KssEkX3wcevY1bsCu/ynZEDdGc4AI9zUDBnf73zdz0xSrBI+4na6Y5J/zzWk7uNrnRo1aRrxXNo+iv5E2+WScDytpVsAehxxkiqmNn0PT6VmkbNzlMpn7wzlue9Oa5Z8HexxwN57VjRoSje7vcU6Ub+6aC+nympY/v43denp0qpbTLK6gth+nPANXrePzXUDqT069KdTS9zlqLl3J71v30SDkBF756jNV2TcjO74UddmM8VNeSK125C/Ip2gj0HFR/fhZDyh6jA4rGHwoiOljJkuWdyxYEA5AIyBj2pwe0l3eZG0ZOMtHjHX0PX8DV6PSvNR8NjaM7nGQMfzpsGmbnbdJgDG0j5mXPpnAP+fetpVIW03R2Qqwvoz1T4SP5vw10xwNu57ggZzj9/JRUfwg4+GGkn/anH/keSivga7vVl6s+6glyo4T9o37/AIa/7ef/AGlXiFvK0U8cicFXDAkdwa9v/aO+/wCGv+3n/wBpV4fbp5s6JnALAE+lfTZVG+Fiu9zKr1ueyNqWn3Xk/wBnwfu/LDYkLEknsBkgAdqiFxHKjzoE8wsAUMeS3PGOuO1ReG7y0TiR/wB2AN0bpkcdOPaur0G60+ye6jlbyfM53k7RgHOCe1b4qUsNB8kXJo+Jko+2cXpc5to5Htt6xIm3kc7Sfp+VTW/lpZymRG3nbtYsMrz37np6VowTz3HmuIFQHcd4ZiG5yxAzg49hU0kX2eGKaG5VLiYlgdoKkY5GAM/lmp9u5RsckpNe6Ybv2RMDovzH19v61DezyMiRyPvAO4CtEP8Aapt5iiQjIACADOfTHP481h6lN/pjRhskcE4ruo+80mjWmuaVl0FjC7+WwR61pWlwrIUeTuSAeRWJHN/tZx61Pby+VMpdcjPfpiuirC6NKtPmubZgZJlmi2gjGAccVsWV5PE6GaeRuejOT/OshLjyoRJ8p4ypzzk0gumd0c/f67ua86tS9qrSWxwSUn8ju8/JnrmkFRWz+bbRSbuoFSg/hXyEo+zk49iHrZjaaf0qU0xxWtKZhOAzHpRTsNSNXQp303OdxtqNztpDS0VrG/QzZWkGx+Kfu3w/gaWRPk445qKN+x5xnn2rsT5lcytYyrraiNvbk9M1zt6nyfdU4Ix6VsavOsVy8Z4HG3A9axLiTtG6++cdq9bDJ2TPbwkGkn3KsH8abunTNOZfRlOOppkZb/ZwepBHSlf12498c13dT0HuRn7/ADt4PXvUmze+TzgZwahyv95vercI++egPGactBy01I3VX+fp65qIt22sfQntVp09agZe4XmhNExkML/xbvqPWopk7hcHJJAqQr90Hoe9G7dn+dUaJ21I45fuEDkcH3ro7F/KeW4T7kQ3eoyRxj8TXP2Vt9ovFRVYkntiuvvbS0srSaOzuVeNG8pyZVJ8xcEqPcE81wY2tTjONLqya8G1dLQxgm9/1J7YqyqfI3oeKbBt2cfmParscfm/3smpnO2/Q86c7EtvFs0u5k74AB6Hk1nZVMgc9hjvW1eH7LZrY7V3vhpD/d9Kwrq2a4hljSXy2KMFI7Z7/rWNKXNeT6lYdXmk3Y9L+F0y3Hw6sZ0ztkmuWHGOtxIelFQ/B3/klmkjPO6fn/tu9FfHV0nVl6s/SYq0UjhP2jfv+Gv+3r/2lXh8DtFOjjqDkV7h+0b/AKzw1/28/wDtKvELWLzblELBQT1NfVZPf6tG3czq21udjZPO3zpKwJHDD2/xxXofhu3tLjw/Ldai20Icbjg9enB6c150F8riMI47AgnAHSug0rVWtYWgkggMMmUaV0JdNw7EenXpXr5lRnVprlPl8VT5tjbXUV/s1oI967iSrjGMentTF+ZFyisBnIfnP/6qW2uNP+eSzml5O4LOBlTnpxnP6UlxN++RvLljjORuOQOn0rghFL7NjyJp83KNdvnIZl56sBnHFZOpQrF14JIJ7HH1rVeRpfkjbYmMsf8A64qhqFxtmGzaQUC5J3V2Ur82hdBNSRSgOzOFwOeSOa2JoIpdISaFf3iZDn2IrFjCtglmI69CefrWtoRaUT277vLZDkAZ+n61piL2Ul0N66a9++xY0gwXVo+9tkgfBDkcg1NMIrdyBExI4XnrXOyK1rcuo3Als5PXj/69aFjd+fuSdmweFZz901lKjvNapmNSj9tPRnZ6FeLLbbCygnotbQX581wkDtaurxyLjPGDkV2Wl3P2q0WZ2Un2r5jNsM4Xqx2ZxKHvW6FwimEU400/NXhU5PR3LqJdhMei0wipaawrop1GmYTppoixTPu1LTTXo06pw1Kewz/IqBk+8XbqQeKs0xhvTHaumEuhi1Y53xDp/wBotlmjX54wdwx1Fckkm58FfnPZ8AfhmvRz/DntXE63p32e7aReY25X1Fe1gquns2exl2ITXs5EKeX8u9lz6Dpx9KimdW6fgOajU/IBu69R6VGW6fN716Kjqeio63EYf7XT86vW3yw5DdSSffFUcfOexPr7itBI9iINvOAT6c0qj0sOptYGbZx155NR/cyfXpSt7cUfwfdxUoxRE9QN/wCOZ5qwRv8Acj8qhYbeD9cD2rRG0RbO6a01KKaNVOw7lzkjiriSSeSsckkjnzJJWd2+Z5HbLMx9en5VQhK/b+F4J71e373/AJ96561CnOam46oupUko8sdmXoTs2g9fX610eiWzP+925x90YrnraJt6CRljB4DSMFBI+v1rpG1mLTbmxsrWdTGHLSTbf3bqAPlDYw3LLnbnsO9fOZ9mLwdFuCuxZflc8wr+zi7FW8t2+0usm4u33jVdrTykeV28uONCzM+AAAOfat7U7vTbJ0d5ZZDKGaFIEMjygDJ2hRz7ntXHaj4ji1WzfS4PLsZbqKSKX7dgbARgMWz8oAOeRknGB3rzaHEFKdOPKtWdmF4WzCrUataMep6T8Lngm+H1nJbptgee5ZFxjCmeTA9KKrfBs/8AFq9H4z/r+P8Ats9Fctb3qja2Prox5Uo32OH/AGjvv+Gv+3n/ANpV4dCWWZWHODnH0r3H9o37/hr/ALev/aVeL6bB9ov4kZWKbskD0HJ/QV9VlC/2aPqzGs0rtnYWlhLcOgjjxv8Au5woz3q3dWb2EwjkCggA5TOOcd+tQJdyRTZSPGBgKeenHer66osuHniiJI44BHPt1/KvoJupe+6Pl6jqc1+hSiDQurt0HOCDiustdTivbF4/IiDv1Cbc8em7pXMmLe7GDaUyMHHPr0rW0y1j/wBY7LnktvyMf0rnxMYyjd7o5MSouPM9yaNt24PuRx2U/j24rM1GNluBsYEYHXg1o35V4nk3ICpAUudpIPsDz6Vnyz7rdidgORjDlunX2/OopPaRnRi7qRnKm+bYeM/yrstOtW02FJ920SLlckcj8K5qwtt1yjvIignjcevPr2+tdZf+W0aKZcMm1VVXDHGMcdCefapxlb3lBbMnGSvaBXnsba4fzJZSBIchRgnJ68fWoZtHilTNujghc5cAKRUkkixPEYN5dPvbwMZz79KhuBNs8xNpQjLCM9MfniueLnpZ2OaDnpZmXKsdrMI9z7/4t4GAR6YNdl4auFghCHhJG+UD1rDgtIr1N8lq4fp5ztx6/jUi3K/aUUL5caD5Bg9qzxUFiKTptF1ZOVrbo78juKawqOzuFuLZJEbOR+tTYr4CSlSqOnLoW0pRUkiP60n3KcRS49a2jNHM4sjNMNSEVGRXdRkjjqpjPuUZoNNNehHVHFJ2IZx/GOo5rO1a2W4s3A25Aypx0Nah+dGz3FVGT5Nm7OP8iu6i2rMdGbUlJdDzxo2RyPTr69aT07fXitTWIVt7w7F+9gg4rN/jyOo619BTlzRTPpqc+eKkIqfOo7Hqa03H3R2AA/KqMY3XKYbBB61fkPUjoe9TUeqM6r1RA5+7jtxVd228Fc/SppD/ALNQyHsetXEcREfp780kh9OBj+dRZ289ecDmlX50Y9fart1NeXW5GNyTK4/P3rWt42lm+TntnoPz7VmfKvBXB6g/WrRv5IrT7PsREPDNjkg+9Z1VJ7DnFytYhub/AHTGQ7XP3VB6YAqexvVlRI5Io3w6kwyKGRx06HvzWKUZ5iR0HHJq9ENqADntz1qK+FpVKfJJXubxfsbODsyx4mub3Skt5rC9udmoxspld1Mq4xlNxBfaMjHzVhabp7RQve3LbIoySYnXKuVGSGHsOxHNdFcXLS2Hkbf38b7opioPlgjDDHvx1/u1VubJLjRZrYfKTFiJUI6ryOfUkc49fSvkKGQuninJx9xH2MOI4/2fGl/y8ej9O5698HTn4XaSRxzNjA4A856KT4OL/wAWr0f6z8f9tnorzq9lVl6mkNjhv2jvv+Gv+3r/ANpV4jaTNb3SSKqkg5wRmvbv2jvv+Gv+3r/2lXidhB9ovIo+uTyPUV9Tk9/q0bdznrW1udjAN28bVK8ZBGOTVlLb5Gccds0W0DS75GdQRyqHPPPbHFXo5GWJ4oJZSpwGTJKsB9M19DOo1pE+Xqy1divDb79rps9yccc/nWv+6itlkEqhwDuUYJ68Y4p1jE3ktINsZ55BGVx255/Ws8QNcTOiN0OME4/lXM2qktXZI4pS55a7IixJcY8hmLh+MZ5B/GtuLTVt3WO++R8jcR8+OP61QgtpEfO5nQDk7wP/AK4qb7TPa2Nyj/OzYAfkgA+/+NZ1XKVlB6Cm3P3Ys2Pstpa+YPPaGQDeFMRJbPv1FPj1nTXeKEWzBwMNPgyHn8iK5KKWX508xMY6YB/mOKmgh/0yIL2weoPFYSwd1ebuyfYKKfM7s1tQh2O7iXevIQFsnr3681Tgt3vXJRWBAAJ7cD9Kv312qwpboq7yADkbelKJVt7QFIlJkXPB79OBVRcowWmpjGUlHbVk1sNPtf8AR55GNxIvDj7qEjjnvWbNDJa3jRlcEenGf6VUmlj+0Etvc7QVJJG0n+fNdDYzQXdisN22XRwqdc4/lUyjKl726YTXs1zb3NHSNV8pER1bYce9dQkqSorr90jiuUbTtqIIZd+CTux1rRivra3h8sTrvXhz9PSvnMyy+OIanSXvHNTxPJdR1Rs4pWHrVGy1KC4+QSLn0zWgfnr5+tSqUJcs1ZHTBxnG63IsUw09vkpPv81rTla2uhzzhe+mpH97rTCPSpGppr0qU9rHDUj3IXFQTxrv396tNUTrvRh7V6VOexxPRnN61brLCxReV54rlicKyheM55rsbtG3sDwBwcVy99btBNgDg8givdws/d5bnu4Kfu8rYRSrFNvfoOQcUst/bJwXwe+c1WmO5M/LVJ0387mJHU57Z/8Ar12Kmm7s74U4yd5E39rRdCzgZySign+Ypr6pbc4aUkcgFAP/AGaqUkPp6/N+dQ+T+AweetbqjHudkaVI0Fn+1OURlcgZwmc4H1pCdu7DdB1rLK+i+9TLOy7Q/KDoD6VbpW22KdJdDQjbzX2Hk5wCTwKgluGd8JIwQcKoPFNMqojbHzkcDn0qJT86kNnI54waSh1YlC12aEZXvy/en79v8sGq8Z+Rak8z15xUNanO1qKX7/jU8F2sVs5lbEaZYkjgYHtVVm6jv+lUdSRpdNlCfwJnJ9uT+nSpqQvE2owUppM93+DfPws0f6zn/wAjyUUfBn/klGj/AFn/APR8lFfm+I/iy9WfYwVkcH+0b9/w1/28/wDtKvFtOl+z38TlVxnBz05r2n9o37/hr/t5/wDaVeJ2kXn3MUfUE8genevqMmv9Xjbuc9a2tz0CyimlheWNlQDnHbPtU4v91n5BjiEgcfOVG45+g/rVO3iZIW8tmCd19akgRfmjZcdydn3a9+UE279D5Wdm2alg/wBofDyqO2HJ/IE81pSQQWszOkmxDjOQzFuP6GsiCXagSNGPQqdh6/hWqtxvh/fquT/CGAOep4JzXFWi+a62OCspc11syOe7uYrZUePzCfujkbcHvVaexlukhkkXYjeuRnHp+FPuUkZE3Imw9MsDg+vv+FTQhpbFEaBTtb7yJ1GO/wD+upvypNCi+VJrRmEPLSZ9isAOBnk/yFOuZJ/lkD4bgbgcYA+lSzBUuW+ZXTqMk5Az055qtJcrsI3ZbODjOAPxruj79nudqV2mixbu1xMjtz7noff655rXu7qO3228vO5QQOuAfxxWZbKzch+o9jjnNR6nJ9ouYgGY7YwM49DWM4800uiMZQU6i8h5t185hHyD046CtaOwnuNkUTKmBu+d8DP+Jqvp8U9x5UVpHvlyWJAGfzrprSK5tNpu7tI3d9zxRxoSce/QDjtXHiq7h7q3OavUa6hYxz2Vs6XSNGRIp65781gs/mvndh84AfgnNdLLcRXt4sYXfAMFhj72eKvSeGNIT50tMN3+d+f1ryKmZU8LJe13kYYenzKU2cZHPNbvhlbPvxXQ2niJkRUmXJHXNV9W0aa3h/0dt9vkkpj7v49TWTbw+bExC/P7ema7XHD4ynzSVw0+JHZLrFtKn3ufSrqyb03jmuLH+j/IknsWHtTodSltbnKyNjuN2Q1edWyenJXp6GalO71udixpjCs6DWYJUXzGVG7+lXw6vyjK49V5rg+rVKOjRzVNb3QjCmZ6k8U8/wCRUEr/AO1XoUVdHnz3KWoRb/3g5BH61hXMK7NknKHrW3c3KxI2WUg9c1kGaG63hC2QOPb2r06HMl5HoYXmSv0Mae02JkLlOm6siZdj7NrHn0rpJo/4FZeevIFU5LNdjCRFfuDk5/wr06dW257FGrbcx27v0HqMmo5G+6Plz0q8LFV+cJu9ug/SnSwK23fbp0xkA/410e07HV7SFzIwr4G7B7Z4pGi7huAOwrSe228iBccfKM4+vWqzp3eLA6YHFaRqXNlUXQrKOiFcHjkfSpVjV9o6Hv70oj+cF1wPpnFOI27cKwx7c/zqnIbl2FO7ofwzSZ9enQdqcknp2zknkUeZBsYFWHHbr/hUt+RmNz67e2cc0HayEHoeGz0xjvSP8zsUb0PXiqWopOumzFFU4Q5Pt0NKXwm1KN5o98+Df/JK9H/7b/8Ao56Kb8GT/wAWp0X/ALb/APo+SivzTEfxZerPrYbHC/tHff8ADX/b1/7SrxnRpFTVYS/3ckY+ox/WvZv2jfv+Gv8At6/9pV4xpKb9ShG5Vw27JGenNfU5N/u8fU5sRs7ncWvzuUDY7HBzWnHaxS7HG1BjBAGTn8az/ubFDKYxkjOMj8ulWbG7bfnzGij54ySTxXuVE3qj5KrGT1Q+aNZfnDLkHjB5P154oikl2YiRSAO2AF9farUiLcbyhcKc4GzGCenarOnCJdkQlZJeqsnAXg57+lc8qlo6q5zudo6lZJGuotkn55I7dq0tOlXetvOm8Ywh2bjn2+Yf1rNktmt3CBl2E5HcVehsJtizl/4cgR8nHrWNVRcN7XMajjY11sYJbZ4p0iDchd6Y2iuIns1ik2fJ1O3GcH3rppmW3eKR1nkGcyJICh/DGP51Ddf2bqVyZ3HkfKPk25PH0xWeHqSpvumLDOVJu7umYNhbLLMySy+WMZLJyM/So5V2fJ9QSAfWul0mbT9PmdLtXEa8q/lZ34+lSzGz1WbetskcEbkliPmcfzFbPFP2j926N5YhqV2tDD0a8ltLxXRmRR97tkV19yY9/n7d8cgDhQSvWqkUll/qI7FAB0byQxH44yaW8u4kC7GyiqPbFclV+2qJ8tmefiJ+0kuVBFKiXcU5RIIQRuCFjnnvXW2tzHd2yzR8hvSuFlu4LiFIkVi33s9Rx2rV03XraytPLZHfHKiNfmYn2J7V5GbYB1qalFe8jei5Req3OlZF+b5a559OW41KWG1/dx4/eEcham/te51X9zp9syFh/rZscfhzWvZWUVlbeXGq7v4m/vGuClWlgaVpP3n0IdPmk2tEc3e6V9k2yRq06YwynqpqKPTppU3eQqLt7kk5/KuqmT5GqDZs/hr08NjpVKavucNetKm7JHI3ltLF8gwBjp71Sgv7u0ciN2TP5V0GvfJ5RXqTjp7VkY3tvfcMDPrzXsUpKcE5K500Z3ppyW5ai8QXPSV0fHLdqutrdpOgIdQa5e5Te5y2PUYIzUJi+TA6dAav6pTeq0NJYSlPXZm/e30D7x5inNZsUu55RG2MggH3xWPNC+7I3f8A1hToZJrd8lvl7g966Vh4xjozpp4aMY+6xl2su8kM5cHnmpbe6lVNjceueKtfY2u0aS33EfxLjpVeS2aKJtjZxxWsZRa5epvzRaUXuTLcqibgyn2ppNzeuI7dN5PTFP0rS/t955M0/lfLux03c9B0zXcW2n21hDiGDgcln5JwP89K46+JjRdkrs5cRWp0GrK7ZwjaDqic7HIxkBCM5PrnA/WqsmnX8WWkicYI4OCD+WRXdrHLLcr5m3JGPkGOB71e+xROjwFm2EnPBPGKx/tCUWuYyWZ2spI8rjlbfsfcBnlTT5Dsf5H5HY4q5rFj9i1J4F6A4FZUz/6RwmcV7NNqolKPU9em1USktmXVjZd33XPvULydnRTgZAFNFwu35k+bGcg4qM3C84bDE9+mKai+xai7j2ZXwNuKqXyMlhMytx5bDGexFWxN/Arq57Yqrqd35WmzEx5JQgNnI54py0ibUU/aJJHvPwa/5JTo3/bf/wBHSUUfBr/klGjf9t//AEdJRX5liX++l6s+rhscL+0d9/w1/wBvX/tKvI/Dls1xqqBP4AzHHXHT+teuftG/f8Nf9vP/ALSryvwlKqav5Z/5aRsoOcbT1z79Mdq+oyh2w0fVnDi21TlY6z7P5W0PGodujZHWpYvJdHzG28deg/pTbkNL88is7r25OAPU88Uee3lRl1Z+TnuPy/Ovc1aPlXdq5Yt2lbCCVgpPABHf61fexlt0YtvGMjdsLDbWW10zuDBsTpkZ2g59eccVsC+kntriOZVd1YjgZ5Hoehx7cVy1XJNJdTnqxno1sNe2jZE/eL68nBIPP55/Gr+nIqIp+2ZCncETLAk9RgVnWt9vSXDGKMJgr3/Egc1rWc6xQrOHjcAlVQ7iFz1bnp07Vy13NRtY5qqaTTIpbOR4XunWX5PvqPl2rjk8/WsC+RZbyKOxguA6LhwT1OecY7e9dLI5vHEfm288aru2yJ8qt0yue/TntUNqrJiONZSMDGzJAI4xzjt6VFGrKLvLoOlVVNX3ZjR2epPgvaSGIHHmFDsBHH3ula8MUUsxgDLGYyQwkcKMj1P+FSSzTv5SSbv3fyh5FxtHsw5xk+tV4LaG412ExS3BjYgvITypPXmqlVnJNy0HKSqeR0NjpcGzf56THOCISML7Z6/yrl/Em5NSe1SLy0BGM5br3zW7dade2UzPYXEjgnoXyQCfeobbxJ++WO/tldkJHm7QD/hXn0J1acnVi+fyIpcqldalLRdAnlRZpmZEY7QoHPTr+db/AIds4IvtEiKrzJIV8zr09Kbc6xbXVsbWxZnmmGxV2kBM9yfatLTLJdPtgiNvJwWPv7Vw5jjKzoS57xb2NIXdTmexZ2bJmwuBQx9ae336Y3pXgRbnZyFNWvbqNx8mKo3d5BaI3mSrvH/LMEbj+FWppViQl2UD1NZFjp8F35t3OvmO79TzjFexg1GMXOb0OOcYydn0My5S71eZGjgZIh0JJx16/wD6qgutNuYnaNU3JgZKfWus8ramEXAHQVSv5Wt0aRFXf7+le1QxTk+WGyMViJRajFaHOjR7nglMDuCearzafOmA8D+xTkda011i5bdtWAH0wf8AGgavJ8wfaWPGEHf8+a7VUqrdHTGdVO7sc1Jtt3xtbNIfLlyRtTHHPWtu902KWFWdWGc89Bmp9PtbK12RTWks5kIIWONW/PNbVMVGFPn6o6oV4tK25zsccsSb4VzjqwHSnz/a1hWTzEkDgAqDubntXoRsNIh23U9pFBGV4WTaDnrgL3P41lvqHh+J5TDaNIcbceVGApHbsa8mlnSrTtTpvTyOnlaXM7HEPP5PDo8Lj+FwV5/Hn8a6PRNdb5YZ90kDYGSc7B/UVcfR9NvUhkgVoUlyqgNvG70OScZ9q5ya3/s3VPJO1GBAYD7pB/pXpKpTxMXFqzMJ+zrJxtqd7LCqIrhsEA7VHA/xprPKiJINpzg5xkdPbmoLGT7RpaHzG8wJtY+uOKmR4vJUuzdASM8V5fK1ozwZK02uxxXiVJk1FJJdh8xeCBgccetc7Mn7539fSug8SanFe3yIi/JECFPr71hllfI+Yc8dOlfSYPmVON1Y+nwvMqcb6MjA9V/yaq3MfccetaATZwV69DUNwnT0rtjLU6YztIyidm7K8Y655qrfln02YHomMDPvVp/v/dzio7gq+mzb+cIcZ9a0qr3GejSfvJ2Pof4M/wDJKNF/7b/+j5KKT4M/8kp0b/tt/wCjpKK/KcR/Fl6s+hjscL+0d9/w1/29f+0q8g8Pru1eLG7ADE47cV7B+0d9/wANf9vX/tKvMPBF61hrrSL94xMo6eor6jKr/VY28zixjtTk/I7K2KvC+/YevJGCw+vSq6SLE7RhcDjnrn+lNnkWJ/LD8nOQjg4/L/GpfJ835Q+8cBSeT/WvdWm+zPkrWu31LdpHaJ87s5x0ACknAq00sEttt3pG4JGUPLe3Hb9Kn07T1VE8yVSDnJJOM/his7VTBZXKR20jGTOGdAETPtiuNtVKnKmcykqk2k9itcph18uVJY1+7jOP15/Sp4ryWL92Hwj8sUyBz29O1Vm+0uv32AbJUjODWvZadO8OZLmLJwQjtyRjjj61tUcYRXNqazcVH3jS0+52WxQRKMfK5IHXpnrz3pmpXP2WaIQN5c+Cx5HXPTg5qSwjZ0M13Na20CEZaXCgZPTJ/qav3tjaXFxFJCiPF5e4TRygq2T1zzkenNeO8TSWI9m3qcEoOH72S90xxN/oySv6fMEcZ9frmqiybr6JElYbjyxfbyTzn/GtDUoPK05wJ4PLU4ADHzGz0wPQVj2sUcsOx2iR9/33fAwRXdScZwcka04px5u52CavaWdp+9nXKkrnO9mx9KwNa1iK6VYrbJUfMPkAyaybXyIrtSsyRupwGf5lJB61ri5kluEnuHt028F0TPBHX1rCGFVCpzpXD2EabT3MNGuUfzC3lknOclD0rRtvENzbv8l3KX772BB/A5FVJrpbh3hd5XRSQoQZBOfrVL7P/pKxpufB49/TNd8qVOqv3kTr5VJXlodXF4wu/l8yOBx6KrAn+lalt4ntLjZ5kTwlzwTgr+dczdaf9ihTzXUuQRsTqufWqLjyoWO7joOa86WW4WqrxVjn5Yz2PQNSuIv7NlkWRXjYYUowOSTT8x2Vgh8zHlx/Ng9cda4CzvZfJ8lpHMYIwu7j/Ct9dPW62bt3Izk46nr6Z/CueeAVO0ZPQ5qtNU2+Zmuut20qbxJ+HQ0y7nW4tmdGUp0xnvVeLSIbVHQYcHGc8fpTTDHAmwcDOQKdOlTjK8Dhk4N+7rY5S7uWt5jt6ZzzWnoP+lXKO6MQPm/Kq2qWX75z2JOK3vD9l9nsPMPDngfSvTr1Iqimt2d1erCNBNbssX372FtvtjNY9l4iuUmls41xIRhGH8OK2rt1ihYvygB5rzTUrpob8Tx70AfPHGeaywtBV4uLQsto+1TTOy1O9u1sBHNI0jzHPzMSBn09KwEXZMAGaNgOecgn9MVcOqR6hbW88e4CIDdv74/nWdcSN5xO7OQT7V0Yegqa5UrHfThJXjLc0lv50RY1k2eW4kXJwFYfX+VQeZJqGqNcSctx+fsKqteSeTiWNHXqGcHj8uv40i6lPyAtvjHeBP8A4mr9lJXcVqaKk0nbqd5ZwrFYIhb95sLMDwcn2qvq0vlaK5VlBxgHvzx0+hNcjHrl3E+C6hDjdsGMDPQL90fXFJe+IPtCJauyIgO4M5IHXOOM56+1ciwVVSTZwxy2p7VSvczG3ebk9u3WnKi9uM/zo+/M5O0Zzg5wPX6U9Y9qElW4I57YNezc9V6DQWTjrj/CmuPkyPToOtSsvQsv9M0wD1bB6013Fcyp4G5Hfr+dUdV+SwZAuCOSfXJFbzovfp61z+qhvJlG5fX64NbSfNBo9HCy5pK59GfBj/klGin/AK7/APo+Sil+DP8AySjRf+2//o+SivyzEfxperPpY7HC/tHf6zw1/wBvX/tKvIvDUrRa1C4bBG4A+mQRXrv7Rv8ArPDX/b1/7SryHw7/AMhI4x9w/wA6+rydXw8V6nFi/wCHL0OtWLbMSeB1BAzzWlpsq+aqSMuD1PK9PwqJJ/3IDqgdxyee9WFRX2pEkRIByHyDyf6V7VSV1Zo+Um7qzN2C6byVj35Q9FKA5GazLmwtvtOfMVy5OAnRf51HDceU7IYvMDEBeCTz9etaWW8lxHHlcBmAyR+Of5VxcrpyuupwKLpNtdTNktW8nES8xnll574q3ZybEczsrnZhSAQRgetJpb/bbkxz7hKAdufYcdvQd6h1G1+weYicmQEYyCRzTb53yPctvmlyM63QLu2WEF23sPlxyxbB6n296vLcW1xK++ReTyu3GPz6Vyfha3nTzXdNkQG0+ZH1747HoM8VvNeW1vMDdbEeYbY1dgFlbHIXPU/T+9Xz2Mw8KeIlUW9t7mNXmlahHWzuZevWUEUKXlpIxIOHjkYbgD3x6Vzq2X2hJSZBgnKrkAnA64rr725guIWQriQhvl6DBzx3wRmsu2Gmxfub2WXyGLbWUltvGOB04PsfpXqYSrKnRtuXRqva1mctLaNbuBubHbua2Le322xcbX4JGTnHFaqxWl7DudmlcnaqMfu56c9QKrTW7WqJI/7uPIjBI4OPeuz617SKUtGXUrc/u9SvFpjTYkdV8vzFHzZG8kZwMA5OOeeAOTxWho2m211cNLbwSERkgq+05B/Kse5mnhmIj4yflO3OQa6TSn+xaDNI8Erl8MUCsNx+vT64rlxMqkIc1732FVbdNa7jjFZec3mRrGR1WQYINZOs6eq2hkhfCDkgkAgmkgEuq6i0jptj6MI8KFB7AGrOpCO3fy23eWedgHC56+1VT5oyST1OWMXTqK0rsr6FY/feO2lndQM71DHBPJH9MVowRyW8zxh5TjBIfquao3niKBPs76bF5DRfeYIM+mPf8amtdVge5munlbzJByXbGDjsPyqJRrSk5yjp+JtiIOcXIS/1a9t8hfKcZ5AFUW1SdvnknYMw52IMDPbmtQ28dwiTRox28knHOf1rN1LS1eLzYJHDE5CnAA5/wrWm6XwtWM6DpaRaszQiVbiwSQtnBO7oORWvFcRpbJngY4IORisiziVNNSBHyxJJL49KffIyW2xFbHAUY54rKUVJ8t9DjqwUpct9LlbWb5XRo45MkdW7Vw2pqzI+VwSeo966V06l+vbOeaxL23+Rj1/xr18GlTsj2sCo0rJGXpGrNaTNCej5BrcSeJ33hp0BPAQggc1xN6jJck9PTFW7DU24ilyfQ+lelWwql766ntVcKpL2kTq3maWY72Z88EudxI7dapvuidsLkDn8Knt9suz0JGaoapKyTbE6YGP51zU4WlynDTjeXKJLL5vIbqOp5qm+7YyHnupPIBz/AFpsMjdDtHuadc/PgpjAPzfhXUoWdjthHldiO31hrf8AdyplAc+hq/FqMNxjZIgI6b+Cc/pWLNHGyZC/vAME9RVNJGidSm7jkdua1eHjPVaM3eHhPVbnYw3DdSnyHpjpUhPqvtWNptx5vyFmIAJCknGSK2WRuu5elcc48krHm1qfJKxBcjZkbutYWrNtsHQruOcA45U//qBrcuT87J8vA61j6xFvsHctnaQwPbrj+tTNv2bOnCaTVz6H+DP/ACSnRj7z8f8AbeSij4M8/CnR/rOf/I0lFfmOI/iy9WfULY4X9o37/hr/ALef/aVeQeH5Gi1RCnU8evTmvX/2jfv+Gv8At5/9pV5V4ZT99NJuYHAX8Cf/AK1fW5Kr4eK9ThxjSpyudzFGt6/8JccZzjofQc0gS2tZnEkrDoSh4yfx4qKximt4fM38E8r5fPX1I/lVO9nubq8AMruQcfOMHj9a9ZR5pNX0PlFC82r6FttQV3V0ihHPDJknj/gWK34nhWXfI2yR4wyl0LDHsw6/5+tc7a2saTKhkV2PHyNkjn6VvRs1raO5SWSMY2YJ2pn26c1zYmK0UTGuoL3UV2MEt/yzID1PRf6VbkMHnI0ayzyLksS2QADx0GcfjWG1vPcTO8aM47iPjB/Kt+ws/s9o0dw7HdyQOMEcdT/SprRjFJ31MaqUEnc2LSSIW+7dEknLI4baTn+vUdKWWe2f/j/to7qWMb7d5EGEyeeSDtPHbrWFef6Pbow3q4PAKgBh7HOc1Z03VluJnidNoIxjaCGx2INeZiMFGvTfNqjno+0oS9vTZbvZLaKzhS0kR444/LDbSfLVQFUZ6dKzpm+0IkibfMCglhwenJJ75q7shimePy8SyghVR8J+A6/hV23dbdGNwrOG+QCP5lHuM1VO2HpqMVsTOt73M92c7BdfZ3Zkd9i8A7QeR071uNLc6rpU0aMuzr8gZWbPY9mqk8U9lcxiO2leIHzeAMgD1piy39xMzmJMTYVRJ8uMDg9MHr2rWqlUs10NFaXvIfeW8q20UCff2rsQA5f6D65py37aVbIP9KgKg5EqfLkf0pZLSW3mW3uImkAjyRCGfPbB5HFOnisnhaEXdzHL0WByQqnPTn/HNQ5J2UtUEbWs3cZa6jbS4mkliilYnBjkwePUGqmqyb4t/mfNghdh6/nz71JLYWiWbwG1iEiEbJkB+Y+/UfhmqrxfatRihDcABc+n5V0UuXm510GowUuaPQ57y5eTtbg8nnipWunt9gfcCTkN04PFdXd6b5VxClm7NgFndOMAHrkdqq3drbTbcwQIoO1mjPLDg9+M11LGRk9rnTHEwla6G2GoXfkrH5cBR1xukkC/qTzW+kcFrYW8z2yTu/DEsSFBqjaaJpsrxfapp0kPCIuAOf51sBNlhs+zXTmN9o2r8zAHr9K8nFVYSnZaHDWcXZ0ypLbRrfvFs2IVyq9171Uvm+8Aq4HH0qze332e8ZUgdyRjAX5+RnnNUXn+0QmVbW4WPByzrwcfjW1Lo2YQpzb5uhlyDZuT8qz5hv8AvdPWrsjq24I2fpVGc/IxC4GenTmvWpHpUlqcxrFrvRn7jpWJFE73KpGrO5PAHeuqk/0jg9sg5/OptMuLTSHll+xRXFwRhGmG5UPrt6H8a9T6xOnSairs9+jiHCHK9TQt9Lay01Zr+SK1d/8AVwPnznPrt7D64rLv49/7wquPb2qvfXdze3Jup5Xkk9XOT68elXopI7izy+4luMgc4HtXJSjVguebu2c0ouLU0YUsbbOGx3/GmwTfwNuFaT2m75UXOeQR1xUf9nXPL+RLgDk4NdyqxtqzojUi1ZlJ4+46eneqE1v8iuOuefQVtPGy/wAGcA5I96rSRr+B61rCoaU6tippUuy/QFuoI/Supl3fKyfJxj2rjJEZJs9x1x2xWrp2t7f3c+5wSMHPA+o71GIpOVpx1HiaDqWnE1Jfvt3wc8jBOKy9YdU02QbuDtC/Xr/StR2byg6N8nY5zisjW0b7AccgMCSPowriq6U2Y4X+Irn0R8Gf+SUaL/23/wDR8lFJ8G/+SUaL/wBt/wD0c9FfmWI/iy9WfUJaHDftHff8Nf8Ab1/7SryTw7M0VxKqdSufyr1v9o37/hr/ALef/aVeQ6Ijb5puqoMEd+a+uyJXox+ZxYtXg0dpp12z3aLK7Sgt8uXOM56Y6Vs/2RJdZ8iLZITliXwCPcH/ABrl4Hbzlf5hjHIG012Gl38EsLqktxGxBDZTeRkf7wHTivTxalT96CPksUpRfNAr2+k7JmA3u+RgIhboeh9BWldX8j20qRx2ojU4bzCVkfJ7DvgjpVrTLG7vbaVIZ1NuMFwEUOWH6/nVDVYW+Y28TRwZ2spYscg9eema8xVvbVLPocV25Jy1HaPNbWk1wLpMpINyjgr+R96us6tC8h2wbfmVOeB9frXLG423+wq3HDDqePYV08UX2pPNTa4EY4j4YY/+uf8A9daV6ajJSb3MsRTs02NaTzbbzEXO4sHbZnJ9/r6VQSOy858tKjHoQigA/j0FWFLRTMElQFlPIAA6ccZ/pVG8mk+4jYL8liM7j6cU6cW9FoOmtbLZkFxeMlzvjlw6nhkOAee2K29O1Ke4eRJFZ5HHyt0IJ6djmueht7l3dSyyKeSeP0FW4r2aymTEmQWHCZwfxNa1qUZR5Y7mtWmmuWO50t22x1gmVXPtwf555rPvoZLh4TCzFw20ptKj179+3WrCwrcIjmRfMwBGmwfOcnPPXgY56c0+2ttty6Ptynz7UcsFx2PbNefCUV6o4uX2KuyWJvkieRrmOQICSiDbgfX+tXYrD7WiyPK0m8ZIkVTnPt0A+lWrWy3/AL6NUEmMBdw2/Q8VVt7/AMr9xM214/kVSWZiBx97v9e9eVXrzlJxoboqEVyqcjO1OyubRG8yeJrQHcIocqc59OgGfeqdpZ2nnJcRSvGJPl/fdeO4Iq7r8n+kxRhWcoCXUk4wefX+VT6bHbXDgm2iEkP3F3HqR6f416FOdSOH5pblTnppomaMEEsXKR/u5BgKANgx9OxqkdNgTdGYN8qfP5hfCLk/dHGTxz0/Gr/nMYWLqsc4Ckx7/XOMngc4IFKGu2h8yODzJTwIi4Udeeeccc14sq9TWTlZFU0oS5VG7Zk20/mzLPKuSjkBRwqZBFR3pb7HfIHbAlRvTqRU7x/ZEut8apJvBx25PX6Ul0N9tqQHBBQ5+nSvXjOMrSWpyrmU2raIytYlZIYZArEMgLMexx2rBkvZHRYwwRB/dzyT+Ndk2mNqelQpu2EDJLKe2ay73SVTaUtGgGOMkseD1OelduFxlDm9le7R2UZKEE5IwEuPk8gInzYO5xzmoZPnfY/HPvxVm6jjixGnJPJJ7n0AqxpulXt180cEmxiFDbTjPrmvSlVp0oupJ2R2Jq10c5dFYn2FeM8k8GmmNdi/N85A+lavi/RptPmRw28MPncLtDEH0rBhv12CJlyf4cDoK7KFRVqanB3O6n79NSjqE8f+775zWb9tntHYJymcle1bMp6gtj0rMv7T5Mp+NdlKz0kdNCUdpLcsQ6mt66K8WHPLMDyxx+VSJeQO7IJMYOMEAc1Qso/KtpXK8sCob3NUZN0VyCre/H9apUYuTS0NvYwk2kdUIldHG7kc4GDWfPbdT82euPXFMsjOyFwygeh6H8DVmd1iTBfL45POM1ik4yte5yqLhKyZj3A3ow3NvAxz6VTEDPMgTnJHFW33b9459Kkij6yDcMdR712p2R3qbigjvmt34X5AcY9QKfqlzHcaU7J8m/blSc+9VRE0rgDjJ7/WodVg8q2TZ9wHDH3rkxcYqm2txwhFzXRn0t8Gf+SUaL9Z/wD0dJRS/Bv/AJJRo3/bf/0c9FflWI/jS9We5HY4X9o7/WeGv+3r/wBpV5FoLL50sb7QSMgnpxXrn7Rv+s8Nf9vP/tKvKdCt18mWc9c7BX12RJ+xjbzODFtKDubRml6RKpGMkBR0rd0qVYoj5m0v/Co7H8Km0t9H/svZPEjz5BDOGAwfXHJ6elV7021vfuLGRjCMFW6k8c+levOr7Vuny2sfMVZe0vCx08LT38KfaGd1jB2LGOV/LrUdrpE9w9wxb7PBH1knbZg4zzWFb3fyKhnwh6ryRn+VW5pn2fuGnVpAC4jyoYDjouM15s8POLtB2ucCpuM7SIhHFEqTD96pPz7CM4P61L9vj+0qLVlKYGScZHT1Hao9P0yeWF3gkYPgjn5e/rn2px0m7t4UJiX5h94SBuM/XFbtwvaTuzR8j0bHX15++dA3HAOXLEH2NENrHcQpIGfeCBk5ZWP9PxrNgt7m7fzEiX92MEDgce571o2vm/KkK7MN85Ofl/xq5RUYpReqFKKgrRepJbfvWeMIM46nC9+2f6GppUkV4oWiwAAwB5P4E0ssS/PMjoH6bi5OePfrRavM8M0lxEk+cqnzquzjrjBP61zyf2jFK92nYtGaOVds7JBHGGcKiBcgc4wBlj6e9TK01rMHaVXjQkIvDLtJ7Yx371jyhfKUuq8AfPk9ux5zU9rrc3nRwW7SOqJhg5O3PrgH09awlh5bx2FODnHTU6xrf7RbJNvxvyu0nG4AZ/kDWfeSeUhQ/JGg+7nIYD2I9aWK5VEKlNidzgBsj6dQfQil1OW2uraLzG537QTwD7nvXBQpVIVG56pnE+W8VHocxDq/lOwfcCeN3P8Anp6V0+neTPCzW0uyRhncAxLYPp/9emWmlW1w7CRE3kcZfPT9DWdqAmtJWFtK/kgDAwNpyfyH5V21HTq+5HQ3coVNI6HQJdeUghGoI8x+8nlEkkduverMPn2s0s23eJcFh0RABgcM4A98d6w7KC53h2i3hwDyOuT6jr1ro/J/c70beuBhQteFmFKlTjyS2Y6dSop3hrYh1XzUtpZk3IOOQ3vVEzzXEN2rO5zGGUEn61p6sjf2I+9sHj+dZlsfnlA3EmHHX73FLK5+0w70+F2JxkfZ1E++ps6Sf+JVCfmJxzjg8Gl1GKKdH3x7jjpUWivs01E2889OetWp22I7llHH15r5vkqUszlNdz2ZTjPCRj2POf7Ja4v2j8ryQZMAlgx/McZrp7C3l0dGAmYRDsV3j68YNRNZyXTp9lhZkiflxgDB5Ix1q+rQbPM2M21C7S7iwJHYLySfoPzr67HY6LgoS1XVHmQVWq1y6FFb2HVYbiK7gQyLGshgyrlkYHB6f/X/ADFeb+JE0/TdbVLDgCNfMQ5+Vj1Xn0ruLu4nu7N5vIu7XeSI4J0IZhgfPt/h5/P8a4648NX92k0/kNIwYhhwG556Hk16uSShFc8p2XY9DDxVGq1N2MaWXzfnRsH+IfSmk70Yuze44p6WLbGwuHz9TmoGbY/lujhz37V9QnHZHoxt9noFvHJcWxREYhX69uaU2kMXM7KX7Af1pTO0WIUXngnPb/OaYsTO7O/JHc9M01fXXQ0u99kXYJo3fbIyxD1OcD8vWpJ447jc6qpI/wBrBGOlUV/iA4HbtVy3ZflB57iolGzujCSs+ZGRIuxyO/elkOzYicOepq1eRbLne7Zz6CqwkVMyOrGNe44JOK6FK6TOmDuk0Vn+/wDeY46Gm3dxF/ZroeHOAB9D/hSecsvG7DH16D8ap30TbEk7ZxnOajF/wXoddON5K59PfBs/8Wo0fvzP/wCjpKKPg1/ySvRv+2//AKPkor8prv8Aey9WetHY4X9o3/WeGv8At6/9pV5Loa7/ADQduOMF2wAe1et/tG/6zw1/29f+0q8q0JlS2fG3fvwTszwR/wDrr67Ib+wjbzOLGO0WbUSN8pDYI7jPb9KmgT5yQquoPQdf502NGTdhUx35zipAnzr6jseTz9K9+TWp89J7ksZ8p9/y7s4xW7aXlotsiSoxkB4BwQcewH86xGjVNpSXL9cdAKSKT5xhcnpke/vXPUpqovQ5qlNTR0Jfz4ZhEzJkjMKDOf8ACrVtb3zWjQGTZBJkAMQAfz5rMsXW6fyk3pIBmPA4JI+oArRaWWLdGZ8CEncj4bkehFedUi0+VbnDUjKOiJriOXT0RHT900eFyAQRn9OTWe7NsaZt52gAsg3DGMjOP50k06zYk8ze7HGecgj9aieON2RCyxZGd8keQfyqoRa1luFOP8whla4fZGjFCME7zgE9+tPS3kt/vp5kYHzbOCBVKCdrJ/MR8FeQw5rRmvpL2FC8bJno3uB1zxWs4yWi2ZpJSTVloTxzQSwuRw5+624sQPwrOvBsucHbvXo0fGfrV6ykg+0ol1EzxcKGBPPPXrV3VrTTdjSQM6ED7o6Hj36fjWHtFTqWa0MVJQnbuV7G+aXcDJhzwcjoPwpHVbq+w8rEQEKVGO3XkmorGf7Lvk8zYMFghzjB/Kpo7i2hi+eWKSRhlsPkZI/+vSmvefKhOFm3FG2LqLfF5ckSD+IFzx6dsflQbS2e53lumcBCACR+FYkxa4m3vI3l8hQhwTk/pVq1naJ0TZ0Jx8/Uf/qrndFrXqckqVleLszYuytvbKY4lEhwSDg8A9cdCatpqS/ZJZhvwvZIiCxPHArLeSKVOWeORuFkyCMnoOv9KuafYiW2f7U6zknIbd2P0rzMXTp+z/eaipc0NtzQmb7Xo8xG45Q438fzrOtI2+1xEsnEZUhWB6U8X/2KZ9OMGUYfLLu45Hp9adCPnsizMN6OCNoxkfQVyYSlLDqSatGT0N8RJVFFrcuaIn+hnEiuMkZHSs66LRXMyJD8kjgdC24ntjim6fd/ZbC6dF3bJPlAAwSTximWd41w7PcyL5EYLvI4A249zwKmjh5U61WvJXiOUuenCnHc1bULb2zJt2M5BZQMYx7VA1vNF5JtYoEjV8SecGXIP90jIz7d+u4Y5i8y+R7cuyrI75AKBCoAztxuYZGMEg4rKudE1DRLPVNUsVk1DVpcrbrOxJghJzsQHO4g9B3447N5leuvjhq5b9z2cDgJTlyVXb+W+z+Zp6lcxafYS+THIJ5O6LnqD/F/nj8KpQi5urBLUowf74fIBKkYOM5IJDEZqloXi+28QWzpND5F/GCDACcMo2jduwByzY25zS6l58X7gRLGRh8jGRnPOc46+le1l3JKnZPVnBjadajV9lNJNdTE1HSUimf7PuIBKlHUhuP61TtoNPf9xdO9s44Ldmx2/WtwzvZvFb6g7Z+V928MNp7ZBIwabN9m1JyEtIwVBwyJncR24JHfr1r6ONefKk/vKhWkl72xzGo6F9nmM1lOtxAMbjwpBPOMdTWZt+Re9dcsX2JxcPZqMZYRzJuX8P8A69UrqwXUne7sIIoAMf6OCck9eBiuyji2vdnqu52U8RzK0mc/s6ZZsnp9KljTphvTpTpEaLeki7SD3HekQ7OP/rV383MjZyuhl4d2F+bPYnvWZdxSW8IA3fMTkY9On61tG1+0TLhlz2D10DaLd/ubaVYDbSgIH2Fwp49cDOayqYuNGyZUcRGna55pugdMeXh/7w5FV7pPKhHzqVY9s9q9cvvhpYy2yfZpWWcDlivyt/hxXnfifw5c6PCkz7Ht2cqroepqJZnRr0ZKD1O7CY6jWmowep9E/Br/AJJRov8A22/9HSUUfBz/AJJRo/1n/wDRz0V+b11+9l6s9+Oxwn7R33/DX/b1/wC0q8m0RX2SvskMZI+ZPUf/AK69a/aO/wBZ4a/7ev8A2lXl2gI11CU3/wCrJwCAAoPPX86+tyFpUI38zgxrtBm9Am/kcHksC4H4/lV9bNtm9ImfbwzoR39O1Z2GR+Wzjg7AP8mtuHXvsdm8IjwxyfMTCsfXPXNezWc/sK583V5rpxM1tzvsDsAPXjinhOkYUnPdATUfmyP87tvUdA/AHNSEbUyFwe+wgjP481TvsQ7l+wk2SrGjYdsAZGOvHWrU/wA+9DJEhjBO3H3u/wCdYyM3RXwRjpgDFX4b5oojGZdic8j+L+VctWm73Rzzpu90BTdhX2gkDaUwM/UVCw27NjO4HH5elX2h/wBG8yB08okAknaWJ9qoKypvAdSc42kYJx/KiEr3COty79nW42FImcCPJOD15+uc1aTTJJYU2RuWAzgc7gB+YFP0qznldHLMinlsgjj1A71viSBIS9v5ryIuN2BnaOPpXDWruL5VqcVavJPljqZX7iytvnXZcj+FzkfUYA281mnWrlbkMbmWSMnaSHIBHp+Va9xMuoIEeNiB92T29+ORXP3kce/y/kj9BxkAnpnHNVRgpfGtSqHLO/MtS3cS2ktyrQKxSRl3oTnaSQcDP41av1spbuLy7UW8vOcAKOf0xVO2i2WPmh+fNBXpg7Qad8svyvLkE5bYOoHB61XIr6dC3voxqGL5ndnGOCN3rWpb20Fw5BlVFxnJxn8O/wCVUjZ21xcRLFLL5xH3TFyMfjzUzWzInkDaJOu0kHnvk5yD7VnUqRatezMaivazszchsbbzkkjgwiDGOSJOO+TSOstu8sgdljOdqgYPX06cA44H5nJqlZrdxPFG0mN/3HDDy2AHp04q5OILh/8ASF3ojjbKHySQd2N3p2xXl1IWldu6MLO/K3oEy/armKQysgByp7rj6/0qxc3WnxPbs8+8K7MAnVS3+e9YutR3+nwxuHVI4yPlXnqe+OO3eobe9ttQdUmXyLg87yOHPvW31ZVYqS+E0p05QjfdGne3llcI8Nr9pd2I4+UBT9BVyxtZE+aNtjHAQbedx71iw28lveLleC+QRyCB6V0lvthsy/7gCTJKsBgg9gOOtcmNf1eh7Om7301GlGdRdEuxSbSLmXxbb6mL2WOOGIQvCFKqw+Yj5uD94qSCD0q5f3tnp95Fa3F/AstwMJE7jewPA4688gUtlJL5KI8Hlx8j14zn+ZqpfaNbahqtvdz2m+Sz+aK5L48vBDDIDAkA9Oor5+GHqUq0ZStruevLGQxMPZzbtHYoajPFsYlYzPLja20hxj7uT/Fjnrn731qO51G9+wKJIVEeQn7t8Hdg4HtxTdUS0leVzfLDNECyjYzF8AnBbdgZPtVbbG8TmWX90Ewg2+YpyOfoffrX1tKnDlSUbWPM95pSm7kumi2u0uIZleO7fdjd0Pryc8ZFQ2iLZX3mI6gRjJEhA3kddrYIwe1Rx3CtC9vBFEgkdQyFySR6YY4bpnNR3c8cXlCeDy0ZiVRMkMc9R/d+lbqEuZp6Jl8rexuWFtFLcs8kWyJ0MpBUkbc8ViXNjbXTzTWc8SESHbGCVwoPXntVh7yTyVgESpZ7wXAz8xHbJH86Se4tpYd0B8mWJBukSMZYjoPlYDnPORUw9pCVyaUWr6mPfzTOkcdxEmIsgFFBBz79KaNKga2MplUkbSyP8rDPp61tyvBLCw3Jd3kmS6umCgH6dPSqeq6h9qs4ndIkKggKFyf84rsp1puygrHRGpLSK0Md9L2RPMsjDZ82105IP5itDSNQ0t7doNSWVMPlZgDz7Ejkke9MgtfttmY4JXM8v8BUgcDjDE1zsul6taXjQIzI3PyvwMZ966eWNdOE5WaOuEY1bxm7M9Ijj822/wBH1mW5hHBXzBk98buSK8/+Jhk8jTotriBPMKFyc5O3I9wO31NUY72dLlEIZHB++DyPyqp4qu5Lu2h+0zyvKrnYryF+COeue4Fc8svlSd73RvgcJKliYy5ro+g/gz/ySjRf+2//AKPkoo+DP/JKNF/7b/8Ao+Sivia/8WXqz65bHC/tG/f8Nf8Abz/7SrzLw9H5VhNLJtCSnauSATjr1+tem/tG/f8ADX/bz/7SrzDSrnzdNSAMqGEnIPfJ457dTX1mRK9GK9Tgx13BpG0RsiWQOrbugwCR/MVIr7kbenXr26/hVW1Kqjq7DnoBgdPY9adNL86+Xv5ABL8ZNfQ8rvY8GUdbIspGqzAuyoB1381ZvXjaZI7dUKIMBk6Nnr196qIGlT536jnkjpQyN9zoBxg9KzcPeu3sZu1yeMLvV/mKj+JOmPxrQSSN/wB2d2442kZzj0/GsuJZF+cPnHbGeD2qaEL5y5XeAR8xyQDn2qKkb9TKcU9bmvDqMaJ5bLsBwrARqS31NSnS1uvMe33iINyxJGPT8O1PaFYrZj5UEhbGGxkgkdB3py3v2eHyo927khgncfWvObd24aHBKT/5d7liKSPRbZ7QSedLJ82UH3CeBk1j3Es8s2+SRn/2ieetKHnLuzvv3D5s8HGe/NOVP3OEbkdeRkj86uFNRd3q2UoqL5nq2XLG+S3O8cJk4yAccdP8mor+a21K8UovlOeOHyOvv9aksI1R+HijBGGZ+do79Oc02xijlv2lRlk8sF+MksQOOMeuKiXKpOS3RNopuS3J763jt7aJEky6fLtwD1HJ9azVNutyEeRgpOCwToPoTmt68sZb9FfyOeFzsOeOucc1mpp7RXLLLFsHGQ6k9DjvzSpVFytN6k06kXHV6mxaWzXXyWt39n43Z2bmYn9T9afbXVy9y1vfxMJSMK6AsD+C9fr2p2kH53jCtnkKAMg4/r+VW9Wt2+weaZ5fNjKsD8qlRnHAVefpmvKr1lCpyy6mMbz5ovoMa2u7i5eQyvkZIbnC4/U0TGN7Te5ggC4Db0PTPOcY71Tad4vKeK5eQEZeM3Bc59wRxVeTUI3mZNyxk9diZc5/z3qlTlOxn7OV+5U1nUHSGawdt+TnBUbgOo6VlW9vPKm+KJnB+6EX09PX8Kkuo1+3kC2eSVjy84yW/A8Z/Or2o6bd2WlJMbm2EUjqhXfsyevTgH+delGrChCMXZNnpU4xSUY9RtjqFza4juNoAztEhGV/DqPyrZt9WvU+VN13Bk4EaY/rwfaua0ez+1TO7xbxH03u20c+i8/kRXUsjQ7II1gheMbnYSbT9Mj1z0Oa5sYoc3La7MKqhCWm4i6zPdXLxyWt1BtH3JF2jg49eeaLrWZfs7xt+7AyoGOCT7EE1QuJru0m8wrFJKSeZCWzz6/rVY3kry/PuMm4/LGOQevQ88VnHC052lKKdjLl968NEVkMnnRSyKj4OSr8/hhuDVmSLzX3iDJkJIWFwST1HHtVwQfapkQR4wBjy8547mtZ7vTbW2+xK+YjwSh3YOeScdMVrVrcrXKtQdXstjFtbaCKzYTrL54k3IHCbQfcZB6+h/OqP+ny3jzTruyDFvRFUBQeT8vUnNdNI6pbokEKvAqAltm7OBnJxj06msqdLu7uVd5UeC0G5eAOuBjC5yaypVLycn/wxdOs2ndWuJPqkdukSvZM5jG6OQuUwRwDisl7iP5w/wC8b+GUgjknljxzVsBr27KCJ43BOcpgKP8A9VbEun2mxP3W91j27wBhsd/Q10c1OnLXdidWNJJNanOPLbbIhJKs8absBEYHqOTz71m3pld0KKiEBfmf6Y711M+lxToxCtGcdcY3fpgfnWMlhJLcoPNYE9WTAHWuqhUhqzopYiHxGSv223dZkkx3DISD0z0qK6u5bub96zSSEAbi2e1dhLoSr5pfdvOFHQ7sn6fyqvNpdtbooRXFwGB2hB0x7DPerji6bd7amkcZTbvbU5S6tJ7KFHnglSOX7jumN30zXK62k++HerCMjKnHGT1/pXrIhjmhczx3OyPaC5wVBJxkkr8oriviBBFFa6eEZS26TJ7/AMPUdqX1zni4vc9LL8Up1lC257j8Gv8Aklejf9t//R8lFL8Gf+SUaL/23/8AR8lFfC4j+LL1Z9WkcJ+0b9/w1/28/wDtKvM9BtdlgZX48xsqD044z/OvTP2jPv8Ahr/t5/8AaVeb6I6y6ake5wVcqcHn1+mK+tyL+BH5nn49vkdjRWH5EBkURg5GDnB+n0pkse2bD7cDOexJ+lbjaSr6W90jOZMghc7sjP0/rWRF1GI/XcBkYxXu06vM3boeDCopXZIE+67svT5s8HP86lUbNrnafZ25qPf1xu2E8cYJx+PPNSrH5uSPud+oH5npQ2Zy8yQSKzkebsQdASM4/CpJZV8lQFXgAtgj/wDXUKRSecqfMc8rknj8+RUjRsv7zzFyvRhyATWTsZvluX7W8lbaZWUpEOATuOPpnk1YkuLa6wYkl3g/MAgx+X+NZCv6/fx0SrkLeUi5ZGkYE7UfJ59cH2rnqU0ndaHPOmty+8a/ZwFdvMQZ5wAAR/OqKS/vSJGYHGCOufX8altmubi/RDCzP/cQH5ue3etlbeG623AjbyHIZWQgsx9c9CM1zyqqm+V6mMv3e+xlXIjV1Ee9wACQ7+vetXR7W2t7OWaZWwx2gx4I4wfp1xViLT4Lez8+7fZgbwpwQwx2HSop4WlS2SFcRtHu2kE4z6gcGuedVVFyo55VOdcq0RclvY5U2xqj4HBK4x/Ln6VmrEptpZJbpvMH/LNz/k1m747eYxrMFIOCyRj5ue3HFdHotkzQrPMyuCcgffP4+hxU1FHDx5rkypqktHuUImaJMyI/luCQG4DA+9aEMU8ttufaLVB134XAHfPYAVpXsFjcQtbx+Wt2PugEBgfw5/OsQvPaW0sLwJBOCT9oLhGcHt05rh9tKvDmjG0vMpQjzWb0Zdj07a6eXLIkjhivlsVwQehGPSsrUljVIpn2wXQL7RCrR8HA+UKuGAwepyKt7jPpztdjzIVA53795PIzyQ39KjvtGudkThp5DsUiF1ZyoJ9hg/nVQj70fayN6MuVyUdibTrqOXyoJH8udCSGB3Zz+NWwkDwmCTbMQRtDYPfnPt+dZVvHOiJJ5cqOSQs44wenpU6WlzvdZ7zGzjYGyy5PXYO1bVYRb0ehxSj714sswQLa73jTYFb7oyEfJx/WpJpfKRnjlijkkwpG3OeMdOSRVTc0TvlJd/O1gpTcAOoGQelJHdW3zmZnd2HykDg+nXpScG3zbkWlu9R8pml2JNveEHC5PIb2xVWWNXm3naBnLMRwD+OaW6Zd+Y1YfKFUBNvf8ePTp9KqXFwyJtdVIPyjPJwPfpXRSg2lbQ0jGTaszWglht0YhVJ4UNGchgewPrWSum6pe6pKXXyB958oR9eg5zVW3+3So88aeXGCOTwPwzV6S9lt7B9yi4l2fLKZm3Rnt+Xp0onGcPg1bOinTUJNdy1Pe3cUMtpGsGyQbWZN27AOCOOmcdKisYW+zO8kzp5bYzvypx7Y5/Osu0id7lCHXeX+Z+Tx745rSvpvNTydzDH3iM4OAfWn7HkfKuu5M7q0ESWNzLO8wg2I7D7xGTjOT0FXooJIoXjuJIpRncFDHPHNZthd21r5ofdl/lYkgY9R61amuIbpxLBNEvlkbA+0EHPvxjHrWdWL5nZaGE4PmtbQdd2q/JIj/wCrzuyMZJ7Y55/CqSeTBM5TcCM4IAzmtqGPfv8A3qyRj1bBBA7EfWqcttaRI9xcTiKNpMRsQ2AM/iaiFRRumTBv4SWWJnHmSeVOBhjmQxtkjjJAI4FZ1rPbRbRdo7+WPvFOBj6ZzWwLZ2tgvySxuMhwwPXv+VYs8TK7wF1jSU43OQhAHPrRT5XfUqlK75H0HLrFoyJb3sDpktK5IwXwDtVeM+ncV5l46uFuNWgZW4MW5gMcMS2c479Oa9BvNP8AtF35cUsskUaAKdvmcEZ7dK4XxxFHELOMRxCRd4LoMbh8pHPtmumlTitYvVn0OUuCrKy1Pevg1/ySvRvrP/6Okopfgz/ySjR/rP8A+j5KK+OxH8aXqz7FbHCftHff8Nf9vX/tKvKtEi/0Z5C+3ccDtjHfP416r+0b9/w1/wBvX/tKvMtJmjWwiztJGRgrnua+xyBfuF8zgxrai7GvBqMibCksoyCCC3T/ADmpN27LDaMgdAM5qrCV/wBbvYc5XJ7CpRcMzvsXJ68jnkfmPzr3nFJ6I8KUVfRWLRO3Ozn1CYz/AFqe2t7p4ftETRICeR0I47elZ6ws+N7/AHfl28jHPpirUKSJgI2M9gwArKpHTRmUlZWTLCKr8eXkjB460+Vew4XGSHPH4UxZJrdMD5AOpz1/KoFmW6uWPl/dXAGcjPvWdnv0Rgotu/REruqOuWUHoOD+dWYRGifaHRSc/e471HdGa3tkwn3Tkhv09jTbW4W44nVwAcjYBjk+hqX70b9BuN43NbTb77FNLPHFHNIUBHmKTs5yeewotL+V9V3SLPIzvtI3Elhj16k+lQxy6bYPiSS6csBjYmFH4lqtabqC2V+s0a5Q8HfydueuO1cE6cW5TUdTCpflta6LmowyxXgtWdtnDBiTnaTjaeen4UXkyeSYY23ufkKkHGB0545NSiWDUNRe4lnWCIARozn7pJ/+uTTdR0GeymW6E8U0Dcq3AGT/ADFc0akIyjCejOSMW7N7IxCzJcqJEUYx0GCMV01vruy2YGLeSDt3Dgn3/CsExtLMsXy7j3zxz9alm09okLoyEAYGMndXXWhTqJKQVFGTXMTeZL5zTxbi+858scr/APWq6bxbuEQXiNLuOdwA3KPT/wDXWbG7RfxbCScBBtJ/CtCOK2lRAlzsc5Em9CAfx6cVhVjFdDOSsT3OnwfY1EbKhflJVBAbHTjqDWRbwT2Uzp5ssLEAbd4Xfn69ufetJZ/Km8iKWCaHocbug6cn8uKnvLeCeHfvwhHDA7gpI6HmsIu2ktUyY1JU/dezKtrfzp+63qjkjh+Exjvjv0qe3fe7mRZckb2Ug78Y9cZ5rOs7mDe8cq57DPIOKnjkW1ufPSJcdvMwST7cduK0qU97IJQW1i/bRfO6SRKIw+SCN+B9R1qlPB++TYzOg4JKkDg+/wBKS9uI7fynhllL4LkxEDGeOoGfzJqCFt8O8q2wcAbiQpA9OozRSjL4tkCp2XMTTNsTIYAjAMb8lePeqDRrLMGbbycc8cD055q9HcKyONjAjJGzpjn9Kru6XE2F5xjcH6KBW8G0VC66DJ9QfyRbOnlpjaMDHA9afHd2iWbxGNnJ7kgf5FAikly/2bzEJGBjkj29afNbwJD9pEeWHAR+COc/XgetO8djT3dLmSL9onTymbfyNyckDHTrViMXHlLJLuCAZ5yQxPbnjPtUaaf9ouIkTa8jAuW7KB9RzVlyyW5jKrz0ymMnNaycdFHc6JcqtZaiWt2vzFkYjq3CqWI9eeay5tQae8/1DRYOFUcZ5/StD7Y28eb042kYxxwAc+1V2lbzgI0ycYG888d8dDVQjZ3aKgkrtrcsrcTxQqxlb5iW++Tn8+citS01RZbfyLpFnjUnAIJP+FZbJvRFMSOVHOxSD/KrttCiOElFwmDlieRg+uFHtXPVjBrVanPUjFpvqaUZtHxslaAKAwVMpkY9AMVWuL27S4luo1bhBGu0Bgc4z97Jx0qpJd3CTP8AvQYYztRUB5x/Kp5Va4t7e3jXBjBZtmQxJ57gdK5vZpNX6mUY8kua90Ut9tFujdbi3kHUo2QBn9eK848Yy/8AE6MAdnSJRgk5zn5v616PqDyb8yymSMjCZmyQe+MkmvP/ABnbr9ot7xPLBlQqyoP7vc9skEdPSuumtLnvZTy+2u+p9CfBn/kk+i/70/8A6Okoo+DP/JKNG+s//o6SivisR/Fl6s+vWxwf7R33/DX/AG8/+0q8s0eNvsBO7aC5wcHsPavVP2jf9Z4a/wC3r/2lXmmhy7LaJdzYGSAhwTz9DX2GQJ+wT9Tz8c/ddjQjuvK2ARt5gJLHnnP4/wBKaW+d2DYJz+ZpHgkd3dI3BIySP69qtWtu3zF484zg469vpX0LcV6niyaSuAXq6JlB+BOfxp/mMr4K7EyBjnnNMZZPO+7wc8gcVJGvm4Qsz+x44rN2MXbdl1JP9G8wx5A5ZsDHX2pdElWG+8x/KQKchpEbb1/2cnt6VUuTcWX7jc8RP3lBK5z9aksV8qZndeo6HuDXPON6ctbXMmkovzOnhs28SPLFKz+TG+6JUxsX07Djn0rI1PT00+/8uFPLeNcMAcAn6dR/nirVpqSpE8XnzwbjkeW+0DP86qSuyzZedTwFDAY4HqOtcNCFSE7fZOWDmpPXTsRmFX+Z9xOAeORViV/tEOxNqYHzAEjIPr2qr9v+9GEw5yPMGf5ZqMs2/l3JPViME118rb1L5X1LiTTxWPmBsIJSCeeTjj+dW7HVZZYWgkZJI8cCTcdp9Rzwagtn32DR7kI8wEn2INAWNHIiwcdVJ4B9exrmnCMr8y1MpWaatqbMNtBvcp5okKEAc/I3bseKrBpHTYVUjGAz57enfFaFlcL9m3s0Rk/hHJOQO5PX+VK0MEu+UbgCcOowuOe35VyKTTd0cDnZtNGWLlX2I8bDGTlOavQwwXHBVip4U4J5+n+NV7uBUTzEXKHkP3z/AJ4pLORlhYFfn5OHTP61rJKUbobs43joXXPyKglZ41IIAAAGe2OnamLcLbvtMrBCfQjr7Gq3nSb2TcgBPA5HH4U2a2ndkcrkNlVLbYwwHuT1rNRitJBGHM7NlgQwLN5kMasp429s+3emXtrJ98Tuka4KgszDJ7jgYp8EDLsyzcnGDzx+ArRbS1e2zAjLIeokYA9ePbpUzmoSV2R7Tle9zn55F3qDucHIzuxn+lH2lkwpwhx90vt4/Sny27ec4kXJHZxzkfjRDp0t186QSyvGCCuO/qPXrXTKpBRu2dUeV7iy3G5BGjLgYYgDIPse5FI0DXEbTB1VEGGjL4Ix3AxSmzeKFy8TKqkg70PTp/KpbOCSXbhUWM9iCQ2R+I9aluNrxYnJJNoYbuG1tlEnQn1GR+WKbJ5lw8ZRGQEA4clO39RUl/Zxywtn7OgJ2qUAGCB1+7yPpSILt0eG3aKQBNrKSEGO2AADnrWfOrJocVC3NHcqSJtm2RwOjjhhzkZH1qaGyjfnzGKgbmIG8gZ/SpJry70/yY49PilkALMZF3Ekjrx2x/niotLka7vna5+zqhBysZ9OOMk4q1OXK2aNPk50ytb2f2h2G/djPCLyQPwqC9kmtJjGrIV4AV/mx/UVdPnXF/M+nxMYrbJYFieAeSCMfrmr2p6Q0Vsk8ksTq+MFMkjPv3rRV486jJ7jUnGSctmZkKK/mCSRA64UqGHPrVlW+Rw/JGTk449uaqQWM8XKIjkZySARg+/4+tWbT/SEcnjBG45569qcrau5FVLdbDJryNHQPF5+XwAOuB2xV2DVlZ38mxtrePeCUuELBWA/vdf0rGnhlur7HzEngHOOR71pQaNKkLB2Y7fnbKg8/XPNZ1qVJpc25TdOEFfcZqFzJcaWlvt3iFwS5wSCewIGQDXnHiy533kFpG+Uhjyf95uT+mB+Fd1Jb70lJRshCN3G3P8An0rifGenyWmqJO20xzRKQyfdJAwce1aQjGEeVHsZS4+0sz6I+DP/ACSjRf8Atv8A+j5KKT4Nj/i1Gjj3n/8AR0lFfDYj+NL1Z9ZHY4b9o77/AIa/7ef/AGlXlekN/oBfdna2Md8H/PrXqf7Ro+fw1/28/wDtKvKrNNlggTbvIznb/WvtOHVekvn+ZwYyzVjWtSz9Nuwdc+3406HUPKcDdkLyd/PHtVa03eSI3Vvm5yCDk/0q00bRYzG2DyMV9BJJtpnjzUb2aN7Sp15ne2aWED5mkTgZ9+e9VLp9184jXC5ypQY4B/LpUNl510giEsSZ6mQ44HPXrSZ8pF3uueduz/8AXXEqaU3qcfIlJsfIzXGwyeaXHA3/ANK2NLu4rdJV2794wC4D4/DjNYcfzRLsk3uz4KYyc1KpfeCi9M/KUz39D2p1KalDlJqQurbFuZ5vtbyOsW84yQAvTjgDj8qk81pflKtnHbrVYy+b0lYgnJGzHNWrcb9pKqcdcgH+dZWUIryMJ6LXoNXcuSVZSeo79feluH2JsC9eAxI6VbnMaQ7AuNvTvnFQwozvvDqDxgnB5xUqV9SFL7T2E00fPL5qMYjH1HQEYP8AKr6ovDptHX5T3xTjDshdyi7/ALvoTnj6UL5cW3K8k/MQN3B/SueUrttGE5c7uiTzIdmNmJC/JwOn9abaztFMdh39SFxuAz607baNbOYRP8uAxkTHPXHAx+O6oQdzt+8whPqecVnG0k9DNxtdM11+zXSZeNUYg8xkAHHt2+tNlg8p0BVi38KnBOPbuRVdbhpUVBuJ5GN/Xj0qRLma3TLKzwt99cZXP0BGKwcZR2Obld9xrs3333ErwVfn+dRTTT3XlSTcxoNibMYJznpnrzjirjwySxI/lkRHJjYDazY/nis27e5t7lEjdPKJIGJD8wPsTiqg1K3c2pLVrqbOnW63DsNzu6DPUqGPYE8kZI5xWxJbXPkpNJ5SSDOVjyRgnqB1/OufjjVvs6ztFJbxuWWMZRQxXbkn+Lhj34raudUtntjMjN5oOWUdFz1rwcXSxUsXGUPhRvzUFh5U2ryZVigb7S87x+dxjbJ83A9M1q2c0DoRZW0UYHfaOv4VzsrwM7TxqoI+m0Y+gqrJf3NvcwyJt8pEGSigZGep/Ou3EYJ4iNupy4dyT3On1Kz32zgqwkHzblwCxx+orj5r5beLyY+W/vuMMPp7Vu3t/c6kkUNsGkjkQF8oDx/WsgadPcXO+6QxpHy5fAGB/M4qsup1KVNxrs2fs+YiaWW4hikuJMwLx+79foeM1vpBbW+lYPl/aXwds6jKA98MM9utc2qWVrrW1xmNXzkABR+JHNbEUW9GkDIYOQEf94CAeNpYfpW2LpudlF2Q5yjFXtoMjgu79N8MnnCNyqxQSklSCw53EYHHY9CKw/tfySp5Cm4O5WIyrdOST0xWrdaYsVsLiy82M2/DtJgiR/UAZHTvVGPUtk0pARN/yNhQoORzyMDNa4dNxfX8DVSja8Vcg/tbULeF4LW2xBcoLfq0mSRknPr14/Sp7GLftjvXeGILkGR8DGOMdua1pJLLyUAvVgiXaVheJZFZgR8x2+hHcVXtb+7lvJZhepboBnmFWPThQOOgHao9pO8mo2X4msp0501ZWkc7e2+9nRXlYA4BR/lx656fnS2IuYn2CXjjBBOR7EdK3byKSKaK+gkjntpPkMwjEe0jsASe3NSWV3Hb2xcW0BnMuUlGFOB+n5Guv61emrK4pVGocrVysbl7dHtyUAbscjJ+p6VHC97O6xG8REI6+ZgcDvWhdRtdvNceUiADc+8jHA46DrWBdNK3zwSuScA7zngemevSnT99eZhTUZbDboyWieWZFJB4ILA81xPi27827gtwrAwx4bec8tzxz0xiu0tbRtVvEjeTG84Zsd+pPWuK8bWjWniKQO+9SilT3wBj+lbya0juz28q5VV5XufRnwa/5JRov/bf/wBHyUUnwZ/5JRov/bf/ANHyUV8HiP40vVn1kdjhf2jfv+Gvpdf+0q8itJJPsafdATO09/8AOa9d/aN+/wCGs/8ATz/7SrzCCyV7NbiNlEewZ+bJUgf1Ir7bhxpUU35/mcGLkluOtJ5N6v8AfHPbP/1600bemx1X1yRj8OaxxFsco7cjBIzn+VXIbiTzsh2GP4ga+iqxvqjy60L6oswytE5jC5A688Y69uK1LTT7u43C3WJ94JCh1HT6mqcwV9j75ctyQeRyKUXDIjQxswC8hfXmuSpzSXu6M5JXfwhFG0SpnaNucAnBP9KeLjys5Vime3y9ffvURuPNw/y/RB/kUsh+UDdg8ZA459x0ot3Ja1940LO73cozMg6ZOD/jVvz2faQqkckgA5/lVG3VYoVTapPUcn/Hir9rqc1rM7xsqE5U4wG/Mc/rXLVT+yrnLOKu2kT2fkyzBZNsXmcHOcqOvfg1qXGiRQOgS5IjYksHAU9eoHAxx0rm5pd8zzld+TvcueWJP68+1WrCXykIDMMk4BJ4Pb9K5p0p6NOxhUpytzRZPdt5VzDCq4yQTluuOAfaiP5NwKtlsAbHHBFQOfNu5ndf3gYr1OMDjirUcy7cRcA8bsnrn16H8appqKIkrJdxrmTe3zK5wNxyD0+tEe7eCeMjHbpVuxnsrWYTX9s0yHptGefzA6U2WS2lm326rHE5yET5inHQ8f41iqj5nHlIe1xXea3dSW2E8EY4/TrU9pq0CQywPBhpQyidBj9MGqTHchBbOR1PH/1jVcpJ98yrgjg5xn8qbpxmveFGKa1NWzuLayfzD85J+dBnpVuR7K7dHWPyRn/lo5xz6fhXPT3Ei7BHt+U525IP8/5VoxPO82+Z1RTjI74P86ynR5feW5E6VveuX7a3aWZh5jGMDLuOOntjNV5I9k235kY8E4Azj+XSrmrPPK8U6R5to0Cl+cZz1wegPSq2pPbJcu2H8t9ojZJFHmLt54HK7Txz1rlhVaa5luTGk5NtPQrgSf8ALRsoRjJ5/L8aSWNZZmD/AHf7zqEzz3OauLLFFb5jRZSc9cqwHs2OOao3F6qOAV2IBwgyP1PJrqi25OyFG99C3DafZJluLa48ngZH3xx16VburOWVPkZd5JIYZYOcc89f+AkVjRa1dxP+7RHU8bpOoGatqlzLM/mXMRikPmMkbgKe56dO9Y1Kc+a7G6c95M04pLZERLm2gm2xgIm4AHLbS/TAAz6frVDUnitLNZEkYoAXa2yWOe/IAwOntWfqOt+Rcy2W5fs6ZVWg37c54DFvve+OM8/SSOy/tW2Sa/1BDbxuM7EZioPAzjkfpWEMPOD9vUb5X0Oz2doxi1oPi11ri0RYrRUiUbQvnrtb9ODVJnX543g8sGQEuJCQq9CMY5zWk9vBCsM1sZdOiznzUYsG9OB7VpSXtzLbO1rcpOkI8yVvuOeOTj2HPP4ZrR1owfuLRmScb+4jmZRH832eVSM8MMjOe2KTTprmK8SR3tzbZJLTymHawBP8POeOMDvV6CGO9vHR4Wdz8xIcRnJ+vHWqMsd/pTyyQSLhCQATkZ78YwcV2uSnF0+rNqUlezRJDfqmrs90LqBwzHduEuT2Icg5HHXNX7SSRbd1tPKJmGHUY3Nnk1hxBm8uRvISNySRHIDtB9VycVqKVl3GII64/wBWgw/Pfp82Pes5QikrE14+9oVRNL86jdhjgoDx9KqtBLvUorAk4LAY/D8q6RZZLfY8TRCUcbXXDge+7NZWs6lJE6IYokkPzbsDJz0yRWtKpJytFaGdOcnK0UQwSyfaY5JIsBTtwuUyPTI5zXA+NruK68TTiBt8UeEU/Nk4653c5zkV6DHeTLltUhFwhwAg+Vmz7ryfxrznxdCsWuGRI1jWaNXEQydgxjGfwos+e7Vj28qS9s29z6P+DP8AySjRf+2//o+Sij4M/wDJKNF/7b/+j5KK+JxH8WXqz6tbHCftG/f8NfS6/wDaVeVaRKyQ7HjyH6EtgV6r+0b9/wANf9vP/tKvJLV4IkiPmZduvHI5r7bhyN6C+f5nDi1dWsaU1qyzK6LmPJyTxg1bhhjiRUPD5+dsZKgfpUlhLHLCfMXjoeOKrkSed5aLjPY5zxXvNt+69LHjOTfuvoXZTGvyRtwCPmz1qKGBmmUpG57bsHj1xUkUfzobh35JyCmQSPapYzHF0jx2DDBrG9lZGTfLsOSCNs5jUEd+akiVUTA3IGPB64H1FEaqkqGRmCgHnpz/ADqVLpvOeNPnyNhycHH1rGbfQ523sJKWZ8h95OPv45FVssuQeAemc4qw26VA5kcv6H0HFXLZI24dYnAOSr4B6eppOXLEjmUVqRIn3VdFx2Pb9eKtwJv2J8xcY5yTyO3pVKVGidz89vjsQRj8K0dJtY5ZkuJtxJcbS42j8ua56svd5jKppHmbKyLN9puSdodZGB3sFOPbNWILRp5hAjoGkPGc/KT2/wAin3Kebqko+Ujc2G4wOevNTBIkTfBuyeNr84J78VlKbcVYxnUuth8NnJFLLEkkZeHIMocqc+gzgEe+KbJ56TMJNqD+FQMgH6qKm08t88cbKEByd4ABz+ta00UUtp59xBwAGymORnseuCa5ZTdOXvanLOq1KzWhzwnkdPVR144BpyyfOw8tfmGMFcYzWuLPTZU2RmV5CM56dPTn+hptraq6PAIGkKnL5wgA7c9ar6xG12he0j0MlbLz3EmxjjGCMke/atWC38xFxtKAfMOxGemDxU8umyRQgiPBPI2f49Kz1FzE5ePzQFJOCQOcUnU9otGQ6jq7O1iwbq2i+SWzWZeCp3bQpz7c1kSs3f5FByTzx7D86szy7JgXi5/Hv7jirogtpYVJlZxk71Rhu59j1oSjS9625rF8iV0VGttQ+wpcCVTAOjBsFcn0POfcVWFvPLtjllbOckH3Pqa1LlYEh8uPzUHBPmEce45x2qil1tR0dXbA+VlcAdc9BVU5SabtuVGbadlYGjli2gN8g4UOP0HAqo21OSmSPyBq7DfyRROsS/e65GDx7g1VluFfd5m3c3TI5x7H/wCtWsOa+qKi5dSpNFbXFzzHsJGSAP8A61RvNPZI4jllWN02NjOGz6/hU4Td/DhOoOc8Dv8ArSXscr2n7tlcccoTngVvppF6o6Yy95J7G3pEGpW9mtxbt5cbDKuGVtvHUhuMfXFQ399LvQTLbXRI27/K2OeOASvyke2TUWj+JJLVIo52nQDsnPH0Oat6rri/usQW2GO7JXnPuK86VKp7a8o3MfejNq25Dp2n337q4FsgjcFY2+Uj16Z9qsXsupLEXeKMMCysNikBjkE4qWz1tXsGgNouCMH532kH16lRx1B/CrEU1t8/kOOMZUPk+mBuO4jPTiueVWftHzx0Iqc8ffsURP8AaIYUi3SCMbVX7y5PXj/64qC50/Yn+kRMlwcymJIThVJ+8WBPQeoq6f7JfWP9CXNvHt8wRswMjnnI3EYIJ7UscP2SWaS0jZInOGidH2kA/LknAz+JFP2sm1yqyKbUL6lCCaG0/c3aoAej4yU/Dvn3qvPeQXCOBE/mlgN4PCge3Tp61auZ1i+0RoqmOTphN24g54OM9ar2M0GyUTxuSSdo2BgCfXNdkVo5tExslz2EWH7VbMIJPLKhjnzMEnr045ry/wARztLq8qPu/dfuwHGCuOo/PNesNJGnlbEYRhCC2Nu//vnkV5h4xijTxJPJDAI4pQHVQWPbBPPPJBPNXGb5rW0PayaX7ySZ9HfBn/klGi/9t/8A0fJRR8Gf+SUaL/23/wDR8lFfE4j+LL1Z9atjhf2jvv8Ahr/t5/8AaVeO2sWyFHdvkbOMjIyK9i/aM+/4a/7ef/aVePn/AFMRR1yo6Zz2r7fhlful8/zOPEPWxoQ3HyZRc5wCOeMfpV9C0v70cHgOMcjB4rKtPMlmVCzYPboBXUQWflQk/KSeQRjIya9+u1F+Z4+Iag7dWIsW9FBVS+MEev41Zj05miJ2o7YyRvGQPcZqBGjT5kZlznYo9femRXas+zdjHU/d/lXFLmex575nsLJti4O05HcZ6ds96WELzsVcnI+cY60lyJOJIJBgdjnOf60luWldAWQOeh9TT+yO3u3uT/dwDtwOCozzUok2Q52s+epHRaZNZz2T4O35uQUGf/1U0ur/ALx238/Nk47dvWo0a0ehm0mOll+1X0W/cQMFieCT/WujsfkdCdxA+bPJwB6ce1czbRebeNK/GPugtyf8K3k/dQny2X96hUjPT161zYhaKKOfFLRRTFnvrRkfMuZCf9XsOCSPWq7Dq5ZOQPuZJqKSBXcPtUupznGAPypzPLvIkTLYAB5J6duahRtszJRiklEsGLcqGN/L53DBwRmr7Xdzb2LJI7FyeN+ORj361Rt5GSZS254wBnk9AO+OaluZvtTrjeQRgA5PX8KzlG8ldGUk20nsQWN/c/aVPloQDhiW5wTXWWrK0K3KbTIgOdmOeuB/9audFtHawoJImSUnqcD9MVoWepLabURcjqe3P41z4iPOrwRhiEp2cVawxNVadDHMVRhwUCgYOMd6rzXn751mjOeyhACO/bpx65rcj/sua2l+02yp5uT945fvzisCe3W3fY6oE+XC5wcZ/M1jQnGUnHltYcI091uzOlf7Q6wx7fMORjoBir9tBd2SOlwVdAASDHwCORyPft0I61WnjWVUcOokP3mwOAOgqw15PLiOVlIUdBH1xXZUTmklsdLlolERpG81g+0oAACFGB6VDcrsT9393O7IYEYP0P64rVgt45bOKdFnPlcudpwwJ9c8EVSupdrolvEkvlkYJQEMPfPf6Gs4VNbRWxEX71itap94dH544HTvTzB57hEb5yQuwtj279ahnDRbHlXgLjuP88+9VkkW635ZSgOAr9PqORXTZv3kaxhd819BkzL9oaOJvmU/Mo5xzjntT4ZNvyfKTjk5AI+mf6VAlnCubiNWQ9ySOPzNSojJbvn5zjLEA8fWtrpqx0SUbWRJ+62uPk3DgKADkmobq7kvcSP8ksXC7QQOBx7VRaWd5l8tGCZwFwDnmp2Vm6Ns5yRg9qr2aum9ylBRauTJZwXrJJd3627gAMEi8w/jgDH5k1ZWbZCqeZkADDFSBn09sVSO3eu3djoGP5cV1GknS3sP9OljMyvkCWUDOCOOSeo4yBmuPESdFOe67CqNSSTMyyu2i4hVfnzlSQwI+hHTNXINS+8k1nsDHG6PKBfoKNUtrS3uQls8mxxlS7jsPU/41QimX50ZnBHcEfz6VMYwqx5rWOWSUr6Fu5eG4mSTc12/O8OvlFefUE5z1p+ly2UVtcR3TTo7JlA6EjPcnFZksMsrpvZtvVgM8j6io7fUv32x40aSI/L5m5iPbGcYPuKJUG6bjF3NYwutNbF66/0f97ALYRqzKvlyNlgD1OTgZxnA6D34HlniG6+269cyEbfmK447cduO1et6rewS2EYgg8uRz+8V0PUY6Z7egHavJPEUaxa3cIq4GQ2M55Iyfz60sPGXs1zbnr5Q1KpJtWZ9M/Bn/klGi/8Abf8A9HyUUfBn/klGi/8Abf8A9HyUV8ZiP4svVn1K2OF/aM+/4a9/tP8A7Srxxo1i8pDuD4G/I7mvY/2jDtk8Ne/2nP8A5CryVpFvbiWceXFuO4InyqPwr7nhn+Bf1/M48Q7M0LPbsUDYM9DjHSt6J/PtxInDAHkZA461ytvceVNgjj2IOfyrqLLy9gRd6O2ep4OTXtYqNtTw8XFrUr7o03fMwUkBeM5OP8adDuRJgWcgN8349Kr3a/cKbh3Bz0qaIefbu6f6wDDDjJHrismtEzNL3blUXEkrvDvQLnAL8dDUuxt6vGzEA5UHtikUb4kRI2ycDgHk1bWLyuX2jOevH86baQSklsi3FP5qLKV2EcHJz0qO7PyfdfeCQu/AHvVjS7JrhJUCqXxkZAP8qZewLEqI8bl1AVtnX61y80eflRxqUfaOKZNYW8lwnmIjGMYVmAP+e9X52+RY0OSoHOccn8uMU3w/cM8ItvkSIsSzbMHH1zWnqdpDvU267HI3Fo33RsMZ4yOT2xXFVrNVuWSOSrL97Z7GCZWR/M2q+eM89qswT7kYP0PzAgj6fXpUbRN5zb3Uke+3mkbcjnO0nrnA6n+ldDSdinZrQkV185fmYZIHPPA4qcsqKnyJk9R2xj8Krxp5r7Ayo5PzZfAH9Kiunkt9R8tGguMkkGNw457cVm0m7CVPm0RcM+/yk28oDjr3+lX47JpULojFEHzsTyAfbrxWcjb3RkibB+8NmRV+11C5lT7KzYjJwxHBIHbNY1VK3udDnqLS6JN81kgTzVQMcbRz35z+VOlvVXc6Jl35yOcnPp6VW1aSRplTdFjHPGMGmufkQhkGAOMnv+NQoJpN7szUU0pMYpa4JSPgjoGGBmrDQMlu0Rbex5bHIwOnWoo5I95cYEnqMYzUpfaiby5bPGWxn8Ov5VUrlO97IWCOa3hMqJmMHaw65/yDTZbmOW33wLiWPqqcZA96RWnlR03SuqHJA9OxpmnGSK5/cSbXJy3muNrZ6j/9dRbd9UCju3ujPk1Jb20lR4pIJUzyZMhsduFqGyb/AEElI2Hr7478VZ1DTllvsxpsc8sx4yR1xjjGfWpJ7W5t7ZQbSVIgvLgbg349K6FOCikup280OVKOlzrNA022uNKY3MCyLICcEZI4HTbyK5G6tpre+lgJEIldsIcEqD64Ofzrd0a/u7LRWk+07wSxCychQB0GO9cxcvJcTNdO7nJy+ztk55/+vXm4KjWhiqspyvF7BCaa5V0Io7ORHG6Vzhfk4Iyex70kvmy5UhQSBzgcAVv2EVpdWbSNqTQyRgCIM46j25OPpWBIFSYp5iuS2C2cjj0zXq0qvPNx7Fxm5PUjgn2p5Y2j1Iyc/T0qRreOXYZEd+ASA+Bg/Wo7S3b7QxCqUX7yuTt4+lS3Fy1w6KlsgI7IOGyevJraXxWRo173ulm1W2VyY/kyCBvbPX3qxMVXqygAcE5Bb+dZaS/d+Ty36fLnvWjFIyw4WRU4IYHgmspwaOacWndsSS5+0WjgxxIDgBioJ/U/0rn5naCYbJWPPU5Bx+NawbZu+87nn2/w/SoRGtxMIx85J+6cDP65rSlaF+xvSajfTQ1LO5+1WLvI7GfbtxtGMA9D7/SvK9ckaXWrsu2SJGUH2HA/QV6m+63h8sK6AjlTx1PbPJrzDXyr63cMiqASCQnTOBn8c/rXMkt1sz0Mnt7SbS0Ppr4M/wDJKNF/7b/+j5KKPgz/AMko0X/tv/6Pkor4bEfxZerPqlscL+0b9/w1/wBvX/tKvF1DInDcd8V7P+0aP3nhr/t5/wDaVeMRjdtwrfhzX3nC7Sw/9dzkr7k8Efz53Jnjg11mmyRrCc7/ADOGXjIx3rk0l28bunQY75robK92om9VQJkA4OM+9e9i4txR5WLi5JGupsbranlOknQKvc/jWbO/2d/3UUkeOpI6H+VPhuJLiYPtAOeTjGKvytA8p898kkbQDlR+defrTdnsef8AA7MZHKJYcv8AZRIM8Z2v+GOP51ZtmsHmVZVlz1ykgIwT/u1g3sTQzOUlTOTypH9K0dKVkhZn5OeOT/n86U6K5eZMKsFycyZ0Npbbd6WF4pc8lJBsY89AQSDWROL2W5fz22ENtO/IYfhUayyo4kYZxyrc/LWzbX0GqbIb6XZc4AjmwFBHUBj9e9cbU6L5nqjls4PmtcdpqLFDIAqkBCSQCpbt396stcRvt2RtG4Hy7Pmxzn1/nmoY7aayvESTmKb5Qw5VlPXk8fjRc27RfMiqRnAw5JGPwx+Rrncozne5xyScr9ymwleZ3O7BPX3NK692dincFsHNacF/FboIp4m2E5YsmcH19eMVavJtCeFZC/znChYyc/kap12mly6C9pJNe6YyxebMo+UBsd89f89q0BpC7HdGVHRcv5jg5HoAV6/jU1jB87TRrKY8/echcD6nNPvZWRJUSJRGx+9n0Pr0rKpVlOSjEzlVk5WiVY0VE/iCKOoPU/yqWzaDeXRckdsdT7ZPNU/OXyWDsyBuh5I/WmGKRcYZCG4AHX8q0cbrVg4Xvdllwt3cFpnZT6SfdH6cVpSaWtvZ+dGrTEoMtC3ycfTH9aypPI2AIuwAYAOOT9cU+LU3tbZ43tkiiYAApwWI7nJwfyrGpCo+Xl2Hytr3SzZaf9ouWI+RTyPlJBx+VJdWV3LbkJDE0gIDNxkZ+px+lVbe4nlw8crxp2y2D71fGrsibH3E78htm3kenYfhSnGpzaGUudSutRbbTpLez4ZhJJ1w3XHYj2NZflsh54didoGB39qsm8Z5mlR3QAZJ46E/gKrzXPyYdsD0TnI/l+taU1JXv1LipX16lyPSbuXypoJFfeeIw3zLz3B/pTJ5FuM28kc8MsR2OEb5Mr1JHTPbOaqNquofbICsmyGMqQE44UnGffDVn3+oXNvqMwntsxSYIOCpGfrkZxUQo1pztP5HZGlzbbmpN5lv5Krcb4pHww7LxkArn5s+wPvio5Z1t8gRJOp+60iAY9gAcYqiG82WHy4ZXBALJ3OP0rRvLu0uvKihtLm1kXiVZB0x0wTznHrVKDhO0tbjlFcqsrWKgSN0l2StG5GNqEqCPcBTn6cVnGFt6blYPkDc/bFaUv2aL54JMMgyS5AJ9/emFvtqM7KvJ+V+/sOPr6V1wbTv0HGTWvQoKyxJhGY56DB+aq/zS3DkbgCegzxzWjLZb87NxAJBbJ5P48imJp00Wx33Orc4j55H/wCut1Vju3qaKcdddSAeWiY2sCB+vrVwnykMw2yoPvkdvzqmWVJcuzB1zgcjmoZDI6HMjYP+2cUW5+ocvNa5pxL9vQOq4weSOQSfw61IY4bfaQvydScAnPufX8Kl0a7ubC2xFPE6dl87HJHXHfpVJrieY5ldOSS3ygf061zrmc2uiMnF8zV9CeXWZIkcFfkOBt6f5xXk10zPdSmRmMhc7i/WvSbm3b778IAcEA45PavPdUK/2lcGP7u89fXvVThFaxPbylRXMon1F8Gf+SUaL/23/wDR8lFHwZ/5JRov/bf/ANHyUV8DiP4svVn0i2OG/aJXfP4aH/Xz/wC0q4ubRtPt7ab7L5p2hQGkHzYrtf2h/wDX+GP+3k/l5VVrx7F3leRvM6boo/lVx69a+oyarOnQjy/1qeHm9WVOUOXqctB4PZLOG8vnit4pASAR8+B9R3qhbJBb3hV45UjJJjV+TjpWtq3jhpW2I3+rG1ETKov5GuVub2fULj7QXYzE55Ykj6c5r6XDQxNS7rbM5aUa1RP2miZ0S/Zopl8t/LznIJ9f/rV0KNo9rpyK8SyzNndwG5/mK5Gw8u62CdXjdRgkHA6/pW1CGR1eWPz4l4x3I96wxNK9lfY4cTT1Su9DGuLCT7T8j4LEYGeQPf0rZtIF2bByR35OcfzpiSrK/CxIATtHSrMM3UlZ1I+VScHk051JOKQqtSUopdiWG1ZnZI1iCkcmQ4HNUJ/4Izw6Nxzz17du9LLK0X34k255JJGf6UyLa7mVFwoyqscZP+NZxT3exEU0rs17G7nt9qiSUiP5ijnKYPoOlbYlkuNjxxQNkDcPsyEs3vx/KuXtpGd0B2kMACT154rotOuPKRz5jBwN3yeg/nXDiaMd7anDiU46rcL2VJf3UlhYLt4Y+UwbcO3ysCBVVLWyleISWqQKF+YxyNkn8cgj/OaSW63zO4zgt1I9e9Sq3moY0XOeh2c8d+nSs1S5ETzzUUXbiCNrZEtZ2AAyFmbB4+nyj8TmszUzqUtvEk9owgQk+cicHPQlhwfrS31zL5KJHFjaPmbnk9M/X3qtZ3lzFNsSSQI5G4ByNw7fWlGnOMebew6MbLmZdtLdbqFGMkHmgjKzOF3Y46mrTWTWSSmaBPNHIUkFeT/QVpXFtBFyYLYyAllIxDtXspPTPblRmqaTR3DyxQXMUMYx8s3yH8wcGuaOIdR82yImnf3djMYWzzJvZhgfMEwccelT+Wrw/wCryACdwB7j1/Cs+ewubfdJNFLg5/eAkqueh3cg8+9W9Etpbh5CW3xxHqWXOMY6ZzXbKcVHm5iqkLR5r7E8T/Z4WGdiHggkc/hgZqtIyrsR9o2nggDPPNXr6CO3TDqoJ5LJgYHoQOlVDAv3A6kHJDD19KVNp+93MYtfF3IAjMgI4GeuM549qiZ4+S6oTwOM5AB9sVNKkmzA2/L1zgdartF94hsEYyCQK3VurN49yGO9lSZTAmCn3mOAPz61NcXlzewr5kSngMX2l2IH196jij+dztw5z6Dge/1q2kmlpYTRz2kr3BGYnRjhQenfH50VGk01G7Oj3bqyMJbtkmYIuFIx0INaguY5dgnkuADyXDeb09cYOB6VTeCN3b5Glcn5iEOAcY9K0xassLPHK1uCD8qHBweuenHTjmtKzi7NaMucoq2hy95PJ9skSNx5bNtUlNgI6ZxWzpm77A5CxSAMNw5J56dDn9KZPpbcyO4MQfaAiZOcf5FCGG1/dh4gDyT0P459a0nKMoKMd0bTnGcEooV7jyn8zfETn7u/HX29jVqHWF4LKI2znMZxxn070sdv50LOYFlAHzOI8hQPU9qqvYx9YWxgZ6gjGfzx+FZ/u5aS3Oe1OWnVGjNOt66ylFldQfmII6+4x0qpJYxO4Ebtv6sJOhA9Mc9qsRm5ihxFuKHhmCc8fXpVFLmWVwXZh5eR0xnNTCLV+V6ImKl0eha27EYLErvx8/PH8gKoypLFMSGbOM44I4/Sp7fUW3oZI1IBySBzj3zwa0J7myuEUusqkNhuAvH16fpT5pU3qrpheUHZq9zKgk2W7CWNSpHIxjB/z6V5bIxeRia9eMEbv+4z5a9DIMgHPHIrzPxBaxWWt3UEMqOofIKcAZ5I/DpUykm7o9vKKkW5K2p9NfBn/klGi/8Abf8A9HyUUfBn/klGi/8Abf8A9HyUV8JiP4svVn0q2OF/aJXfP4aQLlj9pAA/7ZV5a969vbGHzJdxVQT9Ogr1T9oeRornwu6cFTckH/v1Xi5LPk7snvnrX3fDFOMsNdo4cTHmkr7IlBb5vmTnn71SwvGz/vFXGMfeqnjsetTJC3yv2zgEetfUtKxi0jprC3XyVKPvJ+8vB/8Ar1uW0flPgMyFSMkjINc7paNB8jq8b46jHOR/npW7LK3yAbQn8IGQPwxXi4hNyt0PCxUXzNGg0ltKyvJFACeSY8g8fhiq0gg+/wCZKUA4BIbn8aXbGuQG3nqTyTnv+FRXNzs3OjoVA6dR9R0xzXNGGtkckYtuyK7SW29UdlAz8xx2P0qcwfvRHEm8AAjZjp+HPesyTzLjZKVQRkkAg8k/5xW/ZweVDF8qZIHzJyRn+taVHypWZrVtTih8Ns3D+W2MZZvTP4CtG2RdkwCscQscjGeRU9rczfZmj35X0zgkmporXyrS7EbOTKPLIPLAHrXmVKr1TPKnVTepy8U3zsSqBcgc4bn8ea1I5vK2JuYJjcCD39u1UbqDyt4jVkIG5jhgAfTtzT9OgkXElxBPIh43gHA9+ldM3Fxudc4xlHmNdEZ3bYsoc9SB1JPsalSKbzt8KuZd21SF6Z98Z6UguY3hT5Udy3y+Z0xn8MVL5c+9CZPnwCrdeD+tcMpbnnttbiTxSqks0jLhDjeNrMPXPOc/WqqtBFYvhEI3sMvtDn09/rT5pJFuYo9sEgtyxUEfK2QOtaBexu/Jjk2x5QqSemQf4T0AxkdOfl9KwnOVPlurpm0VFrVmSJWt4U+yybC+AQr84+n+Oa1dNvGiyPISNCAWaNMZ+o6n6isuKRVxw4/3MBf/AK9Pjn8rcQ33s4PAwCP61vUpqUWrETu1Yl1go9y7bBzjDZxx/I5FUriZbW3+zvuEkgX5k7D/ABrRjlW4zHMyhx9zzOFP19R9azplZpgkn7l167F29PUDrVUtEovoFKy0fQZFpsr75LF559n8RJPXtinLbzywu4VQUDFh0PH1/wAacmreVbNa3cLPA5U5QFT1B+9+HvWpNcWP9mzPHJGJHjIPknhieMYbJHTJ6fXtUVcRUpys43R1OMmrmFE3m7NqMgPykkcE9+cYJpt3B9n+V1XJyc9wP6Vdmvov3Nh5izxiEuHjTCx5YDblTyepOf8AZ9a0rbRo73TfNWTYxztLoGDDHtggZq/raiuaSstgknCSvpc5C3dt+d/7scHPOB1/CpGupLf545X54IzkY/rV+ezbT7mVPKid0ABwC659elUJbPznzJIsfmuAVOFAz3OOnX0ruVSE9ehtGUZPUjiumunc7f3JPJ5I4Pc+uKc6rbvwySADdj0HXvWxdaZbafpVvGvNtMw3XCSnk9xgjbj2z/DXLXs0bTPGkqhs4XZ0PPrToSjWb5VZG0IqUvd2N628T6fa2AhexlkkMh3MflBz9Kiub6KWFrtFUNI4ZNmTtHTHIwTWbY6XFcbt8rCQY4GCDg1VkMdrebJMmMZB2d/1xVxw1PnfLuNUaXN7u5sm9820wd+SQqlsAj+vSoY2aJGcbRIck84wB6Zo0+1muHWcLvts4O8A7TnGB+FX761XpHGsfPO/7wx368Cp5oxlyoxk4QlyoyTN/pPoQeT1Bz9a0LYxtsLs2APunB/Q8Gqb7kmdzEhXg5x6elRJKqyK5VwFyQBg/wCea1lHmWhbipLQ2J4baWGWRPkcHOU4JP0/wryGd2lnkd3Z3ZiWYnJJ9TXq4n32bhfkQ8Zx6c59hXlV48Ut/cPCmyNpGKLjoCeP0rk5bbnqZMmue59TfBn/AJJRov8A23/9HyUUfBn/AJJRov8A23/9HyUV8NiP4svVn0y2OG/aLH7/AMMf9vX/ALSrxZf4e+e1e1/tE/67wx/29f8AtKvFB8vWv0Dhdf7En5s46/xDinz46H1qSJG4+bAz/ewKao7r2/lVm2k2OzlFYDk7wDX0stFoczZo2MTcpJPyBn/WcH8R9K3tPikdEXc5PQfvOufzrnLYeVMHRVMZ5+TPA9K6i0jjRFKN975lHI7fSvJxNzysY2h6oyYMjOiAbRkj09O/5VUb7NLFseUDnBwf8eMVPeXe3bjkHJ2jjr6E81WtbD7REoO4g/eznjI6+lYRVo80tDlgrLmk7FuGJbq3QeaziPosacY/AYrWhO7bH2BAQOcYz+GelS6XpCyulqG+RuT0ycema1LrR4onbFzsWMcLJ95mx0Hqa86tiIKXKcFavGUrISx27NjrnGQ2DjA6A5Naksv2WzlkeVgAy7SCGJx254/KsyAxpCpRUEqSrkyfNkAdMYyKuyXEl0hNysUke4FVHC9RznqOtedVu5XtoedJJyuzGheC4uWuJ9zqz8iNyOSfcc4q21xFaXgjTzXD8JFJyOenFOeXRrK5CywLGGkYLKjs20AA9s8AGqUsttcak12ih2ByucqAPw/xrSH7yWzskdNr6vY0RK0syObaCHaApjjiK7ue/cmp57xL3/R47eC3I+XLuSM5/urgn2rIe6lgmExZv9ZvynQVq2uoS3e+aSNHkfanmvxj04AwcZzyKxq4fl5ZJbC5mk5MyplbY6llCF93HHHTvVq1v50s/s/mqYyCGzjgenNaV1880okvYocc7GwS/phSR+ecVRkgtn27N4Y52GNwwJJ9GwauNeM/dktiNXBSelyvCLJ3l+1Sz7ACV2DP/wBYVCPKldDbyI8QweVw+ckdOg6flVmayZbZd6J905B+Qj/9fNEGnpZQ7xvRHO3dsJzntnitnKK97mGpx5fMeUR4ZbiTy0SJRw/3s9Og60RRLqsySCXZKMDdImd4Hf045rTl0ywl3xxzRiTzAjAnOGA56H0bOKgvLKWLyZrQ8RknhSScZ7dhgd8enpXCsZTk+VPXoNQktlZmXfQWSXKQHflSxe4R8GTJ4xzgDp2qvqVlbWlo7x7MkbciXdyec5AGa1Mx3vlXUm4W0f8ArIIfkKkHgduprJ1kfcRIGg8sErF5xYKCeOa68O25xgzeDlNp3sZhH7lJI5UwOOCd2T7elaela9c28PkmOOWIA5Wfn8APWs1l8qHftwSflPI61JZyrFud2wGGGXkg5r0KtKNSHLJXOiSXLfcdqWoxPeM6QrB32jnn0qo08UvLtye45JGfT/69TSpA6b3kZHHRfYntVVrfZFvjGVXBAOM5P1/pWtOMIxSWlhwjGy7j7qFfJiPm/fG4nvx71QNkq7tjJt5JZ+304q+qN5KO4cSN/fIwR/u5z+lTWtr0kKebHGc7Twvvx9KuM+SOhpGfs1uVbe3kidF3r5ZGSM7gw/z2pbnRle5Z3lRI8ZUJkZ74x60+4f8A0lxHGiAgggA81JbahLb/ACFUz0w43fzFF5/FHcOefxRLVhJBawoHknI5yD0FJeSJvd4vnQjjepxVaW7aXiXbnsHwBx2xSOytkxt06bEyAKyUPe5nuzn9n73O9ybzvkIMUQC4C5x/Xk1T3K9zlPKHZgcLirsI3w/dyACSdn+c1XlHlOCdxTOVGACB3NWrJs0g1doraxI32BoYo0BaPGQQeCOeleZt/rK9M1GGSW2kFvuDNGdoBA5I4rzNvv8APWpnZJHu5T/Ddj6t+DP/ACSjRf8Atv8A+j5KKPgz/wAko0X/ALb/APo+SivgcR/Fl6s+gWxw37Rf+u8Mf9vX/tKvFMd69u/aGQy3PhhB/wBPR/Lyq8ZiGx8P+NfoPDH+5X82cVd2kLEN+B8ufrzVuOBUdXeVYx1B6immzdPnTcU4+YccGpIl+0OE83ZGvy5PYV785XWj0ONu+qLsU0Fq+Q6OCOdi8An61tR6lA0LeZ6YUjHBxXKNH++xHJlOm7pmrcUEjJsR0wO545rlqUYySbZyVqEZ6tmheXKyumx9+zA65HP4VraSm+E7o2OcAEPiuQMTRTKGZR/FkHPP4V1OhSKls7+awzxkcEmsMTTUaSS1OfFUlGl7rNsyXO1WWVUx8oOzO3j6YH4U9bqe7QWz3c8q4Hyh35I7YPFRRK2zzEftgEMFNWrKX7O+8cFWBYFFzj+fSvIqQVr2u0eQ3ZOy1Ip3n+QhljRcEqSRmtK1LS28w8peRkKf4Rkfj0qtemO4cvEuI8YwOCcetTWJZd8P38qduwnANZ1NYXtY5pu8U7alaSXfMyx+QmByDCCPfjB/lTo2kfCOixqe5QjOPwpUgXe8kjnnIwPmxn6mooVaLJSQxjduw/y07LoXdONkWYrZribyI13uM7cHufrWolq0sL28rJA8TbWJwS2PftWVGzK+8fORhmIww4PqPrVme4jRFLIwkbBYvkc4rCopSdkzCSb0Q3U7S5Xy0klYoAVUnA28e54496qGG8d4nSRpGCDDHjGPfPNSW8bb2kmdnT+EZxj6Z56VKIN2514QHGBzn8f/AK9VH3fd6lKfKuW5MZ7lE8t3Z42ILB8fNx6j/GrKi2ZAyX0kc2fvYzz6nrxWNcRTvMZA28nr0BIA/XgVOT+5VzxGTjAHUD2JqZ0U1puJxWjTNaC5it5ojd3P2onIV8kBR646Gqmp3rLc/aEjcwHCryQMd8Y9ajaKHfETudAV2rgAD8MnNad3aMlsrg4HV2ZFYbevcEda5fZU6clK12xOp7ycmZlrcRpcvJYwbI8F5vMlLBgQOPqMH/vqkfSPt9/Jd3Vyzxv88L5VSx7A5FSmBWtkMceyXAUNG/p6+vbNRx3kdvsgkbEY6l1BCtjqB/8AXrXlau4bmntXJtx3K9x4au/mdYsxIPmw4YY/Q5+grIMflEIVbzPu7EBJH1roLrWr1HSNPIQhACyAfMvY/jnNZ8lvO6NdlVfcfmAYOenpknHua3w1Wql+926HRGTtZmRJc9V8pjIOAoGT/iOKctvJFbPcl4slwjQ4OVB988elaN6WaFAzKH/jTaARgcdsVTZdifKjeW3UEcdfy612KTktNDeNRWskOktfNdHjjUYXJxnOfz/lUDStFuyXQAYZU4HPrnmr0L/aPkTYCBknIHXj+Z6Ul8vlIHkgaQZ5Y4Hf0xn9aFPXlZEZa8rMwzLsY7JXcuMEEZx+tKj/ALrIib1PmZOP1pwt4Xd2+dMdh8365zUscDSzYHMeSpKZGB75rZuJs5K2hAsckuTt4JwqjnA+lLNcLb7I9rZx3PI9q1N8cUO1EPPQuQR0rn3aO4fO9txICgDPAog+d3a0QqT9o3fZGjA37kncwB5AJH9aZCyyuwRWwOrFxirNnBI9siBURSMAnBBPX6iopE8pHQR7Cp+ZcZx2zU3TbSIurtLcjn8v7M38AA+8X+Xj1ryx/v13mq3LRW0wAzmJs4I7iuC/jpVY8tj6DKoNQb7n1d8GP+SU6L/23/8AR8lFHwZP/FqNF/7b/wDo+Sivz/EP99L1Z7q2OP8AjyFe/wDDCPE0mVu8IOrHEeK8kh0e5uEebbsjTO6V+FHPc9K+xHUN8p5+tJ5KcfKtezl2ezwVH2UY3OerQc3dM+R54/KhQJJE5I+V4+Qcdf4v6Vk+fs3D5ST1P419neWn90UeWn90V3x4pqRX8P8AEwhhUt2fF4nZ+RtTHfgVctJ/nXNzBjIyHda+xvLT+4tHlr/dX8qc+K6klZU195bwkZLVnyTexWTu7pfWzHoqiRcfnmrFhd2lunN5b7xzneo/rzX1cUX+6KTYv9xa53xJVcbOH4mMsuUo8rlofNS6tYPtH26DHGMzIuP1/rU8eraejsg1CzT/AGvNGAf8+lfRvl+y/lS+X7LWDzyf8pyvIqX8zPmx9Z0750GoW5weW3ghv61Pa+I7JZljivrUx/xK8wUH8TX0UVFKFH91aTzqTjZwB5DRcdWfOmtavpqbZBq1tzhgI5PM+Y5yPlJxyKpx+LdIdEM1ygIPIAYnA/D+tfS+wUoQYxUrOpctuUI5HQUVFtnz5B4t8MxIh/tKPzS/LGNzgfgM1BeeK/DzzZS/gcA53eU65+vGa+jPLX0pCgrJZtLmvyi/1fw6d7s+crfxrp9vt8vVIyCcMsiOVI/LI/CpV8X6BvV31RXkJ+Y7H7/Vc19EbE/uijYv91abzZt3UQeQYd6Ns+cZ/GGhfMP7R3juoRl/XFUF8TaTK7OdUeNwflO1sY79BX09sU/wijy1/uitFnUkvhLjkWHitGz5zfxX4b+zIU1EmcYHCMBgd+lF34v0S4hy+sO7rGFCojICc+mPp3r6L2L02ijYv91ay/teV17uxKyHD33Z86weKfDqoC+rE44ClXyP/Hagl8WaBvYJeu+ctu2EAEevH1r6R2KP4RR5fstX/a8r35QWQYe97s+cB4w0B7ZopLtTtI8vbGxxk89QOMDJqjdeL9Ct7lPKeW4GwsWROjHtzjpX075a/wB1aPLX+6PypLOGvslrI8PF9T5a/wCE50h5stbzoCRuKIOR7AtwfxqO68a6Uz/uYLgAZA3oOmfrX1TsT+4KQqP7q1os6knpH8TRZPhr7Hy1a+M9GiT54rrzB3VBj/0KoLrxnp8qHZFOCeSdvU/nX1aUUfwikKj+6tNZ3UUr8oLJ8NzXPlGLxfp+zZILpQB0RARn8TT4/GWldXiuif8AdHT86+rNi/3RS7F/uin/AG7U/lK/snDvofKv/Ca6VLMqTw3SQYP3EBI57DIH45pg8UeH94kKX+/HK7VwD2719WbF/uijy1/uioWd1Fsg/sqgtj5Nm8c23zpDp8mzPBMuDjPpjiqq+NU6yWKueej84PvivrzaP7q0bR/dWqWe1P5S1leHS2PjW98RrdJKiwMm9CvL56jHpWB/nnivugIv90UpRfTH0pzz2pLeJ008PCmrQ2OF+DQ/4tRoueP9f/6OkoruwMcH9KK8Kc3OTl3N0rH/2Q==\"},{\"partUri\":\"/media/image2.JPEG\",\"contentType\":\"image/JPEG\",\"content\":\"data:image/JPEG;base64,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set here.\u003c/p\u003e","function_template":"function nanless = removenan(m)\r\nloc=find(nan);\r\nnanless= removenan(m,loc);\r\nend","test_suite":"%% T1\r\ntm=[];\r\nfor t=1:100\r\n rng('default');\r\n m=rand(100,100,100);\r\n m(m\u003e0.7)=nan;\r\n tic\r\n  o=removenan(m);\r\n tm(t)=toc;\r\nend\r\nrng('default');\r\nm=rand(100,100,100);\r\nm(m\u003e0.7)=nan;\r\nm(isnan(m))=[];\r\nt_correct=0.019;\r\nassert(mean(tm)\u003ct_correct)\r\nassert(isequal(o,m'))\r\n\r\n%% T2\r\ntm=[];\r\nfor t=1:100\r\n rng('default');\r\n m=rand(100,10000);\r\n m(m\u003e0.71)=nan;\r\n tic\r\n  o=removenan(m);\r\n tm(t)=toc;\r\nend\r\nrng('default');\r\nm=rand(100,10000);\r\nm(m\u003e0.71)=nan;\r\nm(isnan(m))=[];\r\nt_correct=0.019;\r\nassert(mean(tm)\u003ct_correct)\r\nassert(isequal(o,m'))\r\n\r\n%% T3\r\ntm=[];\r\nfor t=1:100\r\n rng('default');\r\n m=rand(2,500000);\r\n m(m\u003e0.69)=nan;\r\n tic\r\n  o=removenan(m);\r\n tm(t)=toc;\r\nend\r\nrng('default');\r\nm=rand(2,500000);\r\nm(m\u003e0.69)=nan;\r\nm(isnan(m))=[];\r\nt_correct=0.019;\r\nassert(mean(tm)\u003ct_correct)\r\nassert(isequal(o,m'))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":17471,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":44,"test_suite_updated_at":"2013-12-15T04:17:20.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-12-15T03:36:48.000Z","updated_at":"2026-02-13T18:32:27.000Z","published_at":"2013-12-15T04:15:46.000Z","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\",\"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 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1;\r\nz_correct=1;\r\nassert(isequal(Speed(x,y),z_correct))\r\n\r\n%%\r\nx=4;\r\ny=2;\r\nz_correct=2;\r\nassert(isequal(Speed(x,y),z_correct))","published":true,"deleted":false,"likes_count":5,"comments_count":1,"created_by":38003,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":424,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-04-24T05:48:08.000Z","updated_at":"2026-04-15T03:33:00.000Z","published_at":"2015-04-24T05:48:11.000Z","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\u003eCalculate the Speed of car given its Distance travelled and time taken in x and y respectively\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":44241,"title":"NCHOOSEK - Time Optimization","description":"Input\r\nV —— Set of all choices, a vector of N, 1 \u003c N \u003c 100\r\nK —— Number of selected choices, a scalar, 0 \u003c= K \u003c= N\r\nOutput\r\nC —— All combinations of V, equivalent to nchoosek(V, K)\r\nScoring\r\nTime consumed","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: 214.292px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 107.139px; transform-origin: 407px 107.146px; 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; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 40.8611px; 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 20.4306px; transform-origin: 391px 20.4306px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"background-position: 0px 50%; 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=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e —— Set of all choices, a vector of N, 1 \u0026lt; N \u0026lt; 100\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"background-position: 0px 50%; 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=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eK\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e —— Number of selected choices, a scalar, 0 \u0026lt;= K \u0026lt;= N\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; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4306px; 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 10.2083px; transform-origin: 391px 10.2153px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"background-position: 0px 50%; 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=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e —— All combinations of\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eV\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, equivalent to\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"background-position: 0% 0%; block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003enchoosek(V, K)\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; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eScoring\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; \"\u003e\u003cspan style=\"\"\u003eTime consumed\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function c = nchoosekFast(v, k)\r\n  c = nchoosek(v, k);\r\nend","test_suite":"%%\r\nassessFunctionAbsence({'regexp','regexpi','regexprep','str2num','!','system','unix'},'File','nchoosekFast.m')\r\n\r\n%%\r\nv = rand(1,10);\r\nk = randi(10);\r\nassert(isequal(nchoosek(v,k),nchoosekFast(v,k)));\r\n\r\n%%\r\nv = num2cell(randn(5,30),1);\r\nk = randi(4);\r\nassert(isequal(nchoosek(v,k),nchoosekFast(v,k)));\r\n\r\n%%\r\nv = rand(1,100);\r\nassert(isequal(nchoosek(v,1),nchoosekFast(v,1),nchoosekFast(v,100)',v') \u0026\u0026 isempty(nchoosekFast(v,0)))\r\n\r\n%%\r\nv = rand(1,29);\r\nt = tic;\r\nc = nchoosekFast(v,9);\r\nt = toc(t);\r\nassert(isequal(size(c),[10015005 9]) \u0026\u0026 ...\r\n    all(ismember(c(:),v)) \u0026\u0026 ...\r\n    size(unique(sort(c(randperm(end,1e6),:),2),'rows'),1) == 1e6)\r\nfid = fopen('score.p','Wb');\r\nfwrite(fid,uint8(sscanf([...\r\n     '7630312E30307630302E3030000B901C454EFFB100000031000001330000018D483A60'...\r\n     '366BC9545F84AE26323B67424D4E8A7A2E5B7D8ACAA45A1C3C5C8B33E245C95243E3CB'...\r\n     'AF5D0D993BDA70B7AB5DA365A83E8CA87FFC45265E23EF80943784C5F48E6E53D5DA34'...\r\n     'F1F2ECD34683EABE3B7461DC9E8004CC50B2A79D73495F6F625B5365602B2E6C6093D2'...\r\n     '997D371DA457CE82327E686AF512A507B2CB62A375BFD1B283DDD2C01EDEF2771EDAA3'...\r\n     '6ABB4852BA4061E20149688E812EB41A9AF8627EF35755492D2830EB8718BCFE88027E'...\r\n     '6EA960B63A3B3E26E0451B1DCF14F3C20E70D9D93B08E7FF4AE8D82E7CC38042FD38F7'...\r\n     'A14D312EF5652823FEB7E8B52AF5C69F5E7D16B116B5F979EDA77459D6BB61B7971A51'...\r\n     '041227DD601319D667DF62E8DA5E381FDD07A2806FE835BD2569E5315CDFC19C6B6A2B'...\r\n     '4F0FF6BA803F1759ACAB133CCFAB6D5A5D002FC2C5F381F0'],'%2X')));\r\nfclose(fid);\r\nscore(round(3*t))","published":true,"deleted":false,"likes_count":2,"comments_count":8,"created_by":1434,"edited_by":427930,"edited_at":"2023-07-18T13:35:29.000Z","deleted_by":null,"deleted_at":null,"solvers_count":91,"test_suite_updated_at":"2017-12-21T05:50:57.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-06-27T01:24:44.000Z","updated_at":"2026-03-30T19:11:03.000Z","published_at":"2017-06-27T01:38:46.000Z","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\u003eInput\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\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e —— Set of all choices, a vector of N, 1 \u0026lt; N \u0026lt; 100\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\u003eK\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e —— Number of selected choices, a scalar, 0 \u0026lt;= K \u0026lt;= N\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\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\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e —— All combinations of\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\u003eV\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, equivalent to\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\u003enchoosek(V, K)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eScoring\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eTime consumed\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":61036,"title":"The MATLAB Treasure Hunt – Cross the River of Ratios by Finding Successive Proportions in the Data Stream","description":"Following the glowing script, you arrive at the River of Ratios — a flowing stream of numbers.\r\nA carved message on the rocks reads: “Only those who know the balance between every pair shall cross.”\r\nGiven a numeric vector a, calculate the ratio of each element to the previous one.\r\nReturn a vector r such that r(i) = a(i+1)/a(i) for all valid i.\r\nThis will reveal the rhythm of the river and guide your next step!","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; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 141px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 70.5px; transform-origin: 408px 70.5px; 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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eFollowing the glowing script, you arrive at 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; \"\u003eRiver of Ratios\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 flowing stream of numbers.\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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eA carved message on the rocks reads: \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-style: italic; \"\u003e“Only those who know the balance between every pair shall cross.”\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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eGiven a numeric 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003ea\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, calculate 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; \"\u003eratio of each element to the previous one\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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eReturn 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\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 such that \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-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003er(i) = a(i+1)/a(i)\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 for all valid \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-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\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\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: 385px 10.5px; text-align: left; transform-origin: 385px 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=\"\"\u003eThis will reveal the rhythm of the river and guide your next step!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = riverRatios(a)\r\n  y = x;\r\nend","test_suite":"%%\r\na = [2 4 8 16];\r\ny_correct = [2 2 2];\r\nassert(isequal(riverRatios(a),y_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":4953963,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":69,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-10-21T09:38:42.000Z","updated_at":"2026-03-10T14:17:27.000Z","published_at":"2025-10-21T09:38:42.000Z","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\u003eFollowing the glowing script, you arrive at the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRiver of Ratios\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e — a flowing stream of numbers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 carved message on the rocks reads: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e“Only those who know the balance between every pair shall cross.”\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 numeric 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\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, calculate the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eratio of each element to the previous one\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\u003eReturn 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\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e such that \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\u003er(i) = a(i+1)/a(i)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for all valid \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\u003ei\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\u003eThis will reveal the rhythm of the river and guide your next step!\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":958,"title":"ismember: Enhanced Performance for 'rows'  and width - Speed Scoring (66% savings)","description":"The Challenge is to perform very fast the 'ismember' function for a long and wide array.  The width of the array is expanded from 16 to 48.\r\n\r\nFast methods can reduce time by 66%.\r\n\r\nThe data is small integer representing data permutations of items like DNA and Rubik's cube faces and orientations.\r\n\r\n*Input:* Array of uint8 of dimensions (m, 48) with values 0:3\r\n\r\n*Output:* Array Equivalent to ismember(A,B,'rows')\r\n\r\n*Hints:*\r\n\r\n1) Columns can be merged to form a reduced number of columns\r\n2) Unique has the option to provide an Array and a sorting Index\r\n\r\nNote: Enhancements to speed usually improve memory allocation issues.","description_html":"\u003cp\u003eThe Challenge is to perform very fast the 'ismember' function for a long and wide array.  The width of the array is expanded from 16 to 48.\u003c/p\u003e\u003cp\u003eFast methods can reduce time by 66%.\u003c/p\u003e\u003cp\u003eThe data is small integer representing data permutations of items like DNA and Rubik's cube faces and orientations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Array of uint8 of dimensions (m, 48) with values 0:3\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Array Equivalent to ismember(A,B,'rows')\u003c/p\u003e\u003cp\u003e\u003cb\u003eHints:\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1) Columns can be merged to form a reduced number of columns\r\n2) Unique has the option to provide an Array and a sorting Index\u003c/p\u003e\u003cp\u003eNote: Enhancements to speed usually improve memory allocation issues.\u003c/p\u003e","function_template":"function idx = ismember_fast_rows(a,b)\r\n idx=ismember(a,b,'rows');\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',20000);\r\n%%\r\n% Functionality Tests\r\nL=128;\r\na=randi(4,L,48,'uint8')-1;\r\nb=randi(4,L,48,'uint8')-1;\r\nassert(isequal(ismember(a,b,'rows'),ismember_fast_rows(a,b)))\r\n\r\nb=a;\r\nassert(isequal(ismember(a,b,'rows'),ismember_fast_rows(a,b)))\r\n\r\nL=256;\r\na=randi(4,L,48,'uint8')-1;\r\nb=randi(4,L,48,'uint8')-1;\r\na(16:32,:)=b(32:48,:);\r\nassert(isequal(ismember(a,b,'rows'),ismember_fast_rows(a,b)))\r\n\r\n%%\r\n% 2M has a crash for 2x ismember\r\nL=1900000;  % ismember 19.6    fast  C 8.3 2M\r\ntic\r\na=randi(4,L,48,'uint8')-1;\r\nb=randi(4,L,48,'uint8')-1;\r\na(100:200,:)=b(400:500,:); % Put in some matching data\r\ntoc\r\n\r\n\r\nta=clock;\r\nidx = ismember_fast_rows(a,b);\r\nt1=etime(clock,ta)*1000;\r\n\r\nfprintf('Elapsed time = %.0f msec\\n',t1)\r\n\r\nassert(isequal(ismember(a,b,'rows'),idx))\r\n\r\nt2=min(20000,t1); % ismember 1.9M x 48 scores 19000 msec\r\nfeval(@assignin,'caller','score',floor(t2));\r\n\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":21,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-09-24T05:10:17.000Z","updated_at":"2026-01-21T12:28:29.000Z","published_at":"2012-09-24T05:37:58.000Z","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\u003eThe Challenge is to perform very fast the 'ismember' function for a long and wide array. The width of the array is expanded from 16 to 48.\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\u003eFast methods can reduce time by 66%.\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 data is small integer representing data permutations of items like DNA and Rubik's cube faces and orientations.\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\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Array of uint8 of dimensions (m, 48) with values 0:3\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\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Array Equivalent to ismember(A,B,'rows')\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\u003eHints:\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) Columns can be merged to form a reduced number of columns 2) Unique has the option to provide an Array and a sorting 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\u003eNote: Enhancements to speed usually improve memory allocation issues.\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":934,"title":"Find: Faster Alternatives for Large Sorted/Unique Vectors","description":"The Challenge is to create faster Find methods for large unique ascending vectors.\r\n\r\nMethods exist that are 1000 times faster than Find.\r\n\r\nMethods have applications where data files are loaded for multiple searches(Rubik's Mini Cube, DNA).\r\n\r\n*Input:* [a,val]\r\n\r\n*   a: vector [N large,1] of type uint32 that is unique and sorted ascending\r\n*   val: The value to find in the \"a\" vector. Val will exist in \"a\".\r\n\r\n*Output:* [ptr]\r\n\r\n*   ptr: The index in array \"a\" where a(ptr) is val\r\n\r\n*Score:* Time in msec to find 200 random values in a 12,000,000 long vector\r\n\r\n\r\nThe basic find(a==val,1,'first') takes approximately 14 seconds (12M, 200 cases).\r\n\r\nHigh performance find_fast can find 200 values in 4 to 12 milli-seconds for a 12M vector.\r\n\r\nFind time increases linearly with the array size. High performance methods don't appear to incur any increased processing time.\r\n\r\nHints:\r\n\r\n* There are  multiple methods that significantly outperform find.\r\n* Bisect searches\r\n* Predictive searches\r\n* Combining Bisect/Predictive with a Linear Chaser\r\n* \r\n\r\nA static goal vector find time could be enhanced by a Pre-Index array. Not applicable to this challenge.\r\n","description_html":"\u003cp\u003eThe Challenge is to create faster Find methods for large unique ascending vectors.\u003c/p\u003e\u003cp\u003eMethods exist that are 1000 times faster than Find.\u003c/p\u003e\u003cp\u003eMethods have applications where data files are loaded for multiple searches(Rubik's Mini Cube, DNA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [a,val]\u003c/p\u003e\u003cul\u003e\u003cli\u003ea: vector [N large,1] of type uint32 that is unique and sorted ascending\u003c/li\u003e\u003cli\u003eval: The value to find in the \"a\" vector. Val will exist in \"a\".\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e [ptr]\u003c/p\u003e\u003cul\u003e\u003cli\u003eptr: The index in array \"a\" where a(ptr) is val\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eScore:\u003c/b\u003e Time in msec to find 200 random values in a 12,000,000 long vector\u003c/p\u003e\u003cp\u003eThe basic find(a==val,1,'first') takes approximately 14 seconds (12M, 200 cases).\u003c/p\u003e\u003cp\u003eHigh performance find_fast can find 200 values in 4 to 12 milli-seconds for a 12M vector.\u003c/p\u003e\u003cp\u003eFind time increases linearly with the array size. High performance methods don't appear to incur any increased processing time.\u003c/p\u003e\u003cp\u003eHints:\u003c/p\u003e\u003cul\u003e\u003cli\u003eThere are  multiple methods that significantly outperform find.\u003c/li\u003e\u003cli\u003eBisect searches\u003c/li\u003e\u003cli\u003ePredictive searches\u003c/li\u003e\u003cli\u003eCombining Bisect/Predictive with a Linear Chaser\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eA static goal vector find time could be enhanced by a Pre-Index array. Not applicable to this challenge.\u003c/p\u003e","function_template":"function ptr = find_fast(a,val)\r\n  ptr = find(a==val,1,'first');\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',0);\r\n%%\r\nL=1000;\r\na=randi(2^32-1,L*1.2,1,'uint32');\r\na=unique(a,'R2012a');\r\na=a(1:L);\r\n\r\n% Warm-Up Test cases\r\nfor i=1:5:1000\r\n assert(isequal(find_fast(a,a(i)),i))\r\nend\r\n%%\r\n% Timing Performance Case\r\nL=12000000;\r\na=randi(2^32-1,L*1.2,1,'uint32');\r\na=unique(a,'R2012a');\r\na=a(1:L);\r\n\r\nq=200;\r\n\r\nval=zeros(q,1);\r\nfor i=1:q\r\n val(i)=a(randi(L));\r\nend\r\n\r\nt0=clock;\r\nfor i=1:q\r\n ptr=find_fast(a,val(i));\r\nend\r\ndt=etime(clock,t0)*1000;\r\n\r\n\r\nassert(isequal(find_fast(a,val(1)),find(a==val(1),1,'first')))\r\n\r\nfprintf('Your Time = %i msec\\n',floor(dt))\r\n\r\nfeval(@assignin,'caller','score',min(200,floor(dt)));\r\n%   Performance Score\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":37,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-09-07T01:12:34.000Z","updated_at":"2025-12-08T15:39:06.000Z","published_at":"2012-09-07T03:26:59.000Z","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 Challenge is to create faster Find methods for large unique ascending vectors.\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\u003eMethods exist that are 1000 times faster than Find.\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\u003eMethods have applications where data files are loaded for multiple searches(Rubik's Mini Cube, DNA).\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\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [a,val]\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\u003ea: vector [N large,1] of type uint32 that is unique and sorted ascending\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\u003eval: The value to find in the \\\"a\\\" vector. Val will exist in \\\"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\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 [ptr]\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\u003eptr: The index in array \\\"a\\\" where a(ptr) is val\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\u003eScore:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Time in msec to find 200 random values in a 12,000,000 long vector\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 basic find(a==val,1,'first') takes approximately 14 seconds (12M, 200 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:t\u003eHigh performance find_fast can find 200 values in 4 to 12 milli-seconds for a 12M vector.\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\u003eFind time increases linearly with the array size. High performance methods don't appear to incur any increased processing 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHints:\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\u003eThere are multiple methods that significantly outperform find.\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\u003eBisect searches\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\u003ePredictive searches\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\u003eCombining Bisect/Predictive with a Linear Chaser\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\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\u003eA static goal vector find time could be enhanced by a Pre-Index array. Not applicable to this challenge.\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":1888,"title":"Get ranking of a combination","description":"I have the numbers pulled without replacement from the set [1 2 3 4 5 6 7 8 9 10 11 12 13]; They are then ordered from least to greatest.\r\nSo a selection of [3 2 9], [9 2 3] are both considered to be [2 3 9].\r\nThere are 286 unique selections possible. These can be ordered in lexicographic order:\r\nElement   1 = [ 1  2  3]\r\nElement   2 = [ 1  2  4]\r\nElement   3 = [ 1  2  5]\r\nElement   4 = [ 1  2  6]\r\nElement   5 = [ 1  2  7]\r\nElement   6 = [ 1  2  8]\r\nElement   7 = [ 1  2  9]\r\nElement   8 = [ 1  2 10]\r\nElement   9 = [ 1  2 11]\r\nElement  10 = [ 1  2 12]\r\nElement  11 = [ 1  2 13]\r\nElement  12 = [ 1  3  4]\r\nElement  13 = [ 1  3  5]\r\nElement  14 = [ 1  3  6]\r\nElement  15 = [ 1  3  7]\r\n...\r\nElement 285 = [10 12 13]\r\nElement 286 = [11 12 13]\r\nGiven the three ordered values as a row vector, return the element number.\r\nDo this with an eye for speed, though it is not tested for here.\r\nLooking for a way to do this WITHOUT generating the nchoosek matrix.","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: 570.8px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 285.4px; transform-origin: 407px 285.4px; 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: 377px 8px; transform-origin: 377px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI have the numbers pulled without replacement from the set [1 2 3 4 5 6 7 8 9 10 11 12 13]; They are then ordered from least to greatest.\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: 206px 8px; transform-origin: 206px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSo a selection of [3 2 9], [9 2 3] are both considered to be [2 3 9].\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: 276.5px 8px; transform-origin: 276.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThere are 286 unique selections possible. These can be ordered in lexicographic order:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 367.8px; 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 183.9px; transform-origin: 404px 183.9px; 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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e1 = [ 1  2  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e2 = [ 1  2  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e3 = [ 1  2  5]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e4 = [ 1  2  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e5 = [ 1  2  7]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e6 = [ 1  2  8]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e7 = [ 1  2  9]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e8 = [ 1  2 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: 96px 8.5px; tab-size: 4; transform-origin: 96px 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; \"\u003eElement   \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: 56px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 56px 8.5px; \"\u003e9 = [ 1  2 11]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e10 = [ 1  2 12]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e11 = [ 1  2 13]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e12 = [ 1  3  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e13 = [ 1  3  5]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e14 = [ 1  3  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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 36px 8.5px; transform-origin: 36px 8.5px; \"\u003eElement  \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: 60px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 60px 8.5px; \"\u003e15 = [ 1  3  7]\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: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003eElement \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: 64px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 64px 8.5px; \"\u003e285 = [10 12 13]\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: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003eElement \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: 64px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 64px 8.5px; \"\u003e286 = [11 12 13]\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: 237.5px 8px; transform-origin: 237.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven the three ordered values as a row vector, return the element number.\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: 192.5px 8px; transform-origin: 192.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDo this with an eye for speed, though it is not tested for here.\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: 223px 8px; transform-origin: 223px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eLooking for a way to do this WITHOUT generating the nchoosek matrix.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function value = get3HCRank(vec)\r\n  value = vec;\r\nend","test_suite":"%%\r\nfiletext = fileread('get3HCRank.m');\r\nillegal = contains(filetext, 'regexp') || contains(filetext, 'nchoosek') || ...\r\n          contains(filetext, 'elseif') || contains(filetext, 'switch'); \r\nassert(~illegal)\r\n\r\n%%\r\nvec = [1 2 3];\r\ny_correct = 1;\r\nassert(isequal(get3HCRank(vec),y_correct))\r\n\r\n%%\r\nvec = [1 2 4];\r\ny_correct = 2;\r\nassert(isequal(get3HCRank(vec),y_correct))\r\n\r\n%%\r\nvec = [3 5 8];\r\ny_correct = 133;\r\nassert(isequal(get3HCRank(vec),y_correct))\r\n\r\n%%\r\nvec = [5 9 11];\r\ny_correct = 222;\r\nassert(isequal(get3HCRank(vec),y_correct))\r\n\r\n%%\r\nvec = [11 12 13];\r\ny_correct = 286;\r\nassert(isequal(get3HCRank(vec),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":240,"edited_by":223089,"edited_at":"2022-10-30T10:50:12.000Z","deleted_by":null,"deleted_at":null,"solvers_count":53,"test_suite_updated_at":"2022-10-30T10:50:12.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-09-23T15:25:55.000Z","updated_at":"2026-02-08T19:54:25.000Z","published_at":"2013-09-23T15:25:55.000Z","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\u003eI have the numbers pulled without replacement from the set [1 2 3 4 5 6 7 8 9 10 11 12 13]; They are then ordered from least to greatest.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eSo a selection of [3 2 9], [9 2 3] are both considered to be [2 3 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\u003eThere are 286 unique selections possible. These can be ordered in lexicographic order:\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[Element   1 = [ 1  2  3]\\nElement   2 = [ 1  2  4]\\nElement   3 = [ 1  2  5]\\nElement   4 = [ 1  2  6]\\nElement   5 = [ 1  2  7]\\nElement   6 = [ 1  2  8]\\nElement   7 = [ 1  2  9]\\nElement   8 = [ 1  2 10]\\nElement   9 = [ 1  2 11]\\nElement  10 = [ 1  2 12]\\nElement  11 = [ 1  2 13]\\nElement  12 = [ 1  3  4]\\nElement  13 = [ 1  3  5]\\nElement  14 = [ 1  3  6]\\nElement  15 = [ 1  3  7]\\n...\\nElement 285 = [10 12 13]\\nElement 286 = [11 12 13]]]\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\u003eGiven the three ordered values as a row vector, return the element number.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eDo this with an eye for speed, though it is not tested for here.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eLooking for a way to do this WITHOUT generating the nchoosek matrix.\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":52427,"title":"ICFP2021 Hole-In-Wall: Solve Problem 4, Score=0, Bonus GLOBALIST assumed","description":"The ICFP held its annual 3-day contest in July 2021 with Hole-In-Wall. Contest Specification.\r\nThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \r\nThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the Specification when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\r\nValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u003c= Edges*epsilon/1000000.  Lsqr is length squared.\r\nScore is sum of minimum square distances to the figure from each unique hole vertex. \r\nnpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)  \r\nThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\r\nThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\r\nThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use Register Team. Anyone can select Problems Page and then click problem numbers to see the puzzles and to download problem files.\r\n","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: 922px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 461px; transform-origin: 407px 461px; 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: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.icfpconference.org/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP\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: 148.5px 8px; transform-origin: 148.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e held its annual 3-day contest in July 2021 with \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eHole-In-Wall\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: 30.5px 8px; transform-origin: 30.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Contest \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\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: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 168px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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 84px; text-align: left; transform-origin: 384px 84px; 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: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\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: 332px 8px; transform-origin: 332px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\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: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*epsilon/1000000.  Lsqr is length squared.\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 = \"https://www.mathworks.com/matlabcentral/cody/problems/52308\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eScore\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: 253px 8px; transform-origin: 253px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is sum of minimum square distances to the figure from each unique hole vertex. \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: 157.5px 8px; transform-origin: 157.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)\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=\"\"\u003e  \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: 377.5px 8px; transform-origin: 377.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\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: 368.5px 8px; transform-origin: 368.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\u003c/span\u003e\u003c/span\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: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/register\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRegister Team\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: 43.5px 8px; transform-origin: 43.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Anyone can select \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/problems\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblems 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: 259px 8px; transform-origin: 259px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 358px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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 179px; text-align: left; transform-origin: 384px 179px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: top;width: 776px;height: 358px\" 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3dpSCsptUpXMKHhl2/pq1vztKXli/u9kxm9/qX1tOaFJkD84bjalHqAmgW4/SHn46GMfk1nDvqttKxO574fXy5snn5S2H499+5va15PoeGjwt+WDUNrQO01OkPtvGKZ9fbY8cP8Umf/Hh2TV03+WFzb+TTY891dZ+/dlWRkFf/zL0zLXB4bB1qpXaRREgGlGQVAzl8+UiuUV0n25d/dHjTbYulUEWYsYaUBRuYhGgZlKYtqBUlJNkmIKRgB+L5Q5kCmloNzHvyxGQRCTTIMHrh0iW85sl3LxqUAQjHxYXTE2cqV7J88mkwDgc4fbeVPHs9L2jUTP81/4fNqxf/GMz2jbyWRWf6lz2n6s7Xye9rUkOp69sKY2geK588/VvrYu8NuD3yD8FlVtXSfbtvxTXnxhTc5GwbzFK13+CP6ykkZBRJhmFCxatMh9VnfaghdwMAm+9rUbne74inz+839wlykqF9EoME/lvqNbbuG7jlMlFiaMGoAREEwpwO9DbeZAUOU2qcpuFAQpp2mQySTAENeHB/XX/jtiLwhYw229pPeXta8l0bLhnI6xOPaPfPdb8uHR9VL2Y++Jn5Df3DRc+3pSOPf+6EbZ9cnmKccc/P5b39C+XkfQHAj+BhViFDy+ZJU8/uRfXeY9uVK2vryDRkEEmGQUTJgwQVq0aFF94Ry+y7NmjUizZiKTJ+tTFLK5KKSSLRoF5onTBPIY5KNgvQGMGsBzrr8BJtSIMMooCFJK04AmQTKhUVAefv2DEe4w8eBxP/jxj7vnoe71pqP77njyf3pqX0sKR5cyVPXpNnVOT1mbORAkX6PgnXf3y5+eWuUyf4nHS6/QKIgCE4yCPXv2uEF/o0aN5MQ+J7p3eMIXzUuWiDRpIjJ3rr8iIFwcqmGmMA4wzJSjDSidaBSYJU4R6InHITvVVW8gV5mQ8mKsURCkmKYBTYLkQqOgPOiOO85B3WttYNH/6522P9s+82nta0nhbD7rjLTj/dTll2pfm405ECR/o+A9WbD0WYdn5E9PeWx75TUaBRFgglEwbNgwGTVqlHx/zfel3vZ67kVzUNu2iTRqJLJggcihQzUcPuy/ICRcSKrRBnimaUAp0SgwSybc0TVFHFWgF4wAZQLXVW8gF5mS8mKFURAkStOAJkGyoVFQHuKSdqBgUcPSkU0Rw1zNgSCFGAULlz3r8oTPf7bTKIgCE4yCI0eOuMHCyQdOlpPPP9lfW6ORI1O6ZDXDh/svyCCmKFBB0SgwS0gxYsV/TxxV4Km2KQyjlinGjHVGQZBCTAOaBIRGQemJW9qBgkUNS0NtRQwLMQeCFGIU/Hn532ShgkZBZJhgFMAkQMAwY9kMt0ZBsYSLTZWigDtTuBjlaINkiUaBOUp6EUOdkmqcRJ1SUJdM6ntWGwVBcjENaBIQQKOg9MQt7UDBoobFp7Yihi9OulX7m5AP+RoF+2AUrFidYhb8Z/tOGgURUG6jABdr6i7awoULi2oUBIULU1yMqtEGeKZpEH8xMDVHaItwwdKkK0mpGDACYAwocyCqlIJsZFLfi41RECSTaUCTgChoFJSeuKUdBNF9r7CoYXToihge7NJJXnECe93vQD7kbRTsf0/+8vTfZRFYsdql6lUaBQpMKRhet2nTJpk/f76sXbs2bVuQYhsFwes+dCslJ053L9aCgVspjYKwYBLQNIi/gv2NKq9MKCRnmuI8VWJtKQWlMgeUTEvxiKVRECRoGtAkIEFoFJSWuKYdKFjUsDiotIK3enRLO757x/xA+72fL4UYBYtXrnH4ezVVO16nUeAwefJk6dKlS8q6Rx99VDp37izXX3+9XHjhhXLHHXekbA9SbKMAXUldB+JvCCZB66ruaXfOymkUBBU0DXAxi2UaB/EQjQIzxLSD2oXjEpeRFqVOKchGph3f2BsFitefmCN7u6XntwKaBMmERkFpiWvagYJFDaMjXHNg16Mz5KNjG6Qc18MtW8jOJx9P+64vhPyNgv/Kkr+ukScDvJxwo+CVV16Rm266Sc4+++wUo+Ctt95y1z333HPuMi7UOnbsKBs2bKh+TZBSjChAl1LP7h0zjUkAmWIUBIWLWlzccrRBPMTg1AyZUkjORNk+qkB9Z5YjpSAbmTaSJRFGAUyCA5deVH2BGeT1dp+mSZBQaBSUljinHShY1DB/wuZAkH3Dvpt2XPf/79fTXlco+RoF7+7/rzz1zFp5ahX4hyxxeOW1XYk2Cm6++Wa59dZbZe7cuSlGwRNPPOGOIgi+dujQoXL//fenrFOUokaBMgkqWnsXwDbfLQuONqBpYJ9oFJghph1klk1GCsxoGAHBlAJ8L5pkDiiZWAMi9kZBJpPg4PlfkF2PPJBTIUQSH2gUlI64px0oWNQwNzKZA4odzrZDmpSx3ff/Uvv6QsjbKHjvv7L0mX86rJWlzzo4z0k3Cvbu3es+4y580Ch4+OGHZfDgwdXLYOTIkTJ69OiUdQplFASJSqEuJRVVraViwMyUdTYraBqYfHFM1YhGQfnFtIO6ZVoefVjBegMYNYBnG777TJlVIvybG1ujIBuTIPxvaBokBxoFpSPuaQdBWNQwM9mYA0Heuv2WtOMZdRFDRSFGwfK//VOW/e05WfYs+KdsT7hRoAgbBbNnz5YhQ4akvGbUqFEuwXWKUowocGsStHa61nLccfJGGMRNuEhWw21hHGC4LUcbmCcGqOUX0w6yk2lTJZpYbyAXmWpQxdYoyMckCEPTIN7QKCgdSUg7ULCoYTq5mgNB/tvr0rTjGXURQ0W+RsH+9/4rK1avq2b5356TV3e+QaPAIWwUoJDhoEGDUl6DEQVIVQiuUxTbKFAmAWJmGAW4rkQ3i6NZEBQuqJmiYJ5oFJRXHE2Qvcp9rMIpBSbWG8hFphpUsTQKojAJwtA0iB80CkpDUtIOFCxq6FGIOaAoVRFDRf5GwQFZueb5Gv7+vLz6+m4aBQ5ho2Dp0qUpywDGAQyE4DpFsY0CZRJAMAogZRYkRUxRMEcMUssrE3PETVapRxXYmlJQl0w2qGJnFBTDJAhD0yAe0CgoDUlKO1AktahhFOZAkFIVMVTkbRT894D8dc36alY67KBR4BI2ClC7AMtYj2XMftChQwfZtm1b9WuCFNsoCN5IV0YBlNQ4GRfdKkVB3aHjaIPSiUZBeWXacHrThf6KILeYsj2lIBuV4jjmq1gZBaUwCcLQNLAXGgWlIUlpB4okFTWM2hxQlLKIoaIQo2DVPzZU81eHHbtoFICwUQAwqqBz587uxcc555zjzowQ3B6kFDUKlEwuzlUOqQt0piiUTjQKyiemHeSuYk2VCCMAxkDQsIzrKKdiHcOoFBujoBwmQRiaBnZBo6D4JC3tIEicixoWyxwIUsoihopCjIJn1m4MsEFe2/UmjYIIoFFgjnQpClS0YqBaPpl8V9dkRXHcgikF6vsFxkBczYGgTO93sTAKTDAJwtA0MB8aBcUniWkHirgVNSyFORCklEUMFfkaBe/994D87bl/pfDaGzQKooBGgZnCBXzYNKBxULhoFJRPFc6Dyl353hFPQkpBNjK931lvFLzuXLge+vznUi4mFeUyCcLQNDATGgXFJ4lpB4o4FDUstTmg2PXozJIWMVTkbxQclNXrNlXz9+c3yc439tAoiAAaBeZLmQbKOMAzTYP8FDYKFi3y/who0yaRefNEtm71V1AFS6UdrF/vHdstW/wNGunaJOnCd2c2FfvxXQFjQJkDcU4pyEaq35ksu42CF5+TI8c1TLmYVHzwqdNlx+ql+n9XRmgamAONguKS5LQDhY1FDctlDgQpdRFDRd5GwYGD8vfnX0jh9d00CqKARoF9gklA0yA/BYOGCRNEWrTwF3yNGiXSrJlIv34i7dphmkt/A1WQcO7/75iXpW1bpw2cJmjTRmTiRH9jQLo2ocQ1CXTfn0lOKchGNhTPtNooeOfG4WkXk8CUkQR1QdOgvNAoKC5JTjtQ2FLU0ARzQFGOIoaKfI2C/x44KP/YsNljvceu3W/RKIgAGgV2K2gaIEjAMoOE2gWjYM8eL1ht1Cg1KF2zRqSyUmT7dm/54EFvek+spwpTxcaz3GOLYw/t3ClSz/np3r3bW66tTagaqaA3aA4kOaWgLtkwmgCy2ih417kID19MfnDaqVaYBGFoGpQeGgXFJclpB0FMLWpokjkQpBxFDBWFGAX/3PhvWRtg15s0CqKARkF8hGABQYO6u4hhxxxtkCoEDsOGeSMH5sxJDUpnzRK56ip/wRdGFowe7S9QeWm88/jpkfFuSocSjAH8/OzY4S3X1iaUJ5gDrWe29qA5kJXwe5NNuka5ZbVRsOfOW9MuKN9xLuZ0r7UJmgalgUZB8WDaQQ0mFTU01RwIUo4ihorCjIItKex6cy+NggigURBfcerFdMEoOHLE+3vhwtSg9NFHRTp29Bd89XZ+Xvr39xeovBQM2A4fFpk2zTvO48a5q1zV1iZJFYwBGAHK9HPrDcwc7x5L04fSmyBbRhNAsTMK9o79ofa1tkLToHjQKCgeTDuoodxFDW0wBxTlKmKoKMQoeO5fW1J4g0ZBJNAoSIaYouApGDyEg1Lc5W7a1LuzvWKFyN13izRv7o0qoPJTOGBDysGUKSK9eol06lSTiqCUZKMgnFKA5/A5imOJY0pllk3HiUaBRdA0iBYaBcWDaQeplLqooU3mQJByFTFU5G0UHHxf1m16MYU39tAoiAIaBclTMEVBVUZPymiDTEYBhOHxV18t0q2bV8hw5EiRQYP8jVTOyhSw9eghMnasv+AraUYBzIFcpjDMd6rEpMmmqThpFFgKTYPCoVFQHJh2kE4pihraag4oylnEUJGvUXDg4Pvy/AtbU6BREA00CpItFagkJUUhk1Gwb5/I6tX+gq8rrhCZMcNfoHKWCtg2bxa59173z2ohpcPpbilKglGgUgpgDCijLpfRPcFUDipdqImBhy2iURADaBrkB42C4sC0Az3FKGpouzkQpJxFDBWFGAXrN79UDYyC3TQKIoFGARWULkUhTspkFGC2g/r1awrsPfusSJMmInv3estUbgqmHWzc6B1bGAbQrl1eWsf8+d6yUhyNgmBKgTqvYAxgfT6qbapEypNNowkgGgUxg6ZB9tAoKA5MO9ATVVHDOJkDQcpZxFBRiFGw4d/bUnjzrbdpFEQAjQKqNqkUhaBpYLtxUFfqAfLnMUWfs7vSqpXIsmX+BipnhdMOpk4VadhQpGdP73niRH9DQHExCnJNKchVHFWgV9CcskU0CmIMTYPM0CiIHqYd1E4hRQ3jag4oyl3EUFGIUbBxy38ctlXz5l4aBVFAo4DKRso0UMYBnm00DWwLImyWbXd2CxXOERgDyhzINaUgF3FUgV42Gig0ChICTYN0aBRED9MOMpNLUcO4mwNByl3EUFGIUfCvLf9J4c2979AoiAAaBVQ+gklgo2lAo6A0svHObq6qLaWgWOZAWChqyKkSa2Rrn6NRkEBoGnjQKIgeph1kpq6ihkkyBxQmFDFU5GsUHHz/kGcQvPgf2fRilcseGgWRQKOAKlRB0wDBEpZNNQ5oFJRGNt7ZzUbFTinIRbYV7Su2cG4HU11sEY2ChJNk04BGQbQw7SA7dEUN/zX424kyB4LUVsRwuxOw615fTAoxCjZtrUqBRkE00CigohSCJgRPyjhAUGWSaUCjoPiK27D4sDlQzJSCXMSpEmtk8wgWGgWkmqSZBjQKooVpB9mhK2p44MKu2nMyCZhQxFBRiFHwwtaXHap8XpY9e/fRKIgAGgVUMYWgSpkGeC63aUCjoPiy/U43jAEYASqlwCRzICxb76JHLZuPA40CoiUJpgGNgmhh2kFmVFrB848/Ih985tNpx2r3jPu052KcMaWIoaIgo+AlGAU+zt973qZREAU0CqhSSZeiMGTIELnoootk5cqV/quKKxoFxZdNufPod+h/o0ePdvujGjWAZxONgbA4qsCTzYUzaRSQOomraUCjIDqYdqBHmQPhtIJ9g7+dcqzAu9++JuU1ScCUIoaKQoyCzS+9ksJbNAoigUYBVQ6pFIWPfexjzteSc5lfKmZq1pHoGOBg4TFGP0R/tMEcCAvfq3GsB5GtbB/BQqOA5EScTAMaBdHBtIMaajMHgrzx0G/lo2OOSTleh1u3kteWLtC+Po6YVMRQUYhRsOU/21PY+w6NgiigUUCVUzfddJO0bdtWHn/8cX9NccURBcWVbUPA0e/Q/9APbVXcakLkKttnf6BRQPLGdtOARkF0JD3tIBtzIMyBSy9KO2Z7x43WvjaOmFTEUFFQMUN/tgPOehAtNAqoJIlGQXFVYfEQcJuV1KkSbS5iqESjgESCjaYBjYJo0KUdvJ+AtIN8zIEgb932k5RjBpJU1NCkIoaKfI2C/x44KP/Y8O8Udr35Fo2CCKBRQCVJNAqKpzgEbbYqqcc+DmkXNApI5NhiGtAoiIYkpR0Uag4E2bFqcWKLGppWxFCRr1Hwxp698stZf0hh3aatNAoigEYBlSQxkC2e4hC02aykjSqIizlCo4AUFZNNAxoF0RD3tIMozYEwSS1qaFoRQ0W+RsErr+2SoWN/Uc2wsZNl6TP/pFEQATQKqCSJRkHxxLSD8gp926b6EIUqLvtLo4CUDNNMAxoFhRPXtINimgNBkljU0MQihop8jYKXd+ySwTffmcJTq9bSKIgAGgVUkkSjoDiyvfJ8HJSkqRLjtK80CkhZMME0oFFQOHFKOyiVORAmaUUNTSxiqKBRYB40CqgkiUZBccS0AzOUlFEFcdpPGgWk7JTLNKBRUDi2px2UyxwIkrSihiYWMVTQKDAPGgVUkkSjIHrFJVc8DkrKqII4pbnQKCBGUUrTgEZBYdiadmCCORAkSUUNTS1iqKBRYB7lNgoWLfL/oKgSiAFt9GLagVmqbXTH6tUiO3f6C77WrxeZN09kyxZ/hQUKG1O6/cL+YL/WrfNXGCwaBcRYim0a0CgoDJvSDkwzB8IkpaihqUUMFTQKzKOcRsGECSItWvgLFFUC0SiIXixiaJZgEoS/azdtEqms9IJnpTFjRNq2dc4J55Ro00Zk4kR/g+EKzu6g26/Jk0WaNxfp10+kXTuRQYP8DYaKRgFJLOw/hfGexijg8cuPJBQ1NLmIoYJGgXmUwyjYs8e7OG3UiEYBVVrRKIhWTDswU8Fg+tAhkY4dRVq1qgmoN270Amx8F0O4I1+vnsju3d6yqQr2N91+HTkiUr++t3/Q3r3esskjC2gUkMTC/pM/rz29yB0yHjx2R45vJK8/MUf7elI3cS9qaHIRQwWNAvMoh1EwbJjIqFEic+bQKKBKKwa10QrHMy5F5eKkYDrIyJEi48aJ9O6dGlDjbrwSDANcNuzY4a8wVMG0itr2C4bHtm3eMswEGCJITzBVNApIYmH/yR/dsUOgq3styY64FzU0uYihgkaBeZTDKMDFHLRwIY0CqrSiURCtmHZgpjCaAN+3j654Xc4911sXDKiVDh8WmTbNuzOPoNtkBUcTrFghte4X9qdDB5GxY0U6dRIZMcLfYKhoFJDEwv6TP0w7iJ44FzU0vYihgkaBeZSzRgGNAqrUqssoWKSprrl7926ZP3++LFu2zF9DQcHAbdOmTU6wNk9WrVrlLuu0evVq2RmuOkcVTd/c+31p3u6d6kKFOqMAzTFlikivXl5QrVIRTJQavYJ0AtQeqG2/UJsA+3Lffd62Sy4R2b/f32igaBSQxML+kx9MOygecS1qaHoRQwWNAvOgUUAlSZmMggkTJjj9MbVDLnQ6abNmzdwL+fPOO0+6desmR9SQmIRLDQMfMWKEtG7d2gnQ+knHjh2la9eucvDgQf9VnmAkVFZWumYCVRr1HbRfGvb9k/s9C5zu695lx0wHOvXo4W03VWr0CooT9u3r7VN4v+bP9wozYqSEEoyC8QZPykGjgCQW9p/80B03ph1EQxyLGtpQxFBBo8A8aBRQSZLOKNizZ48MGDBAGjVqlGIUHHaiDZgEKzDO2Vf79u1lDoprJFxqvv5169a5BgCOoVKHDh1k+vTp/hLyxA+5BkKrVq1oFJRQCJ5P7P036dR7j3tn3enK7nB9zAqwebPIvff6L/TVv79zfmQecFM2BWsuYL+wP4rgfs2YIXLVVe7LqoWaONg3U0WjgCQW9p/8YNpBcYlbUUMbihgqaBSYB40CKknSGQXDnEhi1KhRrgEQNAqQboBRBFS6VOC2fft2WbJkib/WU58+fWRcIOF95MiR7nJvJ6qjUVBaBadKRFCtDj9mBcBsADAMoF27vCkFcUfeRGWqhRHcL8xu0KBBzX4hTaF9e89AMFU0CkhiYf/JHaYdFJ+4FTW0oYihgkaBedAooJIknVGgUgmQZhA0CmY40UXfvn1lyJAhTvDRwB1xMGnSJH9rshWcfi+orVu3uiMMMNIAwmiMc/2qczQKyiOVIhIMqKGpU0UaNhTp2dN7njjR32CYgrUwdArvF4oZNm7spVLg+cYb/Q2GikYBSSzsP7mjO2ZMO4iWOBU1tKWIoYJGgXmU0yigqFIrU8ARNgpwJ7x+/fpO4OFEHo7Wr18vTZo0kcWLF7vLSVVtgduOHTvc9ILbbrvNXd67d6+0a9dOtvhV52gUlEfBUQU2ShkdcRWNApJY2H9yh2kHpSEuRQ1tKWKooFFgHjQKqLhreSDGqJhZE+BWhW6Ih42CqVOnyhlnnOEveRo4cKBLkpRy/JyfGZgEMAuCx2/NmjVuPYfJSBT3NWjQIHdEBo4rQBrH2LFjXcOFKq1qGwFiuuoaTRAH0SggiYX9JzeYdlA64lDU0KYihgoaBdHy3HPPuXnUTz/9dNo2VBnHtrVr16ZtC0KjgIq78NWogl1lFCDIbd3a/bNaYaNg7ty5aUYBgl9QbDmnrzucWjfbYKZtxVDK8cPPjPMIHj/UKMBICxyvoGAKYBSBAkYC0hCCZgJVGqmaErYp7qMJIBoFJLGw/+SG7ngx7aB42F7U0KYihgoaBdGBi/ALL7xQrr/+evci/Otf/7q88cYb7rZHH31UOnfu7G7Da+644460f6+gUUDFXQhy8RXpPs8cUB3kzpzpv8BX2ChAtf6mTZvKggUL3OXdu3dLy5YtZdmyZe5ysTRihPf5MB98x44iXbuKqNkGM20rllKO34CZ0r1qQPXx27Ztm1u7AccIx0uBGSPCYupB+aRmqbBJSRhNANEoIImF/Sc3mHZQWmwvamhTEUMFjYJowDDfs846S1555ZXqdZdffrnMnj1b3nrrLTn77LPd0QZYX+VERZiabMOGDdWvDUKjgEqCVLALo0BnEkBhowBauXKlm3d/wQUXSOPGjWXChAn+luIINQArKzFlo7/CUYcOIphtMNO2Yqv6+C3vLhXdl1cfP9RxqHA2hBk+fLj3goBoFJRXKmXEFtn2efMVjQKSWNh/sodpB6XH5qKGthUxVNAoiAYUB3vyySdT1g0dOlTuvPNOeeKJJ9xRBOFt999/f8o6hTIKghRLNAqoUivwFelR1do1C4KY9Pja9pvky0vuDKwZIKf1+Yd0HPd4xm3BdVE9wsfJBY/A8aTskW2jCtDX4qrwby6NApJI2H+yR3esmHZQfGwtamhbEUMFjYLisHHjRneEAUYaPPzwwzJ48OCU7bjrN3r06JR1Co4ooJIglW7gBrvOVybukNugrVu9UQT+bIMpyrQtalUfPxgtrausOX5UqmzJ+be1pkI+olFAEgv7T/Yw7aA82FjU0MYihgoaBdGDecsxguCuu+5yl5F+gHnfg68ZNWqUS3CdgkYBFXcFaxLAKFDD6E0PdnfsEGnVSsSfbTBFmbZFrZTj1325tK7qbsXxo9Jly1SJts7SkI9oFJDEwv6THUw7KC+2FTW0sYihgkZBtDz77LNy/vnny69//evqdShkiKrswddhRMHNN9+csk5Bo4CKu4I1CWAUQMosMFVr1og0ayaimyAg07ZiKOX4OccM5/F45wCafPyo2mV6EJ6UIoZKNApIYmH/yQ7dcWLaQemwraihjUUMFTQKogM1CjDVGIqDBdcvXbpUunTpkrIOxgEMhOA6BY0CKu5SQS6kjALI1DviS5aINGmC6Rn9FQFl2lYspRw/5ydH3ZXmiAI7ZfqoArdvOY+kiEYBSSzsP9nBtIPyYlNRQ6+I4bEpn9OGIoYKGgXRsGnTJndmAxQufPPNN6vBjAd79+51jQJUcMdrMftBhw4d3GnMwv8PoFFAJUmm36l0TlNp1EgEMzIeOlQDZhvMtK3USlowFzeZOqogaaMJIBoFJLGw/9QN0w7MwJaihrYWMVTQKIiGW265xb24CPPjH//Y3Y5RBZ07d3YvPs455xyZO3du2v+hoFFAJUmmByEjR6Z9xbtgtsFM20otG+flp2qE88DEqQdN/VzFFH5/aRSQRML+Uze6Y8S0g9JjQ1FDm4sYKmgUmAeNAipJStrdymIqiUFdXGSi0ZNU84lGAUksuv7z3pWXu+uJx/vnnp12jHiOlQfTixraXMRQQaPAPGgUUEkSjYLohMAO57SJQ9ipumWa0ZNU44lGAUkseyb+NK3/kMww7aB8mF7U0OYihgoaBeZBo4BKkmgURKskzXcfN5l2B7/CeSRRNApIYjngXOSH+w/JzIfNTtIeS1J8TC5qaHsRQ0X+RsHrMvhHNAqKAY0CKkmiURC9TC2MR9UtfCebUJQSIwmSem7SKCCJ5cAl6UO5SWY+bHKCUXnxScPUooa2FzFUFGIUDBp9hwz6kQdMAxoF0UCjgEqSaBREryQHebbLlKkSk2w20SggiYWpB/nBc6x8mFjUMA5FDBX5GAUnnnyKVL36unznB7fLwFG3y3d/eIdrGixZ9Q/pfBGNgkKhUUAlSQxoiyNT7kxTuavcQXrSjSYaBSSx6PoPixmm8t/ePdOO0aEz2nFUQRkxrahhHIoYKvIxCj7R7BT5z/bXpf8NE+XbN/282jBY8td/SCcaBQVDo4BKkmgUFEem3Jmmcle560wk3WSiUUASC/tP3bz29CI59Lmz0o7T2yNHaF9Pio9pRQ3jUMRQkY9R0KRpC9n2yk7p+/3b5Jprfybfun6ifPvGn8vilf+Q87tflhL0ktwxxSjYtGmTzJs3T1atWuWvoajoRaOgeOKoAjuF0QRou3KMKmDaCo0CkmDYf7Lj7R/dmHacOKqgfJhU1DAuRQwVYaNg7epVsmDeX2XokBvl+muHa42Cxp/4pGyt2in/7zu3yFcHT5BvDPUMg78sXyPndaNRUCgmGAUjRoyQ1q1bS79+/aRjx47StWtXOXjwoL+VoqITjYLiCYFmEufBj4NwXiBoL7XK9b4miUYBSSzsP9nBUQXmYUpRw7gUMQRPLtoiI298Tbpc8LZ8pu3b0uSEt53d2euwy+HnDhOkwcfHyCc+cZN88pPfkzPO+Jp0Or+HNDrhk7Llpdek59d/Ir3/96dy1YDx0mfQBFm4dLV8oWvPlKCX5E65jYJ169ZJZWWl7Nmzx18j0qFDB5k+fbq/RFHRiUZBccXAz06Vy+RJ6pSIQdEoIImF/Sd7OKrALEwoahiHIoYLHn9RBn7nTWl92iH/4x9xOOxw0GG/Q6pRUFEx1uGHDtc69He4Uho2ai6bX9whX+p9s1z0/34sX/7aT6T3NT+VPy3+m5zT+cspQS/JnXIbBdu3b5clS5b4S5769Okj48aN85coKjrRKCiuOKrAXpXa5Cl3bQRTRKOAJBb2n+zhqALzKHdRQ5uLGD768Db55jfekgYNYAwEdyF3o+DjDU+WTZtflXO6/UDOv+SH0rXXj+Siq8bIvIXPSscvXpIS9JLcMa2Y4datW90RBhhpQFFRi0ZB8cUA0E6VuiAlRxN4olFAEgv7T25wVIFZlLuooY1FDNeu3iTf/tYeObreR+GP7pO7UXDMx5vJxk3b5bPn3iAdOt0kn/+SZxjMeXyVtP/8xSlBL8kdk4yCHTt2SKtWreS2227z11BUtKJRUHyVszgeVZjQbqUoSMkihjWiUUASC/tPbnBUgVmUs6ihjUUMp/36ZTnn8/8NfmQNuRsF9StPkvUbX5HTzrhWPvW56+Sz59wgHTrfJI/8YaW063BRStBLcscUo2DNmjXSrFkzmTx5sr+GoqIXg5PSiKMK7FSpRhUgPYVGkicaBSSxsP/kDkcVmEW5ihraVsTwZ7fiTrCqQ5CJ3I2Co+ufJM9veEWanz5MWrYd7hoGn/7cdTL7d0/Lp87onhL0ktwptlGwPHBzqmJ5zQVoVeAaETUKmjRpInPnzvXXUFRxpIyCTZtE5s0TyTQb56JF/h9UXtIFg+vXe8d9yxZ/RUDZtAlVfBU7iOdoglTRKCCJhf0ndziqwCzKUdTQtiKGN1yPQD/t49ZC7kZBvfpNZd36l+XEU74nzVoNdQ2DUz8zXGY+uFxaf7pbStBLcqfYRgHaXZkFyiiASdDar3e2bds2adSokSxYsEAOHTpUzeHDh70XUFSEQoAyYoTX//r1E+nYUaRrV5HwbJwTJoi0aOEvUHkpfHd6zBiRtm2dNnBixDZtRCZO9Dc4yqZNqNKo2KNBSpXeYItoFJDEwv6TH9pRBU7gyFEF5aHURQ1tKmI4auTr4Y9aB3kYBUc3lXXPvywnnDxEPvHJIa5hcPJpw2T6rGVy6ulfSgl6Se6UYkQB2t59Xt692iSY6RfXHjlypLO9Io3hw4d7L6CoCHXlunFSWSkSmI1TOnQQUbNxYj0C2UaNaBREIRUUbtwoKcd9506RevVEdu/GFKmp26Bgm1ClFUYTFGvmCo4mSBeNApJY2H/yg6MKzKLURQ1tKWJ4x8RX5fjjPwx/1DrI3Sg4yjcKjj9psDRuNtg1DGAW3D9jqbRo1TUl6CW5U4oaBcosgFEQNAkoqtT62vabJDQbp/TpI6Jm4xw2TGTUKJE5c2gURCE1quDIES+1QAmmAL4TduzAFKlIP/I3+Aq2CVV6IZgvxlSJxfp/bRaNApJY2H/yh6MKzGHHXxfLB20/ldYexShqaEsRwwfur5I2p78f/JhZkp9R8NzzL0ujEwdJo6aDXcMAZsFvpj8lzU/pkhL0ktwpplGg7QPLu6csU1QpFb6buXWrdzdbzcaJgBZauJBGQVQKDjVHRtG0aV56QW1GQLhNqNKrWKMKOCViumgUkMTC/pM/HFVgFqUqamhDEcMXX9ggn2z+QfhjZkn+RsFxJw5ygWEAs2Dq/U9JsxYXpAS9JHdKMaJApRu4fWD8ePcClBWvqXIoaBTgbnarViK62ThpFESnYNCJlIMpU0R69RLp1Ck13QDK1CZUaRV1LYFi1z6wVTQKSGJh/ykMjiowh1IUNbSliOFNNyCwT/uYWVKAUfAJzyhwzYKmg+TX056Sk5rTKCiUYhsFwZoE6ANuGsKAmW7gwIJWVKmljII1a0SaNROpbTZOGgXRSjfkvEcPkbFj/QVHdbUJVVqptJGoRINYLxoFJLGw/xQGRxWYRbGLGtpQxPCJ+S/Kp9rkk3KgKNAoUJzoGQVNT+6cEvSS3Cm2URCsSYA+ALlmQfflNAuokgsBK/LhmzQRyTQbJ42CaPXU5h3S9t4pKYFi//5Oe/gDPLJpE6r0iiq4ZxHD2kWjgCQW9p/C0Y4qOIOjCspBsYsa2lDEcPB3d4c/Yo7kbxQ0/MQglxqjYAmNgggotlEQLFyIPqAEs0ANSWZxK6pU6rNtlDujwYIFIocO1RCejZNGQbTCrAdH1T8iwzff6y7v2iXSvLnI/PmYItWbZaKuNqFKr6hGFUSdxhAn0SggiYX9p3A4qsAcilnU0IYihqufeUGOrvdR8CPmQeFGgTIL7vsNjYIoKEWNgkyiWUCVUu1HLnK+V9K/m8KzcdIoiF5Tp4p8rOF/pVvPA9KwocjEid76kSPT20PXJlR5VOioAo4myCwaBSSxsP9EA0cVmEOxihraUMRw7Jid4Y+YB9EaBSc2o1FQKOU2CiBchOKOE80CqthiwFJeMWi0T2ivQr6bC/33cReNApJY2H+igaMKzKEYRQ1tKWJ4WU/nByz1I+YBjQLTMMEoUIJZwKrYVDHFILX84jB0u6RGfeWjQv5tUhQ7o+CdG4ZrX0tIGBoF0cEZEMwh6qKGNhQxXPHUv+XEExHgp33UHInGKAA0CqLBJKMAQiBXWzC3fr3IvHkiW7b4KygqR9EoKL+irqZPFV84b/IZFZDvv0uSrDYK3v7B9WlXeh+e+AnZsXqp9vWEBKFREB0cVWAOURc1tKGI4aTbXw1/xDyhUWAaphkFkGcVpAZ0Y8aItG3rbHNWt2lTk99MUbko3K+o8oijCuxSviMDKpwHlVlWGwV7x44KX+W5fPCp02kWkDqhURAtHFVgBlEWNbShiCEYMqjQ2Q4UNApMw0SjAEJAp+46omJ6ZaXInj3uouzcKVKvnsju3d4yRWUrGgVmiEPS7VOu5g7rUWQnq42CV158zrmIbaC72qNZQOqERkG0cFSBOURV1NCGIobg6v+HgD7to+YBjQLTMNUogFCvAMHEtiNVsmmTv9IRDAP0px07/BUUlaUYuJgjtAWHpdujXFNG8N0NQ4jKLLuNAoddv5uhLbQFDnbrIrvmPqj9d4TQKIgezoBgBlEUNbSliCHo2gVBfdpHzQMaBaZhslEAIZDABScuUjGv+rRpIh07iowb57+AonIQjQJzxFEF9inb4J+jCbKX9UYBgBkAU8C5akuDZgGpDRoF0cNRBeZQaFFDG4oYKj7zGQT2aR83D2gUmIbpRgEEkwAXqH/Y+axMmSLSq5dIp041qQjZavfu3bJy5coU9u7d62+lstWWLVtk3rx5sm7dOn+NPaoreFm0aJH/l76/AOw/FY0waijXmU42bdrk9r9Vq1b5a2pkc9+0Qdm2l0pTWL9+vdseunNGtePWrVv9NclULIwCQLOA5AqNguLAUQVmUGhRQxuKGCoqKz8Kf9Q8oVFgGjYYBZC6+6iGKvfoITJ2rPtn1po0aZLUr19fGjVqVM3ixYv9rVQ2mjx5sjRv3lz69esn7dq1k0GDBvlb7FAmo2DChAnSokULf0lk7ty5KX0F1KtXT4YNG+a/gipUOK8RVGY7RH3EiBHSunVrt/917NhRunbtKgcPHnS32d43bVA27aVGE4wZM0batm0rAwYMkDZt2sjEQAXaUaNGSbNmzarbavz45E6LGxujANAsILlAo6A4cFSBGRRS1NCWIoaKT37yg+BHLQAaBaZhg1GwebPIvfemmgX9+ztBX44jW/v27Sv33Xefv0TlqiNHjrhGy0ZUl3SE0RhYtunurc4o2LNnjxvMwAgIGgVhwVRq2bKl+3oqOmV7lxr9rLKyMuX4d+jQQaZPnx6LvmmLcA4pw1YnbL/t1dtS2mrnzp2uyYZROmvWrHG3bd++3d0GowfmD9YnUbEyCgDNApItNAqKB2dAMIN8ixraUsRQ8bnPHQh/3DyhUWAaNhgFuPZ3rvldwwBmwQW7rpbGzQ/I/Pn+C7IU7lwtW7bMvVg9dOiQv5bKVgjGcLG/bds2dxnHEBf8q1evdpdtkM4owAgB3OGcM2dOrUbB/v373W3B1AQqOmWT+47AcsmSJf6Spz59+si4ceNi0TdtkTJsaxOmRER7ILVACYZBhXMRsGPHDpk1a5ZcddVV/hZPGFkwevRofylZip1RAGgWkGygUVA8OKrADPIpamhTEUPFJT32hT9untAoMA1bUg+mThVp2FCkZ0/v+fSJ92d1F1Lp8OHDbiDRvn17d8gr/ubQ5Nw1bdo09y7u2LFjpVOnTu5QcJukMwoQ1EALFy6s1SjA/vZCcQyqKMq1oj6E3HaYAWrUgO190ybVNqogPDoE37toF6SJwNCBHn30UXc5qN69e0t/DBNLoGJpFACaBaQuaBQUF44qMINcixraVMRQ8c1vvBX+yHlCo8A0bDEKdMLFqi7w0+nll1927z7iGcKdLQwjnwoHgspauPOHIAwpHLi4v+SSS9y77bYoU3+pzSjA0OiGDRsmdmh0qaQK4GUjnL+tWrWS2267zV9jf9+0SbUZOxhNEBRSDqZMmeKabGgbjCwATZs2dUfxrFixQu6+++7q2hJJVFGNgh/+4Bb5/tBb5X+/OVGm/upl+cNjL8kzT2/WXugVA5oFJBM0CooLRxWYQa5FDW0qYqj4wU2vhz9yntAoMA2bjQIIgV+udyKVcMcRF2hUdpo/f75blAx3CZUQjNlUiCwfo2D27NnunWqquKprSLsSDBuMCkLxQqU49E3bFDZ2VBHD2tSjRw93tAeEtISrr75aunXr5rbRyJEjEzvCKzKjYOyY8fL1r90un/vc3dKo0VTnAmpaiNQLspNOOixfvnSfTL5zu6xbu0l78RcFNAtIbdAoKD6cAaH85FLU0LYihooZ06uCH7kAaBREydq1a90L5A0bNqRtw4UYtuE14W1BbDcKIGUWLA/cjER/U6qq8oYpo+hZUEOGDCnrcNfdu0VWrkynXLPvZTp+0IwZM9Jyi5Hfb/qQ4ZT9mlkTyKj9UqrNKEARTDVs2nQhLXzePBHNzIGyfr23zdTZHdFOKvjU9T8INQqaNGnizkgRlK1901ahrdSoAtVWMHmW+421efNmuRcVaANCW6Bo6L59+9JqR1xxxRVuGyZRBRkFP7t1nHytz+3S5vRfylFH/cZpjLA5EERdiOn54hfekwnjd8i2FzdoLwQLgWYB0UGjoPhwVIEZZFvU0LYihoqN6/4l7T6D4D7t4+dINEbBcTQK5Oc//7lcfPHFctNNN8lFF10k99xzT/U25IB27txZrr/+ernwwgvljjvuSPm3QeJgFEDIi62oqrlQRX+DsNi6NYKk9W4VdFVgC0OXMdy1nNMjItZp1CiVevUQ4PgvKLFwzFRQHT5+EHLBGzRo4AYBECrLo+aD6Rf4KfvlGwXB/VKqzSjA3WtsM11Iycc+YQQ3UsC7dkXahLdtzBiRtm3FnSmkTRuRwEx1xgjtNHO5N6pA1/9QqBAzUyxYsMAtVqjAKAJb+6atUueU21atq9zRBN2rBlS3FWafwPetao9du3a537cwr1GUEtvwHQw9++yzrvmDNkui8jIKbpvgGQRtPz3FaQydKaDDa7i6uKDzfrnz9lflxc3RGgY0C0gYGgWlgbUKyk82RQ1tLGIYpN81qFqc9vFzhEZBFDz33HNy1llnySuvvOIuv/TSS/LZz37WuaiukrfeekvOPvts9zXYhnUoHKUbdQDiYhRAuFh1zQL/jqQKMmb6NbeQu4xAA0Ng8RwcumyC4Fm0bIkK4f6KEgsX/jhu6jl8/CAUJmvcuLF7DPF8443O74/hStmvmQO0+wXpjAIUOkS1duRamyzU86usTO07yJbAIBrMGhLchl2BIYURLSZJtVPr5QOkYsDMtHbC8HS0RZjhw4e7223sm7ZKtVX35eOlYvx4b2RB9+Up5xTqv6C2R8+ePd3niQF3CnUL8B3cvXt3t9YEZqNJqnI2CoZ//1Z3BIHeDMiE12jZghEGCx5/UXtBmC80C0gQGgWlgaMKzKCuooY2FjEMcs9d28MfPw9oFEQB7rwoIwDAMMDFBobXP/HEE+4oguDrhw4dKvfff3/KOkWcjALINQnwcPqbLhg0Vai5hhi13LPvqQDAtuNXl6r3a6Z31zMu+6WEKelDMwdKnz4iyJjApA6BmepcwwDHwr+ha5TcdmpdJRXLu8eyneKk6rbCwzmv2Fb5KSej4Mor7pATTrjPOYF1RkBd+F+COdCixQcyftxr2ovCfKFZQBQ0CkoHaxWUn7qKGtpYxDDIiy9skE82/yC8CzkSkVFwomcUND052TUKMHpgpnN1hgrfd955p7vu4YcflsGDB6e8DnfiMEd1cJ1CGQVBbFRKP1MXrzY9xt4qFb3+HFxT3keVN/w7iI0K70NgD+P92Pppqah8XyrWnV2z7vDRUjFtiFR0fF4qxt1Ss97UR6jtqPIr3CYuy7u7I0DUMlW3wr+5dRoF43/yU+lx0SQ5+ui66hBkItRwWVK//kfy/WFvyPNr/6W9OMwHmgUE0CgoHRxVUH4yFTWsvYjhPO3/ZSq3jt8R3IU8KMwowEgClxMHya+n0ShAysFvfvMbt0DUV77yFXdkASq0o0hf8HWYhgoE1yniNqJADVdWfQ53vUwX8sgbNkQ1d39FGVV9/HyjwIbjl41s7Bf5CiMFWrUSCcwc6AopB1OmiPTqJdKpU/lSXDKpup3Gj499O9mu6raaOYBtVYDqNApQsPDMM3KpRVAbNV+A+XBht3flhY0btReH+UCzgNAoKC2sVVB+aitqaGsRwzAbnvuXXNT93fCu5ED+RkG1SVBtFDwlTZsn2ygIggsNFC1EIUNMMxXchhEFN998c8o6RZyMAnXhiiGw6G+4cFXPJmv2bC+fvNxKOX6o9WDJ8atLtvaLfASzqVkzkbrKb/ToIeLPVGeMUtpp/PhYt5PtSmmrmQPYVgUoo1GAooVdLpjsHFxd4J8rXiMVwjXffEs2refIAhINNApKC0cVlB9tUcNTT5EPPn16yjpgSxHDMPdMLqRWQYFGwYkejZoOll/f/5Sc9MkLUoLepPD888+n1Ry44YYb3BkQli5dKl26dEnZBuMABkJwnSJORoG6cIXQ3yB1AWuy+vb1csnLrZTjV+UsOLLh+NUlW/tFrkKNgiZNvNk0gkLh+dBMdYJZAzEDgklKaafx493nOLZTHJTSVjO9jsS2yk8ZjYIvXzrJOai6oD8fvAYqlOuufUN7cZgvNAuSC42C0sNaBeVHV9QwjE1FDHVc8T9v63YrCwowCmAQuCbBIGncbLBM/e1TcnKLZBoFKGR45plnuoYBljFtGKZDxLRhKHQIowAV3NVrO3To4L4m+H8o4mQUqAtXCP1NCRewJgt3gE2YfS/l+PlGAWT68atLtvaLXOSc3u70ms5XgBw6VMPhw96sB/Xre4YBtGuXSPPmIvPne8umKKWdfKMAilM7xUUpbeUbBRDbKnfVahR85erbpUGDqc6Xli7ozwfvC7BQKis/kgk/3aG9OMwXmgXJhEZB6eGogvKjK2oYxqYihjqeXrZZvnzpPt2u1UH+RgEMguNPGizHNxssTZoPkWnTl0rzlql3zpPEAw884E572L9/f/f5nnvuqd6GUQUwDnDxcc4558jcuXNT/m2QuNUosE2oSI9zw7TZ9zA/OmWPRo4Mf9d6+DMHytSpXh2Mnj2958BMdUZqvPOg7NAA50HlL61RMOZH46XymEIKF+rQf0nkA8wCXAjqLhDzhWZB8qBRUB44qqC81FbUUGFjEUMdf3jsJTm74391u5iB/IyCdc+/7JoEJ5w8xDUJmrb8nvx2xlI55bSuKUEvyR0aBZRONAqocopGgT2iUVCYtEZBly5R1SUIorsoy59v/e8e7cVhIWQyCz5o11b2XTvEDS5JgNtvkXe/00/eHn2DvLL5H9rjair4/OF2fu/Ky9P3kUTK3ptHunnx4WP/9g3DtO1EokdX1FBhYxHD2nh87lbXWNbsZi3kbhTU840CGAQnnvI9OenUofLJ04fJA/+3XE5t86WUoJfkDo0CSicaBVQ5RaPAHtEoKExpRsGggT+T446LMuVAobsoy5/GjT+U2TP/o704LIQdq5fKB59KL+xF6uajBg3k1XWrtMfVRGBy6PaDlImPfUx2/H2Ztq1ItLhFDZEUqmkHW4sY1sa0X78sXzj3Pd2uasjDKKh/kjy//hXXIGjeepic8qnvy2lnXCuzHl4hp3+mW0rQS3KHRgGlE40CqpyiUWCPaBQUpjSj4Avn3uVc/OgC/ULRXZQVxlf77NVeGBZKppEFJDPv9v+m9piaCEZC6PaBlI//9rxY21Ykej5sfnLa8T/S5ATta21n3h+2yuWXOT9wqburIXej4OhjTpL1G16RUz79fWn12WulzVkjpN0518tDjz0tbc/snhL0ktyhUUDppDMKFi3y/wgIRfTmzRNZv95fQVERKGgUoG+hj23Z4q8IaPduryjjsmX+Cqrk0hkFq1fX1F1BG61cmY6uPZOoNKNAH+RHge6irHCeXLRFe2FYKDALDp11hv5NSa281+cq7fE0kXdGfE+7D6R8HOzeVdtWJHoOfumCtON/6OzPaV8bB158YYNrLod2OUTuRkH9ypNkw7+2ewbB56+X9ufdKGd3HSmPzv2rnNGxR0rQS3KHRgGlU9gomDBBpEULf8HXQw951fP79XNe77zctHn5KXuljIIxY0TatnWCUScWbdMmtQgjZgrBjCFOjCXnnSfSrZtXHJQqrcJGwaZNqHXnmTsQpuvEjBxB6tUTGTbM2550WW8UjLxxl/aiMAp2PPOkO5xe+8ZEy9s/ukl7LI1k8z/YvoZhVf+xnLd/eH3a8d/33W9pXxsnpt9flWH6xNyNgsoGzeRfL2yX9ud7BsEXe4ySCy77kfzhT8/IWedenBL0ktyhUUDppIyCPXu8IA0X90GjANPuYR2CAgh3DVFNn3cJqSgEowDTOiLgRB+EcIcaASb6GvofTIIVK7xtUPv2InPm+AtUyRQ0CjAlZ8eOIq1a1RgFYS1eLNKyZU27Jl3WGwXdvvSu9mIwKra/sEbe+cF1sm/oQG1htiSz/+tfSWsQ22YNQE2Fd7/bX/b36ytvTRyn3U9SHOLQf2wGbZDk4z/jt1XS7jMwBIKHIHejoEHDk+WFf78q5138Q+ly2Y/kwivHyCV9xsrjf35Wzj7/0pSgl+QOjQJKJ2UU4K7fqFFeABY0CjBfP0YRBNWnj8i99/oLFFWAYBRgdIAyoiAElvgd2bHDSzfAKAKq/AoaBZimc9w4kd699UbB/v3e94gujSmpst4oaNDgiPx5wYvaC0FSXJIeaJDCYP8pLzz+62Xr5g3yu4e2yU037JIvdX1XGnwcJkF2RsFRR31LPtHkcml4fHP599YdctFVY+TSr46VXt8cJ1f2Hy8LlqyWcy/4ckrQS3LHFKNg/fr1zoXlPNnCW9JGSBkFaig3hnkro2CTE71dd90/5ZJL3vNW+Bo4UGTIEH+BogpQsEYBRg9Mm+bdqUYQiv537bXof2+6/Q0DVzG6ZdIkbxu+R7Zu3er/a6rYUkYBRnece677Z5pRsHr1atm5c6ebntSrl7du27Ztblvhu99GqX0KKp/fMeuNAnDHz1/VXgSS4sJAgxQC+0954fFP52+r/iV3/eI/MnpUlXzn21VyWc8qOfecbdLq1PHSrt3N8vnP3ySdO31fvtR1gLPtKrn04gvl+CaflBe3vSa9//enctW3x8tXvjtBvv69W+XPy/4uX/xSz5Sgl+SOCUbBmDFjpG3btjJgwABp06aNTAwmIlNlUbhGgTIKRo0aJc2aNZNOnX4rxx33pIwfXxPQDRrkQVGFKmgUIBabMsULMJs3r5JWrT4vZ565UD72scPy6U/fKQcPHnQLHn784wfkhBO+Lv369XN+T9ql9E2qeIJRsHevOMe8JvUoaBTAvKmsrJQ5c/7kpietWYP6Jg85bdncbavWrVvLWMsKnKh9gimglO/vWCyMgoED3tRe9JHiwkCDFAL7T3nh8U+naus62bbln/LiC2vkhY1/kw3P/VXW/n2ZjB51ndx04/dlxLWDZej3Bsi3+/eVPl+5wjUKTjixhbxUtVO+OniCfH3orfLN4T+TftdNlMUr1sj5F16WEvSS3Cm3UbBx40b3gmuPn7CKOzT16tWT3UhEpsomnVFw0kkfuG21fft2t5Bh795H3Iv8Nbjyd4QRBSxQRkWhoFGgtG7dOjnqqOUycuQBmTpV5IwzRDp06CDTp093+2C9erPkG9/wRrnAPAj2Tap4glEAg7BvX+97AiAtBLH/2rUfSMeOHaVVq1Zy/fVrnfbCCJHD0qhRIzfYhvBd37BhQ2tGkx06dKh6n5RRUMjvWCyMgit6v6296CPFhYEGKQT2n/LC459OPkZBk5NOkf+8slOuufZn0u/6idL/xp/LgJE/lydX/kM6dadRUCjlNgqOHDlSfcEI4ULLuVCSHUhEpsomnVFwwgn/lauuuspdxnR0GGGAO4KjR492111xhTcTAkUVKhgFmzen1ryAQXXppTvd4pqopA+joE+fPjJu3DiZNWuWnHbakykjWoJ9kyqeYBTAFMAoAgUKTSINoXv3P7nt09tZ2bXrq27qyIIFC1wTJyi0472WFDgZOXJk9T4po6CQ37FYGAWdzn9Pe9FHigsDDVII7D/lhcc/nXyMghObnSJVr74u374JBsHtMnDU7fLdH94hS/76D+l8Ua+UoJfkjik1CnCXadq0ae6dGlyEUeWVziho0uSA2z4QahfAKPjiF38q/fv3dyvUI1d81y53M0UVJBgF6FP164trGEDoW5iOE4UMUV2/SZMPne1XuyMN7r9/nvP3LtfAUkIgh75JFVfBYoZKMAtuu+1fcq5ftABt0bjx++73CEwdZTgqDRw4UIZYUOBkxYoVKfsUTD2A8vkdi4VR0KbN+9qLPlJcGGiQQmD/KS88/unkYxQ0PfkUefnV12XgDz2DYNBohx/dIUtW0SiIAlOMAgzVnDJlivTq1Us6depUPYSTKo90RkHz5kekadOmbp0CXDAPH/4HOeqoN5z1m5wggFPTUdFJpR4gxQB57T17es8q7Rt3ak8++atywgn75IILRI4//iM59tifV/fNu+++uzoHniqudEbBl7/8gZxyyvDqdILLL/8f9zII9SaQKnL11Ve765UGDRrkYrL27t3r1r5Q+6QzCvL5HYuFUQB0F32kuDDQIIXA/lNeePzTycsoaN5SXt7xumsOgME/ulMG33ynPLVqrXTuQaOgUEwxCoLq0aOHdcWt4qawUaCE4bW4yO/WrZtbLA7DcE2/wKfsk65GgRLqDqCg5uTJk/01ntg3yyOdUYDj3rdvX1m4cKHLeeed536nY1YAFDK8AnlKAWFEwTDDC5xk2iedsv0di4VRcOqph7QXfaS4MNAghcD+U154/NPJ3yjY5ZoDQWgUREOxjYLly/0/HOE0UKqq8p43b96clpuK4cKoHE2VViltVVVjFKi22rdvnzslWFC44J8xY4a/RFH5K6X/BWYsUP0PWrJkiTRp0kTmokhBQOybpVVKW82s+a5WbYUAGXfcFTB2MGQf5s6yZcukhZpr1RfaCgaCSQr+XkGZ9qmQ37FYGAXnfP6/2os+UlwYaJBCYP8pLzz+6dAoMI9iGwXo+uqiEn9DuJhUtaxQLbp+/fruhRa0a9cud8jwfCQiWyQUt165MhVMGWaTUtrKNwqCbYVicmgrVaDr2WefdYM2DMml6taiRf4fGmXalhSl9D/fKAj2P8y7j2r5KIaHyvMK5IXb1jdxExqj1oOF/nXfIcDEyQBS2so3CoJtFRYC62DhPxgFuCsP4TegQYMG7nd/PoI/hJSGoFBXEG+3dau/Igupz1/b75VarxTcp0J+x2JhFPT88jvaiz5SXBhokEJg/ykvPP7p0Cgwj1KMKED3V8/qomvmTP8FjqZOnepOj9WzZ0/3Odv5p03SpEle4TUnjqlm8WJ/oyVKaauq1tq2Qv4tgrXu3bu704Ph7iBVtyZM8Io/6pRpW5KU0v/Gj0/rf0glcIKoNIYPH+5ut6Vvjhkj0ratuLM3tGlTU3cBgySC3x+gXj0zpxxNaauZA7TfFUEFg2oIbYNAGsPzGzduLHPyLHACQ6Cy0jMFlEaN8mZdQHmKdu1EAoNT6lTQFMD+BZfDCu9Tvr9jsTAKvvW/e7QXfaS4MNAghcD+U154/NOhUWAepahRoC66QKaLSZuFOcTvu89fsFjVbVXVOrZtVUqhlhkCQgR9YTMg07akqrr/OdFdHPsfZnJAYKtq3OFOOMwA3XT7MBpbtqx5rWmqbquZA8rSVpj5ApOwtGpVYxSsWeMd3+3bveWDB73fHKzPVsocUL9XOpMgSsXCKPjxzTu1F32kuDDQIIXA/lNeePzToVFgHsU0CkLdXyrwWN49hbg8jm23Xc5edoN02f3/pNuhSwJb7HiE20UHH7k/Wgx7XE4d9TtpP2e8HNPizcCWzNuS9tD1tzBxeFx45CL54qZvVy932XOl+93YecdXq9fh8aX9vdw+8blFPwysLf9D1y5hSvU4deRj0nrcLDmx99/krHlj3XWfnfVzaXrVKv8V3uPkfk9Kq9GPBNboH2n7Ev79ciiGYmEUPPbINu1FHykuDDRIIbD/lBce/3RoFJhHKUYUVN+haV3l3qku9h2aUuvwYe+uYPv23pBX/G1rsfXg3TQQt7YqtY4c8Z6Rjh0eNZBpW1KVlP6H74xp07w74rrp9lEsv1cvf8FQlbOtVqwQOfdc7+/evWtGFDz6qHdMg8L2/v39hSzk7tfyAVIxYCZHFGTD58/+r3Nht0F70UeKCwMNUgjsP+WFxz8dGgXmUWyjQF1MYlgqTgNMe4ahqnEKAF5+WaRPH+8ZQj01DBnGHPA2KdxWaCP1TBWmTGYAjQJPSep/SDmYMsUzAzp1Sk0vwHD5hg1zGy5fapWzrVCfErUHVJHHoFGA49i0qVenAGbC3XeLNG/u1SvIRsokwAP7o/azmPtlvVEwZNBu7QUfKT4MNEghsP+UFx7/dGgUmEexjQJ1MQnhNIDcYZ4D/JUx1YgR4lz8+QuWSNdWKgCgChONgrqV1P7Xo4c3gkBp9myRDh38BUNVzrbCaC3UhMF5A847zzt+mEkCQoHDq68W6dbNK2Q4cmR2I7xcU2D8TGntPCC1L8U2C6w3Ch6ezbSDcsFAgxQC+0954fFPh0aBeRTbKFAXkxBOA6jKeeBibLnziIMwBdf06f6CryFDchvuaoJ0bQUV825aUkSjoG4lof9h9rzQdPvu90Rwun0Ewbp0BJNUzraCKYBRBAqkeyENYfJkkX37vOkSg7riCpEZM/yFOoTfJfw+QcH9KqbSjILPtpvivLku0C8Ub6ei5KIL35WNz/9Le8FHig8DDVII7D/lhcc/HRoF5lGKGgU6wSRQd25sF+5kYWpE3MmCkHqA4a62TY9IFU80CigIsx7gu8Kfbl927fK+K4LT7SPwRZ+gslMw9QCzHeD44jsYevZZkSZNvHSFuoR0g5nOo9RKMwr6fv3nzvWiLtAvlLRr0oK56xfbtRd7pDQw0CCFwP5TXnj806FRYB7lMgog1CvAIw7C1IiY5g7DiPGMu1sUpUSjgFJC7RLUIOjZ03sOTrePApe4XEANAyo7BY0CCLUf8B3cvbs3deKyZf6GDIJBAKOgHEozCn7+s59Ix453Ox1BF+wXQto1aUH0uuwdeenfG+SVF5+Td24YJvuGDnQvfEnp2P/1r6Q1zHtXXq59LSFh2H/KS1yOP77733YC+Vc2/0Mb/OcCjQLzKKdRAKFeQVxSECiKoii7VO7RbVqj4Nv9J8rHPz7VuW7UBfz5knZNmjdHH/2RTL3vZdckOPKJJvoXEUIISQxHGh8v250AX2cAZAuNAvMot1Gg6hWovFCKoiiKKpXw+1OOlAMlrVEALr5oknPtpQv480V7bZcX3xvszXSAu0jaFxBCCEkc74z4Xlrwnws0Csyj3EYBhIu0uNQroCiKouxQueoSBFWrUfDTn/xUvvjFu5xrL13Qnw/a67qc+cr/2yv//Psm96IOKQfaFxFCCEkc+4Z8Jy34zwUaBeZhglEA4YItLvUKKIqiKLNVzroEQdVqFICRN0yQMz4b1SwI2uu6nOjaZb8seuLFmgu7zf+Qjyor9S8mhBCSGD46+mh5dd2qlMA/V2gUmIcpRgHEegUURVFUsWVSyltGowD88Ae3SOPjf+1ch+mC/1zQXttlzUknHZZlT/477cLuzXsnyZFGx6X9gwM9umkLX5FoeeuWMXKwWxeXvRPGaF9DSG2w/5QXG4//gZ4Xp33fwzDeM+m2tN+HXKFRYB4mGQWsV0BRFEUVW+WuSxBUnUYB+P7QW6XDWfc412M6AyBb0q7tsqbnpfvk94++pL2wA+9cOyTtHx1pcoK8ed9k7esJIYTYx+4Z98mHnzw57ft+36D+2tfnCo0C8zDJKICQfoCRBRRFURQVtUxLc8vKKACjR42XCzpPdq7JdCZANqRd22XFt7+1R/66YrP2ok6xY9ViOdC9a9o/PvilzvLaykXaf0MIIcQedqxZLgcuvSj9e/78L8hrT/1J+29yhUaBeZhmFEAwClivgKIoiopSptQlCCpro0DxP5ffkefUiWnXdxk5/vgP5adjX9NezOnY/Zt75MOmn0j7j/YN+6729YQQQuzhnRuHp32/H2nYUN6853bt6/OBRoF5mGgUqBQE1iugKIqiohB+Vyqch2mpbTkbBeC6aydIt26/yLF2Qdo1npZmJx2WIYN2y58XBIoWZglTEAghJH4UO+VAQaPAPEw0CiBlFlAURVFUoTJhKkSd8jIKFD+46RY555y75eijf+Ncs+nMgSBp13gpVFZ+JJf3ekeeXpY5zSATTEEghJB4UYqUAwWNAvMw1SiAkH5g2jBRiqIoyi7hd8TU35KCjAIF6hf0/frPpdP5d8knm//KuYbLzij4TNuDcs0335K7J2+XVXXUIcgWpiAQQkh8KEXKgYJGgXmYbBRAqFdg4l0giqIoynzh98Pk0WmRGAVhbrphggwedJt8rc/tctmXJ0nnTne5RQl/cNPrMvnO7fLYIy+5Ux1ue3GD9mKtUJiCQAgh9lOqlAMFjQLzMN0oYL0CiqIoKh+p3w/T6hIEVRSjQIfuoqxYMAWBEELsppQpBwoaBeZhulEAwSRgvQKKoigqF5lalyCoWBoFgCkIhBBiL6VMOVDQKCgOq1atkpdeeill3aZNm2T+/Pmydu3alPVhbDAKINQr4JSJFEVRVDaypcZNbI0CwBQEQgixj1KnHChoFETPc889J2eddZZrCqh1jz76qHTu3Fmuv/56ufDCC+WOO+5I+TdBbDEKINQrYAoCRVEUlUk2jUKLtVHAFARCCLGLcqQcKGgURMubb74pvXv3ds0AZRS89dZbcvbZZ7sGAparqqqkY8eOsmHDhpR/q7DJKECeKcwCk/NNKYqiqPLKpro2sTYKAFMQCCHEHsqRcqCgURAtt9xyi9x5553yne98p9ooeOKJJ1zjIPi6oUOHyv3335+yTqGMgiAmy/QK1hRFUVT5ZEtdgiCxNgoAUxAIIcR8ypVyoKBREB3Lli2TK6+80v07aBQ8/PDDMnjw4JTXjhw5UkaPHp2yTmHTiAIlXAiyXgFFURQVFAwCG+oSBJUIo4ApCIQQYjblTDlQ0CiIhldffVUuvfTS6nSCoFEwe/ZsGTJkSMrrR40a5RJcp7DRKIBYr4CiKIpSsmEqRJ0SYRQApiAQQoi5lDPlQEGjIBoQ9F977bWycOFCl6uvvtotWIgZDlDIcNCgQSmvx4iCm2++OWWdwlajwNaLQoqiKCp64ffA9JQDnRJjFACmIBBCiHmUO+VAQaMgGmAKYBSB4vzzz3fTEH7961/L0qVLpUuXLimvh3EAAyG4TmGrUQAh/QAjCyiKoqjkyuZ0tEQZBRlTEJ5mCgIhhJQaE1IOFDQKikMw9WDv3r2uUYCRBljG7AcdOnSQbdu2pfwbhc1GAQSjgPUKKIqikikb6xIElSijADAFgRBCzMGElAMFjYLiEDQKAEYVdO7c2b34OOecc2Tu3Lkprw9iu1GgUhBYr4CiKCpZwvd/hfOwOQUtcUYBYAoCIYSUn90PmJFyoKBRYB62GwUQ6xVQFEUlTxhRZmNdgqASaRQwBYEQQsqLSSkHChoF5hEHowBC+oHNw08piqKo7IXv+zh85yfSKABMQSCEkPJhUsqBgkaBecTFKIAy3V1avVpk505/gaIoirJWv9z9Ozl75QhZuVKq2bvX3+ho2zaRefNE1q/3VxisxBoFgCkIhBBSempNOfhueVIOFDQKzCNORkFt9Qo2bRKprPQuHCmKoih7he/5JpN+JkfX/0gaNZJqFi/2tj/0kEjz5iL9+om0bi0ydqy33lQl2iioPQXhAqYgEEJIETAx5UBBo8A84mQUQDAJYBYoHTok0rGjSKtWNAooiqJsF9INzu/7H7nvPn9FQIcPe6YBzGFo926Rhg1Ftmzxlk1Uoo0CwBQEQggpHSamHChoFJhH3IwCCPUK1JSJI0eKjBsn0rs3jQKKoiibpWrRtGsnsmyZZwTADFZasMAbRRBUnz4i997rLxioxBsFgCkIhBBSfExNOVDQKDCPOBoFEOoV3LNinZx7rrdMo4CiKMpeof4MRoth1EC9eiLt24s0a+b9PWiQ95pZs0Suusr7W2ngQJEhThhqqmgUODAFgRBCiovJKQcKGgXmEVej4Pm9r8ix7bbLsi073GUaBfZq0aJF/l81Wr9+vdOe82RLxGOKde+F98B7rVu3zl9DlVOZ2n7btm3uNrwmCmV6r2L1QUovVX/m5Ze9UQJ4hnY4X/EtW4pMnSoyfbrI1Vd765VgIigjwUTRKPBhCgIhhBQPk1MOFDQKzCOuRgEuDJHHevLC78jChSLnnecVtYoofqBKpAkTJkiLFi38JU9jxoyRtm3byoABA6RNmzYyceJEf0th0r3X5MmTpXnz5tKvXz9p166d068MjjgSoExt/9BDD1W3VevWrZ3zvbAqdpneq1h9kNIL6Qa1zWgDjRghTrDtFTK84gp/pS+MKBg2zF8wUDQKAjAFgRBCosf0lAMFjQLziKtRgBgBowha9n5e2vbe4g5RRRqCE/dRFmjPnj1uENaoUaOU4H3jxo1SWVnpbod27twp9erVk91IVs5Ttb3XkSNHpH79+u57Qnv37nWXObKgPMrU9ocPH3bbb5NfxQ7rGjZsmPfd/kzvVYw+SNUuGAQwCpS2bvVGDgSF1IL+/b26BSGvzzUOYCCYKhoFAZiCQAgh0WJDyoGCRoF5xNUoCAr1Cjr13iPzmHpgjYYNGyajRo2SOXPmpAXvKhiEEKxVON93OzD+OE9lei8EgBjODh06dMgNEFevXu0uU6VVprZfsGCBO4ogqD59+si9eVaxy/RexeiDlF5qyls8K2FUWP36NTMb4LBjOkRMj+g0jWsUYBQZBI+vQQORXbu8ZRNFoyCEm4JwIlMQCCEkCmxIOVDQKDCPJBgFuMhs0HupTJv3hr+GMl0IxqCFzhV/OB0Awh3kadOmSceOHWUcprUoQJneC+/RoUMHdxh7p06dZATGOFNlla7tZ82aJVeFqtgNHDhQhhRYxS5TP4uyD1J6wSTQpRxgakRMg9ijh/ccHCmGUQUwDrCtcWOROXP8DYaKRoEGpiAQQkjh2JJyoKBRYB5JMAogTKuFkQWUXarNKMBw7ylTpkivXr3cAF4NAy9EuvdCvjv+//ucyKR3795yySWXyP79+/2tVDmka/vp06fL1aEqdqgnUWhNiUz9rBh9kKoR0g3UNLdxFo0CDUxBIISQwrAp5UBBo8A8kmIUQDAKknDhGSfVZhQE1aNHj4IL10Hh95o/f75bqA53jpVgFIwfzz5kilTbo5DhFaEqdhhRgLSSqJSpn0XVBylP4boEcRaNglpgCgIhhOSPTSkHChoF5pEko0Dlu2KKLaq8Wh5oAnx9KVXVpCK7CgfvmzdvTss779+/v1uMMDizIerKrVzpMWVKzd+5vNeMGTPShrMj8MT7UdGrrj6Rqe2XLVuW0nbIY//iF38mv/jFn/w1NUJ9Q9QrUTUpg++llOm9Mm2jChe+pyucR7AuQZxFoyADTEEghJDcsS3lQEGjwDySZBRAyixIykWoqcJXlgoM8TeEgDBUjy4teEfFecw8gGAN2rVrlzsl3jXXbHZe565yNXeul7sM8P8fe6xIvXq5vRdmN2jQoEH1e2HWg/bt27sGAhW96uoTtbU9Rn6gzgTaDm04ZozIaacdctp7tvP8oQRnLkQu+0kniRx3nMipp3rTqIbfC58h03tl2kYVLoz8yjQVYtxEoyADTEEghJDcyJRysNPQlAMFjQLzSJpRACH9ICnDWk0VgjF8dalnFaTNDMUH4eAdmjp1qjv1Xc+ePeXYY0+Vc89d7xoCoZdVS70HAsRc3wvF6ho3buwOLcfzjTfe6G+holY2fSLY9nieGHABMKqgadPuctRRH8jxx5/uzmKxc6dnEGGECWpWolo+KuHj/27VSuToo1PfC++tlOm9Mm2j8he+l5P23UyjoA6YgkAIIdljY8qBgkaBeSTRKICSdtfKRKmAEOgC92yEFPRRo7zK5rUZBag9eOKJhb8XVXwV2idgBgRmLhTUF8T/hSn0sA2mgT/bpbz4osjHPlbzXkGTgCq98H2cxIKzNAqygCkIhBBSN7amHChoFJhHUo0C1ison1K+vmYOkIrl3dPI9nHhkYvc5w4LR8sxLd7019Y83P/vW7Ol4vzV3t9VraWi+/KUz0CVX8H2qGhdldIXFLk8Ljx8sXxm2i/kuI4vSetxs6rXV4z8hVS02eb1iTM3ScVX5np9ELA/lE1JTgmjUZAFTEEghJDM2JxyoKBRYB5JNQogmAS4OKXKIwwxdoNA/+uskDu6CxfqRxQcPCjSsCFmMPDuGrsmQRUNIlOlAsaKATML6hNIOUARy169RDp18kYWQP36ecuYh79HD5EGDfyf0pkDXCOBKo/wXZDUEV40CrKEKQiEEFI7NqccKGgUmEeSjQII9Qo4ZWLp5ZoE48e7Q8vxdaaGnOcbGNZmFMyeLdKuXc0wdvc9qqpcs4CpJ2ZJGXczq7wRH4X2CSUYApi5EGZRmzYimO1S1SQ491zvPbAMs4DGYemV9JoxNApygCkIhBCSju0pBwoaBeaRdKMAwp1E3mEujXDH2B0C7psEEL7OIBUY5qPajIK+fUVOOKEm1736vWAWLGedClOE8881b6q88zDfPoGJCEIzFwpms8TMhZisArNdBgsXosaF+v9ds8Dpl0kdAl8O4fxLujlDoyAHmIJACCGpxCHlQEGjwDxoFNQErwwOiit1nBEcBIvU4StNKd+7x7UZBc2aiQQnKkh5LycyVJ+HKp9UsKhMAijfPoEZDTCzgT9zoezaJdK8uTeaYN06L9VAbdu7V6R9+9T3gtTnoXlYfPE40yjIGaYgEEJIDXFIOVDQKDAPGgWeVHBAFUcwCdxg0HkUQzqjAFXu8ZWJfPVMglnA9JPyCMcd/SJKk27qVK8uRc+e3nNw5sJp00QaN/bSEfBc22yX7ggH50GzoHhKcl2CoGgU5AFTEErIi8/JW7eOlXeuGyp77ry1KLz9g+vlrVvGuO+l/QykeJSkfa9z2vfHbN8iEJeUAwWNAvOgUVAjXLgyYIxeCLZMv3OItk9ynnQ5hONt8kieYptbSRaOKc83TzQK8oApCKVhxzNPypHjGqYd52Lx0bEN5LWVbL9SsePZJU77Hqdti2LA9o2WOKUcKGgUmAeNglQhcOFdxOhkg0mgpAJXqviy5VgH02WoaKS+E0w1iEotGgV5whSE4vL6E3Pk0Oc/l3Z8i82HJzeTHauXaj8TiQ4c4w+bp9+JLjYfnPlZ2TX3Qe1nIrkRp5QDBY0C86BRkCp1F5EXsYULwdXJi76dZhKsXy8yb57Ili3+CoOUKYDdtMn73KtW+Ss0Wr267lSHJAtt32PeL2XEllDFwYBMO4bZmgWZPveiRf4flPv9SuOlRjQKCoApCMUBJoHuTmWp+OBTp9MsKCI4tjjGumNfCg5260KzoEDilnKgoFFgHjQK0oWLWN5ZLkxI4ThhwmQ5ucWH/hpPY8aItG3rBOQDvKnqgvnjpkiXNz9ihFcpH/Pwd+wo0rWryMGD/kZfMBIqKz0zgUoX2r5B21fl7AHram17k48hvhNqS03K9LknTNAX2kyiYMTRJEgVjYICYApC9JQ7iFTQLCgObF/7iWPKgYJGgXnQKNArU1BAZdY391wrzQcskkaNUgMkVKRHMLVnj7eMu6/16ons3u0tmyQEMyplAtXyg58b6tBBZPp0f8HRoUOegdCqFY0CnRZvfE2OqvxAfrXnd+6yru1tOIYIdPEIqrbPjf4CQyx8HiRVOKfCx46iUVAwTEGIjkwjCT5o01r2DR2oLVZXCBgV8kG7ttr35J3naMnYvk7gXoz23cf2jZw4phwoaBSYB42C2qUCRSp7IRBoMexxGTVKZM6c1AAJsxDgzqsSAil8xe3Y4a8wTGh7VL5/bPvfZMkSf6WvPn1Exo3zFxyNHOkt9+5NoyAsN53nSBv52aaaA6Nre1uOIfp4cMRRbZ972DDRngdJFPoAziWmdKWLRkEEMAWhcDIFkbhTueuRB7T/LgoQLCJo1L43g8lIYPvGg7imHChoFJgHjYLa5QY4zoMXt9lJjcKAIQDppiyEDh/2pqnDXdhgsG2iVB8IDpfeutUbYYCRBtCKFSLnnuv9TaMgVapwnTLcamt7244hzALs16MrXq/1c9d1HiRJ+G5gyoFeNAoigCkIhVHOIFLBYLJ4sH3jQZxTDhQ0CsyDRkFmIfDNd7js7t27Zf78+bJs2TJ/Tby0yK/QhmBamQTbtm1zgqV5sn79+loDJAw7nzJFpFcvkU6dUof0myi1fwh0cAccQ8xvu83btnevSLt2NYUZswlyN23a5B6jVRmqIqpja4t0+wRz4NQPT5Up66fIypUrXebNWy0///l/U9o+n2NogkbvvV3qt9smy7Z4wyJq+9zZGAU4X3D8tmgqfNr+PYLvz967ezvnvb7So219PWrRKIgIpiDkhwlBpILBZPSwfeNDnFMOFDQKzINGQd3K527YQic6aNasmXsBeN5550m3bt3kiLrFGANNmDDBCX5apATRDz30kDRv3lz69esnrVu3lr59Z9cZIPXoITJ2rL9gsLCf5675nhzf7KBMnuyvdDRokDj76QWDwGlqd39Q3V+nESNGuMcGx6hjx47StWtXORiqiqiOrS3S7dP9H9zv3nEf+uhQqV+/vjRq1KiaxYsXu/9OtX2ux9AU4XOf3/c/cvLC78jtCzfU+rmxT5mac8yYMdK2bVsZMGCAtGnTRiYGqjza/j2C74UvvvdFqaysdI2QsGzr68UQjYIIYQpCbpgURCoYTEYH2zc+xD3lQEGjwDxoFNQtNfw823oFhw8fdi/uV2A8ta/27dvLHCQrW649e/a4AQ0CvpPPP9k9LggGsM9YhzvLEO6CfvzjX3GOw2F3Gdq8WeTe0Kx4/ft7Bd9MF2oUNGnitOPcn7gjJ5QQGOJOssJpdncoetBMUFq3bp0bMOEYKnXo0EGm+1URg8fWluBJt0/Nft1Mmu5v6p43ffv2lfvuuy9j2+dyDE2S+tydeu+Rit5PyAnNPtB+7kxGwcaNG1OOH+6616tXzz1/bP8eQfuf9tFpckavM6RVq1YpRoGNfb1YolEQIUxByB4Tg0gFg8nCYfvGhySkHChoFJgHjYLspHKtsxGGCePuXxw1bNgwGTVqlIxfPl7qba9XbZ4sWLDAvascVJcuP5PGjd/zl7xZD+rX9wwDaNcukebNcby8ZVO1bZtXud7ZRbfC/bcOfdcF+fZhIXCsbdj89u3bZUmoKmKfPn1knJ+sr44tAkFbgqfwPmGY+Un/Okmuu+s6d7ldu3bukPmVK/dm3faZjqGpQlDcoPdSGTFvqb+mRpmMAowOUOYahAC6wvn937Fjh/XfI+gLl/3uMrd/93YaNWgU2NjXiyUaBRHDFIS6MTmIVDCYzB+2b7yoLeVgT4xSDhQ0CsyDRkH2yrZewYwZM9w7qUOGDJEGDRq4d80mTZrkb7VbCGxgDpx84GQ5sc+J/lqRWbNmyVVXXeUvefryl++RY49921/yNHWqiPP1Jj17es+6ufRNE6rah76iXU4Znh7N5hLkbt261b2bjLvykBpSjuHmNgZPODfOP3B+9T7hjjjujuMuOO6MH3XU9+Xoo9+vs+1tNAqgHr0PyFnzxrojbIKqK/UAwrGaNm2am7qhjCObv0dck+D1y+Rcv9Jj2Ciwva9HKRoFRYApCLVjQxCpYDCZO2zfeJGUlAMFjQLzoFGQm5CPX1cKwkgnskReNi78IRQqa9KkSXVuts1CEISRFbf/7faUC3wMn7/66qv9JU+DBg1yiasQDGU7yiQs3DHGcOzbVFXEgGwMnnAsbtp3U8o+vfzyy+6ICTxD2OeWLVvKVLhFMRVGFuRT0wQpB1OmTJFevXpJp06d3JEFtn6PYN9bHWnljiZRxRnDRoESjQIaBUWBKQh6bAoiFQwms4ftGy+SlHKgoFFgHjQKcpOqV4Dn2oRA6IwzzvCXPA0cONDFZmFEhdr38AU+ChleccUV/pIn7C+GGMdZwWOSrdasWePeYZ9cSxK+TcGTCowHvzY44z4pofAhgqK4C8ckWMsiF/Xo0UPGjh1r7fcIzofek3q7oyHQlwFSKLBPMDuColFAo6BoMAUhFRuDSAWDybph+8aPJKUcKGgUREdVVZVb5CrIq6++Wr0dea/IcV27dm3KvwtDoyB3qbvqSjh9lZxmkblz56Zd4Ed9dx036nCDTs3lH4WWBwZKhPcJd4wR/CiFL/CRhx6+4IdxAAMh7lL9ITjSJHz8lJDPj7vC6CO1qZDgCenu6Be6mRfVtq1b/RVZqtZ+4ZsEozaN0u4TUitUoUYlDKPvjyqGCRDOGTyCCh4/aPPmzXJvqMojjg8K/UX9PbJ6tTc1qdLu3SIrV6aCqSqzUW19AvuL8wGmAEYRKGAiIQ0hbCTRKKBRUFSYguBhcxCpYDBZO2zf+JG0lAMFjYLo+NWvfiVnnnmmnH322dX85S9/cbc9+uij0rlzZ7n++uvlwgsvlDvuuCPt3ytoFOSnivG4X+jdMcTpCyEgRD2/Q4cOSdOmTd0CfxAqmGPIdVTzoONaG4Xg+vXz5p+Pyn/AfqgAIGWflqeaBFD4Ah85x1jGegjV3JFXvQtV6xIgmAQVzkOZBeE+AW3bts3NM0e/QB9RID89qHyDpxEjvPdCv+jYUaRrVxE18+KoUd5sAqrPON03a2n7hfOAOXLn7jtr3SfcPcbQeVWsD6kHmD4zDik42QqBc+uq7vrzymmrGTM2uscIhgGE8wXHCCYvjmNU3yNogsrK1NoPKHeAApMo1KnItml0fQIGQcVMfQ0Xph7ULhoFRYQpCPEIIhUMJtNh+8aPJKYcKGgURMe1117r3q0Lr3/rrbdc0+C5555zlzHyAAWyNmzYkPZaQKMgP+EiuWK5V68Ap7C68J/ppyavXLnSzde+4IILpHHjxu584VEINcBwcY8ZBCDcAcRyFCML3H1y9kU9u/vkBDm6IdS6C3wEMAhyMHQa+xyH6SBz0XLngFVUedNF6voEcs5R0T7M8OHDvRf4yid4QvsjEAzMUigdOqB2BFIdvG3bt3vrYR7gc2F9Ngr3C/R5mAQzq5bXuU+YGhFGAvoEnutKTYijXLPAebj9I9AvcDwhpBg0bNhQevbs6T5PDFR5jOJ75NAhzzhy/psUo6BvX7SPv5CjdH3C7fvLnZ3TiEZB7aJRUGSSnIIQpyBSwWCyBrZvPEliyoGCRkF0XHrppbJ06VLXCHjzzTer1z/xxBPuKILga4cOHSr3339/yjrw9ttvy6c//Wk57rjjqqtQU9lLBYYVratSAsJiCs1Ur543ZR+EIABBIIYVRyF14e/uUy0mAVW73D6xvLt7DEvVJyCYAKGZF6VPHxEU0J81SyQ0IYU7smD0aH8hC1X3i+5+QFjlR7lUVsJ5BLNA9QtlEpRCmLUD/cCJ1VOMAowsweAEpCDgeyRXVfcJ7JPzgEFG5Sb87uL395RTTpG9e/e6v8s0CopAElMQ4hhEKhhMsn11/y4OeCkHzdP2O+4pBwoaBdGAUQOf/exn5bLLLpPzzz/f/RtzUmPbww8/LIMHD055Pe76jXaiguA6gFQFdQcQd4JxZ4OjC+pW4NT1KPVj2hCp6LBBKsbeKhWd/iYVI34Z3BrdY4B3Z1xB1a7gcYLJEjiK5Xls/bRUVL4vFevOlopHvyEVHZ8PbpWK3k9IRf//C67J/gGzgH0iK6X0C5xPpX6suFAqzl3r/Y02n/f/vL8PHy0V9T6Uivb/kopmb3h/D/qtty3Xx8wBKd8VVN1Sv7UYTaF+g//4xz+6v8s0CopA0lIQ4hxEKpIcTLJ949m+SU45UNAoiIZ///vf7igBPGMZRcO6du0qDzzwgMyePdstGBZ8PUwEZSQEwYgCTMd19NHORaPTF2eW6vZnTKSGELt3kMePL9ldQtwN7tTJGzaMu4SXXCKyf7+/sUBV7xNGSjhfUaW88xkHVR8/BE9lOn47dnjDzNXMi0hHaNrUq1OwYoXI3XfX1LjIVil9nf0iZ1UfP+eB51IcP6QlYdSAP0NhyogCzFiJESf+zJVun2nZEmkQ3nI2cvdpvFeXgH0id2E0YGunMxxzzDHu7zBHFBSZpKQgJCGIVCQxmGT7+vsaw/ZNcsqBgkZB8UCV6euuu84tZIiq2MFtGFFw8803p6wLgjsby52rPFy00CzITurCH4cLpzOK/eFOa7EvlufPF2nTRiRY/w5GQS7F6WpTyj5VtXb3BfvGACA7pRw/J3gqx/FD3QEULQyXAkAxu6uvFunWzesrGI6ebRHMlP1CXQ72i5ykjp973JxHcLmYQvuiDsHChR7nnSfO74RIaIbCaqEYZrYzV7r70N0fOdO6in0iR413TkL83uJ3F7+/wd9jGgVFJO4pCEkKIhVJCibZvqF9jlH71pZy8G5CUg4UNAqiARXlMXIguA6pBTfccINbt6BLly4p22AcwEAIrgsSTDfAtFzqAoaqXSpwgnA6owq8qldQTM2YkZ5vPmwYplTzFwpQyj45+wKpAICqWynHz6/+XsrjhxoFTZpgek5/ha99+9JrWFxxhdeXslHKfi33Zr9gv8hOYVMAgTVUCrMApgBGEShgIJ17rmciYYrM0MyVMsQJobL9HsFnR19QhTsh9om6hVEE3bt3d8HfEI2CElJ7CkJn61MQkhhEKpIQTLJ949u+TDmogUZBNKxdu9adGlHNbIDUA0yHiJoDGL4IowDVpLENr+nQoYM7NVvw/wgSNAog3O1gKkJmBQ8NTmnILVi2XD89WFRCdfsGDTD/ureM4cXt22cf9GVSyj75RgFUzGAmTko5fr5RAJXi+KG4Jaa3w0x6KEynwMgTFDrEzBgYXg49+6xnKKDvZKOU/fKNAoj9Incpo6AcCqYeYFQB+oQ/c6XbN5COkvX0iE7/xsP9O7BL7BO1S43aw+9rUDQKSkwcUxCSHEQq4hxMsn3j3b5MOaiBRkF0YGpETIOIaZXw/Otf/7p6G0YVwDjAtnPOOUfmzp2b8m/DhI0CSN35wAgDKnshBaHY1b+nTRNp3FikRw/v+cYb/Q0RClXMqfylgqhSCakEoZ8ZFzXz4pQpnpHgnNJu/YI8puJ3hf5N5S9TjAIINU7QJ/A9gudsZ67E9xv7QW7KNFKPRkEZ0KUgfFRZKfu/1Vf23HmrVbxz/VA53OrUtP0BSQkiFZmCycOnniL7vj9IewxNxm3f09i+oO72Haw9hibz7sBvyZHjGqbtT9JSDhQ0CsxEZxQoqQscji7ITkhBcOdMdx42i0ZBYSq1UVAqMUAsTOU0CqKQ+n7DM1W3gqkGtYlGQRnY7lyEfvCp09MuzuME9m/H6qXa/Y8z2Oe4ty1g++qPSxw43LKFbHeCZN3+xx0aBWaSySiAcBeEqQjZCyaB7YE2jYLCRKOA0sl2owD9utgjpuKibFP4aBSUCdydlKM+lnaRHgeSGkQqMt15jgNJG0kQJu5mQZLblkaBmdRlFEC4M4LRBcEiTFTtQr0Cm4NFGgWFiUYBpZPNRgH6dFz7dZRSowiyLQpcMqNg3h8elM0bV2svzpLK++eenXaRbjtHGh2XaJNAAbPg8CkttMfIZj486cREB5IK9PEjjY/XHiObOdT+DO3+JoWwUfD3Z5bIzOm/olFQZrIxCpRgFnB0QXZCUGVrCoLOKFi0yP8jIBRDQ97zqlX+CspVMKDKdIwwxz22oUilDVJGAYrh4XOrOfqDyrQt6QobBZiNYudOfyGkTNtKLYwioHlYt2AS6AoWZlLJjALw8Oxp8s+/L9NeoCWR7c8/I3LUUWkX6zbzzrVDtfuaRPb++AfaY2Qz74z4nnZfk8jeH4/SHiNr+djHZMezS7T7mhSCRsFfVyyU+6feLT+++SYaBWUmF6MAUtWbaRZkls31CsJBwYQJIi1a+Au+MO+60w2kXz+Rjh1FunYVOXjQ35hwKaMg0zFC8ThUmse2du28ee9NF4yCMWNE2raFaSjSpo3IxIn+RkeZtlHOpUDAKICBVFnpmSphZdpWDtn6PVZKwRzIdhRBUCU1CsBvf3OPewGmu0hLIm/eO0k+OrZB2kX7gR7dtMXHTGL/17+S9rn3jv2hdj+TCI5R+Pi8d+XlacfRVNi+mcExCh8fG9r3QM+L0z73R/Xry55Jt2n3M0koo2Dpk/PkV1PukHFjR9EoMIBcjQKlTJWcKU+23olTn3nPHi/oQ1X0oFGAO+AIZLBdqUOH9LnZkyoYBZmO0ZEj3vR0Gzd66zFVIZZNH1nwxY3fSdkn3PGuV09k925vX2rbRnlSRgGmroRxhBkowmZApm3lEPoy6xLUrmDBwnzS8kpuFIApd/1cFv/5D9oLtSSimwXhSJMT5M37JmtfbwoIOsKfm4FkDbYfH7ZvZmw8Prtn3CcffvLktM+9b1AyZzkIA6Pgzwsek184bXvLT0fTKDCEfI0CKNuCTUkW6hXgYZOUUTBsmMioUSJz5qQaBZibf8kSf8FXnz4i48b5CwkXgqtMxwhGAYLobdu89QgOEWRjuLnJuvDIRdVz70MwBfAzh3n4sU+1baM8KaMA01miH4SnLIQybSu1YBAE02ioVKnRdbmkGoRVFqNAwboFHjtWLZYD3btWX7QrDn6ps7y2cpH235gAA8nM0CiIN7Ydnx1rlsuBSy9K+8woTvnaU3/S/pskgd+iP/5+tvzs1h/LrbfcTKPAIAoxCiB1RwUjDCi9bKtXoIwCBH/QwoXpqQeLAkULtm71Al11Rzy4LYnSBVfqGM2du80JAOfJD3/4kjvCYOxYkU6dvDSFTU6kjW2rDC36oGoUHD4sMm2ad+cbQW3wc4e37d69W+bPny/Lli1z/22SBaNgxQqRc8/1lpUZsH79evf4Pfjg9uptl156SH72s02ycuXKFLaUqPiDmr2FUyHqFdWIurIaBYB1Czx2/+Ye+bDpJ9Iu4vcN+6729SbAQDIzNArijW3H550bh6d93iMNG8qb99yufX2SwG8Qfosm3jaWRoGBFGoUKKkLJ44uSJdt848ro0ApbBRMmDDBWfZW4I4xhknfdpu7mLItqQobBeoYdeq0wD1H+vXrJ02aPCGNGv1L7rnnAzdgPPXUfzuvOdPd1tGJsrt27SoHDSv6oIwCpBVMmSLSqxfqLFQ5n/vz1Z/7vPOukl/84gN322c/+7Y0bdpOrrnmGmf9edKtWzc5otynBKpibxO3HoWK9dHuX/3qQ9K2bVvnGA2X+vW3yU03/cbddu65r0uDBt90+kijaurVqyfDMMynBMJ3AFMO0hVMNYhCZTcKAOsWeNiWgsBAMjM0CuKNTceHKQe1g98e/Abht4hGgZlEZRRAuLvCVAS9cNGtAi3TVZtRsGfPHtcQQtACM2DNGpFmzbzCfOFtSVbQKFDH6KabdkhlZaV7nObP94r9nXXW2TJ9+nRZt26dHHXUUhk9+r/+v0I9gw7uNpMU7r/e514uI0ce8NfUfO7Dhw/LMcf8Vb71rZf9LSLt27eXOchjSagqBv1W+vb1zifQocMBJ/ifKE8//bZbzPKqqw44x/N/5JFH3pHzzvNGm2AWCWjx4sXSsmVLt/8UW+i/NAnSVYxUOyOMAsC6BfalIDCQzAyNgnhjy/FhykHt4DcHvz3qd4hGgZlEaRRAuOOCgDHf4k5xFgItG+oV1GYU4G7mqFGj3GDvxBO/IU2aYCi995rgNhoFnlGAGgXqGG3fvt1Z9ooWzJiBoBA1C/rIuHHj3G1XXPGq9O/vbnaltpmk8zb3l3vv9Rcc4XNfeulO53wX2bxZ3G3qcyPdoGnThe42ylPF2FvdUQQKGEjt2x90jTaYAkg3qKh4Qi6++KC7DWkI2LZ//373nCpFSg/rEqRLjSIoRvFeY4wCRdLrFtiUgsBAMjM0CuKNLceHKQfp4DcGvzXh3x8aBWYStVGgBLOAowvSBbPA9HoFtRkFatj4Aw8sl499bL8sWOAV4gMHDx5x89MXOi+mUTDALVTYqJGkHCOAY4RaDh//+EdyzDGfc+/KY9aD9u09AwHaunWrO/oA20wSZj3A7AwwBaBdu7wpHjFCArMeHH10zT7dc8/vnH18W3r2vE8aNGjgjjSZNGmS9w8TquD0iBDMAtQowOiLadOmuakbyhxS26CxY8dKL+RyFFlIjcJnZF2CGsEkKLRgYSYZZxSApNctsCUFgYFkZmgUxBsbjg9TDtJR9Qh0vz00CsykWEYBpKpC0yyokQ31CmozCpS+8pX/hL/2XIYPp1EAwShA9frajtGOHTvkE5/4kRNIvy89eog0bixy443ev8W2Vq1ayW2q6INBgsk1dapIw4YiPXt6zxMnetu8fRojxxzzgbutfv1DUq/eWDcAhlCwr0mTJu4Q+qSqNqNg586dMmXKFNcM6NSpk5teoLahTkVD50CvQQ5LkYX2ZcpBjWAOFGMUQVBGGgUgyXULbElBYCCZGRoF8cb048OUg3SC9Qh00Cgwk2IaBUoYXVDsCy6bhPQDk4f3ho2CsDKZATQKalIPdELA16xZM5mMMeUhZdpmgsI1CpR0n3vq1Klyxhln+EueBg4c6JJUhY0CnXr06OGOIFCaPXu2W/eh2EKfNfk7qZQKFiwsdvqcsUYBSHLdAhtSEBhIZoZGQbwx/fgw5SCVcD0CHTQKzKQURgFUjEJQNgtBl0n1CoIeTkVVjVGgu06mUZCulOM3sybgCh4/1CjAXfW5qrBDQJm2lVMp+7W8xihQ+1Xb58Zy2CgYNGiQS1IVNgo2b94s9waLPjjq37+/a6wq9e3bt+i1KmwqtFpsqVFwxUo1CMtoo0CR1LoFpqcgMJDMDI2CeGPy8WHKQQ211SPQQaPATEplFEDBOzVJl0pBMKVeAb7GVFCojAIEg841c5poFKQr5fj5RkHw+G3bts3N01+wYIEcOnSoGuSnZ9pWbqXsl28UqP3K9Lnx3LRpU3cbtHv3brdq/7Jly9zlJEgdp+rj5xsFav2MGRulfv36rmEA7dq1S5o3b+4WglTCSA2cU8WSDalQpVI5Rr5ZYRSAJNYtMD0FgYFkZmgUxBtTjw9TDmrIVI9AB40CMymlUaCkLsiSProAJkFdw/xLJQQv+DrDNTKMAhXM6JqIRkG6Uo7fzAFpx2/kyJHOdidUDDF8+PCM28qtlP1ajqHYNftV1+deuXKlW2/hggsukMaNG8uECRPc9UmSOl7u8XMewWUIKRqoQdCzZ0/3eaIq+uAIxUNxPFHDoFhCukHS6xIoAzs4kqNUssYoAEmsW2ByCgIDyczQKIg3ph4fphx41FWPQAeNAjMph1EA4a4NLoKTbhaYVK9ABYUwCmozCajaVX38ZsIIi8/xq96v5Zgijv0iVylzAEZB0CQot1iXoPwpcVYZBSCJdQtMTUFgIJkZGgXxxsTjw5QDj2zqEeigUWAm5TIKINzJwV2cUhSNMlnIDy5nCkLoK801Ciq6w8ipWUfVruBxcnEC6vA6GxXeh7jsV6mUcqxaV6Uu+5RLGEVgymimckiNIih3kV3rjAJFkuoWmJqCwEAyMzQK4o1px4cpB7nVI9BBo8BMymkUKMEsSPLoAlPqFVTf+Rww0zULTKmfYIvc47fc6ct+QG3KneNCVd0vZjr75hCX/SqV3PO7yusTpowoSHJdglIXLMwka40CkKS6BSamIDCQzAyNgnhj2vFJespBrvUIdNAoMBMTjAIIF2+4w5NUs6Dcd/hUMIjDj684mAQYKk2zIDspkwAPHD8su8fR8sMX7hfu/i3vTrMgSykTEOe36hflNgvQhkmtSwBzoNyjCIKy2igASapbYFoKAgPJzNAoiDcmHZ+kpxzkU49AB40CMzHFKFAqR+VpU4R6BeWaMlEFgxC+4iAEORhZkPRiZ9kId4xV21UfP98ssFm6fuGaBSh46Tyo2hU224L9olxmAc5ltF/SFJxxx6Q0N+uNApCUugUZUxCeLn0KAgPJzCTdKNj24gZ5fO5WmXzndvnRqNdl4HfelMt7vSNdu+yXr/bZKzdcv0sm3rrDCfCqZPUzL2j/D5MxpX2TnnKQbz0CHTQKzMQ0owAqd4Gpcqpc9QqChxpfc0oIBvGZaBbopY5P0OBJOX6Wx9K19QvsrwnpMqYKxyV8fILHrxxSnylpMinVIKxYGAWKJNQtMCkFgUZBZpJoFCxf8m+58+evyte+uldOb/1++J/XyjHHfCRdLtgvN1y3Sx76v22yZdNG7f9vEqa0b1JTDgqtR6CDRoGZmGgUQME7QEmSGqps0t1amgV6qbZK6nHBftMsSJcyUUw6h6Ek9lXTR6jFyigASahbYEoKAo2CzCTJKHjm6c3uKIGj630U/id50fq0Q+5IBN17mYIJ7bv7gWSmHERRj0AHjQIzMdUoUFIXekkaXYCLeQTmpglDlsuVGmGakm4SKMEkYC2LGplqEiStLoEymvH7YbJiZxSAuNctMCUFQRcovXPDcO1rk8jen4xOOz62GwXh9l385y3y/aFvyCktPgi/NBJ6XLRPfnn3KynvaQp7f/LDtA9cyvZNaspBVPUIdNAoMBPTjQIId4OSlopgalCOz4VHksXgOFU0TTzhvDDR4EO7JOmctSl1LZZGAYh73QITUhD2fW9g2vt/8KnTZdfcB7WvTxI7Vi+VD5s1TTs+736nn/b1JvL2D65P+/wfnvgJd9+w/Z67tsvxx38YfklRuLDbu7J29aa0z1guXn9ijhxqf0baB93/jT7a1xeDJKYcRFmPQAeNAjOxwSiAcIcId4dMK0ZVTCHoMDEYNTUgKoUQdCEopkmQKpgFSU5P8ewz84JxtAtMLdNGOBRDahSBTcVwY2sUKOJct6DcKQjvXD8s7f3BwW5dEm0WIJCGYaI7Njhmun9jInudIEm3D++ffrqMHbhRKiujSTPIlk7n75cZv63SftZSApNAdycfvPvd0gz5rzXloETvX2qKUY9AB40CM7HFKFCCWZCU0QXqTm34In/TJpF580S2bvVXlEEIiuoaYr1okf+HRqtXi+zc6S9YIBzzvvN+J81XfTVln3fvFlm5MpW9e/2NCROOSyazYP16r99u2eKv8GX7McQ+D1r927T+rM7TVav8FWWQag/d+YbjPn++yLJl/gqLZXLBwkyKvVEA4lq3oPYUhAtKk4Lw4nPy0bEN0t4fIFBWd56TRCaT4KPKY2T7C2u0/85IMrTv5orPSvOK13Wbisqn2rwvt098Vf95S0DG9j2mNO2btJSDYtUj0EGjwExsMwogXBTizlESzAKkHwTvVI4aJdKsmUi/fiLt2mGYrb+hDFL52Lq76xMmiLRo4S+EhACqstILomzQiBEiJ7R+W47r90c5o+Mh6epcGh486G2bNEmkfn2RRo1qWLzY25ZUob+G02bGjBFp2xZGn0ibNiITJ/obHNl6DJUx8v1N96X1Z/QZJ251z9OOHSWlz5RK3hiHAdrzbeFC73vkmmtEzjtPpFs3kSNH/I2WCeaATaMIgkqEUQDiWreg3CkIu343Qw6d6VwJhN4fJG1kQaY7zR+0/ZS8Mes32n9nMpnad1HFZdKp4m+6TUXnjp+X3iyos33/rzTtm6SUg2LWI9BBo8BMbDQKlEyvaB2VEIwg8Fqzxrvg377dW4/AA8EI1pdL4aH4e/Z4wSCCPZ1RcOiQFzi1amWHUbBunUi9ysPSZc+V/hqRDh1Epk/3/u7bV+S++7y/qRqpIBXauNHrt+gbEO5s16vn3dGGbDyGyiSYfmh2Wn9GnwnuLxTsM6UQzkt8Pt35dviwZxKsWOEtQ+3bi8yZ4y9YouDMOLamoyXGKABxrVtQ7hQEmAEwBcKfASTFLMgUROJO765HHtD+OxtA++3vkj5yBZTLLDj55A/kl/eUrsihKe2bpJSDYtcj0EGjwExsNgogmwpX5SsEJQjGfzRrs1x1lb/SF+5Yjh7tL5RJMAlUcb9hw7xRDwg6dEbByJEi48aJ9O5th1Hwte03yZeX3OkveerTx9sHCKM6MHQbQS+CMqpGMAoQrOJONe5qKyGAxs/rjh3esm3HUJ2PCMZ1/RlG3pIl3t9KwT5TbKnPh2fd50O6AUYR2CyYw/jety3VIKxEGQWKuNUtKHsKgkOSzYI4mwSKu776Z9cU0O1jucyCDmcdkMce2ab9vFFiSvsmJeWgVPUIdNAoMBPbjQIoeGcprsJF/0mPXuveHQwKAUD//v5CGaWCkxlHZrnLGNocNgpwB/Pcc72/bTAKEOSGh9CjLgTuFuOuMe7M4s447sbiDi3+HjTIfyHlCmaBClpxvKZN8+5wq6DZtmMYNMWy7c/BPlMK4ZjDxKjt882Y4Y3iGDJEpEEDb/QP0j9sUZxGkiXSKABxq1tgwiwISTQLkmASYHYD7BLMANPMgnafOSgvbNyo/dxRYFL7JiHloJT1CHTQKDCTOBgFSuoCMq6jC3645w6pbLrfvWOPIODuu0WaN/dGFZggBIMIrhGkhI0CFKfDnWNVyC5bo2BRhoqIq1evlp0RVkRU7xXcj02bNjmfc54T7G1174BjCPdtt7kvk5df9u4U4xnC9pYtRaZO9ZYpTziOMAv+sPNZmTJFpFcv55qnkzeyINdjuH79erc9toQrIgaUqc/kovB7qf2ASaD685w5z7t9sLb+HO4zxZaX8DEg4/mGUQaoCQHTBkKRySZNzK8LoQxhfM/HRYk1CkDc6haUOwUBJMksSIJJsPjPW+Sz7Q5W75qJZsEN1+/SfvZCMal9k5ByUOp6BDpoFJhJnIwCSA1JjatZ8MVN35YvXf2mW3wMo25x0W/SHVgVZN+4cEmKUYDPiLuYMBAAhj6PHesFKbVpwoQJzv+hyV9whAC+srLSDeSikHovNTICQeGoUaOkWbNm0q9fPznttK9Kw4bvyeTJ/j+oRShihwJxVKqCQTbUo4fX/jrVdgzHjBkjbdu2dQPFNm3ayMRgRURfmfpMLgq/18UrL3Y/P/oHhP58+eXvOAH3Vc5+PKvtz6gdglESdfWZqKSOMZTpfIMJc8YZ7suqNXCgh6mKa4pZoo0CEKe6BSakIIAkmAVJMAnA94e+kbaLppkFp5/+viyY96L28+eLSe2bhJSDctQj0EGjwEziZhRAuPOEC3ybi1zptG+fyLzVO91gQAVcV1zhDSU2TZcsvEsatXjXX/KCFNzVVCCAwrBoXRC1Z88et/0aNWqkDfoOHTokHTt2lFatWhVsFATf6+TzT3aPLQKuNU6UByNi+/btbr55kyYfOZ95qLteCUPKwwXqMJzbhFQQk7R5s8i999YM28fxxTFyDnvWx3DjRkwbXem2F4S7+PXq1ZPdfkXEuvpMLgq/18h3R0rFyxXyz7f+6S5DN9/8oRx//Epp0GCpfOELr6f1Z6/PiMyd6y2XQkEjI9P5hs8UNgpgLJhkOCqpUQRxLVqbeKNAEZe6BW4KwonlTUEAcTYLkmIS/OGxl5wfsw90u2mcWfDd77yp3Yd8MK1945xyUM56BDpoFJhJHI0CJQQOcboLhSJpGDJ81445blDw7LNeMGLinPO4i3lsi7fdYdA6IXipLcYfNmyYezd/zpw52qBv5MiRMm7cOOf/6F2wUaDea/zy8dW559CsWbPkqquukm3bvBzuBQtwl3uA/OAHY9yCe8itx91ZtIcq1Idh5kgFSfr0iGFh1gMcJxgGCGRP3fUFadz8gFtUL9tjeOTIEXcUiRKCeJzbO/APHNXVZ3JR8L3Qf7t80CXlvaBwHwz252CfQV9RoM8US/icMGBqU/Dz4bM0bep9PgheC9I9UFDSJMEYgEFge8HCTKJRECAudQtMSEEAcTQLkmISgK/22avbzWpMMguOrveRPL1ss3Y/csG09o1zykG56xHooFFgJnE2CiBcbOKOVFwuNpHjjSCkdfcqadzqHeMu7pVgFCBeQwCDVISwMhkFCNSghc5/Eg76VqxYIef6FdqiMArwXgiwTj5wspzY50R/rcijjz7qjlpAakfoJ8Jl+HDvdZjWD+2BofR4LtUwc9uE4e4NG4r07ClybMOP5PSJ91cHtrkcw8NOtD1t2jS3bRCoK2XqM/mq/0f9pfOWzmnvpeuDwf5cV5+JWjiOtRlySuHzbeVKr3bCBReING6MlA1/gyHC93USpr6lURAiDnULTElBAHEyC5JkEixb8m/5xCcO63Y1BZPMgqHf263dl2wxrX3jnHJgQj0CHTQKzCTuRoESRhfE7cITAbi6A26yEMQEh0Vnq3DQt3fvXmnXrl11cbkojALMaoDPNmPZjJT3wh3rpk2bunepERjefffd0rx5c7deAVW40BdUwchchJSDKVOmSK9evaRTp07V6QFKURkF+GxIOQi/VzH6YCHC+Y/+GxcFZ7CJU9pYbaJRoCEOdQtMSUEAcTALkmQSgDt//qpuV7WYYhZ84dz35OWX9PtTFya2b1xTDkypR6CDRoGZJMUogJCCEKdUBARb+QTg5ZAKyHMxNsJB36BBg6Rv377uenDeeefJ2LFjBdXp85H6TDh+ugATw8+vvvpq6datm3uHE8PN8Rmo6AQTKTwFZbbq0aOH2/5BFWoUKAMj/JnUe0XdBwsV+m+uZoupgokbx4KFmUSjIAO21y0wJQUB2GwWJM0kAF/7aua0gzCmmAW/f/Ql7f5kwsT2jWPKgWn1CHTQKIgW3G1ZsGCBLF26NG0bApz58+fL2rVr07aFSZJRAAXvWMVBCBIQ2NggfNawWYCvX6XwDcRw0IeADHdwFZiRAEPAJ0+eLLoZ8XDT99Zba+avD75XOCUi/F779u1zp18M6oorrpAZJlaOtFxoCzyUdH1i8+bNci8qIgbUv39/d6RQUGjHk0662B1mr6YGDAqlB7ANRRShlPdyHuifE3dOrPW9MvXBUgvHLC4mQRxHfGUjGgV1YHPdApNSEICNZkESTYJ//n2T82X4vm6XM2KCWTB61OvafaoNE9s3U8rBTktTDkysR6CDRkF04GL4/PPPl+uuu8694/mNb3zDHRKLbcit7ty5s1x//fVy4YUXyh133JH274MkzShQUhemcbh7hYAh37uypRZMgmDRQHwFQwgIneZIUTh4DwuBGoZ9I786/DLEbSiKh///1FO9iu7qvXC8KmamB5jB98JsB/Xr168uYPfss89KkyZN3POMil5oD2Xc6PoEZiJAe8AwgHbt2uWmgsAQDeob33hJjj76P875LdKmjUhwBsVRo0ROPFHkuOO8PoGyJdXv5ZsECLyzfS9I9cFSC58zaK7YKmXchg2fpIhGQRbYXLfApBQEYJNZkESTADw+d6tul7Oi3GbBhd3e1e6TDlPbN24pB6bWI9BBoyAa3nrrLdckWLZsWfW6yy67TObOnetuO/vss+W5555z1+MiDIW4NmzYUP3aMEk1CqA4DXVFkJXLsP5yCkFZRZUXlOFrWAWE4Waoyyi49NK+0qPHK24BvODLUNcOlfRRbR//N97j6KO9ZxynCidCzOa9kJ+O6fYQyGAqRpxzVHHkthMK2DmPitZOD9H0ialTp0pD5/e6Z8+e7vPEoAvgyJtd4YgT1Ld3l3fuFKlXz6vqj1ktKyu9mUPwf592Wo2RFDavoLreS6kcRoF7/jgPPNsspPMkLdUgLBoFWWJz3QKTUhCADWZBUk0CMHnSdt1uZ005zYJ27Q5q9ymMqe0bt5QDk+sR6KBREA1IN8AoAt22J554wh1FEFw3dOhQuf/++1PWBUmyUQDBTMHdLNuLZ6k7orYED26ws9wJ2p2vYZ1JkI2GDfPuEs+Zk24UIEDENHXQiy/6X/d4vwEz83ovqvhyzQKnfWAi5dMn0O6BGRQFdQ7R7hgUMmuWyFVX+Rsc4b0wssDtF8772WKyQWrkg61SowiSmGoQFo2CHLGxboFpKQjAZLMgySYB+NEPd+p2PSfKZRZgpob/vLhBu18KU9s3TikHNtQj0EGjIBoefPBBufbaa2X06NHSoUMHdwTBr371K3fbww8/LIMHD055PQqw4bXBdUFwoRIkqYJZYPvdLaQfmD4cOeUruHWVO+Q8TLaPbx/5jvt8ycK75NgWb/trvUfnaU7A2fJVqbhyvlR86iWpuGSJF4QG39+BKr/CbeK2U559Ao/+hwe67f+Jjtul47jH3XUXPvprdznl/+34vFR0WeX1w8D7myy1j7YKxgAMgrhMV5uPwr+5wd9jGgVZYGPdAtNSEICJZkHSTQLw3e+8qdv9nCmXWfD3Z17Q7hcwuX3jknJgSz0CHTQKouGWW26RM8880w1osYyChSim9Ze//EVmz54tQ4YMSXk9pncDwXVBkmwOhIWLWNzpsvkiFkPrbahXgDu6uGusvpILubG4cGF6jQLMYtipkzdHP+bnb9Agmveiiqeo+gRSDqZMEenVy+sDGFkAmjb1RqCsWCHyk594o07wPnhPG/oERhEEC3DaJnyvchRBqmgU5ImNdQtMS0EAJpkFNAk8rvift3WHIC/KYRYsePxF7X6Z3L5xSTmwqR6BDhoF0fDAAw+4ebPBdRg1AFDIENN3hbfdfPPNKeuC0ChIl80VuFUKgslDqVVAiMEb+DrGMp7zPdxhowA151DI7vDhmvdq3z6a96KKo6j7hBJMIjWDItISrr5a5LzzRE44QeTrX695L7y3yX3CTdWxtC6BSjWwPb2rGKJRUAC21S0wMQUBmGAW0CSooWuX/brDkDelNgv+8Fj6FIkmt29cUg5sq0egg0ZBNKBwVtgoUKMGMFVily5dUrbBOICBEFwXhEaBXhixYWsqgjILTJUKCCF8JUMqMMxHYaMAMxiqfHT1XqhnEMV7UcVRFH0CkxSEZjWU/v1h/GG6SxHMdon/U5kCV1yR+l4mmwVIN7CxLgHM1qQXLMwkGgURYFPdAhNTEEA5zQKaBKn0/cZbukNREKU0C5Y9+e+U/TG9fW1PObC1HoEOGgXR8Oabb8p5553nFi7EMu7QdO3a1TUJMHUbjAJUcMc2zH6AOgbbtm1L+T+C0CioXcE7YbbJ5HoFwZgBX8tKCNbyUdgoWLfOSzVA4Ij3woyGakSBUr7vRRVHUfQJb9YDr92hXbu8mQ0wwgSzHWCbP9ulPPusSJMmqe9lqmytS2DzyKxSiUZBRNhUt8DEFASQySz4oF1b93PvufPWSHnnhu/LofZnaN8ziSYBGDH8Dd3hKJhMZsGGig5ya8VYGVAxsyAGHT1TXr3lNmva1/aUA5vrEeigURAdK1ascGc3+OpXvyrnnHOOTJ48uXobDIPOnTvLNddc427DtInBfxuGRkHdUhe8tt0VQz6zzdXRs5WuRsG0aSKNG3tDz/F8443+BirWmjpVpGFDkZ49vefgrIaoW4CpNOH7tWolYsNslzh/TR4dpJMyWPG9SWUWjYIIsaVugakpCGDH6qXywadOT/tspQafAZ9F9xnjzq3jd+gOSSQ0r3hdNld8Vr+xhJjQvranHNhej0AHjQIzoVGQnWwcQmtDvQKKomoXzl+b6hKgYCFTDbIXjYKIsaVugakpCKDcZkFSRxIofnPfy7rDEhmZRhaUAlNMIJtTDuJQj0AHjQIzoVGQvXCnDHfJcLfMluG0MAlsuyNJUZRddQnUKAKmGuQmGgVFwoa6BaamIAAEch82Tx+SXWw+aPupRJsEALMGHHPMR7rDExkwC9ZWnKvfWEQ+bN7MCJPA1pSDONUj0EGjwExoFOQumAU23TVDvQIbpkykKMoTDAJb6hLAGIBBYPO0suUSjYIiYnrdApNTEMCOZ5fIR8GJhYvMR8ccI6+tLP9+m0CXC6Kd+UBHy4pX5b2KhvqNRcCU9rU15SBu9Qh00CgwExoF+QkXx7iDZsvFMeoVMAWBosyXTaOA8P3HUQT5i0ZBkTG9boHJKQhg+wtr5O0fjJB9QwemFCGMkn3XDpF3bhjmvpfuMySRG67bFe4SReHjFQfl5oqJcnvFaG1xwly4/oTpsvXmica3r40pB3GsR6CDRoGZ0CgoTLZU9lb1Cmych52ikiScp6anHARnhMHfVH6iUVACTK9bYHIKAikPD/3ftnCXMJ4rr3hbuy8mYWPKQVzrEeigUWAmNAoKF1IQbEhFsLGCOkUlSTbUJbDl+84G0SgoIabWLTA9BYGUh9anHQp3CaO5567t2v0wBdtSDuJej0AHjQIzoVEQjYJ32EwWAhHWK6Ao82RDXQJbRlDZIhoFJcbUugWmpyCQ0nPbhOJNkxg1Xbvsl3+u2aTdD1OwKeUgCfUIdNAoMBMaBdFKXUibfLctinoFixYt8v+q0aZNm2TevHmyatUqfw1FUXVp/fr1Mm3xNKlwHuHUoC1btrjn1Lp16/w15ZEyQvH9RkUnGgVlwNS6BUxBIEH+9fy/pMdF+8Jdwkgm3fGqdh9MwaaUg6TUI9BBo8BMaBREL9xtM3lobqH1CiZMmCAtWrTwlzyNGDHCNUj69esnHTt2lK5du8rBgwf9rRRF6TRmzBhp27atHPfmcXLSqJNk4sSJ/haRyZMnS/Pmzd1zql27djJo0CB/S2mFgoVMNSiOaBSUCRPrFjAFgYT55d2vhLuDcfS67B15cfMG7ec3AZtSDpJUj0AHjQIzoVFQHOEOHO6+4S6cicN0kX6Q6zDnPXv2uPvUqFGjFKMAdzsrKyvd7UodOnSQ6dOn+0sURYW1ceNG97z55vvfdM/HnTt3Sr169WT37t1y5MgRqV+/vvsaaO/eve5yKUcWqFEETDUonmgUlBnT6hYwBYGEubDbu+HuYAxH1/tI/vDYS9rPbQq1pRzsMSjlIIn1CHTQKDATGgXFFQJrU+/GIQUhl3oFw4YNk1GjRsmcOXNSjILt27fLkiVL/CVPffr0kXHjxvlLFEWFBTPgZ6/9zD0PIRht+K7YsWOHuw2mwbZt29xthw4dck2F1atXu8vFFowBGAS2TP9qq2gUGIBpdQuYgkCCPDH/RencaX+4SxjBD256XfuZTcGGlIOk1iPQQaPATGgUFF+46MadOdMuulUKQrb1ChC8QAsXLkxLPQhq69atblBTyrufFGWbcP6hLsFLH74k06ZNc1N2guYa1mFkztixY6VTp05uek8phO8pjiIojWgUGIJJdQuYgkDCzPhtlXyqzfvhLlFW+v3vHtmyaaP285qADSkHSa5HoINGgZnQKCidVKFDky7AlVmQizIZBbgb2qpVK7ntttv8NRRF6aSmQkTKwZQpU6RXr16uIaBSeFCbAMv33Xef9O7dWy655BLZv3+/u60YCs7cgr+p4otGgUGYVLeAKQgkzB0TX5VGjT4Md4mycMnF++Tppf/Wfk5TMD3lIOn1CHTQKDATGgWlFVIQTEtFyLVeQW1GwZo1a6RZs2ZuETaKomoXzjfdOdejRw93BMH8+fOlTZs2cvjwYX+Lc212ySVFG5Vk4vdSEkSjwEBMqVvAFAQS5o6fv+rWBQh1i5Jy5hkHZO1qs6dCNDnlgPUIaodGgZnQKCi9gnfuTFHF8u7u3U33b+crVUl3Y1FnFKBGQZMmTWTu3Ln+Gqpc0sxcKbt3ixN8iixb5q+gSi51XuE8wyiezZs3y7333uut9NW/f3935NGMGTPkqquu8td6Qo0QbI9aJo50SopoFBiKCXULmIJAdPzynlekw1kHwt2iJCDdwPSRBCanHLAeQWZoFJgJjYLySV2gm3AXr6J1lVRUefUK8LUKwSRwPl6awkYBCq5hJoQFCxa4RdcUwbuhVGk0YYI4beMv+HKaS5o1E7nmGpHzzhPp1g31JvyNVNGkzh8Vf6vzCiYBzrcZMza6MxnAMIB27drlToeI0QSo79GgQYPqbZj1oH379q6BEJWUYYnvIao8olFgMCbULWAKAtHx2CPb5OIe+8LdomiccMKHbuFCk2sSKExNOWA9grqhUWAmNArKK9zFM2HIL4Kaiu7LvSDG+WpVQY7uY4WNgpEjR7r7EGb4cOf7miqJkNaOeK9Ro1SjAF4NTIIVK/wVjpx4U+bM8ReooipoFjinhJtuUDFgZrV5MHXqVGnoXMP07NnTfZ44caK3wRGKGTZu3NhNR8DzjTfe6G8pXEhhwDnKVIPyikaB4ZhQt4ApCETHCxs3yg3X75KGxx4Jd49I+eIX3jN+CkSFl3LQPG0nyp1ywHoE2UGjwExoFJRfuLOHu3q4u1fO4b+uWYAAwq16rjcJKDM1bJjIqFGeARA0CpBugFEEVPmkzAIYBBUzB1SbBOWQGkXAVAMzRKPAEspZt4ApCCQTCx5/Ub478E1p2vRwuIsURNcu+2XS7a/Kiy9s0L6vaZiYcsB6BLlBo8BMaBSYI5gF5bjLF/padadsQxpCED4Mfxxp4z6fvPA7Uq/FLrVWms4YJQ37/kkaDXlEPtbgoBzV6D1pMuln1dv5KN4jfA65hM61UgrGAAwC06ZpTbJoFFhEOesWMAWB1MXTyzbLkEG7pfVph8LdJGsqKz9yUxruuWu7bHvRDoNAYVrKAesR5A6NAjOhUWCWcDFfrrzh6jufql4BVlBWCfUIgiMKRo4UqV8fw9i95fXrRZo0EVm82Fumii/3vIJxMGCme36V40a+qofCUQRmiUaBZZSzbgFTEEi2LHvy3/KjUa/Lhd3elZYtD7kGQKjruJx00mF3FoO+X3/L6dtV1oweCFNbysG7ZUo5YD2C/KBRYCY0CsxUqS/slUmAwQz4ikVldtcsYFxhlcJGwdSpImec4S/4GjjQgyq+3PNq+QDvfHLOK3Weleq8Cs6wgr8ps0SjwELKVbeg9hSEzkxBIHWycd2/5MlFW9x6A888vVn7GhsxLeWA9Qjyh0aBmdAoMFdIQShVKoIyCSB8zUJu4TUOU7ZKYaMAs1WGjYJBgzyo4so1BcbPdM8jSJ1XpTILSvn9QeUnGgUWU466BUxBICQVU1IOWI+gcGgUmAmNArMVvCNYTAVjCXzVKjnv7E6ZSNmhsFFw6JBI06YiCxZ4y7t3i7RsKbJsmbdMFU9VzgMpB3iGgudVscVUAztEo8ByylG3QJeC8NHHK+Xdb/WVPXfeSkhi2DfwW3LkuIZp50OpUw5YjyAaaBSYCY0CO6Qu/Et9dzAc7FBmK2wUQCtXirRqJXLBBSKNG4tMmOBvoIoqnDdIOSillLFYjhonVO6iURADSl23YPsLa+SDT52eFhwRQirkcMsWsn3j37TnTjFgPYLooFFgJjQK7JGqWl5qs2C888DIAoqishPSDXDelFKYzQCpBhxFYI9oFMSEUtct2DX3QZGjjtIGSoQkmV2PPKA9Z4oB6xFEC40CM6FRYJdwxxB3C3HXsJQBAYyCUgc+FGWjMIqglMaaGkXAVAP7RKMgZpSybsH755ytDZQISSqH2p+hPVeihvUIigONAjOhUWCnYBaUslCZSkFgvQKKql04TyqcR6lSddQoI4wmoOwTjYIYUqq6Bduff4ajCghRfOxjsuOZJdpzJUpYj6B40CgwExoF9gpBQinzkZVZQFGUXkg5KFVdAlW3hKMI7BWNgphSqroFb947ST46tkFa0HSgRzdt8TdCbOdAz4vT+vtH9evLnkm3ac+RKGE9guJCo8BMaBTYr1IGDEg/UNO9URRVI5wXpTg3gjOh4G/KXtEoiDGlqlugmwXhSJMT5M37JmtfT4it7J5xn3z4yZPT+vu+QcWf5YD1CIoPjQIzoVEQD5VyznTkX5e6mjtFmSycD6UYbVPK85wqvmgUJIBi1y3YsWqxHOjeNS14OvilzvLaykXaf0OIbexYs1wOXHpRej8//wvy2lN/0v6bKGA9gtJBo8BMaBTER8E7jcUU6xVQVI3U+VDsugRMNYifaBQkhGLXLdj9m3vkw6afSAui9g37rvb1hNjGOzcOT+vfRxo2lDfvuV37+ihgPYLSQqPATGgUxE8qoCjmXUeYBKxXQFFeykExR9goA7BUtUio0olGQYIodt0CpiCQuFKOlAPWIyg9NArMhEZBPKWqoRfTLEC9Ak6ZSCVZxa7ZgdkMkGrAUQTxFI2ChFHMugVMQSBxpBwpB6xHUB5oFJgJjYL4CncicRcSdyOLFWigXgFTEKgkqpijatQoAqYaxFs0ChJKseoWMAWBxI1SphywHkF5oVFgJjQK4i+YBcUqgIa8bJgFxc7PpijTVKw6HWo0EEYTUPEWjYIEU6y6BUxBIHGhlCkHrEdQfmgUmAmNgmQIwUex8pxLVfGdokxRseoSqPoiHEWQDNEoSDjFqFvAFAQSB0qZcsB6BGZAo8BMaBQkS8UKRBA4sV4BlQTBIIi6LkFwxhL8TSVDNApIUeoWMAWB2E6pUg5Yj8AcaBSYCY2C5KlYc7GzXgEVdxVjKsRinY+U+aJRQKqJum4BUxCIrZQi5YD1CMyDRoGZ0ChIpoJ3MKNSMYIoijJJ6N9Rphww1SDZolFAUoiybgFTEIiNlCLlgPUIzIRGQTQgwFuxYkUaGzZsqH7Npk2bZP78+bJ27dqUf6uDRkGypQKVqO5mIv0AIwsoKm6KMr1GGXUsWJhs0SggaURZt4ApCMQ2ip1ywHoE5kKjIBrmzZsnZ599dgqf/exn5eabb3a3P/roo9K5c2e5/vrr5cILL5Q77rgj7f8IQqOAUlXWozILYBSwXgEVJ0VZlwDmAFINOIqAolFAtERZt4ApCMQWip1ywHoEZkOjoDj85S9/ka5du8orr7wib731lmscPPfcc+423LXq2LFjymiDMDQKKCWMLkD/6dKli0yfPt1fm7tUCgLrFVBxEPpzhfMoJKUG59OVV17pnl9MNaCUaBSQjERRt4ApCMQGiplywHoEdkCjIHpef/11N6hbtGiRu/zEE0+4owiCrxk6dKjcf//9KeuC4EIlCJVsnXrqqc5Xc4WcdNJJ/pr8pMwCirJdUUyFiPMJ51W7du38NVRSFf7NDf4e0yggaURRt4ApCMR0ipVywHoE9kCjIHqQVoC7wGr54YcflsGDB6e8ZuTIkTJ69OiUdUFoDlBB4c5nvXr15MYbb/TX5C+kH0Q9jRxFlVLov1H0YZxPMAoKGalDxU80CkhWRFG3gCkIxFSKlXLAegR2QaMgWt544w03reDZZ5+tXjd79mwZMmRIyutGjRrlElwXhEYBFRaGRkcl1CuIsko8RZVK6LdRjoqBUUBRQdEoIFlTaN2CjCkITzMFgZSHYqUcsB6BfdAoiJZHHnlELr/88pR1KGQ4aNCglHUYUaAKHeqgUUCFFaVRwHoFlI1S/baQugRh0SigwqJRQHKmkLoFTEEgphF1ygHrEdgLjYJoufbaa+XOO+9MWbd06VK3ZkFwHYwDGAjBdUFoFFBhRWkUQDAJWK+AsklR1CUIi0YBFRaNApIXhdQtYAoCMYXdD0SbcsB6BHZDoyBazj//fFm4cGHKur1797pGgVqP2Q86dOgg27ZtS3ldEBoFVFhRGwUQ6hVwykTKBhWrtgaNAiosGgUkb/KtW8AUBGICUaccsB6B/dAoiA4YArjAeOmll9K2YVRB586d5ZprrpFzzjlH5s6dm/aaIDQKqLCKYRRAqFfAFATKZEVdlyAoGgVUWDQKSEHkW7eAKQik3ESZcsB6BPGARoGZ0CigwiqWUYB8b5gFUeZ9U1SUKmY9DRoFVFg0Ckgk6IKnumAKAikXtaYcfDe/lAPdOUHsg0aBmdAooMIqllEAFfOOLUUVomLUJQiKRgEVFo0CEgm64Kkuak9BuIApCKRoFGOWA905QeyDRoGZ0CigwiqmUQAhIKutXsGiRf4fvnbvFlm5MpW9e/2NFBWRYBCougSrV4vs3On+Wa1t20TmzRNZv95fkYdoFFBh0SggkaALnrKBKQik1EQ9ywHQnRPEPmgUmAmNAiqsYhsFkK5ewYQJIi1a+Au+Jk0SqV9fpFGjGhYv9jdSVAQKToW4aZNIZaVnCig99JBI8+Yi/frh3BAZO9bfkKNoFFBh0SggkaALnrKFKQikVESdcqDQnRPEPmgUmAmNAiqsUhgFweBszx6RAQM8EyBsFPTtK3Lfff4CRRVB6IcYUXDokEjHjiKtWtUYBYcPe/0SBgKEES4NG4ps2eIt5yIaBVRYNApIJOiCp2xhCgIpBcVIOVDozgliHzQKzIRGARVWKYwCCOkHGFkwbJjIqFEic+akGwXt2oksW+YFaAjkKCpKBdNgRo4UGTdOpHfvGqNgwQJvFEFQffqI3Huvv5CDaBRQYdEoIJGgC55ygSkIpNgUI+VAoTsniH3QKDATGgVUWKUyCiAYBT894gVqCxemGgW4m1uvnkj79iLNmnl/Dxrkb6SoAhWsS7Bihci557p/phgFs2aJXHWV97fSwIEiQ4b4CzmIRgEVFo0CEgm64ClXmIJAikWxUg4UunOC2AeNAjOhUUCFVUqjQKUgoF5B2Ch4+WXv7i2eoR07RFq2FJk61VumqHyFflfhPPCM4pgYuaLSCYJGwfTpIldf7f2tBLMqH8OKRgEVFo0CEgm64ClXmIJAikExUw4UunOC2AeNAjOhUUCFVUqjAFJmwYyFu9JSD8IaMULkmmv8BYrKUxjJoqZCRNCPWhgwqsB553kFCzHDAQoZXnGF+7JqYUQB0mVyFY0CKiwaBSQSdMFTPrgpCCcyBYFERzFTDhS6c4LYB40CM6FRQIVVaqMAQp74JQvvSjEKtm717ugGhSHf/fv7CxSVh5BuoFIOIJgCGEWgQJoL0hAmT/bqY4TNKxgHMBByFY0CKiwaBSQSdMFTvjAFgURFsVMOFLpzgtgHjQIzoVFAhVUOowDqsHC0nNDiv/6Sd0cXUyOqivNIPcA0dZwekcpXGEWA0QSZFEw9OHLEMwow0gDauFGkQQORXbu85VxEo4AKi0YBiQRd8JQvTEEgUVCKlAOF7pwg9kGjwExoFFBhlcsoQOpBvRa73HoFSpgaEdPT9ejhPeMuL0XlI5XigudMChoFEEYVwKBCH2zc2JudIx/RKKDColFAIkEXPBUCUxBIoZQi5UChOyeIfdAoMBMaBVRY5TIKIJgECOYoKmoh3UDVJSiHaBRQYdEoIJGgC54KhSkIJF9KlXKg0J0TxD5oFJgJjQIqrHIaBRDqFai57SkqCqE/BesSlEM0CqiwaBSQSNAFT4XCFASSD5lSDnZGnHKg0J0TxD5oFJgJjQIqrHIbBRDyyIMpCBSVrzCKwIRRKjQKqLBoFJBI0AVPUcAUBJIrpUw5UOjOCWIfNArMhEYBFZYJRgHyyGEW1JVPTlF1CSaBCaYTjQIqLBoFJBJ0wVNUMAWBZEupUw4UunOC2AeNAjOhUUCFZYJRAJlyJ5iyV+WuSxAUjQIqLBoFJBJ0wVNUMAWBZEM5Ug4UunOC2AeNAjOhUUCFZYpRACHQY70CKh/BICh3XYKgaBRQYdEoIJGgC56ihCkIpC7KkXKg0J0TxD5oFJgJjQIqLJOMAoj1CqhcpWbPMCl1hUYBFRaNAhIJuuApapiCQGqjXCkHCt05QeyDRoGZ0CigwjLNKMh2/nuKUkJ/MSXlQIlGARUWjQISCbrgKWqYgkB0lDPlQKE7J4h90CgwExoFVFimGQUQgj6MLKCoumRSXYKgaBRQYdEoIJGgC56KAVMQSJhyphwodOcEsQ8aBWZCo4AKy0SjAIJRwHoFVCaZVpcgKBoFVFg0Ckgk6IKnYsEUBKIod8qBQndOEPugUWAmNAqosEw1CiBTprqjzBNSUyqch6kpKjQKqLBoFJBI0AVPxYIpCASYkHKg0J0TxD5oFJgJjQIqLJONAtYroGoTRpyYmHKgRKOACotGAYkEXfBUTJiCQExIOVDozgliHzQKzIRGARWWyUYBhPQDU4eXU+UR+oPpfYJGARUWjQISCbrgqdgwBSG5mJJyoNCdE8Q+aBSYCY0CKizTjQLI9LvHVOlkS6FLGgVUWDQKSCTogqdiwxSEZGJSyoFCd04Q+6BRYCY0CqiwbDAKVAoC6xUkWzalotAooMKiUUAiQRc8lQKmICQPk1IOFLpzgtgHjQIzoVFAhWWDUQDBJECQSCVXSDewZWQJjQIqLBoFJBJ0wVOpYApCcjAt5UChOyeIfdAoMBMaBVRYthgFEOoVcMrEZMq2WhU0CqiwaBSQSNAFT6WCKQjJwMSUA4XunCD2QaPATGgUUGHZZBRAyE9nCkKyhFEEto0moVFAhUWjgESCLngqJUxBiD8mphwodOcEsQ8aBWZCo4AKyzajAPnpMAs4ZWJyZGN9ChoFVFg0Ckgk6IKnUsMUhPhiasqBQndOEPugUWAmNAqosGwzCqBC7jBv2rRJ5s2bJ6tWrfLX1Gjbtm3utvXr1/trqHIK7dDjlR5y++u3+2tqlKkdTRCNAiosGgUkEnTBU6lhCkI8MTnlQKE7J4h90CgwExoFVFg2GgUQ8tVzrVcwYsQId3/79esnHTt2lK5du8rBgwfdbQ899JA0b97c3YbXjB071l1PlUdjxoyRk0efLJ/+66elTZs2MnHiRH9L5nY0RTQKqLBoFJBI0AVP5YApCPGjtpSDPQakHCh05wSxDxoFZkKjgArLVqMAyqVewbp166SyslL27NnjrxHp0KGDTJ8+XQ4fPiyNGjVy71JDu3fvlobOb+OWLVvcZaq02rhxo9S/tL60OtLKTTHZuXOn1KtXz22XTO1okmgUUGHRKCCRoAueygVTEOKD6SkHCt05QeyDRoGZ0CigwrLZKMhlXv3t27fLkiVL/CVPffr0kXHjxsmCBQvSjgO23Xvvvf4SVUodOXJETvngFDfFBIIpgMB7x44dGdvRJNEooMKiUUAiQRc8lQumIMQDG1IOFLpzgtgHjQIzoVFAhWWzUQAhmMTIgly1detW98407lDPmjVLrrrqKn+Lp4EDB8qQIUP8JaqUQloJ2hUjPaZNm+amF9RmBATb0STRKKDColFAIkEXPJUTpiDYjw0pBwrdOUHsg0aBmdAooMKy3SiAYBTkUq8Ad6ZbtWolt912m7uMYetXX321+7fSoEGDXKjSCgYBjAIIKQdTpkyRXr16SadOnVLSDaBwO5okGgVUWDQKSCTogqdywxQEe7El5UChOyeIfdAoMBMaBVRYcTAKoGzrFaxZs0aaNWsmkydP9td4hQyvuOIKf8kTRhQMGzbMX6JKIaSQVDgPXSpJjx49UgpM6trRJNEooMKiUUAiQRc8lRumINiJTSkHCt05QeyDRkG0bNiwQebPn+9eHIe3oQAbtq1duzZtWxgaBVRYcTEKwvUK8HOnVOXHnchtb9KkicydO9db4WvZsmXSokULf8kTjAMYCKYKdRfnzRMJzg64e7fIypXpmFaTcXnAzwm2E8wejCjYvHlzWn2I/v37y4AB3kiD2trRJNEooMKiUUAiQRc8mQBTEOzDppQDhe6cIPZBoyA6fv3rX0vnzp3l+uuvl0svvVRGjRpVve3RRx+t3nbhhRfKHXfckfJvw9AooMKKi1EAVYwfXz1sHT95EEwC7OK2bdvcmQ1QuPDQoUPVIA8exfNgFCxcuND9N6i636BBA9m1a5e7bJpGjPD2qV8/kY4dRbp2FcHsgIibnV1MoV49EdMGRqBtlFmg2gntVjHTazt31oP69V3DAEI7YOpKGKKZ2tEk0SigwqJRQCJBFzyZAlMQ7MFLOWie1l6mphwodOcEsQ8aBdGwd+9eOfPMM+W5555zl1999VV3GSML3nrrLTn77LOrt1U5ERGKfmH0QfD/CEKjgAorTkYBTIGK5d5dafzkKZNg5kyRkSNHOusq0hg+fLj7bzGqAMEohrg3btxY5syZ4643TajZV1mJmQD8FY46dECdBX8hoMWLRVq2TH2tCXLbyWkfmAV4dtsL7eZNcuBq6tSp7hSVPXv2dJ8nTpzorq+rHU0RPhNFBUWjgESCLngyBaYg2IGNKQcK3TlB7INGQTTAKPjsZz/rphdg+c0335SzzjpLVq1aJU888YQ7iiD4+qFDh8r999+fsi4IjQIqrDgZBZCb517VWiq6L682CeKk7dsx9N5f8NWnj0h4UoD9+0WQTbFokb/CMCmzoKK1lzIyc7mzIkaiUUCFRaOARIIueDIJpiCYj40pBwrdOUHsg0ZBdMx0Ip3LL7/cTSv4yle+4hb0wvqHH35YBg8enPJa3G0bPXp0yroguFAJQlFxMQpSfvK6L3eL4uEudZA4Ps7f+r9yVOUH8oV1gwJru8tpY2fLJ3qtDqwx4xFuE7edBngjQBRxEI0CCgr/5gZ/j2kUkLzQBU+mwRQEc6kt5eBdw1MOFLpzgtgHjYLoQP0BGASYwu073/mO9OvXT15//XWZPXu2O8978LWoXxCsYRCG5gAVVuxGFPjpBqhXgJz3YOG8uGnHDpFWrUTCswOiXkHDhpgZwF9hoNx2Wu7VJcBlStzaiUYBFRaNAhIJuuDJNGpPQejMFIQyYnPKgUJ3ThD7oFEQDSjYddFFF7n1CNQ6GAWTJk1yCxlinvfg6zGi4Oabb05ZF4RGARVW3GoUqHQD/Py5d7C7L4+lWQAToFkzEd3sgLNne3ULTJXbTuNnuikHaCeVhhCndqJRQIVFo4BEgi54MhGmIJiHzSkHCt05QeyDRkE0PPjgg2npBTACbrjhBlm6dKl06dIlZRuMAxgIwXVBaBRQYcXJKFAmAYSfQDVlIvLg4yTUKGjSxJvlQKe+fdNrFpgkd8RHVWtZ7jzQTpAyC+IiGgVUWDQKSCTogidT0aUgfPTxSnn3W31lz523khKyb+C35MhxDdPaw5aUA4XunCD2QaMgGjC7QYcOHeT55593lzHrwWWXXeYaCCh0CKMAU7phG2Y/wGsxfVjw/whCo4AKK05GQbBwIX4CIbeivhOUxkXO6e1Oe7hggcihQzUEZwfESAN/pkcjhXQDtIv7t99OEMyCuIhGARUWjQISCbrgyVS2v7BGPvjU6d43PTGOwy1byPaNf9O2nanozgliHzQKogPFDM855xy55ppr3Ofx48dXb8Oogs6dO1dvmzt3bsq/DUOjgAorbjUKdBrvP+KgkSO1P/eiZgc8csRb3rnTWzZNMAgGOI+4i0YBFRaNAhIJuuDJZHbNfVDkqKNSf7GIEex65AFtm5mM7pwg9kGjwExoFFBhJcEogFCvAEPdqfIJxx+pIEgJibtoFFBh0SggkaALnkzn/XPOTgtSSXk51P4MbVuZju6cIPZBo8BMaBRQYSXFKFD1CpIQpJoqHH+VchB30SigwqJRQCJBFzyZzvbnn+GoApP42MdkxzNLtG1lOrpzgtgHjQIzoVFAhZUUowBCkIqRBVTphXSDpJgEEI0CKiwaBSQSdMGTDbx57yT56NgGaUHrgR7dtMX3SOEc6Hlx2vH+qH592TPpNm0b2YDunCD2QaPATGgUUGElySiAYBTEpV6BLUpKXYKgaBRQYdEoIJGgC55sQTcLwpEmJ8ib903Wvp7kz+4Z98mHnzw57XjvG2TXLAdhdOcEsQ8aBWZCo4AKK2lGAcR6BaUTUj0qnEfSUj5oFFBh0SggkaALnmxhx6rFcqB717Tg9eCXOstrKxdp/w3JnR1rlsuBSy9KP87nf0Fee+pP2n9jC7pzgtgHjQIzoVFAhZVEo4D1CkonmDJJSjlQolFAhUWjgESCLniyid2/uUc+bPqJtCB237Dval9PcuedG4enHd8jDRvKm/fcrn29TejOCWIfNArMhEYBFVYSjQII6QdJGw5fauH4JvUY0yigwqJRQCJBFzzZBlMQikdcUw4UunOC2AeNAjOhUUCFlVSjAGK9guIp6YUjaRRQYdEoIJGgC55sgykIxSHOKQcK3TlB7INGgZnQKKDCSrJRoFIQWK8gWjG1A5dmzvUZRQVEo4BEgi54shGmIERPnFMOFLpzgtgHjQIzoVFAhZVkowCCSYCglopOSDdIYl2CoGgUUGHRKCCRoAuebIUpCNER95QDhe6cIPZBo8BMaBRQYSXdKIBYryA6JbkuQVA0CqiwaBSQSNAFT7bCFIRoSELKgUJ3ThD7oFFgJjQKqLBoFHjilImFC6MIODrDE40CKiwaBSQSdMGTzTAFoXCSkHKg0J0TxD5oFJgJjQIqLBoFnlivoHAlvS5BUDQKqLBoFJBI0AVPtsMUhPxJSsqBQndOEPugUWAmNAqosGgU1Ih3xPMX6xKkikYBFRaNAhIJuuDJdpiCkB9JSjlQ6M4JYh80CsyERgEVFo2CVKFeAadMzE0wCFiXIFU0CqiwaBSQSNAFT3GAKQi5k6SUA4XunCD2QaPATGgUUGHRKEgX6xVkLzVrBFMOUkWjgAqLRgGJBF3wFBeYgpA9SUs5UOjOCWIfNArMhEYBFRaNgnSpegUMfusWjhNTDtJFo4AKi0YBiQRd8BQXmIKQHUlMOVDozgliHzQKzIRGARUWjQK9EPxiZAFVu1iXoHbRKKDColFAIkEXPMUJpiDUTRJTDhS6c4LYB40CM6FRQIVFo6B2wShgvQK9WJcgs2gUUGHRKCCRoAue4gZTEGonqSkHCt05QeyDRoGZ0CigwqJRkFmsV5AupGRUOA+mZtQuGgVUWDQKSCTogqe4kTEF4enkpiAkOeVAoTsniH3QKDATGgVUWDQKMov1CtIF84QpB5lFo4AKi0YBiQRd8BRHmIKQTpJTDhS6c4LYB40CM6FRQIVFo6BuIf2Aw+w94TjwWNQtGgVUWDQKSCTogqe4whSEGnY/kOyUA4XunCD2QaPATGgUUGHRKMhOrFfAAo+5iEYBFRaNAhIJuuAprjAFwYMpBzXozgliHzQKzIRGARUWjYLspFIQklqvgCkYuYlGARUWjQISCbrgKc4wBYEpB0F05wSxDxoFZkKjgAqLRkH2UsFyEoV0A9YlyF40CqiwaBSQSNAFT3EnySkItaYcfDdZKQcK3TlB7INGgZnQKKDColGQm5JYr4B1CXIXjQIqLBoFJBJ0wVPcqT0F4YJYpyAw5SAd3TlB7INGgZnQKKDColGQu5I0ZSJGESR1FEUholFAhUWjgESCLnhKAklMQWDKQTq6c4LYB40CM6FRQIVFoyB31VavYNMmkXnzRFat8lcEtHu3yPz5IsuW+SssUYv1l8m0eW/Ili3+ioC2bfP2d/16fwVVLRoFVFg0Ckgk6IKnpJCkFASmHOjRnRPEPmgUmAmNAiosGgX5KXynfcQIHEuRfv1EOnYU6dpV5OBBb9vChSLNmolcc43IeeeJdOsmcuSIt81kfW7MAjm57T4ZMECkTRuRiRP9DY4eekikeXNvf7HfY8f6GyhXNAqosGgUkEjQBU9JISkpCEw5qB3dOUHsg0aBmdAooMKiUZC/UK8Aj3XrRCorRfbs8Tc46tBBZPp0kcOHPZNgxQp/g6P27UXmzPEXDNVtGx+XepWHq/dp506RevW8kRHYp0aNvBEUENY1bCjaUQdJFY0CKiwaBSQSdMFTkkhCCgJTDmpHd04Q+6BRYCY0CqiwaBQUJtQreGz732TJEn+Frz59RMaN89INMIrAJiGlovWRNtVGAATDAJcrO3aILFjgjSIICvt7773+AuUcK+dgUVRANApIJOiCp6QR5xQEphxkRndOEPugUWAmNAqosGgUFCZVrwDPSlu3eiMMMNJgxgyRvn1FhjiXNQ0aeHfiJ03yX2iosD9qKkSMHpg2zUungPEBzZolctVV3t9KAwd6+0h5olFAhUWjgESCLnhKGnFNQWDKQd3ozgliHzQKzIRGARUWjYLChaAaIwsg3HFv1UrkttvcRRk5UqR+fS/YhlD4r0kTkcWLvWXThGkQlUkAIeVgyhSRXr1EOnXyRhYgpeLqq/0X+Bo0yIPyRKOACotGAYkEXfCURNwUhBPjlYLAlIO60Z0TxD5oFJgJjQIqLBoF0QgB9uA197v1CCZP9lc6mjpV5Iwz/AVfuPsOTBMMAuxHberRwytaiEKGV1zhr/SF/Rk2zF+gnMs75xqPogKiUUAiQRc8JZU4pSAw5SA7dOcEsQ8aBWZCo4AKi0ZBNEKNgqObvCsT5v7LX+Np7tx0o8DEu+9InahwHiqFYvPm9JoD/fuLOwMCpnhs0cJf6QvGAQwEyhONAiosGgUkEnTBU1KJSwoCUw6yR3dOEPugUWAmNAqosGgUFK5t27zaAw8seENOO9RWXjz0shw65OX347lpU68AIIQZAlq29IJtkxSsSwBt3OilTMAwgHbt8qZDRHFGTO0IowDTPkJ4Leov4DWUJxoFVFg0Ckgk6IKnJBOHFASmHGSP7pwg9kGjwExoFFBh0SgoXKhDEPqJdxnu/PRDK1d6dQsuuECkcWORCRO89aYI6Qa6lAOkTWDaw549veeJE/0NjmB0wDhAOgL2yfTpHkstGgVUWDQKSCTogqekY3MKAlMOckN3ThD7oFFgJjQKqLBoFEQvFDYc7zxsULAQIxWdaBRQYdEoIJGgC56Sjq0pCJlSDnYy5UCL7pwg9kGjwExoFFBh0SiIXmrKxOXOw2TppnakohGNAiosGgUkEnTBE7EzBYEpB7mjOyeIfdAoiJbnnntO5s+fLxs3bkzbtmnTJnfb2rVr07aFoVFAhUWjoDhSQbjJQrpBsC4BFZ1oFFBh0SggkaALnoiHTSkITDnID905QeyDRkF03HrrrXL++efL9ddfL5deeqlMmjSpetujjz4qnTt3drddeOGFcscdd6T82zA0CqiwaBQUT0g/yDTdYDlVW10CKhrRKKDColFAIkEXPBEPW1IQmHKQP7pzgtgHjYJoePbZZ+Wss86SLVu2uMtvvPGGawhg/VtvvSVnn322O9oA26qqqqRjx46yYcOGlP8jCI0CKqgXXnhBGjVqJH/605/8NVTUQv6/aXft8XlMH+1gs3A+wSjA+UVRSjQKSCTogidSgw0pCEw5yB/dOUHsg0ZBNDz88MMyePDglHUYPXDbbbfJE0884ZoGwW1Dhw6V+++/P2VdEFyoBKGSra985SvOz1OFHH/88TJzJoegF0Mm1itgXYLiCGbt8uXL3fMJ5xXOLyrZCv/mBn+PaRSQvNAFTyQVk1MQmHJQGLpzgtgHjYJo+P3vfy+9e/dOWfed73xHbrjhBq2JMHLkSBk9enTKuiA0B6igkMaCgAYjUQYMGOD+jVQE/A3jAEEPVbhMuoPPugTRSRkD3bt3d8G5g2ecTziXcH5RlBKNAhIJuuCJpGJqCgJTDgpHd04Q+6BREA2vvPKKnHfeeW6dgmXLlslvfvOb6poEs2fPliFDhqS8ftSoUS7BdUFoFFBhffDBB/5fnhD8wCSAWYCgBwGPMg446iB/oV5BuadMhEHAugT5C+fG+PHjq88LZQzALAibauHziqJoFJBI0AVPJB0TUxCYclA4unOC2AeNguhADQIYAt/4xjfcO1S33HKLawagkOGgQYNSXosRBTfffHPKuiA0Cqh8pIyD4KgDBExYj+CJyk6oV1CuFAS8L+sSZC81WiBsDGCZI22ofESjgESCLngiekxKQWDKQTTozgliHzQKouG1116TVatWpayDOfDggw/K0qVLpUuXLmnbYCAE1wWhUUBFIXVnFcYBgqfgqAMGUbVL1SsoR30AvC9TDmqXGkkDUwCo0QI0BqioRKOARIIueCJ6TElBYMpBdOjOCWIfNAqiAbMdnHnmmbJ161Z3eeXKlXLuuefKq6++Knv37nWNgoULF7rbMPKgQ4cOsm3btpT/IwiNAqpYUqMO1N1XFWRhPVWjctQrYF2CVNU2WgD9F+tpDFDFEI0CEgm64InUjgkpCEw5iA7dOUHsg0ZBdKAuAaZB7Nu3rzvLAUYSqG34GzULrrnmGjnnnHNk7ty5Kf82DI0CqlRSgVgwXYGjDjwhcC9VvQLWJeBoAcoM0SggkTDt13fJ7x+dKUuffFzW/WOFNpgiqZQzBYEpB4WBPo6+jj6Pvq87J4h90CgwExoFVLmkgrXgqANlHCRx1EEp6hUgxaHCefz/9s4/xKoyjeOiRZCr0B/rum662mIbpmjFlq6zlohpyhZWVISEQbntpuUWEWmGkinqTmusVIYRKUVsDCIahmmpmCyDaCqyySTimjqmxtK2uKy47873nXlv5957dMY798f73PN54MOd854743k899455/M+zztZ+1OIQVJRLUDEFIgCqAiIg86pVQsCLQeXD2IgGyAK4gRRQMQUQRyIwqoDiYV6jmqsV5CFdQlCG4FeN0EMUC1AxBiIAqgKiIN0atGCQMtB5yAGsgmiIE4QBUTMkaw6kDSo96oDtR9Uqi1AP7ceWw70GgnVAiK0Eej1gRggYg5EAdQExMEPVLMFgZaDdBADIBAFcYIoIKxFEAciOVtcL+JALQjlXq9AVQT6udYjVAsUthGEagHEAGEpEAUQBVkWB9VqQaDl4AcQA5AGoiBOEAWE9Qg3jkEcJNsVLN44hhaEcq1XYHldglBRUlgtEMQAQVgORAFESdbEQTVaELLccoAYgK6AKIgTRAFRb5FsVwizzkEcWKk6CLKgHKF2AyvrEgTpk6wW0LnTOGKAqLdAFIAJsiAOKtmCkLWWA8QAlAKiIE4QBUQWIogDUVh1ILEQY5RjvQJ9f3d/RqUitBHoPAQxQLUAkaVAFIBJ6lEcVKoFIQstB4gBKAeIgjhBFBBZjGTVgaRBrFUHWleg1GqAV46/4vr9u5/buXNnx8gPsW/fPrdu3Tp36NChjpHKh/7PQ7WACG0E+v9GDBBZDEQB1AX1Ig4q0YJwsZaDM4ZbDhADUAkQBXGCKCCI9gjiQCRnt2spDkpdr2D27NnuimNXuGlzprmRI0e6hoYGd+7cOb9v7ty5bujQoT7P6667zi1evNiPlzNCtUBhG0GoFkAMEASiAOoUy+KgnC0I9dJygBiAaoAoiBNEAUGkR7jRDeIg2a5QzRtdSYLLWa9g7969rteaXm7lv1Z2jDg3YsQIt3r1anfgwAF31VVXuTNnzvjxEydOuF69erlvvvnGb5caoUKjsFogiAGCIIoDUQCZwJI4KFcLguWWA8QA1AJEQZwgCgiia5FsVwiz5EEcVLrqQOsVdPVPJjaebXR3nrizY6s97rvvPvfSSy+5CxcuuIMHD3aMOi8MlMfXX3/dMdK1CBIlWS2g/wuNIwYIomuBKIBMErs4KEcLgqWWA8QAxACiIE4QBQRRegRxIAqrDiQWyhlar6CzFoS06oOWlhZfRaBKgxDnz593q1at8m0JEgiXitBGoLyCGKBagCC6H4gCgDZiFAfdaUGIveUAMQAxgiiIE0QBQZQvklUHkgblrDoI6xXo8WKh/cnFD1UpMGjQILdo0aKOkfZQy8Frr73m7rrrLjd69OhcK4JCOYRqARHaCHT8iAGCKF8gCgBSiEEclNqCEGPLAWIALIAoiBNEAUFUNoI4EMnZ+FLEgSTAxdYr0J9BTEqC5uZm169fP9fY2NgxUhySAjfddJMbN26cP65QFRGqBRADBFG5QBQAdIFaiYNSWhBiaDlADIBFEAVxUi1RUK9Coh7z4lxVNnTzrRvxIA6S7QpduTEf/NmM3HoFugxRSBDccWRG+0ZbfPLJJ+6aa65xTU1NHSPtsWXLFjd9+vS8aoH+/fu7UaNGRSMFeP3ZCc5V90L/TvL3MaIAoAtUUxxcTgtCe8tB/6LnV7rlADEA9QCiIE6qeUFUj1GPeXGuqhvJdoUwqx/EQVrVQdu9vevxWft6BboMUStCjyODXY/B7S0Jhw8fdn369HEbNmzwwmD+/Pm+YkA/d8CAAa5nz57u3Xff9WKgtbXVi4L169f7740heP3ZCc5V90L/TvL3MaIAoAQqKQ662oJQzZYDxADUI4iCOKnmBVE9Rj3mxbmqfQRxkF51cMRLAbUg5B5nSCq0Lzp44403+u8p5Mknn/Q/+4033nC9e/d2kyZN8o+LFy/247EErz87wbnqXujfSf4+RhQAlIFyi4OutCBUsuUAMQBZAFEQJ7pQAYC4GTJkiJ/579u3r7vyyivbLkEGux4LFrRXEuixxx1+/Oqrr/bPGzhwYOrPAYB4UCR/HyMKACpAOcTBpVoQLtZy8F2JLQeIAcgiiAIAgO5RcBmSStr3AUD8IAoAqkAp4uBiLQj/Gf0rd65hTNH45bQcIAYAEAUAAOVi//7v3KBBF/wliR43bvw+9XkAYAdEAUAN6Ko4uFgLQiGdtRwgBgCKQRQAAHSfIAkkB3RZktxOez4A2ABRABABlxIHaS0IhRS2HCAGADoHUQAA0H2SUkCXJXoMsiD5PACwBaIAIEKS4mDf55vdf38xJFUQiPPXDnD7tm9CDABcJogCAIDuk6wc0KVJ+FqyIHwNAPZAFAAY4K9z/uD+17NnqihomjUz9XsA4NIgCgAAAADSQRQAGOHvw4cVSYKWXw5NfS4AdA6iAAAAACAdRAGAEZa33cx817dPThJ8/6PerrHt5ibtuQDQOYgCAAAAgHQQBQCGWNp2U7Oj7Wbl8/ENXhykPQcAugaiAAAAACAdRAEAAGQSREE22bRpU9HYwYMH3fr1693u3buL9sXMnj17/HFv3769aJ/VnISOWce+f//+on2W8xI7d+50X331Vd6Y1ZyOHDnitm3blsexY8dy+y3ntWHDBrd169aifdZySjtHIvnesvyeUh469ubm5qJ9VvMKn+sHDhwo2lftnBAFAACQSRAF2aOxsdGNHTs2b+yDDz5wY8aMcU8//bS7/fbb3dKlS/P2x8qLL77oj1fHPXXqVPfAAw+4U6dO+X1WcxJLlixxEyZMcM8884wbP368W7FiRW6f5byEbgCGDx/uL/TDmOWcVq5c6YYNG+ZGjRqV4+OPP/b7rOb10Ucfudtuu8099dRTbtq0ae7BBx903377rd9nMad169blnR9xww03uBdeeMHvt/z6e/3113PHPnHiRPfcc8/l9lnN6+WXX/avv5DT8uXLc/tqkROiAAAAMgmiIDscPXrU33jqIjkpCs6ePevHdAOnbc2+jRw5MnUmOyY0e6YbTuUVxqZMmeLWrFljNicRbqRDXpp5102NcrCclzh9+rQXOrrAD6LAek6zZs1yq1evLhq3mpeOWzdpn376aW5s8uTJrqmpyfy5CkjkNDQ0+PeY5ZwkbySpwrGrkkXb+my0mteuXbv859+hQ4f8tsSvPi80XqucEAUAAJBJEAXZQbNnmqnRBX9SFGzcuNFfiCWf+8QTT7i33norbyw2dCG5efPmvDEd97Jly8zmJHTxHy6EhW5mrr/+etfS0mI6L7Fw4UJ/fh599NGcKLCek2Y8VZ6vmxaJkDBuNS+1G6iKIG2f9XMlTp486T//QvuV9c8KSUSV4mtbrz/dZKu1x2pe7733nnv88cfzxlQ9sGjRoprlhCgAAIBMgijIDqF0WGXFSVGQdmH27LPPuueffz5vLHbUy6qLZM2m1UNOmj175513/Ay8bq41ZjkvzVDffffd/uukKLCck86RbtQ0465ZeH0dSr+t5rV27VpfJaHjHDFihJ/BVXuF9tXD+0ql6jNmzMhtW89JnxGqpFJe9957r2/H0rjVvD788EP/mZcc0+fFnDlzapYTogAAADIJoiB7FIoClerPnDkz7zm62Un2usaOZts10/Tqq6/67XrISS0Hb775pr+p0Q2AKgus5qWSaM28hxLhpCiwfK6+/PJLP6OpR23rdaiS9rfffttsXqr6UPm6bkC1rQXjbrnlFl+ub/19pTJ2laqrjD2MWc9Js+36fFD7i95X06dP91UTVvPS59ytt97qq98kF/UZGNYkqFVOiAIAAMgkiILsUSgKtDjUY489lvcczdKEhb5iRxf9ms3Vol5hzHpOhTz88MN+xtBqXrqQ1yy1XntCpe3KRzeh9XauNKOrRQCt5iXJMWnSpLwxHbewfq7ef/99P/ueHLOck9pEtNipKlvCmESBFv+znJdaryQEtIimcpG80mdIrXJCFAAAQCZBFGSPQlGg/urkttDFmC7KkmMxojUKNNupVc2T45Zz+uKLL4p6blV2q4UoreYlKaDZzoDEjtoQJHcsnyu1u2iWMzmmMmidL6t56b1UKArCrK3lcyUkq0IbT8ByTmoTKSzF102z5dff8ePH/RoLyTEdt3KtVU6IAgAAyCSIguxRKAq0doG2Na5tzeaoN/nw4cO558SIFvBS/7QWuNIiXgHNrlnNSehYVfotYaBtHbNKbzV7aDmvJMnWA8s5qSIiueq8Wg90rlSmbzUvvYdU+q33lba1SKPaKXSTZv31J0EVjj1gOSetx6JjDZ8VavHRehm6qbaalxap1XtK7yVt79ixw8tg5VarnBAFAACQSRAF2UMXWbrYSo7pJkA3OCpxv/nmm/1fRkjujxGVo+qvARQyb948v99iTgGVf6uX+pFHHvGPK1asyO2znFcgKQqE5ZzUGy5hpWPXY7IFxmpe27Zt82t+3H///f64Gxsbc/us5qSbTH0+aO2Pwn2WX39aS0LHHI59wYIFuX1W89K6BHovPfTQQ/51qDzCvlrkhCgAAIBMgiiAJFoESxfUafusYjUnHbNmypL9x0k4V/GgY77UsVvNq7W1ldefAcLrr57OlXLRcaftE9XMCVEAAACZBFEAAAAAkA6iAAAAMgmiAAAAACAdRAEAAGQSRAEAAABAOogCAADIJN0RBTPnLstjy+e73a8RBQAAAFAnIAoAACCTlCIKfvzTa93R463ud/P+lIcXBROmpP6iBQAAALAGogAAADJJaaJgoDt6/JT7/fw/57F11x43dsLU1F+0AAAAANZAFAAAQCYpRRT0GzDQ/ePEKTd74V/y+Oxve91vJv429RctAAAAgDUQBQAAkElKEQU/+dnP3bGTp90fl6zKY3vzfjdu8j2pv2gBAAAAbPFP939+8h8Wwy5oNwAAAABJRU5ErkJggg==\" data-image-state=\"image-loaded\" width=\"776\" height=\"358\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function npxy=Solve_ICFP004(hxy,pxy,mseg,epsilon)\r\n% Annealing Solver with Gloabalist Bonus tweak\r\n% Create list of nodes that are placed on hole nodes\r\n% Adjust Segment Max lengths for select segments\r\n% Place hole vertex values into npxy array\r\n% npxy values outside hole are randomly placed inside hole\r\n% as all jiggles are checked for staying in hole\r\n% This routine lacks edge crossing check\r\n% Infinite jiggle traps exist so iterations are limited followed\r\n% by node being randomly placed\r\n% Anneal by moving selected node to/away from goal node with\r\n% directional randomness eg to Bottom Right [1 1;1 0;0 1\r\n% to Top Left the move options would be [-1 -1;-1 0;0 -1]\r\n\r\n npxy=pxy;\r\n np=size(npxy,1); % figure/pose vertices\r\n nhp1=size(hxy,1); % hole vertices\r\n nh=nhp1-1;\r\n nseg=size(mseg,1);\r\n \r\n msegMM=calc_msegMM(pxy,mseg,epsilon,nseg); %Create Min and Max segment integer values\r\n\r\n%By inspection assign these nodes to the hole vertices. See Challenge page Figure \r\n%Revise msegMM for Bonus Stretch of Problem 4\r\n  msegMM([9 46],2)=20*20; % Fit segs 9 and 46 to hole\r\n  msegMM(49,2)=2*msegMM(49,2); %Allow Extend seg 49\r\n  msegMM(50,2)=2*msegMM(50,2); %Allow extend seg 50\r\n%starting at top left\r\n  p2hEdge=[7 27 16 35 42 41 38 23 12 6 5]'; %Assign nodes to hole vertices\r\n  npxy(p2hEdge,:)=hxy(1:nh,:);\r\n \r\n %hplot(hxy,pxy,mseg,nseg,1);\r\n %hplot3(hxy,npxy,mseg,nseg,3,msegMM);\r\n %pause(0.01)\r\n \r\n %Create a simple matrix for indexing in hole positions\r\n %Simple vector calculations determines good/bad node\r\n %May need a -1 check for x or y\r\n xhmax=max(hxy(:,1)); %hole node 0:79 thus 80 47 mod by xchmax\r\n xhmax1=xhmax+1;\r\n yhmax=max(hxy(:,2)); %[x,y]*[1;80]+1 to make in-index\r\n %[x,y]*[1;xhmax+1]+1\r\n dmap=ones(xhmax+1,yhmax+1); % x=column, y=row  hxy(col=x,row=y)\r\n [dx,dy]=find(dmap); %in vector TL2BR across figures L2R,T2B\r\n dxy=[dx dy]-1;\r\n in=inpolygon(dxy(:,1),dxy(:,2),hxy(:,1),hxy(:,2));\r\n hdxy=dxy(:,1)\u003c0; % make logical hdxy of entire board\r\n hdxy(in,:)=1; % [0,0] maps to 1, [1,0] is 2, [79,0] is 80,\r\n %hdxy holds oversized hole map\r\n phdxy=find(hdxy); % Pointer to all in-hole nodes\r\n Lphdxy=nnz(phdxy); % used for outside hole and infinite loops\r\n % new_xy=dxy(phdxy(randi(Lphdxy)),:);\r\n \r\n fprintf('IN-Hole Nodes:%i  HoleV:%i FigV:%i\\n',nnz(hdxy),nh,np);\r\n  \r\n %Problem 4 Solution format in JSON using Bonus from Problem 57\r\n %{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n %\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n %...,[73,45]]}\r\n\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\n% epsilon=200000;\r\n% hxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;\r\n%      5 50;5 5];\r\n% pxy=[10 10;10 25;10 35;15  5;15 30;20 50;30  5;30 30;30 35;30 50;\r\n%      30 55;30 65;35 45;35 60;40  5;40 20;40 30;40 45;40 60;40 80;\r\n%      45 50;45 55;50 95;55 20;55 50;55 55;60  5;60 30;60 35;60 45;\r\n%      60 60;60 80;65 45;65 60;70  5;70 50;70 55;70 65;80 30;80 35;\r\n%      80 50;90 5;90 35];\r\n% mseg=[23 32;32 20;20 23;32 38;38 12;12 20;12 6;38 41;11 10;10 13;13 18;18 21;21 22;22 19;19 14;14 11;34 37;37 36;36 33;33 30;30 25;25 26;26 31;31 34;6 3;43 41;41 36;25 21;10 6;7 4;4 1;1 2;2 5;5 8;15 16;16 17;17 28;28 24;24 27;27 15;16 24;42 39;39 35;8 9;28 29;39 40;43 40;40 29;29 9;9 3];\r\n%Cody mseg cleaned and sorted\r\n%mseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8 9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\n  \r\n%  hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%  pause(0.1);\r\n  \r\n  pstatus=zeros(np,1); %Status 0/1/2/3=Final, nseg attached, \r\n  pstatus(p2hEdge,1)=3; % Assign as Fixed node status\r\n  \r\n  %Find all nodes outside hole and randomly place inside\r\n  % should prioitize these but not implemented.\r\n  \r\n  %Find all segments with issues\r\n  %Grab all nodes of problem segments\r\n  %remove nodes that are pstatus==3\r\n  nodechk=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\n  %Find all nodes that are outside hole and randomly place in-hole\r\n  for i=find(~nodechk)'\r\n   npxy(i,:)=[0 0]; %    *****  Line changed from working program  *****\r\n  end\r\n  \r\n  ztic=tic; % while timer\r\n  iter=0; % purely informational\r\n  badnodes=zeros(1,np);\r\n  %Hole intersect check not required for Problem 4 with given Starts\r\n  while toc(ztic)\u003c10 % repeat until anneal solves, should be quick usually \u003c 1.2s\r\n   iter=iter+1; %Tracking number of anneal iterations for info only\r\n   segchk=ones(nseg,1);\r\n   for i=1:nseg % Find bad segments to identify nodes to jiggle\r\n    segchk(i)=prod(msegMM(i,:)-dist2(npxy(mseg(i,1),:),npxy(mseg(i,2),:)) )\u003e0;\r\n   end\r\n   segnodes=mseg(find(segchk),:);\r\n   badnodes(segnodes(:))=1;\r\n   badnodes(pstatus(:,1)==3)=0; %Remove placed nodes from bad list\r\n  \r\n   if nnz(badnodes)==0  % If no badnodes then problem solved\r\n%    hplot3(hxy,npxy,mseg,nseg,4,msegMM); % hplot3 exists below for out of cody usage\r\n   \r\n    vLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); %Method to evaluate GLOBALIST function\r\n    vLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2);\r\n    ET=epsilon*nseg/1e6;\r\n    ETseg=[[1:nseg]' mseg vLB2 vLN2 vLN2./vLB2 abs(vLN2./vLB2-1)]; % Info table\r\n%    fprintf('%2i %2i %2i  %4i  %4i   %.3f   %.3f\\n',ETseg')\r\n    fprintf('Cody ET Performance: ET Lim:%.3f  Current ET:%.3f\\n',ET,sum(ETseg(:,end)));\r\n   \r\n    fprintf('Solution found in %i iterations,  %.1fs\\n',iter,toc(ztic));\r\n   \r\n%    pause(0.1);\r\n    return; % No bad nodes , return the solution has been found\r\n   end\r\n  \r\n   setbn=find(badnodes);\r\n   %Perfom jiggle on randomized bad nodes\r\n   for jptr= 0                          % *****  Line changed from working program  *****\r\n    jxy=npxy(jptr,:);\r\n   \r\n    jsegs=[find(mseg(:,1)==jptr);find(mseg(:,2)==jptr)];\r\n    Ljsegs=size(jsegs,1);\r\n    jsegnodes=sum(mseg(jsegs,:)-jptr,2)+jptr;\r\n    jsegxy=npxy(jsegnodes,:);\r\n    \r\n   vjsegs=jsegs*0; %1 is valid\r\n   for i=1:Ljsegs\r\n    vjsegs(i)=prod(msegMM(jsegs(i),:)-sum((jxy-jsegxy(i,:)).^2))\u003c=0;\r\n   end\r\n   if nnz(vjsegs==0)==0\r\n    continue; % can this be reached?  if outside hole placement occurs to good/good\r\n   end % ALL Valid\r\n   \r\n   for i=1:Ljsegs\r\n    if vjsegs(i),continue;end % original length okay \r\n    \r\n     subiter=0; %Break out of jiggle inf loop\r\n    if sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2) %too long\r\n     %Perform rand pick of 3 moves until no longer too long\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2)\r\n    % Create quadrant directed approach jiggle\r\n      deltaxy=-sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0];\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop\r\n       deltaxy(:,1)=[0 0 0];  % *****  Line changed from working program  *****\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 0 0];  % *****  Line changed from working program  *****\r\n      end\r\n      \r\n      % add a random directed deltaxy\r\n      tjxy=jxy+[0 0];          % *****  Line changed from working program  *****\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %Banging thru wall\r\n       subiter=subiter+1; % Break out of infinite loop       \r\n       if subiter\u003e10\r\n        subiter=0;\r\n        %Place node at random in-hole\r\n        jxy=[0 0];           %  *****  Line changed from working program  *****\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1); \r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);\r\n     end % while too long\r\n     \r\n    else % must be too short, Push away\r\n     %Perform rand pick of 3 moves until no longer too short\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003cmsegMM(jsegs(i),1)\r\n      deltaxy=sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0]; %mov deltas\r\n    % Create quadrant directed push away jiggle\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop, no 0 0 move\r\n       deltaxy(:,1)=[0 -1 1];\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 -1 1];\r\n      end\r\n      \r\n      tjxy=jxy+deltaxy(randi(3),:); % Randomize selection\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %check jiggle goes outside hole\r\n       subiter=subiter+1; % Break out of locked position\r\n       %Pushing a node into a corner can create inf loop when\r\n       %using directed quadrant push\r\n       if subiter\u003e10\r\n        subiter=0;\r\n        jxy=dxy(phdxy(randi(Lphdxy)),:);  %Place node at random in-hole\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1);\r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);    \r\n     end % while too short\r\n    \r\n    end %  if Long or Short msegMM\r\n    break; %perform jiggle on only first Lseg  (need to randomize?)\r\n   end % i Ljsegs\r\n  \r\n  end % jptr\r\n   badnodes=badnodes*0; % reset badnodes   \r\n%      hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%      pause(0.1);\r\n  end % while 1\r\n  \r\nend %Solve_ICFP004\r\n\r\nfunction d2=dist2(a,b)\r\n% distance squared a-matrix to b-vector \r\n d2=sum((a-b).^2,2);\r\nend %dist2\r\n\r\nfunction msegMM=calc_msegMM(pxy,mseg,epsilon,nseg)\r\n%determine Min and Max integer value of allowed length squared for each segment\r\n%abs(Lsqr(npxy,seg(i))/Lsqr(pxy,seg(i))-1)\u003c= epsilon/1000000.\r\n%mseg has indices of connected vertices [nseg,2].  The nseg may exceed number of vertices.\r\n msegMM=zeros(nseg,2);\r\n for i=1:nseg\r\n  Lseg=sum((pxy(mseg(i,1),:)-pxy(mseg(i,2),:)).^2);\r\n  delta=floor(epsilon*Lseg/1000000);\r\n  msegMM(i,:)=[-delta delta]+Lseg;\r\n end\r\nend % calc_msegMM\r\n\r\n% function [epsilon,hxy,pxy,mseg]=read_problem(pid)\r\n%  path='D:\\Users\\oglraz\\Documents\\MATLAB\\ICFP\\2021_Hole\\all_problems';\r\n%  fid=fopen([path '\\' num2str(pid) '.problem'],'r');\r\n%   pstr=fgetl(fid);\r\n%  fclose(fid)\r\n%  \r\n%  Lpstr=length(pstr);\r\n%  holidx=findstr('\"hole\":[[',pstr); %starting location match\r\n%  epsidx=findstr('\"epsilon\":',pstr);\r\n%  figidx=findstr(',\"figure\"',pstr);\r\n%  edgidx=findstr('\"edges\":[[',pstr);\r\n%  veridx=findstr('\"vertices\":[[',pstr);\r\n%  epsilon=str2num(pstr(epsidx+10:figidx-1));\r\n%  \r\n%  hxy=reshape(str2num(pstr(holidx+8:epsidx-3)),2,[])';\r\n%  hxy=[hxy;hxy(1,:)]; %repeat row1 to close path\r\n%  \r\n%  pxy=reshape(str2num(pstr(veridx+12:Lpstr-3)),2,[])';\r\n%  \r\n%  mseg=reshape(str2num(pstr(edgidx+9:veridx-3)),2,[])'+1;\r\n% end % read_problem\r\n\r\n%Problem 4 JSON\r\n %{\"bonuses\":[{\"bonus\":\"BREAK_A_LEG\",\"problem\":63,\"position\":[95,26]},\r\n %{\"bonus\":\"BREAK_A_LEG\",\"problem\":92,\"position\":[5,32]}],\r\n %\"hole\":[[5,5],[35,15],[65,15],[95,5],[95,50],[70,70],[70,90],[50,95],\r\n %[30,90],[30,70],[5,50]],\"epsilon\":200000,\"figure\":{\r\n %\"edges\":[[22,31],[31,19],[19,22],[31,37],[37,11],[11,19],[11,5],\r\n %[37,40],[10,9],[9,12],[12,17],[17,20],[20,21],[21,18],[18,13],\r\n %[13,10],[33,36],[36,35],[35,32],[32,29],[29,24],[24,25],[25,30],\r\n %[30,33],[5,2],[42,40],[40,35],[24,20],[9,5],[6,3],[3,0],[0,1],[1,4],\r\n %[4,7],[14,15],[15,16],[16,27],[27,23],[23,26],[26,14],[15,23],[41,38],\r\n %[38,34],[7,8],[27,28],[38,39],[42,39],[39,28],[28,8],[8,2]],\r\n %\"vertices\":[[10,10],[10,25],[10,35],[15,5],[15,30],[20,50],[30,5],\r\n %[30,30],[30,35],[30,50],[30,55],[30,65],[35,45],[35,60],[40,5],[40,20],\r\n %[40,30],[40,45],[40,60],[40,80],[45,50],[45,55],[50,95],[55,20],\r\n %[55,50],[55,55],[60,5],[60,30],[60,35],[60,45],[60,60],[60,80],\r\n %[65,45],[65,60],[70,5],[70,50],[70,55],[70,65],[80,30],[80,35],\r\n %[80,50],[90,5],[90,35]]}}\r\n\r\n% function write_bonus_submission(npxy,pid,bonus_type,bonus_prob)\r\n%  fname=['Solution_' num2str(pid) '_' datestr(now,'yyyymmdd_HHMMSS') '.txt'];\r\n%  %fn=['zH' datestr(now,'yyyymmdd_HHMMSS') '.html'];\r\n%  fid=fopen(fname,'wt'); % t for notepad editing\r\n%  \r\n%  %Create ICFP submission with a bonus\r\n%  fprintf('{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf(fid,'{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf('\"vertices\": [');\r\n%  fprintf(fid,'\"vertices\": [');\r\n%  for i=1:size(npxy,1)-1 \r\n%   fprintf('[%i,%i],',npxy(i,:));\r\n%   fprintf(fid,'[%i,%i],',npxy(i,:));\r\n%  end \r\n%  fprintf('[%i,%i]]}\\n',npxy(end,:));\r\n%  fprintf(fid,'[%i,%i]]}\\n',npxy(end,:));\r\n%  fclose(fid);\r\n% end %write_submission_bonus\r\n% \r\n% \r\n% \r\n% function hplot3(vxy,qxy,mseg,Lmseg,id,segMM)\r\n%  segMNM=[segMM(:,1) segMM(:,1)+segMM(:,2) segMM(:,2)];\r\n%  [in] = inpolygon(qxy(:,1),qxy(:,2),vxy(:,1),vxy(:,2)); % inside or on edge\r\n%  figure(id)\r\n%   plot(vxy(:,1),vxy(:,2),'k.-') % hole polygon\r\n%   hold on\r\n%   plot(qxy(in,1),qxy(in,2),'b*') % points inside\r\n%   plot(qxy(~in,1),qxy(~in,2),'ro') % points outside\r\n%   for i=1:size(qxy,1) %length(xq)\r\n%    text(qxy(i,1)+.75,qxy(i,2)-1.5,num2str(i));\r\n%   end\r\n%   \r\n%   for i=1:Lmseg\r\n%    d2seg=(qxy(mseg(i,1),1)-qxy(mseg(i,2),1))^2+(qxy(mseg(i,1),2)-qxy(mseg(i,2),2))^2;\r\n%    if d2seg\u003csegMNM(i,1)\r\n%      plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'b-') % segment too short\r\n%    elseif d2seg\u003esegMNM(i,3)\r\n%      plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'r-') % segment too long\r\n%    else\r\n%     plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'g-') \r\n%    end\r\n%    text(sum(qxy(mseg(i,:),1))/2,sum(qxy(mseg(i,:),2))/2,num2str(i),'Color','b');\r\n%   end\r\n%   \r\n%   %o+*.x_|sd^v\u003e\u003cph\r\n%   %colors ymcrgbwk\r\n%   \r\n%   axis tight\r\n%   axis ij\r\n%   hold off\r\n%end % hplot3\r\n\r\n","test_suite":"%%\r\n% ICFP Problem  4  \r\n% Assume that globalist bonus is enabled so the strict edge lengths are not required\r\n%\r\n%Problem 4 Solution format in JSON using Bonus from Problem 57\r\n%{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n%\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n%...,[73,45]]}\r\ntic\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\nepsilon=200000;\r\nhxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;5 50;5 5];\r\npxy=[10 10;10 25;10 35;15 5;15 30;20 50;30 5;30 30;30 35;30 50;30 55;30 65;35 45;35 60;40 5;40 20;40 30;40 45;40 60;40 80;45 50;45 55;50 95;55 20;55 50;55 55;60 5;60 30;60 35;60 45;60 60;60 80;65 45;65 60;70 5;70 50;70 55;70 65;80 30;80 35;80 50;90 5;90 35];\r\nmseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8  9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\nnseg=size(mseg,1);\r\nnpxy=Solve_ICFP004(hxy,pxy,mseg,epsilon);\r\nvalid=isequal(npxy,round(npxy));\r\nvalid=valid*isequal(size(npxy),size(pxy));\r\nfor i=1:size(hxy,1) % verify all holes covered\r\n valid=valid*(min(sum(abs(npxy-hxy(i,:)),2))==0);\r\nend\r\nin=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\nvalid=valid*(nnz(in==0)==0);\r\n\r\nvLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); % Base seg d2\r\nvLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2); % New seg d2\r\nET=epsilon*nseg/1e6; %Total allowed stretch  10.00\r\nETseg=abs(vLN2./vLB2-1);\r\nETP=sum(ETseg);\r\nvalid=valid*(ETP\u003c=ET);\r\n\r\nfprintf('ET Lim:%.3f  Current ET:%.3f\\n',ET,ETP);\r\nfprintf('%i %i\\n',npxy');\r\ntoc\r\nassert(isequal(valid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2022-09-13T15:37:03.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-08-05T03:23:08.000Z","updated_at":"2022-09-13T15:37:03.000Z","published_at":"2021-08-05T04:53:23.000Z","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 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.icfpconference.org/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e held its annual 3-day contest in July 2021 with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eHole-In-Wall\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Contest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\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:t\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 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\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*epsilon/1000000.  Lsqr is length squared.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\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/52308\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eScore\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003enpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)\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\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/register\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRegister Team\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Anyone can select \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/problems\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblems Page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\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=\\\"358\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"776\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"top\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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With Spaces - Convert Some Messy Data Into A Clean Array","description":"I have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\r\nI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\r\n'- 6.496' =\u003e -6.496\r\n'-10.430' =\u003e -10.430\r\n'+++++++' =\u003e NaN\r\n'+11.664' =\u003e 11.664\r\nOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!","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: 215.795px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.989px 107.898px; transform-origin: 406.996px 107.898px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; 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; \"\u003e\u003cspan style=\"\"\u003eI have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; 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; \"\u003e\u003cspan style=\"\"\u003eI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7614px; 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: 403.991px 40.8807px; transform-origin: 403.999px 40.8807px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; 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: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; 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: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\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-collapse: preserve; 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); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'- 6.496' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; -6.496\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; 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: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; 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: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\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-collapse: preserve; 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); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-10.430' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; -10.430\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; 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: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; 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: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\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-collapse: preserve; 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); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'+++++++' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; NaN\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; 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: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; 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: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\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-collapse: preserve; 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); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'+11.664' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; 11.664\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; 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: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space-collapse: preserve; 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=\"\"\u003eOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = str2numBetter(x)\r\n  y = str2num(cell2mat(x));\r\nend","test_suite":"%%\r\n% This first test should cover every possible case.\r\nx = {'- 6.496';'-10.430';'- 0.493';'+++++++';'+ 6.949';'- 5.368';'+13.214';'+11.664'};\r\nfinalData = [-6.496;-10.430;-0.493';NaN;+6.949;-5.368;+13.214;+11.664];\r\n\r\nassert(isequaln(str2numBetter(x),finalData))\r\n\r\n%%\r\nrng shuffle\r\nn = 30000; % Something small to start you off with.\r\nrNums=10*rand([n 1]);\r\nrNums(rNums\u003e9.99)=NaN;\r\nstr=char(sprintfc('%.3f', rNums, false));\r\nneg=2*randi([0 1],[n 1]);\r\ntens=randi([0 1],[n 1]);\r\nstr = [char(neg+'+') char(17*tens+' ') str];\r\nplus=('+');\r\nstr(isnan(rNums),:)=plus(ones(nnz(isnan(rNums)),7));\r\nx=mat2cell(str,ones(1,n),7);\r\ntic; y=str2numBetter(x); toc % The code I wrote took 0.005872 seconds to run\r\nfinalData = round(-1*(neg-1).*(rNums+10*tens),3);\r\n\r\nassert(isequaln(y,finalData))\r\n\r\n%%\r\nrng shuffle\r\nn = 12000000; % Yes! I have this much data!!!\r\nrNums=10*rand([n 1]);\r\nrNums(rNums\u003e9.99)=NaN; % Real world values overflow\r\nstr=char(sprintfc('%.3f', rNums, false)); % This is what num2str uses\r\nneg=2*randi([0 1],[n 1]); % Some numbers will be random\r\ntens=randi([0 1],[n 1]); % Some numbers will be \u003e10, but in an odd format \r\nstr = [char(neg+'+') char(17*tens+' ') str]; % Add the weird 10's place and multiply by negatives\r\nplus=('+');\r\nstr(isnan(rNums),:)=plus(ones(nnz(isnan(rNums)),7)); % Replace NaN with something more annoying\r\nx=mat2cell(str,ones(1,n),7); % The data is in cells, not an array!\r\ntic; y=str2numBetter(x); toc % The code I wrote takes about 3.5 seconds to run, with a size of 130.\r\nfinalData = round(-1*(neg-1).*(rNums+10*tens),3);\r\n\r\nassert(isequaln(y,finalData))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3115675,"edited_by":3115675,"edited_at":"2023-08-08T14:27:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2023-08-08T14:27:45.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-07T21:52:56.000Z","updated_at":"2023-08-08T14:27:45.000Z","published_at":"2023-08-07T22:00:31.000Z","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 have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still 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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\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['- 6.496' =\u003e -6.496\\n'-10.430' =\u003e -10.430\\n'+++++++' =\u003e NaN\\n'+11.664' =\u003e 11.664]]\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\u003eOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!\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":1237,"title":"It's race time! Write a faster function than the test suite call of unique().","description":"Write a function to get unique elements of a vector faster than unique()! Input will be a vector (of integers or floating point numbers) of any size. The order of the returned vector is unimportant. \r\n\r\nExample:\r\nInput: x = [1 1 2 2 3 3];\r\nOutput: [1 2 3];\r\n\r\nInput: x = [0.1 3.1 2.1 2.0 3.1];\r\nOutput: [0.1 3.1 2.1 2.0]; % or any order","description_html":"\u003cp\u003eWrite a function to get unique elements of a vector faster than unique()! Input will be a vector (of integers or floating point numbers) of any size. The order of the returned vector is unimportant.\u003c/p\u003e\u003cp\u003eExample:\r\nInput: x = [1 1 2 2 3 3];\r\nOutput: [1 2 3];\u003c/p\u003e\u003cp\u003eInput: x = [0.1 3.1 2.1 2.0 3.1];\r\nOutput: [0.1 3.1 2.1 2.0]; % or any order\u003c/p\u003e","function_template":"function y = my_unique(x)\r\n   y = x;\r\nend","test_suite":"%%\r\nx = rand(10000, 1);\r\nz = rand(10000, 1);\r\nx = vertcat(x, z);\r\ntic\r\ny_correct = unique(x);\r\nt_unique = toc\r\n\r\ntic\r\ny_myunique = my_unique(x);\r\nt_myunique = toc\r\n\r\nassert(isequal(sort(my_unique(x)),y_correct) \u0026\u0026 t_unique \u003e t_myunique)\r\n\r\n%%\r\nx = rand(50000, 1);\r\nz = rand(50000, 1);\r\nx = vertcat(x, z);\r\n\r\ntic\r\ny_correct = unique(x);\r\nt_unique = toc\r\n\r\ntic\r\ny_my_unique = my_unique(x);\r\nt_my_unique = toc\r\n\r\nassert(isequal(sort(my_unique(x)),y_correct) \u0026\u0026 t_unique \u003e t_my_unique)\r\n\r\n%%\r\nx = [1; 2; 3; 4; 2; 3; 4; 5;];\r\n\r\ntic\r\ny_correct = unique(x);\r\nt_unique = toc\r\n\r\ntic\r\ny_my_unique = my_unique(x);\r\nt_my_unique = toc\r\n\r\nassert(isequal(sort(my_unique(x)),y_correct) \u0026\u0026 t_unique \u003e t_my_unique)\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":9,"created_by":10338,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":31,"test_suite_updated_at":"2013-02-03T20:33:40.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-01T03:36:13.000Z","updated_at":"2025-09-07T01:43:50.000Z","published_at":"2013-02-01T03:36:13.000Z","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\u003eWrite a function to get unique elements of a vector faster than unique()! Input will be a vector (of integers or floating point numbers) of any size. The order of the returned vector is unimportant.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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: Input: x = [1 1 2 2 3 3]; Output: [1 2 3];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 = [0.1 3.1 2.1 2.0 3.1]; Output: [0.1 3.1 2.1 2.0]; % or any order\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":1940,"title":"Decimation - Optimized for speed","description":"This problem is similar to http://www.mathworks.com/matlabcentral/cody/problems/1092-decimation, only this time the score will be based on how quickly you can determine which person will survive.\r\n\r\nThe sample sizes (num_prisoners) and number of prisoners killed (num_killed) will be larger than in the original problem, but other than that the problem sets are identical.","description_html":"\u003cp\u003eThis problem is similar to \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\"\u003ehttp://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\u003c/a\u003e, only this time the score will be based on how quickly you can determine which person will survive.\u003c/p\u003e\u003cp\u003eThe sample sizes (num_prisoners) and number of prisoners killed (num_killed) will be larger than in the original problem, but other than that the problem sets are identical.\u003c/p\u003e","function_template":"function y = speed_decimation(num_prisoners, num_killed)\r\n  y = num_prisoners+num_killed;\r\nend","test_suite":"%%\r\nassert(isequal(speed_decimation(10,3),4))\r\n%%\r\nassert(isequal(speed_decimation(1024,3),676))\r\n%%\r\nassert(isequal(speed_decimation(2012,50),543))\r\n%%\r\nassert(isequal(speed_decimation(30,5),3))\r\n%%\r\nassert(isequal(speed_decimation(10,10),8))\r\n%%\r\nassert(isequal(speed_decimation(2048,2),1))\r\n%%\r\nassert(isequal(speed_decimation(2048,1024),1773))\r\n%%\r\nt_in=clock;\r\nj=1:50;\r\nv=arrayfun(@(x) speed_decimation(100000,x),j);\r\ncorrect=[100000 68929 92620 32942 40333 54212 27152 67341 42610 77328 82991 13252 91717 6850 45758 71249 38339 86953 63331 66903 72606 83990 87828 46101 99979 47141 16871 60389 51549 76409 42868 78390 79590 27573 95835 53636 36954 39891 45943 63811 71589 70886 49313 4069 93694 96031 20739 41403 93714 60023];\r\nassert(all(isequal(v,correct)));\r\nt_out=etime(clock,t_in)*1000;\r\nfprintf('Actual Time = %.0f msec\\n',t_out)\r\nv=[100000:100000:1000000];\r\nv=arrayfun(@(x) speed_decimation(x,17),v);\r\ncorrect=[38339 162859 151602 99465 462955 559860 337009 546467 563784 364193];\r\nassert(all(isequal(v,correct)));\r\nt_out=etime(clock,t_in)*1000;\r\nfprintf('Actual Time = %.0f msec\\n',t_out)\r\nassert(isequal(speed_decimation(2^20,2^9),210856));\r\nt_out=etime(clock,t_in)*1000;\r\nt2=min(100000,t_out);\r\nfprintf('Actual Time = %.0f msec\\n',t_out)\r\nfeval(@assignin,'caller','score',floor(t2));","published":true,"deleted":false,"likes_count":9,"comments_count":2,"created_by":1615,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":224,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":13,"created_at":"2013-10-16T19:58:38.000Z","updated_at":"2026-04-08T14:54:11.000Z","published_at":"2013-10-16T19:58:38.000Z","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 similar 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/1092-decimation\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, only this time the score will be based on how quickly you can determine which person will survive.\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 sample sizes (num_prisoners) and number of prisoners killed (num_killed) will be larger than in the original problem, but other than that the problem sets are identical.\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":44448,"title":"Project Euler: Problem 14 Longest Collatz sequence","description":"This problem is a hard version of \"Problem 42673. Longest Collatz Sequence\", because of time limits.\r\n\u003chttps://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\u003e\r\n\r\nThe following iterative sequence is defined for the set of positive integers:\r\n\r\nn → n/2 (n is even)\r\nn → 3n + 1 (n is odd)\r\n\r\nUsing the rule above and starting with 13, we generate the following sequence:\r\n\r\n13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1\r\nIt can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.\r\n\r\nWhich starting number, no more than N, produces the longest chain, and how long?\r\nDon't cheat!","description_html":"\u003cp\u003eThis problem is a hard version of \"Problem 42673. Longest Collatz Sequence\", because of time limits. \u003ca href = \"https://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\"\u003ehttps://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\u003c/a\u003e\u003c/p\u003e\u003cp\u003eThe following iterative sequence is defined for the set of positive integers:\u003c/p\u003e\u003cp\u003en → n/2 (n is even)\r\nn → 3n + 1 (n is odd)\u003c/p\u003e\u003cp\u003eUsing the rule above and starting with 13, we generate the following sequence:\u003c/p\u003e\u003cp\u003e13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1\r\nIt can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.\u003c/p\u003e\u003cp\u003eWhich starting number, no more than N, produces the longest chain, and how long?\r\nDon't cheat!\u003c/p\u003e","function_template":"function [num, len] = euler014(N)\r\n  \r\nend","test_suite":"%%\r\nfiletext = fileread('euler014.m');\r\nassert(isempty(strfind(filetext, 'tic')),'tic forbidden');\r\nassert(isempty(strfind(filetext, 'toc')),'toc forbidden');\r\nassert(isempty(strfind(filetext, 'pause')),'pause forbidden');\r\n\r\n%%\r\nN = 1234321;\r\nnum_correct = 1117065;\r\nlen_correct = 528;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 10;\r\nnum_correct = 9;\r\nlen_correct = 20;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 100;\r\nnum_correct = 97;\r\nlen_correct = 119;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1000;\r\nnum_correct = 871;\r\nlen_correct = 179;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1e4;\r\nnum_correct = 6171;\r\nlen_correct = 262;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1e5;\r\nnum_correct = 77031;\r\nlen_correct = 351;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1e6;\r\nnum_correct = 837799;\r\nlen_correct = 525;\r\n[num, len] = euler014(N);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n%%\r\nN = 1e7;\r\nnum_correct = 8400511;\r\nlen_correct = 686;\r\ntic;\r\n[num, len] = euler014(N);\r\nt = toc;\r\nt\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\nassert(t \u003c 1.5);\r\nassert(t \u003e 0.001);\r\n\r\n%%\r\nN = 1e8;\r\nnum_correct = 63728127;\r\nlen_correct = 950;\r\ntic;\r\n[num, len] = euler014(N);\r\nt = toc;\r\nt\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\nassert(t \u003c 15);\r\nassert(t \u003e 1);\r\n\r\n%%\r\nN = randi([7e7, 1e8]);\r\nnum_correct = 63728127;\r\nlen_correct = 950;\r\ntic;\r\n[num, len] = euler014(N);\r\nt = toc;\r\nt\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\nassert(t \u003c 15);\r\nassert(t \u003e 1);\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":9,"created_by":8269,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":"2018-10-10T02:15:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-12-12T01:06:38.000Z","updated_at":"2018-10-26T04:19:54.000Z","published_at":"2017-12-12T01:44:33.000Z","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\",\"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 a hard version of \\\"Problem 42673. Longest Collatz Sequence\\\", because of time limits.\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://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://ww2.mathworks.cn/matlabcentral/cody/problems/42673-longest-collatz-sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\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 following iterative sequence is defined for the set of positive 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en → n/2 (n is even) n → 3n + 1 (n is odd)\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\u003eUsing the rule above and starting with 13, we generate 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=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 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\u003eWhich starting number, no more than N, produces the longest chain, and how long? Don't cheat!\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":937,"title":"Rubik's Mini Cube: Solve Randomized Cube in 11 Moves or Less; Score is by Time (msec)","description":"The Challenge is to Near or Optimally Solve a thoroughly scrambled Mini-Rubik's Cube(2x2x2).\r\n\r\nAny Mini-Cube can be solved in 11 or fewer Face Moves. There are only 3,674,160 unique cube positions, with only 2344 requiring the fulll 11 moves. Cube moves do not count.\r\n\r\nThe Performance metric is cumulative Time to Solve 500 cubes (msec).\r\n\r\nA standard Mini-Rubik's Cube is shown in 3-D and also unfolded to identify the specific Tile-face/Vector numbering.\r\n\r\nThe faces are White-Up / Red-Left / Blue-Front / Orange-Right / Yellow-Down / Green-Back (ULFRDB)\r\n\r\nMoves are denoted as F for clockwise rotation of the Front face. F'(or FP) is CCW and F2 is F twice.  The provided function r_new=rubick_rot_mini(mov,r) implements moves 1-18: UFDLBRU'F'D'L'B'R'U2F2D2L2B2R2.\r\n\r\n\r\n\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/minicube2.png\u003e\u003e\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/miniCube_Map24_200.png\u003e\u003e\r\n\r\n\u003c\u003chttp://mathworks.com/matlabcentral/images/surf.gif\u003e\u003e\r\n\r\n\r\n  \r\n  \r\n  Input: (rubik)\r\n  \r\n  rubik: row vector of size 24\r\n  (The cube started [L=0,U=1,F=2,D=3,B=4,R=5] and then receives forty face moves.\r\n\r\n  Output: move_vec (Numeric of moves to solve)\r\n   move_vec:values 1:27 for UFDLBR U'F'D'L'B'R' U2F2D2L2B2R2 XYZX'Y'Z'X2Y2Z2\r\n\r\n* Example:\r\n* If the cube was randomized by [1 9] UD', one solution is [3 7]  which are the complements in reverse order. \r\n* Scoring is Time in msec to solve 500 cubes\r\n* Cube Moves X, Y, and Z do not constitute a move but are needed in the vector \r\n* A string to numeric value function is provided in the template\r\n* Verifications will be by executing your move vector against the provided Rubik and counting number of face moves.\r\n\r\nThe function rubik_rot_mini(mov,r) is provided in the template. Other functions are provided to re-orient the cube in Cody Challenge 931, Rubik's Mini-Cube.\r\n\r\n\r\nThe Challenge \u003chttp://www.mathworks.com/matlabcentral/cody/problems/931-rubik-s-mini-cube-solve-randomized-mini-cube-score-moves.html Challenge 931 Rubik's Mini-Cube\u003e contains a 3D Mini-Cube Viewer for program development.\r\n\r\n\r\n* \r\n* \u003chttp://kociemba.org/cube.htm Cube Theory: 20-moves Any Cube\u003e \r\n* \u003chttp://peter.stillhq.com/jasmine/rubikscubesolution.html General Cube Info and Middle Layer\u003e\r\n* \u003chttp://www.speedcubing.com/final_layer_print.html SpeedCube Bottom Sequences\u003e\r\n* The site \u003chttp://www.speedcubing.com/CubeSolver/MiniCubeSolver.html MiniCube Solver\u003e claims an outstanding 10 minutes to solve a randomized Mini-Cube. Matlab can achieve any Mini-Cube solution in \u003c 497 usec, independent of moves on an i5/16GB machine.\r\n\r\n\r\n(Note: Mini-Cube can use the full cube moves and ignore edge effects)\r\n\r\nComing Soon: Matlab Tetris\r\n","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: 1316.98px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 658.5px; transform-origin: 407px 658.5px; 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: 294.65px 7.91667px; transform-origin: 294.65px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Challenge is to Near or Optimally Solve a thoroughly scrambled Mini-Rubik's Cube(2x2x2).\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: 377.2px 7.91667px; transform-origin: 377.2px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAny Mini-Cube can be solved in 11 or fewer Face Moves. There are only 3,674,160 unique cube positions, with only 2344 requiring the fulll 11 moves. Cube moves do not count.\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: 221.45px 7.91667px; transform-origin: 221.45px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Performance metric is cumulative Time to Solve 500 cubes (msec).\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: 355.467px 7.91667px; transform-origin: 355.467px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA standard Mini-Rubik's Cube is shown in 3-D and also unfolded to identify the specific Tile-face/Vector numbering.\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: 316.733px 7.91667px; transform-origin: 316.733px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe faces are White-Up / Red-Left / Blue-Front / Orange-Right / Yellow-Down / Green-Back (ULFRDB)\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: 380.533px 7.91667px; transform-origin: 380.533px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMoves are denoted as F for clockwise rotation of the Front face. F'(or FP) is CCW and F2 is F twice. The provided function r_new=rubick_rot_mini(mov,r) implements moves 1-18: UFDLBRU'F'D'L'B'R'U2F2D2L2B2R2.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 138.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; 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 69.4667px; text-align: center; transform-origin: 384px 69.4667px; 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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\" 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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: 0px 7.91667px; transform-origin: 0px 7.91667px; 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: 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: 107.8px 7.91667px; transform-origin: 107.8px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 69.3px 7.91667px; transform-origin: 69.3px 7.91667px; \"\u003erubik: row vector \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: 38.5px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 38.5px 7.91667px; \"\u003eof size 24\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: 304.15px 7.91667px; transform-origin: 304.15px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e(The cube started [L=0,U=1,F=2,D=3,B=4,R=5] and then receives forty face moves.\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: 0px 7.91667px; transform-origin: 0px 7.91667px; 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: 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: 169.4px 7.91667px; transform-origin: 169.4px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eOutput: move_vec (Numeric of moves to solve)\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: 284.9px 7.91667px; transform-origin: 284.9px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 84.7px 7.91667px; transform-origin: 84.7px 7.91667px; \"\u003e move_vec:values 1:27 \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 15.4px 7.91667px; text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 15.4px 7.91667px; \"\u003efor \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 127.05px 7.91667px; transform-origin: 127.05px 7.91667px; \"\u003eUFDLBR U'F'D'L'B'R' U2F2D2L2B2R2 \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: 57.75px 7.91667px; text-decoration: none; text-decoration-color: rgb(196, 0, 0); text-emphasis-color: rgb(196, 0, 0); transform-origin: 57.75px 7.91667px; \"\u003eXYZX'Y'Z'X2Y2Z2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cul style=\"block-size: 122.6px; 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 61.3px; transform-origin: 391px 61.3px; 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: 29.1833px 7.91667px; transform-origin: 29.1833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eExample:\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: 327.667px 7.91667px; transform-origin: 327.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the cube was randomized by [1 9] UD', one solution is [3 7] which are the complements in reverse order.\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: 134.333px 7.91667px; transform-origin: 134.333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eScoring is Time in msec to solve 500 cubes\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: 242.583px 7.91667px; transform-origin: 242.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCube Moves X, Y, and Z do not constitute a move but are needed in the vector\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: 187.1px 7.91667px; transform-origin: 187.1px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA string to numeric value function is provided in the template\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: 355.933px 7.91667px; transform-origin: 355.933px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eVerifications will be by executing your move vector against the provided Rubik and counting number of face moves.\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: 374.067px 7.91667px; transform-origin: 374.067px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function rubik_rot_mini(mov,r) is provided in the template. Other functions are provided to re-orient the cube in Cody Challenge 931, Rubik's Mini-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: 45.5333px 7.91667px; transform-origin: 45.5333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Challenge\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\u003ca target='_blank' href = \"http://www.mathworks.com/matlabcentral/cody/problems/931-rubik-s-mini-cube-solve-randomized-mini-cube-score-moves.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eChallenge 931 Rubik's Mini-Cube\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: 183.883px 7.91667px; transform-origin: 183.883px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e contains a 3D Mini-Cube Viewer for program development.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 122.6px; 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 61.3px; transform-origin: 391px 61.3px; 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\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 = \"http://kociemba.org/cube.htm\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eCube Theory: 20-moves Any Cube\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 = \"http://peter.stillhq.com/jasmine/rubikscubesolution.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eGeneral Cube Info and Middle Layer\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 = \"http://www.speedcubing.com/final_layer_print.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpeedCube Bottom Sequences\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; 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 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 24.9px 7.91667px; transform-origin: 24.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe site\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 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\u003ca target='_blank' href = \"http://www.speedcubing.com/CubeSolver/MiniCubeSolver.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMiniCube Solver\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 276.2px 7.91667px; transform-origin: 276.2px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e claims an outstanding 10 minutes to solve a randomized Mini-Cube. Matlab can achieve any Mini-Cube solution in \u0026lt; 497 usec, independent of moves on an i5/16GB machine.\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: 218.883px 7.91667px; transform-origin: 218.883px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Note: Mini-Cube can use the full cube moves and ignore edge effects)\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: 85.9667px 7.91667px; transform-origin: 85.9667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eComing Soon: Matlab Tetris\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function solve_vec = rubik_solve_mini(r)\r\n% Expect Numeric representation of moves (1:27):\r\n% 1:27 are ufdlbr upfpdplpbprp u2f2d2l2b2r2 xyzxpypzpx2y2z2\r\n solve_vec=[]; \r\n\r\n% One path is to use Challenge 931's, Rubik's Mini Cube, initial Cube re-orientation provided in the template, followed by a solving algorithm that needs only RDB type moves\r\n% Loading an external data file is one method. First solve is not timed.\r\nend\r\n\r\nfunction r=rubik_rot_mini(mov,r)\r\n%mov is 1:27;  1-6 UFDLBR 7-12 U'F;D'L'B'R' 13-18 U2F2D2L2B2R2 \r\n%             19-27 XYZ X'Y'Z' X2Y2Z2  \r\n% X cube-R, Y cube-U,  Z cube-F\r\n%\r\n% r is a 24 element row vector\r\n% r output is a single row vector \r\n%\r\n% vector mov\r\n% r output is array of length(mov) x 24\r\n%\r\n% Perform Rubik Cube face rotations and cube rotations\r\n% L 1:4 U 5:8 F 9:12 D 13:16 B 17:20 R 21:24 \r\n% \r\npersistent vf\r\nif isempty(vf) %\r\nvf=[9 2 11 4 6 8 5 7 21 10 23 12 13 14 15 16 17 3 19 1 20 22 18 24 ; \r\n    1 2 13 15 5 4 7 3 10 12 9 11 22 14 21 16 17 18 19 20 6 8 23 24 ;\r\n    1 19 3 17 5 6 7 8 9 2 11 4 14 16 13 15 24 18 22 20 21 10 23 12 ;\r\n    2 4 1 3 17 18 7 8 5 6 11 12 9 10 15 16 13 14 19 20 21 22 23 24 ;\r\n    7 5 3 4 23 6 24 8 9 10 11 12 13 1 15 2 18 20 17 19 21 22 16 14 ;\r\n    1 2 3 4 5 6 11 12 9 10 15 16 13 14 19 20 17 18 7 8 22 24 21 23 ;\r\n    20 2 18 4 7 5 8 6 1 10 3 12 13 14 15 16 17 23 19 21 9 22 11 24 ;\r\n    1 2 8 6 5 21 7 22 11 9 12 10 3 14 4 16 17 18 19 20 15 13 23 24 ;\r\n    1 10 3 12 5 6 7 8 9 22 11 24 15 13 16 14 4 18 2 20 21 19 23 17 ;\r\n    3 1 4 2 9 10 7 8 13 14 11 12 17 18 15 16 5 6 19 20 21 22 23 24 ;\r\n    14 16 3 4 2 6 1 8 9 10 11 12 13 24 15 23 19 17 20 18 21 22 5 7 ;\r\n    1 2 3 4 5 6 19 20 9 10 7 8 13 14 11 12 17 18 15 16 23 21 24 22 ;\r\n    21 2 23 4 8 7 6 5 20 10 18 12 13 14 15 16 17 11 19 9 1 22 3 24 ;\r\n    1 2 22 21 5 15 7 13 12 11 10 9 8 14 6 16 17 18 19 20 4 3 23 24 ;\r\n    1 22 3 24 5 6 7 8 9 19 11 17 16 15 14 13 12 18 10 20 21 2 23 4 ;\r\n    4 3 2 1 13 14 7 8 17 18 11 12 5 6 15 16 9 10 19 20 21 22 23 24 ;\r\n    24 23 3 4 16 6 14 8 9 10 11 12 13 7 15 5 20 19 18 17 21 22 2 1 ;\r\n    1 2 3 4 5 6 15 16 9 10 19 20 13 14 7 8 17 18 11 12 24 23 22 21 ;\r\n    3 1 4 2 9 10 11 12 13 14 15 16 17 18 19 20 5 6 7 8 22 24 21 23 ;\r\n    9 10 11 12 6 8 5 7 21 22 23 24 15 13 16 14 4 3 2 1 20 19 18 17 ;\r\n    14 16 13 15 2 4 1 3 10 12 9 11 22 24 21 23 19 17 20 18 6 8 5 7 ;\r\n    2 4 1 3 17 18 19 20 5 6 7 8 9 10 11 12 13 14 15 16 23 21 24 22 ;\r\n    20 19 18 17 7 5 8 6 1 2 3 4 14 16 13 15 24 23 22 21 9 10 11 12 ;\r\n    7 5 8 6 23 21 24 22 11 9 12 10 3 1 4 2 18 20 17 19 15 13 16 14 ;\r\n    4 3 2 1 13 14 15 16 17 18 19 20 5 6 7 8 9 10 11 12 24 23 22 21 ;\r\n    21 22 23 24 8 7 6 5 20 19 18 17 16 15 14 13 12 11 10 9 1 2 3 4 ; \r\n    24 23 22 21 16 15 14 13 12 11 10 9 8 7 6 5 20 19 18 17 4 3 2 1 ];\r\nend\r\n\r\nr=r(vf(mov,:));\r\nend\r\n\r\n\r\nfunction move_vec=decode27_movestr_rev001(movestr)\r\n% Active character Inputs: UFDLBRXYZ, GQ are pre-processed\r\n% 1-6 UFDLBR 7-12 U'F;D'L'B'R' 13-18 U2F2D2L2B2R2 19-27 XYZX'Y'Z'X2Y2Z2\r\nmovestr=upper(movestr);\r\nmovestr=strrep(movestr,'''','P'); % simplify further searches\r\n\r\nmovestr=strrep(movestr,'UP',' 7 ');\r\nmovestr=strrep(movestr,'FP',' 8 ');\r\nmovestr=strrep(movestr,'DP',' 9 ');\r\nmovestr=strrep(movestr,'LP',' 10 ');\r\nmovestr=strrep(movestr,'BP',' 11 ');\r\nmovestr=strrep(movestr,'RP',' 12 ');\r\nmovestr=strrep(movestr,'U2',' 13 ');\r\nmovestr=strrep(movestr,'F2',' 14 ');\r\nmovestr=strrep(movestr,'D2',' 15 ');\r\nmovestr=strrep(movestr,'L2',' 16 ');\r\nmovestr=strrep(movestr,'B2',' 17 ');\r\nmovestr=strrep(movestr,'R2',' 18 ');\r\nmovestr=strrep(movestr,'U',' 1 ');\r\nmovestr=strrep(movestr,'F',' 2 ');\r\nmovestr=strrep(movestr,'D',' 3 ');\r\nmovestr=strrep(movestr,'L',' 4 ');\r\nmovestr=strrep(movestr,'B',' 5 ');\r\nmovestr=strrep(movestr,'R',' 6 ');\r\nmovestr=strrep(movestr,'XP',' 22 ');\r\nmovestr=strrep(movestr,'YP',' 23 ');\r\nmovestr=strrep(movestr,'ZP',' 24 ');\r\nmovestr=strrep(movestr,'X2',' 25 ');\r\nmovestr=strrep(movestr,'Y2',' 26 ');\r\nmovestr=strrep(movestr,'Z2',' 27 ');\r\nmovestr=strrep(movestr,'X',' 19 ');\r\nmovestr=strrep(movestr,'Y',' 20 ');\r\nmovestr=strrep(movestr,'Z',' 21 ');\r\n\r\nmove_vec=str2num(movestr);\r\n\r\nend % move_vec","test_suite":"%%\r\nfeval(@assignin,'caller','score',0);\r\n%%\r\nvf=[9 2 11 4 6 8 5 7 21 10 23 12 13 14 15 16 17 3 19 1 20 22 18 24 ; \r\n    1 2 13 15 5 4 7 3 10 12 9 11 22 14 21 16 17 18 19 20 6 8 23 24 ;\r\n    1 19 3 17 5 6 7 8 9 2 11 4 14 16 13 15 24 18 22 20 21 10 23 12 ;\r\n    2 4 1 3 17 18 7 8 5 6 11 12 9 10 15 16 13 14 19 20 21 22 23 24 ;\r\n    7 5 3 4 23 6 24 8 9 10 11 12 13 1 15 2 18 20 17 19 21 22 16 14 ;\r\n    1 2 3 4 5 6 11 12 9 10 15 16 13 14 19 20 17 18 7 8 22 24 21 23 ;\r\n    20 2 18 4 7 5 8 6 1 10 3 12 13 14 15 16 17 23 19 21 9 22 11 24 ;\r\n    1 2 8 6 5 21 7 22 11 9 12 10 3 14 4 16 17 18 19 20 15 13 23 24 ;\r\n    1 10 3 12 5 6 7 8 9 22 11 24 15 13 16 14 4 18 2 20 21 19 23 17 ;\r\n    3 1 4 2 9 10 7 8 13 14 11 12 17 18 15 16 5 6 19 20 21 22 23 24 ;\r\n    14 16 3 4 2 6 1 8 9 10 11 12 13 24 15 23 19 17 20 18 21 22 5 7 ;\r\n    1 2 3 4 5 6 19 20 9 10 7 8 13 14 11 12 17 18 15 16 23 21 24 22 ;\r\n    21 2 23 4 8 7 6 5 20 10 18 12 13 14 15 16 17 11 19 9 1 22 3 24 ;\r\n    1 2 22 21 5 15 7 13 12 11 10 9 8 14 6 16 17 18 19 20 4 3 23 24 ;\r\n    1 22 3 24 5 6 7 8 9 19 11 17 16 15 14 13 12 18 10 20 21 2 23 4 ;\r\n    4 3 2 1 13 14 7 8 17 18 11 12 5 6 15 16 9 10 19 20 21 22 23 24 ;\r\n    24 23 3 4 16 6 14 8 9 10 11 12 13 7 15 5 20 19 18 17 21 22 2 1 ;\r\n    1 2 3 4 5 6 15 16 9 10 19 20 13 14 7 8 17 18 11 12 24 23 22 21 ;\r\n    3 1 4 2 9 10 11 12 13 14 15 16 17 18 19 20 5 6 7 8 22 24 21 23 ;\r\n    9 10 11 12 6 8 5 7 21 22 23 24 15 13 16 14 4 3 2 1 20 19 18 17 ;\r\n    14 16 13 15 2 4 1 3 10 12 9 11 22 24 21 23 19 17 20 18 6 8 5 7 ;\r\n    2 4 1 3 17 18 19 20 5 6 7 8 9 10 11 12 13 14 15 16 23 21 24 22 ;\r\n    20 19 18 17 7 5 8 6 1 2 3 4 14 16 13 15 24 23 22 21 9 10 11 12 ;\r\n    7 5 8 6 23 21 24 22 11 9 12 10 3 1 4 2 18 20 17 19 15 13 16 14 ;\r\n    4 3 2 1 13 14 15 16 17 18 19 20 5 6 7 8 9 10 11 12 24 23 22 21 ;\r\n    21 22 23 24 8 7 6 5 20 19 18 17 16 15 14 13 12 11 10 9 1 2 3 4 ; \r\n    24 23 22 21 16 15 14 13 12 11 10 9 8 7 6 5 20 19 18 17 4 3 2 1 ];\r\n\r\n%r=r(vf(mov,:));  % Standard move, r=rubik_rot_min(mov,r);\r\n\r\nPass=1;\r\nr_fail=0;\r\n% Execute 500 cubes and verify completeness and count moves\r\n % initialize cube\r\n r(1:4)=0;     %Left   0 R\r\n r(5:8)=1;     %Up     1 W\r\n r(9:12)=2;    %Front  2 B\r\n r(13:16)=3;   %Down   3 Y\r\n r(17:20)=4;   %Back   4 G\r\n r(21:24)=5;   %Right  5 O\r\n rnorm=r;\r\n\r\nzcnt=500;\r\nsum_solve=0;\r\nmin_solve=1000;\r\nmax_solve=0;\r\nasolve=199;\r\nmix=40;\r\n\r\ntic\r\n\r\nfor cube_check=1:zcnt %zcnt\u003c100 %500\r\n %zcnt=zcnt+1;\r\n  r=rnorm;\r\n % Initial mix\r\n mov=randi(18,[mix,1]);\r\n for i=1:length(mov)  % Ignoring Move Undos since mix=40\r\n   r=r(vf(mov(i),:));\r\n end\r\n\r\n r_reset=r; % Used in assert\r\n\r\n solve_vec=rubik_solve_mini(r);\r\n\r\n for i=1:length(solve_vec) \r\n  r=r(vf(solve_vec(i),:));\r\n end\r\n\r\n  if all(r(1:4)==r(4)) \u0026\u0026 all(r(5:8)==r(8))  \u0026\u0026 all(r(9:12)==r(9)) \u0026\u0026 ...\r\n     all(r(13:16)==r(13))  \u0026\u0026 all(r(17:20)==r(17)) \u0026\u0026 all(r(21:24)==r(21))\r\n   solve_vec(solve_vec\u003e18)=[]; %   \r\n   lsolve=length(solve_vec);\r\n   if lsolve\u003e11, Pass=0;end % Length Rqmt\r\n   sum_solve=sum_solve+lsolve;\r\n   min_solve=min(min_solve,lsolve);\r\n   max_solve=max(max_solve,lsolve);\r\n   asolve=floor(sum_solve/zcnt);\r\n %  fprintf('Cube Solved Moves=%i  Avg Moves=%i min=%i  max=%i\\n',lsolve,asolve,min_solve,max_solve)\r\n  else % Deug info\r\n   Pass=0;\r\n   r_fail=r_reset;\r\n  % fprintf('\\n\\nCube NOT Solved???\\n\\n') \r\n  % fprintf('%i ',r); % Current ending data\r\n  % fprintf('\\n')\r\n  % fprintf('%i ',r_reset); % Starting Cube\r\n  end\r\n\r\nend % while of cubes\r\ntoc\r\n\r\nassert(isequal(Pass,1),sprintf('Max Len=%i \\n',max_solve)); % Length Exception\r\nassert(isequal(Pass,1),sprintf('%i ',r_fail)); % Output Non-Solved Cube Start\r\n\r\n%if Pass\r\n% feval(@assignin,'caller','score',min(100,floor(asolve)));\r\n%end\r\n\r\nfprintf('Moves: Avg %i   Min %i   Max %i\\n',asolve,min_solve,max_solve)\r\n\r\n%%\r\nvf=[9 2 11 4 6 8 5 7 21 10 23 12 13 14 15 16 17 3 19 1 20 22 18 24 ; \r\n    1 2 13 15 5 4 7 3 10 12 9 11 22 14 21 16 17 18 19 20 6 8 23 24 ;\r\n    1 19 3 17 5 6 7 8 9 2 11 4 14 16 13 15 24 18 22 20 21 10 23 12 ;\r\n    2 4 1 3 17 18 7 8 5 6 11 12 9 10 15 16 13 14 19 20 21 22 23 24 ;\r\n    7 5 3 4 23 6 24 8 9 10 11 12 13 1 15 2 18 20 17 19 21 22 16 14 ;\r\n    1 2 3 4 5 6 11 12 9 10 15 16 13 14 19 20 17 18 7 8 22 24 21 23 ;\r\n    20 2 18 4 7 5 8 6 1 10 3 12 13 14 15 16 17 23 19 21 9 22 11 24 ;\r\n    1 2 8 6 5 21 7 22 11 9 12 10 3 14 4 16 17 18 19 20 15 13 23 24 ;\r\n    1 10 3 12 5 6 7 8 9 22 11 24 15 13 16 14 4 18 2 20 21 19 23 17 ;\r\n    3 1 4 2 9 10 7 8 13 14 11 12 17 18 15 16 5 6 19 20 21 22 23 24 ;\r\n    14 16 3 4 2 6 1 8 9 10 11 12 13 24 15 23 19 17 20 18 21 22 5 7 ;\r\n    1 2 3 4 5 6 19 20 9 10 7 8 13 14 11 12 17 18 15 16 23 21 24 22 ;\r\n    21 2 23 4 8 7 6 5 20 10 18 12 13 14 15 16 17 11 19 9 1 22 3 24 ;\r\n    1 2 22 21 5 15 7 13 12 11 10 9 8 14 6 16 17 18 19 20 4 3 23 24 ;\r\n    1 22 3 24 5 6 7 8 9 19 11 17 16 15 14 13 12 18 10 20 21 2 23 4 ;\r\n    4 3 2 1 13 14 7 8 17 18 11 12 5 6 15 16 9 10 19 20 21 22 23 24 ;\r\n    24 23 3 4 16 6 14 8 9 10 11 12 13 7 15 5 20 19 18 17 21 22 2 1 ;\r\n    1 2 3 4 5 6 15 16 9 10 19 20 13 14 7 8 17 18 11 12 24 23 22 21 ;\r\n    3 1 4 2 9 10 11 12 13 14 15 16 17 18 19 20 5 6 7 8 22 24 21 23 ;\r\n    9 10 11 12 6 8 5 7 21 22 23 24 15 13 16 14 4 3 2 1 20 19 18 17 ;\r\n    14 16 13 15 2 4 1 3 10 12 9 11 22 24 21 23 19 17 20 18 6 8 5 7 ;\r\n    2 4 1 3 17 18 19 20 5 6 7 8 9 10 11 12 13 14 15 16 23 21 24 22 ;\r\n    20 19 18 17 7 5 8 6 1 2 3 4 14 16 13 15 24 23 22 21 9 10 11 12 ;\r\n    7 5 8 6 23 21 24 22 11 9 12 10 3 1 4 2 18 20 17 19 15 13 16 14 ;\r\n    4 3 2 1 13 14 15 16 17 18 19 20 5 6 7 8 9 10 11 12 24 23 22 21 ;\r\n    21 22 23 24 8 7 6 5 20 19 18 17 16 15 14 13 12 11 10 9 1 2 3 4 ; \r\n    24 23 22 21 16 15 14 13 12 11 10 9 8 7 6 5 20 19 18 17 4 3 2 1 ];\r\n\r\n%r=r(vf(mov,:));  % Standard move, r=rubik_rot_min(mov,r);\r\n\r\nPass=1;\r\nr_fail=0;\r\n% Execute 500 cubes and verify completeness and count moves\r\n % initialize cube\r\n r(1:4)=0;     %Left   0 R\r\n r(5:8)=1;     %Up     1 W\r\n r(9:12)=2;    %Front  2 B\r\n r(13:16)=3;   %Down   3 Y\r\n r(17:20)=4;   %Back   4 G\r\n r(21:24)=5;   %Right  5 O\r\n rnorm=r;\r\n\r\n for jrand=1:40  % Ignoring Move Undos since mix=40\r\n   r=r(vf(randi(18),:));\r\n end\r\n\r\n q=500;\r\n ra=zeros(q,24);\r\n for i=1:q\r\n  for jrand=1:10  % Ignoring Move Undos since base mix=40\r\n    r=r(vf(randi(18),:));\r\n  end\r\n % add 10 new moves to prior vector\r\n  ra(i,:)=r;\r\n end\r\n\r\n% The Time Trail section does not check accuracy, that is done above\r\nt0=clock;\r\nfor i=1:q\r\n solve_vec=rubik_solve_mini(ra(q,:));\r\nend\r\ndt=etime(clock,t0)*1000;\r\n\r\n%assert(isequal(find_fast(a,val(1)),find(a==val(1),1,'first')))\r\nfprintf('Your Time = %i msec\\n',floor(dt))\r\nfeval(@assignin,'caller','score',min(2000,floor(dt)));\r\n%   Performance Score","published":true,"deleted":false,"likes_count":5,"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":"2012-09-09T17:48:25.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-09-09T06:26:08.000Z","updated_at":"2025-11-17T16:25:58.000Z","published_at":"2012-09-09T16:33:58.000Z","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 Challenge is to Near or Optimally Solve a thoroughly scrambled Mini-Rubik's Cube(2x2x2).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eAny Mini-Cube can be solved in 11 or fewer Face Moves. There are only 3,674,160 unique cube positions, with only 2344 requiring the fulll 11 moves. Cube moves do not count.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 Performance metric is cumulative Time to Solve 500 cubes (msec).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 standard Mini-Rubik's Cube is shown in 3-D and also unfolded to identify the specific Tile-face/Vector numbering.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 faces are White-Up / Red-Left / Blue-Front / Orange-Right / Yellow-Down / Green-Back (ULFRDB)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eMoves are denoted as F for clockwise rotation of the Front face. F'(or FP) is CCW and F2 is F twice. The provided function r_new=rubick_rot_mini(mov,r) implements moves 1-18: UFDLBRU'F'D'L'B'R'U2F2D2L2B2R2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"center\\\"/\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=\\\"center\\\"/\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=\\\"center\\\"/\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=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\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: (rubik)\\n\\nrubik: row vector of size 24\\n(The cube started [L=0,U=1,F=2,D=3,B=4,R=5] and then receives forty face moves.\\n\\nOutput: move_vec (Numeric of moves to solve)\\n move_vec:values 1:27 for UFDLBR U'F'D'L'B'R' U2F2D2L2B2R2 XYZX'Y'Z'X2Y2Z2]]\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\u003eExample:\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 the cube was randomized by [1 9] UD', one solution is [3 7] which are the complements in reverse order.\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\u003eScoring is Time in msec to solve 500 cubes\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\u003eCube Moves X, Y, and Z do not constitute a move but are needed in the 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:t\u003eA string to numeric value function is provided in the template\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\u003eVerifications will be by executing your move vector against the provided Rubik and counting number of face moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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 function rubik_rot_mini(mov,r) is provided in the template. Other functions are provided to re-orient the cube in Cody Challenge 931, Rubik's Mini-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\u003eThe Challenge\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/931-rubik-s-mini-cube-solve-randomized-mini-cube-score-moves.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eChallenge 931 Rubik's Mini-Cube\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contains a 3D Mini-Cube Viewer for program development.\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\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:hyperlink w:docLocation=\\\"http://kociemba.org/cube.htm\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCube Theory: 20-moves Any Cube\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=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://peter.stillhq.com/jasmine/rubikscubesolution.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGeneral Cube Info and Middle Layer\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=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.speedcubing.com/final_layer_print.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpeedCube Bottom Sequences\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=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe site\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.speedcubing.com/CubeSolver/MiniCubeSolver.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMiniCube Solver\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e claims an outstanding 10 minutes to solve a randomized Mini-Cube. Matlab can achieve any Mini-Cube solution in \u0026lt; 497 usec, independent of moves on an i5/16GB machine.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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: Mini-Cube can use the full cube moves and ignore edge effects)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\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\u003eComing Soon: Matlab 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Euler: Problem 14 Longest Collatz sequence(harder version)","description":"This problem is a harder version of \"Problem 44448. Project Euler: Problem 14 Longest Collatz sequence\", because of time limits. \u003chttps://ww2.mathworks.cn/matlabcentral/cody/problems/44448\u003e \r\n\r\nThe following iterative sequence is defined for the set of positive integers:\r\nn → n/2 (n is even) n → 3n + 1 (n is odd)\r\nUsing the rule above and starting with 13, we generate the following sequence:\r\n13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.\r\nWhich starting number, no more than N, produces the longest chain, and how long? Don't cheat!","description_html":"\u003cp\u003eThis problem is a harder version of \"Problem 44448. Project Euler: Problem 14 Longest Collatz sequence\", because of time limits. \u003ca href = \"https://ww2.mathworks.cn/matlabcentral/cody/problems/44448\"\u003ehttps://ww2.mathworks.cn/matlabcentral/cody/problems/44448\u003c/a\u003e\u003c/p\u003e\u003cp\u003eThe following iterative sequence is defined for the set of positive integers:\r\nn → n/2 (n is even) n → 3n + 1 (n is odd)\r\nUsing the rule above and starting with 13, we generate the following sequence:\r\n13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.\r\nWhich starting number, no more than N, produces the longest chain, and how long? Don't cheat!\u003c/p\u003e","function_template":"function y = euler014_v2(x)\r\n  y = x;\r\nend","test_suite":"1\r\n%%\r\nassessFunctionAbsence({'tic','toc','pause','etime','clock','now','str2num','timer'},'FileName','euler014_v2.m')\r\n\r\n2\r\n%%\r\nN = 2e8;\r\nnum_correct = 169941673;\r\nlen_correct = 954;\r\ntic\r\n[num, len] = euler014_v2(N);\r\nt=toc\r\nassert(t\u003e1);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n3\r\n%%\r\nN = 4e8;\r\nnum_correct = 268549803;\r\nlen_correct = 965;\r\ntic\r\n[num, len] = euler014_v2(N);\r\nt=toc\r\nassert(t\u003e1);\r\nassert(isequal(num, num_correct));\r\nassert(isequal(len, len_correct));\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":3668,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2018-11-11T06:11:05.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-11-09T16:59:43.000Z","updated_at":"2018-11-11T06:11:05.000Z","published_at":"2018-11-09T16:59:43.000Z","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 a harder version of \\\"Problem 44448. Project Euler: Problem 14 Longest Collatz sequence\\\", because of time limits.\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://ww2.mathworks.cn/matlabcentral/cody/problems/44448\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://ww2.mathworks.cn/matlabcentral/cody/problems/44448\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\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 following iterative sequence is defined for the set of positive integers: n → n/2 (n is even) n → 3n + 1 (n is odd) Using the rule above and starting with 13, we generate the following sequence: 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1. Which starting number, no more than N, produces the longest chain, and how long? Don't cheat!\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":908,"title":"AVIRIS Inscribed Rectangle Bit Mask - Speed Test","description":"The AVIRIS data sometimes is provided uncropped. This creates edge regions with values of \"-50\". Shown is AVIRIS Moffett Field image Layer 1 (400nm) and a zoom of the top-right corner. The dark blue on the edges is non-imaged ground.\r\n\r\nTo expedite compression the unused mask can be created with an interior excluded rectangle of no Zeros and maximum pixels. The challenge is to quickly and accurately determine the maximum non-zero inscribed rectangle for this image and some other test cases.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/AVIRIS_L001.jpg\u003e\u003e \r\n\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/AVIRIS_L001_TR.jpg\u003e\u003e\r\n\r\n\r\n*Input:* m ( 2-D array with zero and non-zero values )\r\n\r\n*Output:* [idxtlc rmnr rmnc]\r\n\r\n* idxtlc : array index of top left corner\r\n* rmnr : numer of rows of the non-zero rectangle mask\r\n* rmnc : number of columns of the non-zero rectangle mask\r\n\r\n*Score:* Time ( msec ) Based upon time to process the 1924x753 AVIRIS image.\r\n\r\n(If time-out occurs I suggest downloading the mat file using the test suite code and then optimize with profiler.)\r\n\r\n*Passing:* Minimum number of pixels in rectangle\r\n\r\n\r\n\r\n*Example:* \r\n\r\n* m=[ 1 1 0 1 1......idxtlc = 3  ( index of TLC row 3, col 1) \r\n* ........1 0 1 1 1......rmnr = 3 ( rectangle mask number rows )\r\n* ........1 1 1 1 1......rmnc = 5 ( rectangle mask number columns )\r\n* ........1 1 1 1 1......Maximum rectangle pixels of 15\r\n* ........1 1 1 1 1 ]\r\n \r\n \r\nFor the AVIRIS image the zero edge bit mask encoding can be reduced 89% using an inscribed rectangle.\r\n\r\nNote: Additional test cases may be invoked if hard coded solution achieves best score. ","description_html":"\u003cp\u003eThe AVIRIS data sometimes is provided uncropped. This creates edge regions with values of \"-50\". Shown is AVIRIS Moffett Field image Layer 1 (400nm) and a zoom of the top-right corner. The dark blue on the edges is non-imaged ground.\u003c/p\u003e\u003cp\u003eTo expedite compression the unused mask can be created with an interior excluded rectangle of no Zeros and maximum pixels. The challenge is to quickly and accurately determine the maximum non-zero inscribed rectangle for this image and some other test cases.\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/AVIRIS_L001.jpg\"\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/AVIRIS_L001_TR.jpg\"\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m ( 2-D array with zero and non-zero values )\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e [idxtlc rmnr rmnc]\u003c/p\u003e\u003cul\u003e\u003cli\u003eidxtlc : array index of top left corner\u003c/li\u003e\u003cli\u003ermnr : numer of rows of the non-zero rectangle mask\u003c/li\u003e\u003cli\u003ermnc : number of columns of the non-zero rectangle mask\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eScore:\u003c/b\u003e Time ( msec ) Based upon time to process the 1924x753 AVIRIS image.\u003c/p\u003e\u003cp\u003e(If time-out occurs I suggest downloading the mat file using the test suite code and then optimize with profiler.)\u003c/p\u003e\u003cp\u003e\u003cb\u003ePassing:\u003c/b\u003e Minimum number of pixels in rectangle\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003em=[ 1 1 0 1 1......idxtlc = 3  ( index of TLC row 3, col 1)\u003c/li\u003e\u003cli\u003e........1 0 1 1 1......rmnr = 3 ( rectangle mask number rows )\u003c/li\u003e\u003cli\u003e........1 1 1 1 1......rmnc = 5 ( rectangle mask number columns )\u003c/li\u003e\u003cli\u003e........1 1 1 1 1......Maximum rectangle pixels of 15\u003c/li\u003e\u003cli\u003e........1 1 1 1 1 ]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eFor the AVIRIS image the zero edge bit mask encoding can be reduced 89% using an inscribed rectangle.\u003c/p\u003e\u003cp\u003eNote: Additional test cases may be invoked if hard coded solution achieves best score.\u003c/p\u003e","function_template":"function [idxtlc rmnr rmnc]=rect_mask(m)\r\n  idxtlc=1; rmnr=0; rmnc=0;\r\nend","test_suite":"global dt\r\ndt=0;\r\nm=ones(5); m(7)=0;\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(m);\r\ndt=etime(clock,t0)*1000; % ms\r\ndt\r\n\r\n[x y]=ind2sub(size(m),idxtlc);\r\npass=~any(any(m(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=15,sprintf('Expected 15 pixels, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n%%\r\nm=ones(5); m(7)=0; m(11)=0;\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(m);\r\ndt=etime(clock,t0)*1000; % ms\r\ndt\r\n\r\n[x y]=ind2sub(size(m),idxtlc);\r\npass=~any(any(m(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=15,sprintf('Expected 15 pixels, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n%%\r\n m=zeros(6);m(15)=1;\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(m);\r\ndt=etime(clock,t0)*1000; % ms\r\ndt\r\n\r\n[x y]=ind2sub(size(m),idxtlc);\r\npass=~any(any(m(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=1,sprintf('Expected 1 pixel, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n%%\r\nm=ones(8); m(2,:)=0;  m(7,:)=0;  m(4,2)=0;  m(5,7)=0;\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(m);\r\ndt=etime(clock,t0)*1000; % ms\r\ndt\r\n\r\n[x y]=ind2sub(size(m),idxtlc);\r\npass=~any(any(m(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=16,sprintf('Expected 16 pixels, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n%%\r\n% Load aviris file Layer 1 ; 1.8MB mat file\r\nglobal dt\r\ntic % approx 2.5 sec to write and load\r\nurlwrite('http://tinyurl.com/matlab-avmofL001','aviris_moffett_L001.mat');\r\ntoc\r\nload('aviris_moffett_L001.mat');\r\ntoc\r\n\r\n% Array variable is L001\r\n%Files also posted are L010,L023,L157, and L158 with same tinyurl format\r\n% L010 and L023 have high contrast L157 and L158 are in atmospheric notch\r\n\r\n% Time Trial File 1924 x 753\r\nt0=clock;\r\n[idxtlc rmnr rmnc]=rect_mask(L001);\r\ndt=etime(clock,t0)*1000; % ms\r\n\r\n[x y]=ind2sub(size(L001),idxtlc);\r\npass=~any(any(L001(x:x+rmnr-1,y:y+rmnc-1)==0));\r\n\r\nassert(rmnr*rmnc\u003e=1282281,sprintf('Expected 1282281 pixels, only have %i\\n',rmnr*rmnc))\r\nassert(isequal(pass,1),sprintf('Not all non-zero [%i,%i] [%i,%i]\\n',x,y,x+rmnr-1,y+rmnc-1))\r\n\r\nfprintf('Time to process %.0f msec\\n',dt);\r\nfprintf('idx=%i x=%i y=%i  rect_nr=%i  rect_nc=%i\\n',idxtlc,x,y,rmnr,rmnc)\r\n\r\n%%\r\nglobal dt\r\n%Write file based on time in test 1\r\nnet_time=uint32(dt);\r\n% net_time in ms\r\n% Create graph data\r\nnet_time=min(4000,net_time); % Limit graph y-axis\r\nfh=fopen('rect_mask.m','wt');\r\nfeval(@assignin,'caller','score',net_time)\r\nfprintf(fh,'%s\\n',repmat('1;',[1,round(net_time/2)]));\r\nfclose(fh);","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2012-08-15T00:11:30.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-08-13T00:01:09.000Z","updated_at":"2012-08-15T00:22:27.000Z","published_at":"2012-08-13T05:16:56.000Z","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\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.JPEG\"}],\"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 AVIRIS data sometimes is provided uncropped. This creates edge regions with values of \\\"-50\\\". Shown is AVIRIS Moffett Field image Layer 1 (400nm) and a zoom of the top-right corner. The dark blue on the edges is non-imaged ground.\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 expedite compression the unused mask can be created with an interior excluded rectangle of no Zeros and maximum pixels. The challenge is to quickly and accurately determine the maximum non-zero inscribed rectangle for this image and some other test 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: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=\\\"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\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=\\\"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\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 m ( 2-D array with zero and non-zero values )\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\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [idxtlc rmnr rmnc]\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\u003eidxtlc : array index of top left corner\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\u003ermnr : numer of rows of the non-zero rectangle mask\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\u003ermnc : number of columns of the non-zero rectangle mask\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\u003eScore:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Time ( msec ) Based upon time to process the 1924x753 AVIRIS image.\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(If time-out occurs I suggest downloading the mat file using the test suite code and then optimize with profiler.)\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\u003ePassing:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Minimum number of pixels in rectangle\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:\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\u003em=[ 1 1 0 1 1......idxtlc = 3 ( index of TLC row 3, col 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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e........1 0 1 1 1......rmnr = 3 ( rectangle mask number rows )\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........1 1 1 1 1......rmnc = 5 ( rectangle mask number columns )\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........1 1 1 1 1......Maximum rectangle pixels of 15\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........1 1 1 1 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\u003eFor the AVIRIS image the zero edge bit mask encoding can be reduced 89% using an inscribed rectangle.\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\u003eNote: Additional test cases may be invoked if hard coded solution achieves best 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\"},{\"partUri\":\"/media/image2.JPEG\",\"contentType\":\"image/JPEG\",\"content\":\"data:image/JPEG;base64,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