{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2025-12-14T01:33:56.000Z","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":"2025-12-14T00:00:00.000Z","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":58877,"title":"Neural Net Image Convolution: Return only Valid portion of conv2","description":"This challenge is to return only the Valid portion of a 2-D convolution. Valid is deemed to be where all elements of the kernel are applied against the image. The (1,1) point with a ones(3) kernel would be invalid while point (2,2) would return a Valid convolution value. The images will be even-square,m, and the kernels will be odd-square,K.The m=ones(6) and K=ones(3) would return a 4x4 matrix of 9s.\r\nmValidConv2=create_ValidConv2(m,K)\r\nIn neural net processing of the Mnist database of digits(0-9) one Convolutional Neural Network method uses 9x9 kernels on the 28x28 images to return the Valid 20x20 central core for the next processing step. The third image includes a black border of four around Valid to maintain scale.\r\n  \r\nThe entire Matlab code from Convolutional Neural Networks in Matlab by Nuruzzaman Faruqui , a Matlab Neural Net Tutorial for Mnist processing, is included in the function template. Loading the Mnist Train/Test Images/Labels from posted MAT files or THE MNIST DATABASE of handwritten digits are provided. The Neural Net tutorial explains Neural Net processing for Digit Recognition. The neural net training method uses 784 input nodes,  20 convolutions(9x9), ReLU, Pooling, batch sampling, single hidden layer of 100 nodes, updating parameters via backwards propagation, and updating convolution kernels. The digit prediction accuracy is \u003e94% after 50s of training. The final feature kernels are very amorphous and unexpected.","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: 545.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 272.75px; transform-origin: 407px 272.75px; vertical-align: baseline; \"\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: 369.5px 8px; transform-origin: 369.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to return only the Valid portion of a 2-D convolution. Valid is deemed to be where all elements of the kernel are applied against the image. The (1,1) point with a ones(3) kernel would be invalid while point (2,2) would return a Valid convolution value. The images will be even-square,m, and the kernels will be odd-square,K.The m=ones(6) and K=ones(3) would return a 4x4 matrix of 9s.\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: 121.5px 8px; transform-origin: 121.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003emValidConv2=create_ValidConv2(m,K)\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: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn neural net processing of the Mnist database of digits(0-9) one Convolutional Neural Network method uses 9x9 kernels on the 28x28 images to return the Valid 20x20 central core for the next processing step. The third image includes a black border of four around Valid to maintain scale.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 194.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 97.25px; text-align: left; transform-origin: 384px 97.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 252px;height: 189px\" 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\" data-image-state=\"image-loaded\" width=\"252\" height=\"189\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 91px 8px; transform-origin: 91px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe entire Matlab code from \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.youtube.com/watch?v=ZOXOwYUVCqw\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eConvolutional Neural Networks in Matlab by Nuruzzaman Faruqui\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: 70px 8px; transform-origin: 70px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e , a Matlab Neural Net Tutorial for Mnist processing, is included in the function template. Loading the Mnist Train/Test Images/Labels from posted MAT files or \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eTHE MNIST DATABASE of handwritten digits\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: 181.5px 8px; transform-origin: 181.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e are provided. The Neural Net tutorial explains Neural Net processing for Digit Recognition. The neural net training method uses 784 input nodes,  20 convolutions(9x9), ReLU, Pooling, batch sampling, single hidden layer of 100 nodes, updating parameters via backwards propagation, and updating convolution kernels. The digit prediction accuracy is \u0026gt;94% after 50s of training. The final feature kernels are very amorphous and unexpected.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function mValidConv2=create_ValidConv2(m,K)\r\n  mValidConv2=conv2(m,K,'same');\r\nend\r\n\r\n% Code from Convolutional Neural Network in Matlab by Nuruzzaman Faruqui\r\n% Youtube:  https://www.youtube.com/watch?v=ZOXOwYUVCqw\r\n% I have made comments noted by raz and a couple speed enhancements\r\n% I also provide alternate direct source for MNIST files as .mat files\r\n%{\r\nfunction MNIST_Process\r\n%Based upon https://www.youtube.com/watch?v=ZOXOwYUVCqw\r\n%784 input nodes;20 conv 9x9 filters,ReLU;2x2 submatrix Pooling layer,single hidden layer 100 nodes\r\n\r\n%Matlab Convolutional Neural Network in Matlab\r\n%Processing MNIST digits using conv, contents, \r\n%MNIST files  http://yann.lecun.com/exdb/mnist/\r\n%Create filters\r\n%File Exchange NN https://www.mathworks.com/matlabcentral/fileexchange/59223-convolution-neural-network-simple-code-simple-to-use?s_tid=ta_fx_results\r\n%\r\n%use Test_images.mat if avail, use MNIST ubyte files if avail, load Test_images.mat\r\ndir_struct=dir;\r\nfilenames={dir_struct.name};\r\nfn='Test_images.mat';\r\nptr=find(ismember(filenames,fn));\r\nif ~isempty(ptr) % Test images/labels mat files exist\r\n tic;\r\n load('Test_images'); % images [28,28,10000] uint8\r\n load('Test_labels'); % labels [10000,1] uint8 values 0:9\r\n fprintf('Images:%i  Time:%.2f\\n',size(images,3),toc);\r\n Images=double(images)/255; %raz values must be 0:1 or bad things happen\r\n Labels=double(labels);\r\n Labels(Labels==0)=10; % 0 remapped to 10\r\n\r\nelse % Check for Mnist file\r\n %MNIST files source  http://yann.lecun.com/exdb/mnist/\r\n fn='t10k-images.idx3-ubyte'; %NMIST gzip expanded file for Test Images 10K\r\n filenames={dir_struct.name};\r\n ptr=find(ismember(filenames,fn));\r\n if ~isempty(ptr)\r\n  tic\r\n  Images=loadMNISTImages(fn);\r\n  fprintf('MNIST Images Process Time: %.2f\\n',toc); %raz\r\n  Images=reshape(Images,28,28,[]); % Un-vectorize the images\r\n  %Images already scaled by 1/255 in loadMNISTImages\r\n \r\n  Labels=loadMNISTLabels('t10k-labels.idx1-ubyte');\r\n  Labels(Labels==0)=10; % 0 to 10\r\nelse % download Test_images.mat, Test_labels.mat and load\r\n fnameTestI='Test_images.mat'; %load('Test_images.mat') creates images(28,28,10000) 1.6MB\r\n fnameTrainI='Train_images.mat'; %load('Train_images.mat') creates images(28,28,60000)  9.9MB\r\n fnameTestL='Test_labels.mat'; %load('Test_images.mat') creates labels(10000,1) 5KB\r\n fnameTrainL='Train_labels.mat'; %load('Train_images.mat') creates labels(60000,1) 30KB\r\n \r\n %Google Drive Dowloads need to come from shared files\r\n% Tweak link: file/d/ to uc?export=download\u0026id=   while removing /view?usp=sharing\r\n% https://drive.google.com/file/d/1v3GsGgP3p905wzdvUqypL_-djYmxiyzK/view?usp=sharing\r\n% https://drive.google.com/uc?export=download\u0026id=1v3GsGgP3p905wzdvUqypL_-djYmxiyzK\r\n \r\n\r\n  urlTeI='https://drive.google.com/uc?export=download\u0026id=10ReGWlfSvr9p6uq6FQlqw6vw3UXgWo57';\r\n  urlTeL='https://drive.google.com/uc?export=download\u0026id=105zTcVgItZ2JjFiUkJulU33R7lXNl29-';\r\n  urlTrI='https://drive.google.com/uc?export=download\u0026id=1HJZssH6_9OcQ8mXwS_DtvsS_Kk86BDFY';\r\n  urlTrL='https://drive.google.com/uc?export=download\u0026id=1KNTn2eNXbQkQJNe6xhO1LMmvkDRP-XLq';\r\n \r\n  tic\r\n  urlwrite(urlTeI,fnameTestI); %Writing GoogleDrive MNIST_Test_images.pdf  Test_images.mat \r\n  fprintf('Download 1.6MB Time: %.1f  sec\\n\\n',toc); %1.6MB download Time, about 1.1 sec\r\n \r\n  tic\r\n  urlwrite(urlTeL,fnameTestL); %Writing GoogleDrive MNIST_Test_labels.pdf  Test_labels.mat\r\n  fprintf('Download 5KB Time: %.1f  sec\\n\\n',toc); %5KB download Time, about 0.5 sec\r\n \r\n  tic\r\n  urlwrite(urlTrI,fnameTrainI); %Writing GoogleDrive MNIST_Train_images.pdf  Train_images.mat\r\n  fprintf('Download 9.9MB Time: %.1f  sec\\n\\n',toc); %9.9MB download Time, about 4.1 sec\r\n \r\n  tic\r\n  urlwrite(urlTrL,fnameTrainL); %Writing GoogleDrive MNIST_Train_labels.pdf  Train_labels.mat\r\n  fprintf('Download 30KB Time: %.1f  sec\\n\\n',toc); %30KB download Time, about 0.6 sec\r\n\r\n  tic\r\n  load('Test_images'); % images [28,28,10000] uint8\r\n  load('Test_labels'); % labels [10000,1] uint8 values 0:9\r\n  fprintf('Images:%i  Time:%.2f\\n',size(images,3),toc); %.05s\r\n  Images=double(images)/255; %raz values must be 0:1 or bad things happen\r\n  Labels=double(labels);\r\n  Labels(Labels==0)=10; % 0 to 10\r\n end % if MNIST ubyte files\r\n\r\nend % if Test_images.mat\r\n\r\n\r\nrng(1); %raz not liked by curretn matlab\r\n\r\nW1=.01*randn([9 9 20]); %Conv Kernels   base .01\r\nW5=(2*rand(100,2000)-1)*sqrt(6)/sqrt(360+2000); %Hidden layer coeff of 100 pooled\r\nWo=(2*rand(10, 100)-1)*sqrt(6)/sqrt( 10+ 100); %Output coeff to find 0:9 best match\r\n\r\nX=Images(:,:,1:8000);\r\nD=Labels(1:8000);\r\nztic=tic;\r\nfor epoch=1:3  %Repeat cycles of all Training data\r\n fprintf('epoch:%i  Time:%.2f\\n',epoch,toc(ztic));\r\n [W1, W5, Wo]=MnistConv(W1,W5,Wo,X,D);\r\nend %epoch\r\n\r\n%save('MnistConv.mat'); % Used for Post analysis, see video\r\n\r\nX=Images(:,:,8001:10000);\r\nD=Labels(8001:10000);\r\nacc=0;\r\nN=length(D);\r\n\r\nfor k=1:N\r\n x=X(:,:,k);\r\n y1=Conv(x,W1); %Neural Net Processing  W1 kernels on image-x\r\n y2=ReLU(y1);   %Rectification process\r\n y3=Pool(y2);   %Take 20x20 conv images into 100 values\r\n y4=reshape(y3,[],1);\r\n v5=W5*y4;      %Hidden layer weights\r\n y5=ReLU(v5);   %Rectification process\r\n v=Wo*y5;       %Wo Output weight processing\r\n y=Softmax(v);  %create vector length 10 of expected probability, uses exp(x) smoothing\r\n                %Needed in training but not sure why in evaluation\r\n \r\n [~,i]=max(y);\r\n if i==D(k)\r\n  acc=acc+1;\r\n end\r\nend %k\r\n\r\n acc=acc/N;\r\n fprintf('Accuracy is %.2f%%  Time:%.2f\\n',100*acc,toc(ztic));  %raz output/time\r\n\r\nend% MNIST_Process\r\n\r\nfunction [W1, W5,Wo]=MnistConv(W1, W5,Wo,X,D)\r\n alpha=.01;\r\n beta=0.95;\r\n z=reshape(1:100,10,10); % raz\r\n kmap=kron(z,ones(2)); % raz\r\n \r\n momentum1=zeros(size(W1));\r\n momentum5=zeros(size(W5));\r\n momentumo=zeros(size(Wo));\r\n \r\n N=length(D);\r\n \r\n bsize=100;\r\n blist=1:bsize:(N-bsize+1);\r\n \r\n for batch=1:length(blist) % Batch/Updates, each Batch creates updates to Params\r\n  dW1=zeros(size(W1));\r\n  dW5=zeros(size(W5));\r\n  dWo=zeros(size(Wo));\r\n  \r\n  begin=blist(batch);\r\n  for k=begin:begin+bsize-1  %Subset Group of Training samples for param adjust\r\n   x=X(:,:,k);\r\n   y1=Conv(x,W1); %8K/5.8s  3.5s\r\n   y2=ReLU(y1);\r\n   y3=Pool(y2);\r\n   y4=reshape(y3,[],1);\r\n   v5=W5*y4;\r\n   y5=ReLU(v5);\r\n   v=Wo*y5;\r\n   y=Softmax(v); %10 Probability Bins for [1-9 0] \r\n   \r\n   d=zeros(10,1);\r\n   d(sub2ind(size(d),D(k),1))=1;\r\n   \r\n    e=d-y; % raz should this be (e-y)^2*sign(e-y)?\r\n    delta=e;\r\n    e5=Wo'*delta;\r\n    delta5=(y5\u003e0).*e5;\r\n    e4=W5'*delta5;\r\n    e3=reshape(e4,size(y3));\r\n    e2=zeros(size(y2));\r\n    W3=ones(size(y2))/(2*2);\r\n    \r\n    for c=1:20\r\n     %e2(:,:,c)=kron(e3(:,:,c),ones([2 2])).*W3(:,:,c); %160K/4.5s\r\n     e3c=e3(:,:,c); %160K/.27 raz\r\n     e2(:,:,c)=e3c(kmap).*W3(:,:,c);  %160K/.8s  raz\r\n    end %c\r\n    \r\n    delta2=(y2\u003e0).*e2;\r\n    delta1_x=zeros(size(W1)); \r\n    \r\n    for c=1:20\r\n     delta1_x(:,:,c)=conv2(x(:,:),rot90(delta2(:,:,c),2),'valid'); %160K/5.3s\r\n    end %c\r\n    \r\n    dW1=dW1+delta1_x; %error per kernel plane\r\n    dW5=dW5+delta5*y4'; %8K/4.2s  100x2000\r\n    dWo=dWo+delta*y5';\r\n  end %k\r\n  dW1=dW1/bsize;\r\n  dW5=dW5/bsize;\r\n  dWo=dWo/bsize;\r\n  \r\n  momentum1=alpha*dW1+beta*momentum1;\r\n  W1       = W1+momentum1;\r\n  \r\n  momentum5=alpha*dW5+beta*momentum5;\r\n  W5       = W5+momentum5;\r\n  \r\n  momentumo=alpha*dWo+beta*momentumo;\r\n  Wo       = Wo+momentumo;\r\n  \r\n end % batch\r\n \r\nend %MnistConv\r\n\r\nfunction rng(x)\r\n randn('seed',x);\r\n rand('seed',x);\r\nend % rng\r\n\r\nfunction y=Pool(x)\r\n [xrow,xcol,numFilters]=size(x);\r\n y=zeros(xrow/2,xcol/2,numFilters);\r\n for k=1:numFilters\r\n  filter=ones(2)/(2*2);\r\n  image=conv2(x(:,:,k),filter,'valid');\r\n  y(:,:,k)=image(1:2:end,1:2:end);\r\n end\r\n\r\nend %Pool\r\n\r\nfunction y=Conv(x,W)\r\n [wrow,wcol,numFilters]=size(W);\r\n [xrow,xcol,~         ]=size(x);\r\n \r\n yrow=xrow-wrow+1;\r\n ycol=xcol-wcol+1;\r\n \r\n y=zeros(yrow,ycol,numFilters);\r\n \r\n for k=1:numFilters\r\n  filter=W(:,:,k);\r\n  filter=rot90(squeeze(filter),2); %200K/2.5s  raz remove test/92.15%\r\n  y(:,:,k)=conv2(x,filter,'valid'); %200K/3.7s  3.5s\r\n end\r\nend %Conv\r\n\r\nfunction y=Softmax(x)\r\n ex=exp(x);\r\n y=ex/sum(ex);\r\nend %Softmax\r\n\r\nfunction y=ReLU(x)\r\n y=max(0,x);\r\nend %ReLU\r\n\r\n\r\n\r\nfunction images=loadMNISTImages(filename)\r\n%\r\nfp=fopen(filename,'rb');\r\nassert(fp~=-1,['Could not open', filename,'']);\r\nmagic=fread(fp,1,'int32',0,'ieee-be');\r\nassert(magic==2051,['Bad magic number in', filename, '']); %2051 Images\r\nnumImages=fread(fp,1,'int32',0,'ieee-be');\r\nnumRows=fread(fp,1,'int32',0,'ieee-be');\r\nnumCols=fread(fp,1,'int32',0,'ieee-be');\r\nimages=fread(fp,inf,'unsigned char=\u003eunsigned char');\r\nfclose(fp);\r\nfprintf('Images:%i  rows:%i  cols:%i\\n',numImages,numRows,numCols); %raz\r\n\r\nimages=reshape(images,numCols,numRows,numImages);\r\nimages=permute(images,[2 1 3]); \r\n\r\nimages=reshape(images,size(images,1)*size(images,2),size(images,3)); %raz Will be reverted\r\nimages=double(images)/255;\r\n \r\nend %loadMNISTImages\r\n\r\nfunction labels=loadMNISTLabels(filename)\r\n%\r\nfp=fopen(filename,'rb');\r\nassert(fp~=-1,['Could not open', filename,'']);\r\nmagic=fread(fp,1,'int32',0,'ieee-be');\r\nassert(magic==2049,['Bad magic number in ', filename,'']);\r\nnumLabels=fread(fp,1,'int32',0,'ieee-be');\r\nlabels=fread(fp,inf,'unsigned char');\r\nassert(size(labels,1)==numLabels,'Mismatch in label count');\r\nfclose(fp);\r\nfprintf('Labels:%i\\n',numLabels); %raz\r\n\r\n\r\nend %loadMNISTLabels\r\n%}","test_suite":"%%\r\n%Google Drive Dowloads need to come from shared files\r\n% Tweak link: file/d/ to uc?export=download\u0026id=   while removing /view?usp=sharing\r\n% https://drive.google.com/file/d/1v3GsGgP3p905wzdvUqypL_-djYmxiyzK/view?usp=sharing\r\n% https://drive.google.com/uc?export=download\u0026id=1v3GsGgP3p905wzdvUqypL_-djYmxiyzK\r\n fnameTestI='Test_images.mat'; %load('Test_images.mat') creates images(28,28,10000) 1.6MB\r\n fnameTrainI='Train_images.mat'; %load('Train_images.mat') creates images(28,28,60000)  9.9MB\r\n fnameTestL='Test_labels.mat'; %load('Test_images.mat') creates labels(10000,1) 5KB\r\n fnameTrainL='Train_labels.mat'; %load('Train_images.mat') creates labels(60000,1) 30KB\r\n \r\n urlTeI='https://drive.google.com/uc?export=download\u0026id=10ReGWlfSvr9p6uq6FQlqw6vw3UXgWo57';\r\n urlTeL='https://drive.google.com/uc?export=download\u0026id=105zTcVgItZ2JjFiUkJulU33R7lXNl29-';\r\n urlTrI='https://drive.google.com/uc?export=download\u0026id=1HJZssH6_9OcQ8mXwS_DtvsS_Kk86BDFY';\r\n urlTrL='https://drive.google.com/uc?export=download\u0026id=1KNTn2eNXbQkQJNe6xhO1LMmvkDRP-XLq';\r\n \r\n % fn.mat  https://drive.google.com/file/d/10GsOZTIjzMIuO7xAYIqLT1zIq9Cyubl-/view?usp=drive_link\r\n % Google Gives warning thus aborts urlwrite\r\n %\r\n %fname='Mnist_Test_images.pdf'\r\n %Fake name of .pdf on GoogleDrive,  write as a mat\r\n \r\n %url='https://drive.google.com/file/d/1mgxzsmVQNXgqHEdd61QR2r0STm3N9lgG/view?usp=drive_link';\r\n %ptr=strfind(url,'/view'); % Tweaking the url\r\n %url(ptr:end)=[];\r\n %url=strrep(url,'file/d/','uc?export=download\u0026id=');\r\n \r\n tic\r\n urlwrite(urlTeI,fnameTestI); %Writing GoogleDrive MNIST_Test_images.pdf  Test_images.mat\r\n fprintf('Download 1.6MB Time: %.1f  sec\\n',toc); %1.6MB download Time, about 1.5 sec\r\n \r\n tic\r\n urlwrite(urlTeL,fnameTestL); %Writing GoogleDrive MNIST_Test_labels.pdf  Test_labels.mat\r\n fprintf('Download 5KB Time: %.1f  sec\\n',toc); %5KB download Time, about .5 sec\r\n \r\n global labels images \r\n \r\n load('Test_images'); % images [28,28,10000] uint8\r\n load('Test_labels'); % labels [10000,1] uint8 values 0:9\r\n \r\n %Showing a Label and an image from Mnist\r\n ptr=randi(10000);\r\n fprintf('Label:%i\\n',labels(ptr));\r\n figure(1);imagesc(images(:,:,ptr)); colormap gray;\r\n \r\n \r\nvalid=1;\r\nassert(valid)\r\n%%\r\n%Showing a Label and an image from Mnist\r\n global labels images\r\n valid=1;\r\n ptr=randi(10000);\r\n fprintf('Label:%i\\n',labels(ptr));\r\n m=double(images(:,:,ptr))/255;\r\n figure(2);imagesc(m); colormap gray;\r\n \r\n K=repmat([0 0 .5 1 1 1 .5 0 0],9,1); % Vert Bar\r\n mValidConv2=create_ValidConv2(m,K);\r\n figure(3);imagesc(mValidConv2); colormap gray;\r\n mc=conv2(m,K,'same');\r\n if ~isequal(mc(5:end-4,5:end-4),mValidConv2)\r\n  valid=0;\r\n end\r\n \r\nassert(valid)\r\n\r\n%%\r\n%Showing a Label and an image from Mnist\r\n global labels images\r\n valid=1;\r\n ptr=randi(10000);\r\n fprintf('Label:%i\\n',labels(ptr));\r\n m=double(images(:,:,ptr))/255;\r\n figure(2);imagesc(m); colormap gray;\r\n \r\n K=repmat([0 0 .5 1 1 1 .5 0 0]',1,9); %Horiz Bar\r\n mValidConv2=create_ValidConv2(m,K);\r\n figure(3);imagesc(mValidConv2); colormap gray;\r\n mc=conv2(m,K,'same');\r\n if ~isequal(mc(5:end-4,5:end-4),mValidConv2)\r\n  valid=0;\r\n end\r\n \r\nassert(valid)\r\n\r\n%%\r\n%Showing a Label and an image from Mnist\r\n global labels images\r\n valid=1;\r\n m=rand(24);\r\n K=repmat([0 .5 1 1 1 .5 0]',1,7); %Horiz Bar\r\n mValidConv2=create_ValidConv2(m,K)\r\n mc=conv2(m,K,'same');\r\n if ~isequal(mc(4:end-3,4:end-3),mValidConv2)\r\n  valid=0;\r\n end\r\n \r\nassert(valid)\r\n\r\n%%\r\n%Showing a Label and an image from Mnist\r\n global labels images\r\n valid=1;\r\n m=rand(8);\r\n K=ones(3)/9;\r\n mValidConv2=create_ValidConv2(m,K)\r\n mc=conv2(m,K,'same');\r\n if ~isequal(mc(2:end-1,2:end-1),mValidConv2)\r\n  valid=0;\r\n end\r\n \r\nassert(valid)\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-17T19:33:19.000Z","deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-17T14:03:38.000Z","updated_at":"2025-12-09T23:45:29.000Z","published_at":"2023-08-17T19:33:20.000Z","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\u003eThis challenge is to return only the Valid portion of a 2-D convolution. Valid is deemed to be where all elements of the kernel are applied against the image. The (1,1) point with a ones(3) kernel would be invalid while point (2,2) would return a Valid convolution value. The images will be even-square,m, and the kernels will be odd-square,K.The m=ones(6) and K=ones(3) would return a 4x4 matrix of 9s.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003emValidConv2=create_ValidConv2(m,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:t\u003eIn neural net processing of the Mnist database of digits(0-9) one Convolutional Neural Network method uses 9x9 kernels on the 28x28 images to return the Valid 20x20 central core for the next processing step. The third image includes a black border of four around Valid to maintain scale.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\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=\\\"189\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"252\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"189\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"252\\\"/\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\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"189\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"252\\\"/\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=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe entire Matlab code from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.youtube.com/watch?v=ZOXOwYUVCqw\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eConvolutional Neural Networks in Matlab by Nuruzzaman Faruqui\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e , a Matlab Neural Net Tutorial for Mnist processing, is included in the function template. Loading the Mnist Train/Test Images/Labels from posted MAT files or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eTHE MNIST DATABASE of handwritten digits\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e are provided. The Neural Net tutorial explains Neural Net processing for Digit Recognition. The neural net training method uses 784 input nodes,  20 convolutions(9x9), ReLU, Pooling, batch sampling, single hidden layer of 100 nodes, updating parameters via backwards propagation, and updating convolution kernels. The digit prediction accuracy is \u0026gt;94% after 50s of training. The final feature kernels are very amorphous and 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Reverse Game of Life - Zoo of Stills and Oscillators","description":"\u003chttp://www.kaggle.com/c/conway-s-reverse-game-of-life Kaggle's Conway's Reverse Game of Life\u003e contest inspires this Reverse Life Zoo challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003chttp://mathworld.wolfram.com/GameofLife.html Game of Life at Wolfram\u003e. \u003chttp://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Wiki Life\u003e.\r\n\r\n\r\n  1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n  2. Any live cell with two or three live neighbors lives on to the next generation.\r\n  3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n  4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\r\n\r\n\r\nDetermine the Zoo of life at its previous Snapshot. The output must evolve to the given Zoo Snapshot.  It is a Zoo since all animals are in their private enclosures and do not interact with any other animals. Animals are stable or cyclic oscillators.\r\n\r\n*Input:* Zoo , an [m,n] array\r\n\r\n*Output:* ZooPre, image of the Zoo at its prior iteration\r\n\r\n\r\n*Example:*\r\n\r\n  Zoo        ZooPre\r\n  0000000000  000000000  \r\n  0110000100  011000000  \r\n  0110000100  011001110  \r\n  0000000100  000000000  \r\n  0000000000  000000000\r\n\r\n*Additional References:*\r\n\u003chttp://www.conwaylife.com/wiki/Oscillator Oscillators\u003e, \u003chttp://www.conwaylife.com/wiki/Still_life Still Life\u003e\r\n\r\n*Next:* Small Island - Prior Snapshot Prediction","description_html":"\u003cp\u003e\u003ca href = \"http://www.kaggle.com/c/conway-s-reverse-game-of-life\"\u003eKaggle's Conway's Reverse Game of Life\u003c/a\u003e contest inspires this Reverse Life Zoo challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003ca href = \"http://mathworld.wolfram.com/GameofLife.html\"\u003eGame of Life at Wolfram\u003c/a\u003e. \u003ca href = \"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\"\u003eWiki Life\u003c/a\u003e.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n2. Any live cell with two or three live neighbors lives on to the next generation.\r\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\u003c/pre\u003e\u003cp\u003eDetermine the Zoo of life at its previous Snapshot. The output must evolve to the given Zoo Snapshot.  It is a Zoo since all animals are in their private enclosures and do not interact with any other animals. Animals are stable or cyclic oscillators.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Zoo , an [m,n] array\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e ZooPre, image of the Zoo at its prior iteration\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eZoo        ZooPre\r\n0000000000  000000000  \r\n0110000100  011000000  \r\n0110000100  011001110  \r\n0000000100  000000000  \r\n0000000000  000000000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAdditional References:\u003c/b\u003e \u003ca href = \"http://www.conwaylife.com/wiki/Oscillator\"\u003eOscillators\u003c/a\u003e, \u003ca href = \"http://www.conwaylife.com/wiki/Still_life\"\u003eStill Life\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eNext:\u003c/b\u003e Small Island - Prior Snapshot Prediction\u003c/p\u003e","function_template":"function ZooPre=Zoo_prior(Zoo)\r\n ZooPre=Zoo;\r\nend","test_suite":"%%\r\n block=[0 0 0 0;0 1 1 0;0 1 1 0;0 0 0 0];\r\n Zoo=repmat(block,3,1);\r\n\r\n ZooPre=Zoo_prior(Zoo);\r\n mc=conv2(single(ZooPre),[1 1 1;1 0 1;1 1 1],'same');\r\n ZooChk=~(mc\u003c2 | mc\u003e3) \u0026 ((ZooPre \u0026 mc==2) | (ZooPre \u0026 mc==3) | (~ZooPre \u0026 mc==3)); \r\n\r\n \r\n   assert(all(all(ZooChk==Zoo))) \r\n%  figure(1);imagesc(ZooPre)\r\n%  figure(2);imagesc(Zoo)\r\n\r\n%%\r\ncaterer1=[0 0 0 0 0 0 0 0 0 0;0 0 0 1 0 0 0 0 0 0;0 1 0 0 0 1 1 1 1 0;0 1 0 0 0 1 0 0 0 0;0 1 0 0 0 0 0 0 0 0;0 0 0 0 1 0 0 0 0 0;0 0 1 1 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0];\r\n\r\ncaterer2=[0     0     0     0     0     0     0     0     0     0;\r\n     0     0     0     0     0     0     1     1     0     0;\r\n     0     0     1     0     1     1     1     1     0     0;\r\n     1     1     1     0     0     1     0     1     0     0;\r\n     0     0     0     0     0     0     0     0     0     0;\r\n     0     0     1     1     0     0     0     0     0     0;\r\n     0     0     0     1     0     0     0     0     0     0;\r\n     0     0     0     0     0     0     0     0     0     0];\r\n\r\ncaterer3=[0     0     0     0     0     0     0     0     0     0;\r\n     0     0     0     0     0     0     0     1     0     0;\r\n     0     0     1     1     1     0     0     0     1     0;\r\n     0     1     1     1     1     1     0     1     0     0;\r\n     0     0     0     1     0     0     0     0     0     0;\r\n     0     0     1     1     0     0     0     0     0     0;\r\n     0     0     1     1     0     0     0     0     0     0;\r\n     0     0     0     0     0     0     0     0     0     0];\r\n\r\n Zoo=[caterer1 zeros(8,1) caterer2 zeros(8,1) caterer3] ;\r\n\r\n ZooPre=Zoo_prior(Zoo);\r\n mc=conv2(single(ZooPre),[1 1 1;1 0 1;1 1 1],'same');\r\n ZooChk=~(mc\u003c2 | mc\u003e3) \u0026 ((ZooPre \u0026 mc==2) | (ZooPre \u0026 mc==3) | (~ZooPre \u0026 mc==3)); \r\n\r\n \r\n   assert(all(all(ZooChk==Zoo))) \r\n%  figure(1);imagesc(ZooPre)\r\n%  figure(2);imagesc(Zoo)\r\n\r\n%%\r\ncaterer1=[0 0 0 0 0 0 0 0 0 0;0 0 0 1 0 0 0 0 0 0;0 1 0 0 0 1 1 1 1 0;0 1 0 0 0 1 0 0 0 0;0 1 0 0 0 0 0 0 0 0;0 0 0 0 1 0 0 0 0 0;0 0 1 1 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0]; % 8x10\r\nLoaf=[0 0 0 0 0 0;0 0 1 1 0 0;0 1 0 0 1 0;0 0 1 0 1 0;0 0 0 1 0 0;0 0 0 0 0 0]; % 6x6\r\nblinker=[0 0 0 0 0;0 0 0 0 0;0 1 1 1 0;0 0 0 0 0;0 0 0 0 0]; % 5x5\r\n\r\n\r\n\r\nZoo=[[Loaf;Loaf'] zeros(12,1) [caterer1;zeros(4,10)] [blinker;blinker';zeros(2,5)] ] ;\r\n\r\n ZooPre=Zoo_prior(Zoo);\r\n mc=conv2(single(ZooPre),[1 1 1;1 0 1;1 1 1],'same');\r\n ZooChk=~(mc\u003c2 | mc\u003e3) \u0026 ((ZooPre \u0026 mc==2) | (ZooPre \u0026 mc==3) | (~ZooPre \u0026 mc==3)); \r\n\r\n \r\n   assert(all(all(ZooChk==Zoo))) \r\n%  figure(1);imagesc(ZooPre)\r\n%  figure(2);imagesc(Zoo)\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":19,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-09T04:53:18.000Z","updated_at":"2026-02-13T15:21:36.000Z","published_at":"2013-11-09T05:22:10.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/conway-s-reverse-game-of-life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKaggle's Conway's Reverse Game of Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest inspires this Reverse Life Zoo challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References:\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://mathworld.wolfram.com/GameofLife.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGame of Life at Wolfram\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWiki Life\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\\n2. Any live cell with two or three live neighbors lives on to the next generation.\\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\\n5. No wrap around. Beyond edge is zero. Eight Neighbors.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDetermine the Zoo of life at its previous Snapshot. The output must evolve to the given Zoo Snapshot. It is a Zoo since all animals are in their private enclosures and do not interact with any other animals. Animals are stable or cyclic oscillators.\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 Zoo , an [m,n] array\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 ZooPre, image of the Zoo at its prior iteration\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Zoo        ZooPre\\n0000000000  000000000  \\n0110000100  011000000  \\n0110000100  011001110  \\n0000000100  000000000  \\n0000000000  000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAdditional References:\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.conwaylife.com/wiki/Oscillator\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOscillators\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conwaylife.com/wiki/Still_life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eStill Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNext:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Small Island - Prior Snapshot Prediction\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":1976,"title":"Kaggle: Reverse Game of Life - Create Isle prior State","description":"\u003chttp://www.kaggle.com/c/conway-s-reverse-game-of-life Kaggle's Conway's Reverse Game of Life\u003e contest inspires this Isle Single Evolution step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003chttp://mathworld.wolfram.com/GameofLife.html Game of Life at Wolfram\u003e. \u003chttp://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Wiki Life\u003e.\r\n\r\n\r\n  1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n  2. Any live cell with two or three live neighbors lives on to the next generation.\r\n  3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n  4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\r\n\r\nCreate an Isle that will evolve in one step to the given Isle state. An Isle is a morphing matrix that may contain non-periodic and interacting animals. The Isle is pre-evolved and will have \u003e10 live cells. Multiple solutions are possible. Imperfect solutions are allowed. This is a Performance Challenge.\r\n\r\n*Input:* Isle, 10x10 binary, 100 Isles\r\n\r\n*Output:* Isle_Predict,  a matrix that will evolve to Isle\r\n\r\n*Scoring:* 1000 * ( Errors / Total_points )\r\n\r\n*Example:*  (Errors=0 and 100 Total Points)\r\n\r\n  Isle        Isle_Predict\r\n\r\n  0001000000 0001000000\r\n  0010100000 0010100000\r\n  0100100000 0100100000\r\n  1101100000 0101000000\r\n  0100100000 0111000000\r\n  0000101000 0010100000\r\n  0010111100 0010111100\r\n  0001000010 0001000100\r\n  0000110100 0000111100\r\n  0000110000 0000010000\r\n\r\n*Next:* Isle_Predict vs Actual Specific Isle Predecessor. Study in Probability.","description_html":"\u003cp\u003e\u003ca href = \"http://www.kaggle.com/c/conway-s-reverse-game-of-life\"\u003eKaggle's Conway's Reverse Game of Life\u003c/a\u003e contest inspires this Isle Single Evolution step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003ca href = \"http://mathworld.wolfram.com/GameofLife.html\"\u003eGame of Life at Wolfram\u003c/a\u003e. \u003ca href = \"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\"\u003eWiki Life\u003c/a\u003e.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n2. Any live cell with two or three live neighbors lives on to the next generation.\r\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\u003c/pre\u003e\u003cp\u003eCreate an Isle that will evolve in one step to the given Isle state. An Isle is a morphing matrix that may contain non-periodic and interacting animals. The Isle is pre-evolved and will have \u0026gt;10 live cells. Multiple solutions are possible. Imperfect solutions are allowed. This is a Performance Challenge.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Isle, 10x10 binary, 100 Isles\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Isle_Predict,  a matrix that will evolve to Isle\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e 1000 * ( Errors / Total_points )\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e  (Errors=0 and 100 Total Points)\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eIsle        Isle_Predict\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003e0001000000 0001000000\r\n0010100000 0010100000\r\n0100100000 0100100000\r\n1101100000 0101000000\r\n0100100000 0111000000\r\n0000101000 0010100000\r\n0010111100 0010111100\r\n0001000010 0001000100\r\n0000110100 0000111100\r\n0000110000 0000010000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eNext:\u003c/b\u003e Isle_Predict vs Actual Specific Isle Predecessor. Study in Probability.\u003c/p\u003e","function_template":"function Isle_Predict=predict_prior_isle(Isle)\r\n% Given Isle find prior Isle state to produce minimal Isle errors\r\n Isle_Predict=zeros(size(Isle));\r\nend","test_suite":"assignin('caller','score',200);\r\n%%\r\ntic\r\nN=10; % size of Isle\r\nQisles=100; % Qty of Isles to process to find error rate\r\n\r\nrng(15,'twister'); % creation fixed set of Isles\r\n\r\nvalid=0; \r\n\r\nwhile valid\u003cQisles\r\n q=floor(N/4)+randi(floor(N*N/4));\r\n idx=randperm(N*N);\r\n \r\n mb=zeros(N);\r\n mb(idx(1:q))=1;\r\n\r\n for i=1:10 % Pre-Evolve\r\n  %mb0=mb;\r\n  mc=conv2(single(mb),[1 1 1;1 0 1;1 1 1],'same');\r\n  mb=~(mc\u003c2 | mc\u003e3) \u0026 ((mb \u0026 mc==2) | (mb \u0026 mc==3) | (~mb \u0026 mc==3)); \r\n end\r\n\r\n if nnz(mb)\u003eN*N/10  % Avoid empty isles\r\n%   figure(1);imagesc(mb0)\r\n%   figure(2);imagesc(mb)\r\n  valid=valid+1;\r\n  Isle{valid}=mb;\r\n  \r\n end\r\n\r\nend % while  to create cases\r\n\r\nerrTot=0;\r\nfor i=1:Qisles\r\n\r\n isle_predict=predict_prior_isle(Isle{i});\r\n  \r\n m1=isle_predict; % Evolve to calculate errors\r\n mc=conv2(single(m1),[1 1 1;1 0 1;1 1 1],'same');\r\n isle_created=~(mc\u003c2 | mc\u003e3) \u0026 ((m1 \u0026 mc==2) | (m1 \u0026 mc==3) | (~m1 \u0026 mc==3));\r\n  \r\n err=N*N-nnz(Isle{i}==isle_created);\r\n errTot=errTot+err;\r\n\r\nend % Qisles\r\ntoc\r\nassignin('caller','score',min(200,floor(1000*errTot/N/N/Qisles)));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":6,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2013-11-10T02:23:31.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-09T19:43:29.000Z","updated_at":"2026-02-13T15:23:16.000Z","published_at":"2013-11-09T20:16:15.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/conway-s-reverse-game-of-life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKaggle's Conway's Reverse Game of Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest inspires this Isle Single Evolution step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References:\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://mathworld.wolfram.com/GameofLife.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGame of Life at Wolfram\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWiki Life\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\\n2. Any live cell with two or three live neighbors lives on to the next generation.\\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\\n5. No wrap around. Beyond edge is zero. Eight Neighbors.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate an Isle that will evolve in one step to the given Isle state. An Isle is a morphing matrix that may contain non-periodic and interacting animals. The Isle is pre-evolved and will have \u0026gt;10 live cells. Multiple solutions are possible. Imperfect solutions are allowed. This is a Performance Challenge.\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 Isle, 10x10 binary, 100 Isles\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 Isle_Predict, a matrix that will evolve to Isle\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 1000 * ( Errors / Total_points )\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\u003cw:r\u003e\u003cw:t\u003e (Errors=0 and 100 Total Points)\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[Isle        Isle_Predict\\n\\n0001000000 0001000000\\n0010100000 0010100000\\n0100100000 0100100000\\n1101100000 0101000000\\n0100100000 0111000000\\n0000101000 0010100000\\n0010111100 0010111100\\n0001000010 0001000100\\n0000110100 0000111100\\n0000110000 0000010000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNext:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Isle_Predict vs Actual Specific Isle Predecessor. Study in Probability.\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":1966,"title":"Kaggle: Reverse Game of Life - Single Move to One Cell Case","description":"\u003chttp://www.kaggle.com/c/conway-s-reverse-game-of-life Kaggle's Conway's Reverse Game of Life\u003e contest inspires this Single Reverse step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003chttp://mathworld.wolfram.com/GameofLife.html Game of Life at Wolfram\u003e. \u003chttp://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Wiki Life\u003e.\r\n\r\n\r\n  1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n  2. Any live cell with two or three live neighbors lives on to the next generation.\r\n  3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n  4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\r\n\r\n\r\nFind all the 4x4 Life matrices that upon one evolution produce a single living cell. This is a single evolution reversal set. The 4x4 gets appended by a ring of zeros for processing. The single living cell may be anywhere in the final 6x6 matrix, but none are expected to occur in the outer ring for a valid solution.\r\n\r\n*Input:* None\r\n\r\n*Output:* Cell array of unique 4x4 matrices that evolve to a single cell\r\n\r\n*Scoring:* 600 - (Number of valid unique 4x4 matrices)\r\n\r\n*Examples:*  A few valid matrices that produce a single cell\r\n\r\n  0000  0010  1101\r\n  0001  1000  1110\r\n  0000  0000  0100\r\n  0101  0110  1001","description_html":"\u003cp\u003e\u003ca href = \"http://www.kaggle.com/c/conway-s-reverse-game-of-life\"\u003eKaggle's Conway's Reverse Game of Life\u003c/a\u003e contest inspires this Single Reverse step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003ca href = \"http://mathworld.wolfram.com/GameofLife.html\"\u003eGame of Life at Wolfram\u003c/a\u003e. \u003ca href = \"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\"\u003eWiki Life\u003c/a\u003e.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n2. Any live cell with two or three live neighbors lives on to the next generation.\r\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\u003c/pre\u003e\u003cp\u003eFind all the 4x4 Life matrices that upon one evolution produce a single living cell. This is a single evolution reversal set. The 4x4 gets appended by a ring of zeros for processing. The single living cell may be anywhere in the final 6x6 matrix, but none are expected to occur in the outer ring for a valid solution.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e None\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Cell array of unique 4x4 matrices that evolve to a single cell\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e 600 - (Number of valid unique 4x4 matrices)\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e  A few valid matrices that produce a single cell\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e0000  0010  1101\r\n0001  1000  1110\r\n0000  0000  0100\r\n0101  0110  1001\r\n\u003c/pre\u003e","function_template":"function mcell=Life\r\n  mcell{1}=zeros(4);\r\nend","test_suite":"%%\r\nassignin('caller','score',200);\r\n\r\n%%\r\n[mcell] = Life;\r\n\r\n[nr,nc]=size(mcell);\r\n% check for uniqueness\r\nvalid=1;\r\ntic\r\nfor k=1:nc-1\r\n  mk=mcell{k};\r\n for p=k+1:nc\r\n  mp=mcell{p};\r\n  mkp=mk==mp;\r\n  if all(mkp(:))\r\n   valid=0;\r\n  end\r\n end\r\nend\r\nassert(valid==1,sprintf('Not all unique solutions'));\r\ntoc\r\n\r\n% run an evolution and verify\r\ntic\r\nfor k=1:nc\r\n  m=mcell{k};\r\n  m=[zeros(1,6);zeros(4,1) m zeros(4,1);zeros(1,6)];\r\n  mc=conv2(m,[1 1 1;1 0 1;1 1 1],'same');\r\n  m=~(mc\u003c2 | mc\u003e3) \u0026 ((m \u0026 mc==2) | (m \u0026 mc==3) | (~m \u0026 mc==3)); \r\n  assert(isequal(nnz(m),1),sprintf('Non-Single survivor solution'))\r\nend\r\ntoc\r\nassignin('caller','score',min(200,max(0,600-nc)));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":"2019-07-16T14:11:36.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-31T02:08:53.000Z","updated_at":"2026-02-13T15:19:14.000Z","published_at":"2013-10-31T03:12:32.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/conway-s-reverse-game-of-life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKaggle's Conway's Reverse Game of Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest inspires this Single Reverse step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References:\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://mathworld.wolfram.com/GameofLife.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGame of Life at Wolfram\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWiki Life\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\\n2. Any live cell with two or three live neighbors lives on to the next generation.\\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\\n5. No wrap around. Beyond edge is zero. Eight Neighbors.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind all the 4x4 Life matrices that upon one evolution produce a single living cell. This is a single evolution reversal set. The 4x4 gets appended by a ring of zeros for processing. The single living cell may be anywhere in the final 6x6 matrix, but none are expected to occur in the outer ring for a valid solution.\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 None\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 Cell array of unique 4x4 matrices that evolve to a single cell\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 600 - (Number of valid unique 4x4 matrices)\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\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A few valid matrices that produce a single cell\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[0000  0010  1101\\n0001  1000  1110\\n0000  0000  0100\\n0101  0110  1001]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1970,"title":"Kaggle: Reverse Game of Life - Periods of Oscillators","description":"\u003chttp://www.kaggle.com/c/conway-s-reverse-game-of-life Kaggle's Conway's Reverse Game of Life\u003e contest inspires this Period of Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003chttp://mathworld.wolfram.com/GameofLife.html Game of Life at Wolfram\u003e. \u003chttp://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Wiki Life\u003e.\r\n\r\n\r\n  1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n  2. Any live cell with two or three live neighbors lives on to the next generation.\r\n  3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n  4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\r\n\r\n\r\nDetermine the period of life for a given binary matrix. A stable configuration, Still Life, has a cycle of 1.\r\n\r\n*Input:* M , an [m,n] array\r\n\r\n*Output:* N, Period of Life cycle (Period \u003c11)\r\n\r\n\r\n*Examples:*  A few matrices of varying periods\r\n\r\n  N=1   N=2    N=3 Caterer\r\n  0000  00000  0000000000\r\n  0110  00000  0001000000\r\n  0110  01110  0100011110\r\n  0000  00000  0100010000\r\n        00000  0100000000\r\n               0000100000\r\n               0011000000\r\n               0000000000\r\n\r\n*Additional References:*\r\n\u003chttp://www.conwaylife.com/wiki/Oscillator Oscillators\u003e, \u003chttp://www.conwaylife.com/wiki/Still_life Still Life\u003e","description_html":"\u003cp\u003e\u003ca href = \"http://www.kaggle.com/c/conway-s-reverse-game-of-life\"\u003eKaggle's Conway's Reverse Game of Life\u003c/a\u003e contest inspires this Period of Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003ca href = \"http://mathworld.wolfram.com/GameofLife.html\"\u003eGame of Life at Wolfram\u003c/a\u003e. \u003ca href = \"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\"\u003eWiki Life\u003c/a\u003e.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n2. Any live cell with two or three live neighbors lives on to the next generation.\r\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\u003c/pre\u003e\u003cp\u003eDetermine the period of life for a given binary matrix. A stable configuration, Still Life, has a cycle of 1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e M , an [m,n] array\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e N, Period of Life cycle (Period \u0026lt;11)\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e  A few matrices of varying periods\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eN=1   N=2    N=3 Caterer\r\n0000  00000  0000000000\r\n0110  00000  0001000000\r\n0110  01110  0100011110\r\n0000  00000  0100010000\r\n      00000  0100000000\r\n             0000100000\r\n             0011000000\r\n             0000000000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAdditional References:\u003c/b\u003e \u003ca href = \"http://www.conwaylife.com/wiki/Oscillator\"\u003eOscillators\u003c/a\u003e, \u003ca href = \"http://www.conwaylife.com/wiki/Still_life\"\u003eStill Life\u003c/a\u003e\u003c/p\u003e","function_template":"function N=Life_Period(M)\r\n%\r\nN=0;\r\n\r\nend","test_suite":"%Block  Still 1\r\nm=[0 0 0 0;0 1 1 0;0 1 1 0;0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Beehive  Still 1\r\nm=[0 0 0 0 0 0;0 0 1 1 0 0;0 1 0 0 1 0;0 0 1 1 0 0;0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Loaf  Still 1\r\nm=[0 0 0 0 0 0;0 0 1 1 0 0;0 1 0 0 1 0;0 0 1 0 1 0;0 0 0 1 0 0;0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Boat  Still 1\r\nm=[0 0 0 0 0;0 1 1 0 0;0 1 0 1 0;0 0 1 0 0;0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Table Still 1\r\nm=[0 0 0 0 0 0 0;0 1 1 0 1 1 0;0 0 1 0 1 0 0;0 0 1 0 1 0 0;0 1 1 0 1 1 0;0 0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Blinker  Osc 2\r\nm=[0 0 0 0 0;0 0 0 0 0;0 1 1 1 0;0 0 0 0 0;0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==2)\r\n%%\r\n%Toad  Osc 2\r\nm=[0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 1 1 0;0 1 1 1 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==2)\r\n%%\r\n%Beacon  Osc 2\r\nm=[0 0 0 0 0 0;0 1 1 0 0 0;0 1 1 0 0 0;0 0 0 1 1 0;0 0 0 1 1 0;0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==2)\r\n%%\r\n%Caterer  Osc 3\r\nm=[0 0 0 0 0 0 0 0 0 0;0 0 0 1 0 0 0 0 0 0;0 1 0 0 0 1 1 1 1 0;0 1 0 0 0 1 0 0 0 0;0 1 0 0 0 0 0 0 0 0;0 0 0 0 1 0 0 0 0 0;0 0 1 1 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0;];\r\nn=Life_Period(m);\r\nassert(n==3)","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-04T04:25:13.000Z","updated_at":"2026-02-13T15:20:35.000Z","published_at":"2013-11-04T04:46:57.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/conway-s-reverse-game-of-life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKaggle's Conway's Reverse Game of Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest inspires this Period of Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References:\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://mathworld.wolfram.com/GameofLife.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGame of Life at Wolfram\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWiki Life\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\\n2. Any live cell with two or three live neighbors lives on to the next generation.\\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\\n5. No wrap around. Beyond edge is zero. Eight Neighbors.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDetermine the period of life for a given binary matrix. A stable configuration, Still Life, has a cycle of 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: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 , an [m,n] array\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 N, Period of Life cycle (Period \u0026lt;11)\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\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A few matrices of varying periods\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[N=1   N=2    N=3 Caterer\\n0000  00000  0000000000\\n0110  00000  0001000000\\n0110  01110  0100011110\\n0000  00000  0100010000\\n      00000  0100000000\\n             0000100000\\n             0011000000\\n             0000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAdditional References:\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.conwaylife.com/wiki/Oscillator\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOscillators\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conwaylife.com/wiki/Still_life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eStill Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42290,"title":"GJam 2015 Rd1B: Noisy Neighbors","description":"This Challenge is derived from \u003chttps://code.google.com/codejam/contest/8224486/dashboard#s=p1 GJam 2015 Rd 1B: Noisy Neighbors\u003e. Fastest completion - 8 minutes.\r\n\r\nDetermine minimum number of adjacencies for N placed people in an RxC hotel matrix\r\n\r\nInput: m, an RxC zeros array; N number of rooms to be filled\r\n\r\nOutput: NN, minimum number of common walls\r\n\r\nExamples: Small Case 1\u003c=R*C\u003c=16, 0\u003c=N\u003c=R*C\r\n\r\n  [1 1 1;1 0 1;1 1 1] has minimum 8 common walls vs [1 1 1;1 1 1;1 1 0] has 10 common\r\n  [1;0;0;1] has 0 common walls\r\n  ones(2,3) has 7 common walls\r\n\r\n\r\nTheory: The small case can be solved with brute force using vector set with nchoosek followed by processing of convolutions. The large case has 10000 rooms making brute force hopeless.\r\n\r\n\r\nAdditional GJam solutions can be found at \u003chttp://www.go-hero.net/jam/15 Example GJam Matlab solutions\u003e. Select Find Solutions, change Language to Matlab. The Test Suite, at the bottom, contains a full GJam Matlab solution.","description_html":"\u003cp\u003eThis Challenge is derived from \u003ca href = \"https://code.google.com/codejam/contest/8224486/dashboard#s=p1\"\u003eGJam 2015 Rd 1B: Noisy Neighbors\u003c/a\u003e. Fastest completion - 8 minutes.\u003c/p\u003e\u003cp\u003eDetermine minimum number of adjacencies for N placed people in an RxC hotel matrix\u003c/p\u003e\u003cp\u003eInput: m, an RxC zeros array; N number of rooms to be filled\u003c/p\u003e\u003cp\u003eOutput: NN, minimum number of common walls\u003c/p\u003e\u003cp\u003eExamples: Small Case 1\u0026lt;=R*C\u0026lt;=16, 0\u0026lt;=N\u0026lt;=R*C\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e[1 1 1;1 0 1;1 1 1] has minimum 8 common walls vs [1 1 1;1 1 1;1 1 0] has 10 common\r\n[1;0;0;1] has 0 common walls\r\nones(2,3) has 7 common walls\r\n\u003c/pre\u003e\u003cp\u003eTheory: The small case can be solved with brute force using vector set with nchoosek followed by processing of convolutions. The large case has 10000 rooms making brute force hopeless.\u003c/p\u003e\u003cp\u003eAdditional GJam solutions can be found at \u003ca href = \"http://www.go-hero.net/jam/15\"\u003eExample GJam Matlab solutions\u003c/a\u003e. Select Find Solutions, change Language to Matlab. The Test Suite, at the bottom, contains a full GJam Matlab solution.\u003c/p\u003e","function_template":"function NN = Noisy_Neighbors(m,N)\r\n% m is an RxC zeros array\r\n% N is number of cells to be occupied\r\n% NN is number of edge adjacencies\r\n% Goal is to minimize NN\r\n\r\n  NN=0;\r\nend","test_suite":"tic\r\n%%\r\nm=zeros(5,2);N=8;\r\nNNexp=7;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=14;\r\nNNexp=18;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(8,2);N=12;\r\nNNexp=10;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,3);N=6;\r\nNNexp=3;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,6);N=12;\r\nNNexp=16;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=5;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=11;\r\nNNexp=8;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(7,2);N=13;\r\nNNexp=16;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=15;\r\nNNexp=14;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=9;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=12;\r\nNNexp=17;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=13;\r\nNNexp=14;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,4);N=5;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=5;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=16;\r\nNNexp=15;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,5);N=8;\r\nNNexp=7;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=4;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=10;\r\nNNexp=6;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=9;\r\nNNexp=3;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,2);N=2;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=15;\r\nNNexp=20;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,2);N=4;\r\nNNexp=4;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=11;\r\nNNexp=8;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=9;\r\nNNexp=1;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=16;\r\nNNexp=24;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=9;\r\nNNexp=1;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=15;\r\nNNexp=22;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,5);N=9;\r\nNNexp=10;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=10;\r\nNNexp=6;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=15;\r\nNNexp=14;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,2);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=2;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,2);N=4;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=4;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,7);N=13;\r\nNNexp=16;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,3);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(7,2);N=11;\r\nNNexp=10;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,1);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,6);N=9;\r\nNNexp=7;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=3;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=15;\r\nNNexp=22;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,2);N=6;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,4);N=12;\r\nNNexp=17;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,3);N=6;\r\nNNexp=7;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,1);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=16;\r\nNNexp=15;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,2);N=2;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,3);N=9;\r\nNNexp=12;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(9,1);N=6;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=12;\r\nNNexp=11;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,2);N=3;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(7,2);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,3);N=4;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=9;\r\nNNexp=3;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,4);N=6;\r\nNNexp=4;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=12;\r\nNNexp=11;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=3;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,13);N=9;\r\nNNexp=4;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=2;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,9);N=6;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(6,2);N=12;\r\nNNexp=16;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=8;\r\nNNexp=4;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=5;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=14;\r\nNNexp=18;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=7;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(6,2);N=4;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=13;\r\nNNexp=14;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\ntoc\r\n%%\r\n% function GJam_Rd1B_2015b\r\n%  fn='B-small-practice.in';\r\n%  [data] = read_file(fn); % \r\n%  fidG = fopen('B-small-practice.out', 'w');\r\n%  \r\n% tic\r\n% for i=1:size(data,2) % Cell array has N cols of cases\r\n%  NN = Noisy_Neighbors(data{i});\r\n%  fprintf(fidG,'Case #%i: %i\\n',i,NN);\r\n%  \r\n%  fprintf('Case #%i: %i\\n',i,NN);   \r\n% end\r\n% toc\r\n% fclose(fidG);\r\n% end\r\n% \r\n% function val=Noisy_Neighbors(v)\r\n%  r=v(1);c=v(2);N=v(3);\r\n%  m=zeros(r,c);\r\n%  if N==0\r\n%   val=0;\r\n%   return;\r\n%  end\r\n%  \r\n% ptrset=find(m==0);\r\n% \r\n% val=Inf;\r\n% tlocs=nchoosek(ptrset,N);\r\n% for i=1:size(tlocs,1)\r\n%  m=m*0;\r\n%  tlocv=tlocs(i,:);\r\n%  m(tlocv)=1;\r\n%  vchk=nnz(conv2(m,[1 1])==2)+nnz(conv2(m,[1;1])==2);\r\n%  \r\n%  if vchk\u003cval\r\n%   val=vchk;\r\n%  end\r\n%  if val==0,break;end;\r\n% end\r\n%  \r\n% end\r\n% \r\n% function [d] = read_file(fn)\r\n% d={};\r\n% fid=fopen(fn);\r\n% fgetl(fid); % Total Count ignore\r\n% ptr=0;\r\n% while ~feof(fid)\r\n%  ptr=ptr+1;\r\n%  v=str2num(fgetl(fid)); % r,c,N\r\n%  \r\n%  d{ptr}=v;\r\n%  \r\n% end % feof\r\n%  fclose(fid);\r\n% \r\n% end % read_file\r\n%%\r\ntoc\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":5,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-05-03T17:57:59.000Z","updated_at":"2015-05-03T18:31:40.000Z","published_at":"2015-05-03T18:31:40.000Z","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 Challenge is derived from\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://code.google.com/codejam/contest/8224486/dashboard#s=p1\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGJam 2015 Rd 1B: Noisy Neighbors\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Fastest completion - 8 minutes.\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\u003eDetermine minimum number of adjacencies for N placed people in an RxC hotel matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: m, an RxC zeros array; N number of rooms to be filled\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\u003eOutput: NN, minimum number of common walls\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\u003eExamples: Small Case 1\u0026lt;=R*C\u0026lt;=16, 0\u0026lt;=N\u0026lt;=R*C\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[[1 1 1;1 0 1;1 1 1] has minimum 8 common walls vs [1 1 1;1 1 1;1 1 0] has 10 common\\n[1;0;0;1] has 0 common walls\\nones(2,3) has 7 common walls]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTheory: The small case can be solved with brute force using vector set with nchoosek followed by processing of convolutions. The large case has 10000 rooms making brute force hopeless.\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\u003eAdditional GJam solutions can be found at\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.go-hero.net/jam/15\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eExample GJam Matlab solutions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Select Find Solutions, change Language to Matlab. The Test Suite, at the bottom, contains a full GJam Matlab solution.\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":1102,"title":"USC Fall 2012 ACM : Find Largest Water Concentration","description":"This Challenge is to solve Question B, Water?, of the \u003chttp://contest.usc.edu/index.php/Fall12/Home USC ACM Fall 2012 Contest\u003e.\r\n\r\nGiven a character array A of minerals(A:Z) and a vector, iswater, of Water bearing minerals(A:Z) and a minimum rectangular size to evaluate, determine the Region with the Maximum water density. Report the larger region if two have the same density. \r\n\r\nSpecifically, report the Total water symbols and the Area of the highest density region. \r\n\r\n\r\nInput: [ A, iswater, s ]\r\n\r\nOutput: [Total Water Symbols, Area of Rectangle]; \r\n\r\n\r\nThe full \u003chttp://contest.usc.edu/index.php/Fall12/Home?action=download\u0026upname=water.in.txt USC data file\u003e\r\n\r\nExample:\r\n\r\nInput: A, iswater, s\r\n\r\n  Matrix A\r\n  ITTTTHHHHTTTT\r\n  IXYOXOOOOOOXI\r\n  IOOOXOOOOOXXJ\r\n  IYOOOOXOOOOOI\r\n  IXXOYOOYOOOOI\r\n  IAAAAAXAXAAAJ\r\n  \r\n  iswater vector\r\n  YX\r\n  \r\n  s is 3; This is the minimum allowed rectangle size\r\n  \r\n\r\nOutput: [8 16] as there are 8 \"water symbols\" in a 4x4=16 square TLC:(2,2)\r\n\r\n\r\n\r\n\u003chttp://contest.usc.edu/index.php/Fall12/Home?action=download\u0026upname=water.rongqiqiu.cpp.txt The Winning B solution\u003e is very large.\r\n","description_html":"\u003cp\u003eThis Challenge is to solve Question B, Water?, of the \u003ca href=\"http://contest.usc.edu/index.php/Fall12/Home\"\u003eUSC ACM Fall 2012 Contest\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eGiven a character array A of minerals(A:Z) and a vector, iswater, of Water bearing minerals(A:Z) and a minimum rectangular size to evaluate, determine the Region with the Maximum water density. Report the larger region if two have the same density.\u003c/p\u003e\u003cp\u003eSpecifically, report the Total water symbols and the Area of the highest density region.\u003c/p\u003e\u003cp\u003eInput: [ A, iswater, s ]\u003c/p\u003e\u003cp\u003eOutput: [Total Water Symbols, Area of Rectangle];\u003c/p\u003e\u003cp\u003eThe full \u003ca href=\"http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026amp;upname=water.in.txt\"\u003eUSC data file\u003c/a\u003e\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cp\u003eInput: A, iswater, s\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eMatrix A\r\nITTTTHHHHTTTT\r\nIXYOXOOOOOOXI\r\nIOOOXOOOOOXXJ\r\nIYOOOOXOOOOOI\r\nIXXOYOOYOOOOI\r\nIAAAAAXAXAAAJ\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eiswater vector\r\nYX\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003es is 3; This is the minimum allowed rectangle size\r\n\u003c/pre\u003e\u003cp\u003eOutput: [8 16] as there are 8 \"water symbols\" in a 4x4=16 square TLC:(2,2)\u003c/p\u003e\u003cp\u003e\u003ca href=\"http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026amp;upname=water.rongqiqiu.cpp.txt\"\u003eThe Winning B solution\u003c/a\u003e is very large.\u003c/p\u003e","function_template":"function [w,w_area]=Water_analysis(A,iswater,s) \r\n w=0;\r\n w_area=s^2;\r\n \r\nend","test_suite":"%%\r\n%http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026upname=water.in.txt\r\ntic\r\nurlwrite('http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026upname=water.in.txt','water_in.txt');\r\ntoc\r\n%%\r\n fid=fopen('water_in.txt','r');\r\n w_area_expect=[8 16;100 100;0 1;1 1;5 9;186 675;23 110; 480 2500;102 2500;61 400; ...\r\n     73 930;160 441; 678 800; 55 361; 53 195;216 1296;199 1110;81 576;232 552;12 30;9 25;15 25;25 143; ...\r\n     66 120;2500 2500;270 1680;264 900;292 1365;748 2401;244 609;315 1296;214 1188];\r\n \r\n qty=fscanf(fid,'%i',1);\r\n for q=1:qty\r\n  n = fscanf(fid,'%i %i %i\\n',3)'; % rows / cols / min rect\r\n  s=n(3);\r\n  n=n(1:2);\r\n \r\n  strv=fgetl(fid);\r\n  iswater=strv;\r\n  \r\n  A=zeros(n); % Format is rows, columns\r\n  for i=1:n(1)\r\n   strv = fgetl(fid);\r\n   A(i,:) = strv;\r\n  end\r\n  A=char(A);\r\n\r\n  [w,w_area]=Water_analysis(A,iswater,s);\r\n  \r\n  assert(isequal([w w_area],w_area_expect(q,:)));\r\n\r\n end\r\n \r\n fprintf('Processing Complete\\n\\n')\r\n fclose(fid);\r\ntoc\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":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-12-07T02:38:08.000Z","updated_at":"2025-11-21T09:56:08.000Z","published_at":"2012-12-07T04:04:40.000Z","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 Challenge is to solve Question B, Water?, of the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://contest.usc.edu/index.php/Fall12/Home\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUSC ACM Fall 2012 Contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a character array A of minerals(A:Z) and a vector, iswater, of Water bearing minerals(A:Z) and a minimum rectangular size to evaluate, determine the Region with the Maximum water density. Report the larger region if two have the same density.\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\u003eSpecifically, report the Total water symbols and the Area of the highest density region.\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\u003eInput: [ A, iswater, s ]\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\u003eOutput: [Total Water Symbols, Area of 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\u003eThe full\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://contest.usc.edu/index.php/Fall12/Home?action=download\u0026amp;upname=water.in.txt\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUSC data file\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: A, iswater, s\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[Matrix A\\nITTTTHHHHTTTT\\nIXYOXOOOOOOXI\\nIOOOXOOOOOXXJ\\nIYOOOOXOOOOOI\\nIXXOYOOYOOOOI\\nIAAAAAXAXAAAJ\\n\\niswater vector\\nYX\\n\\ns is 3; This is the minimum allowed rectangle size]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput: [8 16] as there are 8 \\\"water symbols\\\" in a 4x4=16 square TLC:(2,2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026amp;upname=water.rongqiqiu.cpp.txt\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eThe Winning B solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is very large.\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":56478,"title":"IQpuzzler Preparation #3: Detect isolated groups of zeros in a matrix","description":"Return true if any small, rectangular shaped, isolated groups of zeros (size 1x1, 1x2, 2x1, 1x3, 3x1 or 2x2) are present in a matrix, with all vertically and horizontally adjacent elements being non-zero.\r\nThe matrix will always be 3-by-3 or larger.\r\nExamples:\r\n    11     2     3     2     9    10    10    11     6     7     4\r\n    12     4    13     6     1    10     1    10     5     6     9\r\n     2     8    13    12     0     0     0     5    10     9     9\r\n    12    13     7    11    13     9     1    13    11    10     3\r\n     9    13    11    13     9     3     2     1     3    10     2\r\n==\u003e true\r\n\r\n    11     2     3     2     9    10    10    11     6     7     4\r\n    12     4    13     6     1    10     1    10     5     6     9\r\n     2     8    13    12     0     0     0     1    10     9     9\r\n    12    13     7    11    13     0     1    13    11    10     3\r\n     9    13    11    13     9     3     2     1     3    10     2\r\n==\u003e false\r\n\r\n     7    10    13    11     5     5     4     1     2     3     8\r\n    13     0     8     4     3    11    10     1     8    11     4\r\n     5     0     2    11     4     8    10     7     7     5     9\r\n     8     0     2     4     9     8     5    11     1     7     9\r\n     3    12     4    13     7    12     8    13     5     3    10\r\n==\u003e true\r\n\r\nBackground:\r\nThis function or variations of it may be useful in some board games like Go, or in the upcoming IQpuzzler challenge.\r\nHint:\r\nThe built-in function conv2 may be helpful for your implementation.","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: 762.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 332.5px 381.25px; transform-origin: 332.5px 381.25px; 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: 309.5px 21px; text-align: left; transform-origin: 309.5px 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: 306.5px 7.7px; transform-origin: 306.5px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn true if any small, rectangular shaped, isolated groups of zeros (size 1x1, 1x2, 2x1, 1x3, 3x1 or 2x2) are present in a matrix, with all vertically and horizontally adjacent elements being non-zero.\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 129.142px 7.7px; transform-origin: 129.142px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe matrix will always be 3-by-3 or larger.\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32.675px 7.7px; transform-origin: 32.675px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eExamples:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; 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: 329.5px 51.0833px; transform-origin: 329.5px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    11     2     3     2     9    10    10    11     6     7     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    12     4    13     6     1    10     1    10     5     6     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     2     8    13    12     0     0     0     5    10     9     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    12    13     7    11    13     9     1    13    11    10     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     9    13    11    13     9     3     2     1     3    10     2\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 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: 26.275px 7.7px; transform-origin: 26.275px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e==\u0026gt; true\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 7.7px; transform-origin: 0px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; 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: 329.5px 51.0833px; transform-origin: 329.5px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    11     2     3     2     9    10    10    11     6     7     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    12     4    13     6     1    10     1    10     5     6     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     2     8    13    12     0     0     0     1    10     9     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    12    13     7    11    13     0     1    13    11    10     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     9    13    11    13     9     3     2     1     3    10     2\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 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margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 7.7px; transform-origin: 0px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; 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: 329.5px 51.0833px; transform-origin: 329.5px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; 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perspective-origin: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     7    10    13    11     5     5     4     1     2     3     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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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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; 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padding-inline-start: 4px; padding-left: 4px; perspective-origin: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     3    12     4    13     7    12     8    13     5     3    10\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 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: 26.275px 7.7px; transform-origin: 26.275px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e==\u0026gt; true\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 7.7px; transform-origin: 0px 7.7px; 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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 39.2917px 7.7px; transform-origin: 39.2917px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBackground:\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: 309.5px 21px; text-align: left; transform-origin: 309.5px 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: 294.833px 7.7px; transform-origin: 294.833px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis function or variations of it may be useful in some board games like Go, or in the upcoming IQpuzzler challenge.\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.3917px 7.7px; transform-origin: 14.3917px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHint:\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 206.167px 7.7px; transform-origin: 206.167px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe built-in function conv2 may be helpful for your implementation.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function result=FindHoles2(board)\r\n    result=false;\r\nend","test_suite":"%%\r\nboard=magic(3);\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=zeros(5,9);\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=ones(7,3);\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=ones(5,11);\r\nboard(1)=0;\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[11     2     3     2     9    10    10    11     6     7     4\r\n       12     4    13     6     1    10     1    10     5     6     9\r\n        2     8    13    12     0     0     0     5    10     9     9\r\n       12    13     7    11    13     9     1    13    11    10     3\r\n        9    13    11    13     9     3     2     1     3    10     2];\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[11     2     3     2     9    10    10    11     6     7     4\r\n       12     4    13     6     1    10     1    10     5     6     9\r\n        2     8    13    12     0     0     0     5    10     9     9\r\n       12    13     7    11    13     0     1    13    11    10     3\r\n        9    13    11    13     9     3     2     1     3    10     2];\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     0     8     4     3    11    10     1     8    11     4\r\n        5     0     2    11     4     8    10     7     7     5     9\r\n        8     0     2     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     5     8     4     3    11    10     1     8    11     4\r\n        5     0     2    11     4     8    10     7     7     5     9\r\n        8     0     2     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     5     8     4     3    11    10     1     8    11     4\r\n        5     0     0    11     4     8    10     7     7     5     9\r\n        8     0     2     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     5     8     4     3    11    10     1     8    11     4\r\n        5     0     0    11     4     8    10     7     7     5     9\r\n        8     0     0     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     5     8     4     3    11    10     1     8    11     4\r\n        5     0     0    11     4     8    10     7     7     5     9\r\n        0     0     0     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=mod(spiral(20),40);\r\nassert(FindHoles2(board));","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":2414210,"edited_by":2414210,"edited_at":"2022-11-17T13:01:48.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:17:45.000Z","updated_at":"2022-11-17T13:01:48.000Z","published_at":"2022-11-07T18:17:45.000Z","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\u003eReturn true if any small, rectangular shaped, isolated groups of zeros (size 1x1, 1x2, 2x1, 1x3, 3x1 or 2x2) are present in a matrix, with all vertically and horizontally adjacent elements being non-zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe matrix will always be 3-by-3 or larger.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[    11     2     3     2     9    10    10    11     6     7     4\\n    12     4    13     6     1    10     1    10     5     6     9\\n     2     8    13    12     0     0     0     5    10     9     9\\n    12    13     7    11    13     9     1    13    11    10     3\\n     9    13    11    13     9     3     2     1     3    10     2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e==\u0026gt; true\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[    11     2     3     2     9    10    10    11     6     7     4\\n    12     4    13     6     1    10     1    10     5     6     9\\n     2     8    13    12     0     0     0     1    10     9     9\\n    12    13     7    11    13     0     1    13    11    10     3\\n     9    13    11    13     9     3     2     1     3    10     2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e==\u0026gt; false\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[     7    10    13    11     5     5     4     1     2     3     8\\n    13     0     8     4     3    11    10     1     8    11     4\\n     5     0     2    11     4     8    10     7     7     5     9\\n     8     0     2     4     9     8     5    11     1     7     9\\n     3    12     4    13     7    12     8    13     5     3    10]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e==\u0026gt; true\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBackground:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\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 function or variations of it may be useful in some board games like Go, or in the upcoming IQpuzzler challenge.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\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 built-in function conv2 may be helpful for your implementation.\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":42341,"title":"LMI Twins : Fuzzy Image matching","description":"The image below from \u003chttp://logicmastersindia.com/2016/04P/ Logic Masters India Marathon 2016\u003e is puzzle \"Twins Co-Ordinates\" with PDF password of \"TCO_cLiEsh\".\r\n\r\nThe challenge is to find the six matching image squares given a [612,1078,3] PNG file screen captured using Snipping Tool. The matching squares may be rotated but are not reflected. The cells will not match perfectly and the PNG file is oversize into the white around the whole black frame. The image grid is 10x18. Return a 6x4 array of [r1,c1,r2,c2;...] where cell [r1,c1] is a match for [r2,c2] with r(1:10) and c(1:18).\r\nA pscript to obfuscate the solution is implemented. The method for creating a Cody pscript is included in the Test Suite.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Twins.PNG\u003e\u003e\r\n\r\nMethod Hints:\r\nMerge into a BW 612x1078 image;Trim outer white space;Locate Top Left corner of each cell;create cell array of 180  squares;grab only 55x55 squares to enable rotation check;Problem size is only  4*180*179/2;Filter positive and negative deltas separately with a + convolution; check residual delta \u003c threshold.","description_html":"\u003cp\u003eThe image below from \u003ca href = \"http://logicmastersindia.com/2016/04P/\"\u003eLogic Masters India Marathon 2016\u003c/a\u003e is puzzle \"Twins Co-Ordinates\" with PDF password of \"TCO_cLiEsh\".\u003c/p\u003e\u003cp\u003eThe challenge is to find the six matching image squares given a [612,1078,3] PNG file screen captured using Snipping Tool. The matching squares may be rotated but are not reflected. The cells will not match perfectly and the PNG file is oversize into the white around the whole black frame. The image grid is 10x18. Return a 6x4 array of [r1,c1,r2,c2;...] where cell [r1,c1] is a match for [r2,c2] with r(1:10) and c(1:18).\r\nA pscript to obfuscate the solution is implemented. The method for creating a Cody pscript is included in the Test Suite.\u003c/p\u003e\u003cimg src = \"https://sites.google.com/site/razapor/matlab_cody/Twins.PNG\"\u003e\u003cp\u003eMethod Hints:\r\nMerge into a BW 612x1078 image;Trim outer white space;Locate Top Left corner of each cell;create cell array of 180  squares;grab only 55x55 squares to enable rotation check;Problem size is only  4*180*179/2;Filter positive and negative deltas separately with a + convolution; check residual delta \u0026lt; threshold.\u003c/p\u003e","function_template":"function m=LMI_Twins(fn)\r\n% Grab png \r\nm=[];\r\npict=imread(fn); % [612,1078,3] size png,  sum(pict,3) may be useful\r\n% useful sub_pict size of 55x55\r\n\r\nend\r\n","test_suite":"%%\r\n% obfuscated check function\r\ntic\r\nfh = fopen('Eval_Twins.p','wb');\r\nfwrite(fh, hex2dec(reshape('7630312E30307630302E30300002901CC14FDFB100000081000000D80000012A48DF9D54B1FA232306002212C284B03C55EA944CADBBEC102FFDF678B732282B51740342451D4E91F086ABE90CD97AEA717741F995FCF6ED6A372B6C44524D61F2179DA76458DBA2B9AA01529272E90AD613EA63642EEECCDE54CD649A84090DE86A418D761593CBABBD4B17018634CA5451EA6A6F4386B000CBE73A136AFE2CE5946FD8FD16225562B3414B8C6268F085E3E3E8F1CFCFBBA469FCC5E0343472A562EAD16FFC1B7D27730414ECC1D42F6EB98289E998DA2EA3F4F820A10F9B23E296CDE97331C46563B702180857770F7A14412389BC1BA2',2,[]).'));\r\nfclose(fh);\r\nrehash path;\r\ntoc\r\n%%\r\ntic\r\nurl='https://sites.google.com/site/razapor/matlab_cody/Twins.PNG?attredirects=0\u0026d=1';\r\nurlwrite(url,'Twins.PNG');\r\nfprintf('url write complete %.1f\\n',toc)\r\n\r\nfn='Twins.PNG';\r\nm=LMI_Twins(fn);\r\nassert(Eval_Twins(m));\r\nfprintf('Process Complete %.1f\\n',toc)\r\n%%\r\n% Creation Process for a Cody p file\r\n% Create function  func_name.m\r\n% pcode func_name\r\n% fn='func_name.p';\r\n% fr=fopen(fn,'r');\r\n% m=fread(fr);\r\n% fclose(fr);\r\n% fw=fopen([fn(1:end-1) 'txt'],'w');\r\n% fprintf(fw,'%s',dec2hex(m)');\r\n% fclose(fw);\r\n% use wordpad to open func_name.txt\r\n% In cody in first block write:\r\n% fh = fopen('func_name.p','wb');\r\n% fwrite(fh, hex2dec(reshape('INSERT WORDPAD TEXT HERE',2,[]).'));\r\n% fclose(fh);\r\n% rehash path;","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-05-28T19:12:46.000Z","updated_at":"2016-05-12T01:56:20.000Z","published_at":"2016-05-12T01:56:20.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"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.png\"}],\"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 image below from\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://logicmastersindia.com/2016/04P/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLogic Masters India Marathon 2016\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is puzzle \\\"Twins Co-Ordinates\\\" with PDF password of \\\"TCO_cLiEsh\\\".\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 challenge is to find the six matching image squares given a [612,1078,3] PNG file screen captured using Snipping Tool. The matching squares may be rotated but are not reflected. The cells will not match perfectly and the PNG file is oversize into the white around the whole black frame. The image grid is 10x18. Return a 6x4 array of [r1,c1,r2,c2;...] where cell [r1,c1] is a match for [r2,c2] with r(1:10) and c(1:18). A pscript to obfuscate the solution is implemented. The method for creating a Cody pscript is included in the Test Suite.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw: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:r\u003e\u003cw:t\u003eMethod Hints: Merge into a BW 612x1078 image;Trim outer white space;Locate Top Left corner of each cell;create cell array of 180 squares;grab only 55x55 squares to enable rotation check;Problem size is only 4*180*179/2;Filter positive and negative deltas separately with a + convolution; check residual delta \u0026lt; 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\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":58877,"title":"Neural Net Image Convolution: Return only Valid portion of conv2","description":"This challenge is to return only the Valid portion of a 2-D convolution. Valid is deemed to be where all elements of the kernel are applied against the image. The (1,1) point with a ones(3) kernel would be invalid while point (2,2) would return a Valid convolution value. The images will be even-square,m, and the kernels will be odd-square,K.The m=ones(6) and K=ones(3) would return a 4x4 matrix of 9s.\r\nmValidConv2=create_ValidConv2(m,K)\r\nIn neural net processing of the Mnist database of digits(0-9) one Convolutional Neural Network method uses 9x9 kernels on the 28x28 images to return the Valid 20x20 central core for the next processing step. The third image includes a black border of four around Valid to maintain scale.\r\n  \r\nThe entire Matlab code from Convolutional Neural Networks in Matlab by Nuruzzaman Faruqui , a Matlab Neural Net Tutorial for Mnist processing, is included in the function template. Loading the Mnist Train/Test Images/Labels from posted MAT files or THE MNIST DATABASE of handwritten digits are provided. The Neural Net tutorial explains Neural Net processing for Digit Recognition. The neural net training method uses 784 input nodes,  20 convolutions(9x9), ReLU, Pooling, batch sampling, single hidden layer of 100 nodes, updating parameters via backwards propagation, and updating convolution kernels. The digit prediction accuracy is \u003e94% after 50s of training. The final feature kernels are very amorphous and unexpected.","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: 545.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 272.75px; transform-origin: 407px 272.75px; vertical-align: baseline; \"\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: 369.5px 8px; transform-origin: 369.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to return only the Valid portion of a 2-D convolution. Valid is deemed to be where all elements of the kernel are applied against the image. The (1,1) point with a ones(3) kernel would be invalid while point (2,2) would return a Valid convolution value. The images will be even-square,m, and the kernels will be odd-square,K.The m=ones(6) and K=ones(3) would return a 4x4 matrix of 9s.\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: 121.5px 8px; transform-origin: 121.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003emValidConv2=create_ValidConv2(m,K)\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: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn neural net processing of the Mnist database of digits(0-9) one Convolutional Neural Network method uses 9x9 kernels on the 28x28 images to return the Valid 20x20 central core for the next processing step. The third image includes a black border of four around Valid to maintain scale.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 194.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 97.25px; text-align: left; transform-origin: 384px 97.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 252px;height: 189px\" 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\" data-image-state=\"image-loaded\" width=\"252\" height=\"189\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 91px 8px; transform-origin: 91px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe entire Matlab code from \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.youtube.com/watch?v=ZOXOwYUVCqw\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eConvolutional Neural Networks in Matlab by Nuruzzaman Faruqui\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: 70px 8px; transform-origin: 70px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e , a Matlab Neural Net Tutorial for Mnist processing, is included in the function template. Loading the Mnist Train/Test Images/Labels from posted MAT files or \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eTHE MNIST DATABASE of handwritten digits\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: 181.5px 8px; transform-origin: 181.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e are provided. The Neural Net tutorial explains Neural Net processing for Digit Recognition. The neural net training method uses 784 input nodes,  20 convolutions(9x9), ReLU, Pooling, batch sampling, single hidden layer of 100 nodes, updating parameters via backwards propagation, and updating convolution kernels. The digit prediction accuracy is \u0026gt;94% after 50s of training. The final feature kernels are very amorphous and unexpected.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function mValidConv2=create_ValidConv2(m,K)\r\n  mValidConv2=conv2(m,K,'same');\r\nend\r\n\r\n% Code from Convolutional Neural Network in Matlab by Nuruzzaman Faruqui\r\n% Youtube:  https://www.youtube.com/watch?v=ZOXOwYUVCqw\r\n% I have made comments noted by raz and a couple speed enhancements\r\n% I also provide alternate direct source for MNIST files as .mat files\r\n%{\r\nfunction MNIST_Process\r\n%Based upon https://www.youtube.com/watch?v=ZOXOwYUVCqw\r\n%784 input nodes;20 conv 9x9 filters,ReLU;2x2 submatrix Pooling layer,single hidden layer 100 nodes\r\n\r\n%Matlab Convolutional Neural Network in Matlab\r\n%Processing MNIST digits using conv, contents, \r\n%MNIST files  http://yann.lecun.com/exdb/mnist/\r\n%Create filters\r\n%File Exchange NN https://www.mathworks.com/matlabcentral/fileexchange/59223-convolution-neural-network-simple-code-simple-to-use?s_tid=ta_fx_results\r\n%\r\n%use Test_images.mat if avail, use MNIST ubyte files if avail, load Test_images.mat\r\ndir_struct=dir;\r\nfilenames={dir_struct.name};\r\nfn='Test_images.mat';\r\nptr=find(ismember(filenames,fn));\r\nif ~isempty(ptr) % Test images/labels mat files exist\r\n tic;\r\n load('Test_images'); % images [28,28,10000] uint8\r\n load('Test_labels'); % labels [10000,1] uint8 values 0:9\r\n fprintf('Images:%i  Time:%.2f\\n',size(images,3),toc);\r\n Images=double(images)/255; %raz values must be 0:1 or bad things happen\r\n Labels=double(labels);\r\n Labels(Labels==0)=10; % 0 remapped to 10\r\n\r\nelse % Check for Mnist file\r\n %MNIST files source  http://yann.lecun.com/exdb/mnist/\r\n fn='t10k-images.idx3-ubyte'; %NMIST gzip expanded file for Test Images 10K\r\n filenames={dir_struct.name};\r\n ptr=find(ismember(filenames,fn));\r\n if ~isempty(ptr)\r\n  tic\r\n  Images=loadMNISTImages(fn);\r\n  fprintf('MNIST Images Process Time: %.2f\\n',toc); %raz\r\n  Images=reshape(Images,28,28,[]); % Un-vectorize the images\r\n  %Images already scaled by 1/255 in loadMNISTImages\r\n \r\n  Labels=loadMNISTLabels('t10k-labels.idx1-ubyte');\r\n  Labels(Labels==0)=10; % 0 to 10\r\nelse % download Test_images.mat, Test_labels.mat and load\r\n fnameTestI='Test_images.mat'; %load('Test_images.mat') creates images(28,28,10000) 1.6MB\r\n fnameTrainI='Train_images.mat'; %load('Train_images.mat') creates images(28,28,60000)  9.9MB\r\n fnameTestL='Test_labels.mat'; %load('Test_images.mat') creates labels(10000,1) 5KB\r\n fnameTrainL='Train_labels.mat'; %load('Train_images.mat') creates labels(60000,1) 30KB\r\n \r\n %Google Drive Dowloads need to come from shared files\r\n% Tweak link: file/d/ to uc?export=download\u0026id=   while removing /view?usp=sharing\r\n% https://drive.google.com/file/d/1v3GsGgP3p905wzdvUqypL_-djYmxiyzK/view?usp=sharing\r\n% https://drive.google.com/uc?export=download\u0026id=1v3GsGgP3p905wzdvUqypL_-djYmxiyzK\r\n \r\n\r\n  urlTeI='https://drive.google.com/uc?export=download\u0026id=10ReGWlfSvr9p6uq6FQlqw6vw3UXgWo57';\r\n  urlTeL='https://drive.google.com/uc?export=download\u0026id=105zTcVgItZ2JjFiUkJulU33R7lXNl29-';\r\n  urlTrI='https://drive.google.com/uc?export=download\u0026id=1HJZssH6_9OcQ8mXwS_DtvsS_Kk86BDFY';\r\n  urlTrL='https://drive.google.com/uc?export=download\u0026id=1KNTn2eNXbQkQJNe6xhO1LMmvkDRP-XLq';\r\n \r\n  tic\r\n  urlwrite(urlTeI,fnameTestI); %Writing GoogleDrive MNIST_Test_images.pdf  Test_images.mat \r\n  fprintf('Download 1.6MB Time: %.1f  sec\\n\\n',toc); %1.6MB download Time, about 1.1 sec\r\n \r\n  tic\r\n  urlwrite(urlTeL,fnameTestL); %Writing GoogleDrive MNIST_Test_labels.pdf  Test_labels.mat\r\n  fprintf('Download 5KB Time: %.1f  sec\\n\\n',toc); %5KB download Time, about 0.5 sec\r\n \r\n  tic\r\n  urlwrite(urlTrI,fnameTrainI); %Writing GoogleDrive MNIST_Train_images.pdf  Train_images.mat\r\n  fprintf('Download 9.9MB Time: %.1f  sec\\n\\n',toc); %9.9MB download Time, about 4.1 sec\r\n \r\n  tic\r\n  urlwrite(urlTrL,fnameTrainL); %Writing GoogleDrive MNIST_Train_labels.pdf  Train_labels.mat\r\n  fprintf('Download 30KB Time: %.1f  sec\\n\\n',toc); %30KB download Time, about 0.6 sec\r\n\r\n  tic\r\n  load('Test_images'); % images [28,28,10000] uint8\r\n  load('Test_labels'); % labels [10000,1] uint8 values 0:9\r\n  fprintf('Images:%i  Time:%.2f\\n',size(images,3),toc); %.05s\r\n  Images=double(images)/255; %raz values must be 0:1 or bad things happen\r\n  Labels=double(labels);\r\n  Labels(Labels==0)=10; % 0 to 10\r\n end % if MNIST ubyte files\r\n\r\nend % if Test_images.mat\r\n\r\n\r\nrng(1); %raz not liked by curretn matlab\r\n\r\nW1=.01*randn([9 9 20]); %Conv Kernels   base .01\r\nW5=(2*rand(100,2000)-1)*sqrt(6)/sqrt(360+2000); %Hidden layer coeff of 100 pooled\r\nWo=(2*rand(10, 100)-1)*sqrt(6)/sqrt( 10+ 100); %Output coeff to find 0:9 best match\r\n\r\nX=Images(:,:,1:8000);\r\nD=Labels(1:8000);\r\nztic=tic;\r\nfor epoch=1:3  %Repeat cycles of all Training data\r\n fprintf('epoch:%i  Time:%.2f\\n',epoch,toc(ztic));\r\n [W1, W5, Wo]=MnistConv(W1,W5,Wo,X,D);\r\nend %epoch\r\n\r\n%save('MnistConv.mat'); % Used for Post analysis, see video\r\n\r\nX=Images(:,:,8001:10000);\r\nD=Labels(8001:10000);\r\nacc=0;\r\nN=length(D);\r\n\r\nfor k=1:N\r\n x=X(:,:,k);\r\n y1=Conv(x,W1); %Neural Net Processing  W1 kernels on image-x\r\n y2=ReLU(y1);   %Rectification process\r\n y3=Pool(y2);   %Take 20x20 conv images into 100 values\r\n y4=reshape(y3,[],1);\r\n v5=W5*y4;      %Hidden layer weights\r\n y5=ReLU(v5);   %Rectification process\r\n v=Wo*y5;       %Wo Output weight processing\r\n y=Softmax(v);  %create vector length 10 of expected probability, uses exp(x) smoothing\r\n                %Needed in training but not sure why in evaluation\r\n \r\n [~,i]=max(y);\r\n if i==D(k)\r\n  acc=acc+1;\r\n end\r\nend %k\r\n\r\n acc=acc/N;\r\n fprintf('Accuracy is %.2f%%  Time:%.2f\\n',100*acc,toc(ztic));  %raz output/time\r\n\r\nend% MNIST_Process\r\n\r\nfunction [W1, W5,Wo]=MnistConv(W1, W5,Wo,X,D)\r\n alpha=.01;\r\n beta=0.95;\r\n z=reshape(1:100,10,10); % raz\r\n kmap=kron(z,ones(2)); % raz\r\n \r\n momentum1=zeros(size(W1));\r\n momentum5=zeros(size(W5));\r\n momentumo=zeros(size(Wo));\r\n \r\n N=length(D);\r\n \r\n bsize=100;\r\n blist=1:bsize:(N-bsize+1);\r\n \r\n for batch=1:length(blist) % Batch/Updates, each Batch creates updates to Params\r\n  dW1=zeros(size(W1));\r\n  dW5=zeros(size(W5));\r\n  dWo=zeros(size(Wo));\r\n  \r\n  begin=blist(batch);\r\n  for k=begin:begin+bsize-1  %Subset Group of Training samples for param adjust\r\n   x=X(:,:,k);\r\n   y1=Conv(x,W1); %8K/5.8s  3.5s\r\n   y2=ReLU(y1);\r\n   y3=Pool(y2);\r\n   y4=reshape(y3,[],1);\r\n   v5=W5*y4;\r\n   y5=ReLU(v5);\r\n   v=Wo*y5;\r\n   y=Softmax(v); %10 Probability Bins for [1-9 0] \r\n   \r\n   d=zeros(10,1);\r\n   d(sub2ind(size(d),D(k),1))=1;\r\n   \r\n    e=d-y; % raz should this be (e-y)^2*sign(e-y)?\r\n    delta=e;\r\n    e5=Wo'*delta;\r\n    delta5=(y5\u003e0).*e5;\r\n    e4=W5'*delta5;\r\n    e3=reshape(e4,size(y3));\r\n    e2=zeros(size(y2));\r\n    W3=ones(size(y2))/(2*2);\r\n    \r\n    for c=1:20\r\n     %e2(:,:,c)=kron(e3(:,:,c),ones([2 2])).*W3(:,:,c); %160K/4.5s\r\n     e3c=e3(:,:,c); %160K/.27 raz\r\n     e2(:,:,c)=e3c(kmap).*W3(:,:,c);  %160K/.8s  raz\r\n    end %c\r\n    \r\n    delta2=(y2\u003e0).*e2;\r\n    delta1_x=zeros(size(W1)); \r\n    \r\n    for c=1:20\r\n     delta1_x(:,:,c)=conv2(x(:,:),rot90(delta2(:,:,c),2),'valid'); %160K/5.3s\r\n    end %c\r\n    \r\n    dW1=dW1+delta1_x; %error per kernel plane\r\n    dW5=dW5+delta5*y4'; %8K/4.2s  100x2000\r\n    dWo=dWo+delta*y5';\r\n  end %k\r\n  dW1=dW1/bsize;\r\n  dW5=dW5/bsize;\r\n  dWo=dWo/bsize;\r\n  \r\n  momentum1=alpha*dW1+beta*momentum1;\r\n  W1       = W1+momentum1;\r\n  \r\n  momentum5=alpha*dW5+beta*momentum5;\r\n  W5       = W5+momentum5;\r\n  \r\n  momentumo=alpha*dWo+beta*momentumo;\r\n  Wo       = Wo+momentumo;\r\n  \r\n end % batch\r\n \r\nend %MnistConv\r\n\r\nfunction rng(x)\r\n randn('seed',x);\r\n rand('seed',x);\r\nend % rng\r\n\r\nfunction y=Pool(x)\r\n [xrow,xcol,numFilters]=size(x);\r\n y=zeros(xrow/2,xcol/2,numFilters);\r\n for k=1:numFilters\r\n  filter=ones(2)/(2*2);\r\n  image=conv2(x(:,:,k),filter,'valid');\r\n  y(:,:,k)=image(1:2:end,1:2:end);\r\n end\r\n\r\nend %Pool\r\n\r\nfunction y=Conv(x,W)\r\n [wrow,wcol,numFilters]=size(W);\r\n [xrow,xcol,~         ]=size(x);\r\n \r\n yrow=xrow-wrow+1;\r\n ycol=xcol-wcol+1;\r\n \r\n y=zeros(yrow,ycol,numFilters);\r\n \r\n for k=1:numFilters\r\n  filter=W(:,:,k);\r\n  filter=rot90(squeeze(filter),2); %200K/2.5s  raz remove test/92.15%\r\n  y(:,:,k)=conv2(x,filter,'valid'); %200K/3.7s  3.5s\r\n end\r\nend %Conv\r\n\r\nfunction y=Softmax(x)\r\n ex=exp(x);\r\n y=ex/sum(ex);\r\nend %Softmax\r\n\r\nfunction y=ReLU(x)\r\n y=max(0,x);\r\nend %ReLU\r\n\r\n\r\n\r\nfunction images=loadMNISTImages(filename)\r\n%\r\nfp=fopen(filename,'rb');\r\nassert(fp~=-1,['Could not open', filename,'']);\r\nmagic=fread(fp,1,'int32',0,'ieee-be');\r\nassert(magic==2051,['Bad magic number in', filename, '']); %2051 Images\r\nnumImages=fread(fp,1,'int32',0,'ieee-be');\r\nnumRows=fread(fp,1,'int32',0,'ieee-be');\r\nnumCols=fread(fp,1,'int32',0,'ieee-be');\r\nimages=fread(fp,inf,'unsigned char=\u003eunsigned char');\r\nfclose(fp);\r\nfprintf('Images:%i  rows:%i  cols:%i\\n',numImages,numRows,numCols); %raz\r\n\r\nimages=reshape(images,numCols,numRows,numImages);\r\nimages=permute(images,[2 1 3]); \r\n\r\nimages=reshape(images,size(images,1)*size(images,2),size(images,3)); %raz Will be reverted\r\nimages=double(images)/255;\r\n \r\nend %loadMNISTImages\r\n\r\nfunction labels=loadMNISTLabels(filename)\r\n%\r\nfp=fopen(filename,'rb');\r\nassert(fp~=-1,['Could not open', filename,'']);\r\nmagic=fread(fp,1,'int32',0,'ieee-be');\r\nassert(magic==2049,['Bad magic number in ', filename,'']);\r\nnumLabels=fread(fp,1,'int32',0,'ieee-be');\r\nlabels=fread(fp,inf,'unsigned char');\r\nassert(size(labels,1)==numLabels,'Mismatch in label count');\r\nfclose(fp);\r\nfprintf('Labels:%i\\n',numLabels); %raz\r\n\r\n\r\nend %loadMNISTLabels\r\n%}","test_suite":"%%\r\n%Google Drive Dowloads need to come from shared files\r\n% Tweak link: file/d/ to uc?export=download\u0026id=   while removing /view?usp=sharing\r\n% https://drive.google.com/file/d/1v3GsGgP3p905wzdvUqypL_-djYmxiyzK/view?usp=sharing\r\n% https://drive.google.com/uc?export=download\u0026id=1v3GsGgP3p905wzdvUqypL_-djYmxiyzK\r\n fnameTestI='Test_images.mat'; %load('Test_images.mat') creates images(28,28,10000) 1.6MB\r\n fnameTrainI='Train_images.mat'; %load('Train_images.mat') creates images(28,28,60000)  9.9MB\r\n fnameTestL='Test_labels.mat'; %load('Test_images.mat') creates labels(10000,1) 5KB\r\n fnameTrainL='Train_labels.mat'; %load('Train_images.mat') creates labels(60000,1) 30KB\r\n \r\n urlTeI='https://drive.google.com/uc?export=download\u0026id=10ReGWlfSvr9p6uq6FQlqw6vw3UXgWo57';\r\n urlTeL='https://drive.google.com/uc?export=download\u0026id=105zTcVgItZ2JjFiUkJulU33R7lXNl29-';\r\n urlTrI='https://drive.google.com/uc?export=download\u0026id=1HJZssH6_9OcQ8mXwS_DtvsS_Kk86BDFY';\r\n urlTrL='https://drive.google.com/uc?export=download\u0026id=1KNTn2eNXbQkQJNe6xhO1LMmvkDRP-XLq';\r\n \r\n % fn.mat  https://drive.google.com/file/d/10GsOZTIjzMIuO7xAYIqLT1zIq9Cyubl-/view?usp=drive_link\r\n % Google Gives warning thus aborts urlwrite\r\n %\r\n %fname='Mnist_Test_images.pdf'\r\n %Fake name of .pdf on GoogleDrive,  write as a mat\r\n \r\n %url='https://drive.google.com/file/d/1mgxzsmVQNXgqHEdd61QR2r0STm3N9lgG/view?usp=drive_link';\r\n %ptr=strfind(url,'/view'); % Tweaking the url\r\n %url(ptr:end)=[];\r\n %url=strrep(url,'file/d/','uc?export=download\u0026id=');\r\n \r\n tic\r\n urlwrite(urlTeI,fnameTestI); %Writing GoogleDrive MNIST_Test_images.pdf  Test_images.mat\r\n fprintf('Download 1.6MB Time: %.1f  sec\\n',toc); %1.6MB download Time, about 1.5 sec\r\n \r\n tic\r\n urlwrite(urlTeL,fnameTestL); %Writing GoogleDrive MNIST_Test_labels.pdf  Test_labels.mat\r\n fprintf('Download 5KB Time: %.1f  sec\\n',toc); %5KB download Time, about .5 sec\r\n \r\n global labels images \r\n \r\n load('Test_images'); % images [28,28,10000] uint8\r\n load('Test_labels'); % labels [10000,1] uint8 values 0:9\r\n \r\n %Showing a Label and an image from Mnist\r\n ptr=randi(10000);\r\n fprintf('Label:%i\\n',labels(ptr));\r\n figure(1);imagesc(images(:,:,ptr)); colormap gray;\r\n \r\n \r\nvalid=1;\r\nassert(valid)\r\n%%\r\n%Showing a Label and an image from Mnist\r\n global labels images\r\n valid=1;\r\n ptr=randi(10000);\r\n fprintf('Label:%i\\n',labels(ptr));\r\n m=double(images(:,:,ptr))/255;\r\n figure(2);imagesc(m); colormap gray;\r\n \r\n K=repmat([0 0 .5 1 1 1 .5 0 0],9,1); % Vert Bar\r\n mValidConv2=create_ValidConv2(m,K);\r\n figure(3);imagesc(mValidConv2); colormap gray;\r\n mc=conv2(m,K,'same');\r\n if ~isequal(mc(5:end-4,5:end-4),mValidConv2)\r\n  valid=0;\r\n end\r\n \r\nassert(valid)\r\n\r\n%%\r\n%Showing a Label and an image from Mnist\r\n global labels images\r\n valid=1;\r\n ptr=randi(10000);\r\n fprintf('Label:%i\\n',labels(ptr));\r\n m=double(images(:,:,ptr))/255;\r\n figure(2);imagesc(m); colormap gray;\r\n \r\n K=repmat([0 0 .5 1 1 1 .5 0 0]',1,9); %Horiz Bar\r\n mValidConv2=create_ValidConv2(m,K);\r\n figure(3);imagesc(mValidConv2); colormap gray;\r\n mc=conv2(m,K,'same');\r\n if ~isequal(mc(5:end-4,5:end-4),mValidConv2)\r\n  valid=0;\r\n end\r\n \r\nassert(valid)\r\n\r\n%%\r\n%Showing a Label and an image from Mnist\r\n global labels images\r\n valid=1;\r\n m=rand(24);\r\n K=repmat([0 .5 1 1 1 .5 0]',1,7); %Horiz Bar\r\n mValidConv2=create_ValidConv2(m,K)\r\n mc=conv2(m,K,'same');\r\n if ~isequal(mc(4:end-3,4:end-3),mValidConv2)\r\n  valid=0;\r\n end\r\n \r\nassert(valid)\r\n\r\n%%\r\n%Showing a Label and an image from Mnist\r\n global labels images\r\n valid=1;\r\n m=rand(8);\r\n K=ones(3)/9;\r\n mValidConv2=create_ValidConv2(m,K)\r\n mc=conv2(m,K,'same');\r\n if ~isequal(mc(2:end-1,2:end-1),mValidConv2)\r\n  valid=0;\r\n end\r\n \r\nassert(valid)\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-17T19:33:19.000Z","deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-17T14:03:38.000Z","updated_at":"2025-12-09T23:45:29.000Z","published_at":"2023-08-17T19:33:20.000Z","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\u003eThis challenge is to return only the Valid portion of a 2-D convolution. Valid is deemed to be where all elements of the kernel are applied against the image. The (1,1) point with a ones(3) kernel would be invalid while point (2,2) would return a Valid convolution value. The images will be even-square,m, and the kernels will be odd-square,K.The m=ones(6) and K=ones(3) would return a 4x4 matrix of 9s.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003emValidConv2=create_ValidConv2(m,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:t\u003eIn neural net processing of the Mnist database of digits(0-9) one Convolutional Neural Network method uses 9x9 kernels on the 28x28 images to return the Valid 20x20 central core for the next processing step. The third image includes a black border of four around Valid to maintain scale.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\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=\\\"189\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"252\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"189\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"252\\\"/\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\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"189\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"252\\\"/\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=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe entire Matlab code from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.youtube.com/watch?v=ZOXOwYUVCqw\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eConvolutional Neural Networks in Matlab by Nuruzzaman Faruqui\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e , a Matlab Neural Net Tutorial for Mnist processing, is included in the function template. Loading the Mnist Train/Test Images/Labels from posted MAT files or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eTHE MNIST DATABASE of handwritten digits\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e are provided. The Neural Net tutorial explains Neural Net processing for Digit Recognition. The neural net training method uses 784 input nodes,  20 convolutions(9x9), ReLU, Pooling, batch sampling, single hidden layer of 100 nodes, updating parameters via backwards propagation, and updating convolution kernels. The digit prediction accuracy is \u0026gt;94% after 50s of training. The final feature kernels are very amorphous and 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Reverse Game of Life - Zoo of Stills and Oscillators","description":"\u003chttp://www.kaggle.com/c/conway-s-reverse-game-of-life Kaggle's Conway's Reverse Game of Life\u003e contest inspires this Reverse Life Zoo challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003chttp://mathworld.wolfram.com/GameofLife.html Game of Life at Wolfram\u003e. \u003chttp://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Wiki Life\u003e.\r\n\r\n\r\n  1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n  2. Any live cell with two or three live neighbors lives on to the next generation.\r\n  3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n  4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\r\n\r\n\r\nDetermine the Zoo of life at its previous Snapshot. The output must evolve to the given Zoo Snapshot.  It is a Zoo since all animals are in their private enclosures and do not interact with any other animals. Animals are stable or cyclic oscillators.\r\n\r\n*Input:* Zoo , an [m,n] array\r\n\r\n*Output:* ZooPre, image of the Zoo at its prior iteration\r\n\r\n\r\n*Example:*\r\n\r\n  Zoo        ZooPre\r\n  0000000000  000000000  \r\n  0110000100  011000000  \r\n  0110000100  011001110  \r\n  0000000100  000000000  \r\n  0000000000  000000000\r\n\r\n*Additional References:*\r\n\u003chttp://www.conwaylife.com/wiki/Oscillator Oscillators\u003e, \u003chttp://www.conwaylife.com/wiki/Still_life Still Life\u003e\r\n\r\n*Next:* Small Island - Prior Snapshot Prediction","description_html":"\u003cp\u003e\u003ca href = \"http://www.kaggle.com/c/conway-s-reverse-game-of-life\"\u003eKaggle's Conway's Reverse Game of Life\u003c/a\u003e contest inspires this Reverse Life Zoo challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003ca href = \"http://mathworld.wolfram.com/GameofLife.html\"\u003eGame of Life at Wolfram\u003c/a\u003e. \u003ca href = \"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\"\u003eWiki Life\u003c/a\u003e.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n2. Any live cell with two or three live neighbors lives on to the next generation.\r\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\u003c/pre\u003e\u003cp\u003eDetermine the Zoo of life at its previous Snapshot. The output must evolve to the given Zoo Snapshot.  It is a Zoo since all animals are in their private enclosures and do not interact with any other animals. Animals are stable or cyclic oscillators.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Zoo , an [m,n] array\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e ZooPre, image of the Zoo at its prior iteration\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eZoo        ZooPre\r\n0000000000  000000000  \r\n0110000100  011000000  \r\n0110000100  011001110  \r\n0000000100  000000000  \r\n0000000000  000000000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAdditional References:\u003c/b\u003e \u003ca href = \"http://www.conwaylife.com/wiki/Oscillator\"\u003eOscillators\u003c/a\u003e, \u003ca href = \"http://www.conwaylife.com/wiki/Still_life\"\u003eStill Life\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eNext:\u003c/b\u003e Small Island - Prior Snapshot Prediction\u003c/p\u003e","function_template":"function ZooPre=Zoo_prior(Zoo)\r\n ZooPre=Zoo;\r\nend","test_suite":"%%\r\n block=[0 0 0 0;0 1 1 0;0 1 1 0;0 0 0 0];\r\n Zoo=repmat(block,3,1);\r\n\r\n ZooPre=Zoo_prior(Zoo);\r\n mc=conv2(single(ZooPre),[1 1 1;1 0 1;1 1 1],'same');\r\n ZooChk=~(mc\u003c2 | mc\u003e3) \u0026 ((ZooPre \u0026 mc==2) | (ZooPre \u0026 mc==3) | (~ZooPre \u0026 mc==3)); \r\n\r\n \r\n   assert(all(all(ZooChk==Zoo))) \r\n%  figure(1);imagesc(ZooPre)\r\n%  figure(2);imagesc(Zoo)\r\n\r\n%%\r\ncaterer1=[0 0 0 0 0 0 0 0 0 0;0 0 0 1 0 0 0 0 0 0;0 1 0 0 0 1 1 1 1 0;0 1 0 0 0 1 0 0 0 0;0 1 0 0 0 0 0 0 0 0;0 0 0 0 1 0 0 0 0 0;0 0 1 1 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0];\r\n\r\ncaterer2=[0     0     0     0     0     0     0     0     0     0;\r\n     0     0     0     0     0     0     1     1     0     0;\r\n     0     0     1     0     1     1     1     1     0     0;\r\n     1     1     1     0     0     1     0     1     0     0;\r\n     0     0     0     0     0     0     0     0     0     0;\r\n     0     0     1     1     0     0     0     0     0     0;\r\n     0     0     0     1     0     0     0     0     0     0;\r\n     0     0     0     0     0     0     0     0     0     0];\r\n\r\ncaterer3=[0     0     0     0     0     0     0     0     0     0;\r\n     0     0     0     0     0     0     0     1     0     0;\r\n     0     0     1     1     1     0     0     0     1     0;\r\n     0     1     1     1     1     1     0     1     0     0;\r\n     0     0     0     1     0     0     0     0     0     0;\r\n     0     0     1     1     0     0     0     0     0     0;\r\n     0     0     1     1     0     0     0     0     0     0;\r\n     0     0     0     0     0     0     0     0     0     0];\r\n\r\n Zoo=[caterer1 zeros(8,1) caterer2 zeros(8,1) caterer3] ;\r\n\r\n ZooPre=Zoo_prior(Zoo);\r\n mc=conv2(single(ZooPre),[1 1 1;1 0 1;1 1 1],'same');\r\n ZooChk=~(mc\u003c2 | mc\u003e3) \u0026 ((ZooPre \u0026 mc==2) | (ZooPre \u0026 mc==3) | (~ZooPre \u0026 mc==3)); \r\n\r\n \r\n   assert(all(all(ZooChk==Zoo))) \r\n%  figure(1);imagesc(ZooPre)\r\n%  figure(2);imagesc(Zoo)\r\n\r\n%%\r\ncaterer1=[0 0 0 0 0 0 0 0 0 0;0 0 0 1 0 0 0 0 0 0;0 1 0 0 0 1 1 1 1 0;0 1 0 0 0 1 0 0 0 0;0 1 0 0 0 0 0 0 0 0;0 0 0 0 1 0 0 0 0 0;0 0 1 1 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0]; % 8x10\r\nLoaf=[0 0 0 0 0 0;0 0 1 1 0 0;0 1 0 0 1 0;0 0 1 0 1 0;0 0 0 1 0 0;0 0 0 0 0 0]; % 6x6\r\nblinker=[0 0 0 0 0;0 0 0 0 0;0 1 1 1 0;0 0 0 0 0;0 0 0 0 0]; % 5x5\r\n\r\n\r\n\r\nZoo=[[Loaf;Loaf'] zeros(12,1) [caterer1;zeros(4,10)] [blinker;blinker';zeros(2,5)] ] ;\r\n\r\n ZooPre=Zoo_prior(Zoo);\r\n mc=conv2(single(ZooPre),[1 1 1;1 0 1;1 1 1],'same');\r\n ZooChk=~(mc\u003c2 | mc\u003e3) \u0026 ((ZooPre \u0026 mc==2) | (ZooPre \u0026 mc==3) | (~ZooPre \u0026 mc==3)); \r\n\r\n \r\n   assert(all(all(ZooChk==Zoo))) \r\n%  figure(1);imagesc(ZooPre)\r\n%  figure(2);imagesc(Zoo)\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":19,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-09T04:53:18.000Z","updated_at":"2026-02-13T15:21:36.000Z","published_at":"2013-11-09T05:22:10.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/conway-s-reverse-game-of-life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKaggle's Conway's Reverse Game of Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest inspires this Reverse Life Zoo challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References:\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://mathworld.wolfram.com/GameofLife.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGame of Life at Wolfram\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWiki Life\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\\n2. Any live cell with two or three live neighbors lives on to the next generation.\\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\\n5. No wrap around. Beyond edge is zero. Eight Neighbors.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDetermine the Zoo of life at its previous Snapshot. The output must evolve to the given Zoo Snapshot. It is a Zoo since all animals are in their private enclosures and do not interact with any other animals. Animals are stable or cyclic oscillators.\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 Zoo , an [m,n] array\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 ZooPre, image of the Zoo at its prior iteration\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Zoo        ZooPre\\n0000000000  000000000  \\n0110000100  011000000  \\n0110000100  011001110  \\n0000000100  000000000  \\n0000000000  000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAdditional References:\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.conwaylife.com/wiki/Oscillator\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOscillators\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conwaylife.com/wiki/Still_life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eStill Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNext:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Small Island - Prior Snapshot Prediction\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":1976,"title":"Kaggle: Reverse Game of Life - Create Isle prior State","description":"\u003chttp://www.kaggle.com/c/conway-s-reverse-game-of-life Kaggle's Conway's Reverse Game of Life\u003e contest inspires this Isle Single Evolution step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003chttp://mathworld.wolfram.com/GameofLife.html Game of Life at Wolfram\u003e. \u003chttp://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Wiki Life\u003e.\r\n\r\n\r\n  1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n  2. Any live cell with two or three live neighbors lives on to the next generation.\r\n  3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n  4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\r\n\r\nCreate an Isle that will evolve in one step to the given Isle state. An Isle is a morphing matrix that may contain non-periodic and interacting animals. The Isle is pre-evolved and will have \u003e10 live cells. Multiple solutions are possible. Imperfect solutions are allowed. This is a Performance Challenge.\r\n\r\n*Input:* Isle, 10x10 binary, 100 Isles\r\n\r\n*Output:* Isle_Predict,  a matrix that will evolve to Isle\r\n\r\n*Scoring:* 1000 * ( Errors / Total_points )\r\n\r\n*Example:*  (Errors=0 and 100 Total Points)\r\n\r\n  Isle        Isle_Predict\r\n\r\n  0001000000 0001000000\r\n  0010100000 0010100000\r\n  0100100000 0100100000\r\n  1101100000 0101000000\r\n  0100100000 0111000000\r\n  0000101000 0010100000\r\n  0010111100 0010111100\r\n  0001000010 0001000100\r\n  0000110100 0000111100\r\n  0000110000 0000010000\r\n\r\n*Next:* Isle_Predict vs Actual Specific Isle Predecessor. Study in Probability.","description_html":"\u003cp\u003e\u003ca href = \"http://www.kaggle.com/c/conway-s-reverse-game-of-life\"\u003eKaggle's Conway's Reverse Game of Life\u003c/a\u003e contest inspires this Isle Single Evolution step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003ca href = \"http://mathworld.wolfram.com/GameofLife.html\"\u003eGame of Life at Wolfram\u003c/a\u003e. \u003ca href = \"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\"\u003eWiki Life\u003c/a\u003e.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n2. Any live cell with two or three live neighbors lives on to the next generation.\r\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\u003c/pre\u003e\u003cp\u003eCreate an Isle that will evolve in one step to the given Isle state. An Isle is a morphing matrix that may contain non-periodic and interacting animals. The Isle is pre-evolved and will have \u0026gt;10 live cells. Multiple solutions are possible. Imperfect solutions are allowed. This is a Performance Challenge.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Isle, 10x10 binary, 100 Isles\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Isle_Predict,  a matrix that will evolve to Isle\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e 1000 * ( Errors / Total_points )\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e  (Errors=0 and 100 Total Points)\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eIsle        Isle_Predict\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003e0001000000 0001000000\r\n0010100000 0010100000\r\n0100100000 0100100000\r\n1101100000 0101000000\r\n0100100000 0111000000\r\n0000101000 0010100000\r\n0010111100 0010111100\r\n0001000010 0001000100\r\n0000110100 0000111100\r\n0000110000 0000010000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eNext:\u003c/b\u003e Isle_Predict vs Actual Specific Isle Predecessor. Study in Probability.\u003c/p\u003e","function_template":"function Isle_Predict=predict_prior_isle(Isle)\r\n% Given Isle find prior Isle state to produce minimal Isle errors\r\n Isle_Predict=zeros(size(Isle));\r\nend","test_suite":"assignin('caller','score',200);\r\n%%\r\ntic\r\nN=10; % size of Isle\r\nQisles=100; % Qty of Isles to process to find error rate\r\n\r\nrng(15,'twister'); % creation fixed set of Isles\r\n\r\nvalid=0; \r\n\r\nwhile valid\u003cQisles\r\n q=floor(N/4)+randi(floor(N*N/4));\r\n idx=randperm(N*N);\r\n \r\n mb=zeros(N);\r\n mb(idx(1:q))=1;\r\n\r\n for i=1:10 % Pre-Evolve\r\n  %mb0=mb;\r\n  mc=conv2(single(mb),[1 1 1;1 0 1;1 1 1],'same');\r\n  mb=~(mc\u003c2 | mc\u003e3) \u0026 ((mb \u0026 mc==2) | (mb \u0026 mc==3) | (~mb \u0026 mc==3)); \r\n end\r\n\r\n if nnz(mb)\u003eN*N/10  % Avoid empty isles\r\n%   figure(1);imagesc(mb0)\r\n%   figure(2);imagesc(mb)\r\n  valid=valid+1;\r\n  Isle{valid}=mb;\r\n  \r\n end\r\n\r\nend % while  to create cases\r\n\r\nerrTot=0;\r\nfor i=1:Qisles\r\n\r\n isle_predict=predict_prior_isle(Isle{i});\r\n  \r\n m1=isle_predict; % Evolve to calculate errors\r\n mc=conv2(single(m1),[1 1 1;1 0 1;1 1 1],'same');\r\n isle_created=~(mc\u003c2 | mc\u003e3) \u0026 ((m1 \u0026 mc==2) | (m1 \u0026 mc==3) | (~m1 \u0026 mc==3));\r\n  \r\n err=N*N-nnz(Isle{i}==isle_created);\r\n errTot=errTot+err;\r\n\r\nend % Qisles\r\ntoc\r\nassignin('caller','score',min(200,floor(1000*errTot/N/N/Qisles)));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":6,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2013-11-10T02:23:31.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-09T19:43:29.000Z","updated_at":"2026-02-13T15:23:16.000Z","published_at":"2013-11-09T20:16:15.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/conway-s-reverse-game-of-life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKaggle's Conway's Reverse Game of Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest inspires this Isle Single Evolution step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References:\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://mathworld.wolfram.com/GameofLife.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGame of Life at Wolfram\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWiki Life\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\\n2. Any live cell with two or three live neighbors lives on to the next generation.\\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\\n5. No wrap around. Beyond edge is zero. Eight Neighbors.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate an Isle that will evolve in one step to the given Isle state. An Isle is a morphing matrix that may contain non-periodic and interacting animals. The Isle is pre-evolved and will have \u0026gt;10 live cells. Multiple solutions are possible. Imperfect solutions are allowed. This is a Performance Challenge.\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 Isle, 10x10 binary, 100 Isles\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 Isle_Predict, a matrix that will evolve to Isle\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 1000 * ( Errors / Total_points )\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\u003cw:r\u003e\u003cw:t\u003e (Errors=0 and 100 Total Points)\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[Isle        Isle_Predict\\n\\n0001000000 0001000000\\n0010100000 0010100000\\n0100100000 0100100000\\n1101100000 0101000000\\n0100100000 0111000000\\n0000101000 0010100000\\n0010111100 0010111100\\n0001000010 0001000100\\n0000110100 0000111100\\n0000110000 0000010000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNext:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Isle_Predict vs Actual Specific Isle Predecessor. Study in Probability.\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":1966,"title":"Kaggle: Reverse Game of Life - Single Move to One Cell Case","description":"\u003chttp://www.kaggle.com/c/conway-s-reverse-game-of-life Kaggle's Conway's Reverse Game of Life\u003e contest inspires this Single Reverse step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003chttp://mathworld.wolfram.com/GameofLife.html Game of Life at Wolfram\u003e. \u003chttp://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Wiki Life\u003e.\r\n\r\n\r\n  1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n  2. Any live cell with two or three live neighbors lives on to the next generation.\r\n  3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n  4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\r\n\r\n\r\nFind all the 4x4 Life matrices that upon one evolution produce a single living cell. This is a single evolution reversal set. The 4x4 gets appended by a ring of zeros for processing. The single living cell may be anywhere in the final 6x6 matrix, but none are expected to occur in the outer ring for a valid solution.\r\n\r\n*Input:* None\r\n\r\n*Output:* Cell array of unique 4x4 matrices that evolve to a single cell\r\n\r\n*Scoring:* 600 - (Number of valid unique 4x4 matrices)\r\n\r\n*Examples:*  A few valid matrices that produce a single cell\r\n\r\n  0000  0010  1101\r\n  0001  1000  1110\r\n  0000  0000  0100\r\n  0101  0110  1001","description_html":"\u003cp\u003e\u003ca href = \"http://www.kaggle.com/c/conway-s-reverse-game-of-life\"\u003eKaggle's Conway's Reverse Game of Life\u003c/a\u003e contest inspires this Single Reverse step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003ca href = \"http://mathworld.wolfram.com/GameofLife.html\"\u003eGame of Life at Wolfram\u003c/a\u003e. \u003ca href = \"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\"\u003eWiki Life\u003c/a\u003e.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n2. Any live cell with two or three live neighbors lives on to the next generation.\r\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\u003c/pre\u003e\u003cp\u003eFind all the 4x4 Life matrices that upon one evolution produce a single living cell. This is a single evolution reversal set. The 4x4 gets appended by a ring of zeros for processing. The single living cell may be anywhere in the final 6x6 matrix, but none are expected to occur in the outer ring for a valid solution.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e None\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Cell array of unique 4x4 matrices that evolve to a single cell\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e 600 - (Number of valid unique 4x4 matrices)\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e  A few valid matrices that produce a single cell\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e0000  0010  1101\r\n0001  1000  1110\r\n0000  0000  0100\r\n0101  0110  1001\r\n\u003c/pre\u003e","function_template":"function mcell=Life\r\n  mcell{1}=zeros(4);\r\nend","test_suite":"%%\r\nassignin('caller','score',200);\r\n\r\n%%\r\n[mcell] = Life;\r\n\r\n[nr,nc]=size(mcell);\r\n% check for uniqueness\r\nvalid=1;\r\ntic\r\nfor k=1:nc-1\r\n  mk=mcell{k};\r\n for p=k+1:nc\r\n  mp=mcell{p};\r\n  mkp=mk==mp;\r\n  if all(mkp(:))\r\n   valid=0;\r\n  end\r\n end\r\nend\r\nassert(valid==1,sprintf('Not all unique solutions'));\r\ntoc\r\n\r\n% run an evolution and verify\r\ntic\r\nfor k=1:nc\r\n  m=mcell{k};\r\n  m=[zeros(1,6);zeros(4,1) m zeros(4,1);zeros(1,6)];\r\n  mc=conv2(m,[1 1 1;1 0 1;1 1 1],'same');\r\n  m=~(mc\u003c2 | mc\u003e3) \u0026 ((m \u0026 mc==2) | (m \u0026 mc==3) | (~m \u0026 mc==3)); \r\n  assert(isequal(nnz(m),1),sprintf('Non-Single survivor solution'))\r\nend\r\ntoc\r\nassignin('caller','score',min(200,max(0,600-nc)));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":"2019-07-16T14:11:36.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-31T02:08:53.000Z","updated_at":"2026-02-13T15:19:14.000Z","published_at":"2013-10-31T03:12:32.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/conway-s-reverse-game-of-life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKaggle's Conway's Reverse Game of Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest inspires this Single Reverse step in Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References:\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://mathworld.wolfram.com/GameofLife.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGame of Life at Wolfram\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWiki Life\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\\n2. Any live cell with two or three live neighbors lives on to the next generation.\\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\\n5. No wrap around. Beyond edge is zero. Eight Neighbors.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind all the 4x4 Life matrices that upon one evolution produce a single living cell. This is a single evolution reversal set. The 4x4 gets appended by a ring of zeros for processing. The single living cell may be anywhere in the final 6x6 matrix, but none are expected to occur in the outer ring for a valid solution.\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 None\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 Cell array of unique 4x4 matrices that evolve to a single cell\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 600 - (Number of valid unique 4x4 matrices)\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\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A few valid matrices that produce a single cell\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[0000  0010  1101\\n0001  1000  1110\\n0000  0000  0100\\n0101  0110  1001]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1970,"title":"Kaggle: Reverse Game of Life - Periods of Oscillators","description":"\u003chttp://www.kaggle.com/c/conway-s-reverse-game-of-life Kaggle's Conway's Reverse Game of Life\u003e contest inspires this Period of Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003chttp://mathworld.wolfram.com/GameofLife.html Game of Life at Wolfram\u003e. \u003chttp://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Wiki Life\u003e.\r\n\r\n\r\n  1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n  2. Any live cell with two or three live neighbors lives on to the next generation.\r\n  3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n  4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\r\n\r\n\r\nDetermine the period of life for a given binary matrix. A stable configuration, Still Life, has a cycle of 1.\r\n\r\n*Input:* M , an [m,n] array\r\n\r\n*Output:* N, Period of Life cycle (Period \u003c11)\r\n\r\n\r\n*Examples:*  A few matrices of varying periods\r\n\r\n  N=1   N=2    N=3 Caterer\r\n  0000  00000  0000000000\r\n  0110  00000  0001000000\r\n  0110  01110  0100011110\r\n  0000  00000  0100010000\r\n        00000  0100000000\r\n               0000100000\r\n               0011000000\r\n               0000000000\r\n\r\n*Additional References:*\r\n\u003chttp://www.conwaylife.com/wiki/Oscillator Oscillators\u003e, \u003chttp://www.conwaylife.com/wiki/Still_life Still Life\u003e","description_html":"\u003cp\u003e\u003ca href = \"http://www.kaggle.com/c/conway-s-reverse-game-of-life\"\u003eKaggle's Conway's Reverse Game of Life\u003c/a\u003e contest inspires this Period of Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References: \u003ca href = \"http://mathworld.wolfram.com/GameofLife.html\"\u003eGame of Life at Wolfram\u003c/a\u003e. \u003ca href = \"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\"\u003eWiki Life\u003c/a\u003e.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\r\n2. Any live cell with two or three live neighbors lives on to the next generation.\r\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\r\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\r\n5. No wrap around. Beyond edge is zero. Eight Neighbors.\r\n\u003c/pre\u003e\u003cp\u003eDetermine the period of life for a given binary matrix. A stable configuration, Still Life, has a cycle of 1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e M , an [m,n] array\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e N, Period of Life cycle (Period \u0026lt;11)\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e  A few matrices of varying periods\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eN=1   N=2    N=3 Caterer\r\n0000  00000  0000000000\r\n0110  00000  0001000000\r\n0110  01110  0100011110\r\n0000  00000  0100010000\r\n      00000  0100000000\r\n             0000100000\r\n             0011000000\r\n             0000000000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAdditional References:\u003c/b\u003e \u003ca href = \"http://www.conwaylife.com/wiki/Oscillator\"\u003eOscillators\u003c/a\u003e, \u003ca href = \"http://www.conwaylife.com/wiki/Still_life\"\u003eStill Life\u003c/a\u003e\u003c/p\u003e","function_template":"function N=Life_Period(M)\r\n%\r\nN=0;\r\n\r\nend","test_suite":"%Block  Still 1\r\nm=[0 0 0 0;0 1 1 0;0 1 1 0;0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Beehive  Still 1\r\nm=[0 0 0 0 0 0;0 0 1 1 0 0;0 1 0 0 1 0;0 0 1 1 0 0;0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Loaf  Still 1\r\nm=[0 0 0 0 0 0;0 0 1 1 0 0;0 1 0 0 1 0;0 0 1 0 1 0;0 0 0 1 0 0;0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Boat  Still 1\r\nm=[0 0 0 0 0;0 1 1 0 0;0 1 0 1 0;0 0 1 0 0;0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Table Still 1\r\nm=[0 0 0 0 0 0 0;0 1 1 0 1 1 0;0 0 1 0 1 0 0;0 0 1 0 1 0 0;0 1 1 0 1 1 0;0 0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==1)\r\n%%\r\n%Blinker  Osc 2\r\nm=[0 0 0 0 0;0 0 0 0 0;0 1 1 1 0;0 0 0 0 0;0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==2)\r\n%%\r\n%Toad  Osc 2\r\nm=[0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 1 1 0;0 1 1 1 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==2)\r\n%%\r\n%Beacon  Osc 2\r\nm=[0 0 0 0 0 0;0 1 1 0 0 0;0 1 1 0 0 0;0 0 0 1 1 0;0 0 0 1 1 0;0 0 0 0 0 0];\r\nn=Life_Period(m);\r\nassert(n==2)\r\n%%\r\n%Caterer  Osc 3\r\nm=[0 0 0 0 0 0 0 0 0 0;0 0 0 1 0 0 0 0 0 0;0 1 0 0 0 1 1 1 1 0;0 1 0 0 0 1 0 0 0 0;0 1 0 0 0 0 0 0 0 0;0 0 0 0 1 0 0 0 0 0;0 0 1 1 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0;];\r\nn=Life_Period(m);\r\nassert(n==3)","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-04T04:25:13.000Z","updated_at":"2026-02-13T15:20:35.000Z","published_at":"2013-11-04T04:46:57.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/conway-s-reverse-game-of-life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKaggle's Conway's Reverse Game of Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest inspires this Period of Life challenge. The kaggle contest runs from Oct-14-2013 thru Mar-02-2014. References:\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://mathworld.wolfram.com/GameofLife.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGame of Life at Wolfram\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWiki Life\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=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.\\n2. Any live cell with two or three live neighbors lives on to the next generation.\\n3. Any live cell with more than three live neighbors dies, as if by overcrowding.\\n4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.\\n5. No wrap around. Beyond edge is zero. Eight Neighbors.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDetermine the period of life for a given binary matrix. A stable configuration, Still Life, has a cycle of 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: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 , an [m,n] array\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 N, Period of Life cycle (Period \u0026lt;11)\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\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A few matrices of varying periods\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[N=1   N=2    N=3 Caterer\\n0000  00000  0000000000\\n0110  00000  0001000000\\n0110  01110  0100011110\\n0000  00000  0100010000\\n      00000  0100000000\\n             0000100000\\n             0011000000\\n             0000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAdditional References:\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.conwaylife.com/wiki/Oscillator\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOscillators\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conwaylife.com/wiki/Still_life\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eStill Life\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42290,"title":"GJam 2015 Rd1B: Noisy Neighbors","description":"This Challenge is derived from \u003chttps://code.google.com/codejam/contest/8224486/dashboard#s=p1 GJam 2015 Rd 1B: Noisy Neighbors\u003e. Fastest completion - 8 minutes.\r\n\r\nDetermine minimum number of adjacencies for N placed people in an RxC hotel matrix\r\n\r\nInput: m, an RxC zeros array; N number of rooms to be filled\r\n\r\nOutput: NN, minimum number of common walls\r\n\r\nExamples: Small Case 1\u003c=R*C\u003c=16, 0\u003c=N\u003c=R*C\r\n\r\n  [1 1 1;1 0 1;1 1 1] has minimum 8 common walls vs [1 1 1;1 1 1;1 1 0] has 10 common\r\n  [1;0;0;1] has 0 common walls\r\n  ones(2,3) has 7 common walls\r\n\r\n\r\nTheory: The small case can be solved with brute force using vector set with nchoosek followed by processing of convolutions. The large case has 10000 rooms making brute force hopeless.\r\n\r\n\r\nAdditional GJam solutions can be found at \u003chttp://www.go-hero.net/jam/15 Example GJam Matlab solutions\u003e. Select Find Solutions, change Language to Matlab. The Test Suite, at the bottom, contains a full GJam Matlab solution.","description_html":"\u003cp\u003eThis Challenge is derived from \u003ca href = \"https://code.google.com/codejam/contest/8224486/dashboard#s=p1\"\u003eGJam 2015 Rd 1B: Noisy Neighbors\u003c/a\u003e. Fastest completion - 8 minutes.\u003c/p\u003e\u003cp\u003eDetermine minimum number of adjacencies for N placed people in an RxC hotel matrix\u003c/p\u003e\u003cp\u003eInput: m, an RxC zeros array; N number of rooms to be filled\u003c/p\u003e\u003cp\u003eOutput: NN, minimum number of common walls\u003c/p\u003e\u003cp\u003eExamples: Small Case 1\u0026lt;=R*C\u0026lt;=16, 0\u0026lt;=N\u0026lt;=R*C\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e[1 1 1;1 0 1;1 1 1] has minimum 8 common walls vs [1 1 1;1 1 1;1 1 0] has 10 common\r\n[1;0;0;1] has 0 common walls\r\nones(2,3) has 7 common walls\r\n\u003c/pre\u003e\u003cp\u003eTheory: The small case can be solved with brute force using vector set with nchoosek followed by processing of convolutions. The large case has 10000 rooms making brute force hopeless.\u003c/p\u003e\u003cp\u003eAdditional GJam solutions can be found at \u003ca href = \"http://www.go-hero.net/jam/15\"\u003eExample GJam Matlab solutions\u003c/a\u003e. Select Find Solutions, change Language to Matlab. The Test Suite, at the bottom, contains a full GJam Matlab solution.\u003c/p\u003e","function_template":"function NN = Noisy_Neighbors(m,N)\r\n% m is an RxC zeros array\r\n% N is number of cells to be occupied\r\n% NN is number of edge adjacencies\r\n% Goal is to minimize NN\r\n\r\n  NN=0;\r\nend","test_suite":"tic\r\n%%\r\nm=zeros(5,2);N=8;\r\nNNexp=7;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=14;\r\nNNexp=18;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(8,2);N=12;\r\nNNexp=10;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,3);N=6;\r\nNNexp=3;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,6);N=12;\r\nNNexp=16;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=5;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=11;\r\nNNexp=8;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(7,2);N=13;\r\nNNexp=16;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=15;\r\nNNexp=14;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=9;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=12;\r\nNNexp=17;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=13;\r\nNNexp=14;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,4);N=5;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=5;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=16;\r\nNNexp=15;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,5);N=8;\r\nNNexp=7;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=4;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=10;\r\nNNexp=6;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=9;\r\nNNexp=3;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,2);N=2;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=15;\r\nNNexp=20;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,2);N=4;\r\nNNexp=4;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=11;\r\nNNexp=8;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=9;\r\nNNexp=1;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=16;\r\nNNexp=24;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=9;\r\nNNexp=1;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=15;\r\nNNexp=22;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,5);N=9;\r\nNNexp=10;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=10;\r\nNNexp=6;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=15;\r\nNNexp=14;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,2);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=2;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,2);N=4;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=4;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,7);N=13;\r\nNNexp=16;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,3);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(7,2);N=11;\r\nNNexp=10;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,1);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,6);N=9;\r\nNNexp=7;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=3;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=15;\r\nNNexp=22;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,2);N=6;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,4);N=12;\r\nNNexp=17;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,3);N=6;\r\nNNexp=7;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,1);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=16;\r\nNNexp=15;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,2);N=2;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,3);N=9;\r\nNNexp=12;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(9,1);N=6;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=12;\r\nNNexp=11;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,2);N=3;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(7,2);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=6;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,3);N=4;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,15);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(16,1);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=9;\r\nNNexp=3;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(15,1);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(2,4);N=6;\r\nNNexp=4;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=7;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=12;\r\nNNexp=11;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,4);N=1;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=8;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,16);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=3;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=0;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,13);N=9;\r\nNNexp=4;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=2;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(1,9);N=6;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(6,2);N=12;\r\nNNexp=16;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=8;\r\nNNexp=4;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=5;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(5,3);N=14;\r\nNNexp=18;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(4,3);N=7;\r\nNNexp=2;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(6,2);N=4;\r\nNNexp=0;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\nm=zeros(3,5);N=13;\r\nNNexp=14;\r\nNN=Noisy_Neighbors(m,N);\r\nassert(isequal(NNexp,NN))\r\n%%\r\ntoc\r\n%%\r\n% function GJam_Rd1B_2015b\r\n%  fn='B-small-practice.in';\r\n%  [data] = read_file(fn); % \r\n%  fidG = fopen('B-small-practice.out', 'w');\r\n%  \r\n% tic\r\n% for i=1:size(data,2) % Cell array has N cols of cases\r\n%  NN = Noisy_Neighbors(data{i});\r\n%  fprintf(fidG,'Case #%i: %i\\n',i,NN);\r\n%  \r\n%  fprintf('Case #%i: %i\\n',i,NN);   \r\n% end\r\n% toc\r\n% fclose(fidG);\r\n% end\r\n% \r\n% function val=Noisy_Neighbors(v)\r\n%  r=v(1);c=v(2);N=v(3);\r\n%  m=zeros(r,c);\r\n%  if N==0\r\n%   val=0;\r\n%   return;\r\n%  end\r\n%  \r\n% ptrset=find(m==0);\r\n% \r\n% val=Inf;\r\n% tlocs=nchoosek(ptrset,N);\r\n% for i=1:size(tlocs,1)\r\n%  m=m*0;\r\n%  tlocv=tlocs(i,:);\r\n%  m(tlocv)=1;\r\n%  vchk=nnz(conv2(m,[1 1])==2)+nnz(conv2(m,[1;1])==2);\r\n%  \r\n%  if vchk\u003cval\r\n%   val=vchk;\r\n%  end\r\n%  if val==0,break;end;\r\n% end\r\n%  \r\n% end\r\n% \r\n% function [d] = read_file(fn)\r\n% d={};\r\n% fid=fopen(fn);\r\n% fgetl(fid); % Total Count ignore\r\n% ptr=0;\r\n% while ~feof(fid)\r\n%  ptr=ptr+1;\r\n%  v=str2num(fgetl(fid)); % r,c,N\r\n%  \r\n%  d{ptr}=v;\r\n%  \r\n% end % feof\r\n%  fclose(fid);\r\n% \r\n% end % read_file\r\n%%\r\ntoc\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":5,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-05-03T17:57:59.000Z","updated_at":"2015-05-03T18:31:40.000Z","published_at":"2015-05-03T18:31:40.000Z","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 Challenge is derived from\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://code.google.com/codejam/contest/8224486/dashboard#s=p1\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGJam 2015 Rd 1B: Noisy Neighbors\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Fastest completion - 8 minutes.\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\u003eDetermine minimum number of adjacencies for N placed people in an RxC hotel matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: m, an RxC zeros array; N number of rooms to be filled\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\u003eOutput: NN, minimum number of common walls\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\u003eExamples: Small Case 1\u0026lt;=R*C\u0026lt;=16, 0\u0026lt;=N\u0026lt;=R*C\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[[1 1 1;1 0 1;1 1 1] has minimum 8 common walls vs [1 1 1;1 1 1;1 1 0] has 10 common\\n[1;0;0;1] has 0 common walls\\nones(2,3) has 7 common walls]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTheory: The small case can be solved with brute force using vector set with nchoosek followed by processing of convolutions. The large case has 10000 rooms making brute force hopeless.\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\u003eAdditional GJam solutions can be found at\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.go-hero.net/jam/15\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eExample GJam Matlab solutions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Select Find Solutions, change Language to Matlab. The Test Suite, at the bottom, contains a full GJam Matlab solution.\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":1102,"title":"USC Fall 2012 ACM : Find Largest Water Concentration","description":"This Challenge is to solve Question B, Water?, of the \u003chttp://contest.usc.edu/index.php/Fall12/Home USC ACM Fall 2012 Contest\u003e.\r\n\r\nGiven a character array A of minerals(A:Z) and a vector, iswater, of Water bearing minerals(A:Z) and a minimum rectangular size to evaluate, determine the Region with the Maximum water density. Report the larger region if two have the same density. \r\n\r\nSpecifically, report the Total water symbols and the Area of the highest density region. \r\n\r\n\r\nInput: [ A, iswater, s ]\r\n\r\nOutput: [Total Water Symbols, Area of Rectangle]; \r\n\r\n\r\nThe full \u003chttp://contest.usc.edu/index.php/Fall12/Home?action=download\u0026upname=water.in.txt USC data file\u003e\r\n\r\nExample:\r\n\r\nInput: A, iswater, s\r\n\r\n  Matrix A\r\n  ITTTTHHHHTTTT\r\n  IXYOXOOOOOOXI\r\n  IOOOXOOOOOXXJ\r\n  IYOOOOXOOOOOI\r\n  IXXOYOOYOOOOI\r\n  IAAAAAXAXAAAJ\r\n  \r\n  iswater vector\r\n  YX\r\n  \r\n  s is 3; This is the minimum allowed rectangle size\r\n  \r\n\r\nOutput: [8 16] as there are 8 \"water symbols\" in a 4x4=16 square TLC:(2,2)\r\n\r\n\r\n\r\n\u003chttp://contest.usc.edu/index.php/Fall12/Home?action=download\u0026upname=water.rongqiqiu.cpp.txt The Winning B solution\u003e is very large.\r\n","description_html":"\u003cp\u003eThis Challenge is to solve Question B, Water?, of the \u003ca href=\"http://contest.usc.edu/index.php/Fall12/Home\"\u003eUSC ACM Fall 2012 Contest\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eGiven a character array A of minerals(A:Z) and a vector, iswater, of Water bearing minerals(A:Z) and a minimum rectangular size to evaluate, determine the Region with the Maximum water density. Report the larger region if two have the same density.\u003c/p\u003e\u003cp\u003eSpecifically, report the Total water symbols and the Area of the highest density region.\u003c/p\u003e\u003cp\u003eInput: [ A, iswater, s ]\u003c/p\u003e\u003cp\u003eOutput: [Total Water Symbols, Area of Rectangle];\u003c/p\u003e\u003cp\u003eThe full \u003ca href=\"http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026amp;upname=water.in.txt\"\u003eUSC data file\u003c/a\u003e\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cp\u003eInput: A, iswater, s\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eMatrix A\r\nITTTTHHHHTTTT\r\nIXYOXOOOOOOXI\r\nIOOOXOOOOOXXJ\r\nIYOOOOXOOOOOI\r\nIXXOYOOYOOOOI\r\nIAAAAAXAXAAAJ\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eiswater vector\r\nYX\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003es is 3; This is the minimum allowed rectangle size\r\n\u003c/pre\u003e\u003cp\u003eOutput: [8 16] as there are 8 \"water symbols\" in a 4x4=16 square TLC:(2,2)\u003c/p\u003e\u003cp\u003e\u003ca href=\"http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026amp;upname=water.rongqiqiu.cpp.txt\"\u003eThe Winning B solution\u003c/a\u003e is very large.\u003c/p\u003e","function_template":"function [w,w_area]=Water_analysis(A,iswater,s) \r\n w=0;\r\n w_area=s^2;\r\n \r\nend","test_suite":"%%\r\n%http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026upname=water.in.txt\r\ntic\r\nurlwrite('http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026upname=water.in.txt','water_in.txt');\r\ntoc\r\n%%\r\n fid=fopen('water_in.txt','r');\r\n w_area_expect=[8 16;100 100;0 1;1 1;5 9;186 675;23 110; 480 2500;102 2500;61 400; ...\r\n     73 930;160 441; 678 800; 55 361; 53 195;216 1296;199 1110;81 576;232 552;12 30;9 25;15 25;25 143; ...\r\n     66 120;2500 2500;270 1680;264 900;292 1365;748 2401;244 609;315 1296;214 1188];\r\n \r\n qty=fscanf(fid,'%i',1);\r\n for q=1:qty\r\n  n = fscanf(fid,'%i %i %i\\n',3)'; % rows / cols / min rect\r\n  s=n(3);\r\n  n=n(1:2);\r\n \r\n  strv=fgetl(fid);\r\n  iswater=strv;\r\n  \r\n  A=zeros(n); % Format is rows, columns\r\n  for i=1:n(1)\r\n   strv = fgetl(fid);\r\n   A(i,:) = strv;\r\n  end\r\n  A=char(A);\r\n\r\n  [w,w_area]=Water_analysis(A,iswater,s);\r\n  \r\n  assert(isequal([w w_area],w_area_expect(q,:)));\r\n\r\n end\r\n \r\n fprintf('Processing Complete\\n\\n')\r\n fclose(fid);\r\ntoc\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":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-12-07T02:38:08.000Z","updated_at":"2025-11-21T09:56:08.000Z","published_at":"2012-12-07T04:04:40.000Z","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 Challenge is to solve Question B, Water?, of the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://contest.usc.edu/index.php/Fall12/Home\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUSC ACM Fall 2012 Contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a character array A of minerals(A:Z) and a vector, iswater, of Water bearing minerals(A:Z) and a minimum rectangular size to evaluate, determine the Region with the Maximum water density. Report the larger region if two have the same density.\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\u003eSpecifically, report the Total water symbols and the Area of the highest density region.\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\u003eInput: [ A, iswater, s ]\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\u003eOutput: [Total Water Symbols, Area of 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\u003eThe full\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://contest.usc.edu/index.php/Fall12/Home?action=download\u0026amp;upname=water.in.txt\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eUSC data file\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: A, iswater, s\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[Matrix A\\nITTTTHHHHTTTT\\nIXYOXOOOOOOXI\\nIOOOXOOOOOXXJ\\nIYOOOOXOOOOOI\\nIXXOYOOYOOOOI\\nIAAAAAXAXAAAJ\\n\\niswater vector\\nYX\\n\\ns is 3; This is the minimum allowed rectangle size]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput: [8 16] as there are 8 \\\"water symbols\\\" in a 4x4=16 square TLC:(2,2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://contest.usc.edu/index.php/Fall12/Home?action=download\u0026amp;upname=water.rongqiqiu.cpp.txt\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eThe Winning B solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is very large.\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":56478,"title":"IQpuzzler Preparation #3: Detect isolated groups of zeros in a matrix","description":"Return true if any small, rectangular shaped, isolated groups of zeros (size 1x1, 1x2, 2x1, 1x3, 3x1 or 2x2) are present in a matrix, with all vertically and horizontally adjacent elements being non-zero.\r\nThe matrix will always be 3-by-3 or larger.\r\nExamples:\r\n    11     2     3     2     9    10    10    11     6     7     4\r\n    12     4    13     6     1    10     1    10     5     6     9\r\n     2     8    13    12     0     0     0     5    10     9     9\r\n    12    13     7    11    13     9     1    13    11    10     3\r\n     9    13    11    13     9     3     2     1     3    10     2\r\n==\u003e true\r\n\r\n    11     2     3     2     9    10    10    11     6     7     4\r\n    12     4    13     6     1    10     1    10     5     6     9\r\n     2     8    13    12     0     0     0     1    10     9     9\r\n    12    13     7    11    13     0     1    13    11    10     3\r\n     9    13    11    13     9     3     2     1     3    10     2\r\n==\u003e false\r\n\r\n     7    10    13    11     5     5     4     1     2     3     8\r\n    13     0     8     4     3    11    10     1     8    11     4\r\n     5     0     2    11     4     8    10     7     7     5     9\r\n     8     0     2     4     9     8     5    11     1     7     9\r\n     3    12     4    13     7    12     8    13     5     3    10\r\n==\u003e true\r\n\r\nBackground:\r\nThis function or variations of it may be useful in some board games like Go, or in the upcoming IQpuzzler challenge.\r\nHint:\r\nThe built-in function conv2 may be helpful for your implementation.","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: 762.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 332.5px 381.25px; transform-origin: 332.5px 381.25px; 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: 309.5px 21px; text-align: left; transform-origin: 309.5px 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: 306.5px 7.7px; transform-origin: 306.5px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn true if any small, rectangular shaped, isolated groups of zeros (size 1x1, 1x2, 2x1, 1x3, 3x1 or 2x2) are present in a matrix, with all vertically and horizontally adjacent elements being non-zero.\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 129.142px 7.7px; transform-origin: 129.142px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe matrix will always be 3-by-3 or larger.\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32.675px 7.7px; transform-origin: 32.675px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eExamples:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; 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: 329.5px 51.0833px; transform-origin: 329.5px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    11     2     3     2     9    10    10    11     6     7     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    12     4    13     6     1    10     1    10     5     6     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     2     8    13    12     0     0     0     5    10     9     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    12    13     7    11    13     9     1    13    11    10     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     9    13    11    13     9     3     2     1     3    10     2\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 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: 26.275px 7.7px; transform-origin: 26.275px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e==\u0026gt; true\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 7.7px; transform-origin: 0px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; 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: 329.5px 51.0833px; transform-origin: 329.5px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    11     2     3     2     9    10    10    11     6     7     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    12     4    13     6     1    10     1    10     5     6     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     2     8    13    12     0     0     0     1    10     9     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    12    13     7    11    13     0     1    13    11    10     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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     9    13    11    13     9     3     2     1     3    10     2\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 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margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 7.7px; transform-origin: 0px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; 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: 329.5px 51.0833px; transform-origin: 329.5px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; 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perspective-origin: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     7    10    13    11     5     5     4     1     2     3     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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: 0.55px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.55px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.55px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.55px; 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; 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padding-inline-start: 4px; padding-left: 4px; perspective-origin: 329.5px 10.2167px; transform-origin: 329.5px 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: 254.1px 7.975px; tab-size: 4; transform-origin: 254.1px 7.975px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     3    12     4    13     7    12     8    13     5     3    10\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 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: 26.275px 7.7px; transform-origin: 26.275px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e==\u0026gt; true\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 7.7px; transform-origin: 0px 7.7px; 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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 39.2917px 7.7px; transform-origin: 39.2917px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBackground:\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: 309.5px 21px; text-align: left; transform-origin: 309.5px 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: 294.833px 7.7px; transform-origin: 294.833px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis function or variations of it may be useful in some board games like Go, or in the upcoming IQpuzzler challenge.\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.3917px 7.7px; transform-origin: 14.3917px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHint:\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: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 206.167px 7.7px; transform-origin: 206.167px 7.7px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe built-in function conv2 may be helpful for your implementation.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function result=FindHoles2(board)\r\n    result=false;\r\nend","test_suite":"%%\r\nboard=magic(3);\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=zeros(5,9);\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=ones(7,3);\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=ones(5,11);\r\nboard(1)=0;\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[11     2     3     2     9    10    10    11     6     7     4\r\n       12     4    13     6     1    10     1    10     5     6     9\r\n        2     8    13    12     0     0     0     5    10     9     9\r\n       12    13     7    11    13     9     1    13    11    10     3\r\n        9    13    11    13     9     3     2     1     3    10     2];\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[11     2     3     2     9    10    10    11     6     7     4\r\n       12     4    13     6     1    10     1    10     5     6     9\r\n        2     8    13    12     0     0     0     5    10     9     9\r\n       12    13     7    11    13     0     1    13    11    10     3\r\n        9    13    11    13     9     3     2     1     3    10     2];\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     0     8     4     3    11    10     1     8    11     4\r\n        5     0     2    11     4     8    10     7     7     5     9\r\n        8     0     2     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     5     8     4     3    11    10     1     8    11     4\r\n        5     0     2    11     4     8    10     7     7     5     9\r\n        8     0     2     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     5     8     4     3    11    10     1     8    11     4\r\n        5     0     0    11     4     8    10     7     7     5     9\r\n        8     0     2     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     5     8     4     3    11    10     1     8    11     4\r\n        5     0     0    11     4     8    10     7     7     5     9\r\n        8     0     0     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(FindHoles2(board));\r\n%%\r\nboard=[ 7    10    13    11     5     5     4     1     2     3     8\r\n       13     5     8     4     3    11    10     1     8    11     4\r\n        5     0     0    11     4     8    10     7     7     5     9\r\n        0     0     0     4     9     8     5    11     1     7     9\r\n        3    12     4    13     7    12     8    13     5     3    10];\r\nassert(~FindHoles2(board));\r\n%%\r\nboard=mod(spiral(20),40);\r\nassert(FindHoles2(board));","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":2414210,"edited_by":2414210,"edited_at":"2022-11-17T13:01:48.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:17:45.000Z","updated_at":"2022-11-17T13:01:48.000Z","published_at":"2022-11-07T18:17:45.000Z","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\u003eReturn true if any small, rectangular shaped, isolated groups of zeros (size 1x1, 1x2, 2x1, 1x3, 3x1 or 2x2) are present in a matrix, with all vertically and horizontally adjacent elements being non-zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe matrix will always be 3-by-3 or larger.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[    11     2     3     2     9    10    10    11     6     7     4\\n    12     4    13     6     1    10     1    10     5     6     9\\n     2     8    13    12     0     0     0     5    10     9     9\\n    12    13     7    11    13     9     1    13    11    10     3\\n     9    13    11    13     9     3     2     1     3    10     2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e==\u0026gt; true\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[    11     2     3     2     9    10    10    11     6     7     4\\n    12     4    13     6     1    10     1    10     5     6     9\\n     2     8    13    12     0     0     0     1    10     9     9\\n    12    13     7    11    13     0     1    13    11    10     3\\n     9    13    11    13     9     3     2     1     3    10     2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e==\u0026gt; false\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[     7    10    13    11     5     5     4     1     2     3     8\\n    13     0     8     4     3    11    10     1     8    11     4\\n     5     0     2    11     4     8    10     7     7     5     9\\n     8     0     2     4     9     8     5    11     1     7     9\\n     3    12     4    13     7    12     8    13     5     3    10]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e==\u0026gt; true\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBackground:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\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 function or variations of it may be useful in some board games like Go, or in the upcoming IQpuzzler challenge.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\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 built-in function conv2 may be helpful for your implementation.\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":42341,"title":"LMI Twins : Fuzzy Image matching","description":"The image below from \u003chttp://logicmastersindia.com/2016/04P/ Logic Masters India Marathon 2016\u003e is puzzle \"Twins Co-Ordinates\" with PDF password of \"TCO_cLiEsh\".\r\n\r\nThe challenge is to find the six matching image squares given a [612,1078,3] PNG file screen captured using Snipping Tool. The matching squares may be rotated but are not reflected. The cells will not match perfectly and the PNG file is oversize into the white around the whole black frame. The image grid is 10x18. Return a 6x4 array of [r1,c1,r2,c2;...] where cell [r1,c1] is a match for [r2,c2] with r(1:10) and c(1:18).\r\nA pscript to obfuscate the solution is implemented. The method for creating a Cody pscript is included in the Test Suite.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Twins.PNG\u003e\u003e\r\n\r\nMethod Hints:\r\nMerge into a BW 612x1078 image;Trim outer white space;Locate Top Left corner of each cell;create cell array of 180  squares;grab only 55x55 squares to enable rotation check;Problem size is only  4*180*179/2;Filter positive and negative deltas separately with a + convolution; check residual delta \u003c threshold.","description_html":"\u003cp\u003eThe image below from \u003ca href = \"http://logicmastersindia.com/2016/04P/\"\u003eLogic Masters India Marathon 2016\u003c/a\u003e is puzzle \"Twins Co-Ordinates\" with PDF password of \"TCO_cLiEsh\".\u003c/p\u003e\u003cp\u003eThe challenge is to find the six matching image squares given a [612,1078,3] PNG file screen captured using Snipping Tool. The matching squares may be rotated but are not reflected. The cells will not match perfectly and the PNG file is oversize into the white around the whole black frame. The image grid is 10x18. Return a 6x4 array of [r1,c1,r2,c2;...] where cell [r1,c1] is a match for [r2,c2] with r(1:10) and c(1:18).\r\nA pscript to obfuscate the solution is implemented. The method for creating a Cody pscript is included in the Test Suite.\u003c/p\u003e\u003cimg src = \"https://sites.google.com/site/razapor/matlab_cody/Twins.PNG\"\u003e\u003cp\u003eMethod Hints:\r\nMerge into a BW 612x1078 image;Trim outer white space;Locate Top Left corner of each cell;create cell array of 180  squares;grab only 55x55 squares to enable rotation check;Problem size is only  4*180*179/2;Filter positive and negative deltas separately with a + convolution; check residual delta \u0026lt; threshold.\u003c/p\u003e","function_template":"function m=LMI_Twins(fn)\r\n% Grab png \r\nm=[];\r\npict=imread(fn); % [612,1078,3] size png,  sum(pict,3) may be useful\r\n% useful sub_pict size of 55x55\r\n\r\nend\r\n","test_suite":"%%\r\n% obfuscated check function\r\ntic\r\nfh = fopen('Eval_Twins.p','wb');\r\nfwrite(fh, hex2dec(reshape('7630312E30307630302E30300002901CC14FDFB100000081000000D80000012A48DF9D54B1FA232306002212C284B03C55EA944CADBBEC102FFDF678B732282B51740342451D4E91F086ABE90CD97AEA717741F995FCF6ED6A372B6C44524D61F2179DA76458DBA2B9AA01529272E90AD613EA63642EEECCDE54CD649A84090DE86A418D761593CBABBD4B17018634CA5451EA6A6F4386B000CBE73A136AFE2CE5946FD8FD16225562B3414B8C6268F085E3E3E8F1CFCFBBA469FCC5E0343472A562EAD16FFC1B7D27730414ECC1D42F6EB98289E998DA2EA3F4F820A10F9B23E296CDE97331C46563B702180857770F7A14412389BC1BA2',2,[]).'));\r\nfclose(fh);\r\nrehash path;\r\ntoc\r\n%%\r\ntic\r\nurl='https://sites.google.com/site/razapor/matlab_cody/Twins.PNG?attredirects=0\u0026d=1';\r\nurlwrite(url,'Twins.PNG');\r\nfprintf('url write complete %.1f\\n',toc)\r\n\r\nfn='Twins.PNG';\r\nm=LMI_Twins(fn);\r\nassert(Eval_Twins(m));\r\nfprintf('Process Complete %.1f\\n',toc)\r\n%%\r\n% Creation Process for a Cody p file\r\n% Create function  func_name.m\r\n% pcode func_name\r\n% fn='func_name.p';\r\n% fr=fopen(fn,'r');\r\n% m=fread(fr);\r\n% fclose(fr);\r\n% fw=fopen([fn(1:end-1) 'txt'],'w');\r\n% fprintf(fw,'%s',dec2hex(m)');\r\n% fclose(fw);\r\n% use wordpad to open func_name.txt\r\n% In cody in first block write:\r\n% fh = fopen('func_name.p','wb');\r\n% fwrite(fh, hex2dec(reshape('INSERT WORDPAD TEXT HERE',2,[]).'));\r\n% fclose(fh);\r\n% rehash path;","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-05-28T19:12:46.000Z","updated_at":"2016-05-12T01:56:20.000Z","published_at":"2016-05-12T01:56:20.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"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.png\"}],\"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 image below from\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://logicmastersindia.com/2016/04P/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLogic Masters India Marathon 2016\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is puzzle \\\"Twins Co-Ordinates\\\" with PDF password of \\\"TCO_cLiEsh\\\".\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 challenge is to find the six matching image squares given a [612,1078,3] PNG file screen captured using Snipping Tool. The matching squares may be rotated but are not reflected. The cells will not match perfectly and the PNG file is oversize into the white around the whole black frame. The image grid is 10x18. Return a 6x4 array of [r1,c1,r2,c2;...] where cell [r1,c1] is a match for [r2,c2] with r(1:10) and c(1:18). A pscript to obfuscate the solution is implemented. The method for creating a Cody pscript is included in the Test Suite.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw: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:r\u003e\u003cw:t\u003eMethod Hints: Merge into a BW 612x1078 image;Trim outer white space;Locate Top Left corner of each cell;create cell array of 180 squares;grab only 55x55 squares to enable rotation check;Problem size is only 4*180*179/2;Filter positive and negative deltas separately with a + convolution; check residual delta \u0026lt; 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