SVMtoy

 

SVMtoy

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[label_matrix, instance_matrix] = libsvmread('ex8b.txt'); options = ''; %  contour_level = [-1 0 1]; contour_level = [-0.2  0.2 1 2];     % function svmtoy(label_matrix, instance_matrix, options, contour_level) %% svmtoy(label_matrix, instance_matrix, options, contour_level) %% label_matrix: N by 1, has to be two-class %% instance_matrix: N by 2 %% options: default '', %%          see libsvm-mat-8 README, has to be a classification formulation. %% contour_level: default [0 0], %%                change to [-1 0 1] for showing the +/- 1 margin. %% %% svmtoy shows the two-class classification boundary of the 2-D data %% based on libsvm-mat-2.8 %% %% Hsuan-Tien Lin, htlin at caltech.edu, 2006/04/07   % if nargin <= 1 %   instance_matrix = []; % elseif nargin == 2    %   options = '' % end % % if nargin <= 3 %   contour_level = [-1 0 1]; % end   N = size(label_matrix, 1); if N <= 0   fprintf(2, 'number of data should be positive\n');   return; end   if size(label_matrix, 2) ~= 1   fprintf(2, 'the label matrix should have only one column\n');   return; end   if size(instance_matrix, 1) ~= N   fprintf(2, ['the label and instance matrices should have the same ' ...               'number of rows\n']);   return; end   if size(instance_matrix, 2) ~= 2   fprintf(2, 'svmtoy only works for 2-D data\n');   return; end   mdl = svmtrain(label_matrix, instance_matrix, options);   nclass = mdl.nr_class; svmtype = mdl.Parameters(1);   if nclass ~= 2 || svmtype >= 2   fprintf(2, ['cannot plot the decision boundary for these ' ...               'SVM problems\n']);   return end   minX = min(instance_matrix(:, 1)); maxX = max(instance_matrix(:, 1)); minY = min(instance_matrix(:, 2)); maxY = max(instance_matrix(:, 2));   gridX = (maxX - minX) ./ 100; gridY = (maxY - minY) ./ 100;   minX = minX - 10 * gridX; maxX = maxX + 10 * gridX; minY = minY - 10 * gridY; maxY = maxY + 10 * gridY;   [bigX, bigY] = meshgrid(minX:gridX:maxX, minY:gridY:maxY);   mdl.Parameters(1) = 3; % the trick to get the decision values ntest=size(bigX, 1) * size(bigX, 2); instance_test=[reshape(bigX, ntest, 1), reshape(bigY, ntest, 1)]; label_test = zeros(size(instance_test, 1), 1);   [Z]= svmpredict(label_test, instance_test, mdl);   bigZ = reshape(Z, size(bigX, 1), size(bigX, 2));   clf; hold on;   ispos = (label_matrix == label_matrix(1)); pos = find(ispos); neg = find(~ispos);   plot(instance_matrix(pos, 1), instance_matrix(pos, 2), 'o'); plot(instance_matrix(neg, 1), instance_matrix(neg, 2), 'x');   contour(bigX, bigY, bigZ, contour_level);   title(options);