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function [features, targets, label] = k_means(train_features, train_targets, Nmu, region, plot_on) %Reduce the number of data points using the k-means algorithm %Inputs: % train_features - Input features % train_targets - Input targets % Nmu - Number of output data points % region - Decision region vector: [-x x -y y number_of_points] % plot_on - Plot stages of the algorithm % %Outputs % features - New features % targets - New targets % label - The labels given for each of the original features if (nargin < 5), plot_on = 0; end [D,L] = size(train_features); dist = zeros(Nmu,L); label = zeros(1,L); %Initialize the mu's mu = randn(D,Nmu); mu = sqrtm(cov(train_features',1))*mu + mean(train_features')'*ones(1,Nmu); old_mu = zeros(D,Nmu); switch Nmu, case 0, mu = []; label = []; case 1, mu = mean(train_features')'; label = ones(1,L); otherwise while (sum(sum(mu == old_mu)) == 0), old_mu = mu; %Classify all the features to one of the mu's for i = 1:Nmu, dist(i,:) = sum((train_features - mu(:,i)*ones(1,L)).^2); end %Label the points [m,label] = min(dist); %Recompute the mu's for i = 1:Nmu, mu(:,i) = mean(train_features(:,find(label == i))')'; end if (plot_on == 1), plot_process(mu) end end end %Make the decision region targets = zeros(1,Nmu); if (Nmu > 1), for i = 1:Nmu, if (length(train_targets(:,find(label == i))) > 0), targets(i) = (sum(train_targets(:,find(label == i)))/length(train_targets(:,find(label == i))) > .5); end end else %There is only one center targets = (sum(train_targets)/length(train_targets) > .5); end features = mu;