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function [features, targets] = LVQ1(train_features, train_targets, Nmu, region, plot_on) %Reduce the number of data points using linear vector quantization %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 %OR % D - Decision region if (nargin < 5), plot_on = 0; end alpha = 0.9; L = length(train_targets); dist = zeros(Nmu,L); label = zeros(1,L); %Initialize the mu's mu = randn(2,Nmu); mu = sqrtm(cov(train_features',1))*mu + mean(train_features')'*ones(1,Nmu); mu_target= rand(1,Nmu)>0.5; old_mu = zeros(2,Nmu); while (sum(sum(abs(mu - old_mu))) > 0.1), 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); %Label the mu's for i = 1:Nmu, if (length(train_targets(:,find(label == i))) > 0), mu_target(i) = (sum(train_targets(:,find(label == i)))/length(train_targets(:,find(label == i))) > .5); end end %Recompute the mu's for i = 1:Nmu, indices = find(label == i); if ~isempty(indices), Q = ones(2,1) * (2*(train_targets(indices) == mu_target(i)) - 1); mu(:,i) = mu(:,i) + mean(((train_features(:,indices)-mu(:,i)*ones(1,length(indices))).*Q)')'*alpha; end end alpha = 0.95 * alpha; if (plot_on == 1), plot_process(mu) 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; if (nargout == 1), features = Nearest_Neighbor(features, targets, 1, region); end