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function [patterns, targets, label, J] = BIMSEC(train_patterns, train_targets, params, plot_on) %Reduce the number of data points using the basic iterative MSE clustering algorithm %Inputs: % train_patterns - Input patterns % train_targets - Input targets % params - Algorithm parameters: [Number of output data points, Number of attempts] % plot_on - Plot stages of the algorithm % %Outputs % patterns - New patterns % targets - New targets % label - The labels given for each of the original patterns if (nargin < 4), plot_on = 0; end [Nmu, Ntries] = process_params(params); [D,L] = size(train_patterns); dist = zeros(Nmu,L); label = zeros(1,L); Uc = unique(train_targets); %Initialize the mu's mu = randn(D,Nmu); mu = sqrtm(cov(train_patterns',1))*mu + mean(train_patterns')'*ones(1,Nmu); ro = zeros(1,Nmu); n = zeros(1,Nmu); Ji = zeros(1,Nmu); J = 1; iter = 1; if (Nmu == 1), mu = mean(train_patterns')'; label = ones(1,L); else %Assign each example to one of the mu's %Compute distances dist = zeros(Nmu, L); for i = 1:Nmu, dist(i,:) = sqrt(sum((mu(:,i)*ones(1,L) - train_patterns).^2)); end [m, label] = min(dist); n = hist(label, Nmu); while (Ntries > 0), iter = iter + 1; J(iter) = 0; %Select a sample x_hat r = randperm(L); x_hat = train_patterns(:,r(1)); %i <- argmin||mi - x_hat|| dist = sqrt(sum((mu - x_hat * ones(1,Nmu)).^2)); i = find(dist == min(dist)); %Compute ro if n(i) ~= 1 if (n(i) ~=1), for j = 1:Nmu, if (i ~= j), ro(j) = n(j)/(n(j)+1)*dist(j)^2; else ro(j) = n(j)/(n(j)-1)*dist(j)^2; end end %Transfer x_hat if needed [m, k] = find(min(ro) == ro); if (k ~= i), label(r(1)) = k; n(i) = n(i) - 1; n(k) = n(k) + 1; %Recompute Je, and the mu's for j = 1:Nmu, indexes = find(label == j); mu(:,j) = mean(train_patterns(:,indexes)')'; Ji(j) = sum(sum((mu(:,j)*ones(1,length(indexes)) - train_patterns(:,indexes)).^2)); end J(iter) = sum(Ji); end end %Plot the centers during the process plot_process(mu, plot_on) if (J(iter) == J(iter-1)), Ntries = Ntries - 1; end end end %Classify all the patterns to one of the mu's (1-NN) dist = zeros(Nmu,L); for i = 1:Nmu, dist(i,:) = sum((train_patterns - mu(:,i)*ones(1,L)).^2); end %Label the points [m,label] = min(dist); targets = zeros(1,Nmu); for i = 1:Nmu, N = hist(train_targets(:,find(label == i)), Uc); [m, max_l] = max(N); targets(i) = Uc(max_l); end patterns = mu;