www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/BIMSEC.m
function [features, targets, label] = BIMSEC(train_features, train_targets, params, region, plot_on) %Reduce the number of data points using the basic iterative MSE clustering algorithm %Inputs: % train_features - Input features % train_targets - Input targets % params - Algorithm parameters: [Number of output data points, Number of attempts] % 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 [Nmu, Ntries] = process_params(params); [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); ro = zeros(1,Nmu); n = zeros(1,Nmu); Ji = zeros(1,Nmu); oldJ = 0; J = 1; if (Nmu == 1), mu = mean(train_features')'; 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_features).^2)); end [m, label] = min(dist); n = hist(label, Nmu); while (Ntries > 0), %Select a sample x_hat r = randperm(L); x_hat = train_features(:,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_features(:,indexes)')'; Ji(j) = sum(sum((mu(:,j)*ones(1,length(indexes)) - train_features(:,indexes)).^2)); end oldJ = J; J = sum(Ji); end end %disp(['Distance to convergence is ' num2str(abs(J-oldJ))]) if (plot_on == 1), plot_process(mu) end if (J == oldJ), Ntries = Ntries - 1; 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;