www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/fuzzy_k_means.m
function [features, targets] = fuzzy_k_means(train_features, train_targets, Nmu, region, plot_on) %Reduce the number of data points using the fuzzy 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 if (nargin < 5), plot_on = 0; end b = 2; 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); old_mu = zeros(2,Nmu); %Initialize the P's P = randn(Nmu,L); old_P = zeros(Nmu,L); while ((sum(sum(mu == old_mu)) == 0) & (sum(sum(P == old_P)) == 0)), old_mu = mu; old_P = P; %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 %Recompute P's for i = 1:Nmu, P(i,:) = (1./dist(i,:)).^(1/(b-1)); end P = P ./ (ones(Nmu,1) * sum(P)); %Recompute the mu's for i = 1:Nmu, mu(:,i) = (sum((((ones(2,1)*P(i,:)).^b).*train_features)')./sum(((ones(2,1)*P(i,:)).^b)'))'; end if (plot_on == 1), plot_process(mu) end end %Make the decision region [m,label] = max(P); 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;