fuzzy_k_means.rar_K._fuzzy k means_k means_k-means 识别_k-means聚类分

function [patterns, targets] = fuzzy_k_means(train_patterns, train_targets, Nmu, plot_on) %Reduce the number of data points using the fuzzy k-means algorithm %Inputs: % train_patterns - Input patterns % train_targets - Input targets % Nmu - Number of output data points % plot_on - Plot stages of the algorithm % %Outputs % patterns - New patterns % targets - New targets if (nargin < 4), plot_on = 0; end b = 2; L = length(train_targets); dist = zeros(Nmu,L); label = zeros(1,L); Dim = size(train_patterns,1); %Initialize the mu's mu = randn(Dim,Nmu); mu = sqrtm(cov(train_patterns',1))*mu + mean(train_patterns')'*ones(1,Nmu); old_mu = zeros(Dim,Nmu); %Initialize the P's P = randn(Nmu,L); old_P = zeros(Nmu,L); while ((sum(sum(abs(mu - old_mu) > 1e-5)) + sum(sum(abs(P - old_P) > 1e-5)) > 0)), old_mu = mu; old_P = P; %Classify all the patterns to one of the mu's for i = 1:Nmu, dist(i,:) = sum((train_patterns - mu(:,i)*ones(1,L)).^2); end %Recompute P's P = (1./dist).^(1/(b-1)); P = P ./ (ones(Nmu,1) * sum(P)); %Recompute the mu's P = P.^b; mu = (train_patterns * P') ./ (ones(Dim,1)*sum(P')); %Plot centers during training plot_process(mu, plot_on) end %Classify the patterns [m,label] = max(P); targets = zeros(1,Nmu); Uc = unique(train_targets); 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;