混合高斯模型（GMM）

% Clustering by Gaussian Mixture Model % Author : T. N. Vikram % Place: International School of Information Management % Reference : Algorithm Collections For Digital Signal Processing % Applications Using Matlab - E.S. Gopi , Kluwer 2007 % Email: vikram@isim.ac.in % GMM Example % x1 = 10 + sqrt(3) * randn(5,3); % x2 = 20 + sqrt(5) * randn(5,3); % x3 = 25 + sqrt(2) * randn(5,3); % Input = [x1 x2 x3]; % No_of_Clusters = 2; % No_of_Iterations = 5; % [INDEX,Mu, Variances] = GMM(Input, No_of_Clusters,No_of_Iterations); function [INDEX, Mu, Variances] = GMM(Input, No_of_Clusters,Limit) % Initialize_the_Cluster_Centroid [IDX, Initial_Centroids] = kmeans(Input',No_of_Clusters); Mu = Initial_Centroids'; Limit = 10; for Iterations = 1:Limit [No_of_Features_within_Data,No_of_Data_Points] = size(Input); Probability_of_Cluster_given_Point(1:No_of_Clusters,1:No_of_Data_Points) = 0.0; [PC,INDEX] = Cluster_Probability(Input,Mu); %Initialize Cluster Covariances COVAR(1:No_of_Features_within_Data,1:No_of_Clusters) = 0.0; for i=1:No_of_Clusters COVAR(:,i) = Cluster_Covariance(Input(:,IDX==i)); end %Initialize the probability matrix P(Cluster/Point) Variances = COVAR; for i=1:No_of_Clusters for j=1:No_of_Data_Points Probability_of_Cluster_given_Point(i,j) = Probability_of_Cluster_given_X(Input(:,j),Mu,Variances,PC,i); end; end; % New Means Mu1(1:No_of_Clusters,1:No_of_Features_within_Data) = 0.0; for i=1:No_of_Clusters Mu1(i,:) = Compute_Mean_for_Cluster(Input,Mu,Variances,PC,i); end; %disp(Iterations); %disp(Mu1); Mu = Mu1'; end;