EM算法的matlab实现

function varargout = gmm(X, K_or_centroids)
% ============================================================
% Expectation-Maximization iteration implementation of
% Gaussian Mixture Model.
%
% PX = GMM(X, K_OR_CENTROIDS)
% [PX MODEL] = GMM(X, K_OR_CENTROIDS)
%
%  - X: N-by-D data matrix.
%  - K_OR_CENTROIDS: either K indicating the number of
%       components or a K-by-D matrix indicating the
%       choosing of the initial K centroids.
%
%  - PX: N-by-K matrix indicating the probability of each
%       component generating each point.
%  - MODEL: a structure containing the parameters for a GMM:
%       MODEL.Miu: a K-by-D matrix.
%       MODEL.Sigma: a D-by-D-by-K matrix.
%       MODEL.Pi: a 1-by-K vector.
% ============================================================
 
    threshold = 1e-15;
    [N, D] = size(X);
 
    if isscalar(K_or_centroids)
        K = K_or_centroids;
        % randomly pick centroids
        rndp = randperm(N);
        centroids = X(rndp(1:K), :);
    else