function [Alpha, Mu, Sigma] = GMM_EM(Data, Alpha0, Mu0, Sigma0)

%% EM迭代停止条件

loglik_threshold = 1e-10;

%%初始化参数

[dim, N] = size(Data);

M = size(Mu0,2);

loglik_old = -realmax;

nbStep = 0;

Mu = Mu0;

Sigma = Sigma0;

Alpha = Alpha0;

Epsilon = 0.0001;

while (nbStep < 1200)

nbStep = nbStep+1;

%% E-步骤%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for i=1:M

% PDF of each point

Pxi(:,i) = GaussPDF(Data, Mu(:,i), Sigma(:,:,i));

end

%计算后验概率beta(i|x)

Pix_tmp = repmat(Alpha,[N 1]).*Pxi;

Pix = Pix_tmp ./ (repmat(sum(Pix_tmp,2),[1 M])+realmin);

Beta = sum(Pix);

%% M-步骤%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for i=1:M

%更新权值

Alpha(i) = Beta(i) / N;

%更新均值

Mu(:,i) = Data*Pix(:,i) / Beta(i);

%更新方差

Data_tmp1 = Data - repmat(Mu(:,i),1,N);

Sigma(:,:,i) = (repmat(Pix(:,i)',dim, 1) .* Data_tmp1*Data_tmp1') / Beta(i);

%% Add a tiny variance to avoid numerical instability

Sigma(:,:,i) = Sigma(:,:,i) + 1E-5.*diag(ones(dim,1));

end

%  %% Stopping criterion 1 %%%%%%%%%%%%%%%%%%%%

%  for i=1:M

%Compute the new probability p(x|i)

%    Pxi(:,i) = GaussPDF(Data, Mu(:,i), Sigma(i));

%  end

%Compute the log likelihood

%  F = Pxi*Alpha';

%  F(find(F

%  loglik = mean(log(F));

%Stop the process depending on the increase of the log likelihood

%  if abs((loglik/loglik_old)-1) < loglik_threshold

%    break;

%  end

%  loglik_old = loglik;

%% Stopping criterion 2 %%%%%%%%%%%%%%%%%%%%

v = [sum(abs(Mu - Mu0)), abs(Alpha - Alpha0)];

s = abs(Sigma-Sigma0);

v2 = 0;

for i=1:M

v2 = v2 + det(s(:,:,i));

end

if ((sum(v) + v2) < Epsilon)

break;

end

Mu0 = Mu;

Sigma0 = Sigma;

Alpha0 = Alpha;

end

nbStep麻烦大神帮忙看下，这个是我找到的高斯混合模型的代码， 但是上面%%% Stopping criterion 1 %%%%%%%%%%%%%%%%%%%% 及其以下代码， 前面应不应该加% 这个符号呢，文中是加了的，但是我觉得加了%不就成注释了？