function model=mlcgmm(data,cov_type)% MLCGMM Maximal Likelihood estimation of Gaussian mixture model.% % Synopsis:%  model = mlcgmm(X)%  model = mlcgmm(X,cov_type)%  model = mlcgmm(data)%  model = mlcgmm(data,cov_type)% % Description:%  It computes Maximum Likelihood estimation of parameters%  of Gaussian mixture model for given labeled data sample%  (complete data).%%  model = mlcgmm(X) computes parameters (model.Mean,model.Cov)%   of a single Gaussian distribution for given sample of column %   vectors X (all labels are assumed to be 1).%%  model = mlcgmm(X,cov_type) specifies shape of covariance matrix:%   cov_type = 'full'      full covariance matrix (default)%   cov_type = 'diag'      diagonal covarinace matrix%   cov_type = 'spherical' spherical covariance matrix%%  model = mlcgmm(data) computes parameters of a Gaussian mixture model%   from a given labeled data sample%     data.X ... samples,%     data.y .. labels.%   It estimates parameters of ncomp=max(data.y) Gaussians and%   a priory probabilities Prior [1 x ncomp] using Maximum-Likelihood %   principle.%% Input:%  X [dim x num_data] Data sample.%  data.X [dim x num_data] Data sample.%  data.y [1 x num_data] Data labels.%  cov_type [string] Type of covariacne matrix (see above).%% Output:%  model [struct] Estimated Gaussian mixture model:%   .Mean [dim x ncomp] Mean vectors.%   .Cov [dim x dim x ncomp] Covariance matrices.%   .Prior [1 x ncomp] Estimated a priory probabilities.%  % Example:%  data = load('riply_trn');%  model = mlcgmm( data );%  figure; hold on; ppatterns(data); pgauss( model );%  figure; hold on; ppatterns(data); pgmm( model );%% See also %  EMGMM, MMGAUSS, PDFGMM.%% About: Statistical Pattern Recognition Toolbox% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac% <a href="http://www.cvut.cz">Czech Technical University Prague</a>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 17-aug-2004, VF, labels y do not have to form a sequence 1,2,...,max_y% 2-may-2004, VF% 29-apr-2004, VF% 19-sep-2003, VF% 27-feb-2003, VF% processing of  inputsdata=c2s(data);if ~isstruct(data),  data.X = data;  data.y = ones(1,size(data.X,2));end if nargin < 2, cov_type = 'full'; end[dim,num_data] = size(data.X);labels = unique(data.y);model.Mean = zeros(dim,max(labels));model.Cov = zeros(dim,dim,max(labels));for i=1:length(labels),      inx = find(data.y==labels(i));   n = length(inx);   model.Mean(:,i) = sum(data.X(:,inx),2)/n;   XC=data.X(:,inx)-model.Mean(:,i)*ones(1,n);   switch cov_type,     case 'full',        model.Cov(:,:,i) = XC*XC'/n;     case 'diag',        model.Cov(:,:,i) = diag(sum(XC.^2,2)/n);     case 'spherical'       model.Cov(:,:,i) = eye(dim,dim)*sum(sum(XC.^2))/(n*dim);     otherwise       error('Wrong cov_type.');   end      model.Prior(i) = n/num_data;   model.y(i) = labels(i);endmodel.cov_type = cov_type;model.fun = 'pdfgmm';return;