www.pudn.com > training_gmm.rar > trGmms.m

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function [GMMs,errLog,options,time] = trGmms(data,feat_type,feat_sel,m)  
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% 
%train one type GMMs corresponding to selected features. 
%input: 
%     data = train data with labels; 
%     feat_type = feature type; 
%     m = mix number 
%output: 
%     GMMs = gauss mix models; 
%     errlog = log likehood of each iteration; 
%     time = run time. 
% 
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%% Written by Qianyong Gao                      %% 
%% Data:May 11,2006                             %% 
%% Update:June 27,2006 by Qianyong Gao          %% 
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tic; 
if nargin < 3 
    m = 32; 
end 
label = unique(data(:,end)); 
if length(label) > 1 
    error('data belong to different kind of audio'); 
end     
options = foptions; 
options(14) = 12; 
options(1) = 1.0; 
 
GMMs = opt_gmm_init(data,m,options); 
data(:,end) = []; 
options(14) = 128; 
[GMMs.centres,post] = mykmeans(GMMs.centres,data,options); 
cluster_size = sum(post,1); 
GMMs.priors = cluster_size / sum(cluster_size);  
 
GMM_WIDTH = eps; 
GMMs.covars = []; 
%ncentres = GMMs.ncentres; 
%centres = GMMs.centres; 
%temp_data = data; 
for j = 1:GMMs.ncentres 
    % Pick out data points belonging to this centre 
    c = data(find(post(:,j)),:); 
    if size(c,1) == 0% (size(data,1)/GMMs.ncentres)*1E-1 
        warning(['the ',num2str(j),'th is zero cluster']); 
        %GMMs.priors(j) = []; 
        %centres(j,:) = []; 
        %ncentres = ncentres - 1; 
        %temp_data(find(post(:,j)),:) = []; 
        GMMs.covars = [GMMs.covars;eps*ones(1,GMMs.nin)]; 
    else     
        diffs = c - (ones(size(c,1),1)*GMMs.centres(j,:)); 
        covj = sum((diffs.*diffs),1)/size(c,1); 
        %if ~any(covj>eps) 
        %    warning('some identical input train data') 
        %    centres(j,:) = []; 
        %    GMMs.priors(j) = []; 
        %    ncentres = ncentres - 1; 
        %    temp_data(find(post(:,j)),:) = []; 
        %elseif any(covj>eps) 
        %    GMMs.covars = [GMMs.covars;covj]; 
        if any(covj