www.pudn.com > HMM1.zip > mixgauss_classifier_train.m

function mixgauss = mixgauss_classifier_train(trainFeatures, trainLabels, nc, varargin) 
% function mixgauss = mixgauss_classifier_train(trainFeatures, trainLabels, nclusters, varargin) 
% trainFeatures(:,i) for i'th example 
% trainLabels should be 0,1 
% To evaluate performance on a tets set, use 
% mixgauss = mixgauss_classifier_train(trainFeatures, trainLabels, nc, 'testFeatures', tf, 'testLabels', tl) 
 
[testFeatures, testLabels, max_iter, thresh, cov_type, mu, Sigma, priorC, method, ... 
 cov_prior, verbose, prune_thresh] = process_options(... 
    varargin, 'testFeatures', [], 'testLabels', [], ... 
     'max_iter', 10, 'thresh', 0.01, 'cov_type', 'diag', ... 
    'mu', [], 'Sigma', [], 'priorC', [], 'method', 'kmeans', ... 
    'cov_prior', [], 'verbose', 0, 'prune_thresh', 0); 
 
Nclasses = 2; % max([trainLabels testLabels]) + 1; 
 
pos = find(trainLabels == 1); 
neg = find(trainLabels == 0); 
 
if verbose, fprintf('fitting pos\n'); end 
[mixgauss.pos.mu, mixgauss.pos.Sigma, mixgauss.pos.prior] = ... 
    mixgauss_em(trainFeatures(:, pos), nc, varargin{:}); 
 
if verbose, fprintf('fitting neg\n'); end 
[mixgauss.neg.mu, mixgauss.neg.Sigma, mixgauss.neg.prior] = ... 
    mixgauss_em(trainFeatures(:, neg), nc, varargin{:}); 
 
 
if ~isempty(priorC) 
  mixgauss.priorC = priorC; 
else 
  mixgauss.priorC = normalize([length(pos) length(neg)]); 
end