diff toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_classifier_train.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_classifier_train.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,33 @@
+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