?? #mixgauss_classifier_train.m#
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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?? 快捷鍵說(shuō)明
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