function [L,hits,error_rate] = threshold_te(model,test_set,sample_weights,true_labels)%% TESTING THRESHOLD CLASSIFIER%%   Testing of the basic linear classifier where seperation hyperplane is %	perpedicular to one dimension.%%	[L,hits,error_rate] = threshold_te(model,test_set,sample_weights,true_labels)%%		model: the model that is outputed from threshold_tr. It consists of%			1) min_error: training error%			2) min_error_thr: threshold value%			3) pos_neg: whether up-direction shows the positive region (label:2, 'pos') or%				the negative region (label:1, 'neg')%		test_set: an NxD-matrix, each row is a testing sample in the D dimensional feature%			space.%		sample_weights: an Nx1-vector, each entry is the weight of the corresponding test sample%		true_labels: Nx1 dimensional vector, each entry is the corresponding label (either 1 or 2)%		%		L: an Nx2-matrix showing likelihoods of each class %		hits: the number of hits%		error_rate: the error rate with the sample weights%		%% Bug Reporting: Please contact the author for bug reporting and comments.%% Cuneyt Mertayak% email: cuneyt.mertayak@gmail.com% version: 1.0% date: 21/05/2007feat = test_set(:,model.dim);if(strcmp(model.pos_neg,'pos'))	ind = (feat>model.min_error_thr)+1;else	ind = (feat<model.min_error_thr)+1;endhits = sum(ind==true_labels);error_rate = sum(sample_weights(ind~=true_labels));L = zeros(length(feat),2);L(ind==1,1) = 1;L(ind==2,2) = 1;