www.gusucode.com > Adaboost算法训练人脸图像和非人脸图像，通过迭代得到由多个弱分类器组合而成的强分类器，实现图片里的人脸检测。 > Adaboost算法训练人脸图像和非人脸图像，通过迭代得到由多个弱分类器组合而成的强分类器，实现图片里的人脸检测。/myfacedet02/threshold_te.m

    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: 21/05/2007

feat = 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;
end

hits = 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;