classf_svm_te.m

function [pred, prob] = classf_svm_te(model,Xtest)
%Classify XTEST based on the trained MODEL
%	MODEL: result of CLASSF_SVM_TR.
%	XTEST: a matrix, each row is a sample.
% Return:
%	PRED : predicted labels for XTEST
%	PROB : the confidence of PRED returned by libsvm
% 
%	Dependency: libsvm toolbox
% 
%	Ke YAN, 2016, Tsinghua Univ. http://yanke23.com, xjed09@gmail.com

[pred, ~, prob] = ...
	svmpredict(ones(size(Xtest,1),1),Xtest,model,'-b 1');

end

%{
-s svm_type : set type of SVM (default 0)
	0 -- C-SVC		(multi-class classification)
	1 -- nu-SVC		(multi-class classification)
	2 -- one-class SVM	
	3 -- epsilon-SVR	(regression)
	4 -- nu-SVR		(regression)
-t kernel_type : set type of kernel function (default 2)
	0 -- linear: u'*v
	1 -- polynomial: (gamma*u'*v + coef0)^degree
	2 -- radial basis function: exp(-gamma*|u-v|^2)
	3 -- sigmoid: tanh(gamma*u'*v + coef0)
	4 -- precomputed kernel (kernel values in training_set_file)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/num_features)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
-v n: n-fold cross validation mode
-q : quiet mode (no outputs)
%}