function predicted=cosmo_classify_matlabsvm(samples_train, targets_train, samples_test, opt)
% SVM multi-classifier using matlab's SVM implementation
%
% predicted=cosmo_classify_matlabsvm(samples_train, targets_train, samples_test, opt)
%
% Inputs
% samples_train PxR training data for P samples and R features
% targets_train Px1 training data classes
% samples_test QxR test data
% opt (optional) struct with options for svm_classify
%
% Output
% predicted Qx1 predicted data classes for samples_test
%
% Notes:
% - this function uses matlab's builtin svmtrain function, which has
% the same name as LIBSVM's version. Use of this function is not
% supported when LIBSVM's svmtrain precedes in the matlab path; in
% that case, adjust the path or use cosmo_classify_libsvm instead.
% - for a guide on svm classification, see
% http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
% note that cosmo_crossvalidate and cosmo_crossvalidation_measure
% provide an option 'normalization' to perform data scaling.
% - As of Matlab 2017a (maybe earlier), Matlab gives the warning that
% 'svmtrain will be removed in a future release. Use fitcsvm instead.'
% however fitcsvm gives different results than svmtrain; as a result
% cosmo_classify_matlabcsvm gives different results than
% cosmo_classify_matlabsvm. In this function the warning message is
% . suppressed.
% - As of Matlab 2018a, this function cannot be used anymore. Use
% cosmo_classify_matlabcsvm instead.
%
% See also svmtrain, svmclassify, cosmo_classify_svm,
% cosmo_classify_libsvm, cosmo_classify_matlabcsvm
%
% # For CoSMoMVPA's copyright information and license terms, #
% # see the COPYING file distributed with CoSMoMVPA. #
