function predicted=cosmo_classify_matlabcsvm(samples_train, targets_train, samples_test, opt)
% svm classifier wrapper (around fitcsvm)
%
% predicted=cosmo_classify_matlabcsvm(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 struct with options. supports any option that
% fitcsvm supports
%
% Output:
% predicted Qx1 predicted data classes for samples_test
%
% Notes:
% - this function uses Matlab's builtin fitcsvm function, which was the
% successor of svmtrain.
% - Matlab's SVM classifier is rather slow, especially for multi-class
% data (more than two classes). When classification takes a long time,
% consider using libsvm.
% - 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.
%
% See also fitcsvm, svmclassify, cosmo_classify_matlabsvm.
%
% # For CoSMoMVPA's copyright information and license terms, #
% # see the COPYING file distributed with CoSMoMVPA. #
