function predicted=cosmo_classify_meta_feature_selection(samples_train, targets_train, samples_test, opt)
% meta classifier that uses feature selection on the training data
%
% predicted=cosmo_classify_meta_feature_selection(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 the following fields:
% .child_classifier handle to classifier to use (e.g.
% @cosmo_classify_matlabsvm).
% .feature_selector handle to feature selector (e.g.
% @cosmo_anova_feature_selector).
% .feature_selection_ratio_to_keep ratio of how many features to
% keep. Should be in between 0 and
% 1.
% Output:
% predicted Qx1 predicted data classes for samples_test
%
% Example:
% ds_tl=cosmo_synthetic_dataset('nchunks',5,'ntargets',5,...
% 'type','meeg');
% %
% measure_args=struct();
% measure=@cosmo_crossvalidation_measure;
% measure_args.classifier=@cosmo_classify_meta_feature_selection;
% measure_args.child_classifier=@cosmo_classify_lda;
% measure_args.feature_selector=@cosmo_anova_feature_selector;
% measure_args.feature_selection_ratio_to_keep=.6;
% measure_args.partitions=cosmo_nchoosek_partitioner(ds_tl,1);
% nbrhood=cosmo_interval_neighborhood(ds_tl,'time','radius',0);
% %
% res=cosmo_searchlight(ds_tl,nbrhood,measure,measure_args,...
% 'progress',false);
% cosmo_disp(res.samples)
% %|| [ 0.28 0.52 ]
%
% # For CoSMoMVPA's copyright information and license terms, #
% # see the COPYING file distributed with CoSMoMVPA. #
