cosmo classify matlabsvm skl

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.           #

    if nargin < 4
        opt = struct();
    end

    [ntrain, nfeatures] = size(samples_train);
    [ntest, nfeatures_] = size(samples_test);
    ntrain_ = numel(targets_train);

    if nfeatures ~= nfeatures_ || ntrain_ ~= ntrain
        error('illegal input size');
    end

    classes = unique(targets_train);
    nclasses = numel(classes);

    if nclasses < 2 || nfeatures == 0
        % matlab's svm cannot deal with empty data, so predict all
        % test samples as the class of the first sample
        predicted = targets_train(1) * (ones(ntest, 1));
        return
    end

    % number of pair-wise comparisons
    ncombi = nclasses * (nclasses - 1) / 2;

    % allocate space for all predictions
    all_predicted = zeros(ntest, ncombi);

    % Consider all pairwise comparisons (over classes)
    % and store the predictions in all_predicted
    pos = 0;
    for k = 1:(nclasses - 1)
        for j = (k + 1):nclasses
            pos = pos + 1;
            % classify between 2 classes only (from classes(k) and classes(j)).
            %%%% >>> Your code here <<< %%%%
            all_predicted(:, pos) = pred;
        end
    end

    % find the classes that were predicted most often.
    % ties are handled by cosmo_winner_indices
    [winners, test_classes] = cosmo_winner_indices(all_predicted);

    predicted = test_classes(winners);