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#include "precomp.hpp"

namespace cv { namespace ml {

struct PairDI
{
    double d;
    int    i;
};

struct CmpPairDI
{
    bool operator ()(const PairDI& e1, const PairDI& e2) const
    {
        return (e1.d < e2.d) || (e1.d == e2.d && e1.i < e2.i);
    }
};

void createConcentricSpheresTestSet( int num_samples, int num_features, int num_classes,
                                     OutputArray _samples, OutputArray _responses)
{
    if( num_samples < 1 )
        CV_Error( CV_StsBadArg, "num_samples parameter must be positive" );

    if( num_features < 1 )
        CV_Error( CV_StsBadArg, "num_features parameter must be positive" );

    if( num_classes < 1 )
        CV_Error( CV_StsBadArg, "num_classes parameter must be positive" );

    int i, cur_class;

    _samples.create( num_samples, num_features, CV_32F );
    _responses.create( 1, num_samples, CV_32S );

    Mat responses = _responses.getMat();

    Mat mean = Mat::zeros(1, num_features, CV_32F);
    Mat cov = Mat::eye(num_features, num_features, CV_32F);

    // fill the feature values matrix with random numbers drawn from standard normal distribution
    randMVNormal( mean, cov, num_samples, _samples );
    Mat samples = _samples.getMat();

    // calculate distances from the origin to the samples and put them
    // into the sequence along with indices
    std::vector<PairDI> dis(samples.rows);

    for( i = 0; i < samples.rows; i++ )
    {
        PairDI& elem = dis[i];
        elem.i = i;
        elem.d = norm(samples.row(i), NORM_L2);
    }

    std::sort(dis.begin(), dis.end(), CmpPairDI());

    // assign class labels
    num_classes = std::min( num_samples, num_classes );
    for( i = 0, cur_class = 0; i < num_samples; ++cur_class )
    {
        int last_idx = num_samples * (cur_class + 1) / num_classes - 1;
        double max_dst = dis[last_idx].d;
        max_dst = std::max( max_dst, dis[i].d );

        for( ; i < num_samples && dis[i].d <= max_dst; ++i )
            responses.at<int>(i) = cur_class;
    }
}

}}

/* End of file. */
