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#include "test_precomp.hpp"

namespace opencv_test { namespace {

using cv::ml::TrainData;
using cv::ml::EM;
using cv::ml::KNearest;

void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 )
{
    CV_TRACE_FUNCTION();
    float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
    float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
    float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
    means.create(3, 2, type);
    Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
    Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
    Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
    means.resize(3), covs.resize(3);

    Mat mr0 = means.row(0);
    m0.convertTo(mr0, type);
    c0.convertTo(covs[0], type);

    Mat mr1 = means.row(1);
    m1.convertTo(mr1, type);
    c1.convertTo(covs[1], type);

    Mat mr2 = means.row(2);
    m2.convertTo(mr2, type);
    c2.convertTo(covs[2], type);
}

// generate points sets by normal distributions
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType )
{
    CV_TRACE_FUNCTION();
    vector<int>::const_iterator sit = sizes.begin();
    int total = 0;
    for( ; sit != sizes.end(); ++sit )
        total += *sit;
    CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
    CV_Assert( !data.empty() && data.rows == total );
    CV_Assert( data.type() == dataType );

    labels.create( data.rows, 1, labelType );

    randn( data, Scalar::all(-1.0), Scalar::all(1.0) );
    vector<Mat> means(sizes.size());
    for(int i = 0; i < _means.rows; i++)
        means[i] = _means.row(i);
    vector<Mat>::const_iterator mit = means.begin(), cit = covs.begin();
    int bi, ei = 0;
    sit = sizes.begin();
    for( int p = 0, l = 0; sit != sizes.end(); ++sit, ++mit, ++cit, l++ )
    {
        bi = ei;
        ei = bi + *sit;
        assert( mit->rows == 1 && mit->cols == data.cols );
        assert( cit->rows == data.cols && cit->cols == data.cols );
        for( int i = bi; i < ei; i++, p++ )
        {
            Mat r = data.row(i);
            r =  r * (*cit) + *mit;
            if( labelType == CV_32FC1 )
                labels.at<float>(p, 0) = (float)l;
            else if( labelType == CV_32SC1 )
                labels.at<int>(p, 0) = l;
            else
            {
                CV_DbgAssert(0);
            }
        }
    }
}

int maxIdx( const vector<int>& count )
{
    int idx = -1;
    int maxVal = -1;
    vector<int>::const_iterator it = count.begin();
    for( int i = 0; it != count.end(); ++it, i++ )
    {
        if( *it > maxVal)
        {
            maxVal = *it;
            idx = i;
        }
    }
    assert( idx >= 0);
    return idx;
}

bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq=true )
{
    size_t total = 0, nclusters = sizes.size();
    for(size_t i = 0; i < sizes.size(); i++)
        total += sizes[i];

    assert( !labels.empty() );
    assert( labels.total() == total && (labels.cols == 1 || labels.rows == 1));
    assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );

    bool isFlt = labels.type() == CV_32FC1;

    labelsMap.resize(nclusters);

    vector<bool> buzy(nclusters, false);
    int startIndex = 0;
    for( size_t clusterIndex = 0; clusterIndex < sizes.size(); clusterIndex++ )
    {
        vector<int> count( nclusters, 0 );
        for( int i = startIndex; i < startIndex + sizes[clusterIndex]; i++)
        {
            int lbl = isFlt ? (int)labels.at<float>(i) : labels.at<int>(i);
            CV_Assert(lbl < (int)nclusters);
            count[lbl]++;
            CV_Assert(count[lbl] < (int)total);
        }
        startIndex += sizes[clusterIndex];

        int cls = maxIdx( count );
        CV_Assert( !checkClusterUniq || !buzy[cls] );

        labelsMap[clusterIndex] = cls;

        buzy[cls] = true;
    }

    if(checkClusterUniq)
    {
        for(size_t i = 0; i < buzy.size(); i++)
            if(!buzy[i])
                return false;
    }

    return true;
}

bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true, bool checkClusterUniq=true )
{
    err = 0;
    CV_Assert( !labels.empty() && !origLabels.empty() );
    CV_Assert( labels.rows == 1 || labels.cols == 1 );
    CV_Assert( origLabels.rows == 1 || origLabels.cols == 1 );
    CV_Assert( labels.total() == origLabels.total() );
    CV_Assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
    CV_Assert( origLabels.type() == labels.type() );

    vector<int> labelsMap;
    bool isFlt = labels.type() == CV_32FC1;
    if( !labelsEquivalent )
    {
        if( !getLabelsMap( labels, sizes, labelsMap, checkClusterUniq ) )
            return false;

        for( int i = 0; i < labels.rows; i++ )
            if( isFlt )
                err += labels.at<float>(i) != labelsMap[(int)origLabels.at<float>(i)] ? 1.f : 0.f;
            else
                err += labels.at<int>(i) != labelsMap[origLabels.at<int>(i)] ? 1.f : 0.f;
    }
    else
    {
        for( int i = 0; i < labels.rows; i++ )
            if( isFlt )
                err += labels.at<float>(i) != origLabels.at<float>(i) ? 1.f : 0.f;
            else
                err += labels.at<int>(i) != origLabels.at<int>(i) ? 1.f : 0.f;
    }
    err /= (float)labels.rows;
    return true;
}

//--------------------------------------------------------------------------------------------
class CV_KMeansTest : public cvtest::BaseTest {
public:
    CV_KMeansTest() {}
protected:
    virtual void run( int start_from );
};

void CV_KMeansTest::run( int /*start_from*/ )
{
    CV_TRACE_FUNCTION();
    const int iters = 100;
    int sizesArr[] = { 5000, 7000, 8000 };
    int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];

    Mat data( pointsCount, 2, CV_32FC1 ), labels;
    vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
    Mat means;
    vector<Mat> covs;
    defaultDistribs( means, covs );
    generateData( data, labels, sizes, means, covs, CV_32FC1, CV_32SC1 );

    int code = cvtest::TS::OK;
    float err;
    Mat bestLabels;
    // 1. flag==KMEANS_PP_CENTERS
    kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_PP_CENTERS, noArray() );
    if( !calcErr( bestLabels, labels, sizes, err , false ) )
    {
        ts->printf( cvtest::TS::LOG, "Bad output labels if flag==KMEANS_PP_CENTERS.\n" );
        code = cvtest::TS::FAIL_INVALID_OUTPUT;
    }
    else if( err > 0.01f )
    {
        ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_PP_CENTERS.\n", err );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    // 2. flag==KMEANS_RANDOM_CENTERS
    kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_RANDOM_CENTERS, noArray() );
    if( !calcErr( bestLabels, labels, sizes, err, false ) )
    {
        ts->printf( cvtest::TS::LOG, "Bad output labels if flag==KMEANS_RANDOM_CENTERS.\n" );
        code = cvtest::TS::FAIL_INVALID_OUTPUT;
    }
    else if( err > 0.01f )
    {
        ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_RANDOM_CENTERS.\n", err );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    // 3. flag==KMEANS_USE_INITIAL_LABELS
    labels.copyTo( bestLabels );
    RNG rng;
    for( int i = 0; i < 0.5f * pointsCount; i++ )
        bestLabels.at<int>( rng.next() % pointsCount, 0 ) = rng.next() % 3;
    kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_USE_INITIAL_LABELS, noArray() );
    if( !calcErr( bestLabels, labels, sizes, err, false ) )
    {
        ts->printf( cvtest::TS::LOG, "Bad output labels if flag==KMEANS_USE_INITIAL_LABELS.\n" );
        code = cvtest::TS::FAIL_INVALID_OUTPUT;
    }
    else if( err > 0.01f )
    {
        ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_USE_INITIAL_LABELS.\n", err );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    ts->set_failed_test_info( code );
}

//--------------------------------------------------------------------------------------------
class CV_KNearestTest : public cvtest::BaseTest {
public:
    CV_KNearestTest() {}
protected:
    virtual void run( int start_from );
};

void CV_KNearestTest::run( int /*start_from*/ )
{
    int sizesArr[] = { 500, 700, 800 };
    int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];

    // train data
    Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
    vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
    Mat means;
    vector<Mat> covs;
    defaultDistribs( means, covs );
    generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );

    // test data
    Mat testData( pointsCount, 2, CV_32FC1 ), testLabels, bestLabels;
    generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );

    int code = cvtest::TS::OK;

    // KNearest default implementation
    Ptr<KNearest> knearest = KNearest::create();
    knearest->train(trainData, ml::ROW_SAMPLE, trainLabels);
    knearest->findNearest(testData, 4, bestLabels);
    float err;
    if( !calcErr( bestLabels, testLabels, sizes, err, true ) )
    {
        ts->printf( cvtest::TS::LOG, "Bad output labels.\n" );
        code = cvtest::TS::FAIL_INVALID_OUTPUT;
    }
    else if( err > 0.01f )
    {
        ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) on test data.\n", err );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    // KNearest KDTree implementation
    Ptr<KNearest> knearestKdt = KNearest::create();
    knearestKdt->setAlgorithmType(KNearest::KDTREE);
    knearestKdt->train(trainData, ml::ROW_SAMPLE, trainLabels);
    knearestKdt->findNearest(testData, 4, bestLabels);
    if( !calcErr( bestLabels, testLabels, sizes, err, true ) )
    {
        ts->printf( cvtest::TS::LOG, "Bad output labels.\n" );
        code = cvtest::TS::FAIL_INVALID_OUTPUT;
    }
    else if( err > 0.01f )
    {
        ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) on test data.\n", err );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    ts->set_failed_test_info( code );
}

class EM_Params
{
public:
    EM_Params(int _nclusters=10, int _covMatType=EM::COV_MAT_DIAGONAL, int _startStep=EM::START_AUTO_STEP,
           const cv::TermCriteria& _termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON),
           const cv::Mat* _probs=0, const cv::Mat* _weights=0,
           const cv::Mat* _means=0, const std::vector<cv::Mat>* _covs=0)
        : nclusters(_nclusters), covMatType(_covMatType), startStep(_startStep),
        probs(_probs), weights(_weights), means(_means), covs(_covs), termCrit(_termCrit)
    {}

    int nclusters;
    int covMatType;
    int startStep;

    // all 4 following matrices should have type CV_32FC1
    const cv::Mat* probs;
    const cv::Mat* weights;
    const cv::Mat* means;
    const std::vector<cv::Mat>* covs;

    cv::TermCriteria termCrit;
};

//--------------------------------------------------------------------------------------------
class CV_EMTest : public cvtest::BaseTest
{
public:
    CV_EMTest() {}
protected:
    virtual void run( int start_from );
    int runCase( int caseIndex, const EM_Params& params,
                  const cv::Mat& trainData, const cv::Mat& trainLabels,
                  const cv::Mat& testData, const cv::Mat& testLabels,
                  const vector<int>& sizes);
};

int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
                        const cv::Mat& trainData, const cv::Mat& trainLabels,
                        const cv::Mat& testData, const cv::Mat& testLabels,
                        const vector<int>& sizes )
{
    int code = cvtest::TS::OK;

    cv::Mat labels;
    float err;

    Ptr<EM> em = EM::create();
    em->setClustersNumber(params.nclusters);
    em->setCovarianceMatrixType(params.covMatType);
    em->setTermCriteria(params.termCrit);
    if( params.startStep == EM::START_AUTO_STEP )
        em->trainEM( trainData, noArray(), labels, noArray() );
    else if( params.startStep == EM::START_E_STEP )
        em->trainE( trainData, *params.means, *params.covs,
                    *params.weights, noArray(), labels, noArray() );
    else if( params.startStep == EM::START_M_STEP )
        em->trainM( trainData, *params.probs,
                    noArray(), labels, noArray() );

    // check train error
    if( !calcErr( labels, trainLabels, sizes, err , false, false ) )
    {
        ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
        code = cvtest::TS::FAIL_INVALID_OUTPUT;
    }
    else if( err > 0.008f )
    {
        ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on train data.\n", caseIndex, err );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    // check test error
    labels.create( testData.rows, 1, CV_32SC1 );
    for( int i = 0; i < testData.rows; i++ )
    {
        Mat sample = testData.row(i);
        Mat probs;
        labels.at<int>(i) = static_cast<int>(em->predict2( sample, probs )[1]);
    }
    if( !calcErr( labels, testLabels, sizes, err, false, false ) )
    {
        ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
        code = cvtest::TS::FAIL_INVALID_OUTPUT;
    }
    else if( err > 0.008f )
    {
        ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on test data.\n", caseIndex, err );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    return code;
}

void CV_EMTest::run( int /*start_from*/ )
{
    int sizesArr[] = { 500, 700, 800 };
    int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];

    // Points distribution
    Mat means;
    vector<Mat> covs;
    defaultDistribs( means, covs, CV_64FC1 );

    // train data
    Mat trainData( pointsCount, 2, CV_64FC1 ), trainLabels;
    vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
    generateData( trainData, trainLabels, sizes, means, covs, CV_64FC1, CV_32SC1 );

    // test data
    Mat testData( pointsCount, 2, CV_64FC1 ), testLabels;
    generateData( testData, testLabels, sizes, means, covs, CV_64FC1, CV_32SC1 );

    EM_Params params;
    params.nclusters = 3;
    Mat probs(trainData.rows, params.nclusters, CV_64FC1, cv::Scalar(1));
    params.probs = &probs;
    Mat weights(1, params.nclusters, CV_64FC1, cv::Scalar(1));
    params.weights = &weights;
    params.means = &means;
    params.covs = &covs;

    int code = cvtest::TS::OK;
    int caseIndex = 0;
    {
        params.startStep = EM::START_AUTO_STEP;
        params.covMatType = EM::COV_MAT_GENERIC;
        int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
        code = currCode == cvtest::TS::OK ? code : currCode;
    }
    {
        params.startStep = EM::START_AUTO_STEP;
        params.covMatType = EM::COV_MAT_DIAGONAL;
        int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
        code = currCode == cvtest::TS::OK ? code : currCode;
    }
    {
        params.startStep = EM::START_AUTO_STEP;
        params.covMatType = EM::COV_MAT_SPHERICAL;
        int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
        code = currCode == cvtest::TS::OK ? code : currCode;
    }
    {
        params.startStep = EM::START_M_STEP;
        params.covMatType = EM::COV_MAT_GENERIC;
        int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
        code = currCode == cvtest::TS::OK ? code : currCode;
    }
    {
        params.startStep = EM::START_M_STEP;
        params.covMatType = EM::COV_MAT_DIAGONAL;
        int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
        code = currCode == cvtest::TS::OK ? code : currCode;
    }
    {
        params.startStep = EM::START_M_STEP;
        params.covMatType = EM::COV_MAT_SPHERICAL;
        int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
        code = currCode == cvtest::TS::OK ? code : currCode;
    }
    {
        params.startStep = EM::START_E_STEP;
        params.covMatType = EM::COV_MAT_GENERIC;
        int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
        code = currCode == cvtest::TS::OK ? code : currCode;
    }
    {
        params.startStep = EM::START_E_STEP;
        params.covMatType = EM::COV_MAT_DIAGONAL;
        int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
        code = currCode == cvtest::TS::OK ? code : currCode;
    }
    {
        params.startStep = EM::START_E_STEP;
        params.covMatType = EM::COV_MAT_SPHERICAL;
        int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
        code = currCode == cvtest::TS::OK ? code : currCode;
    }

    ts->set_failed_test_info( code );
}

class CV_EMTest_SaveLoad : public cvtest::BaseTest {
public:
    CV_EMTest_SaveLoad() {}
protected:
    virtual void run( int /*start_from*/ )
    {
        int code = cvtest::TS::OK;
        const int nclusters = 2;

        Mat samples = Mat(3,1,CV_64FC1);
        samples.at<double>(0,0) = 1;
        samples.at<double>(1,0) = 2;
        samples.at<double>(2,0) = 3;

        Mat labels;

        Ptr<EM> em = EM::create();
        em->setClustersNumber(nclusters);
        em->trainEM(samples, noArray(), labels, noArray());

        Mat firstResult(samples.rows, 1, CV_32SC1);
        for( int i = 0; i < samples.rows; i++)
            firstResult.at<int>(i) = static_cast<int>(em->predict2(samples.row(i), noArray())[1]);

        // Write out
        string filename = cv::tempfile(".xml");
        {
            FileStorage fs = FileStorage(filename, FileStorage::WRITE);
            try
            {
                fs << "em" << "{";
                em->write(fs);
                fs << "}";
            }
            catch(...)
            {
                ts->printf( cvtest::TS::LOG, "Crash in write method.\n" );
                ts->set_failed_test_info( cvtest::TS::FAIL_EXCEPTION );
            }
        }

        em.release();

        // Read in
        try
        {
            em = Algorithm::load<EM>(filename);
        }
        catch(...)
        {
            ts->printf( cvtest::TS::LOG, "Crash in read method.\n" );
            ts->set_failed_test_info( cvtest::TS::FAIL_EXCEPTION );
        }

        remove( filename.c_str() );

        int errCaseCount = 0;
        for( int i = 0; i < samples.rows; i++)
            errCaseCount = std::abs(em->predict2(samples.row(i), noArray())[1] - firstResult.at<int>(i)) < FLT_EPSILON ? 0 : 1;

        if( errCaseCount > 0 )
        {
            ts->printf( cvtest::TS::LOG, "Different prediction results before writing and after reading (errCaseCount=%d).\n", errCaseCount );
            code = cvtest::TS::FAIL_BAD_ACCURACY;
        }

        ts->set_failed_test_info( code );
    }
};

class CV_EMTest_Classification : public cvtest::BaseTest
{
public:
    CV_EMTest_Classification() {}
protected:
    virtual void run(int)
    {
        // This test classifies spam by the following way:
        // 1. estimates distributions of "spam" / "not spam"
        // 2. predict classID using Bayes classifier for estimated distributions.

        string dataFilename = string(ts->get_data_path()) + "spambase.data";
        Ptr<TrainData> data = TrainData::loadFromCSV(dataFilename, 0);

        if( data.empty() )
        {
            ts->printf(cvtest::TS::LOG, "File with spambase dataset cann't be read.\n");
            ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
            return;
        }

        Mat samples = data->getSamples();
        CV_Assert(samples.cols == 57);
        Mat responses = data->getResponses();

        vector<int> trainSamplesMask(samples.rows, 0);
        int trainSamplesCount = (int)(0.5f * samples.rows);
        for(int i = 0; i < trainSamplesCount; i++)
            trainSamplesMask[i] = 1;
        RNG rng(0);
        for(size_t i = 0; i < trainSamplesMask.size(); i++)
        {
            int i1 = rng(static_cast<unsigned>(trainSamplesMask.size()));
            int i2 = rng(static_cast<unsigned>(trainSamplesMask.size()));
            std::swap(trainSamplesMask[i1], trainSamplesMask[i2]);
        }

        Mat samples0, samples1;
        for(int i = 0; i < samples.rows; i++)
        {
            if(trainSamplesMask[i])
            {
                Mat sample = samples.row(i);
                int resp = (int)responses.at<float>(i);
                if(resp == 0)
                    samples0.push_back(sample);
                else
                    samples1.push_back(sample);
            }
        }
        Ptr<EM> model0 = EM::create();
        model0->setClustersNumber(3);
        model0->trainEM(samples0, noArray(), noArray(), noArray());

        Ptr<EM> model1 = EM::create();
        model1->setClustersNumber(3);
        model1->trainEM(samples1, noArray(), noArray(), noArray());

        Mat trainConfusionMat(2, 2, CV_32SC1, Scalar(0)),
            testConfusionMat(2, 2, CV_32SC1, Scalar(0));
        const double lambda = 1.;
        for(int i = 0; i < samples.rows; i++)
        {
            Mat sample = samples.row(i);
            double sampleLogLikelihoods0 = model0->predict2(sample, noArray())[0];
            double sampleLogLikelihoods1 = model1->predict2(sample, noArray())[0];

            int classID = sampleLogLikelihoods0 >= lambda * sampleLogLikelihoods1 ? 0 : 1;

            if(trainSamplesMask[i])
                trainConfusionMat.at<int>((int)responses.at<float>(i), classID)++;
            else
                testConfusionMat.at<int>((int)responses.at<float>(i), classID)++;
        }
//        std::cout << trainConfusionMat << std::endl;
//        std::cout << testConfusionMat << std::endl;

        double trainError = (double)(trainConfusionMat.at<int>(1,0) + trainConfusionMat.at<int>(0,1)) / trainSamplesCount;
        double testError = (double)(testConfusionMat.at<int>(1,0) + testConfusionMat.at<int>(0,1)) / (samples.rows - trainSamplesCount);
        const double maxTrainError = 0.23;
        const double maxTestError = 0.26;

        int code = cvtest::TS::OK;
        if(trainError > maxTrainError)
        {
            ts->printf(cvtest::TS::LOG, "Too large train classification error (calc = %f, valid=%f).\n", trainError, maxTrainError);
            code = cvtest::TS::FAIL_INVALID_TEST_DATA;
        }
        if(testError > maxTestError)
        {
            ts->printf(cvtest::TS::LOG, "Too large test classification error (calc = %f, valid=%f).\n", testError, maxTestError);
            code = cvtest::TS::FAIL_INVALID_TEST_DATA;
        }

        ts->set_failed_test_info(code);
    }
};

TEST(ML_KMeans, accuracy) { CV_KMeansTest test; test.safe_run(); }
TEST(ML_KNearest, accuracy) { CV_KNearestTest test; test.safe_run(); }
TEST(ML_EM, accuracy) { CV_EMTest test; test.safe_run(); }
TEST(ML_EM, save_load) { CV_EMTest_SaveLoad test; test.safe_run(); }
TEST(ML_EM, classification) { CV_EMTest_Classification test; test.safe_run(); }

TEST(ML_KNearest, regression_12347)
{
    Mat xTrainData = (Mat_<float>(5,2) << 1, 1.1, 1.1, 1, 2, 2, 2.1, 2, 2.1, 2.1);
    Mat yTrainLabels = (Mat_<float>(5,1) << 1, 1, 2, 2, 2);
    Ptr<KNearest> knn = KNearest::create();
    knn->train(xTrainData, ml::ROW_SAMPLE, yTrainLabels);

    Mat xTestData = (Mat_<float>(2,2) << 1.1, 1.1, 2, 2.2);
    Mat zBestLabels, neighbours, dist;
    // check output shapes:
    int K = 16, Kexp = std::min(K, xTrainData.rows);
    knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
    EXPECT_EQ(xTestData.rows, zBestLabels.rows);
    EXPECT_EQ(neighbours.cols, Kexp);
    EXPECT_EQ(dist.cols, Kexp);
    // see if the result is still correct:
    K = 2;
    knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
    EXPECT_EQ(1, zBestLabels.at<float>(0,0));
    EXPECT_EQ(2, zBestLabels.at<float>(1,0));
}

}} // namespace