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#include "test_precomp.hpp"

using namespace std;
using namespace cv;

void defaultDistribs( vector<Mat>& means, vector<Mat>& covs )
{
    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};
    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);
    m0.copyTo(means[0]), c0.copyTo(covs[0]);
    m1.copyTo(means[1]), c1.copyTo(covs[1]);
    m2.copyTo(means[2]), c2.copyTo(covs[2]);
}

// generate points sets by normal distributions
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const vector<Mat>& means, const vector<Mat>& covs, int labelType )
{
    vector<int>::const_iterator sit = sizes.begin();
    int total = 0;
    for( ; sit != sizes.end(); ++sit )
        total += *sit;
    assert( means.size() == sizes.size() && covs.size() == sizes.size() );
    assert( !data.empty() && data.rows == total );
    assert( data.type() == CV_32FC1 );
    
    labels.create( data.rows, 1, labelType );

    randn( data, Scalar::all(0.0), Scalar::all(1.0) );
    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(1, data.cols, CV_32FC1, data.ptr<float>(i));
            r =  r * (*cit) + *mit; 
            if( labelType == CV_32FC1 )
                labels.at<float>(p, 0) = (float)l;
            else
                labels.at<int>(p, 0) = l;
        }
    }
}

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 )
{
    int total = 0, setCount = (int)sizes.size();
    vector<int>::const_iterator sit = sizes.begin();
    for( ; sit != sizes.end(); ++sit )
        total += *sit;
    assert( !labels.empty() );
    assert( labels.rows == total && labels.cols == 1 );
    assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );

    bool isFlt = labels.type() == CV_32FC1;
    labelsMap.resize(setCount);
    vector<int>::iterator lmit = labelsMap.begin();
    vector<bool> buzy(setCount, false);
    int bi, ei = 0;
    for( sit = sizes.begin(); sit != sizes.end(); ++sit, ++lmit )
    {
        vector<int> count( setCount, 0 );
        bi = ei;
        ei = bi + *sit;
        if( isFlt )
        {
            for( int i = bi; i < ei; i++ )
                count[(int)labels.at<float>(i, 0)]++;
        }
        else
        {
            for( int i = bi; i < ei; i++ )
                count[labels.at<int>(i, 0)]++;
        }
  
        *lmit = maxIdx( count );
        if( buzy[*lmit] )
            return false;
        buzy[*lmit] = true;
    }
    return true;    
}

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

    vector<int> labelsMap;
    bool isFlt = labels.type() == CV_32FC1;
    if( !labelsEquivalent )
    {
        getLabelsMap( labels, sizes, labelsMap );
        for( int i = 0; i < labels.rows; i++ )
            if( isFlt )
                err += labels.at<float>(i, 0) != labelsMap[(int)origLabels.at<float>(i, 0)];
            else
                err += labels.at<int>(i, 0) != labelsMap[origLabels.at<int>(i, 0)];
    }
    else
    {
        for( int i = 0; i < labels.rows; i++ )
            if( isFlt )
                err += labels.at<float>(i, 0) != origLabels.at<float>(i, 0);
            else
                err += labels.at<int>(i, 0) != origLabels.at<int>(i, 0);
    }
    return (float)err / (float)labels.rows;
}

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

void CV_KMeansTest::run( int /*start_from*/ )
{
    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]) );
    vector<Mat> means, covs;
    defaultDistribs( means, covs );
    generateData( data, labels, sizes, means, covs, CV_32SC1 );
    
    int code = cvtest::TS::OK;
    Mat bestLabels;
    // 1. flag==KMEANS_PP_CENTERS
    kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_PP_CENTERS, OutputArray() );
    if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
    {
        ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
        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, OutputArray() );
    if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
    {
        ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
        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, OutputArray() );
    if( calcErr( bestLabels, labels, sizes, false ) > 0.01f )
    {
        ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" );
        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]) );
    vector<Mat> means, covs;
    defaultDistribs( means, covs );
    generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1 );

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

    int code = cvtest::TS::OK;
    KNearest knearest;
    knearest.train( trainData, trainLabels );
    knearest.find_nearest( testData, 4, &bestLabels );
    if( calcErr( bestLabels, testLabels, sizes, true ) > 0.01f )
    {
        ts->printf( cvtest::TS::LOG, "bad accuracy on test data" );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }
    ts->set_failed_test_info( code );
}

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

void CV_EMTest::run( int /*start_from*/ )
{
    int sizesArr[] = { 5000, 7000, 8000 };
    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]) );
    vector<Mat> means, covs;
    defaultDistribs( means, covs );
    generateData( trainData, trainLabels, sizes, means, covs, CV_32SC1 );

    // test data
    Mat testData( pointsCount, 2, CV_32FC1 ), testLabels, bestLabels;
    generateData( testData, testLabels, sizes, means, covs, CV_32SC1 );

    int code = cvtest::TS::OK;
    ExpectationMaximization em;
    CvEMParams params;
    params.nclusters = 3;
    em.train( trainData, Mat(), params, &bestLabels );

    // check train error
    if( calcErr( bestLabels, trainLabels, sizes, true ) > 0.002f )
    {
        ts->printf( cvtest::TS::LOG, "bad accuracy on train data" );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }

    // check test error
    bestLabels.create( testData.rows, 1, CV_32SC1 );
    for( int i = 0; i < testData.rows; i++ )
    {
        Mat sample( 1, testData.cols, CV_32FC1, testData.ptr<float>(i));
        bestLabels.at<int>(i,0) = (int)em.predict( sample, 0 );
    }
    if( calcErr( bestLabels, testLabels, sizes, true ) > 0.005f )
    {
        ts->printf( cvtest::TS::LOG, "bad accuracy on test data" );
        code = cvtest::TS::FAIL_BAD_ACCURACY;
    }
    
    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_EMTest, accuracy) { CV_EMTest test; test.safe_run(); }