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#include "test_precomp.hpp"
using namespace std;
using namespace cv;
using cv::ml::TrainData;
using cv::ml::EM;
using cv::ml::KNearest;
static
void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 )
{
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
static
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType )
{
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);
}
}
}
}
static
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;
}
static
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;
}
static
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*/ )
{
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 writeing 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);
}
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(); }