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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
CV_ENUM(EM_START_STEP, EM::START_AUTO_STEP, EM::START_M_STEP, EM::START_E_STEP)
CV_ENUM(EM_COV_MAT, EM::COV_MAT_GENERIC, EM::COV_MAT_DIAGONAL, EM::COV_MAT_SPHERICAL)
typedef testing::TestWithParam< tuple<EM_START_STEP, EM_COV_MAT> > ML_EM_Params;
TEST_P(ML_EM_Params, accuracy)
{
const int nclusters = 3;
const int sizesArr[] = { 500, 700, 800 };
const vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
const int pointsCount = sizesArr[0] + sizesArr[1] + sizesArr[2];
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs, CV_64FC1 );
Mat trainData(pointsCount, 2, CV_64FC1 );
Mat trainLabels;
generateData( trainData, trainLabels, sizes, means, covs, CV_64FC1, CV_32SC1 );
Mat testData( pointsCount, 2, CV_64FC1 );
Mat testLabels;
generateData( testData, testLabels, sizes, means, covs, CV_64FC1, CV_32SC1 );
Mat probs(trainData.rows, nclusters, CV_64FC1, cv::Scalar(1));
Mat weights(1, nclusters, CV_64FC1, cv::Scalar(1));
TermCriteria termCrit(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, FLT_EPSILON);
int startStep = get<0>(GetParam());
int covMatType = get<1>(GetParam());
cv::Mat labels;
Ptr<EM> em = EM::create();
em->setClustersNumber(nclusters);
em->setCovarianceMatrixType(covMatType);
em->setTermCriteria(termCrit);
if( startStep == EM::START_AUTO_STEP )
em->trainEM( trainData, noArray(), labels, noArray() );
else if( startStep == EM::START_E_STEP )
em->trainE( trainData, means, covs, weights, noArray(), labels, noArray() );
else if( startStep == EM::START_M_STEP )
em->trainM( trainData, probs, noArray(), labels, noArray() );
{
SCOPED_TRACE("Train");
float err = 1000;
EXPECT_TRUE(calcErr( labels, trainLabels, sizes, err , false, false ));
EXPECT_LE(err, 0.008f);
}
{
SCOPED_TRACE("Test");
float err = 1000;
labels.create( testData.rows, 1, CV_32SC1 );
for( int i = 0; i < testData.rows; i++ )
{
Mat sample = testData.row(i);
Mat out_probs;
labels.at<int>(i) = static_cast<int>(em->predict2( sample, out_probs )[1]);
}
EXPECT_TRUE(calcErr( labels, testLabels, sizes, err, false, false ));
EXPECT_LE(err, 0.008f);
}
}
INSTANTIATE_TEST_CASE_P(/**/, ML_EM_Params,
testing::Combine(
testing::Values(EM::START_AUTO_STEP, EM::START_M_STEP, EM::START_E_STEP),
testing::Values(EM::COV_MAT_GENERIC, EM::COV_MAT_DIAGONAL, EM::COV_MAT_SPHERICAL)
));
//==================================================================================================
TEST(ML_EM, save_load)
{
const int nclusters = 2;
Mat_<double> samples(3, 1);
samples << 1., 2., 3.;
std::vector<double> firstResult;
string filename = cv::tempfile(".xml");
{
Mat labels;
Ptr<EM> em = EM::create();
em->setClustersNumber(nclusters);
em->trainEM(samples, noArray(), labels, noArray());
for( int i = 0; i < samples.rows; i++)
{
Vec2d res = em->predict2(samples.row(i), noArray());
firstResult.push_back(res[1]);
}
{
FileStorage fs = FileStorage(filename, FileStorage::WRITE);
ASSERT_NO_THROW(fs << "em" << "{");
ASSERT_NO_THROW(em->write(fs));
ASSERT_NO_THROW(fs << "}");
}
}
{
Ptr<EM> em;
ASSERT_NO_THROW(em = Algorithm::load<EM>(filename));
for( int i = 0; i < samples.rows; i++)
{
SCOPED_TRACE(i);
Vec2d res = em->predict2(samples.row(i), noArray());
EXPECT_DOUBLE_EQ(firstResult[i], res[1]);
}
}
remove(filename.c_str());
}
//==================================================================================================
TEST(ML_EM, classification)
{
// 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 = findDataFile("spambase.data");
Ptr<TrainData> data = TrainData::loadFromCSV(dataFilename, 0);
ASSERT_FALSE(data.empty());
Mat samples = data->getSamples();
ASSERT_EQ(samples.cols, 57);
Mat responses = data->getResponses();
vector<int> trainSamplesMask(samples.rows, 0);
const int trainSamplesCount = (int)(0.5f * samples.rows);
const int testSamplesCount = samples.rows - trainSamplesCount;
for(int i = 0; i < trainSamplesCount; i++)
trainSamplesMask[i] = 1;
RNG &rng = cv::theRNG();
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());
// confusion matrices
Mat_<int> trainCM(2, 2, 0);
Mat_<int> testCM(2, 2, 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;
int resp = (int)responses.at<float>(i);
EXPECT_TRUE(resp == 0 || resp == 1);
if(trainSamplesMask[i])
trainCM(resp, classID)++;
else
testCM(resp, classID)++;
}
EXPECT_LE((double)(trainCM(1,0) + trainCM(0,1)) / trainSamplesCount, 0.23);
EXPECT_LE((double)(testCM(1,0) + testCM(0,1)) / testSamplesCount, 0.26);
}
}} // namespace
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