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Commit 5ff1faba authored by Maksim Shabunin's avatar Maksim Shabunin Committed by Alexander Alekhin
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Merge pull request #15959 from mshabunin:refactor-ml-tests 

ml: refactored tests

* use parametrized tests where appropriate
* use stable theRNG in most tests
* use modern style with EXPECT_/ASSERT_ checks
parent 9e906d9e
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modules/ml/test/test_ann.cpp 0 → 100644
View file @ 5ff1faba
// 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"
// #define GENERATE_TESTDATA
namespace opencv_test { namespace {
struct Activation
{
int id;
const char * name;
};
void PrintTo(const Activation &a, std::ostream *os) { *os << a.name; }
Activation activation_list[] =
{
{ ml::ANN_MLP::IDENTITY, "identity" },
{ ml::ANN_MLP::SIGMOID_SYM, "sigmoid_sym" },
{ ml::ANN_MLP::GAUSSIAN, "gaussian" },
{ ml::ANN_MLP::RELU, "relu" },
{ ml::ANN_MLP::LEAKYRELU, "leakyrelu" },
};
typedef testing::TestWithParam< Activation > ML_ANN_Params;
TEST_P(ML_ANN_Params, ActivationFunction)
{
const Activation &activation = GetParam();
const string dataname = "waveform";
const string data_path = findDataFile(dataname + ".data");
const string model_name = dataname + "_" + activation.name + ".yml";
Ptr<TrainData> tdata = TrainData::loadFromCSV(data_path, 0);
ASSERT_FALSE(tdata.empty());
// hack?
const uint64 old_state = theRNG().state;
theRNG().state = 1027401484159173092;
tdata->setTrainTestSplit(500);
theRNG().state = old_state;
Mat_<int> layerSizes(1, 4);
layerSizes(0, 0) = tdata->getNVars();
layerSizes(0, 1) = 100;
layerSizes(0, 2) = 100;
layerSizes(0, 3) = tdata->getResponses().cols;
Mat testSamples = tdata->getTestSamples();
Mat rx, ry;
{
Ptr<ml::ANN_MLP> x = ml::ANN_MLP::create();
x->setActivationFunction(activation.id);
x->setLayerSizes(layerSizes);
x->setTrainMethod(ml::ANN_MLP::RPROP, 0.01, 0.1);
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 300, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE);
ASSERT_TRUE(x->isTrained());
x->predict(testSamples, rx);
#ifdef GENERATE_TESTDATA
x->save(cvtest::TS::ptr()->get_data_path() + model_name);
#endif
}
{
const string model_path = findDataFile(model_name);
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(model_path);
ASSERT_TRUE(y);
y->predict(testSamples, ry);
EXPECT_MAT_NEAR(rx, ry, FLT_EPSILON);
}
}
INSTANTIATE_TEST_CASE_P(/**/, ML_ANN_Params, testing::ValuesIn(activation_list));
//==================================================================================================
CV_ENUM(ANN_MLP_METHOD, ANN_MLP::RPROP, ANN_MLP::ANNEAL)
typedef tuple<ANN_MLP_METHOD, string, int> ML_ANN_METHOD_Params;
typedef TestWithParam<ML_ANN_METHOD_Params> ML_ANN_METHOD;
TEST_P(ML_ANN_METHOD, Test)
{
int methodType = get<0>(GetParam());
string methodName = get<1>(GetParam());
int N = get<2>(GetParam());
String folder = string(cvtest::TS::ptr()->get_data_path());
String original_path = findDataFile("waveform.data");
string dataname = "waveform_" + methodName;
string weight_name = dataname + "_init_weight.yml.gz";
string model_name = dataname + ".yml.gz";
string response_name = dataname + "_response.yml.gz";
Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0);
ASSERT_FALSE(tdata2.empty());
Mat samples = tdata2->getSamples()(Range(0, N), Range::all());
Mat responses(N, 3, CV_32FC1, Scalar(0));
for (int i = 0; i < N; i++)
responses.at<float>(i, static_cast<int>(tdata2->getResponses().at<float>(i, 0))) = 1;
Ptr<TrainData> tdata = TrainData::create(samples, ml::ROW_SAMPLE, responses);
ASSERT_FALSE(tdata.empty());
// hack?
const uint64 old_state = theRNG().state;
theRNG().state = 0;
tdata->setTrainTestSplitRatio(0.8);
theRNG().state = old_state;
Mat testSamples = tdata->getTestSamples();
// train 1st stage
Ptr<ml::ANN_MLP> xx = ml::ANN_MLP_ANNEAL::create();
Mat_<int> layerSizes(1, 4);
layerSizes(0, 0) = tdata->getNVars();
layerSizes(0, 1) = 30;
layerSizes(0, 2) = 30;
layerSizes(0, 3) = tdata->getResponses().cols;
xx->setLayerSizes(layerSizes);
xx->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
xx->setTrainMethod(ml::ANN_MLP::RPROP);
xx->setTermCriteria(TermCriteria(TermCriteria::COUNT, 1, 0.01));
xx->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE);
#ifdef GENERATE_TESTDATA
{
FileStorage fs;
fs.open(cvtest::TS::ptr()->get_data_path() + weight_name, FileStorage::WRITE + FileStorage::BASE64);
xx->write(fs);
}
#endif
// train 2nd stage
Mat r_gold;
Ptr<ml::ANN_MLP> x = ml::ANN_MLP_ANNEAL::create();
{
const string weight_file = findDataFile(weight_name);
FileStorage fs;
fs.open(weight_file, FileStorage::READ);
x->read(fs.root());
}
x->setTrainMethod(methodType);
if (methodType == ml::ANN_MLP::ANNEAL)
{
x->setAnnealEnergyRNG(RNG(CV_BIG_INT(0xffffffff)));
x->setAnnealInitialT(12);
x->setAnnealFinalT(0.15);
x->setAnnealCoolingRatio(0.96);
x->setAnnealItePerStep(11);
}
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 100, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE + ml::ANN_MLP::UPDATE_WEIGHTS);
ASSERT_TRUE(x->isTrained());
#ifdef GENERATE_TESTDATA
x->save(cvtest::TS::ptr()->get_data_path() + model_name);
x->predict(testSamples, r_gold);
{
FileStorage fs_response(cvtest::TS::ptr()->get_data_path() + response_name, FileStorage::WRITE + FileStorage::BASE64);
fs_response << "response" << r_gold;
}
#endif
{
const string response_file = findDataFile(response_name);
FileStorage fs_response(response_file, FileStorage::READ);
fs_response["response"] >> r_gold;
}
ASSERT_FALSE(r_gold.empty());
// verify
const string model_file = findDataFile(model_name);
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(model_file);
ASSERT_TRUE(y);
Mat rx, ry;
for (int j = 0; j < 4; j++)
{
rx = x->getWeights(j);
ry = y->getWeights(j);
EXPECT_MAT_NEAR(rx, ry, FLT_EPSILON) << "Weights are not equal for layer: " << j;
}
x->predict(testSamples, rx);
y->predict(testSamples, ry);
EXPECT_MAT_NEAR(ry, rx, FLT_EPSILON) << "Predict are not equal to result of the saved model";
EXPECT_MAT_NEAR(r_gold, rx, FLT_EPSILON) << "Predict are not equal to 'gold' response";
}
INSTANTIATE_TEST_CASE_P(/*none*/, ML_ANN_METHOD,
testing::Values(
ML_ANN_METHOD_Params(ml::ANN_MLP::RPROP, "rprop", 5000),
ML_ANN_METHOD_Params(ml::ANN_MLP::ANNEAL, "anneal", 1000)
// ML_ANN_METHOD_Params(ml::ANN_MLP::BACKPROP, "backprop", 500) -----> NO BACKPROP TEST
)
);
}} // namespace
modules/ml/test/test_bayes.cpp 0 → 100644
View file @ 5ff1faba
// 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 {
TEST(ML_NBAYES, regression_5911)
{
int N=12;
Ptr<ml::NormalBayesClassifier> nb = cv::ml::NormalBayesClassifier::create();
// data:
float X_data[] = {
1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4,
5,5,5,5, 5,5,5,5, 5,5,5,5, 5,5,5,5,
4,3,2,1, 4,3,2,1, 4,3,2,1, 4,3,2,1
};
Mat_<float> X(N, 4, X_data);
// labels:
int Y_data[] = { 0,0,0,0, 1,1,1,1, 2,2,2,2 };
Mat_<int> Y(N, 1, Y_data);
nb->train(X, ml::ROW_SAMPLE, Y);
// single prediction:
Mat R1,P1;
for (int i=0; i<N; i++)
{
Mat r,p;
nb->predictProb(X.row(i), r, p);
R1.push_back(r);
P1.push_back(p);
}
// bulk prediction (continuous memory):
Mat R2,P2;
nb->predictProb(X, R2, P2);
EXPECT_EQ(255 * R2.total(), sum(R1 == R2)[0]);
EXPECT_EQ(255 * P2.total(), sum(P1 == P2)[0]);
// bulk prediction, with non-continuous memory storage
Mat R3_(N, 1+1, CV_32S),
P3_(N, 3+1, CV_32F);
nb->predictProb(X, R3_.col(0), P3_.colRange(0,3));
Mat R3 = R3_.col(0).clone(),
P3 = P3_.colRange(0,3).clone();
EXPECT_EQ(255 * R3.total(), sum(R1 == R3)[0]);
EXPECT_EQ(255 * P3.total(), sum(P1 == P3)[0]);
}
}} // namespace
modules/ml/test/test_em.cpp 0 → 100644
View file @ 5ff1faba
// 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
modules/ml/test/test_emknearestkmeans.cpp deleted 100644 → 0
View file @ 9e906d9e
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modules/ml/test/test_gbttest.cpp deleted 100644 → 0
View file @ 9e906d9e
#include "test_precomp.hpp"
#if 0
using namespace std;
class CV_GBTreesTest : public cvtest::BaseTest
{
public:
CV_GBTreesTest();
~CV_GBTreesTest();
protected:
void run(int);
int TestTrainPredict(int test_num);
int TestSaveLoad();
int checkPredictError(int test_num);
int checkLoadSave();
string model_file_name1;
string model_file_name2;
string* datasets;
string data_path;
CvMLData* data;
CvGBTrees* gtb;
vector<float> test_resps1;
vector<float> test_resps2;
int64 initSeed;
};
int _get_len(const CvMat* mat)
{
return (mat->cols > mat->rows) ? mat->cols : mat->rows;
}
CV_GBTreesTest::CV_GBTreesTest()
{
int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52),
CV_BIG_INT(0x0000a17166072c7c),
CV_BIG_INT(0x0201b32115cd1f9a),
CV_BIG_INT(0x0513cb37abcd1234),
CV_BIG_INT(0x0001a2b3c4d5f678)
};
int seedCount = sizeof(seeds)/sizeof(seeds[0]);
cv::RNG& rng = cv::theRNG();
initSeed = rng.state;
rng.state = seeds[rng(seedCount)];
datasets = 0;
data = 0;
gtb = 0;
}
CV_GBTreesTest::~CV_GBTreesTest()
{
if (data)
delete data;
delete[] datasets;
cv::theRNG().state = initSeed;
}
int CV_GBTreesTest::TestTrainPredict(int test_num)
{
int code = cvtest::TS::OK;
int weak_count = 200;
float shrinkage = 0.1f;
float subsample_portion = 0.5f;
int max_depth = 5;
bool use_surrogates = false;
int loss_function_type = 0;
switch (test_num)
{
case (1) : loss_function_type = CvGBTrees::SQUARED_LOSS; break;
case (2) : loss_function_type = CvGBTrees::ABSOLUTE_LOSS; break;
case (3) : loss_function_type = CvGBTrees::HUBER_LOSS; break;
case (0) : loss_function_type = CvGBTrees::DEVIANCE_LOSS; break;
default :
{
ts->printf( cvtest::TS::LOG, "Bad test_num value in CV_GBTreesTest::TestTrainPredict(..) function." );
return cvtest::TS::FAIL_BAD_ARG_CHECK;
}
}
int dataset_num = test_num == 0 ? 0 : 1;
if (!data)
{
data = new CvMLData();
data->set_delimiter(',');
if (data->read_csv(datasets[dataset_num].c_str()))
{
ts->printf( cvtest::TS::LOG, "File reading error." );
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
if (test_num == 0)
{
data->set_response_idx(57);
data->set_var_types("ord[0-56],cat[57]");
}
else
{
data->set_response_idx(13);
data->set_var_types("ord[0-2,4-13],cat[3]");
subsample_portion = 0.7f;
}
int train_sample_count = cvFloor(_get_len(data->get_responses())*0.5f);
CvTrainTestSplit spl( train_sample_count );
data->set_train_test_split( &spl );
}
data->mix_train_and_test_idx();
if (gtb) delete gtb;
gtb = new CvGBTrees();
bool tmp_code = true;
tmp_code = gtb->train(data, CvGBTreesParams(loss_function_type, weak_count,
shrinkage, subsample_portion,
max_depth, use_surrogates));
if (!tmp_code)
{
ts->printf( cvtest::TS::LOG, "Model training was failed.");
return cvtest::TS::FAIL_INVALID_OUTPUT;
}
code = checkPredictError(test_num);
return code;
}
int CV_GBTreesTest::checkPredictError(int test_num)
{
if (!gtb)
return cvtest::TS::FAIL_GENERIC;
//float mean[] = {5.430247f, 13.5654f, 12.6569f, 13.1661f};
//float sigma[] = {0.4162694f, 3.21161f, 3.43297f, 3.00624f};
float mean[] = {5.80226f, 12.68689f, 13.49095f, 13.19628f};
float sigma[] = {0.4764534f, 3.166919f, 3.022405f, 2.868722f};
float current_error = gtb->calc_error(data, CV_TEST_ERROR);
if ( abs( current_error - mean[test_num]) > 6*sigma[test_num] )
{
ts->printf( cvtest::TS::LOG, "Test error is out of range:\n"
"abs(%f/*curEr*/ - %f/*mean*/ > %f/*6*sigma*/",
current_error, mean[test_num], 6*sigma[test_num] );
return cvtest::TS::FAIL_BAD_ACCURACY;
}
return cvtest::TS::OK;
}
int CV_GBTreesTest::TestSaveLoad()
{
if (!gtb)
return cvtest::TS::FAIL_GENERIC;
model_file_name1 = cv::tempfile();
model_file_name2 = cv::tempfile();
gtb->save(model_file_name1.c_str());
gtb->calc_error(data, CV_TEST_ERROR, &test_resps1);
gtb->load(model_file_name1.c_str());
gtb->calc_error(data, CV_TEST_ERROR, &test_resps2);
gtb->save(model_file_name2.c_str());
return checkLoadSave();
}
int CV_GBTreesTest::checkLoadSave()
{
int code = cvtest::TS::OK;
// 1. compare files
ifstream f1( model_file_name1.c_str() ), f2( model_file_name2.c_str() );
string s1, s2;
int lineIdx = 0;
CV_Assert( f1.is_open() && f2.is_open() );
for( ; !f1.eof() && !f2.eof(); lineIdx++ )
{
getline( f1, s1 );
getline( f2, s2 );
if( s1.compare(s2) )
{
ts->printf( cvtest::TS::LOG, "first and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
}
if( !f1.eof() || !f2.eof() )
{
ts->printf( cvtest::TS::LOG, "First and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
f1.close();
f2.close();
// delete temporary files
remove( model_file_name1.c_str() );
remove( model_file_name2.c_str() );
// 2. compare responses
CV_Assert( test_resps1.size() == test_resps2.size() );
vector<float>::const_iterator it1 = test_resps1.begin(), it2 = test_resps2.begin();
for( ; it1 != test_resps1.end(); ++it1, ++it2 )
{
if( fabs(*it1 - *it2) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG, "Responses predicted before saving and after loading are different" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
}
return code;
}
void CV_GBTreesTest::run(int)
{
string dataPath = string(ts->get_data_path());
datasets = new string[2];
datasets[0] = dataPath + string("spambase.data"); /*string("dataset_classification.csv");*/
datasets[1] = dataPath + string("housing_.data"); /*string("dataset_regression.csv");*/
int code = cvtest::TS::OK;
for (int i = 0; i < 4; i++)
{
int temp_code = TestTrainPredict(i);
if (temp_code != cvtest::TS::OK)
{
code = temp_code;
break;
}
else if (i==0)
{
temp_code = TestSaveLoad();
if (temp_code != cvtest::TS::OK)
code = temp_code;
delete data;
data = 0;
}
delete gtb;
gtb = 0;
}
delete data;
data = 0;
ts->set_failed_test_info( code );
}
/////////////////////////////////////////////////////////////////////////////
//////////////////// test registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
TEST(ML_GBTrees, regression) { CV_GBTreesTest test; test.safe_run(); }
#endif
modules/ml/test/test_kmeans.cpp 0 → 100644
View file @ 5ff1faba
// 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 {
TEST(ML_KMeans, accuracy)
{
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 );
TermCriteria termCriteria( TermCriteria::COUNT, iters, 0.0);
{
SCOPED_TRACE("KMEANS_PP_CENTERS");
float err = 1000;
Mat bestLabels;
kmeans( data, 3, bestLabels, termCriteria, 0, KMEANS_PP_CENTERS, noArray() );
EXPECT_TRUE(calcErr( bestLabels, labels, sizes, err , false ));
EXPECT_LE(err, 0.01f);
}
{
SCOPED_TRACE("KMEANS_RANDOM_CENTERS");
float err = 1000;
Mat bestLabels;
kmeans( data, 3, bestLabels, termCriteria, 0, KMEANS_RANDOM_CENTERS, noArray() );
EXPECT_TRUE(calcErr( bestLabels, labels, sizes, err, false ));
EXPECT_LE(err, 0.01f);
}
{
SCOPED_TRACE("KMEANS_USE_INITIAL_LABELS");
float err = 1000;
Mat bestLabels;
labels.copyTo( bestLabels );
RNG &rng = cv::theRNG();
for( int i = 0; i < 0.5f * pointsCount; i++ )
bestLabels.at<int>( rng.next() % pointsCount, 0 ) = rng.next() % 3;
kmeans( data, 3, bestLabels, termCriteria, 0, KMEANS_USE_INITIAL_LABELS, noArray() );
EXPECT_TRUE(calcErr( bestLabels, labels, sizes, err, false ));
EXPECT_LE(err, 0.01f);
}
}
}} // namespace
modules/ml/test/test_knearest.cpp 0 → 100644
View file @ 5ff1faba
// 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 {
using cv::ml::TrainData;
using cv::ml::EM;
using cv::ml::KNearest;
TEST(ML_KNearest, accuracy)
{
int sizesArr[] = { 500, 700, 800 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
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 );
Mat testData( pointsCount, 2, CV_32FC1 );
Mat testLabels;
generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
{
SCOPED_TRACE("Default");
Mat bestLabels;
float err = 1000;
Ptr<KNearest> knn = KNearest::create();
knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
knn->findNearest(testData, 4, bestLabels);
EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
EXPECT_LE(err, 0.01f);
}
{
// TODO: broken
#if 0
SCOPED_TRACE("KDTree");
Mat bestLabels;
float err = 1000;
Ptr<KNearest> knn = KNearest::create();
knn->setAlgorithmType(KNearest::KDTREE);
knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
knn->findNearest(testData, 4, bestLabels);
EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
EXPECT_LE(err, 0.01f);
#endif
}
}
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
modules/ml/test/test_lr.cpp
View file @ 5ff1faba
/////////////////////////////////////////////////////////////////////////////////////// // This file is part of OpenCV project.
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // 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.
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV. // This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
...@@ -11,92 +8,16 @@ ...@@ -11,92 +8,16 @@
// Rahul Kavi rahulkavi[at]live[at]com // Rahul Kavi rahulkavi[at]live[at]com
// //
// contains a subset of data from the popular Iris Dataset (taken from "http://archive.ics.uci.edu/ml/datasets/Iris")
// # You are free to use, change, or redistribute the code in any way you wish for
// # non-commercial purposes, but please maintain the name of the original author.
// # This code comes with no warranty of any kind.
// #
// # You are free to use, change, or redistribute the code in any way you wish for
// # non-commercial purposes, but please maintain the name of the original author.
// # This code comes with no warranty of any kind.
// # Logistic Regression ALGORITHM
// License Agreement
// For Open Source Computer Vision Library
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
#include "test_precomp.hpp" #include "test_precomp.hpp"
namespace opencv_test { namespace { namespace opencv_test { namespace {
bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error) TEST(ML_LR, accuracy)
{
CV_TRACE_FUNCTION();
error = 0.0f;
float accuracy = 0.0f;
Mat _p_labels_temp;
Mat _o_labels_temp;
_p_labels.convertTo(_p_labels_temp, CV_32S);
_o_labels.convertTo(_o_labels_temp, CV_32S);
CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
accuracy = (float)countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
error = 1 - accuracy;
return true;
}
//--------------------------------------------------------------------------------------------
class CV_LRTest : public cvtest::BaseTest
{
public:
CV_LRTest() {}
protected:
virtual void run( int start_from );
};
void CV_LRTest::run( int /*start_from*/ )
{ {
CV_TRACE_FUNCTION(); std::string dataFileName = findDataFile("iris.data");
// initialize variables from the popular Iris Dataset
string dataFileName = ts->get_data_path() + "iris.data";
Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0); Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << dataFileName; ASSERT_FALSE(tdata.empty());
// run LR classifier train classifier
Ptr<LogisticRegression> p = LogisticRegression::create(); Ptr<LogisticRegression> p = LogisticRegression::create();
p->setLearningRate(1.0); p->setLearningRate(1.0);
p->setIterations(10001); p->setIterations(10001);
...@@ -105,121 +26,54 @@ void CV_LRTest::run( int /*start_from*/ ) ...@@ -105,121 +26,54 @@ void CV_LRTest::run( int /*start_from*/ )
p->setMiniBatchSize(10); p->setMiniBatchSize(10);
p->train(tdata); p->train(tdata);
// predict using the same data
Mat responses; Mat responses;
p->predict(tdata->getSamples(), responses); p->predict(tdata->getSamples(), responses);
// calculate error float error = 1000;
int test_code = cvtest::TS::OK; EXPECT_TRUE(calculateError(responses, tdata->getResponses(), error));
float error = 0.0f; EXPECT_LE(error, 0.05f);
if(!calculateError(responses, tdata->getResponses(), error))
{
ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if(error > 0.05f)
{
ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
test_code = cvtest::TS::FAIL_BAD_ACCURACY;
}
{
FileStorage s("debug.xml", FileStorage::WRITE);
s << "original" << tdata->getResponses();
s << "predicted1" << responses;
s << "learnt" << p->get_learnt_thetas();
s << "error" << error;
s.release();
}
ts->set_failed_test_info(test_code);
} }
//-------------------------------------------------------------------------------------------- //==================================================================================================
class CV_LRTest_SaveLoad : public cvtest::BaseTest
{
public:
CV_LRTest_SaveLoad(){}
protected:
virtual void run(int start_from);
};
void CV_LRTest_SaveLoad::run( int /*start_from*/ ) TEST(ML_LR, save_load)
{ {
CV_TRACE_FUNCTION(); string dataFileName = findDataFile("iris.data");
int code = cvtest::TS::OK;
// initialize variables from the popular Iris Dataset
string dataFileName = ts->get_data_path() + "iris.data";
Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0); Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << dataFileName; ASSERT_FALSE(tdata.empty());
Mat responses1, responses2; Mat responses1, responses2;
Mat learnt_mat1, learnt_mat2; Mat learnt_mat1, learnt_mat2;
// train and save the classifier
String filename = tempfile(".xml"); String filename = tempfile(".xml");
try
{ {
// run LR classifier train classifier
Ptr<LogisticRegression> lr1 = LogisticRegression::create(); Ptr<LogisticRegression> lr1 = LogisticRegression::create();
lr1->setLearningRate(1.0); lr1->setLearningRate(1.0);
lr1->setIterations(10001); lr1->setIterations(10001);
lr1->setRegularization(LogisticRegression::REG_L2); lr1->setRegularization(LogisticRegression::REG_L2);
lr1->setTrainMethod(LogisticRegression::BATCH); lr1->setTrainMethod(LogisticRegression::BATCH);
lr1->setMiniBatchSize(10); lr1->setMiniBatchSize(10);
lr1->train(tdata); ASSERT_NO_THROW(lr1->train(tdata));
lr1->predict(tdata->getSamples(), responses1); ASSERT_NO_THROW(lr1->predict(tdata->getSamples(), responses1));
ASSERT_NO_THROW(lr1->save(filename));
learnt_mat1 = lr1->get_learnt_thetas(); learnt_mat1 = lr1->get_learnt_thetas();
lr1->save(filename);
} }
catch(...)
{
ts->printf(cvtest::TS::LOG, "Crash in write method.\n" );
ts->set_failed_test_info(cvtest::TS::FAIL_EXCEPTION);
}
// and load to another
try
{ {
Ptr<LogisticRegression> lr2 = Algorithm::load<LogisticRegression>(filename); Ptr<LogisticRegression> lr2;
lr2->predict(tdata->getSamples(), responses2); ASSERT_NO_THROW(lr2 = Algorithm::load<LogisticRegression>(filename));
ASSERT_NO_THROW(lr2->predict(tdata->getSamples(), responses2));
learnt_mat2 = lr2->get_learnt_thetas(); learnt_mat2 = lr2->get_learnt_thetas();
} }
catch(...) // compare difference in prediction outputs and stored inputs
{ EXPECT_MAT_NEAR(responses1, responses2, 0.f);
ts->printf(cvtest::TS::LOG, "Crash in write method.\n" );
ts->set_failed_test_info(cvtest::TS::FAIL_EXCEPTION);
}
CV_Assert(responses1.rows == responses2.rows);
// compare difference in learnt matrices before and after loading from disk
Mat comp_learnt_mats; Mat comp_learnt_mats;
comp_learnt_mats = (learnt_mat1 == learnt_mat2); comp_learnt_mats = (learnt_mat1 == learnt_mat2);
comp_learnt_mats = comp_learnt_mats.reshape(1, comp_learnt_mats.rows*comp_learnt_mats.cols); comp_learnt_mats = comp_learnt_mats.reshape(1, comp_learnt_mats.rows*comp_learnt_mats.cols);
comp_learnt_mats.convertTo(comp_learnt_mats, CV_32S); comp_learnt_mats.convertTo(comp_learnt_mats, CV_32S);
comp_learnt_mats = comp_learnt_mats/255; comp_learnt_mats = comp_learnt_mats/255;
// compare difference in prediction outputs and stored inputs
// check if there is any difference between computed learnt mat and retrieved mat // check if there is any difference between computed learnt mat and retrieved mat
EXPECT_EQ(comp_learnt_mats.rows, sum(comp_learnt_mats)[0]);
float errorCount = 0.0;
errorCount += 1 - (float)countNonZero(responses1 == responses2)/responses1.rows;
errorCount += 1 - (float)sum(comp_learnt_mats)[0]/comp_learnt_mats.rows;
if(errorCount>0)
{
ts->printf( cvtest::TS::LOG, "Different prediction results before writing and after reading (errorCount=%d).\n", errorCount );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
remove( filename.c_str() ); remove( filename.c_str() );
ts->set_failed_test_info( code );
} }
TEST(ML_LR, accuracy) { CV_LRTest test; test.safe_run(); }
TEST(ML_LR, save_load) { CV_LRTest_SaveLoad test; test.safe_run(); }
}} // namespace }} // namespace
modules/ml/test/test_mltests.cpp
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modules/ml/test/test_mltests2.cpp deleted 100644 → 0
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modules/ml/test/test_precomp.hpp
View file @ 5ff1faba
...@@ -2,10 +2,15 @@ ...@@ -2,10 +2,15 @@
#define __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__
#include "opencv2/ts.hpp" #include "opencv2/ts.hpp"
#include <opencv2/ts/cuda_test.hpp> // EXPECT_MAT_NEAR
#include "opencv2/ml.hpp" #include "opencv2/ml.hpp"
#include "opencv2/core/core_c.h" #include "opencv2/core/core_c.h"
#include <fstream>
using std::ifstream;
namespace opencv_test { namespace opencv_test {
using namespace cv::ml; using namespace cv::ml;
#define CV_NBAYES "nbayes" #define CV_NBAYES "nbayes"
...@@ -19,8 +24,6 @@ using namespace cv::ml; ...@@ -19,8 +24,6 @@ using namespace cv::ml;
#define CV_ERTREES "ertrees" #define CV_ERTREES "ertrees"
#define CV_SVMSGD "svmsgd" #define CV_SVMSGD "svmsgd"
enum { CV_TRAIN_ERROR=0, CV_TEST_ERROR=1 };
using cv::Ptr; using cv::Ptr;
using cv::ml::StatModel; using cv::ml::StatModel;
using cv::ml::TrainData; using cv::ml::TrainData;
...@@ -34,58 +37,14 @@ using cv::ml::Boost; ...@@ -34,58 +37,14 @@ using cv::ml::Boost;
using cv::ml::RTrees; using cv::ml::RTrees;
using cv::ml::SVMSGD; using cv::ml::SVMSGD;
class CV_MLBaseTest : public cvtest::BaseTest void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 );
{ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType );
public: int maxIdx( const vector<int>& count );
CV_MLBaseTest( const char* _modelName ); bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq=true );
virtual ~CV_MLBaseTest(); bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true, bool checkClusterUniq=true );
protected:
virtual int read_params( CvFileStorage* fs );
virtual void run( int startFrom );
virtual int prepare_test_case( int testCaseIdx );
virtual std::string& get_validation_filename();
virtual int run_test_case( int testCaseIdx ) = 0;
virtual int validate_test_results( int testCaseIdx ) = 0;
int train( int testCaseIdx );
float get_test_error( int testCaseIdx, std::vector<float> *resp = 0 );
void save( const char* filename );
void load( const char* filename );
Ptr<TrainData> data;
std::string modelName, validationFN;
std::vector<std::string> dataSetNames;
cv::FileStorage validationFS;
Ptr<StatModel> model;
std::map<int, int> cls_map;
int64 initSeed;
};
class CV_AMLTest : public CV_MLBaseTest
{
public:
CV_AMLTest( const char* _modelName );
virtual ~CV_AMLTest() {}
protected:
virtual int run_test_case( int testCaseIdx );
virtual int validate_test_results( int testCaseIdx );
};
class CV_SLMLTest : public CV_MLBaseTest
{
public:
CV_SLMLTest( const char* _modelName );
virtual ~CV_SLMLTest() {}
protected:
virtual int run_test_case( int testCaseIdx );
virtual int validate_test_results( int testCaseIdx );
std::vector<float> test_resps1, test_resps2; // predicted responses for test data // used in LR test
std::string fname1, fname2; bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error);
};
} // namespace } // namespace
......
modules/ml/test/test_rtrees.cpp 0 → 100644
View file @ 5ff1faba
// 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 {
TEST(ML_RTrees, getVotes)
{
int n = 12;
int count, i;
int label_size = 3;
int predicted_class = 0;
int max_votes = -1;
int val;
// RTrees for classification
Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
//data
Mat data(n, 4, CV_32F);
randu(data, 0, 10);
//labels
Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
rt->train(data, ml::ROW_SAMPLE, labels);
//run function
Mat test(1, 4, CV_32F);
Mat result;
randu(test, 0, 10);
rt->getVotes(test, result, 0);
//count vote amount and find highest vote
count = 0;
const int* result_row = result.ptr<int>(1);
for( i = 0; i < label_size; i++ )
{
val = result_row[i];
//predicted_class = max_votes < val? i;
if( max_votes < val )
{
max_votes = val;
predicted_class = i;
}
count += val;
}
EXPECT_EQ(count, (int)rt->getRoots().size());
EXPECT_EQ(result.at<float>(0, predicted_class), rt->predict(test));
}
}} // namespace
modules/ml/test/test_save_load.cpp
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modules/ml/test/test_svmsgd.cpp
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modules/ml/test/test_svmtrainauto.cpp
View file @ 5ff1faba
/*M/////////////////////////////////////////////////////////////////////////////////////// // This file is part of OpenCV project.
// // It is subject to the license terms in the LICENSE file found in the top-level directory
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // of this distribution and at http://opencv.org/license.html.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp" #include "test_precomp.hpp"
...@@ -46,21 +9,11 @@ namespace opencv_test { namespace { ...@@ -46,21 +9,11 @@ namespace opencv_test { namespace {
using cv::ml::SVM; using cv::ml::SVM;
using cv::ml::TrainData; using cv::ml::TrainData;
//-------------------------------------------------------------------------------------------- static Ptr<TrainData> makeRandomData(int datasize)
class CV_SVMTrainAutoTest : public cvtest::BaseTest {
public:
CV_SVMTrainAutoTest() {}
protected:
virtual void run( int start_from );
};
void CV_SVMTrainAutoTest::run( int /*start_from*/ )
{ {
int datasize = 100;
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 ); cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S ); cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
RNG &rng = cv::theRNG();
RNG rng(0);
for (int i = 0; i < datasize; ++i) for (int i = 0; i < datasize; ++i)
{ {
int response = rng.uniform(0, 2); // Random from {0, 1}. int response = rng.uniform(0, 2); // Random from {0, 1}.
...@@ -68,36 +21,14 @@ void CV_SVMTrainAutoTest::run( int /*start_from*/ ) ...@@ -68,36 +21,14 @@ void CV_SVMTrainAutoTest::run( int /*start_from*/ )
samples.at<float>( i, 1 ) = rng.uniform(0.f, 0.5f) + response * 0.5f; samples.at<float>( i, 1 ) = rng.uniform(0.f, 0.5f) + response * 0.5f;
responses.at<int>( i, 0 ) = response; responses.at<int>( i, 0 ) = response;
} }
return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
cv::Ptr<SVM> svm = SVM::create();
svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {0.25f, 0.25f};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
float result0 = svm->predict( test_point0 );
float test_data1[2] = {0.75f, 0.75f};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
float result1 = svm->predict( test_point1 );
if ( fabs( result0 - 0 ) > 0.001 || fabs( result1 - 1 ) > 0.001 )
{
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
} }
TEST(ML_SVM, trainauto) { CV_SVMTrainAutoTest test; test.safe_run(); } static Ptr<TrainData> makeCircleData(int datasize, float scale_factor, float radius)
TEST(ML_SVM, trainauto_sigmoid)
{ {
const int datasize = 100; // Populate samples with data that can be split into two concentric circles
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 ); cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S ); cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
const float scale_factor = 0.5;
const float radius = 2.0;
// Populate samples with data that can be split into two concentric circles
for (int i = 0; i < datasize; i+=2) for (int i = 0; i < datasize; i+=2)
{ {
const float pi = 3.14159f; const float pi = 3.14159f;
...@@ -115,41 +46,74 @@ TEST(ML_SVM, trainauto_sigmoid) ...@@ -115,41 +46,74 @@ TEST(ML_SVM, trainauto_sigmoid)
samples.at<float>( i + 1, 1 ) = y * scale_factor; samples.at<float>( i + 1, 1 ) = y * scale_factor;
responses.at<int>( i + 1, 0 ) = 1; responses.at<int>( i + 1, 0 ) = 1;
} }
return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
}
static Ptr<TrainData> makeRandomData2(int datasize)
{
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
RNG &rng = cv::theRNG();
for (int i = 0; i < datasize; ++i)
{
int response = rng.uniform(0, 2); // Random from {0, 1}.
samples.at<float>( i, 0 ) = 0;
samples.at<float>( i, 1 ) = (0.5f - response) * rng.uniform(0.f, 1.2f) + response;
responses.at<int>( i, 0 ) = response;
}
return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
}
//==================================================================================================
cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses ); TEST(ML_SVM, trainauto)
{
const int datasize = 100;
cv::Ptr<TrainData> data = makeRandomData(datasize);
ASSERT_TRUE(data);
cv::Ptr<SVM> svm = SVM::create(); cv::Ptr<SVM> svm = SVM::create();
svm->setKernel(SVM::SIGMOID); ASSERT_TRUE(svm);
svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {0.25f, 0.25f};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
float result0 = svm->predict( test_point0 );
float test_data1[2] = {0.75f, 0.75f};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
float result1 = svm->predict( test_point1 );
EXPECT_NEAR(result0, 0, 0.001);
EXPECT_NEAR(result1, 1, 0.001);
}
TEST(ML_SVM, trainauto_sigmoid)
{
const int datasize = 100;
const float scale_factor = 0.5;
const float radius = 2.0;
cv::Ptr<TrainData> data = makeCircleData(datasize, scale_factor, radius);
ASSERT_TRUE(data);
cv::Ptr<SVM> svm = SVM::create();
ASSERT_TRUE(svm);
svm->setKernel(SVM::SIGMOID);
svm->setGamma(10.0); svm->setGamma(10.0);
svm->setCoef0(-10.0); svm->setCoef0(-10.0);
svm->trainAuto( data, 10 ); // 2-fold cross validation. svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {radius, radius}; float test_data0[2] = {radius, radius};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 ); cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
ASSERT_EQ(0, svm->predict( test_point0 )); EXPECT_FLOAT_EQ(svm->predict( test_point0 ), 0);
float test_data1[2] = {scale_factor * radius, scale_factor * radius}; float test_data1[2] = {scale_factor * radius, scale_factor * radius};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 ); cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
ASSERT_EQ(1, svm->predict( test_point1 )); EXPECT_FLOAT_EQ(svm->predict( test_point1 ), 1);
} }
TEST(ML_SVM, trainAuto_regression_5369) TEST(ML_SVM, trainAuto_regression_5369)
{ {
int datasize = 100; const int datasize = 100;
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 ); Ptr<TrainData> data = makeRandomData2(datasize);
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
RNG rng(0); // fixed!
for (int i = 0; i < datasize; ++i)
{
int response = rng.uniform(0, 2); // Random from {0, 1}.
samples.at<float>( i, 0 ) = 0;
samples.at<float>( i, 1 ) = (0.5f - response) * rng.uniform(0.f, 1.2f) + response;
responses.at<int>( i, 0 ) = response;
}
cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
cv::Ptr<SVM> svm = SVM::create(); cv::Ptr<SVM> svm = SVM::create();
svm->trainAuto( data, 10 ); // 2-fold cross validation. svm->trainAuto( data, 10 ); // 2-fold cross validation.
...@@ -164,16 +128,8 @@ TEST(ML_SVM, trainAuto_regression_5369) ...@@ -164,16 +128,8 @@ TEST(ML_SVM, trainAuto_regression_5369)
EXPECT_EQ(1., result1); EXPECT_EQ(1., result1);
} }
class CV_SVMGetSupportVectorsTest : public cvtest::BaseTest { TEST(ML_SVM, getSupportVectors)
public:
CV_SVMGetSupportVectorsTest() {}
protected:
virtual void run( int startFrom );
};
void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ )
{ {
int code = cvtest::TS::OK;
// Set up training data // Set up training data
int labels[4] = {1, -1, -1, -1}; int labels[4] = {1, -1, -1, -1};
float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} }; float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
...@@ -181,19 +137,18 @@ void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ ) ...@@ -181,19 +137,18 @@ void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ )
Mat labelsMat(4, 1, CV_32SC1, labels); Mat labelsMat(4, 1, CV_32SC1, labels);
Ptr<SVM> svm = SVM::create(); Ptr<SVM> svm = SVM::create();
ASSERT_TRUE(svm);
svm->setType(SVM::C_SVC); svm->setType(SVM::C_SVC);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6)); svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
// Test retrieval of SVs and compressed SVs on linear SVM // Test retrieval of SVs and compressed SVs on linear SVM
svm->setKernel(SVM::LINEAR); svm->setKernel(SVM::LINEAR);
svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat); svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
Mat sv = svm->getSupportVectors(); Mat sv = svm->getSupportVectors();
CV_Assert(sv.rows == 1); // by default compressed SV returned EXPECT_EQ(1, sv.rows); // by default compressed SV returned
sv = svm->getUncompressedSupportVectors(); sv = svm->getUncompressedSupportVectors();
CV_Assert(sv.rows == 3); EXPECT_EQ(3, sv.rows);
// Test retrieval of SVs and compressed SVs on non-linear SVM // Test retrieval of SVs and compressed SVs on non-linear SVM
svm->setKernel(SVM::POLY); svm->setKernel(SVM::POLY);
...@@ -201,15 +156,9 @@ void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ ) ...@@ -201,15 +156,9 @@ void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ )
svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat); svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
sv = svm->getSupportVectors(); sv = svm->getSupportVectors();
CV_Assert(sv.rows == 3); EXPECT_EQ(3, sv.rows);
sv = svm->getUncompressedSupportVectors(); sv = svm->getUncompressedSupportVectors();
CV_Assert(sv.rows == 0); // inapplicable for non-linear SVMs EXPECT_EQ(0, sv.rows); // inapplicable for non-linear SVMs
ts->set_failed_test_info(code);
} }
TEST(ML_SVM, getSupportVectors) { CV_SVMGetSupportVectorsTest test; test.safe_run(); }
}} // namespace }} // namespace
modules/ml/test/test_utils.cpp 0 → 100644
View file @ 5ff1faba
// 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 {
void defaultDistribs( Mat& means, vector<Mat>& covs, int type)
{
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 )
{
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;
CV_Assert( mit->rows == 1 && mit->cols == data.cols );
CV_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;
}
}
CV_Assert( idx >= 0);
return idx;
}
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq)
{
size_t total = 0, nclusters = sizes.size();
for(size_t i = 0; i < sizes.size(); i++)
total += sizes[i];
CV_Assert( !labels.empty() );
CV_Assert( labels.total() == total && (labels.cols == 1 || labels.rows == 1));
CV_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, bool checkClusterUniq)
{
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;
}
bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error)
{
error = 0.0f;
float accuracy = 0.0f;
Mat _p_labels_temp;
Mat _o_labels_temp;
_p_labels.convertTo(_p_labels_temp, CV_32S);
_o_labels.convertTo(_o_labels_temp, CV_32S);
CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
accuracy = (float)countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
error = 1 - accuracy;
return true;
}
} // namespace
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