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#include "test_precomp.hpp"

using namespace cv;
using namespace std;
using cv::ml::SVM;
using cv::ml::TrainData;

//--------------------------------------------------------------------------------------------
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 responses = cv::Mat::zeros( datasize, 1, CV_32S );

    RNG rng(0);
    for (int i = 0; i < datasize; ++i)
    {
        int response = rng.uniform(0, 2);  // Random from {0, 1}.
        samples.at<float>( i, 0 ) = 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;
    }

    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(); }


TEST(ML_SVM, trainAuto_regression_5369)
{
    int datasize = 100;
    cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
    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();
    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_EQ(0., result0);
    EXPECT_EQ(1., result1);
}