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//
//#include "test_precomp.hpp"
//
//using namespace cv;
//using namespace std;
//
//CV_AMLTest::CV_AMLTest( const char* _modelName ) : CV_MLBaseTest( _modelName )
//{
//    validationFN = "avalidation.xml";
//}
//
//int CV_AMLTest::run_test_case( int testCaseIdx )
//{
//    int code = cvtest::TS::OK;
//    code = prepare_test_case( testCaseIdx );
//
//    if (code == cvtest::TS::OK)
//    {
//        //#define GET_STAT
//#ifdef GET_STAT
//        const char* data_name = ((CvFileNode*)cvGetSeqElem( dataSetNames, testCaseIdx ))->data.str.ptr;
//        printf("%s, %s      ", name, data_name);
//        const int icount = 100;
//        float res[icount];
//        for (int k = 0; k < icount; k++)
//        {
//#endif
//            data->shuffleTrainTest();
//            code = train( testCaseIdx );
//#ifdef GET_STAT
//            float case_result = get_error();
//
//            res[k] = case_result;
//        }
//        float mean = 0, sigma = 0;
//        for (int k = 0; k < icount; k++)
//        {
//            mean += res[k];
//        }
//        mean = mean /icount;
//        for (int k = 0; k < icount; k++)
//        {
//            sigma += (res[k] - mean)*(res[k] - mean);
//        }
//        sigma = sqrt(sigma/icount);
//        printf("%f, %f\n", mean, sigma);
//#endif
//    }
//    return code;
//}
//
//int CV_AMLTest::validate_test_results( int testCaseIdx )
//{
//    int iters;
//    float mean, sigma;
//    // read validation params
//    FileNode resultNode =
//        validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["result"];
//    resultNode["iter_count"] >> iters;
//    if ( iters > 0)
//    {
//        resultNode["mean"] >> mean;
//        resultNode["sigma"] >> sigma;
//        model->save(format("/Users/vp/tmp/dtree/testcase_%02d.cur.yml", testCaseIdx));
//        float curErr = get_test_error( testCaseIdx );
//        const int coeff = 4;
//        ts->printf( cvtest::TS::LOG, "Test case = %d; test error = %f; mean error = %f (diff=%f), %d*sigma = %f\n",
//                                testCaseIdx, curErr, mean, abs( curErr - mean), coeff, coeff*sigma );
//        if ( abs( curErr - mean) > coeff*sigma )
//        {
//            ts->printf( cvtest::TS::LOG, "abs(%f - %f) > %f - OUT OF RANGE!\n", curErr, mean, coeff*sigma, coeff );
//            return cvtest::TS::FAIL_BAD_ACCURACY;
//        }
//        else
//            ts->printf( cvtest::TS::LOG, ".\n" );
//
//    }
//    else
//    {
//        ts->printf( cvtest::TS::LOG, "validation info is not suitable" );
//        return cvtest::TS::FAIL_INVALID_TEST_DATA;
//    }
//    return cvtest::TS::OK;
//}
//
//TEST(ML_DTree, regression) { CV_AMLTest test( CV_DTREE ); test.safe_run(); }
//TEST(ML_Boost, regression) { CV_AMLTest test( CV_BOOST ); test.safe_run(); }
//TEST(ML_RTrees, regression) { CV_AMLTest test( CV_RTREES ); test.safe_run(); }
//TEST(DISABLED_ML_ERTrees, regression) { CV_AMLTest test( CV_ERTREES ); test.safe_run(); }
//
//TEST(ML_NBAYES, regression_5911)
//{
//    int N=12;
//    Ptr<ml::NormalBayesClassifier> nb = cv::ml::NormalBayesClassifier::create();
//
//    // data:
//    Mat_<float> X(N,4);
//    X << 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;
//
//    // labels:
//    Mat_<int> Y(N,1);
//    Y << 0,0,0,0, 1,1,1,1, 2,2,2,2;
//    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(sum(R1 == R2)[0], 255 * R2.total());
//    EXPECT_EQ(sum(P1 == P2)[0], 255 * P2.total());
//
//    // 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(sum(R1 == R3)[0], 255 * R3.total());
//    EXPECT_EQ(sum(P1 == P3)[0], 255 * P3.total());
//}
//
///* End of file. */
