opencv/modules/ml/test/test_mltests.cpp

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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(); }

/* End of file. */