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#include "mltest.h"

CV_AMLTest::CV_AMLTest( const char* _modelName, const char* _testName ) :
    CV_MLBaseTest( _modelName, _testName, "train-predict" )
{
    validationFN = "avalidation.xml";
}

int CV_AMLTest::run_test_case( int testCaseIdx )
{
    int code = CvTS::OK;
    code = prepare_test_case( testCaseIdx );

    if (code == CvTS::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.mix_train_and_test_idx();
            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;
        float curErr = get_error( testCaseIdx, CV_TEST_ERROR );
        const int coeff = 4;
        ts->printf( CvTS::LOG, "Test case = %d; test error = %f; mean error = %f (diff=%f), %d*sigma = %f",
                                testCaseIdx, curErr, mean, abs( curErr - mean), coeff, coeff*sigma );
        if ( abs( curErr - mean) > coeff*sigma )
        {
            ts->printf( CvTS::LOG, "abs(%f - %f) > %f - OUT OF RANGE!\n", curErr, mean, coeff*sigma, coeff );
            return CvTS::FAIL_BAD_ACCURACY;
        }
        else
            ts->printf( CvTS::LOG, ".\n" );

    }
    else
    {
        ts->printf( CvTS::LOG, "validation info is not suitable" );
        return CvTS::FAIL_INVALID_TEST_DATA;
    }
    return CvTS::OK;
}

CV_AMLTest amldtree( CV_DTREE, "adtree" );
CV_AMLTest amlboost( CV_BOOST, "aboost" );
CV_AMLTest amlrtrees( CV_RTREES, "artrees" );
CV_AMLTest amlertrees( CV_ERTREES, "aertrees" );

/* End of file. */
