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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// 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 "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 );
if ( abs( curErr - mean) > 6*sigma )
{
ts->printf( CvTS::LOG, "in test case %d test error is out of range:\n"
"abs(%f/*curEr*/ - %f/*mean*/ > %f/*6*sigma*/", testCaseIdx, curErr, mean, 6*sigma );
return CvTS::FAIL_BAD_ACCURACY;
}
}
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. */