131 lines
4.7 KiB
C++
131 lines
4.7 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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using namespace cv;
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using namespace std;
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CV_AMLTest::CV_AMLTest( const char* _modelName ) : CV_MLBaseTest( _modelName )
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{
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validationFN = "avalidation.xml";
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}
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int CV_AMLTest::run_test_case( int testCaseIdx )
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{
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int code = cvtest::TS::OK;
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code = prepare_test_case( testCaseIdx );
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if (code == cvtest::TS::OK)
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{
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//#define GET_STAT
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#ifdef GET_STAT
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const char* data_name = ((CvFileNode*)cvGetSeqElem( dataSetNames, testCaseIdx ))->data.str.ptr;
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printf("%s, %s ", name, data_name);
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const int icount = 100;
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float res[icount];
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for (int k = 0; k < icount; k++)
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{
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#endif
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data.mix_train_and_test_idx();
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code = train( testCaseIdx );
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#ifdef GET_STAT
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float case_result = get_error();
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res[k] = case_result;
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}
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float mean = 0, sigma = 0;
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for (int k = 0; k < icount; k++)
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{
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mean += res[k];
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}
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mean = mean /icount;
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for (int k = 0; k < icount; k++)
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{
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sigma += (res[k] - mean)*(res[k] - mean);
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}
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sigma = sqrt(sigma/icount);
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printf("%f, %f\n", mean, sigma);
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#endif
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}
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return code;
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}
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int CV_AMLTest::validate_test_results( int testCaseIdx )
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{
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int iters;
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float mean, sigma;
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// read validation params
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FileNode resultNode =
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validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["result"];
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resultNode["iter_count"] >> iters;
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if ( iters > 0)
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{
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resultNode["mean"] >> mean;
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resultNode["sigma"] >> sigma;
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float curErr = get_error( testCaseIdx, CV_TEST_ERROR );
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const int coeff = 4;
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ts->printf( cvtest::TS::LOG, "Test case = %d; test error = %f; mean error = %f (diff=%f), %d*sigma = %f",
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testCaseIdx, curErr, mean, abs( curErr - mean), coeff, coeff*sigma );
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if ( abs( curErr - mean) > coeff*sigma )
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{
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ts->printf( cvtest::TS::LOG, "abs(%f - %f) > %f - OUT OF RANGE!\n", curErr, mean, coeff*sigma, coeff );
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return cvtest::TS::FAIL_BAD_ACCURACY;
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}
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else
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ts->printf( cvtest::TS::LOG, ".\n" );
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}
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else
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{
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ts->printf( cvtest::TS::LOG, "validation info is not suitable" );
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return cvtest::TS::FAIL_INVALID_TEST_DATA;
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}
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return cvtest::TS::OK;
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}
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TEST(ML_DTree, regression) { CV_AMLTest test( CV_DTREE ); test.safe_run(); }
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TEST(ML_Boost, regression) { CV_AMLTest test( CV_BOOST ); test.safe_run(); }
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TEST(ML_RTrees, regression) { CV_AMLTest test( CV_RTREES ); test.safe_run(); }
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TEST(ML_ERTrees, regression) { CV_AMLTest test( CV_ERTREES ); test.safe_run(); }
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/* End of file. */
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