mirror of
https://github.com/opencv/opencv.git
synced 2024-12-15 09:49:13 +08:00
4a297a2443
- removed tr1 usage (dropped in C++17) - moved includes of vector/map/iostream/limits into ts.hpp - require opencv_test + anonymous namespace (added compile check) - fixed norm() usage (must be from cvtest::norm for checks) and other conflict functions - added missing license headers
225 lines
7.0 KiB
C++
225 lines
7.0 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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namespace opencv_test {
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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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CV_TRACE_FUNCTION();
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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->shuffleTrainTest();
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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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CV_TRACE_FUNCTION();
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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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model->save(format("/Users/vp/tmp/dtree/testcase_%02d.cur.yml", testCaseIdx));
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float curErr = get_test_error( testCaseIdx );
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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\n",
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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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namespace {
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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(DISABLED_ML_ERTrees, regression) { CV_AMLTest test( CV_ERTREES ); test.safe_run(); }
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TEST(ML_NBAYES, regression_5911)
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{
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int N=12;
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Ptr<ml::NormalBayesClassifier> nb = cv::ml::NormalBayesClassifier::create();
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// data:
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Mat_<float> X(N,4);
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X << 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4,
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5,5,5,5, 5,5,5,5, 5,5,5,5, 5,5,5,5,
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4,3,2,1, 4,3,2,1, 4,3,2,1, 4,3,2,1;
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// labels:
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Mat_<int> Y(N,1);
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Y << 0,0,0,0, 1,1,1,1, 2,2,2,2;
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nb->train(X, ml::ROW_SAMPLE, Y);
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// single prediction:
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Mat R1,P1;
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for (int i=0; i<N; i++)
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{
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Mat r,p;
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nb->predictProb(X.row(i), r, p);
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R1.push_back(r);
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P1.push_back(p);
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}
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// bulk prediction (continuous memory):
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Mat R2,P2;
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nb->predictProb(X, R2, P2);
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EXPECT_EQ(sum(R1 == R2)[0], 255 * R2.total());
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EXPECT_EQ(sum(P1 == P2)[0], 255 * P2.total());
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// bulk prediction, with non-continuous memory storage
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Mat R3_(N, 1+1, CV_32S),
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P3_(N, 3+1, CV_32F);
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nb->predictProb(X, R3_.col(0), P3_.colRange(0,3));
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Mat R3 = R3_.col(0).clone(),
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P3 = P3_.colRange(0,3).clone();
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EXPECT_EQ(sum(R1 == R3)[0], 255 * R3.total());
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EXPECT_EQ(sum(P1 == P3)[0], 255 * P3.total());
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}
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TEST(ML_RTrees, getVotes)
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{
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int n = 12;
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int count, i;
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int label_size = 3;
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int predicted_class = 0;
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int max_votes = -1;
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int val;
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// RTrees for classification
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Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
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//data
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Mat data(n, 4, CV_32F);
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randu(data, 0, 10);
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//labels
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Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
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rt->train(data, ml::ROW_SAMPLE, labels);
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//run function
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Mat test(1, 4, CV_32F);
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Mat result;
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randu(test, 0, 10);
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rt->getVotes(test, result, 0);
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//count vote amount and find highest vote
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count = 0;
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const int* result_row = result.ptr<int>(1);
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for( i = 0; i < label_size; i++ )
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{
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val = result_row[i];
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//predicted_class = max_votes < val? i;
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if( max_votes < val )
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{
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max_votes = val;
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predicted_class = i;
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}
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count += val;
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}
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EXPECT_EQ(count, (int)rt->getRoots().size());
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EXPECT_EQ(result.at<float>(0, predicted_class), rt->predict(test));
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}
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}} // namespace
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/* End of file. */
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