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5ff1fababc
ml: refactored tests * use parametrized tests where appropriate * use stable theRNG in most tests * use modern style with EXPECT_/ASSERT_ checks
54 lines
1.8 KiB
C++
54 lines
1.8 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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TEST(ML_KMeans, accuracy)
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{
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const int iters = 100;
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int sizesArr[] = { 5000, 7000, 8000 };
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int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
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Mat data( pointsCount, 2, CV_32FC1 ), labels;
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vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
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Mat means;
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vector<Mat> covs;
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defaultDistribs( means, covs );
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generateData( data, labels, sizes, means, covs, CV_32FC1, CV_32SC1 );
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TermCriteria termCriteria( TermCriteria::COUNT, iters, 0.0);
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{
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SCOPED_TRACE("KMEANS_PP_CENTERS");
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float err = 1000;
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Mat bestLabels;
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kmeans( data, 3, bestLabels, termCriteria, 0, KMEANS_PP_CENTERS, noArray() );
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EXPECT_TRUE(calcErr( bestLabels, labels, sizes, err , false ));
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EXPECT_LE(err, 0.01f);
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}
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{
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SCOPED_TRACE("KMEANS_RANDOM_CENTERS");
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float err = 1000;
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Mat bestLabels;
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kmeans( data, 3, bestLabels, termCriteria, 0, KMEANS_RANDOM_CENTERS, noArray() );
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EXPECT_TRUE(calcErr( bestLabels, labels, sizes, err, false ));
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EXPECT_LE(err, 0.01f);
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}
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{
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SCOPED_TRACE("KMEANS_USE_INITIAL_LABELS");
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float err = 1000;
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Mat bestLabels;
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labels.copyTo( bestLabels );
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RNG &rng = cv::theRNG();
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for( int i = 0; i < 0.5f * pointsCount; i++ )
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bestLabels.at<int>( rng.next() % pointsCount, 0 ) = rng.next() % 3;
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kmeans( data, 3, bestLabels, termCriteria, 0, KMEANS_USE_INITIAL_LABELS, noArray() );
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EXPECT_TRUE(calcErr( bestLabels, labels, sizes, err, false ));
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EXPECT_LE(err, 0.01f);
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}
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}
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}} // namespace
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