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Open Source Computer Vision Library
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78 lines
2.5 KiB
78 lines
2.5 KiB
5 years ago
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// 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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using cv::ml::TrainData;
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using cv::ml::EM;
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using cv::ml::KNearest;
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TEST(ML_KNearest, accuracy)
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{
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int sizesArr[] = { 500, 700, 800 };
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int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
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Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
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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( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
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Mat testData( pointsCount, 2, CV_32FC1 );
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Mat testLabels;
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generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
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{
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SCOPED_TRACE("Default");
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Mat bestLabels;
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float err = 1000;
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Ptr<KNearest> knn = KNearest::create();
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knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
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knn->findNearest(testData, 4, bestLabels);
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EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
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EXPECT_LE(err, 0.01f);
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}
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{
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// TODO: broken
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#if 0
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SCOPED_TRACE("KDTree");
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Mat bestLabels;
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float err = 1000;
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Ptr<KNearest> knn = KNearest::create();
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knn->setAlgorithmType(KNearest::KDTREE);
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knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
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knn->findNearest(testData, 4, bestLabels);
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EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
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EXPECT_LE(err, 0.01f);
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#endif
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}
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}
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TEST(ML_KNearest, regression_12347)
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{
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Mat xTrainData = (Mat_<float>(5,2) << 1, 1.1, 1.1, 1, 2, 2, 2.1, 2, 2.1, 2.1);
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Mat yTrainLabels = (Mat_<float>(5,1) << 1, 1, 2, 2, 2);
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Ptr<KNearest> knn = KNearest::create();
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knn->train(xTrainData, ml::ROW_SAMPLE, yTrainLabels);
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Mat xTestData = (Mat_<float>(2,2) << 1.1, 1.1, 2, 2.2);
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Mat zBestLabels, neighbours, dist;
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// check output shapes:
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int K = 16, Kexp = std::min(K, xTrainData.rows);
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knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
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EXPECT_EQ(xTestData.rows, zBestLabels.rows);
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EXPECT_EQ(neighbours.cols, Kexp);
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EXPECT_EQ(dist.cols, Kexp);
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// see if the result is still correct:
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K = 2;
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knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
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EXPECT_EQ(1, zBestLabels.at<float>(0,0));
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EXPECT_EQ(2, zBestLabels.at<float>(1,0));
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}
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}} // namespace
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