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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
165 lines
5.6 KiB
C++
165 lines
5.6 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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using cv::ml::SVM;
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using cv::ml::TrainData;
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static Ptr<TrainData> makeRandomData(int datasize)
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{
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cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
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cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
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RNG &rng = cv::theRNG();
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for (int i = 0; i < datasize; ++i)
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{
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int response = rng.uniform(0, 2); // Random from {0, 1}.
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samples.at<float>( i, 0 ) = rng.uniform(0.f, 0.5f) + response * 0.5f;
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samples.at<float>( i, 1 ) = rng.uniform(0.f, 0.5f) + response * 0.5f;
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responses.at<int>( i, 0 ) = response;
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}
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return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
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}
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static Ptr<TrainData> makeCircleData(int datasize, float scale_factor, float radius)
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{
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// Populate samples with data that can be split into two concentric circles
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cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
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cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
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for (int i = 0; i < datasize; i+=2)
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{
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const float pi = 3.14159f;
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const float angle_rads = (i/datasize) * pi;
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const float x = radius * cos(angle_rads);
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const float y = radius * cos(angle_rads);
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// Larger circle
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samples.at<float>( i, 0 ) = x;
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samples.at<float>( i, 1 ) = y;
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responses.at<int>( i, 0 ) = 0;
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// Smaller circle
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samples.at<float>( i + 1, 0 ) = x * scale_factor;
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samples.at<float>( i + 1, 1 ) = y * scale_factor;
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responses.at<int>( i + 1, 0 ) = 1;
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}
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return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
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}
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static Ptr<TrainData> makeRandomData2(int datasize)
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{
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cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
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cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
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RNG &rng = cv::theRNG();
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for (int i = 0; i < datasize; ++i)
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{
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int response = rng.uniform(0, 2); // Random from {0, 1}.
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samples.at<float>( i, 0 ) = 0;
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samples.at<float>( i, 1 ) = (0.5f - response) * rng.uniform(0.f, 1.2f) + response;
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responses.at<int>( i, 0 ) = response;
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}
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return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
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}
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//==================================================================================================
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TEST(ML_SVM, trainauto)
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{
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const int datasize = 100;
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cv::Ptr<TrainData> data = makeRandomData(datasize);
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ASSERT_TRUE(data);
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cv::Ptr<SVM> svm = SVM::create();
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ASSERT_TRUE(svm);
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svm->trainAuto( data, 10 ); // 2-fold cross validation.
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float test_data0[2] = {0.25f, 0.25f};
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cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
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float result0 = svm->predict( test_point0 );
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float test_data1[2] = {0.75f, 0.75f};
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cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
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float result1 = svm->predict( test_point1 );
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EXPECT_NEAR(result0, 0, 0.001);
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EXPECT_NEAR(result1, 1, 0.001);
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}
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TEST(ML_SVM, trainauto_sigmoid)
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{
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const int datasize = 100;
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const float scale_factor = 0.5;
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const float radius = 2.0;
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cv::Ptr<TrainData> data = makeCircleData(datasize, scale_factor, radius);
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ASSERT_TRUE(data);
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cv::Ptr<SVM> svm = SVM::create();
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ASSERT_TRUE(svm);
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svm->setKernel(SVM::SIGMOID);
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svm->setGamma(10.0);
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svm->setCoef0(-10.0);
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svm->trainAuto( data, 10 ); // 2-fold cross validation.
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float test_data0[2] = {radius, radius};
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cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
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EXPECT_FLOAT_EQ(svm->predict( test_point0 ), 0);
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float test_data1[2] = {scale_factor * radius, scale_factor * radius};
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cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
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EXPECT_FLOAT_EQ(svm->predict( test_point1 ), 1);
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}
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TEST(ML_SVM, trainAuto_regression_5369)
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{
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const int datasize = 100;
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Ptr<TrainData> data = makeRandomData2(datasize);
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cv::Ptr<SVM> svm = SVM::create();
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svm->trainAuto( data, 10 ); // 2-fold cross validation.
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float test_data0[2] = {0.25f, 0.25f};
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cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
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float result0 = svm->predict( test_point0 );
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float test_data1[2] = {0.75f, 0.75f};
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cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
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float result1 = svm->predict( test_point1 );
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EXPECT_EQ(0., result0);
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EXPECT_EQ(1., result1);
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}
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TEST(ML_SVM, getSupportVectors)
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{
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// Set up training data
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int labels[4] = {1, -1, -1, -1};
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float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
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Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
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Mat labelsMat(4, 1, CV_32SC1, labels);
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Ptr<SVM> svm = SVM::create();
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ASSERT_TRUE(svm);
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svm->setType(SVM::C_SVC);
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svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
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// Test retrieval of SVs and compressed SVs on linear SVM
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svm->setKernel(SVM::LINEAR);
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svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
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Mat sv = svm->getSupportVectors();
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EXPECT_EQ(1, sv.rows); // by default compressed SV returned
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sv = svm->getUncompressedSupportVectors();
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EXPECT_EQ(3, sv.rows);
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// Test retrieval of SVs and compressed SVs on non-linear SVM
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svm->setKernel(SVM::POLY);
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svm->setDegree(2);
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svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
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sv = svm->getSupportVectors();
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EXPECT_EQ(3, sv.rows);
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sv = svm->getUncompressedSupportVectors();
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EXPECT_EQ(0, sv.rows); // inapplicable for non-linear SVMs
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}
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}} // namespace
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