157 lines
5.2 KiB
C++
157 lines
5.2 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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static const int TEST_VALUE_LIMIT = 500;
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enum
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{
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UNIFORM_SAME_SCALE,
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UNIFORM_DIFFERENT_SCALES
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};
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CV_ENUM(SVMSGD_TYPE, UNIFORM_SAME_SCALE, UNIFORM_DIFFERENT_SCALES)
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typedef std::vector< std::pair<float,float> > BorderList;
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static void makeData(RNG &rng, int samplesCount, const Mat &weights, float shift, const BorderList & borders, Mat &samples, Mat & responses)
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{
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int featureCount = weights.cols;
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samples.create(samplesCount, featureCount, CV_32FC1);
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
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responses.create(samplesCount, 1, CV_32FC1);
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for (int i = 0 ; i < samplesCount; i++)
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{
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double res = samples.row(i).dot(weights) + shift;
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responses.at<float>(i) = res > 0 ? 1.f : -1.f;
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}
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}
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//==================================================================================================
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typedef tuple<SVMSGD_TYPE, int, double> ML_SVMSGD_Param;
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typedef testing::TestWithParam<ML_SVMSGD_Param> ML_SVMSGD_Params;
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TEST_P(ML_SVMSGD_Params, scale_and_features)
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{
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const int type = get<0>(GetParam());
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const int featureCount = get<1>(GetParam());
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const double precision = get<2>(GetParam());
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RNG &rng = cv::theRNG();
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Mat_<float> weights(1, featureCount);
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rng.fill(weights, RNG::UNIFORM, -1, 1);
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const float shift = static_cast<float>(rng.uniform(-featureCount, featureCount));
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BorderList borders;
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float lowerLimit = -TEST_VALUE_LIMIT;
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float upperLimit = TEST_VALUE_LIMIT;
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if (type == UNIFORM_SAME_SCALE)
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{
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
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}
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else if (type == UNIFORM_DIFFERENT_SCALES)
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{
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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{
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int crit = rng.uniform(0, 2);
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if (crit > 0)
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borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
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else
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borders.push_back(std::pair<float,float>(lowerLimit/1000, upperLimit/1000));
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}
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}
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ASSERT_FALSE(borders.empty());
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Mat trainSamples;
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Mat trainResponses;
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int trainSamplesCount = 10000;
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makeData(rng, trainSamplesCount, weights, shift, borders, trainSamples, trainResponses);
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ASSERT_EQ(trainResponses.type(), CV_32FC1);
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Mat testSamples;
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Mat testResponses;
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int testSamplesCount = 100000;
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makeData(rng, testSamplesCount, weights, shift, borders, testSamples, testResponses);
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ASSERT_EQ(testResponses.type(), CV_32FC1);
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Ptr<TrainData> data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses);
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ASSERT_TRUE(data);
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cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
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ASSERT_TRUE(svmsgd);
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svmsgd->train(data);
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Mat responses;
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svmsgd->predict(testSamples, responses);
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ASSERT_EQ(responses.type(), CV_32FC1);
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ASSERT_EQ(responses.rows, testSamplesCount);
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int errCount = 0;
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for (int i = 0; i < testSamplesCount; i++)
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if (responses.at<float>(i) * testResponses.at<float>(i) < 0)
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errCount++;
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float err = (float)errCount / testSamplesCount;
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EXPECT_LE(err, precision);
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}
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ML_SVMSGD_Param params_list[] = {
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ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 2, 0.01),
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ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 5, 0.01),
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ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 100, 0.02),
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ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 2, 0.01),
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ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 5, 0.01),
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ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 100, 0.01),
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};
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INSTANTIATE_TEST_CASE_P(/**/, ML_SVMSGD_Params, testing::ValuesIn(params_list));
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//==================================================================================================
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TEST(ML_SVMSGD, twoPoints)
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{
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Mat samples(2, 2, CV_32FC1);
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samples.at<float>(0,0) = 0;
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samples.at<float>(0,1) = 0;
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samples.at<float>(1,0) = 1000;
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samples.at<float>(1,1) = 1;
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Mat responses(2, 1, CV_32FC1);
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responses.at<float>(0) = -1;
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responses.at<float>(1) = 1;
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cv::Ptr<TrainData> trainData = TrainData::create(samples, cv::ml::ROW_SAMPLE, responses);
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Mat realWeights(1, 2, CV_32FC1);
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realWeights.at<float>(0) = 1000;
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realWeights.at<float>(1) = 1;
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float realShift = -500000.5;
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float normRealWeights = static_cast<float>(cv::norm(realWeights)); // TODO cvtest
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realWeights /= normRealWeights;
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realShift /= normRealWeights;
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cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
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svmsgd->setOptimalParameters();
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svmsgd->train( trainData );
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Mat foundWeights = svmsgd->getWeights();
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float foundShift = svmsgd->getShift();
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float normFoundWeights = static_cast<float>(cv::norm(foundWeights)); // TODO cvtest
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foundWeights /= normFoundWeights;
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foundShift /= normFoundWeights;
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EXPECT_LE(cv::norm(Mat(foundWeights - realWeights)), 0.001); // TODO cvtest
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EXPECT_LE(std::abs((foundShift - realShift) / realShift), 0.05);
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}
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}} // namespace
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