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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
static const int TEST_VALUE_LIMIT = 500;
enum
{
UNIFORM_SAME_SCALE,
UNIFORM_DIFFERENT_SCALES
};
CV_ENUM(SVMSGD_TYPE, UNIFORM_SAME_SCALE, UNIFORM_DIFFERENT_SCALES)
typedef std::vector< std::pair<float,float> > BorderList;
static void makeData(RNG &rng, int samplesCount, const Mat &weights, float shift, const BorderList & borders, Mat &samples, Mat & responses)
{
int featureCount = weights.cols;
samples.create(samplesCount, featureCount, CV_32FC1);
for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
responses.create(samplesCount, 1, CV_32FC1);
for (int i = 0 ; i < samplesCount; i++)
{
double res = samples.row(i).dot(weights) + shift;
responses.at<float>(i) = res > 0 ? 1.f : -1.f;
}
}
//==================================================================================================
typedef tuple<SVMSGD_TYPE, int, double> ML_SVMSGD_Param;
typedef testing::TestWithParam<ML_SVMSGD_Param> ML_SVMSGD_Params;
TEST_P(ML_SVMSGD_Params, scale_and_features)
{
const int type = get<0>(GetParam());
const int featureCount = get<1>(GetParam());
const double precision = get<2>(GetParam());
RNG &rng = cv::theRNG();
Mat_<float> weights(1, featureCount);
rng.fill(weights, RNG::UNIFORM, -1, 1);
const float shift = static_cast<float>(rng.uniform(-featureCount, featureCount));
BorderList borders;
float lowerLimit = -TEST_VALUE_LIMIT;
float upperLimit = TEST_VALUE_LIMIT;
if (type == UNIFORM_SAME_SCALE)
{
for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
}
else if (type == UNIFORM_DIFFERENT_SCALES)
{
for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
{
int crit = rng.uniform(0, 2);
if (crit > 0)
borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
else
borders.push_back(std::pair<float,float>(lowerLimit/1000, upperLimit/1000));
}
}
ASSERT_FALSE(borders.empty());
Mat trainSamples;
Mat trainResponses;
int trainSamplesCount = 10000;
makeData(rng, trainSamplesCount, weights, shift, borders, trainSamples, trainResponses);
ASSERT_EQ(trainResponses.type(), CV_32FC1);
Mat testSamples;
Mat testResponses;
int testSamplesCount = 100000;
makeData(rng, testSamplesCount, weights, shift, borders, testSamples, testResponses);
ASSERT_EQ(testResponses.type(), CV_32FC1);
Ptr<TrainData> data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses);
ASSERT_TRUE(data);
cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
ASSERT_TRUE(svmsgd);
svmsgd->train(data);
Mat responses;
svmsgd->predict(testSamples, responses);
ASSERT_EQ(responses.type(), CV_32FC1);
ASSERT_EQ(responses.rows, testSamplesCount);
int errCount = 0;
for (int i = 0; i < testSamplesCount; i++)
if (responses.at<float>(i) * testResponses.at<float>(i) < 0)
errCount++;
float err = (float)errCount / testSamplesCount;
EXPECT_LE(err, precision);
}
ML_SVMSGD_Param params_list[] = {
ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 2, 0.01),
ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 5, 0.01),
ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 100, 0.02),
ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 2, 0.01),
ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 5, 0.01),
ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 100, 0.01),
};
INSTANTIATE_TEST_CASE_P(/**/, ML_SVMSGD_Params, testing::ValuesIn(params_list));
//==================================================================================================
TEST(ML_SVMSGD, twoPoints)
{
Mat samples(2, 2, CV_32FC1);
samples.at<float>(0,0) = 0;
samples.at<float>(0,1) = 0;
samples.at<float>(1,0) = 1000;
samples.at<float>(1,1) = 1;
Mat responses(2, 1, CV_32FC1);
responses.at<float>(0) = -1;
responses.at<float>(1) = 1;
cv::Ptr<TrainData> trainData = TrainData::create(samples, cv::ml::ROW_SAMPLE, responses);
Mat realWeights(1, 2, CV_32FC1);
realWeights.at<float>(0) = 1000;
realWeights.at<float>(1) = 1;
float realShift = -500000.5;
float normRealWeights = static_cast<float>(cv::norm(realWeights)); // TODO cvtest
realWeights /= normRealWeights;
realShift /= normRealWeights;
cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
svmsgd->setOptimalParameters();
svmsgd->train( trainData );
Mat foundWeights = svmsgd->getWeights();
float foundShift = svmsgd->getShift();
float normFoundWeights = static_cast<float>(cv::norm(foundWeights)); // TODO cvtest
foundWeights /= normFoundWeights;
foundShift /= normFoundWeights;
EXPECT_LE(cv::norm(Mat(foundWeights - realWeights)), 0.001); // TODO cvtest
EXPECT_LE(std::abs((foundShift - realShift) / realShift), 0.05);
}
}} // namespace
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