Refactored SVMSGD class

modules/ml/test/test_svmsgd.cpp

@@ -0,0 +1,182 @@
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+
+#include "test_precomp.hpp"
+#include "opencv2/highgui.hpp"
+
+using namespace cv;
+using namespace cv::ml;
+using cv::ml::SVMSGD;
+using cv::ml::TrainData;
+
+
+
+class CV_SVMSGDTrainTest : public cvtest::BaseTest
+{
+public:
+    CV_SVMSGDTrainTest(Mat _weights, float _shift);
+private:
+    virtual void run( int start_from );
+    float decisionFunction(Mat sample, Mat weights, float shift);
+
+    cv::Ptr<TrainData> data;
+    cv::Mat testSamples;
+    cv::Mat testResponses;
+    static const int TEST_VALUE_LIMIT = 50;
+};
+
+CV_SVMSGDTrainTest::CV_SVMSGDTrainTest(Mat weights, float shift)
+{
+    int datasize = 100000;
+    int varCount = weights.cols;
+    cv::Mat samples = cv::Mat::zeros( datasize, varCount, CV_32FC1 );
+    cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32FC1 );
+    cv::RNG rng(0);
+
+    float lowerLimit = -TEST_VALUE_LIMIT;
+    float upperLimit = TEST_VALUE_LIMIT;
+
+
+    rng.fill(samples, RNG::UNIFORM, lowerLimit, upperLimit);
+    for (int sampleIndex = 0; sampleIndex < datasize; sampleIndex++)
+    {
+        responses.at<float>( sampleIndex ) = decisionFunction(samples.row(sampleIndex), weights, shift) > 0 ? 1 : -1;
+    }
+
+    data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
+
+    int testSamplesCount = 100000;
+
+    testSamples.create(testSamplesCount, varCount, CV_32FC1);
+    rng.fill(testSamples, RNG::UNIFORM, lowerLimit, upperLimit);
+    testResponses.create(testSamplesCount, 1, CV_32FC1);
+
+    for (int i = 0 ; i < testSamplesCount; i++)
+    {
+        testResponses.at<float>(i) = decisionFunction(testSamples.row(i), weights, shift) > 0 ? 1 : -1;
+    }
+}
+
+void CV_SVMSGDTrainTest::run( int /*start_from*/ )
+{
+    cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
+
+    svmsgd->setOptimalParameters(SVMSGD::ASGD);
+
+    svmsgd->train( data );
+
+    Mat responses;
+
+    svmsgd->predict(testSamples, responses);
+
+    int errCount = 0;
+    int testSamplesCount = testSamples.rows;
+
+    for (int i = 0; i < testSamplesCount; i++)
+    {
+        if (responses.at<float>(i) * testResponses.at<float>(i) < 0 )
+            errCount++;
+    }
+
+    float err = (float)errCount / testSamplesCount;
+    std::cout << "err " << err << std::endl;
+
+    if ( err > 0.01 )
+    {
+        ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
+    }
+}
+
+float CV_SVMSGDTrainTest::decisionFunction(Mat sample, Mat weights, float shift)
+{
+    return sample.dot(weights) + shift;
+}
+
+TEST(ML_SVMSGD, train0)
+{
+    int varCount = 2;
+
+    Mat weights;
+    weights.create(1, varCount, CV_32FC1);
+    weights.at<float>(0) = 1;
+    weights.at<float>(1) = 0;
+
+    float shift = 5;
+
+    CV_SVMSGDTrainTest test(weights, shift);
+    test.safe_run();
+}
+
+TEST(ML_SVMSGD, train1)
+{
+    int varCount = 5;
+
+    Mat weights;
+    weights.create(1, varCount, CV_32FC1);
+
+    float lowerLimit = -1;
+    float upperLimit = 1;
+    cv::RNG rng(0);
+    rng.fill(weights, RNG::UNIFORM, lowerLimit, upperLimit);
+
+    float shift = rng.uniform(-5.f, 5.f);
+
+    CV_SVMSGDTrainTest test(weights, shift);
+    test.safe_run();
+}
+
+TEST(ML_SVMSGD, train2)
+{
+    int varCount = 100;
+
+    Mat weights;
+    weights.create(1, varCount, CV_32FC1);
+
+    float lowerLimit = -1;
+    float upperLimit = 1;
+    cv::RNG rng(0);
+    rng.fill(weights, RNG::UNIFORM, lowerLimit, upperLimit);
+
+    float shift = rng.uniform(-1000.f, 1000.f);
+
+    CV_SVMSGDTrainTest test(weights, shift);
+    test.safe_run();
+}