Deleted functions makeTrainData() and makeTestData() in test_svmsgd.cpp.

Added function makeData() in test_svmsgd.cpp.

modules/ml/include/opencv2/ml.hpp

@@ -1626,10 +1626,10 @@ public:
      * stepDecreasingPower = 1;
      * termCrit.maxCount = 100000;
      * termCrit.epsilon = 0.00001;
-     * @param svmsgdType is the type of SVMSGD classifier. Legal values are SvmsgdType::SGD and SvmsgdType::ASGD.
-     * Recommended value is SvmsgdType::ASGD (by default).
-     * @param marginType is the type of margin constraint. Legal values are MarginType::SOFT_MARGIN and MarginType::HARD_MARGIN.
-     * Default value is MarginType::SOFT_MARGIN.
+     * @param svmsgdType is the type of SVMSGD classifier. Legal values are SVMSGD::SvmsgdType::SGD and SVMSGD::SvmsgdType::ASGD.
+     * Recommended value is SVMSGD::SvmsgdType::ASGD (by default).
+     * @param marginType is the type of margin constraint. Legal values are SVMSGD::MarginType::SOFT_MARGIN and SVMSGD::MarginType::HARD_MARGIN.
+     * Default value is SVMSGD::MarginType::SOFT_MARGIN.
     */
     CV_WRAP virtual void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN) = 0;
 

modules/ml/src/svmsgd.cpp

@@ -142,6 +142,7 @@ void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
     int samplesCount = samples.rows;
 
     average = Mat(1, featuresCount, samples.type());
+    CV_Assert(average.type() ==  CV_32FC1);
     for (int featureIndex = 0; featureIndex < featuresCount; featureIndex++)
     {
         average.at<float>(featureIndex) = static_cast<float>(mean(samples.col(featureIndex))[0]);
@@ -170,11 +171,11 @@ void SVMSGDImpl::makeExtendedTrainSamples(const Mat &trainSamples, Mat &extended
     cv::hconcat(normalizedTrainSamples, onesCol, extendedTrainSamples);
 }
 
-void SVMSGDImpl::updateWeights(InputArray _sample, bool firstClass, float stepSize, Mat& weights)
+void SVMSGDImpl::updateWeights(InputArray _sample, bool positive, float stepSize, Mat& weights)
 {
     Mat sample = _sample.getMat();
 
-    int response = firstClass ? 1 : -1; // ensure that trainResponses are -1 or 1
+    int response = positive ? 1 : -1; // ensure that trainResponses are -1 or 1
 
     if ( sample.dot(weights) * response > 1)
     {
@@ -197,6 +198,7 @@ float SVMSGDImpl::calcShift(InputArray _samples, InputArray _responses) const
 
     Mat trainResponses = _responses.getMat();
 
+    CV_Assert(trainResponses.type() ==  CV_32FC1);
     for (int samplesIndex = 0; samplesIndex < trainSamplesCount; samplesIndex++)
     {
         Mat currentSample = trainSamples.row(samplesIndex);
@@ -261,7 +263,7 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
 
     RNG rng(0);
 
-    CV_Assert (params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS);
+    CV_Assert ((params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS) && (trainResponses.type() == CV_32FC1));
     int maxCount = (params.termCrit.type & TermCriteria::COUNT) ? params.termCrit.maxCount : INT_MAX;
     double epsilon = (params.termCrit.type & TermCriteria::EPS) ? params.termCrit.epsilon : 0;
 
@@ -300,7 +302,7 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
     weights_ = extendedWeights(roi);
     weights_ *= multiplier;
 
-    CV_Assert(params.marginType == SOFT_MARGIN || params.marginType == HARD_MARGIN);
+    CV_Assert((params.marginType == SOFT_MARGIN || params.marginType == HARD_MARGIN) && (extendedWeights.type() ==  CV_32FC1));
 
     if (params.marginType == SOFT_MARGIN)
     {
@@ -332,7 +334,7 @@ float SVMSGDImpl::predict( InputArray _samples, OutputArray _results, int ) cons
     else
     {
         CV_Assert( nSamples == 1 );
-        results = Mat(1, 1, CV_32F, &result);
+        results = Mat(1, 1, CV_32FC1, &result);
     }
 
     for (int sampleIndex = 0; sampleIndex < nSamples; sampleIndex++)

modules/ml/test/test_svmsgd.cpp

@@ -62,8 +62,7 @@ public:
 private:
     virtual void run( int start_from );
     static float decisionFunction(const Mat &sample, const Mat &weights, float shift);
-    void makeTrainData(Mat weights, float shift);
-    void makeTestData(Mat weights, float shift);
+    void makeData(int samplesCount, Mat weights, float shift, RNG rng, Mat &samples, Mat & responses);
     void generateSameBorders(int featureCount);
     void generateDifferentBorders(int featureCount);
 
@@ -108,46 +107,28 @@ void CV_SVMSGDTrainTest::generateDifferentBorders(int featureCount)
     }
 }
 
-void CV_SVMSGDTrainTest::makeTrainData(Mat weights, float shift)
+float CV_SVMSGDTrainTest::decisionFunction(const Mat &sample, const Mat &weights, float shift)
 {
-    int datasize = 100000;
-    int featureCount = weights.cols;
-    RNG rng(0);
-
-    cv::Mat samples = cv::Mat::zeros(datasize, featureCount, CV_32FC1);
-
-    for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
-    {
-        rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
-    }
-
-    cv::Mat responses = cv::Mat::zeros(datasize, 1, CV_32FC1);
-    for (int sampleIndex = 0; sampleIndex < datasize; sampleIndex++)
-    {
-        responses.at<float>(sampleIndex) = decisionFunction(samples.row(sampleIndex), weights, shift) > 0 ? 1.f : -1.f;
-    }
-
-    data = TrainData::create(samples, cv::ml::ROW_SAMPLE, responses);
+    return static_cast<float>(sample.dot(weights)) + shift;
 }
 
-void CV_SVMSGDTrainTest::makeTestData(Mat weights, float shift)
+void CV_SVMSGDTrainTest::makeData(int samplesCount, Mat weights, float shift, RNG rng, Mat &samples, Mat & responses)
 {
-    int testSamplesCount = 100000;
     int featureCount = weights.cols;
-    cv::RNG rng(42);
 
-    testSamples.create(testSamplesCount, featureCount, CV_32FC1);
+    samples.create(samplesCount, featureCount, CV_32FC1);
     for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
     {
-        rng.fill(testSamples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
+        rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
     }
 
-    testResponses.create(testSamplesCount, 1, CV_32FC1);
+    responses.create(samplesCount, 1, CV_32FC1);
 
-    for (int i = 0 ; i < testSamplesCount; i++)
+    for (int i = 0 ; i < samplesCount; i++)
     {
-        testResponses.at<float>(i) = decisionFunction(testSamples.row(i), weights, shift) > 0 ? 1.f : -1.f;
+        responses.at<float>(i) = decisionFunction(samples.row(i), weights, shift) > 0 ? 1.f : -1.f;
     }
+
 }
 
 CV_SVMSGDTrainTest::CV_SVMSGDTrainTest(const Mat &weights, float shift, TrainDataType _type, double _precision)
@@ -169,13 +150,16 @@ CV_SVMSGDTrainTest::CV_SVMSGDTrainTest(const Mat &weights, float shift, TrainDat
         CV_Error(CV_StsBadArg, "Unknown train data type");
     }
 
-    makeTrainData(weights, shift);
-    makeTestData(weights, shift);
-}
+    RNG rng(0);
 
-float CV_SVMSGDTrainTest::decisionFunction(const Mat &sample, const Mat &weights, float shift)
-{
-    return static_cast<float>(sample.dot(weights)) + shift;
+    Mat trainSamples;
+    Mat trainResponses;
+    int trainSamplesCount = 10000;
+    makeData(trainSamplesCount, weights, shift, rng, trainSamples, trainResponses);
+    data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses);
+
+    int testSamplesCount = 100000;
+    makeData(testSamplesCount, weights, shift, rng, testSamples, testResponses);
 }
 
 void CV_SVMSGDTrainTest::run( int /*start_from*/ )
@@ -205,7 +189,6 @@ void CV_SVMSGDTrainTest::run( int /*start_from*/ )
     }
 }
 
-
 void makeWeightsAndShift(int featureCount, Mat &weights, float &shift)
 {
     weights.create(1, featureCount, CV_32FC1);
@@ -253,7 +236,7 @@ TEST(ML_SVMSGD, trainSameScale100)
     float shift = 0;
     makeWeightsAndShift(featureCount, weights, shift);
 
-    CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
+    CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE, 0.02);
     test.safe_run();
 }