Deleted default value for parameters in docs.

Added some asserts.

modules/ml/include/opencv2/ml.hpp

@@ -1542,7 +1542,7 @@ The other parameters may be described as follows:
   Recommended value for SGD model is 0.0001, for ASGD model is 0.00001.
 
 - Initial step size parameter is the initial value for the step size \f$\gamma(t)\f$.
-  You will have to find the best \f$\gamma_0\f$ for your problem.
+  You will have to find the best initial step for your problem.
 
 - Step decreasing power is the power parameter for \f$\gamma(t)\f$ decreasing by the formula, mentioned above.
   Recommended value for SGD model is 1, for ASGD model is 0.75.
@@ -1605,31 +1605,15 @@ public:
     */
     CV_WRAP virtual float getShift() = 0;
 
-
     /** @brief Creates empty model.
-        Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to
-        find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
+     * Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to
+     * find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
     */
     CV_WRAP static Ptr<SVMSGD> create();
 
     /** @brief Function sets optimal parameters values for chosen SVM SGD model.
-     * If chosen type is ASGD, function sets the following values for parameters of model:
-     * marginRegularization = 0.00001;
-     * initialStepSize = 0.05;
-     * stepDecreasingPower = 0.75;
-     * termCrit.maxCount = 100000;
-     * termCrit.epsilon = 0.00001;
-     *
-     * If SGD:
-     * marginRegularization = 0.0001;
-     * initialStepSize = 0.05;
-     * stepDecreasingPower = 1;
-     * termCrit.maxCount = 100000;
-     * termCrit.epsilon = 0.00001;
-     * @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.
+     * @param svmsgdType is the type of SVMSGD classifier.
+     * @param marginType is the type of margin constraint.
     */
     CV_WRAP virtual void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN) = 0;
 
@@ -1645,20 +1629,19 @@ public:
     /** @copybrief getMarginType @see getMarginType */
     CV_WRAP virtual void setMarginType(int marginType) = 0;
 
-
-    /** @brief Parameter marginRegularization of a %SVMSGD optimization problem. Default value is 0. */
+    /** @brief Parameter marginRegularization of a %SVMSGD optimization problem. */
     /** @see setMarginRegularization */
     CV_WRAP virtual float getMarginRegularization() const = 0;
     /** @copybrief getMarginRegularization @see getMarginRegularization */
     CV_WRAP virtual void setMarginRegularization(float marginRegularization) = 0;
 
-    /** @brief Parameter initialStepSize of a %SVMSGD optimization problem. Default value is 0. */
+    /** @brief Parameter initialStepSize of a %SVMSGD optimization problem. */
     /** @see setInitialStepSize */
     CV_WRAP virtual float getInitialStepSize() const = 0;
     /** @copybrief getInitialStepSize @see getInitialStepSize */
     CV_WRAP virtual void setInitialStepSize(float InitialStepSize) = 0;
 
-    /** @brief Parameter stepDecreasingPower of a %SVMSGD optimization problem. Default value is 0. */
+    /** @brief Parameter stepDecreasingPower of a %SVMSGD optimization problem. */
     /** @see setStepDecreasingPower */
     CV_WRAP virtual float getStepDecreasingPower() const = 0;
     /** @copybrief getStepDecreasingPower @see getStepDecreasingPower */

modules/ml/src/svmsgd.cpp

@@ -97,7 +97,7 @@ public:
     CV_IMPL_PROPERTY_S(cv::TermCriteria, TermCriteria, params.termCrit)
 
 private:
-    void updateWeights(InputArray sample, bool isPositive, float stepSize, Mat &weights);
+    void updateWeights(InputArray sample, bool positive, float stepSize, Mat &weights);
 
     void writeParams( FileStorage &fs ) const;
 
@@ -111,8 +111,6 @@ private:
 
     static void makeExtendedTrainSamples(const Mat &trainSamples, Mat &extendedTrainSamples, Mat &average, float &multiplier);
 
-
-
     // Vector with SVM weights
     Mat weights_;
     float shift_;
@@ -263,11 +261,12 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
 
     RNG rng(0);
 
-    CV_Assert ((params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS) && (trainResponses.type() == CV_32FC1));
+    CV_Assert (params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS);
     int maxCount = (params.termCrit.type & TermCriteria::COUNT) ? params.termCrit.maxCount : INT_MAX;
     double epsilon = (params.termCrit.type & TermCriteria::EPS) ? params.termCrit.epsilon : 0;
 
     double err = DBL_MAX;
+    CV_Assert (trainResponses.type() == CV_32FC1);
     // Stochastic gradient descent SVM
     for (int iter = 0; (iter < maxCount) && (err > epsilon); iter++)
     {
@@ -288,8 +287,8 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
         }
         else
         {
-             err = norm(extendedWeights - previousWeights);
-             extendedWeights.copyTo(previousWeights);
+            err = norm(extendedWeights - previousWeights);
+            extendedWeights.copyTo(previousWeights);
         }
     }
 
@@ -316,7 +315,6 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
     return true;
 }
 
-
 float SVMSGDImpl::predict( InputArray _samples, OutputArray _results, int ) const
 {
     float result = 0;
@@ -417,17 +415,6 @@ void SVMSGDImpl::writeParams( FileStorage& fs ) const
         fs << "iterations" << params.termCrit.maxCount;
     fs << "}";
 }
-
-void SVMSGDImpl::read(const FileNode& fn)
-{
-    clear();
-
-    readParams(fn);
-
-    fn["weights"] >> weights_;
-    fn["shift"] >> shift_;
-}
-
 void SVMSGDImpl::readParams( const FileNode& fn )
 {
     String svmsgdTypeStr = (String)fn["svmsgdType"];
@@ -443,7 +430,7 @@ void SVMSGDImpl::readParams( const FileNode& fn )
     String marginTypeStr = (String)fn["marginType"];
     int marginType =
             marginTypeStr == "SOFT_MARGIN" ? SOFT_MARGIN :
-                                     marginTypeStr == "HARD_MARGIN" ? HARD_MARGIN : -1;
+                                             marginTypeStr == "HARD_MARGIN" ? HARD_MARGIN : -1;
 
     if( marginType < 0 )
         CV_Error( CV_StsParseError, "Missing or invalid margin type" );
@@ -460,16 +447,22 @@ void SVMSGDImpl::readParams( const FileNode& fn )
     params.stepDecreasingPower = (float)fn["stepDecreasingPower"];
 
     FileNode tcnode = fn["term_criteria"];
-    if( !tcnode.empty() )
-    {
-        params.termCrit.epsilon = (double)tcnode["epsilon"];
-        params.termCrit.maxCount = (int)tcnode["iterations"];
-        params.termCrit.type = (params.termCrit.epsilon > 0 ? TermCriteria::EPS : 0) +
-                (params.termCrit.maxCount > 0 ? TermCriteria::COUNT : 0);
-    }
-    else
-        params.termCrit = TermCriteria( TermCriteria::EPS + TermCriteria::COUNT, 100000, FLT_EPSILON );
+    CV_Assert(!tcnode.empty());
+    params.termCrit.epsilon = (double)tcnode["epsilon"];
+    params.termCrit.maxCount = (int)tcnode["iterations"];
+    params.termCrit.type = (params.termCrit.epsilon > 0 ? TermCriteria::EPS : 0) +
+            (params.termCrit.maxCount > 0 ? TermCriteria::COUNT : 0);
+    CV_Assert ((params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS));
+}
 
+void SVMSGDImpl::read(const FileNode& fn)
+{
+    clear();
+
+    readParams(fn);
+
+    fn["weights"] >> weights_;
+    fn["shift"] >> shift_;
 }
 
 void SVMSGDImpl::clear()
@@ -492,7 +485,7 @@ void SVMSGDImpl::setOptimalParameters(int svmsgdType, int marginType)
     case SGD:
         params.svmsgdType = SGD;
         params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
-                            (marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
+                                                          (marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
         params.marginRegularization = 0.0001f;
         params.initialStepSize = 0.05f;
         params.stepDecreasingPower = 1.f;
@@ -502,7 +495,7 @@ void SVMSGDImpl::setOptimalParameters(int svmsgdType, int marginType)
     case ASGD:
         params.svmsgdType = ASGD;
         params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
-                            (marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
+                                                          (marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
         params.marginRegularization = 0.00001f;
         params.initialStepSize = 0.05f;
         params.stepDecreasingPower = 0.75f;

modules/ml/test/test_svmsgd.cpp

@@ -62,7 +62,7 @@ public:
 private:
     virtual void run( int start_from );
     static float decisionFunction(const Mat &sample, const Mat &weights, float shift);
-    void makeData(int samplesCount, Mat weights, float shift, RNG rng, Mat &samples, Mat & responses);
+    void makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses);
     void generateSameBorders(int featureCount);
     void generateDifferentBorders(int featureCount);
 
@@ -112,7 +112,7 @@ float CV_SVMSGDTrainTest::decisionFunction(const Mat &sample, const Mat &weights
     return static_cast<float>(sample.dot(weights)) + shift;
 }
 
-void CV_SVMSGDTrainTest::makeData(int samplesCount, Mat weights, float shift, RNG rng, Mat &samples, Mat & responses)
+void CV_SVMSGDTrainTest::makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses)
 {
     int featureCount = weights.cols;
 
@@ -175,6 +175,7 @@ void CV_SVMSGDTrainTest::run( int /*start_from*/ )
     int errCount = 0;
     int testSamplesCount = testSamples.rows;
 
+    CV_Assert((responses.type() == CV_32FC1) && (testResponses.type() == CV_32FC1));
     for (int i = 0; i < testSamplesCount; i++)
     {
         if (responses.at<float>(i) * testResponses.at<float>(i) < 0)

samples/cpp/train_svmsgd.cpp

@@ -91,6 +91,7 @@ bool findCrossPointWithBorders(const Mat &weights, float shift, const std::pair<
     int yMin = std::min(segment.first.y, segment.second.y);
     int yMax = std::max(segment.first.y, segment.second.y);
 
+    CV_Assert(weights.type() == CV_32FC1);
     CV_Assert(xMin == xMax || yMin == yMax);
 
     if (xMin == xMax && weights.at<float>(1) != 0)
@@ -146,6 +147,7 @@ void redraw(Data data, const Point points[2])
     Point center;
     int radius = 3;
     Scalar color;
+    CV_Assert((data.samples.type() == CV_32FC1) && (data.responses.type() == CV_32FC1));
     for (int i = 0; i < data.samples.rows; i++)
     {
         center.x = static_cast<int>(data.samples.at<float>(i,0));
@@ -160,14 +162,14 @@ void redraw(Data data, const Point points[2])
 
 void addPointRetrainAndRedraw(Data &data, int x, int y, int response)
 {
-    Mat currentSample(1, 2, CV_32F);
+    Mat currentSample(1, 2, CV_32FC1);
 
     currentSample.at<float>(0,0) = (float)x;
     currentSample.at<float>(0,1) = (float)y;
     data.samples.push_back(currentSample);
     data.responses.push_back(response);
 
-    Mat weights(1, 2, CV_32F);
+    Mat weights(1, 2, CV_32FC1);
     float shift = 0;
 
     if (doTrain(data.samples, data.responses, weights, shift))