export SVM::trainAuto to python #7224 (#8373)

* export SVM::trainAuto to python #7224

* workaround for ABI compatibility of SVM::trainAuto

* add parameter comments to new SVM::trainAuto function

* Export ParamGrid member variables

modules/ml/include/opencv2/ml.hpp

@@ -104,7 +104,7 @@ enum SampleTypes
 It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate
 being computed by cross-validation.
  */
-class CV_EXPORTS ParamGrid
+class CV_EXPORTS_W ParamGrid
 {
 public:
     /** @brief Default constructor */
@@ -112,8 +112,8 @@ public:
     /** @brief Constructor with parameters */
     ParamGrid(double _minVal, double _maxVal, double _logStep);
 
-    double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
-    double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
+    CV_PROP_RW double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
+    CV_PROP_RW double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
     /** @brief Logarithmic step for iterating the statmodel parameter.
 
     The grid determines the following iteration sequence of the statmodel parameter values:
@@ -122,7 +122,15 @@ public:
     \f[\texttt{minVal} * \texttt{logStep} ^n <  \texttt{maxVal}\f]
     The grid is logarithmic, so logStep must always be greater then 1. Default value is 1.
     */
-    double logStep;
+    CV_PROP_RW double logStep;
+
+    /** @brief Creates a ParamGrid Ptr that can be given to the %SVM::trainAuto method
+
+    @param minVal minimum value of the parameter grid
+    @param maxVal maximum value of the parameter grid
+    @param logstep Logarithmic step for iterating the statmodel parameter
+    */
+    CV_WRAP static Ptr<ParamGrid> create(double minVal=0., double maxVal=0., double logstep=1.);
 };
 
 /** @brief Class encapsulating training data.
@@ -691,6 +699,46 @@ public:
                     ParamGrid degreeGrid = getDefaultGrid(DEGREE),
                     bool balanced=false) = 0;
 
+    /** @brief Trains an %SVM with optimal parameters
+
+    @param samples training samples
+    @param layout See ml::SampleTypes.
+    @param responses vector of responses associated with the training samples.
+    @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
+        subset is used to test the model, the others form the train set. So, the %SVM algorithm is
+    @param Cgrid grid for C
+    @param gammaGrid grid for gamma
+    @param pGrid grid for p
+    @param nuGrid grid for nu
+    @param coeffGrid grid for coeff
+    @param degreeGrid grid for degree
+    @param balanced If true and the problem is 2-class classification then the method creates more
+        balanced cross-validation subsets that is proportions between classes in subsets are close
+        to such proportion in the whole train dataset.
+
+    The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
+    nu, coef0, degree. Parameters are considered optimal when the cross-validation
+    estimate of the test set error is minimal.
+
+    This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
+    offers rudimentary parameter options.
+
+    This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
+    regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
+    the usual %SVM with parameters specified in params is executed.
+    */
+    CV_WRAP bool trainAuto(InputArray samples,
+            int layout,
+            InputArray responses,
+            int kFold = 10,
+            Ptr<ParamGrid> Cgrid = SVM::getDefaultGridPtr(SVM::C),
+            Ptr<ParamGrid> gammaGrid  = SVM::getDefaultGridPtr(SVM::GAMMA),
+            Ptr<ParamGrid> pGrid      = SVM::getDefaultGridPtr(SVM::P),
+            Ptr<ParamGrid> nuGrid     = SVM::getDefaultGridPtr(SVM::NU),
+            Ptr<ParamGrid> coeffGrid  = SVM::getDefaultGridPtr(SVM::COEF),
+            Ptr<ParamGrid> degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE),
+            bool balanced=false);
+
     /** @brief Retrieves all the support vectors
 
     The method returns all the support vectors as a floating-point matrix, where support vectors are
@@ -733,6 +781,16 @@ public:
      */
     static ParamGrid getDefaultGrid( int param_id );
 
+    /** @brief Generates a grid for %SVM parameters.
+
+    @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
+    generated for the parameter with this ID.
+
+    The function generates a grid pointer for the specified parameter of the %SVM algorithm.
+    The grid may be passed to the function SVM::trainAuto.
+     */
+    CV_WRAP static Ptr<ParamGrid> getDefaultGridPtr( int param_id );
+
     /** Creates empty model.
     Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
     find the best parameters for your problem, it can be done with SVM::trainAuto. */