ENH: Parameters optimization for svm machine learning model, using default opencv grid

Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.h

@@ -115,6 +115,9 @@ public:
   itkGetMacro(P, double);
   itkSetMacro(P, double);
 
+  itkGetMacro(ParameterOptimization, bool);
+  itkSetMacro(ParameterOptimization, bool);
+
 protected:
   /** Constructor */
   SVMMachineLearningModel();
@@ -141,6 +144,7 @@ private:
   double m_C;
   double m_Nu;
   double m_P;
+  bool m_ParameterOptimization;
 };
 } // end namespace otb
 

Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.txx

@@ -63,7 +63,18 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
   CvSVMParams params( m_SVMType, m_KernelType, m_Degree, m_Gamma, m_Coef0, m_C, m_Nu, m_P, NULL , term_crit );
 
   // Train the SVM
-  m_SVMModel->train(samples, labels, cv::Mat(), cv::Mat(), params);
+  if (!m_ParameterOptimization)
+    m_SVMModel->train(samples, labels, cv::Mat(), cv::Mat(), params);
+  else
+    //Trains SVM with optimal parameters.
+    //train_auto(const Mat& trainData, const Mat& responses, const Mat& varIdx, const Mat& sampleIdx,
+    //CvSVMParams params, int k_fold=10, CvParamGrid Cgrid=CvSVM::get_default_grid(CvSVM::C),
+    //CvParamGrid gammaGrid=CvSVM::get_default_grid(CvSVM::GAMMA),
+    //CvParamGrid pGrid=CvSVM::get_default_grid(CvSVM::P), CvParamGrid nuGrid=CvSVM::get_default_grid(CvSVM::NU),
+    //CvParamGrid coeffGrid=CvSVM::get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid=CvSVM::get_default_grid(CvSVM::DEGREE),
+    //bool balanced=false)
+    //We used default parameters grid. If not enough, those grids should be expose to the user.
+    m_SVMModel->train_auto(samples, labels, cv::Mat(), cv::Mat(), params);
 }
 
 template <class TInputValue, class TOutputValue>