ENH: implement regression in GBTree ML model

Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.h

@@ -130,6 +130,9 @@ protected:
   /** Predict values using the model */
   virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
 
+  virtual void TrainRegression();
+  virtual TargetSampleType PredictRegression(const InputSampleType& input) const;
+
 private:
   GradientBoostedTreeMachineLearningModel(const Self &); //purposely not implemented
   void operator =(const Self&); //purposely not implemented
@@ -142,7 +145,6 @@ private:
   double m_SubSamplePortion;
   int m_MaxDepth;
   bool m_UseSurrogates;
-  bool m_IsRegression;
 
 
 };

Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.txx

@@ -36,8 +36,7 @@ GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
  m_Shrinkage(0.01),
  m_SubSamplePortion(0.8),
  m_MaxDepth(3),
- m_UseSurrogates(false),
- m_IsRegression(false)
+ m_UseSurrogates(false)
 {
 }
 
@@ -62,7 +61,6 @@ GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
   cv::Mat labels;
   otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(),labels);
 
-
   CvGBTreesParams params = CvGBTreesParams(m_LossFunctionType, m_WeakCount, m_Shrinkage, m_SubSamplePortion,
                                            m_MaxDepth, m_UseSurrogates);
 
@@ -70,12 +68,20 @@ GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
   cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U );
   var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL) ); // all inputs are numerical
 
-  if (!m_IsRegression) //Classification
+  if (!this->m_RegressionMode) //Classification
     var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
 
   m_GBTreeModel->train(samples,CV_ROW_SAMPLE,labels,cv::Mat(),cv::Mat(),var_type,cv::Mat(),params, false);
 }
 
+template <class TInputValue, class TOutputValue>
+void
+GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
+::TrainRegression()
+{
+  this->TrainClassification();
+}
+
 template <class TInputValue, class TOutputValue>
 typename GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
 ::TargetSampleType
@@ -104,6 +110,15 @@ GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
   return target;
 }
 
+template <class TInputValue, class TOutputValue>
+typename GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
+::TargetSampleType
+GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
+::PredictRegression(const InputSampleType & input) const
+{
+  return this->PredictClassification(input, NULL);
+}
+
 template <class TInputValue, class TOutputValue>
 void
 GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>