ENH: implement SVM regression mode

Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h

@@ -126,14 +126,21 @@ protected:
   /** PrintSelf method */
   void PrintSelf(std::ostream& os, itk::Indent indent) const;
 
-  /** Train the machine learning model */
+  /** Train the machine learning model for classification*/
   virtual void TrainClassification();
-  /** Predict values using the model */
+  /** Predict values using the model for classification*/
   virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
 
+  /** Train the machine learning model for regression*/
+  virtual void TrainRegression();
+  /** Predict values using the model for regression*/
+  virtual TargetSampleType PredictRegression(const InputSampleType& input) const;
+
 private:
   SVMMachineLearningModel(const Self &); //purposely not implemented
   void operator =(const Self&); //purposely not implemented
+  virtual void InternalTrain();
+  virtual TargetSampleType InternalPredict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
 
   CvSVM * m_SVMModel;
   int m_SVMType;

Modules/Learning/Supervised/include/otbSVMMachineLearningModel.txx

@@ -64,7 +64,7 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
 template <class TInputValue, class TOutputValue>
 void
 SVMMachineLearningModel<TInputValue,TOutputValue>
-::TrainClassification()
+::InternalTrain()
 {
   //convert listsample to opencv matrix
   cv::Mat samples;
@@ -105,11 +105,21 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
 
 }
 
+/** Train the machine learning model for classification*/
+template <class TInputValue, class TOutputValue>
+void
+SVMMachineLearningModel<TInputValue,TOutputValue>
+::TrainClassification()
+{
+  m_SVMType = CvSVM::C_SVC;
+  this->InternalTrain();
+}
+
 template <class TInputValue, class TOutputValue>
 typename SVMMachineLearningModel<TInputValue,TOutputValue>
 ::TargetSampleType
 SVMMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+::InternalPredict(const InputSampleType & input, ConfidenceValueType *quality) const
 {
   //convert listsample to Mat
   cv::Mat sample;
@@ -130,15 +140,43 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
   return target;
 }
 
+template <class TInputValue, class TOutputValue>
+typename SVMMachineLearningModel<TInputValue,TOutputValue>
+::TargetSampleType
+SVMMachineLearningModel<TInputValue,TOutputValue>
+::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+{
+  return this->InternalPredict(input, quality);
+}
+
+/** Train the machine learning model for regression*/
+template <class TInputValue, class TOutputValue>
+void
+SVMMachineLearningModel<TInputValue,TOutputValue>
+::TrainRegression()
+{
+  m_SVMType = CvSVM::NU_SVR;
+  this->InternalTrain();
+}
+
+template <class TInputValue, class TOutputValue>
+typename SVMMachineLearningModel<TInputValue,TOutputValue>
+::TargetSampleType
+SVMMachineLearningModel<TInputValue,TOutputValue>
+::PredictRegression(const InputSampleType & input) const
+{
+  return this->InternalPredict(input, false);
+}
+
 template <class TInputValue, class TOutputValue>
 void
 SVMMachineLearningModel<TInputValue,TOutputValue>
 ::Save(const std::string & filename, const std::string & name)
 {
-       if (name == "")
-              m_SVMModel->save(filename.c_str(), 0);
-       else
-              m_SVMModel->save(filename.c_str(), name.c_str());
+  if (name == "")
+    m_SVMModel->save(filename.c_str(), 0);
+  else
+    m_SVMModel->save(filename.c_str(), name.c_str());
 }
 
 template <class TInputValue, class TOutputValue>
@@ -147,9 +185,9 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
 ::Load(const std::string & filename, const std::string & name)
 {
   if (name == "")
-         m_SVMModel->load(filename.c_str(), 0);
+    m_SVMModel->load(filename.c_str(), 0);
   else
-         m_SVMModel->load(filename.c_str(), name.c_str());
+    m_SVMModel->load(filename.c_str(), name.c_str());
 }
 
 template <class TInputValue, class TOutputValue>
@@ -157,22 +195,22 @@ bool
 SVMMachineLearningModel<TInputValue,TOutputValue>
 ::CanReadFile(const std::string & file)
 {
-   std::ifstream ifs;
-   ifs.open(file.c_str());
+  std::ifstream ifs;
+  ifs.open(file.c_str());
 
-   if(!ifs)
-   {
-      std::cerr<<"Could not read file "<<file<<std::endl;
-      return false;
-   }
+  if(!ifs)
+    {
+    std::cerr<<"Could not read file "<<file<<std::endl;
+    return false;
+    }
 
-   while (!ifs.eof())
-   {
-      std::string line;
-      std::getline(ifs, line);
+  while (!ifs.eof())
+    {
+    std::string line;
+    std::getline(ifs, line);
 
-      //if (line.find(m_SVMModel->getName()) != std::string::npos)
-      if (line.find(CV_TYPE_NAME_ML_SVM) != std::string::npos)
+    //if (line.find(m_SVMModel->getName()) != std::string::npos)
+    if (line.find(CV_TYPE_NAME_ML_SVM) != std::string::npos)
       {
          //std::cout<<"Reading a "<<CV_TYPE_NAME_ML_SVM<<" model"<<std::endl;
          return true;