ENH: implement prediction for ANN regression

Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h

@@ -186,6 +186,9 @@ protected:
   /** 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:
   NeuralNetworkMachineLearningModel(const Self &); //purposely not implemented
   void operator =(const Self&); //purposely not implemented

Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.txx

@@ -223,7 +223,7 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TrainRegressi
   if ( nbLayers == 0 )
     itkExceptionMacro(<< "Number of layers in the Neural Network must be >= 3")
 
-  cv::Mat layers = cv::Mat(nbLayers, 1, CV_32SC1);
+      cv::Mat layers = cv::Mat(nbLayers, 1, CV_32SC1);
   for (unsigned int i = 0; i < nbLayers; i++)
     {
     layers.row(i) = m_LayerSizes[i];
@@ -251,6 +251,23 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TrainRegressi
   m_ANNModel->train(samples, matOutputANN, cv::Mat(), cv::Mat(), params);
 }
 
+template<class TInputValue, class TOutputValue>
+typename NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TargetSampleType NeuralNetworkMachineLearningModel<
+  TInputValue, TOutputValue>::PredictRegression(const InputSampleType & input) const
+{
+  //convert listsample to Mat
+  cv::Mat sample;
+
+  otb::SampleToMat<InputSampleType>(input, sample);
+
+  cv::Mat response;
+  m_ANNModel->predict(sample, response);
+
+  TargetSampleType target;
+  target[0] = response.at<float> (0, 0);
+  return target;
+}
+
 template<class TInputValue, class TOutputValue>
 void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::Save(const std::string & filename,
                                                                         const std::string & name)