ENH: API change of Shark's RF

Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h

@@ -34,6 +34,7 @@
 #pragma GCC diagnostic ignored "-Wcast-align"
 #pragma GCC diagnostic ignored "-Wunknown-pragmas"
 #endif
+#include <shark/Models/Classifier.h>
 #include "otb_shark.h"
 #include "shark/Algorithms/Trainers/RFTrainer.h"
 #if defined(__GNUC__) || defined(__clang__)
@@ -154,8 +155,8 @@ private:
   SharkRandomForestsMachineLearningModel(const Self &); //purposely not implemented
   void operator =(const Self&); //purposely not implemented
 
-  shark::RFClassifier m_RFModel;
-  shark::RFTrainer m_RFTrainer;
+  shark::RFClassifier<unsigned int> m_RFModel;
+  shark::RFTrainer<unsigned int> m_RFTrainer;
 
   unsigned int m_NumberOfTrees;
   unsigned int m_MTry;

Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx

@@ -32,7 +32,6 @@
 #pragma GCC diagnostic ignored "-Woverloaded-virtual"
 #pragma GCC diagnostic ignored "-Wignored-qualifiers"
 #endif
-#include <shark/Models/Converter.h>
 #if defined(__GNUC__) || defined(__clang__)
 #pragma GCC diagnostic pop
 #endif
@@ -82,7 +81,7 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
   m_RFTrainer.setMTry(m_MTry);
   m_RFTrainer.setNTrees(m_NumberOfTrees);
   m_RFTrainer.setNodeSize(m_NodeSize);
-  m_RFTrainer.setOOBratio(m_OobRatio);
+  //  m_RFTrainer.setOOBratio(m_OobRatio);
   m_RFTrainer.train(m_RFModel, TrainSamples);
 
 }
@@ -125,13 +124,11 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
     }
   if (quality != ITK_NULLPTR)
     {
-    shark::RealVector probas = m_RFModel(samples);
+    shark::RealVector probas = m_RFModel.decisionFunction()(samples);
     (*quality) = ComputeConfidence(probas, m_ComputeMargin);
     }
-  shark::ArgMaxConverter<shark::RFClassifier> amc;
-  amc.decisionFunction() = m_RFModel;
-  unsigned int res;
-  amc.eval(samples, res);
+  unsigned int res{0};
+  m_RFModel.eval(samples, res);
   TargetSampleType target;
   target[0] = static_cast<TOutputValue>(res);
   return target;
@@ -163,7 +160,7 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
   
   if(quality != ITK_NULLPTR)
     {
-    shark::Data<shark::RealVector> probas = m_RFModel(inputSamples);
+    shark::Data<shark::RealVector> probas = m_RFModel.decisionFunction()(inputSamples);
     unsigned int id = startIndex;
     for(shark::RealVector && p : probas.elements())
       {
@@ -175,9 +172,7 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
       }
     }
     
-  shark::ArgMaxConverter<shark::RFClassifier> amc;
-  amc.decisionFunction() = m_RFModel;
-  auto prediction = amc(inputSamples);
+  auto prediction = m_RFModel(inputSamples);
   unsigned int id = startIndex;
   for(const auto& p : prediction.elements())
     {