ENH: use the InputSampleList from mother class

Modules/Applications/AppDimensionalityReduction/include/otbDimensionalityReductionTrainSOM.txx

@@ -158,7 +158,7 @@ void TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
     radius[i]=std::stoi(n[i]);
     }
   dimredTrainer->SetNeighborhoodSizeInit(radius);
-  dimredTrainer->SetListSample(trainingListSample);
+  dimredTrainer->SetInputListSample(trainingListSample);
   dimredTrainer->Train();
   dimredTrainer->Save(modelPath);
 }

Modules/Learning/DimensionalityReductionLearning/include/otbSOMModel.h

@@ -100,8 +100,6 @@ public:
   itkGetMacro(RandomInit, bool);
   itkSetMacro(Seed, unsigned int);
   itkGetMacro(Seed, unsigned int);
-  itkGetObjectMacro(ListSample, InputListSampleType);
-  itkSetObjectMacro(ListSample, InputListSampleType);
 
   bool CanReadFile(const std::string & filename);
   bool CanWriteFile(const std::string & filename);
@@ -140,8 +138,6 @@ private:
   bool m_RandomInit;
   /** Seed for random initialization */
   unsigned int m_Seed;
-  /** The input list sample */
-  ListSamplePointerType m_ListSample;
   /** Behavior of the Learning weightening (link to the beta coefficient) */
   SOMLearningBehaviorFunctorType m_BetaFunctor;
   /** Behavior of the Neighborhood extent */

Modules/Learning/DimensionalityReductionLearning/include/otbSOMModel.txx

@@ -72,7 +72,7 @@ SOMModel<TInputValue,  MapDimension>::Train()
 {
   typedef otb::SOM<InputListSampleType, MapType>    EstimatorType;
   typename EstimatorType::Pointer estimator = EstimatorType::New();
-  estimator->SetListSample(m_ListSample);
+  estimator->SetListSample(this->GetInputListSample());
   estimator->SetMapSize(m_MapSize);
   estimator->SetNeighborhoodSizeInit(m_NeighborhoodSizeInit);
   estimator->SetNumberOfIterations(m_NumberOfIterations);