ENH: remove normalization from Shark Kmeans machine learning model

Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h

@@ -125,10 +125,6 @@ public:
   /** Set the number of class for the kMeans algorithm.*/
   itkSetMacro( K, unsigned );
 
-  /** If true, normalized input data sample list */
-  itkGetMacro( Normalized, bool );
-  itkSetMacro( Normalized, bool );
-
   /** Initialize the centroids for the kmeans algorithm */
   void SetCentroidsFromData(const shark::Data<shark::RealVector> & data)
   {
@@ -150,9 +146,6 @@ protected:
   virtual void DoPredictBatch(const InputListSampleType *, const unsigned int &startIndex, const unsigned int &size,
                               TargetListSampleType *, ConfidenceListSampleType * = nullptr, ProbaListSampleType * = nullptr) const override;
 
-  template<typename DataType>
-  DataType NormalizeData(const DataType &data) const;
-  
   template<typename DataType>
   shark::Normalizer<> TrainNormalizer(const DataType &data) const;
 
@@ -164,7 +157,6 @@ private:
   void operator=(const Self &) = delete;
 
   // Parameters set by the user
-  bool m_Normalized;
   unsigned int m_K;
   unsigned int m_MaximumNumberOfIterations;
   bool m_CanRead;

Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.hxx

@@ -52,7 +52,7 @@ namespace otb
 template<class TInputValue, class TOutputValue>
 SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
 ::SharkKMeansMachineLearningModel() :
-        m_Normalized( false ), m_K(2), m_MaximumNumberOfIterations( 10 )
+        m_K(2), m_MaximumNumberOfIterations( 10 )
 {
   // Default set HardClusteringModel
   this->m_ConfidenceIndex = true;
@@ -77,30 +77,11 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
   otb::Shark::ListSampleToSharkVector( this->GetInputListSample(), vector_data );
   shark::Data<shark::RealVector> data = shark::createDataFromRange( vector_data );
 
-  // Normalized input value if necessary
-  if( m_Normalized )
-  {
-    auto normalizer = TrainNormalizer(data);
-    data = normalizer(data);
-  }
-
   // Use a Hard Clustering Model for classification
   shark::kMeans( data, m_K, m_Centroids, m_MaximumNumberOfIterations );
   m_ClusteringModel = boost::make_shared<ClusteringModelType>( &m_Centroids );
 }
 
-template<class TInputValue, class TOutputValue>
-template<typename DataType>
-DataType
-SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
-::NormalizeData(const DataType &data) const
-{
-  shark::Normalizer<> normalizer;
-  shark::NormalizeComponentsUnitVariance<> normalizingTrainer( true );//zero mean
-  normalizingTrainer.train( normalizer, data );
-  return normalizer( data );
-}
-
 template<class TInputValue, class TOutputValue>
 template<typename DataType>
 shark::Normalizer<>