ENH: Expose K parameter to control number of neighbors used in KNN machine...

ENH: Expose K parameter to control number of neighbors used in KNN machine learning. Also expose IsRegression parameter.

Code/UtilitiesAdapters/OpenCV/otbKNearestNeighborsMachineLearningModel.h

@@ -53,6 +53,20 @@ public:
   itkNewMacro(Self);
   itkTypeMacro(KNearestNeighborsMachineLearningModel, itk::MachineLearningModel);
 
+  /** Setters/Getters to the number of neighbors to use
+   *  Default is 32
+   *  see @http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
+   */
+  itkGetMacro(K, int);
+  itkSetMacro(K, int);
+
+  /** Setters/Getters to IsRegression flag
+   *  Default is False
+   *  see @http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
+   */
+  itkGetMacro(IsRegression, bool);
+  itkSetMacro(IsRegression, bool);
+
   /** Train the machine learning model */
   virtual void Train();
 
@@ -89,6 +103,7 @@ private:
 
   CvKNearest * m_KNearestModel;
   int m_K;
+  int m_IsRegression;
 };
 } // end namespace otb
 

Code/UtilitiesAdapters/OpenCV/otbKNearestNeighborsMachineLearningModel.txx

@@ -26,10 +26,10 @@ namespace otb
 
 template <class TInputValue, class TOutputValue>
 KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
-::KNearestNeighborsMachineLearningModel()
+::KNearestNeighborsMachineLearningModel() :
+ m_K(10), m_IsRegression(false)
 {
   m_KNearestModel = new CvKNearest;
-  m_K = 10;
 }
 
 
@@ -54,7 +54,7 @@ KNearestNeighborsMachineLearningModel<TInputValue,TOutputValue>
   otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(),labels);
 
   //train the KNN model
-  m_KNearestModel->train(samples,labels,cv::Mat(),false, m_K,false);
+  m_KNearestModel->train(samples,labels,cv::Mat(),m_IsRegression, m_K,false);
 }
 
 template <class TInputValue, class TOutputValue>