ENH: use the random fores confidence estimation in the MachineLearningModel

Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h

@@ -24,8 +24,9 @@
 #include "itkFixedArray.h"
 #include "otbMachineLearningModel.h"
 #include "itkVariableSizeMatrix.h"
+#include "otbCvRTrees.h"
 
-class CvRTrees;
+class CvRTreesWrapper;
 
 namespace otb
 {
@@ -53,7 +54,7 @@ public:
 
 
   //opencv typedef
-  typedef CvRTrees RFType;
+  typedef CvRTreesWrapper RFType;
 
   /** Run-time type information (and related methods). */
   itkNewMacro(Self);
@@ -145,7 +146,7 @@ private:
   RandomForestsMachineLearningModel(const Self &); //purposely not implemented
   void operator =(const Self&); //purposely not implemented
 
-  CvRTrees * m_RFModel;
+  CvRTreesWrapper * m_RFModel;
   /** The depth of the tree. A low value will likely underfit and conversely a
    * high value will likely overfit. The optimal value can be obtained using cross
    * validation or other suitable methods. */
@@ -189,7 +190,7 @@ private:
    * first category. */
   std::vector<float> m_Priors;
   /** If true then variable importance will be calculated and then it can be
-   * retrieved by CvRTrees::get_var_importance(). */
+   * retrieved by CvRTreesWrapper::get_var_importance(). */
   bool m_CalculateVariableImportance;
   /** The size of the randomly selected subset of features at each tree node and
    * that are used to find the best split(s). If you set it to 0 then the size will

Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx

@@ -29,17 +29,17 @@ namespace otb
 template <class TInputValue, class TOutputValue>
 RandomForestsMachineLearningModel<TInputValue,TOutputValue>
 ::RandomForestsMachineLearningModel() :
- m_RFModel (new CvRTrees),
- m_MaxDepth(5),
- m_MinSampleCount(10),
- m_RegressionAccuracy(0.01),
- m_ComputeSurrogateSplit(false),
- m_MaxNumberOfCategories(10),
- m_CalculateVariableImportance(false),
- m_MaxNumberOfVariables(0),
- m_MaxNumberOfTrees(100),
- m_ForestAccuracy(0.01),
- m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS)
+  m_RFModel (new CvRTreesWrapper),
+  m_MaxDepth(5),
+  m_MinSampleCount(10),
+  m_RegressionAccuracy(0.01),
+  m_ComputeSurrogateSplit(false),
+  m_MaxNumberOfCategories(10),
+  m_CalculateVariableImportance(false),
+  m_MaxNumberOfVariables(0),
+  m_MaxNumberOfTrees(100),
+  m_ForestAccuracy(0.01),
+  m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS)
 {
   this->m_ConfidenceIndex = true;
   this->m_IsRegressionSupported = true;
@@ -125,7 +125,7 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
 
   if (quality != NULL)
     {
-    (*quality) = m_RFModel->predict_prob(sample);
+    (*quality) = m_RFModel->predict_confidence(sample);
     }
 
   return target[0];
@@ -158,23 +158,23 @@ bool
 RandomForestsMachineLearningModel<TInputValue,TOutputValue>
 ::CanReadFile(const std::string & file)
 {
-   std::ifstream ifs;
-   ifs.open(file.c_str());
+  std::ifstream ifs;
+  ifs.open(file.c_str());
 
-   if(!ifs)
-   {
-      std::cerr<<"Could not read file "<<file<<std::endl;
-      return false;
-   }
+  if(!ifs)
+    {
+    std::cerr<<"Could not read file "<<file<<std::endl;
+    return false;
+    }
 
 
-   while (!ifs.eof())
-   {
-      std::string line;
-      std::getline(ifs, line);
+  while (!ifs.eof())
+    {
+    std::string line;
+    std::getline(ifs, line);
 
-      //if (line.find(m_RFModel->getName()) != std::string::npos)
-      if (line.find(CV_TYPE_NAME_ML_RTREES) != std::string::npos)
+    //if (line.find(m_RFModel->getName()) != std::string::npos)
+    if (line.find(CV_TYPE_NAME_ML_RTREES) != std::string::npos)
       {
          //std::cout<<"Reading a "<<CV_TYPE_NAME_ML_RTREES<<" model"<<std::endl;
          return true;