GBRTree.h

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#ifndef EGAMMAOBJECTS_GBRTree
#define EGAMMAOBJECTS_GBRTree

//                                                                      //
// GBRForest                                                            //
//                                                                      //
// A fast minimal implementation of Gradient-Boosted Regression Trees   //
// which has been especially optimized for size on disk and in memory.  //                                                                  
//                                                                      //
// Designed to be built from TMVA-trained trees, but could also be      //
// generalized to otherwise-trained trees, classification,              //
//  or other boosting methods in the future                             //
//                                                                      //
//  Josh Bendavid - MIT                                                 //

// The decision tree is implemented here as a set of two arrays, one for
// intermediate nodes, containing the variable index and cut value, as well
// as the indices of the 'left' and 'right' daughter nodes.  Positive indices
// indicate further intermediate nodes, whereas negative indices indicate
// terminal nodes, which are stored simply as a vector of regression responses


#include "CondFormats/Serialization/interface/Serializable.h"

#include <vector>
#include <map>

  namespace TMVA {
    class DecisionTree;
    class DecisionTreeNode;
  }

  class GBRTree {

    public:

       GBRTree();
       explicit GBRTree(const TMVA::DecisionTree *tree, double scale, bool useyesnoleaf, bool adjustboundary);
       virtual ~GBRTree();
       
       double GetResponse(const float* vector) const;
       int TerminalIndex(const float *vector) const;
       
       std::vector<float> &Responses() { return fResponses; }       
       const std::vector<float> &Responses() const { return fResponses; }
       
       std::vector<unsigned char> &CutIndices() { return fCutIndices; }
       const std::vector<unsigned char> &CutIndices() const { return fCutIndices; }
       
       std::vector<float> &CutVals() { return fCutVals; }
       const std::vector<float> &CutVals() const { return fCutVals; }
       
       std::vector<int> &LeftIndices() { return fLeftIndices; }
       const std::vector<int> &LeftIndices() const { return fLeftIndices; } 
       
       std::vector<int> &RightIndices() { return fRightIndices; }
       const std::vector<int> &RightIndices() const { return fRightIndices; }
       

       
    protected:      
        unsigned int CountIntermediateNodes(const TMVA::DecisionTreeNode *node);
        unsigned int CountTerminalNodes(const TMVA::DecisionTreeNode *node);
      
        void AddNode(const TMVA::DecisionTreeNode *node, double scale, bool isregression, bool useyesnoleaf, bool adjustboundary);
        
    std::vector<unsigned char> fCutIndices;
    std::vector<float> fCutVals;
    std::vector<int> fLeftIndices;
    std::vector<int> fRightIndices;
    std::vector<float> fResponses;  
        
  
  COND_SERIALIZABLE;
};

//_______________________________________________________________________
inline double GBRTree::GetResponse(const float* vector) const {
  return fResponses[TerminalIndex(vector)];
}

//_______________________________________________________________________
inline int GBRTree::TerminalIndex(const float* vector) const {
   int index = 0;
  do {
     auto r = fRightIndices[index];
     auto l = fLeftIndices[index];  
    index =  vector[fCutIndices[index]] > fCutVals[index] ? r : l;
  } while (index>0);
  return -index;
}

#endif