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00001 00002 #ifndef EGAMMAOBJECTS_GBRForest 00003 #define EGAMMAOBJECTS_GBRForest 00004 00006 // // 00007 // GBRForest // 00008 // // 00009 // A fast minimal implementation of Gradient-Boosted Regression Trees // 00010 // which has been especially optimized for size on disk and in memory. // 00011 // // 00012 // Designed to be built from TMVA-trained trees, but could also be // 00013 // generalized to otherwise-trained trees, classification, // 00014 // or other boosting methods in the future // 00015 // // 00016 // Josh Bendavid - MIT // 00018 00019 #include <vector> 00020 #include "GBRTree.h" 00021 #include <math.h> 00022 #include <stdio.h> 00023 00024 namespace TMVA { 00025 class MethodBDT; 00026 } 00027 00028 class GBRForest { 00029 00030 public: 00031 00032 GBRForest(); 00033 explicit GBRForest(const TMVA::MethodBDT *bdt); 00034 virtual ~GBRForest(); 00035 00036 double GetResponse(const float* vector) const; 00037 double GetClassifier(const float* vector) const; 00038 00039 void SetInitialResponse(double response) { fInitialResponse = response; } 00040 00041 std::vector<GBRTree> &Trees() { return fTrees; } 00042 const std::vector<GBRTree> &Trees() const { return fTrees; } 00043 00044 protected: 00045 double fInitialResponse; 00046 std::vector<GBRTree> fTrees; 00047 00048 }; 00049 00050 //_______________________________________________________________________ 00051 inline double GBRForest::GetResponse(const float* vector) const { 00052 double response = fInitialResponse; 00053 for (std::vector<GBRTree>::const_iterator it=fTrees.begin(); it!=fTrees.end(); ++it) { 00054 response += it->GetResponse(vector); 00055 } 00056 return response; 00057 } 00058 00059 //_______________________________________________________________________ 00060 inline double GBRForest::GetClassifier(const float* vector) const { 00061 double response = GetResponse(vector); 00062 return 2.0/(1.0+exp(-2.0*response))-1; //MVA output between -1 and 1 00063 } 00064 00065 #endif
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