library: libTMVA
#include "TMVA_MethodBDT.h"

TMVA_MethodBDT

class description - source file - inheritance tree (.pdf)

class TMVA_MethodBDT : public TMVA_MethodBase

Inheritance Chart:

TObject

TMVA_MethodBase

TMVA_MethodBDT

    private:

      void AdaBoost(vector<TMVA_Event*>, TMVA_DecisionTree* dt)
      void Bagging(vector<TMVA_Event*>, Int_t iTree)
      void EpsilonBoost(vector<TMVA_Event*>, TMVA_DecisionTree* dt)
      void InitBDT()

    public:

                       TMVA_MethodBDT(TString jobName, vector<TString>* theVariables, TTree* theTree, TString theOption = 100:AdaBoost:GiniIndex:10:0.002:20:-1, TDirectory* theTargetDir = 0)
                       TMVA_MethodBDT(vector<TString>* theVariables, TString theWeightFile, TDirectory* theTargetDir = NULL)
                       TMVA_MethodBDT(const TMVA_MethodBDT&)
               virtual ~TMVA_MethodBDT()
                  void Boost(vector<TMVA_Event*>, TMVA_DecisionTree* dt, Int_t iTree)
        static TClass* Class()
      virtual Double_t GetMvaValue(TMVA_Event* e)
          virtual void InitEventSample()
       virtual TClass* IsA() const
       TMVA_MethodBDT& operator=(const TMVA_MethodBDT&)
          virtual void ReadWeightsFromFile()
          virtual void ShowMembers(TMemberInspector& insp, char* parent)
          virtual void Streamer(TBuffer& b)
                  void StreamerNVirtual(TBuffer& b)
          virtual void Train()
          virtual void WriteHistosToFile()
          virtual void WriteWeightsToFile()

Data Members

    private:

                        Double_t fAdaBoostBeta    
             vector<TMVA_Event*> fEventSample     
                           Int_t fNTrees          
      vector<TMVA_DecisionTree*> fForest          
                         TString fBoostType       
            TMVA_SeparationBase* fSepType         
                           Int_t fNodeMinEvents   
                        Double_t fNodeMinSepGain  
                           Int_t fNCuts           
                        Double_t fSignalFraction

Class Description

 Analysis of Boosted Decision Trees

_______________________________________________________________________

TMVA_MethodBDT( TString jobName, vector<TString>* theVariables, TTree* theTree, TString theOption, TDirectory* theTargetDir ) : TMVA_MethodBase( jobName, theVariables, theTree, theOption, theTargetDir )

TMVA_MethodBDT( vector<TString> *theVariables, TString theWeightFile, TDirectory* theTargetDir ) : TMVA_MethodBase( theVariables, theWeightFile, theTargetDir )

void InitBDT( void )

~TMVA_MethodBDT( void )

void InitEventSample( void )

 write all Events from the Tree into a vector of TMVA_Events, that are
 more easily manipulated
 should never be called without existing trainingTree

void Train( void )

 default sanity checks

void Boost( vector<TMVA_Event*> eventSample, TMVA_DecisionTree *dt, Int_t iTree )

void AdaBoost( vector<TMVA_Event*> eventSample, TMVA_DecisionTree *dt )

void EpsilonBoost( vector<TMVA_Event*> /*eventSample*/, TMVA_DecisionTree * /*dt*/ )

void Bagging( vector<TMVA_Event*> eventSample, Int_t iTree )

 call it Bootstrapping, re-sampling or whatever you like, in the end it is nothing
 else but applying "random Weights" to each event.

void WriteWeightsToFile( void )

 write coefficients to file

void ReadWeightsFromFile( void )

 read coefficients from file

Double_t GetMvaValue(TMVA_Event *e)

void WriteHistosToFile( void )

Inline Functions

                TClass* Class()
                TClass* IsA() const
                   void ShowMembers(TMemberInspector& insp, char* parent)
                   void Streamer(TBuffer& b)
                   void StreamerNVirtual(TBuffer& b)
         TMVA_MethodBDT TMVA_MethodBDT(const TMVA_MethodBDT&)
        TMVA_MethodBDT& operator=(const TMVA_MethodBDT&)

Author: Andreas Hoecker, Helge Voss, Kai Voss
Last update: root/tmva $Id: TMVA_MethodBDT.cxx,v 1.1 2006/05/08 12:46:31 brun Exp $
Copyright (c) 2005: *

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