library: libTMVA
#include "TMVA_MethodPDERS.h"

TMVA_MethodPDERS

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

class TMVA_MethodPDERS : public TMVA_MethodBase

Inheritance Chart:

TObject

TMVA_MethodBase

TMVA_MethodPDERS

    private:

      Float_t GetError(Float_t countS, Float_t countB, Float_t sumW2S, Float_t sumW2B) const
         void InitPDERS()
      Float_t RScalc(TMVA_Event* e)
         void SetVolumeElement()

    protected:

      TMVA_BinarySearchTree* GetBinaryTreeBkg() const
      TMVA_BinarySearchTree* GetBinaryTreeSig() const
                    Double_t KernelEstimate(TMVA_Event&, vector<TMVA_Event*>&, TMVA_Volume&)

    public:

                               TMVA_MethodPDERS(TString jobName, vector<TString>* theVariables, TTree* theTree = 0, TString theOption = Adaptive:100:200:50:0.99, TDirectory* theTargetDir = 0)
                               TMVA_MethodPDERS(vector<TString>* theVariables, TString theWeightFile, TDirectory* theTargetDir = NULL)
                               TMVA_MethodPDERS(const TMVA_MethodPDERS&)
                       virtual ~TMVA_MethodPDERS()
                static TClass* Class()
              virtual Double_t GetMvaValue(TMVA_Event* e)
                      Double_t GetVolumeContentForRoot(Double_t)
               static Double_t IGetVolumeContentForRoot(Double_t)
               virtual TClass* IsA() const
             TMVA_MethodPDERS& operator=(const TMVA_MethodPDERS&)
                  virtual void ReadWeightsFromFile()
                  virtual void ShowMembers(TMemberInspector& insp, char* parent)
                  virtual void Streamer(TBuffer& b)
                          void StreamerNVirtual(TBuffer& b)
      static TMVA_MethodPDERS* ThisPDERS()
                  virtual void Train()
                  virtual void WriteHistosToFile()
                  virtual void WriteWeightsToFile()

Data Members

    private:

      TMVA_MethodPDERS::VolumeRangeMode fVRangeMode            
                 TMVA_BinarySearchTree* fBinaryTreeS           
                 TMVA_BinarySearchTree* fBinaryTreeB           
                       vector<Float_t>* fDelta                 
                       vector<Float_t>* fShift                 
                                Float_t fScaleS                
                                Float_t fScaleB                
                                Float_t fDeltaFrac             
                                Float_t fNEventsMin            
                                Float_t fNEventsMax            
                                Float_t fMaxVIterations        
                                Float_t fInitialScale          
                                 TFile* fFin                   
                                 Bool_t fInitializedVolumeEle  
               static TMVA_MethodPDERS* fgThisPDERS            

    protected:

      TMVA_Volume* fHelpVolume  
             Int_t fFcnCall     

    public:

      static const TMVA_MethodPDERS::VolumeRangeMode kMinMax    
      static const TMVA_MethodPDERS::VolumeRangeMode kRMS       
      static const TMVA_MethodPDERS::VolumeRangeMode kAdaptive

Class Description

 Multidimensional Likelihood using the "Probability density
 estimator range search" (PDERS) method

_______________________________________________________________________

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

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

void InitPDERS( void )

~TMVA_MethodPDERS( void )

void Train( void )

--------------------------------------------------------------
 this is a dummy training: the preparation work to do is the construction
 of the binary tree as a pointer chain. It is easier to directly save the
 trainingTree in the weight file, and to rebuild the binary tree in the
 test phase from scratch

Double_t GetMvaValue( TMVA_Event *e )

 init the size of a volume element using a defined fraction of the
 volume containing the entire events

void SetVolumeElement( void )

 init relative scales

Double_t IGetVolumeContentForRoot( Double_t scale )

Double_t GetVolumeContentForRoot( Double_t scale )

 search for events in rescaled volume
 retrieve the class object

Float_t RScalc( TMVA_Event *e )

Double_t KernelEstimate( TMVA_Event& event, vector<TMVA_Event*>& events, TMVA_Volume& v )

 define gaussian sigmas

Float_t GetError( Float_t countS, Float_t countB, Float_t sumW2S, Float_t sumW2B ) const

void WriteWeightsToFile( void )

 write coefficients to file

void ReadWeightsFromFile( void )

 read coefficients from file

void WriteHistosToFile( void )

Inline Functions

             TMVA_MethodPDERS* ThisPDERS()
        TMVA_BinarySearchTree* GetBinaryTreeSig() const
        TMVA_BinarySearchTree* GetBinaryTreeBkg() const
                       TClass* Class()
                       TClass* IsA() const
                          void ShowMembers(TMemberInspector& insp, char* parent)
                          void Streamer(TBuffer& b)
                          void StreamerNVirtual(TBuffer& b)
              TMVA_MethodPDERS TMVA_MethodPDERS(const TMVA_MethodPDERS&)
             TMVA_MethodPDERS& operator=(const TMVA_MethodPDERS&)

Author: Andreas Hoecker, Helge Voss, Kai Voss
Last update: root/tmva $Id: TMVA_MethodPDERS.cxx,v 1.6 2006/05/10 07:36:12 brun Exp $
Copyright (c) 2005: *

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