/usr/include/root/TMVA/MethodPDERS.h is in libroot-tmva-dev 5.34.30-0ubuntu8.
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// Author: Andreas Hoecker, Yair Mahalalel, Joerg Stelzer, Helge Voss, Kai Voss
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : MethodPDERS *
* Web : http://tmva.sourceforge.net *
* *
* Description: *
* Multidimensional Likelihood using the "Probability density estimator *
* range search" (PDERS) method suggested in *
* T. Carli and B. Koblitz, NIM A 501, 576 (2003) *
* *
* The multidimensional PDFs for signal and background are modeled *
* by counting the events in the "vicinity" of a test point. The volume *
* that describes "vicinity" is user-defined through the option string. *
* A search method based on binary-trees is used to improve the selection *
* efficiency of the volume search. *
* *
* Authors (alphabetical): *
* Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
* Yair Mahalalel <Yair.Mahalalel@cern.ch> - CERN, Switzerland *
* Peter Speckmayer <peter.speckmayer@cern.ch> - CERN, Switzerland *
* Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
* Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada *
* *
* Copyright (c) 2005: *
* CERN, Switzerland *
* U. of Victoria, Canada *
* MPI-K Heidelberg, Germany *
* *
* Redistribution and use in source and binary forms, with or without *
* modification, are permitted according to the terms listed in LICENSE *
* (http://tmva.sourceforge.net/LICENSE) *
**********************************************************************************/
#ifndef ROOT_TMVA_MethodPDERS
#define ROOT_TMVA_MethodPDERS
//////////////////////////////////////////////////////////////////////////
// //
// MethodPDERS //
// //
// Multidimensional Likelihood using the "Probability density //
// estimator range search" (PDERS) method //
// //
//////////////////////////////////////////////////////////////////////////
#ifndef ROOT_TMVA_MethodBase
#include "TMVA/MethodBase.h"
#endif
#ifndef ROOT_TMVA_BinarySearchTree
#include "TMVA/BinarySearchTree.h"
#endif
#ifndef ROOT_TMVA_TVector
#ifndef ROOT_TVector
#include "TVector.h"
#endif
#endif
namespace TMVA {
class Volume;
class Event;
class MethodPDERS : public MethodBase {
public:
MethodPDERS( const TString& jobName,
const TString& methodTitle,
DataSetInfo& theData,
const TString& theOption,
TDirectory* theTargetDir = 0 );
MethodPDERS( DataSetInfo& theData,
const TString& theWeightFile,
TDirectory* theTargetDir = NULL );
virtual ~MethodPDERS( void );
virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets );
// training method
void Train( void );
// write weights to file
void WriteWeightsToStream( TFile& rf ) const;
void AddWeightsXMLTo( void* parent ) const;
// read weights from file
void ReadWeightsFromStream( std::istream& istr );
void ReadWeightsFromStream( TFile& istr );
void ReadWeightsFromXML( void* wghtnode );
// calculate the MVA value
Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 );
// calculate the MVA value
const std::vector<Float_t>& GetRegressionValues();
public:
// for root finder
static Double_t IGetVolumeContentForRoot( Double_t );
Double_t GetVolumeContentForRoot( Double_t );
// static pointer to this object
static MethodPDERS* ThisPDERS( void );
protected:
// make ROOT-independent C++ class for classifier response (classifier-specific implementation)
void MakeClassSpecific( std::ostream&, const TString& ) const;
// get help message text
void GetHelpMessage() const;
Volume* fHelpVolume; // auxiliary variable
Int_t fFcnCall; // number of external function calls (RootFinder)
// accessors
BinarySearchTree* GetBinaryTree( void ) const { return fBinaryTree; }
Double_t CKernelEstimate( const Event&, std::vector<const BinarySearchTreeNode*>&, Volume& );
void RKernelEstimate( const Event&, std::vector<const BinarySearchTreeNode*>&, Volume&, std::vector<Float_t> *pdfSum );
Double_t ApplyKernelFunction( Double_t normalized_distance );
Double_t KernelNormalization( Double_t pdf );
Double_t GetNormalizedDistance( const TMVA::Event &base_event,
const BinarySearchTreeNode &sample_event,
Double_t *dim_normalization);
Double_t NormSinc( Double_t x );
Double_t LanczosFilter( Int_t level, Double_t x );
// ranking of input variables
const Ranking* CreateRanking() { return 0; }
private:
// the option handling methods
void DeclareOptions();
void ProcessOptions();
// calculate the averages of the input variables needed for adaptive training
void CalcAverages();
// create binary search trees for signal and background
void CreateBinarySearchTree( Types::ETreeType type );
// get sample of training events
void GetSample( const Event &e, std::vector<const BinarySearchTreeNode*>& events, Volume *volume);
// option
TString fVolumeRange; // option volume range
TString fKernelString; // option kernel estimator
enum EVolumeRangeMode {
kUnsupported = 0,
kMinMax,
kRMS,
kAdaptive,
kUnscaled,
kkNN
} fVRangeMode;
enum EKernelEstimator {
kBox = 0,
kSphere,
kTeepee,
kGauss,
kSinc3, // the sinc enumerators must be consecutive and in order!
kSinc5,
kSinc7,
kSinc9,
kSinc11,
kLanczos2,
kLanczos3,
kLanczos5,
kLanczos8,
kTrim
} fKernelEstimator;
BinarySearchTree* fBinaryTree; // binary tree
std::vector<Float_t>* fDelta; // size of volume
std::vector<Float_t>* fShift; // volume center
std::vector<Float_t> fAverageRMS; // average RMS of signal and background
Float_t fScaleS; // weight for signal events
Float_t fScaleB; // weight for background events
Float_t fDeltaFrac; // fraction of RMS
Double_t fGaussSigma; // size of Gauss in adaptive volume
Double_t fGaussSigmaNorm;// size of Gauss in adaptive volume (normalised to dimensions)
Double_t fNRegOut; // number of output dimensions for regression
// input for adaptive volume adjustment
Float_t fNEventsMin; // minimum number of events in adaptive volume
Float_t fNEventsMax; // maximum number of events in adaptive volume
Float_t fMaxVIterations;// maximum number of iterations to adapt volume size
Float_t fInitialScale; // initial scale for adaptive volume
Bool_t fInitializedVolumeEle; // is volume element initialized ?
Int_t fkNNMin; // min number of events in kNN tree
Int_t fkNNMax; // max number of events in kNN tree
Double_t fMax_distance; // maximum distance
Bool_t fPrinted; // print
Bool_t fNormTree; // binary-search tree is normalised
void SetVolumeElement ( void );
Double_t CRScalc ( const Event& );
void RRScalc ( const Event&, std::vector<Float_t>* count );
Float_t GetError ( Float_t countS, Float_t countB,
Float_t sumW2S, Float_t sumW2B ) const;
// this carrier
static MethodPDERS* fgThisPDERS; // this pointer (required by root finder)
void UpdateThis();
void Init( void );
ClassDef(MethodPDERS,0) // Multi-dimensional probability density estimator range search (PDERS) method
};
} // namespace TMVA
#endif // MethodPDERS_H
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