// @(#)root/tmva $Id$
// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss, Peter Speckmayer, Eckhard von Toerne, Jan Therhaag
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : MethodLikelihood *
* Web : http://tmva.sourceforge.net *
* *
* Description: *
* Likelihood analysis ("non-parametric approach") *
* Also implemented is a "diagonalized likelihood approach", *
* which improves over the uncorrelated likelihood ansatz by transforming *
* linearly the input variables into a diagonal space, using the square-root *
* of the covariance matrix. This approach can be chosen by inserting *
* the letter "D" into the option string. *
* *
* Authors (alphabetical): *
* Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
* Peter Speckmayer <Peter.Speckmazer@cern.ch> - CERN, Switzerland *
* Joerg Stelzer <Joerg.Stelzer@cern.ch> - CERN, Switzerland *
* Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
* Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada *
* Jan Therhaag <Jan.Therhaag@cern.ch> - U of Bonn, Germany *
* Eckhard v. Toerne <evt@uni-bonn.de> - U of Bonn, Germany *
* *
* Copyright (c) 2005-2011: *
* CERN, Switzerland *
* U. of Victoria, Canada *
* MPI-K Heidelberg, Germany *
* U. of Bonn, 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_MethodLikelihood
#define ROOT_TMVA_MethodLikelihood
//////////////////////////////////////////////////////////////////////////
// //
// MethodLikelihood //
// //
// Likelihood analysis ("non-parametric approach") //
// Also implemented is a "diagonalized likelihood approach", //
// which improves over the uncorrelated likelihood ansatz by //
// transforming linearly the input variables into a diagonal space, //
// using the square-root of the covariance matrix //
// //
//////////////////////////////////////////////////////////////////////////
#include "TMVA/MethodBase.h"
#include "TMVA/PDF.h"
class TH1D;
namespace TMVA {
class MethodLikelihood : public MethodBase {
public:
MethodLikelihood( const TString& jobName,
const TString& methodTitle,
DataSetInfo& theData,
const TString& theOption = "");
MethodLikelihood( DataSetInfo& theData,
const TString& theWeightFile);
virtual ~MethodLikelihood();
virtual Bool_t HasAnalysisType( Types::EAnalysisType type,
UInt_t numberClasses, UInt_t numberTargets );
// training method
void Train();
// 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
// the argument is used for internal ranking tests
Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 );
// write method specific histos to target file
void WriteMonitoringHistosToFile() const;
// ranking of input variables
const Ranking* CreateRanking();
virtual void WriteOptionsToStream ( std::ostream& o, const TString& prefix ) const;
protected:
void DeclareCompatibilityOptions();
// make ROOT-independent C++ class for classifier response (classifier-specific implementation)
void MakeClassSpecific( std::ostream&, const TString& ) const;
// header and auxiliary classes
void MakeClassSpecificHeader( std::ostream&, const TString& = "" ) const;
// get help message text
void GetHelpMessage() const;
private:
// returns transformed or non-transformed output
Double_t TransformLikelihoodOutput( Double_t ps, Double_t pb ) const;
// the option handling methods
void Init();
void DeclareOptions();
void ProcessOptions();
// options
Double_t fEpsilon; // minimum number of likelihood (to avoid zero)
Bool_t fTransformLikelihoodOutput; // likelihood output is sigmoid-transformed
Int_t fDropVariable; // for ranking test
std::vector<TH1*>* fHistSig; // signal PDFs (histograms)
std::vector<TH1*>* fHistBgd; // background PDFs (histograms)
std::vector<TH1*>* fHistSig_smooth; // signal PDFs (smoothed histograms)
std::vector<TH1*>* fHistBgd_smooth; // background PDFs (smoothed histograms)
PDF* fDefaultPDFLik; // pdf that contains default definitions
std::vector<PDF*>* fPDFSig; // list of PDFs (signal)
std::vector<PDF*>* fPDFBgd; // list of PDFs (background)
// default initialisation called by all constructors
// obsolete variables kept for backward combatibility
Int_t fNsmooth; // number of smooth passes
Int_t* fNsmoothVarS; // number of smooth passes
Int_t* fNsmoothVarB; // number of smooth passes
Int_t fAverageEvtPerBin; // average events per bin; used to calculate fNbins
Int_t* fAverageEvtPerBinVarS; // average events per bin; used to calculate fNbins
Int_t* fAverageEvtPerBinVarB; // average events per bin; used to calculate fNbins
TString fBorderMethodString; // the method to take care about "border" effects (string)
Float_t fKDEfineFactor; // fine tuning factor for Adaptive KDE
TString fKDEiterString; // Number of iterations (string)
TString fKDEtypeString; // Kernel type to use for KDE (string)
TString* fInterpolateString; // which interpolation method used for reference histograms (individual for each variable)
ClassDef(MethodLikelihood,0); // Likelihood analysis ("non-parametric approach")
};
} // namespace TMVA
#endif // MethodLikelihood_H