00001 // @(#)root/tmva $Id: MethodLikelihood.h 36966 2010-11-26 09:50:13Z evt $ 00002 // Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss 00003 00004 /********************************************************************************** 00005 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * 00006 * Package: TMVA * 00007 * Class : MethodLikelihood * 00008 * Web : http://tmva.sourceforge.net * 00009 * * 00010 * Description: * 00011 * Likelihood analysis ("non-parametric approach") * 00012 * Also implemented is a "diagonalized likelihood approach", * 00013 * which improves over the uncorrelated likelihood ansatz by transforming * 00014 * linearly the input variables into a diagonal space, using the square-root * 00015 * of the covariance matrix. This approach can be chosen by inserting * 00016 * the letter "D" into the option string. * 00017 * * 00018 * Authors (alphabetical): * 00019 * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland * 00020 * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany * 00021 * Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada * 00022 * * 00023 * Copyright (c) 2005: * 00024 * CERN, Switzerland * 00025 * U. of Victoria, Canada * 00026 * MPI-K Heidelberg, Germany * 00027 * * 00028 * Redistribution and use in source and binary forms, with or without * 00029 * modification, are permitted according to the terms listed in LICENSE * 00030 * (http://tmva.sourceforge.net/LICENSE) * 00031 **********************************************************************************/ 00032 00033 #ifndef ROOT_TMVA_MethodLikelihood 00034 #define ROOT_TMVA_MethodLikelihood 00035 00036 ////////////////////////////////////////////////////////////////////////// 00037 // // 00038 // MethodLikelihood // 00039 // // 00040 // Likelihood analysis ("non-parametric approach") // 00041 // Also implemented is a "diagonalized likelihood approach", // 00042 // which improves over the uncorrelated likelihood ansatz by // 00043 // transforming linearly the input variables into a diagonal space, // 00044 // using the square-root of the covariance matrix // 00045 // // 00046 ////////////////////////////////////////////////////////////////////////// 00047 00048 #ifndef ROOT_TMVA_MethodBase 00049 #include "TMVA/MethodBase.h" 00050 #endif 00051 #ifndef ROOT_TMVA_PDF 00052 #include "TMVA/PDF.h" 00053 #endif 00054 00055 class TH1D; 00056 00057 namespace TMVA { 00058 00059 class MethodLikelihood : public MethodBase { 00060 00061 public: 00062 00063 MethodLikelihood( const TString& jobName, 00064 const TString& methodTitle, 00065 DataSetInfo& theData, 00066 const TString& theOption = "", 00067 TDirectory* theTargetDir = 0 ); 00068 00069 MethodLikelihood( DataSetInfo& theData, 00070 const TString& theWeightFile, 00071 TDirectory* theTargetDir = NULL ); 00072 00073 virtual ~MethodLikelihood(); 00074 00075 virtual Bool_t HasAnalysisType( Types::EAnalysisType type, 00076 UInt_t numberClasses, UInt_t numberTargets ); 00077 00078 // training method 00079 void Train(); 00080 00081 // write weights to file 00082 void WriteWeightsToStream( TFile& rf ) const; 00083 void AddWeightsXMLTo( void* parent ) const; 00084 00085 // read weights from file 00086 void ReadWeightsFromStream( istream& istr ); 00087 void ReadWeightsFromStream( TFile& istr ); 00088 void ReadWeightsFromXML( void* wghtnode ); 00089 // calculate the MVA value 00090 // the argument is used for internal ranking tests 00091 Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 ); 00092 00093 // write method specific histos to target file 00094 void WriteMonitoringHistosToFile() const; 00095 00096 // ranking of input variables 00097 const Ranking* CreateRanking(); 00098 00099 virtual void WriteOptionsToStream ( ostream& o, const TString& prefix ) const; 00100 00101 protected: 00102 00103 void DeclareCompatibilityOptions(); 00104 00105 // make ROOT-independent C++ class for classifier response (classifier-specific implementation) 00106 void MakeClassSpecific( std::ostream&, const TString& ) const; 00107 00108 // header and auxiliary classes 00109 void MakeClassSpecificHeader( std::ostream&, const TString& = "" ) const; 00110 00111 // get help message text 00112 void GetHelpMessage() const; 00113 00114 private: 00115 00116 // returns transformed or non-transformed output 00117 Double_t TransformLikelihoodOutput( Double_t ps, Double_t pb ) const; 00118 00119 // the option handling methods 00120 void Init(); 00121 void DeclareOptions(); 00122 void ProcessOptions(); 00123 00124 // options 00125 Double_t fEpsilon; // minimum number of likelihood (to avoid zero) 00126 Bool_t fTransformLikelihoodOutput; // likelihood output is sigmoid-transformed 00127 00128 Int_t fDropVariable; // for ranking test 00129 00130 std::vector<TH1*>* fHistSig; // signal PDFs (histograms) 00131 std::vector<TH1*>* fHistBgd; // background PDFs (histograms) 00132 std::vector<TH1*>* fHistSig_smooth; // signal PDFs (smoothed histograms) 00133 std::vector<TH1*>* fHistBgd_smooth; // background PDFs (smoothed histograms) 00134 00135 PDF* fDefaultPDFLik; // pdf that contains default definitions 00136 std::vector<PDF*>* fPDFSig; // list of PDFs (signal) 00137 std::vector<PDF*>* fPDFBgd; // list of PDFs (background) 00138 00139 // default initialisation called by all constructors 00140 00141 // obsolete variables kept for backward combatibility 00142 Int_t fNsmooth; // number of smooth passes 00143 Int_t* fNsmoothVarS; // number of smooth passes 00144 Int_t* fNsmoothVarB; // number of smooth passes 00145 Int_t fAverageEvtPerBin; // average events per bin; used to calculate fNbins 00146 Int_t* fAverageEvtPerBinVarS; // average events per bin; used to calculate fNbins 00147 Int_t* fAverageEvtPerBinVarB; // average events per bin; used to calculate fNbins 00148 TString fBorderMethodString; // the method to take care about "border" effects (string) 00149 Float_t fKDEfineFactor; // fine tuning factor for Adaptive KDE 00150 TString fKDEiterString; // Number of iterations (string) 00151 TString fKDEtypeString; // Kernel type to use for KDE (string) 00152 TString* fInterpolateString; // which interpolation method used for reference histograms (individual for each variable) 00153 00154 ClassDef(MethodLikelihood,0) // Likelihood analysis ("non-parametric approach") 00155 }; 00156 00157 } // namespace TMVA 00158 00159 #endif // MethodLikelihood_H
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