// @(#)root/tmva $Id: MethodFisher.h,v 1.2 2006/05/23 13:03:15 brun Exp $ // Author: Andreas Hoecker, Xavier Prudent, Joerg Stelzer, Helge Voss, Kai Voss /********************************************************************************** * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * * Package: TMVA * * Class : MethodFisher * * * * Description: * * Analysis of Fisher discriminant (Fisher or Mahalanobis approach) * * * * Original author of this Fisher-Discriminant implementation: * * Andre Gaidot, CEA-France; * * (Translation from FORTRAN) * * * * Authors (alphabetical): * * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland * * Xavier Prudent <prudent@lapp.in2p3.fr> - LAPP, France * * Helge Voss <Helge.Voss@cern.ch> - MPI-KP Heidelberg, Germany * * Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada * * * * Copyright (c) 2005: * * CERN, Switzerland, * * U. of Victoria, Canada, * * MPI-KP Heidelberg, Germany, * * LAPP, Annecy, France * * * * Redistribution and use in source and binary forms, with or without * * modification, are permitted according to the terms listed in LICENSE * * (http://mva.sourceforge.net/license.txt) * * * * File and Version Information: * * $Id: MethodFisher.h,v 1.2 2006/05/23 13:03:15 brun Exp $ **********************************************************************************/ #ifndef ROOT_TMVA_MethodFisher #define ROOT_TMVA_MethodFisher ////////////////////////////////////////////////////////////////////////// // // // MethodFisher // // // // Analysis of Fisher discriminant (Fisher or Mahalanobis approach) // // // ////////////////////////////////////////////////////////////////////////// #ifndef ROOT_TMVA_MethodBase #include "TMVA/MethodBase.h" #endif #ifndef ROOT_TMVA_TMatrix #include "TMatrix.h" #endif #ifndef ROOT_TMatrixD #include "TMatrixD.h" #endif class TH1D; namespace TMVA { class MethodFisher : public MethodBase { public: MethodFisher( TString jobName, vector<TString>* theVariables, TTree* theTree = 0, TString theOption = "Fisher", TDirectory* theTargetDir = 0 ); MethodFisher( vector<TString> *theVariables, TString theWeightFile, TDirectory* theTargetDir = NULL ); virtual ~MethodFisher( void ); // training method virtual void Train( void ); // write weights to file virtual void WriteWeightsToFile( void ); // read weights from file virtual void ReadWeightsFromFile( void ); // calculate the MVA value virtual Double_t GetMvaValue( Event *e ); // write method specific histos to target file virtual void WriteHistosToFile( void ) ; enum FisherMethod { kFisher, kMahalanobis }; virtual FisherMethod GetMethod( void ) { return fFisherMethod; } protected: private: Int_t fNevt; // total number of events Int_t fNsig; // number of signal events Int_t fNbgd; // number of background events // event matrices: (first index: variable, second index: event) TMatrixF *fSig; // variables for signal TMatrixF *fBgd; // variables for background // Initialization and allocation void Init( void ); // get mean value of variables void GetMean( void ); // get matrix of covariance within class void GetCov_WithinClass( void ); // get matrix of covariance between class void GetCov_BetweenClass( void ); // and the full covariance matrix void GetCov_Full( void ); // get discriminating power void GetDiscrimPower( void ); // nice output void PrintCoefficients( void ); // get Fisher coefficients void GetFisherCoeff( void ); // matrix of variables means: S, B, S+B vs. variables TMatrixD *fMeanMatx; // covariance matrices TMatrixD *fBetw; // between-class matrix TMatrixD *fWith; // within-class matrix TMatrixD *fCov; // full covariance matrix // discriminating power vector<Double_t> *fDiscrimPow; // Fisher coefficients vector<Double_t> *fFisherCoeff; Double_t fF0; // method to be used (Fisher or Mahalanobis) FisherMethod fFisherMethod; // default initialisation called by all constructors void InitFisher( void ); ClassDef(MethodFisher,0) //Analysis of Fisher discriminant (Fisher or Mahalanobis approach) }; } // namespace TMVA #endif // MethodFisher_H