/usr/include/root/TMVA/MethodHMatrix.h is in libroot-tmva-dev 5.34.14-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : MethodHMatrix *
* Web : http://tmva.sourceforge.net *
* *
* Description: *
* H-Matrix method, which is implemented as a simple comparison of *
* chi-squared estimators for signal and background, taking into account *
* the linear correlations between the input variables. *
* Method is (also) used by D0 Collaboration (FNAL) for electron *
* identification; for more information, see, eg, *
* http://www-d0.fnal.gov/d0dist/dist/packages/tau_hmchisq/devel/doc/ *
* *
* Authors (alphabetical): *
* Andreas Hoecker <Andreas.Hocker@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_MethodHMatrix
#define ROOT_TMVA_MethodHMatrix
//////////////////////////////////////////////////////////////////////////
// //
// MethodHMatrix //
// //
// H-Matrix method, which is implemented as a simple comparison of //
// chi-squared estimators for signal and background, taking into //
// account the linear correlations between the input variables //
// //
//////////////////////////////////////////////////////////////////////////
#ifndef ROOT_TMVA_MethodBase
#include "TMVA/MethodBase.h"
#endif
#ifndef ROOT_TMVA_TMatrixDfwd
#ifndef ROOT_TMatrixDfwd
#include "TMatrixDfwd.h"
#endif
#endif
#ifndef ROOT_TMVA_TVectorD
#ifndef ROOT_TVectorD
#include "TVectorD.h"
#endif
#endif
namespace TMVA {
class MethodHMatrix : public MethodBase {
public:
MethodHMatrix( const TString& jobName,
const TString& methodTitle,
DataSetInfo& theData,
const TString& theOption = "",
TDirectory* theTargetDir = 0 );
MethodHMatrix( DataSetInfo& theData,
const TString& theWeightFile,
TDirectory* theTargetDir = NULL );
virtual ~MethodHMatrix();
virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets );
// training method
void Train();
using MethodBase::ReadWeightsFromStream;
// write weights to file
void AddWeightsXMLTo( void* parent ) const;
// read weights from file
void ReadWeightsFromStream( std::istream& istr );
void ReadWeightsFromXML( void* wghtnode );
// calculate the MVA value
Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 );
// ranking of input variables
const Ranking* CreateRanking() { return 0; }
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;
private:
// the option handling methods
void DeclareOptions();
void ProcessOptions();
// returns chi2 estimator for given type (signal or background)
Double_t GetChi2( Types::ESBType );
// compute correlation matrices
void ComputeCovariance( Bool_t, TMatrixD* );
// arrays of input evt vs. variable
TMatrixD* fInvHMatrixS; // inverse H-matrix (signal)
TMatrixD* fInvHMatrixB; // inverse H-matrix (background)
TVectorD* fVecMeanS; // vector of mean values (signal)
TVectorD* fVecMeanB; // vector of mean values (background)
// default initialisation method called by all constructors
void Init();
ClassDef(MethodHMatrix,0) // H-Matrix method, a simple comparison of chi-squared estimators for signal and background
};
} // namespace TMVA
#endif
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