/usr/include/root/TMVA/MethodFisher.h is in libroot-tmva-dev 5.34.30-0ubuntu8.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | // @(#)root/tmva $Id$
// 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 *
* Web : http://tmva.sourceforge.net *
* *
* 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-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 *
* 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://tmva.sourceforge.net/LICENSE) *
**********************************************************************************/
#ifndef ROOT_TMVA_MethodFisher
#define ROOT_TMVA_MethodFisher
//////////////////////////////////////////////////////////////////////////
// //
// MethodFisher //
// //
// Analysis of Fisher discriminant (Fisher or Mahalanobis approach) //
// //
//////////////////////////////////////////////////////////////////////////
#include <vector>
#ifndef ROOT_TMVA_MethodBase
#include "TMVA/MethodBase.h"
#endif
#ifndef ROOT_TMatrixDfwd
#include "TMatrixDfwd.h"
#endif
class TH1D;
namespace TMVA {
class MethodFisher : public MethodBase {
public:
MethodFisher( const TString& jobName,
const TString& methodTitle,
DataSetInfo& dsi,
const TString& theOption = "Fisher",
TDirectory* theTargetDir = 0 );
MethodFisher( DataSetInfo& dsi,
const TString& theWeightFile,
TDirectory* theTargetDir = NULL );
virtual ~MethodFisher( void );
virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets );
// training method
void Train( void );
using MethodBase::ReadWeightsFromStream;
// write weights to stream
void AddWeightsXMLTo ( void* parent ) const;
// read weights from stream
void ReadWeightsFromStream( std::istream & i );
void ReadWeightsFromXML ( void* wghtnode );
// calculate the MVA value
Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 );
enum EFisherMethod { kFisher, kMahalanobis };
EFisherMethod GetFisherMethod( void ) { return fFisherMethod; }
// ranking of input variables
const Ranking* CreateRanking();
// nice output
void PrintCoefficients( 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;
private:
// the option handling methods
void DeclareOptions();
void ProcessOptions();
// Initialization and allocation of matrices
void InitMatrices( 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 );
// get Fisher coefficients
void GetFisherCoeff( void );
// matrix of variables means: S, B, S+B vs. variables
TMatrixD *fMeanMatx;
// method to be used
TString fTheMethod; // Fisher or Mahalanobis
EFisherMethod fFisherMethod; // Fisher or Mahalanobis
// covariance matrices
TMatrixD *fBetw; // between-class matrix
TMatrixD *fWith; // within-class matrix
TMatrixD *fCov; // full covariance matrix
// number of events (sumOfWeights)
Double_t fSumOfWeightsS; // sum-of-weights for signal training events
Double_t fSumOfWeightsB; // sum-of-weights for background training events
std::vector<Double_t>* fDiscrimPow; // discriminating power
std::vector<Double_t>* fFisherCoeff; // Fisher coefficients
Double_t fF0; // offset
// default initialisation called by all constructors
void Init( void );
ClassDef(MethodFisher,0) // Analysis of Fisher discriminant (Fisher or Mahalanobis approach)
};
} // namespace TMVA
#endif // MethodFisher_H
|