/usr/include/root/TMVA/MethodTMlpANN.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 : MethodTMlpANN *
* Web : http://tmva.sourceforge.net *
* *
* Description: *
* Implementation of interface for Root-integrated artificial neural *
* network: TMultiLayerPerceptron, author: Christophe.Delaere@cern.ch *
* for a manual, see *
* http://root.cern.ch/root/html/TMultiLayerPerceptron.html *
* *
* 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_MethodTMlpANN
#define ROOT_TMVA_MethodTMlpANN
//////////////////////////////////////////////////////////////////////////
// //
// MethodTMlpANN //
// //
// Implementation of interface for Root-integrated artificial neural //
// network: TMultiLayerPerceptron //
// //
//////////////////////////////////////////////////////////////////////////
#ifndef ROOT_TMVA_MethodBase
#include "TMVA/MethodBase.h"
#endif
class TMultiLayerPerceptron;
namespace TMVA {
class MethodTMlpANN : public MethodBase {
public:
MethodTMlpANN( const TString& jobName,
const TString& methodTitle,
DataSetInfo& theData,
const TString& theOption = "3000:N-1:N-2",
TDirectory* theTargetDir = 0 );
MethodTMlpANN( DataSetInfo& theData,
const TString& theWeightFile,
TDirectory* theTargetDir = NULL );
virtual ~MethodTMlpANN( 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 file
void AddWeightsXMLTo( void* parent ) const;
// read weights from file
void ReadWeightsFromStream( std::istream& istr );
void ReadWeightsFromXML(void* wghtnode);
// calculate the MVA value ...
// - here it is just a dummy, as it is done in the overwritten
// - PrepareEvaluationtree... ugly but necessary due to the strucure
// of TMultiLayerPercepton in ROOT grr... :-(
Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 );
void SetHiddenLayer(TString hiddenlayer = "" ) { fHiddenLayer=hiddenlayer; }
// ranking of input variables
const Ranking* CreateRanking() { return 0; }
// make ROOT-independent C++ class
void MakeClass( const TString& classFileName = TString("") ) const;
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();
void CreateMLPOptions( TString );
// option string
TString fLayerSpec; // Layer specification option
TMultiLayerPerceptron* fMLP; // the TMLP
TTree* fLocalTrainingTree; // local copy of training tree
TString fHiddenLayer; // string containig the hidden layer structure
Int_t fNcycles; // number of training cylcles
Double_t fValidationFraction; // fraction of events in training tree used for cross validation
TString fMLPBuildOptions; // option string to build the mlp
TString fLearningMethod; // the learning method (given via option string)
// default initialisation called by all constructors
void Init( void );
ClassDef(MethodTMlpANN,0) // Implementation of interface for TMultiLayerPerceptron
};
} // namespace TMVA
#endif
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