/usr/include/root/TMVA/MethodCFMlpANN.h is in libroot-tmva-dev 5.34.19+dfsg-1.2.
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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 : MethodCFMlpANN *
* Web : http://tmva.sourceforge.net *
* *
* Description: *
* Interface for Clermond-Ferrand artificial neural network. *
* The ANN code has been translated from FORTRAN77 (f2c); *
* see files: MethodCFMlpANN_f2c_mlpl3.cpp *
* MethodCFMlpANN_f2c_datacc.cpp *
* *
* -------------------------------------------------------------------- *
* Reference for the original FORTRAN version: *
* Authors : J. Proriol and contributions from ALEPH-Clermont-Fd *
* Team members. Contact : gaypas@afal11.cern.ch *
* *
* Copyright: Laboratoire Physique Corpusculaire *
* Universite de Blaise Pascal, IN2P3/CNRS *
* -------------------------------------------------------------------- *
* *
* Usage: options are given through Factory: *
* factory->BookMethod( "MethodCFMlpANN", OptionsString ); *
* *
* where: *
* TString OptionsString = "n_training_cycles:n_hidden_layers" *
* *
* default is: n_training_cycles = 5000, n_layers = 4 *
* note that the number of hidden layers in the NN is *
* *
* n_hidden_layers = n_layers - 2 *
* *
* since there is one input and one output layer. The number of *
* nodes (neurons) is predefined to be *
* *
* n_nodes[i] = nvars + 1 - i (where i=1..n_layers) *
* *
* with nvars being the number of variables used in the NN. *
* Hence, the default case is: n_neurons(layer 1 (input)) : nvars *
* n_neurons(layer 2 (hidden)): nvars-1 *
* n_neurons(layer 3 (hidden)): nvars-1 *
* n_neurons(layer 4 (out)) : 2 *
* *
* This artificial neural network usually needs a relatively large *
* number of cycles to converge (8000 and more). Overtraining can *
* be efficienctly tested by comparing the signal and background *
* output of the NN for the events that were used for training and *
* an independent data sample (with equal properties). If the separation *
* performance is significantly better for the training sample, the *
* NN interprets statistical effects, and is hence overtrained. In *
* this case, the number of cycles should be reduced, or the size *
* of the training sample increased. *
* *
* 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_MethodCFMlpANN
#define ROOT_TMVA_MethodCFMlpANN
//////////////////////////////////////////////////////////////////////////
// //
// MethodCFMlpANN //
// //
// Interface for Clermond-Ferrand artificial neural network //
// //
//////////////////////////////////////////////////////////////////////////
#include <iosfwd>
#ifndef ROOT_TMVA_MethodBase
#include "TMVA/MethodBase.h"
#endif
#ifndef ROOT_TMVA_MethodCFMlpANN_Utils
#include "TMVA/MethodCFMlpANN_Utils.h"
#endif
#ifndef ROOT_TMatrixF
#include "TMatrixF.h"
#endif
namespace TMVA {
class MethodCFMlpANN : public MethodBase, MethodCFMlpANN_Utils {
public:
MethodCFMlpANN( const TString& jobName,
const TString& methodTitle,
DataSetInfo& theData,
const TString& theOption = "3000:N-1:N-2",
TDirectory* theTargetDir = 0 );
MethodCFMlpANN( DataSetInfo& theData,
const TString& theWeightFile,
TDirectory* theTargetDir = NULL );
virtual ~MethodCFMlpANN( 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
Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 );
// data accessors for external functions
Double_t GetData ( Int_t isel, Int_t ivar ) const { return (*fData)(isel, ivar); }
Int_t GetClass( Int_t ivar ) const { return (*fClass)[ivar]; }
// static pointer to this object (required for external functions
static MethodCFMlpANN* This( void );
// 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;
// header and auxiliary classes
void MakeClassSpecificHeader( std::ostream&, const TString& = "" ) const;
// get help message text
void GetHelpMessage() const;
Int_t DataInterface( Double_t*, Double_t*, Int_t*, Int_t*, Int_t*, Int_t*,
Double_t*, Int_t*, Int_t* );
private:
void PrintWeights( std::ostream & o ) const;
// the option handling methods
void DeclareOptions();
void ProcessOptions();
static MethodCFMlpANN* fgThis; // this carrier
// LUTs
TMatrixF *fData; // the (data,var) string
std::vector<Int_t> *fClass; // the event class (1=signal, 2=background)
Int_t fNlayers; // number of layers (including input and output layers)
Int_t fNcycles; // number of training cycles
Int_t* fNodes; // number of nodes per layer
// additional member variables for the independent NN::Evaluation phase
Double_t** fYNN; // weights
TString fLayerSpec; // the hidden layer specification string
// auxiliary member functions
Double_t EvalANN( std::vector<Double_t>&, Bool_t& isOK );
void NN_ava ( Double_t* );
Double_t NN_fonc( Int_t, Double_t ) const;
// default initialisation
void Init( void );
ClassDef(MethodCFMlpANN,0) // Interface for Clermond-Ferrand artificial neural network
};
} // namespace TMVA
#endif
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