/usr/include/root/TMVA/MethodCFMlpANN_Utils.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.
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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_utils *
* Web : http://tmva.sourceforge.net *
* *
* Reference for the original FORTRAN version "mlpl3.F": *
* Authors : J. Proriol and contributions from ALEPH-Clermont-Fd *
* Team members *
* Copyright: Laboratoire Physique Corpusculaire *
* Universite de Blaise Pascal, IN2P3/CNRS *
* Description: *
* Utility routine, obtained via f2c from original mlpl3.F FORTRAN routine *
* *
* 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_Utils
#define ROOT_TMVA_MethodCFMlpANN_Utils
#ifndef ROOT_TMVA_MethodCFMlpANN_def
#include "TMVA/MethodCFMlpANN_def.h"
#endif
#ifndef ROOT_TMVA_MsgLogger
#include "TMVA/MsgLogger.h"
#endif
#ifndef ROOT_Rtypes
#include "Rtypes.h"
#endif
#include <cstdlib>
//////////////////////////////////////////////////////////////////////////
// //
// MethodCFMlpANN_Utils //
// //
// Implementation of Clermond-Ferrand artificial neural network //
// //
//////////////////////////////////////////////////////////////////////////
namespace TMVA {
class MethodCFMlpANN_Utils {
public:
MethodCFMlpANN_Utils();
virtual ~MethodCFMlpANN_Utils();
protected:
void Train_nn( Double_t *tin2, Double_t *tout2, Int_t *ntrain,
Int_t *ntest, Int_t *nvar2, Int_t *nlayer,
Int_t *nodes, Int_t *ncycle );
void Entree_new( Int_t *, char *, Int_t *ntrain, Int_t *ntest,
Int_t *numlayer, Int_t *nodes, Int_t *numcycle,
Int_t );
virtual Int_t DataInterface( Double_t*, Double_t*, Int_t*, Int_t*, Int_t*, Int_t*,
Double_t*, Int_t*, Int_t* ) = 0;
Double_t Fdecroi(Int_t *i__);
Double_t Sen3a(void);
void Wini ();
void En_avant (Int_t *ievent);
void En_avant2 (Int_t *ievent);
void En_arriere(Int_t *ievent);
void Leclearn (Int_t *ktest, Double_t *tout2, Double_t *tin2);
void Out (Int_t *iii, Int_t *maxcycle);
void Cout (Int_t *, Double_t *xxx);
void Innit (char *det, Double_t *tout2, Double_t *tin2, Int_t );
void TestNN ();
void Inl ();
void GraphNN (Int_t *ilearn, Double_t *, Double_t *, char *, Int_t);
void Foncf (Int_t *i__, Double_t *u, Double_t *f);
void Cout2 (Int_t * /*i1*/, Double_t *yyy);
void Lecev2 (Int_t *ktest, Double_t *tout2, Double_t *tin2);
void Arret (const char* mot );
void CollectVar(Int_t *nvar, Int_t *class__, Double_t *xpg);
protected:
static Int_t fg_100; // constant
static Int_t fg_0; // constant
static Int_t fg_max_nVar_; // static maximum number of input variables
static Int_t fg_max_nNodes_; // maximum number of nodes per variable
static Int_t fg_999; // constant
static const char* fg_MethodName; // method name for print
Double_t W_ref(const Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3) const {
return wNN [(a_3*max_nNodes_ + a_2)*max_nLayers_ + a_1 - 187];
}
Double_t& W_ref(Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3) {
return wNN [((a_3)*max_nNodes_ + (a_2))*max_nLayers_ + a_1 - 187];
}
Double_t Ww_ref(const Double_t wwNN[], Int_t a_1,Int_t a_2) const {
return wwNN[(a_2)*max_nLayers_ + a_1 - 7];
}
Double_t& Ww_ref(Double_t wwNN[], Int_t a_1,Int_t a_2) {
return wwNN[(a_2)*max_nLayers_ + a_1 - 7];
}
// ANN training parameters
struct {
Double_t epsmin, epsmax, eeps, eta;
Int_t layerm, lclass, nevl, nblearn, nunilec, nunisor, nunishort, nunap;
Int_t nvar, itest, ndiv, ichoi, ndivis, nevt;
} fParam_1;
// ANN training results
struct {
Double_t xmax[max_nVar_], xmin[max_nVar_];
Int_t nclass[max_Events_], mclass[max_Events_], iclass;
} fVarn_1;
// dynamic data table
class VARn2 {
public:
VARn2() : fNevt(0), fNvar(0) {
fxx = 0;
}
~VARn2() {
Delete();
}
void Create( Int_t nevt, Int_t nvar ) {
fNevt = nevt+1; fNvar = nvar+1; // fortran array style 1...N
fxx = new Double_t*[fNevt];
for (Int_t i=0; i<fNevt; i++) fxx[i] = new Double_t[fNvar];
}
Double_t operator=( Double_t val ) { return val; }
Double_t &operator()( Int_t ievt, Int_t ivar ) const {
if (0 != fxx && ievt < fNevt && ivar < fNvar) return fxx[ievt][ivar];
else {
printf( "*** ERROR in varn3_(): fxx is zero pointer ==> abort ***\n") ;
std::exit(1);
return fxx[0][0];
}
}
void Delete( void ) {
if (0 != fxx) for (Int_t i=0; i<fNevt; i++) if (0 != fxx[i]) delete [] fxx[i];
delete[] fxx;
fxx=0;
}
Double_t** fxx;
Int_t fNevt;
Int_t fNvar;
} fVarn2_1, fVarn3_1;
// ANN weights
struct {
Double_t x[max_nLayers_*max_nNodes_];
Double_t y[max_nLayers_*max_nNodes_];
Double_t o[max_nNodes_];
Double_t w[max_nLayers_*max_nNodes_*max_nNodes_];
Double_t ww[max_nLayers_*max_nNodes_];
Double_t cut[max_nNodes_];
Double_t deltaww[max_nLayers_*max_nNodes_];
Int_t neuron[max_nLayers_];
} fNeur_1;
// ANN weights
struct {
Double_t coef[max_nNodes_], temp[max_nLayers_], demin, demax;
Double_t del[max_nLayers_*max_nNodes_];
Double_t delw[max_nLayers_*max_nNodes_*max_nNodes_];
Double_t delta[max_nLayers_*max_nNodes_*max_nNodes_];
Double_t delww[max_nLayers_*max_nNodes_];
Int_t idde;
} fDel_1;
// flags and stuff (don't ask me...)
struct {
Double_t ancout, tolcou;
Int_t ieps;
} fCost_1;
void SetLogger(MsgLogger *l) { fLogger = l; }
private:
MsgLogger * fLogger;
MsgLogger& ULog() { if (fLogger) return *fLogger; return *(fLogger = new MsgLogger("CFMLP_Utils")); } // avoiding control reaches end of non-void function warning
public:
ClassDef(MethodCFMlpANN_Utils,0) // Implementation of Clermond-Ferrand artificial neural network
};
} // namespace TMVA
#endif
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