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MethodCFMlpANN_Utils.h
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1 // @(#)root/tmva $Id$
2 // Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
3 
4 /**********************************************************************************
5  * Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
6  * Package: TMVA *
7  * Class : MethodCFMlpANN_utils *
8  * Web : http://tmva.sourceforge.net *
9  * *
10  * Reference for the original FORTRAN version "mlpl3.F": *
11  * Authors : J. Proriol and contributions from ALEPH-Clermont-Fd *
12  * Team members *
13  * Copyright: Laboratoire Physique Corpusculaire *
14  * Universite de Blaise Pascal, IN2P3/CNRS *
15  * Description: *
16  * Utility routine, obtained via f2c from original mlpl3.F FORTRAN routine *
17  * *
18  * Authors (alphabetical): *
19  * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
20  * Xavier Prudent <prudent@lapp.in2p3.fr> - LAPP, France *
21  * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
22  * Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada *
23  * *
24  * Copyright (c) 2005: *
25  * CERN, Switzerland *
26  * U. of Victoria, Canada *
27  * MPI-K Heidelberg, Germany *
28  * LAPP, Annecy, France *
29  * *
30  * Redistribution and use in source and binary forms, with or without *
31  * modification, are permitted according to the terms listed in LICENSE *
32  * (http://tmva.sourceforge.net/LICENSE) *
33  **********************************************************************************/
34 
35 #ifndef ROOT_TMVA_MethodCFMlpANN_Utils
36 #define ROOT_TMVA_MethodCFMlpANN_Utils
37 
38 #include "TMVA/MethodCFMlpANN_def.h"
39 #include "TMVA/MsgLogger.h"
40 
41 #include "Rtypes.h"
42 
43 #include <cstdlib>
44 //////////////////////////////////////////////////////////////////////////
45 // //
46 // MethodCFMlpANN_Utils //
47 // //
48 // Implementation of Clermond-Ferrand artificial neural network //
49 // //
50 //////////////////////////////////////////////////////////////////////////
51 
52 namespace TMVA {
53 
54  class MethodCFMlpANN_Utils {
55 
56  public:
57 
58  MethodCFMlpANN_Utils();
59  virtual ~MethodCFMlpANN_Utils();
60 
61  protected:
62 
63  void Train_nn( Double_t *tin2, Double_t *tout2, Int_t *ntrain,
64  Int_t *ntest, Int_t *nvar2, Int_t *nlayer,
65  Int_t *nodes, Int_t *ncycle );
66 
67  void Entree_new( Int_t *, char *, Int_t *ntrain, Int_t *ntest,
68  Int_t *numlayer, Int_t *nodes, Int_t *numcycle,
69  Int_t );
70 
71  virtual Int_t DataInterface( Double_t*, Double_t*, Int_t*, Int_t*, Int_t*, Int_t*,
72  Double_t*, Int_t*, Int_t* ) = 0;
73 
74  Double_t Fdecroi(Int_t *i__);
75  Double_t Sen3a(void);
76 
77  void Wini ();
78  void En_avant (Int_t *ievent);
79  void En_avant2 (Int_t *ievent);
80  void En_arriere(Int_t *ievent);
81  void Leclearn (Int_t *ktest, Double_t *tout2, Double_t *tin2);
82  void Out (Int_t *iii, Int_t *maxcycle);
83  void Cout (Int_t *, Double_t *xxx);
84  void Innit (char *det, Double_t *tout2, Double_t *tin2, Int_t );
85  void TestNN ();
86  void Inl ();
87  void GraphNN (Int_t *ilearn, Double_t *, Double_t *, char *, Int_t);
88  void Foncf (Int_t *i__, Double_t *u, Double_t *f);
89  void Cout2 (Int_t * /*i1*/, Double_t *yyy);
90  void Lecev2 (Int_t *ktest, Double_t *tout2, Double_t *tin2);
91  void Arret (const char* mot );
92  void CollectVar(Int_t *nvar, Int_t *class__, Double_t *xpg);
93 
94  protected:
95 
96  Int_t fg_100; // constant
97  Int_t fg_0; // constant
98  static const Int_t fg_max_nVar_; // static maximum number of input variables
99  static const Int_t fg_max_nNodes_; // maximum number of nodes per variable
100  Int_t fg_999; // constant
101  static const char* const fg_MethodName; // method name for print
102 
103  Double_t W_ref(const Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3) const {
104  return wNN [(a_3*max_nNodes_ + a_2)*max_nLayers_ + a_1 - 187];
105  }
106  Double_t& W_ref(Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3) {
107  return wNN [((a_3)*max_nNodes_ + (a_2))*max_nLayers_ + a_1 - 187];
108  }
109 
110  Double_t Ww_ref(const Double_t wwNN[], Int_t a_1,Int_t a_2) const {
111  return wwNN[(a_2)*max_nLayers_ + a_1 - 7];
112  }
113  Double_t& Ww_ref(Double_t wwNN[], Int_t a_1,Int_t a_2) {
114  return wwNN[(a_2)*max_nLayers_ + a_1 - 7];
115  }
116 
117  // ANN training parameters
118  struct {
119  Double_t epsmin, epsmax, eeps, eta;
120  Int_t layerm, lclass, nevl, nblearn, nunilec, nunisor, nunishort, nunap;
121  Int_t nvar, itest, ndiv, ichoi, ndivis, nevt;
122  } fParam_1;
123 
124  // ANN training results
125  struct {
126  Double_t xmax[max_nVar_], xmin[max_nVar_];
127  Int_t nclass[max_Events_], mclass[max_Events_], iclass;
128  } fVarn_1;
129 
130  // dynamic data table
131  class VARn2 {
132  public:
133  VARn2() : fNevt(0), fNvar(0) {
134  fxx = 0;
135  }
136  ~VARn2() {
137  Delete();
138  }
139  void Create( Int_t nevt, Int_t nvar ) {
140  fNevt = nevt+1; fNvar = nvar+1; // fortran array style 1...N
141  fxx = new Double_t*[fNevt];
142  for (Int_t i=0; i<fNevt; i++) fxx[i] = new Double_t[fNvar];
143  }
144  Double_t operator=( Double_t val ) { return val; }
145  Double_t &operator()( Int_t ievt, Int_t ivar ) const {
146  if (0 != fxx && ievt < fNevt && ivar < fNvar) return fxx[ievt][ivar];
147  else {
148  printf( "*** ERROR in varn3_(): fxx is zero pointer ==> abort ***\n") ;
149  std::exit(1);
150  return fxx[0][0];
151  }
152  }
153  void Delete( void ) {
154  if (0 != fxx) for (Int_t i=0; i<fNevt; i++) if (0 != fxx[i]) delete [] fxx[i];
155  delete[] fxx;
156  fxx=0;
157  }
158 
159  Double_t** fxx;
160  Int_t fNevt;
161  Int_t fNvar;
162  } fVarn2_1, fVarn3_1;
163 
164  // ANN weights
165  struct {
166  Double_t x[max_nLayers_*max_nNodes_];
167  Double_t y[max_nLayers_*max_nNodes_];
168  Double_t o[max_nNodes_];
169  Double_t w[max_nLayers_*max_nNodes_*max_nNodes_];
170  Double_t ww[max_nLayers_*max_nNodes_];
171  Double_t cut[max_nNodes_];
172  Double_t deltaww[max_nLayers_*max_nNodes_];
173  Int_t neuron[max_nLayers_];
174  } fNeur_1;
175 
176  // ANN weights
177  struct {
178  Double_t coef[max_nNodes_], temp[max_nLayers_], demin, demax;
179  Double_t del[max_nLayers_*max_nNodes_];
180  Double_t delw[max_nLayers_*max_nNodes_*max_nNodes_];
181  Double_t delta[max_nLayers_*max_nNodes_*max_nNodes_];
182  Double_t delww[max_nLayers_*max_nNodes_];
183  Int_t idde;
184  } fDel_1;
185 
186  // flags and stuff (don't ask me...)
187  struct {
188  Double_t ancout, tolcou;
189  Int_t ieps;
190  } fCost_1;
191 
192  void SetLogger(MsgLogger *l) { fLogger = l; }
193 
194  private:
195  MsgLogger * fLogger;
196  MsgLogger& ULog() { if (fLogger) return *fLogger; return *(fLogger = new MsgLogger("CFMLP_Utils")); } // avoiding control reaches end of non-void function warning
197 
198  public:
199 
200  ClassDef(MethodCFMlpANN_Utils,0); // Implementation of Clermond-Ferrand artificial neural network
201  };
202 
203 } // namespace TMVA
204 
205 #endif
MethodCFMlpANN_def.h