35#ifndef ROOT_TMVA_MethodCFMlpANN_Utils
36#define ROOT_TMVA_MethodCFMlpANN_Utils
91 void Arret (
const char* mot );
104 return wNN [(a_3*max_nNodes_ + a_2)*max_nLayers_ + a_1 - 187];
107 return wNN [((a_3)*max_nNodes_ + (a_2))*max_nLayers_ + a_1 - 187];
111 return wwNN[(a_2)*max_nLayers_ + a_1 - 7];
114 return wwNN[(a_2)*max_nLayers_ + a_1 - 7];
148 printf(
"*** ERROR in varn3_(): fxx is zero pointer ==> abort ***\n") ;
#define ClassDef(name, id)
Double_t operator=(Double_t val)
void Create(Int_t nevt, Int_t nvar)
Double_t & operator()(Int_t ievt, Int_t ivar) const
Implementation of Clermond-Ferrand artificial neural network.
Double_t delta[max_nLayers_ *max_nNodes_ *max_nNodes_]
void Foncf(Int_t *i__, Double_t *u, Double_t *f)
struct TMVA::MethodCFMlpANN_Utils::@166 fDel_1
void Out(Int_t *iii, Int_t *maxcycle)
MethodCFMlpANN_Utils()
default constructor
void Innit(char *det, Double_t *tout2, Double_t *tin2, Int_t)
void Entree_new(Int_t *, char *, Int_t *ntrain, Int_t *ntest, Int_t *numlayer, Int_t *nodes, Int_t *numcycle, Int_t)
Double_t temp[max_nLayers_]
void CollectVar(Int_t *nvar, Int_t *class__, Double_t *xpg)
[smart comments to be added]
void Leclearn(Int_t *ktest, Double_t *tout2, Double_t *tin2)
[smart comments to be added]
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 & Ww_ref(Double_t wwNN[], Int_t a_1, Int_t a_2)
struct TMVA::MethodCFMlpANN_Utils::@165 fNeur_1
struct TMVA::MethodCFMlpANN_Utils::@163 fParam_1
void GraphNN(Int_t *ilearn, Double_t *, Double_t *, char *, Int_t)
[smart comments to be added]
Double_t cut[max_nNodes_]
class TMVA::MethodCFMlpANN_Utils::VARn2 fVarn3_1
Double_t w[max_nLayers_ *max_nNodes_ *max_nNodes_]
static const Int_t fg_max_nVar_
class TMVA::MethodCFMlpANN_Utils::VARn2 fVarn2_1
Double_t del[max_nLayers_ *max_nNodes_]
void En_avant2(Int_t *ievent)
[smart comments to be added]
Double_t Ww_ref(const Double_t wwNN[], Int_t a_1, Int_t a_2) const
Double_t Fdecroi(Int_t *i__)
[smart comments to be added]
void En_arriere(Int_t *ievent)
[smart comments to be added]
Double_t & W_ref(Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3)
Int_t neuron[max_nLayers_]
void Cout(Int_t *, Double_t *xxx)
[smart comments to be added]
Double_t y[max_nLayers_ *max_nNodes_]
Int_t mclass[max_Events_]
Double_t delw[max_nLayers_ *max_nNodes_ *max_nNodes_]
static const Int_t fg_max_nNodes_
Double_t W_ref(const Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3) const
Double_t Sen3a(void)
[smart comments to be added]
Double_t deltaww[max_nLayers_ *max_nNodes_]
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)
Double_t x[max_nLayers_ *max_nNodes_]
void SetLogger(MsgLogger *l)
void Wini()
[smart comments to be added]
struct TMVA::MethodCFMlpANN_Utils::@164 fVarn_1
Double_t coef[max_nNodes_]
Double_t ww[max_nLayers_ *max_nNodes_]
void En_avant(Int_t *ievent)
[smart comments to be added]
void Cout2(Int_t *, Double_t *yyy)
[smart comments to be added]
void TestNN()
[smart comments to be added]
Double_t delww[max_nLayers_ *max_nNodes_]
void Lecev2(Int_t *ktest, Double_t *tout2, Double_t *tin2)
[smart comments to be added]
virtual ~MethodCFMlpANN_Utils()
Destructor.
void Arret(const char *mot)
Int_t nclass[max_Events_]
struct TMVA::MethodCFMlpANN_Utils::@167 fCost_1
static const char *const fg_MethodName
void Inl()
[smart comments to be added]
ostringstream derivative to redirect and format output
create variable transformations