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class TMVA::MethodCFMlpANN
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library: libTMVA
#include "MethodCFMlpANN.h"
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class TMVA::MethodCFMlpANN: public TMVA::MethodBase, private TMVA::MethodCFMlpANN_Utils


 
/* Interface to Clermond-Ferrand artificial neural network

The CFMlpANN belong to the class of Multilayer Perceptrons (MLP), which are feed-forward networks according to the following propagation schema:

Schema for artificial neural network
The input layer contains as many neurons as input variables used in the MVA. The output layer contains two neurons for the signal and background event classes. In between the input and output layers are a variable number of k hidden layers with arbitrary numbers of neurons. (While the structure of the input and output layers is determined by the problem, the hidden layers can be configured by the user through the option string of the method booking.)
As indicated in the sketch, all neuron inputs to a layer are linear combinations of the neuron output of the previous layer. The transfer from input to output within a neuron is performed by means of an "activation function". In general, the activation function of a neuron can be zero (deactivated), one (linear), or non-linear. The above example uses a sigmoid activation function. The transfer function of the output layer is usually linear. As a consequence: an ANN without hidden layer should give identical discrimination power as a linear discriminant analysis (Fisher). In case of one hidden layer, the ANN computes a linear combination of sigmoid.
The learning method used by the CFMlpANN is only stochastic. */

Function Members (Methods)

public:
virtual~MethodCFMlpANN()
voidTObject::AbstractMethod(const char* method) const
voidTObject::AbstractMethod(const char* method) const
virtual voidTObject::AppendPad(Option_t* option = "")
virtual voidTObject::AppendPad(Option_t* option = "")
TDirectory*TMVA::MethodBase::BaseDir() const
virtual voidTObject::Browse(TBrowser* b)
virtual voidTObject::Browse(TBrowser* b)
voidTMVA::Configurable::CheckForUnusedOptions() const
static TClass*Class()
virtual const char*TObject::ClassName() const
virtual const char*TObject::ClassName() const
virtual voidTObject::Clear(Option_t* = "")
virtual voidTObject::Clear(Option_t* = "")
virtual TObject*TObject::Clone(const char* newname = "") const
virtual TObject*TObject::Clone(const char* newname = "") const
virtual Int_tTObject::Compare(const TObject* obj) const
virtual Int_tTObject::Compare(const TObject* obj) const
TMVA::ConfigurableTMVA::Configurable::Configurable(const TString& theOption = "")
virtual voidTObject::Copy(TObject& object) const
virtual voidTObject::Copy(TObject& object) const
voidTMVA::MethodBase::CreateMVAPdfs()
virtual const TMVA::Ranking*CreateRanking()
TMVA::DataSet&TMVA::MethodBase::Data() const
virtual voidTObject::Delete(Option_t* option = "")
virtual voidTObject::Delete(Option_t* option = "")
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() const
virtual voidTObject::DrawClass() const
virtual TObject*TObject::DrawClone(Option_t* option = "") const
virtual TObject*TObject::DrawClone(Option_t* option = "") const
virtual voidTObject::Dump() const
virtual voidTObject::Dump() const
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
Int_tGetClass(Int_t ivar) const
Double_tGetData(Int_t isel, Int_t ivar) const
virtual Option_t*TObject::GetDrawOption() const
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
static Long_tTObject::GetDtorOnly()
Double_tTMVA::MethodBase::GetEffForRoot(Double_t)
virtual Double_tTMVA::MethodBase::GetEfficiency(TString, TTree*, Double_t& err)
TMVA::Event&TMVA::MethodBase::GetEvent() const
Double_tTMVA::MethodBase::GetEventVal(Int_t ivar) const
Double_tTMVA::MethodBase::GetEventWeight() const
virtual const char*TObject::GetIconName() const
virtual const char*TObject::GetIconName() const
const TString&TMVA::MethodBase::GetInputExp(int i) const
const TString&TMVA::MethodBase::GetInputVar(int i) const
const TString&TMVA::MethodBase::GetJobName() const
const TString&TMVA::MethodBase::GetMethodName() const
const TString&TMVA::MethodBase::GetMethodTitle() const
TMVA::Types::EMVATMVA::MethodBase::GetMethodType() const
virtual Double_tTMVA::MethodBase::GetmuTransform(TTree*)
virtual Double_tGetMvaValue()
virtual const char*TMVA::MethodBase::GetName() const
Int_tTMVA::MethodBase::GetNvar() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
static Bool_tTObject::GetObjectStat()
virtual Double_tTMVA::MethodBase::GetOptimalSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const
virtual Option_t*TObject::GetOption() const
virtual Option_t*TObject::GetOption() const
const TString&TMVA::Configurable::GetOptions() const
virtual Double_tTMVA::MethodBase::GetProba(Double_t mvaVal, Double_t ap_sig)
const TStringTMVA::MethodBase::GetProbaName() const
virtual Double_tTMVA::MethodBase::GetRarity(Double_t mvaVal, TMVA::Types::ESBType reftype = Types::kBackground) const
Double_tTMVA::MethodBase::GetRMS(Int_t ivar) const
virtual Double_tTMVA::MethodBase::GetSeparation(TH1*, TH1*) const
virtual Double_tTMVA::MethodBase::GetSeparation(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
Double_tTMVA::MethodBase::GetSignalReferenceCut() const
virtual Double_tTMVA::MethodBase::GetSignificance() const
TTree*TMVA::MethodBase::GetTestTree() const
const TString&TMVA::MethodBase::GetTestvarName() const
static TMVA::MethodBase*TMVA::MethodBase::GetThisBase()
virtual const char*TObject::GetTitle() const
virtual const char*TObject::GetTitle() const
virtual Double_tTMVA::MethodBase::GetTrainingEfficiency(TString)
TTree*TMVA::MethodBase::GetTrainingTree() const
virtual UInt_tTObject::GetUniqueID() const
virtual UInt_tTObject::GetUniqueID() const
TMVA::Types::EVariableTransformTMVA::MethodBase::GetVariableTransform() const
TMVA::VariableTransformBase&TMVA::MethodBase::GetVarTransform() const
TStringTMVA::MethodBase::GetWeightFileDir() const
TStringTMVA::MethodBase::GetWeightFileName() const
Double_tTMVA::MethodBase::GetXmax(Int_t ivar) const
Double_tTMVA::MethodBase::GetXmax(const TString& var) const
Double_tTMVA::MethodBase::GetXmin(Int_t ivar) const
Double_tTMVA::MethodBase::GetXmin(const TString& var) const
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual ULong_tTObject::Hash() const
virtual ULong_tTObject::Hash() const
Bool_tTMVA::MethodBase::HasTrainingTree() const
Bool_tTMVA::MethodBase::Help() const
static Double_tTMVA::MethodBase::IGetEffForRoot(Double_t)
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual voidTObject::Inspect() const
virtual voidTObject::Inspect() const
voidTObject::InvertBit(UInt_t f)
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
virtual Bool_tTObject::IsFolder() const
Bool_tTMVA::MethodBase::IsMVAPdfs() const
Bool_tTMVA::MethodBase::IsNormalised() const
Bool_tTMVA::MethodBase::IsOK() const
Bool_tTObject::IsOnHeap() const
Bool_tTObject::IsOnHeap() const
Bool_tTMVA::MethodBase::IsSignalEvent() const
virtual Bool_tTMVA::MethodBase::IsSignalLike()
virtual Bool_tTObject::IsSortable() const
virtual Bool_tTObject::IsSortable() const
Bool_tTObject::IsZombie() const
Bool_tTObject::IsZombie() const
virtual voidTObject::ls(Option_t* option = "") const
virtual voidTObject::ls(Option_t* option = "") const
virtual voidTMVA::MethodBase::MakeClass(const TString& classFileName = "") const
voidTObject::MayNotUse(const char* method) const
voidTObject::MayNotUse(const char* method) const
TDirectory*TMVA::MethodBase::MethodBaseDir() const
TMVA::MethodCFMlpANNMethodCFMlpANN(TMVA::DataSet& theData, TString theWeightFile, TDirectory* theTargetDir = NULL)
TMVA::MethodCFMlpANNMethodCFMlpANN(TString jobName, TString methodTitle, TMVA::DataSet& theData, TString theOption = "3000:N-1:N-2", TDirectory* theTargetDir = 0)
Double_tTMVA::MethodBase::Norm(Int_t ivar, Double_t x) const
Double_tTMVA::MethodBase::Norm(TString var, Double_t x) const
virtual Bool_tTObject::Notify()
virtual Bool_tTObject::Notify()
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr, void* vp)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz, void* vp)
void*TObject::operator new[](size_t sz, void* vp)
TMVA::IMethod&TMVA::IMethod::operator=(const TMVA::IMethod&)
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTObject::Paint(Option_t* option = "")
voidTMVA::Configurable::ParseOptions(Bool_t verbose = kTRUE)
virtual voidTObject::Pop()
virtual voidTObject::Pop()
virtual voidTMVA::MethodBase::PrepareEvaluationTree(TTree* theTestTree)
virtual voidTObject::Print(Option_t* option = "") const
virtual voidTObject::Print(Option_t* option = "") const
virtual voidTMVA::MethodBase::PrintHelpMessage() const
voidTMVA::Configurable::PrintOptions() const
virtual Int_tTObject::Read(const char* name)
virtual Int_tTObject::Read(const char* name)
virtual Bool_tTMVA::MethodBase::ReadEvent(TTree* tr, UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
voidTMVA::MethodBase::ReadStateFromFile()
voidTMVA::MethodBase::ReadStateFromStream(istream& tf)
voidTMVA::MethodBase::ReadStateFromStream(TFile& rf)
virtual Bool_tTMVA::MethodBase::ReadTestEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
virtual Bool_tTMVA::MethodBase::ReadTrainingEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
virtual voidReadWeightsFromStream(istream& istr)
virtual voidTObject::RecursiveRemove(TObject* obj)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidTObject::ResetBit(UInt_t f)
voidTObject::ResetBit(UInt_t f)
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") const
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") const
virtual voidTObject::SavePrimitive(ostream& out, Option_t* option = "")
virtual voidTObject::SavePrimitive(ostream& out, Option_t* option = "")
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
voidTObject::SetBit(UInt_t f, Bool_t set)
virtual voidTObject::SetDrawOption(Option_t* option = "")
virtual voidTObject::SetDrawOption(Option_t* option = "")
static voidTObject::SetDtorOnly(void* obj)
static voidTObject::SetDtorOnly(void* obj)
voidTMVA::MethodBase::SetHelp(Bool_t h = kTRUE)
voidTMVA::MethodBase::SetJobName(TString jobName)
voidTMVA::MethodBase::SetMethodName(TString methodName)
voidTMVA::MethodBase::SetMethodTitle(TString methodTitle)
voidTMVA::MethodBase::SetMethodType(TMVA::Types::EMVA methodType)
voidTMVA::Configurable::SetName(const char* n)
voidTMVA::MethodBase::SetNormalised(Bool_t norm)
voidTMVA::MethodBase::SetNvar(Int_t n)
static voidTObject::SetObjectStat(Bool_t stat)
static voidTObject::SetObjectStat(Bool_t stat)
voidTMVA::Configurable::SetOptions(const TString& s)
virtual voidTObject::SetUniqueID(UInt_t uid)
virtual voidTObject::SetUniqueID(UInt_t uid)
voidTMVA::MethodBase::SetVariableTransform(TMVA::Types::EVariableTransform m)
voidTMVA::MethodBase::SetVerbose(Bool_t v = kTRUE)
voidTMVA::MethodBase::SetWeightFileDir(TString fileDir)
voidTMVA::MethodBase::SetWeightFileName(TString)
voidTMVA::MethodBase::SetXmax(Int_t ivar, Double_t x)
voidTMVA::MethodBase::SetXmax(const TString& var, Double_t x)
voidTMVA::MethodBase::SetXmin(Int_t ivar, Double_t x)
voidTMVA::MethodBase::SetXmin(const TString& var, Double_t x)
virtual voidShowMembers(TMemberInspector& insp, char* parent)
virtual voidStreamer(TBuffer& b)
voidStreamerNVirtual(TBuffer& b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
virtual voidTMVA::MethodBase::Test(TTree* theTestTree = 0)
Bool_tTObject::TestBit(UInt_t f) const
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
virtual voidTMVA::MethodBase::TestInit(TTree* theTestTree = 0)
static TMVA::MethodCFMlpANN*This()
virtual voidTrain()
voidTMVA::MethodBase::TrainMethod()
virtual voidTObject::UseCurrentStyle()
virtual voidTObject::UseCurrentStyle()
Bool_tTMVA::MethodBase::Verbose() const
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
voidTMVA::MethodBase::WriteEvaluationHistosToFile(TDirectory* targetDir = 0)
virtual voidTMVA::MethodBase::WriteMonitoringHistosToFile() const
voidTMVA::MethodBase::WriteStateToFile() const
voidTMVA::MethodBase::WriteStateToStream(TFile& rf) const
voidTMVA::MethodBase::WriteStateToStream(ostream& tf, Bool_t isClass = kFALSE) const
virtual voidWriteWeightsToStream(ostream& o) const
protected:
Bool_tTMVA::MethodBase::CheckSanity(TTree* theTree = 0)
virtual Int_tDataInterface(Double_t*, Double_t*, Int_t*, Int_t*, Int_t*, Int_t*, Double_t*, Int_t*, Int_t*)
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTMVA::Configurable::EnableLooseOptions(Bool_t b = kTRUE)
TMVA::MethodBase::ECutOrientationTMVA::MethodBase::GetCutOrientation() const
virtual voidGetHelpMessage() const
const TString&TMVA::MethodBase::GetInternalVarName(Int_t ivar) const
const TString&TMVA::MethodBase::GetOriginalVarName(Int_t ivar) const
const TString&TMVA::MethodBase::GetTestvarPrefix() const
UInt_tTMVA::MethodBase::GetTrainingROOTVersionCode() const
TStringTMVA::MethodBase::GetTrainingROOTVersionString() const
UInt_tTMVA::MethodBase::GetTrainingTMVAVersionCode() const
TStringTMVA::MethodBase::GetTrainingTMVAVersionString() const
TMVA::Types::ESBTypeTMVA::MethodBase::GetVariableTransformType() const
TDirectory*TMVA::MethodBase::LocalTDir() const
Bool_tTMVA::Configurable::LooseOptionCheckingEnabled() const
virtual voidMakeClassSpecific(ostream&, const TString&) const
virtual voidMakeClassSpecificHeader(ostream&, const TString& = "") const
voidTObject::MakeZombie()
voidTObject::MakeZombie()
voidTMVA::Configurable::ReadOptionsFromStream(istream& istr)
voidTMVA::Configurable::ResetSetFlag()
voidTMVA::MethodBase::ResetThisBase()
voidTMVA::MethodBase::SetSignalReferenceCut(Double_t cut)
voidTMVA::MethodBase::SetTestvarName(const TString& v = "")
voidTMVA::MethodBase::SetTestvarPrefix(TString prefix)
voidTMVA::MethodBase::SetVariableTransformType(TMVA::Types::ESBType t)
voidTMVA::MethodBase::Statistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Bool_t norm = kFALSE)
Bool_tTMVA::MethodBase::TxtWeightsOnly() const
voidTMVA::Configurable::WriteOptionsToStream(ostream& o, const TString& prefix) const
private:
voidTMVA::MethodCFMlpANN_Utils::Arret(const char* mot)
voidTMVA::MethodCFMlpANN_Utils::CollectVar(Int_t* nvar, Int_t* class__, Double_t* xpg)
voidTMVA::MethodCFMlpANN_Utils::Cout(Int_t*, Double_t* xxx)
voidTMVA::MethodCFMlpANN_Utils::Cout2(Int_t*, Double_t* yyy)
virtual voidDeclareOptions()
voidTMVA::MethodCFMlpANN_Utils::En_arriere(Int_t* ievent)
voidTMVA::MethodCFMlpANN_Utils::En_avant(Int_t* ievent)
voidTMVA::MethodCFMlpANN_Utils::En_avant2(Int_t* ievent)
voidTMVA::MethodCFMlpANN_Utils::Entree_new(Int_t*, char*, Int_t* ntrain, Int_t* ntest, Int_t* numlayer, Int_t* nodes, Int_t* numcycle, Int_t)
Double_tEvalANN(vector<Double_t>&, Bool_t& isOK)
Double_tTMVA::MethodCFMlpANN_Utils::Fdecroi(Int_t* i__)
voidTMVA::MethodCFMlpANN_Utils::Foncf(Int_t* i__, Double_t* u, Double_t* f)
voidTMVA::MethodCFMlpANN_Utils::GraphNN(Int_t* ilearn, Double_t*, Double_t*, char*, Int_t)
voidInitCFMlpANN()
voidTMVA::MethodCFMlpANN_Utils::Inl()
voidTMVA::MethodCFMlpANN_Utils::Innit(char* det, Double_t* tout2, Double_t* tin2, Int_t)
voidTMVA::MethodCFMlpANN_Utils::Lecev2(Int_t* ktest, Double_t* tout2, Double_t* tin2)
voidTMVA::MethodCFMlpANN_Utils::Leclearn(Int_t* ktest, Double_t* tout2, Double_t* tin2)
voidNN_ava(Double_t*)
Double_tNN_fonc(Int_t, Double_t) const
voidTMVA::MethodCFMlpANN_Utils::Out(Int_t* iii, Int_t* maxcycle)
voidPrintWeights(ostream& o) const
virtual voidProcessOptions()
Double_tTMVA::MethodCFMlpANN_Utils::Sen3a()
voidTMVA::MethodCFMlpANN_Utils::TestNN()
voidTMVA::MethodCFMlpANN_Utils::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_tTMVA::MethodCFMlpANN_Utils::W_ref(const Double_t* wNN, Int_t a_1, Int_t a_2, Int_t a_3) const
Double_t&TMVA::MethodCFMlpANN_Utils::W_ref(Double_t* wNN, Int_t a_1, Int_t a_2, Int_t a_3)
voidTMVA::MethodCFMlpANN_Utils::Wini()
Double_tTMVA::MethodCFMlpANN_Utils::Ww_ref(const Double_t* wwNN, Int_t a_1, Int_t a_2) const
Double_t&TMVA::MethodCFMlpANN_Utils::Ww_ref(Double_t* wwNN, Int_t a_1, Int_t a_2)

Data Members

public:
enum TMVA::MethodBase::EWeightFileType { kROOT
kTEXT
};
enum TMVA::MethodBase::ECutOrientation { kNegative
kPositive
};
enum TObject::EStatusBits { kCanDelete
kMustCleanup
kObjInCanvas
kIsReferenced
kHasUUID
kCannotPick
kNoContextMenu
kInvalidObject
};
enum TObject::[unnamed] { kIsOnHeap
kNotDeleted
kZombie
kBitMask
kSingleKey
kOverwrite
kWriteDelete
};
enum TObject::EStatusBits { kCanDelete
kMustCleanup
kObjInCanvas
kIsReferenced
kHasUUID
kCannotPick
kNoContextMenu
kInvalidObject
};
enum TObject::[unnamed] { kIsOnHeap
kNotDeleted
kZombie
kBitMask
kSingleKey
kOverwrite
kWriteDelete
};
protected:
TMVA::MethodBase::ECutOrientationTMVA::MethodBase::fCutOrientation+1 if Sig>Bkg, -1 otherwise
TH1*TMVA::MethodBase::fEffBefficiency plot (background)
TH1*TMVA::MethodBase::fEffBvsSbackground efficiency versus signal efficiency
TH1*TMVA::MethodBase::fEffSefficiency plot (signal)
TGraph*TMVA::MethodBase::fGraphBgraphs used for splines for efficiency (background)
TGraph*TMVA::MethodBase::fGraphSgraphs used for splines for efficiency (signal)
TGraph*TMVA::MethodBase::fGraphTrainBgraphs used for splines for training efficiency (background)
TGraph*TMVA::MethodBase::fGraphTrainEffBvsSgraphs used for splines for training signal eff. versus background eff.
TGraph*TMVA::MethodBase::fGraphTrainSgraphs used for splines for training efficiency (signal)
TGraph*TMVA::MethodBase::fGrapheffBvsSgraphs used for splines for signal eff. versus background eff.
TH1*TMVA::MethodBase::fHistB_highbinMVA plots used for efficiency calculations (background)
TH1*TMVA::MethodBase::fHistB_plotbinMVA plots used for graphics representation (background)
TH1*TMVA::MethodBase::fHistBhatBworking histograms needed for mu-transform (background)
TH1*TMVA::MethodBase::fHistBhatSworking histograms needed for mu-transform (signal)
TH1*TMVA::MethodBase::fHistMuBmu-transform (background)
TH1*TMVA::MethodBase::fHistMuSmu-transform (signal)
TH1*TMVA::MethodBase::fHistS_highbinMVA plots used for efficiency calculations (signal)
TH1*TMVA::MethodBase::fHistS_plotbinMVA plots used for graphics representation (signal)
vector<TString>*TMVA::MethodBase::fInputVarsvector of input variables used in MVA
Bool_tTMVA::MethodBase::fIsOKstatus of sanity checks
TMVA::MsgLoggerTMVA::MethodBase::fLoggermessage logger
TMVA::PDF*TMVA::MethodBase::fMVAPdfBbackground MVA PDF
TMVA::PDF*TMVA::MethodBase::fMVAPdfSsignal MVA PDF
Double_tTMVA::MethodBase::fMode
Int_tTMVA::MethodBase::fNbinsnumber of bins in representative histograms
Int_tTMVA::MethodBase::fNbinsHnumber of bins in evaluation histograms
Int_tTMVA::MethodBase::fNbinsMVAPdfnumber of bins used in histogram that creates PDF
Int_tTMVA::MethodBase::fNsmoothMVAPdfnumber of times a histogram is smoothed before creating the PDF
TH1*TMVA::MethodBase::fProbaB_plotbinP(MVA) plots used for graphics representation (background)
TH1*TMVA::MethodBase::fProbaS_plotbinP(MVA) plots used for graphics representation (signal)
TMVA::Ranking*TMVA::MethodBase::fRankingranking
TH1*TMVA::MethodBase::fRarityB_plotbinR(MVA) plots used for graphics representation (background)
TH1*TMVA::MethodBase::fRarityS_plotbinR(MVA) plots used for graphics representation (signal)
TH1*TMVA::MethodBase::fRejBvsSbackground rejection (=1-eff.) versus signal efficiency
TMVA::PDF*TMVA::MethodBase::fSplBPDFs of MVA distribution (background)
TMVA::TSpline1*TMVA::MethodBase::fSplRefBhelper splines for RootFinder (background)
TMVA::TSpline1*TMVA::MethodBase::fSplRefShelper splines for RootFinder (signal)
TMVA::PDF*TMVA::MethodBase::fSplSPDFs of MVA distribution (signal)
TMVA::PDF*TMVA::MethodBase::fSplTrainBPDFs of training MVA distribution (background)
TSpline*TMVA::MethodBase::fSplTrainEffBvsSsplines for training signal eff. versus background eff.
TMVA::TSpline1*TMVA::MethodBase::fSplTrainRefBhelper splines for RootFinder (background)
TMVA::TSpline1*TMVA::MethodBase::fSplTrainRefShelper splines for RootFinder (signal)
TMVA::PDF*TMVA::MethodBase::fSplTrainSPDFs of training MVA distribution (signal)
TSpline*TMVA::MethodBase::fSpleffBvsSsplines for signal eff. versus background eff.
TH1*TMVA::MethodBase::fTrainEffBTraining efficiency plot (background)
TH1*TMVA::MethodBase::fTrainEffBvsSTraining background efficiency versus signal efficiency
TH1*TMVA::MethodBase::fTrainEffSTraining efficiency plot (signal)
TH1*TMVA::MethodBase::fTrainRejBvsSTraining background rejection (=1-eff.) versus signal efficiency
Double_tTMVA::MethodBase::fX
TH1*TMVA::MethodBase::finvBeffvsSeffinverse background eff (1/eff.) versus signal efficiency
private:
vector<Int_t>*fClassthe event class (1=signal, 2=background)
TMVA::MethodCFMlpANN_Utils::TMVA::MethodCFMlpANN_Utils::fCost_1
TMatrix*fDatathe (data,var) string
TMVA::MethodCFMlpANN_Utils::TMVA::MethodCFMlpANN_Utils::fDel_1
TStringfLayerSpecthe hidden layer specification string
Int_tfNcyclesnumber of training cycles
TMVA::MethodCFMlpANN_Utils::TMVA::MethodCFMlpANN_Utils::fNeur_1
Int_tfNlayersnumber of layers (including input and output layers)
Int_t*fNodesnumber of nodes per layer
TMVA::MethodCFMlpANN_Utils::TMVA::MethodCFMlpANN_Utils::fParam_1
TMVA::MethodCFMlpANN_Utils::VARn2TMVA::MethodCFMlpANN_Utils::fVarn2_1
TMVA::MethodCFMlpANN_Utils::VARn2TMVA::MethodCFMlpANN_Utils::fVarn3_1
TMVA::MethodCFMlpANN_Utils::TMVA::MethodCFMlpANN_Utils::fVarn_1
Double_t**fYNNweights
static TMVA::MethodCFMlpANN*fgThisthis carrier
static Int_tTMVA::MethodCFMlpANN_Utils::fg_0constant
static Int_tTMVA::MethodCFMlpANN_Utils::fg_100constant
static Int_tTMVA::MethodCFMlpANN_Utils::fg_999constant
static Int_tTMVA::MethodCFMlpANN_Utils::fg_max_nNodes_maximum number of nodes per variable
static Int_tTMVA::MethodCFMlpANN_Utils::fg_max_nVar_static maximum number of input variables

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

MethodCFMlpANN(TString jobName, TString methodTitle, TMVA::DataSet& theData, TString theOption = "3000:N-1:N-2", TDirectory* theTargetDir = 0)
 standard constructor
 option string: "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.

MethodCFMlpANN(TMVA::DataSet& theData, TString theWeightFile, TDirectory* theTargetDir = NULL)
 construction from weight file
void DeclareOptions()
 define the options (their key words) that can be set in the option string
 know options: NCycles=xx              :the number of training cycles
               HiddenLayser="N-1,N-2"  :the specification of the hidden layers
void ProcessOptions()
 decode the options in the option string
void InitCFMlpANN( void )
 default initialisation called by all constructors
~MethodCFMlpANN( void )
 destructor
void Train( void )
 training of the Clement-Ferrand NN classifier
Double_t GetMvaValue()
 returns CFMlpANN output (normalised within [0,1])
Double_t EvalANN(vector<Double_t>& , Bool_t& isOK)
 evaluates NN value as function of input variables
void NN_ava(Double_t* )
 auxiliary functions
Double_t NN_fonc(Int_t , Double_t ) const
 activation function
void ReadWeightsFromStream(istream& istr)
 read back the weight from the training from file (stream)
Int_t DataInterface(Double_t* , Double_t* , Int_t* , Int_t* , Int_t* , Int_t* , Double_t* , Int_t* , Int_t* )
 data interface function
void WriteWeightsToStream(ostream& o) const
 write number of variables and classes
void PrintWeights(ostream& o) const
 write the weights of the neural net
void MakeClassSpecific(ostream& , const TString& ) const
 write specific classifier response
void MakeClassSpecificHeader(ostream& , const TString& = "") const
 write specific classifier response for header
void GetHelpMessage()
 get help message text

 typical length of text line:
         "|--------------------------------------------------------------|"
Double_t GetData(Int_t isel, Int_t ivar) const
 data accessors for external functions
{ return (*fData)(isel, ivar); }
Int_t GetClass(Int_t ivar) const
{ return (*fClass)[ivar]; }
MethodCFMlpANN* This( void )
 static pointer to this object (required for external functions
{ return fgThis; }
const Ranking* CreateRanking()
 ranking of input variables
{ return 0; }

Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
Last update: root/tmva $Id: MethodCFMlpANN.cxx,v 1.16 2007/06/21 10:28:08 brun Exp $
Copyright (c) 2005: *

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