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class TMVA::MethodANNBase
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library: libTMVA
#include "MethodANNBase.h"
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class TMVA::MethodANNBase: public TMVA::MethodBase


Base class for all TMVA methods using artificial neural networks


Function Members (Methods)

 
    This is an abstract class, constructors will not be documented.
    Look at the header to check for available constructors.

public:
virtual~MethodANNBase()
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
Bool_tDebug() const
virtual voidDeclareOptions()
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
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
voidInitANNBase()
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
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
virtual voidPrintNetwork()
voidTMVA::Configurable::PrintOptions() const
virtual voidProcessOptions()
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 = "")
voidSetActivation(TMVA::TActivation* activation)
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)
voidSetNeuronInputCalculator(TMVA::TNeuronInput* inputCalculator)
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)
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 voidWriteMonitoringHistosToFile() 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:
virtual voidBuildNetwork(vector<Int_t>* layout, vector<Double_t>* weights = NULL)
Bool_tTMVA::MethodBase::CheckSanity(TTree* theTree = 0)
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)
voidForceNetworkCalculations()
voidForceNetworkInputs(Int_t ignoreIndex = -1)
TMVA::MethodBase::ECutOrientationTMVA::MethodBase::GetCutOrientation() const
virtual voidTMVA::IMethod::GetHelpMessage() const
TMVA::TNeuron*GetInputNeuron(Int_t index)
const TString&TMVA::MethodBase::GetInternalVarName(Int_t ivar) const
Double_tGetNetworkOutput()
const TString&TMVA::MethodBase::GetOriginalVarName(Int_t ivar) const
TMVA::TNeuron*GetOutputNeuron()
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 voidTMVA::MethodBase::MakeClassSpecificHeader(ostream&, const TString& = "") const
voidTObject::MakeZombie()
voidTObject::MakeZombie()
Int_tNumCycles()
vector<Int_t>*ParseLayoutString(TString layerSpec)
voidPrintMessage(TString message, Bool_t force = kFALSE) const
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
voidWaitForKeyboard()
voidTMVA::Configurable::WriteOptionsToStream(ostream& o, const TString& prefix) const
private:
voidAddPreLinks(TMVA::TNeuron* neuron, TObjArray* prevLayer)
voidBuildLayer(Int_t numNeurons, TObjArray* curLayer, TObjArray* prevLayer, Int_t layerIndex, Int_t numLayers)
voidBuildLayers(vector<Int_t>* layout)
voidDeleteNetwork()
voidDeleteNetworkLayer(TObjArray*& layer)
voidForceWeights(vector<Double_t>* weights)
voidInitWeights()
voidPrintLayer(TObjArray* layer)
voidPrintNeuron(TMVA::TNeuron* neuron)

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::TActivation*fActivationactivation function to be used for hidden layers
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)
TH1F*fEstimatorHistTestmonitors convergence of independent test sample
TH1F*fEstimatorHistTrainmonitors convergence of training sample
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)
TMVA::TActivation*fIdentityactivation for input and output layers
TMVA::TNeuronInput*fInputCalculatorinput calculator for all neurons
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
TObjArray*fNetworkTObjArray of TObjArrays representing network
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.
TObjArray*fSynapsesarray of pointers to synapses, no structural data
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
TRandom3*frgenrandom number generator for various uses
private:
TObjArray*fInputLayercache this for fast access
TStringfLayerSpeclayout specification option
Int_tfNcyclesnumber of epochs to train
TStringfNeuronInputTypename of neuron input calculator class
TStringfNeuronTypename of neuron activation function class
TMVA::TNeuron*fOutputNeuroncache this for fast access
static const Bool_tfgDEBUGdebug flag
static const Bool_tfgFIXED_SEEDfix rand generator seed

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

void DeclareOptions()
 define the options (their key words) that can be set in the option string
 here the options valid for ALL MVA methods are declared.
 know options: NCycles=xx              :the number of training cycles
               Normalize=kTRUE,kFALSe  :if normalised in put variables should be used
               HiddenLayser="N-1,N-2"  :the specification of the hidden layers
               NeuronType=sigmoid,tanh,radial,linar  : the type of activation function
                                                       used at the neuronn

void ProcessOptions()
 decode the options in the option string
vector<Int_t>* ParseLayoutString(TString layerSpec)
 parse layout specification string and return a vector, each entry
 containing the number of neurons to go in each successive layer
void InitANNBase()
 initialize ANNBase object
~MethodANNBase()
 destructor
void DeleteNetwork()
 delete/clear network
void DeleteNetworkLayer(TObjArray*& layer)
 delete a network layer
void BuildNetwork(vector<Int_t>* layout, vector<Double_t>* weights = NULL)
 build network given a layout (number of neurons in each layer)
 and optional weights array
void BuildLayers(vector<Int_t>* layout)
 build the network layers
void BuildLayer(Int_t numNeurons, TObjArray* curLayer, TObjArray* prevLayer, Int_t layerIndex, Int_t numLayers)
 build a single layer with neurons and synapses connecting this
 layer to the previous layer
void AddPreLinks(TMVA::TNeuron* neuron, TObjArray* prevLayer)
 add synapses connecting a neuron to its preceding layer
void InitWeights()
 initialize the synapse weights randomly
void ForceWeights(vector<Double_t>* weights)
 force the synapse weights
void ForceNetworkInputs(Int_t ignoreIndex = -1)
 force the input values of the input neurons
 force the value for each input neuron
void ForceNetworkCalculations()
 calculate input values to each neuron
void PrintMessage(TString message, Bool_t force = kFALSE) const
 print messages, turn off printing by setting verbose and debug flag appropriately
void WaitForKeyboard()
 wait for keyboard input, for debugging
void PrintNetwork()
 print network representation, for debugging
void PrintLayer(TObjArray* layer)
 print a single layer, for debugging
void PrintNeuron(TMVA::TNeuron* neuron)
 print a neuron, for debugging
Double_t GetMvaValue()
 get the mva value generated by the NN
void WriteWeightsToStream(ostream& o) const
 write the weights stream
void ReadWeightsFromStream(istream& istr)
 destroy/clear the network then read it back in from the weights file
const TMVA::Ranking* CreateRanking()
 compute ranking of input variables by summing function of weights
void WriteMonitoringHistosToFile()
 write histograms to file
void MakeClassSpecific(ostream& , const TString& ) const
 write specific classifier response
void SetActivation(TMVA::TActivation* activation)
 setters for subclasses
void SetNeuronInputCalculator(TMVA::TNeuronInput* inputCalculator)
void Train()
 this will have to be overridden by every subclass
Bool_t Debug()
{ return fgDEBUG; }
Double_t GetNetworkOutput()
{ return GetOutputNeuron()->GetActivationValue(); }
Int_t NumCycles()
 accessors
{ return fNcycles; }
TNeuron* GetInputNeuron(Int_t index)
{ return (TNeuron*)fInputLayer->At(index); }
TNeuron* GetOutputNeuron()
{ return fOutputNeuron; }

Author: Andreas Hoecker, Matt Jachowski
Last update: root/tmva $Id: MethodANNBase.cxx,v 1.13 2007/06/19 13:26:21 brun Exp $
Copyright (c) 2005: *

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