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class TMVA::MethodCuts: public TMVA::MethodBase, public TMVA::IFitterTarget


MethodCuts

Multivariate optimisation of signal efficiency for given background
efficiency, using rectangular minimum and maximum requirements on
input variables

Function Members (Methods)

public:

virtual	~MethodCuts()
void	TObject::AbstractMethod(const char* method) const
void	TMVA::Configurable::AddOptionsXMLTo(void* parent) const
void	TMVA::MethodBase::AddOutput(TMVA::Types::ETreeType type, TMVA::Types::EAnalysisType analysisType)
virtual void	AddWeightsXMLTo(void* parent) const
virtual void	TObject::AppendPad(Option_t* option = "")
TDirectory*	TMVA::MethodBase::BaseDir() const
virtual void	TObject::Browse(TBrowser* b)
void	TMVA::Configurable::CheckForUnusedOptions() const
virtual void	CheckSetup()
static TClass*	Class()
virtual const char*	TObject::ClassName() const
virtual void	TNamed::Clear(Option_t* option = "")
virtual TObject*	TNamed::Clone(const char* newname = "") const
virtual Int_t	TNamed::Compare(const TObject* obj) const
Double_t	ComputeEstimator(vector<Double_t>&)
TMVA::Configurable	TMVA::Configurable::Configurable(const TString& theOption = "")
TMVA::Configurable	TMVA::Configurable::Configurable(const TMVA::Configurable&)
virtual void	TNamed::Copy(TObject& named) const
virtual const TMVA::Ranking*	CreateRanking()
TMVA::DataSet*	TMVA::MethodBase::Data() const
TMVA::DataSetInfo&	TMVA::MethodBase::DataInfo() const
virtual void	TMVA::MethodBase::DeclareCompatibilityOptions()
virtual void	DeclareOptions()
virtual void	TObject::Delete(Option_t* option = "")MENU
void	TMVA::MethodBase::DisableWriting(Bool_t setter)
virtual Int_t	TObject::DistancetoPrimitive(Int_t px, Int_t py)
Bool_t	TMVA::MethodBase::DoMulticlass() const
Bool_t	TMVA::MethodBase::DoRegression() const
virtual void	TObject::Draw(Option_t* option = "")
virtual void	TObject::DrawClass() constMENU
virtual TObject*	TObject::DrawClone(Option_t* option = "") constMENU
virtual void	TObject::Dump() constMENU
static TMVA::MethodCuts*	DynamicCast(TMVA::IMethod* method)
virtual void	TObject::Error(const char* method, const char* msgfmt) const
virtual Double_t	EstimatorFunction(vector<Double_t>&)
Double_t	EstimatorFunction(Int_t ievt1, Int_t ievt2)
virtual void	TObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual void	TObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual void	TObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
void	TMVA::MethodBase::ExitFromTraining()
virtual void	TObject::Fatal(const char* method, const char* msgfmt) const
virtual void	TNamed::FillBuffer(char*& buffer)
virtual TObject*	TObject::FindObject(const char* name) const
virtual TObject*	TObject::FindObject(const TObject* obj) const
TMVA::Types::EAnalysisType	TMVA::MethodBase::GetAnalysisType() const
const char*	TMVA::Configurable::GetConfigDescription() const
const char*	TMVA::Configurable::GetConfigName() const
UInt_t	TMVA::MethodBase::GetCurrentIter()
Double_t	GetCuts(Double_t effS, vector<Double_t>& cutMin, vector<Double_t>& cutMax) const
Double_t	GetCuts(Double_t effS, Double_t* cutMin, Double_t* cutMax) const
virtual Option_t*	TObject::GetDrawOption() const
static Long_t	TObject::GetDtorOnly()
virtual Double_t	GetEfficiency(const TString&, TMVA::Types::ETreeType, Double_t&)
const TMVA::Event*	TMVA::MethodBase::GetEvent() const
const TMVA::Event*	TMVA::MethodBase::GetEvent(const TMVA::Event* ev) const
const TMVA::Event*	TMVA::MethodBase::GetEvent(Long64_t ievt) const
const TMVA::Event*	TMVA::MethodBase::GetEvent(Long64_t ievt, TMVA::Types::ETreeType type) const
const vector<TMVA::Event*>&	TMVA::MethodBase::GetEventCollection(TMVA::Types::ETreeType type)
TFile*	TMVA::MethodBase::GetFile() const
virtual const char*	TObject::GetIconName() const
const TString&	TMVA::MethodBase::GetInputLabel(Int_t i) const
const char*	TMVA::MethodBase::GetInputTitle(Int_t i) const
const TString&	TMVA::MethodBase::GetInputVar(Int_t i) const
TMultiGraph*	TMVA::MethodBase::GetInteractiveTrainingError()
const TString&	TMVA::MethodBase::GetJobName() const
virtual Double_t	TMVA::MethodBase::GetKSTrainingVsTest(Char_t SorB, TString opt = "X")
virtual Double_t	TMVA::MethodBase::GetMaximumSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const
UInt_t	TMVA::MethodBase::GetMaxIter()
Double_t	TMVA::MethodBase::GetMean(Int_t ivar) const
const TString&	TMVA::MethodBase::GetMethodName() const
TMVA::Types::EMVA	TMVA::MethodBase::GetMethodType() const
TString	TMVA::MethodBase::GetMethodTypeName() const
virtual vector<Float_t>	TMVA::MethodBase::GetMulticlassEfficiency(vector<vector<Float_t> >& purity)
virtual vector<Float_t>	TMVA::MethodBase::GetMulticlassTrainingEfficiency(vector<vector<Float_t> >& purity)
virtual const vector<Float_t>&	TMVA::MethodBase::GetMulticlassValues()
Double_t	GetmuTransform(TTree*)
virtual Double_t	GetMvaValue(Double_t* err = 0, Double_t* errUpper = 0)
virtual const char*	TMVA::MethodBase::GetName() const
UInt_t	TMVA::MethodBase::GetNEvents() const
UInt_t	TMVA::MethodBase::GetNTargets() const
UInt_t	TMVA::MethodBase::GetNvar() const
UInt_t	TMVA::MethodBase::GetNVariables() const
virtual char*	TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_t	TObject::GetObjectStat()
virtual Option_t*	TObject::GetOption() const
const TString&	TMVA::Configurable::GetOptions() const
virtual Double_t	TMVA::MethodBase::GetProba(const TMVA::Event* ev)
virtual Double_t	TMVA::MethodBase::GetProba(Double_t mvaVal, Double_t ap_sig)
const TString	TMVA::MethodBase::GetProbaName() const
virtual Double_t	GetRarity(Double_t, TMVA::Types::ESBType) const
virtual void	TMVA::MethodBase::GetRegressionDeviation(UInt_t tgtNum, TMVA::Types::ETreeType type, Double_t& stddev, Double_t& stddev90Percent) const
virtual const vector<Float_t>&	TMVA::MethodBase::GetRegressionValues()
const vector<Float_t>&	TMVA::MethodBase::GetRegressionValues(const TMVA::Event*const ev)
Double_t	TMVA::MethodBase::GetRMS(Int_t ivar) const
virtual Double_t	TMVA::MethodBase::GetROCIntegral(TH1D* histS, TH1D* histB) const
virtual Double_t	TMVA::MethodBase::GetROCIntegral(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
virtual Double_t	GetSeparation(TH1, TH1) const
virtual Double_t	GetSeparation(TMVA::PDF* = 0, TMVA::PDF* = 0) const
Double_t	TMVA::MethodBase::GetSignalReferenceCut() const
Double_t	TMVA::MethodBase::GetSignalReferenceCutOrientation() const
virtual Double_t	GetSignificance() const
const TMVA::Event*	TMVA::MethodBase::GetTestingEvent(Long64_t ievt) const
Double_t	TMVA::MethodBase::GetTestTime() const
const TString&	TMVA::MethodBase::GetTestvarName() const
virtual const char*	TNamed::GetTitle() const
virtual Double_t	GetTrainingEfficiency(const TString&)
const TMVA::Event*	TMVA::MethodBase::GetTrainingEvent(Long64_t ievt) const
UInt_t	TMVA::MethodBase::GetTrainingROOTVersionCode() const
TString	TMVA::MethodBase::GetTrainingROOTVersionString() const
UInt_t	TMVA::MethodBase::GetTrainingTMVAVersionCode() const
TString	TMVA::MethodBase::GetTrainingTMVAVersionString() const
Double_t	TMVA::MethodBase::GetTrainTime() const
TMVA::TransformationHandler&	TMVA::MethodBase::GetTransformationHandler(Bool_t takeReroutedIfAvailable = true)
const TMVA::TransformationHandler&	TMVA::MethodBase::GetTransformationHandler(Bool_t takeReroutedIfAvailable = true) const
virtual UInt_t	TObject::GetUniqueID() const
TString	TMVA::MethodBase::GetWeightFileName() const
Double_t	TMVA::MethodBase::GetXmax(Int_t ivar) const
Double_t	TMVA::MethodBase::GetXmin(Int_t ivar) const
virtual Bool_t	TObject::HandleTimer(TTimer* timer)
virtual Bool_t	HasAnalysisType(TMVA::Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
virtual ULong_t	TNamed::Hash() const
Bool_t	TMVA::MethodBase::HasMVAPdfs() const
TMVA::IFitterTarget	TMVA::IFitterTarget::IFitterTarget()
TMVA::IFitterTarget	TMVA::IFitterTarget::IFitterTarget(const TMVA::IFitterTarget&)
TMVA::IMethod	TMVA::IMethod::IMethod()
TMVA::IMethod	TMVA::IMethod::IMethod(const TMVA::IMethod&)
virtual void	TObject::Info(const char* method, const char* msgfmt) const
virtual Bool_t	TObject::InheritsFrom(const char* classname) const
virtual Bool_t	TObject::InheritsFrom(const TClass* cl) const
void	TMVA::MethodBase::InitIPythonInteractive()
virtual void	TObject::Inspect() constMENU
void	TObject::InvertBit(UInt_t f)
virtual TClass*	IsA() const
virtual Bool_t	TObject::IsEqual(const TObject* obj) const
virtual Bool_t	TObject::IsFolder() const
Bool_t	TMVA::MethodBase::IsModelPersistence()
Bool_t	TObject::IsOnHeap() const
virtual Bool_t	TMVA::MethodBase::IsSignalLike()
virtual Bool_t	TMVA::MethodBase::IsSignalLike(Double_t mvaVal)
Bool_t	TMVA::MethodBase::IsSilentFile()
virtual Bool_t	TNamed::IsSortable() const
Bool_t	TObject::IsZombie() const
TMVA::MsgLogger&	TMVA::Configurable::Log() const
virtual void	TNamed::ls(Option_t* option = "") const
virtual void	TMVA::MethodBase::MakeClass(const TString& classFileName = TString("")) const
void	TObject::MayNotUse(const char* method) const
TMVA::MethodBase	TMVA::MethodBase::MethodBase(const TMVA::MethodBase&)
TMVA::MethodBase	TMVA::MethodBase::MethodBase(TMVA::Types::EMVA methodType, TMVA::DataSetInfo& dsi, const TString& weightFile)
TMVA::MethodBase	TMVA::MethodBase::MethodBase(const TString& jobName, TMVA::Types::EMVA methodType, const TString& methodTitle, TMVA::DataSetInfo& dsi, const TString& theOption = "")
TDirectory*	TMVA::MethodBase::MethodBaseDir() const
TMVA::MethodCuts	MethodCuts(const TMVA::MethodCuts&)
TMVA::MethodCuts	MethodCuts(TMVA::DataSetInfo& theData, const TString& theWeightFile)
TMVA::MethodCuts	MethodCuts(const TString& jobName, const TString& methodTitle, TMVA::DataSetInfo& theData, const TString& theOption = "MC:150:10000:")
virtual Bool_t	TObject::Notify()
void	TObject::Obsolete(const char* method, const char* asOfVers, const char* removedFromVers) const
void	TObject::operator delete(void* ptr)
void	TObject::operator delete(void* ptr, void* vp)
void	TObject::operator delete[](void* ptr)
void	TObject::operator delete[](void* ptr, void* vp)
void*	TObject::operator new(size_t sz)
void*	TObject::operator new(size_t sz, void* vp)
void*	TObject::operator new[](size_t sz)
void*	TObject::operator new[](size_t sz, void* vp)
TMVA::MethodCuts&	operator=(const TMVA::MethodCuts&)
virtual map<TString,Double_t>	TMVA::MethodBase::OptimizeTuningParameters(TString fomType = "ROCIntegral", TString fitType = "FitGA")
virtual void	TObject::Paint(Option_t* option = "")
virtual void	TMVA::Configurable::ParseOptions()
virtual void	TObject::Pop()
virtual void	TNamed::Print(Option_t* option = "") const
void	PrintCuts(Double_t effS) const
virtual void	TMVA::MethodBase::PrintHelpMessage() const
void	TMVA::Configurable::PrintOptions() const
virtual void	ProcessOptions()
void	TMVA::MethodBase::ProcessSetup()
virtual void	TMVA::IFitterTarget::ProgressNotifier(TString, TString)
virtual Int_t	TObject::Read(const char* name)
void	TMVA::Configurable::ReadOptionsFromStream(istream& istr)
void	TMVA::Configurable::ReadOptionsFromXML(void* node)
void	TMVA::MethodBase::ReadStateFromFile()
void	TMVA::MethodBase::ReadStateFromStream(istream& tf)
void	TMVA::MethodBase::ReadStateFromStream(TFile& rf)
void	TMVA::MethodBase::ReadStateFromXMLString(const char* xmlstr)
virtual void	ReadWeightsFromStream(istream& i)
virtual void	ReadWeightsFromXML(void* wghtnode)
virtual void	TObject::RecursiveRemove(TObject* obj)
void	TMVA::MethodBase::RerouteTransformationHandler(TMVA::TransformationHandler* fTargetTransformation)
virtual void	TMVA::MethodBase::Reset()
void	TObject::ResetBit(UInt_t f)
virtual void	TObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual void	TObject::SavePrimitive(ostream& out, Option_t* option = "")
virtual void	TMVA::MethodBase::SetAnalysisType(TMVA::Types::EAnalysisType type)
void	TMVA::MethodBase::SetBaseDir(TDirectory* methodDir)
void	TObject::SetBit(UInt_t f)
void	TObject::SetBit(UInt_t f, Bool_t set)
void	TMVA::Configurable::SetConfigDescription(const char* d)
void	TMVA::Configurable::SetConfigName(const char* n)
virtual void	TObject::SetDrawOption(Option_t* option = "")MENU
static void	TObject::SetDtorOnly(void* obj)
void	TMVA::MethodBase::SetFile(TFile* file)
void	TMVA::MethodBase::SetMethodBaseDir(TDirectory* methodDir)
void	TMVA::MethodBase::SetMethodDir(TDirectory* methodDir)
void	TMVA::MethodBase::SetModelPersistence(Bool_t status)
void	TMVA::Configurable::SetMsgType(TMVA::EMsgType t)
virtual void	TNamed::SetName(const char* name)MENU
virtual void	TNamed::SetNameTitle(const char* name, const char* title)
static void	TObject::SetObjectStat(Bool_t stat)
void	TMVA::Configurable::SetOptions(const TString& s)
void	TMVA::MethodBase::SetSignalReferenceCut(Double_t cut)
void	TMVA::MethodBase::SetSignalReferenceCutOrientation(Double_t cutOrientation)
void	TMVA::MethodBase::SetSilentFile(Bool_t status)
void	SetTestSignalEfficiency(Double_t effS)
void	TMVA::MethodBase::SetTestTime(Double_t testTime)
void	TMVA::MethodBase::SetTestvarName(const TString& v = "")
virtual void	TNamed::SetTitle(const char* title = "")MENU
void	TMVA::MethodBase::SetTrainTime(Double_t trainTime)
virtual void	TMVA::MethodBase::SetTuneParameters(map<TString,Double_t> tuneParameters)
virtual void	TObject::SetUniqueID(UInt_t uid)
void	TMVA::MethodBase::SetupMethod()
virtual void	ShowMembers(TMemberInspector& insp) const
virtual Int_t	TNamed::Sizeof() const
virtual void	Streamer(TBuffer&)
void	StreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b)
virtual void	TObject::SysError(const char* method, const char* msgfmt) const
Bool_t	TObject::TestBit(UInt_t f) const
Int_t	TObject::TestBits(UInt_t f) const
virtual void	TestClassification()
virtual void	TMVA::MethodBase::TestMulticlass()
virtual void	TMVA::MethodBase::TestRegression(Double_t& bias, Double_t& biasT, Double_t& dev, Double_t& devT, Double_t& rms, Double_t& rmsT, Double_t& mInf, Double_t& mInfT, Double_t& corr, TMVA::Types::ETreeType type)
virtual void	Train()
bool	TMVA::MethodBase::TrainingEnded()
void	TMVA::MethodBase::TrainMethod()
virtual void	TObject::UseCurrentStyle()
virtual void	TObject::Warning(const char* method, const char* msgfmt) const
virtual Int_t	TObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_t	TObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
virtual void	TMVA::MethodBase::WriteEvaluationHistosToFile(TMVA::Types::ETreeType treetype)
virtual void	WriteMonitoringHistosToFile() const
void	TMVA::Configurable::WriteOptionsToStream(ostream& o, const TString& prefix) const
void	TMVA::MethodBase::WriteStateToFile() const

protected:

virtual void	TObject::DoError(int level, const char* location, const char* fmt, va_list va) const
void	TMVA::Configurable::EnableLooseOptions(Bool_t b = kTRUE)
virtual void	GetHelpMessage() const
const TString&	TMVA::MethodBase::GetInternalVarName(Int_t ivar) const
virtual vector<Double_t>	TMVA::MethodBase::GetMvaValues(Long64_t firstEvt = 0, Long64_t lastEvt = -1, Bool_t logProgress = false)
const TString&	TMVA::MethodBase::GetOriginalVarName(Int_t ivar) const
const TString&	TMVA::Configurable::GetReferenceFile() const
const TString&	TMVA::MethodBase::GetWeightFileDir() const
Bool_t	TMVA::MethodBase::HasTrainingTree() const
Bool_t	TMVA::MethodBase::Help() const
Bool_t	TMVA::MethodBase::IgnoreEventsWithNegWeightsInTraining() const
Bool_t	TMVA::MethodBase::IsConstructedFromWeightFile() const
Bool_t	TMVA::MethodBase::IsNormalised() const
Bool_t	TMVA::Configurable::LooseOptionCheckingEnabled() const
virtual void	MakeClassSpecific(ostream&, const TString&) const
virtual void	TMVA::MethodBase::MakeClassSpecificHeader(ostream&, const TString& = "") const
void	TObject::MakeZombie()
void	TMVA::MethodBase::NoErrorCalc(Double_tconst err, Double_tconst errUpper)
void	TMVA::Configurable::ResetSetFlag()
void	TMVA::MethodBase::SetNormalised(Bool_t norm)
void	TMVA::MethodBase::SetWeightFileDir(TString fileDir)
void	TMVA::MethodBase::SetWeightFileName(TString)
void	TMVA::MethodBase::Statistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&)
Bool_t	TMVA::MethodBase::TxtWeightsOnly() const
Bool_t	TMVA::MethodBase::Verbose() const
void	TMVA::Configurable::WriteOptionsReferenceToFile()

private:

void	CreateVariablePDFs()
void	GetEffsfromPDFs(Double_t* cutMin, Double_t* cutMax, Double_t& effS, Double_t& effB)
void	GetEffsfromSelection(Double_t* cutMin, Double_t* cutMax, Double_t& effS, Double_t& effB)
virtual void	Init()
void	MatchCutsToPars(vector<Double_t>&, Double_t, Double_t)
void	MatchCutsToPars(vector<Double_t>&, Double_t, Double_t, Int_t ibin)
void	MatchParsToCuts(const vector<Double_t>&, Double_t, Double_t)
void	MatchParsToCuts(Double_t, Double_t, Double_t*)

Data Members

public:

Bool_t	TMVA::MethodBase::fSetupCompleted	is method setup
const TMVA::Event*	TMVA::MethodBase::fTmpEvent	! temporary event when testing on a different DataSet than the own one
static const Double_t	fgMaxAbsCutVal
static TObject::(anonymous)	TObject::kBitMask
static TObject::EStatusBits	TObject::kCanDelete
static TObject::EStatusBits	TObject::kCannotPick
static TObject::EStatusBits	TObject::kHasUUID
static TObject::EStatusBits	TObject::kInvalidObject
static TObject::(anonymous)	TObject::kIsOnHeap
static TObject::EStatusBits	TObject::kIsReferenced
static TObject::EStatusBits	TObject::kMustCleanup
static TObject::EStatusBits	TObject::kNoContextMenu
static TObject::(anonymous)	TObject::kNotDeleted
static TObject::EStatusBits	TObject::kObjInCanvas
static TObject::(anonymous)	TObject::kOverwrite
static TMVA::MethodBase::EWeightFileType	TMVA::MethodBase::kROOT
static TObject::(anonymous)	TObject::kSingleKey
static TMVA::MethodBase::EWeightFileType	TMVA::MethodBase::kTEXT
static TObject::(anonymous)	TObject::kWriteDelete
static TObject::(anonymous)	TObject::kZombie

protected:

TMVA::Types::EAnalysisType	TMVA::MethodBase::fAnalysisType	method-mode : true --> regression, false --> classification
UInt_t	TMVA::MethodBase::fBackgroundClass	index of the Background-class
bool	TMVA::MethodBase::fExitFromTraining
UInt_t	TMVA::MethodBase::fIPyCurrentIter
UInt_t	TMVA::MethodBase::fIPyMaxIter
vector<TString>*	TMVA::MethodBase::fInputVars	vector of input variables used in MVA
TMVA::IPythonInteractive*	TMVA::MethodBase::fInteractive
TMVA::MsgLogger*	TMVA::Configurable::fLogger	! message logger
vector<Float_t>*	TMVA::MethodBase::fMulticlassReturnVal	holds the return-values for the multiclass classification
TString	TNamed::fName	object identifier
Int_t	TMVA::MethodBase::fNbins	number of bins in input variable histograms
Int_t	TMVA::MethodBase::fNbinsH	number of bins in evaluation histograms
Int_t	TMVA::MethodBase::fNbinsMVAoutput	number of bins in MVA output histograms
TMVA::Ranking*	TMVA::MethodBase::fRanking	pointer to ranking object (created by derived classifiers)
vector<Float_t>*	TMVA::MethodBase::fRegressionReturnVal	holds the return-values for the regression
TMVA::Results*	TMVA::MethodBase::fResults
UInt_t	TMVA::MethodBase::fSignalClass	index of the Signal-class
TString	TNamed::fTitle	object title

private:

TString*	fAllVarsI	what to do with variables
TMVA::BinarySearchTree*	fBinaryTreeB
TMVA::BinarySearchTree*	fBinaryTreeS
Double_t**	fCutMax	maximum requirement
Double_t**	fCutMin	minimum requirement
vector<TMVA::Interval*>	fCutRange	allowed ranges for cut optimisation
Double_t*	fCutRangeMax	maximum of allowed cut range
Double_t*	fCutRangeMin	minimum of allowed cut range
TH1*	fEffBvsSLocal	intermediate eff. background versus eff signal histo
TMVA::MethodCuts::EEffMethod	fEffMethod	chosen efficiency calculation method
TString	fEffMethodS	chosen efficiency calculation method (string)
Double_t	fEffRef	reference efficiency
Double_t	fEffSMax	used to test optimized signal efficiency
Double_t	fEffSMin	used to test optimized signal efficiency
TMVA::MethodCuts::EFitMethodType	fFitMethod	chosen fit method
TString	fFitMethodS	chosen fit method (string)
vector<TMVA::MethodCuts::EFitParameters>*	fFitParams	vector for series of fit methods
vector<Double_t>*	fMeanB	means of variables (background)
vector<Double_t>*	fMeanS	means of variables (signal)
Bool_t	fNegEffWarning	flag risen in case of negative efficiency warning
Int_t	fNpar	number of parameters in fit (default: 2*Nvar)
TRandom*	fRandom	random generator for MC optimisation method
vector<Int_t>*	fRangeSign	used to match cuts to fit parameters (and vice versa)
vector<Double_t>*	fRmsB	RMSs of variables (background)
vector<Double_t>*	fRmsS	RMSs of variables (signal)
Double_t	fTestSignalEff	used to test optimized signal efficiency
Double_t*	fTmpCutMax	temporary maximum requirement
Double_t*	fTmpCutMin	temporary minimum requirement
vector<TH1>	fVarHistB	reference histograms (background)
vector<TH1>	fVarHistB_smooth	smoothed reference histograms (background)
vector<TH1>	fVarHistS	reference histograms (signal)
vector<TH1>	fVarHistS_smooth	smoothed reference histograms (signal)
vector<TMVA::PDF>	fVarPdfB	reference PDFs (background)
vector<TMVA::PDF>	fVarPdfS	reference PDFs (signal)
static TMVA::MethodCuts::EFitParameters	kForceMax
static TMVA::MethodCuts::EFitParameters	kForceMin
static TMVA::MethodCuts::EFitParameters	kForceSmart
static TMVA::MethodCuts::EFitParameters	kNotEnforced
static TMVA::MethodCuts::EFitMethodType	kUseEventScan
static TMVA::MethodCuts::EEffMethod	kUseEventSelection
static TMVA::MethodCuts::EFitMethodType	kUseGeneticAlgorithm
static TMVA::MethodCuts::EFitMethodType	kUseMinuit
static TMVA::MethodCuts::EFitMethodType	kUseMonteCarlo
static TMVA::MethodCuts::EFitMethodType	kUseMonteCarloEvents
static TMVA::MethodCuts::EEffMethod	kUsePDFs
static TMVA::MethodCuts::EFitMethodType	kUseSimulatedAnnealing

Class Charts

Inheritance Chart:

TMVA::IMethod

TObject

←

TNamed

←

TMVA::Configurable

←

TMVA::MethodBase

TMVA::IFitterTarget

←

TMVA::MethodCuts

Function documentation

MethodCuts(const TString& jobName, const TString& methodTitle, TMVA::DataSetInfo& theData, const TString& theOption = "MC:150:10000:")

MethodCuts* DynamicCast(TMVA::IMethod* method)

 this is a workaround which is necessary since CINT is not capable of handling dynamic casts

{ return dynamic_cast<MethodCuts*>(method); }

virtual ~MethodCuts( void )

Bool_t HasAnalysisType(TMVA::Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)

void Train( void )

 training method

void AddWeightsXMLTo(void* parent) const

void ReadWeightsFromStream(istream& i)

void ReadWeightsFromXML(void* wghtnode)

Double_t GetMvaValue(Double_t* err = 0, Double_t* errUpper = 0)

 calculate the MVA value (for CUTs this is just a dummy)

void WriteMonitoringHistosToFile( void )

 write method specific histos to target file

void TestClassification()

 test the method

Double_t GetSeparation(TH1* , TH1* ) const

 also overwrite --> not computed for cuts

{ return -1; }

Double_t GetSeparation(TMVA::PDF* = 0, TMVA::PDF* = 0) const

{ return -1; }

Double_t GetSignificance( void )

{ return -1; }

Double_t GetmuTransform(TTree* )

{ return -1; }

Double_t GetEfficiency(const TString& , TMVA::Types::ETreeType , Double_t& )

Double_t GetTrainingEfficiency(const TString& )

Double_t GetRarity(Double_t , TMVA::Types::ESBType ) const

 rarity distributions (signal or background (default) is uniform in [0,1])

{ return 0; }

Double_t ComputeEstimator(vector<Double_t>& )

 accessors for Minuit

Double_t EstimatorFunction(vector<Double_t>& )

Double_t EstimatorFunction(Int_t ievt1, Int_t ievt2)

void SetTestSignalEfficiency(Double_t effS)

{ fTestSignalEff = effS; }

void PrintCuts(Double_t effS) const

 retrieve cut values for given signal efficiency

Double_t GetCuts(Double_t effS, vector<Double_t>& cutMin, vector<Double_t>& cutMax) const

Double_t GetCuts(Double_t effS, Double_t* cutMin, Double_t* cutMax) const

const Ranking* CreateRanking()

 ranking of input variables (not available for cuts)

{ return 0; }

void DeclareOptions()

void ProcessOptions()

void CheckSetup()

 no check of options at this place

{}

void MakeClassSpecific(ostream& , const TString& ) const

 make ROOT-independent C++ class for classifier response (classifier-specific implementation)

void GetHelpMessage() const

 get help message text

void MatchParsToCuts( const std::vector<Double_t>&, Double_t*, Double_t* )

 the definition of fit parameters can be different from the actual
 cut requirements; these functions provide the matching

void MatchParsToCuts(Double_t* , Double_t* , Double_t* )

void MatchCutsToPars(vector<Double_t>& , Double_t* , Double_t* )

void MatchCutsToPars(vector<Double_t>& , Double_t** , Double_t** , Int_t ibin)

void CreateVariablePDFs( void )

 creates PDFs in case these are used to compute efficiencies
 (corresponds to: EffMethod == kUsePDFs)

void GetEffsfromSelection(Double_t* cutMin, Double_t* cutMax, Double_t& effS, Double_t& effB)

 returns signal and background efficiencies for given cuts - using event counting

void Init( void )

 returns signal and background efficiencies for given cuts - using PDFs
 default initialisation method called by all constructors