Multivariate optimisation of signal efficiency for given background efficiency, applying rectangular minimum and maximum requirements.

Also implemented is a "decorrelate/diagonalized cuts approach", which improves over the uncorrelated cuts approach by transforming linearly the input variables into a diagonal space, using the square-root of the covariance matrix.

Other optimisation criteria, such as maximising the signal significance- squared, \( \frac{S^2}{(S+B)} \), with S and B being the signal and background yields, correspond to a particular point in the optimised background rejection versus signal efficiency curve. This working point requires the knowledge of the expected yields, which is not the case in general. Note also that for rare signals, Poissonian statistics should be used, which modifies the significance criterion.

The rectangular cut of a volume in the variable space is performed using a binary tree to sort the training events. This provides a significant reduction in computing time (up to several orders of magnitudes, depending on the complexity of the problem at hand).

Technically, optimisation is achieved in TMVA by two methods:

Monte Carlo generation using uniform priors for the lower cut value, and the cut width, thrown within the variable ranges.
A Genetic Algorithm (GA) searches for the optimal ("fittest") cut sample. The GA is configurable by many external settings through the option string. For difficult cases (such as many variables), some tuning may be necessary to achieve satisfying results

Attempts to use Minuit fits (Simplex ot Migrad) instead have not shown superior results, and often failed due to convergence at local minima.

The tests we have performed so far showed that in generic applications, the GA is superior to MC sampling, and hence GA is the default method. It is worthwhile trying both anyway.

Decorrelated (or "diagonalized") Cuts**

See class description for Method Likelihood for a detailed explanation.

Definition at line 61 of file MethodCuts.h.

Public Member Functions
	MethodCuts (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="MC:150:10000:")
	standard constructor

	MethodCuts (DataSetInfo &theData, const TString &theWeightFile)
	construction from weight file

virtual	~MethodCuts (void)
	destructor

void	AddWeightsXMLTo (void *parent) const
	create XML description for LD classification and regression (for arbitrary number of output classes/targets)

void	CheckSetup ()
	check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)

Double_t	ComputeEstimator (std::vector< Double_t > &)
	returns estimator for "cut fitness" used by GA.

const Ranking *	CreateRanking ()

void	DeclareOptions ()
	define the options (their key words) that can be set in the option string.

Double_t	EstimatorFunction (Int_t ievt1, Int_t ievt2)
	for full event scan

Double_t	EstimatorFunction (std::vector< Double_t > &)
	returns estimator for "cut fitness" used by GA

Double_t	GetCuts (Double_t effS, Double_t cutMin, Double_t cutMax) const
	retrieve cut values for given signal efficiency assume vector of correct size !!

Double_t	GetCuts (Double_t effS, std::vector< Double_t > &cutMin, std::vector< Double_t > &cutMax) const
	retrieve cut values for given signal efficiency

Double_t	GetEfficiency (const TString &, Types::ETreeType, Double_t &)
	Overloaded function to create background efficiency (rejection) versus signal efficiency plot (first call of this function).

Double_t	GetmuTransform (TTree *)

Double_t	GetMvaValue (Double_t err=0, Double_t errUpper=0)
	cut evaluation: returns 1.0 if event passed, 0.0 otherwise

Double_t	GetRarity (Double_t, Types::ESBType) const
	compute rarity:

Double_t	GetSeparation (PDF =0, PDF =0) const
	compute "separation" defined as

Double_t	GetSeparation (TH1 , TH1 ) const
	compute "separation" defined as

Double_t	GetSignificance (void) const
	compute significance of mean difference

Double_t	GetTrainingEfficiency (const TString &)
	Overloaded function to create background efficiency (rejection) versus signal efficiency plot (first call of this function).

virtual Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
	Cuts can only handle classification with 2 classes.

void	PrintCuts (Double_t effS) const
	print cuts

void	ProcessOptions ()
	process user options.

virtual void	ReadWeightsFromStream (std::istream &)=0

void	ReadWeightsFromStream (std::istream &i)
	read the cuts from stream

virtual void	ReadWeightsFromStream (TFile &)

void	ReadWeightsFromXML (void *wghtnode)
	read coefficients from xml weight file

void	SetTestSignalEfficiency (Double_t effS)

void	TestClassification ()
	nothing to test

void	Train (void)
	training method: here the cuts are optimised for the training sample

void	WriteMonitoringHistosToFile (void) const
	write histograms and PDFs to file for monitoring purposes

Public Member Functions inherited from TMVA::MethodBase
	MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
	standard constructor

	MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile)
	constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles

virtual	~MethodBase ()
	destructor

void	AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType)

TDirectory *	BaseDir () const
	returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored

DataSet *	Data () const

DataSetInfo &	DataInfo () const

virtual void	DeclareCompatibilityOptions ()
	options that are used ONLY for the READER to ensure backward compatibility they are hence without any effect (the reader is only reading the training options that HAD been used at the training of the .xml weight file at hand

void	DisableWriting (Bool_t setter)

Bool_t	DoMulticlass () const

Bool_t	DoRegression () const

void	ExitFromTraining ()

Types::EAnalysisType	GetAnalysisType () const

UInt_t	GetCurrentIter ()

const Event *	GetEvent () const

const Event *	GetEvent (const TMVA::Event *ev) const

const Event *	GetEvent (Long64_t ievt) const

const Event *	GetEvent (Long64_t ievt, Types::ETreeType type) const

const std::vector< TMVA::Event * > &	GetEventCollection (Types::ETreeType type)
	returns the event collection (i.e.

TFile *	GetFile () const

const TString &	GetInputLabel (Int_t i) const

const char *	GetInputTitle (Int_t i) const

const TString &	GetInputVar (Int_t i) const

TMultiGraph *	GetInteractiveTrainingError ()

const TString &	GetJobName () const

virtual Double_t	GetKSTrainingVsTest (Char_t SorB, TString opt="X")

virtual Double_t	GetMaximumSignificance (Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const
	plot significance, \( \frac{S}{\sqrt{S^2 + B^2}} \), curve for given number of signal and background events; returns cut for maximum significance also returned via reference is the maximum significance

UInt_t	GetMaxIter ()

Double_t	GetMean (Int_t ivar) const

const TString &	GetMethodName () const

Types::EMVA	GetMethodType () const

TString	GetMethodTypeName () const

virtual TMatrixD	GetMulticlassConfusionMatrix (Double_t effB, Types::ETreeType type)
	Construct a confusion matrix for a multiclass classifier.

virtual std::vector< Float_t >	GetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity)

virtual std::vector< Float_t >	GetMulticlassTrainingEfficiency (std::vector< std::vector< Float_t > > &purity)

virtual const std::vector< Float_t > &	GetMulticlassValues ()

Double_t	GetMvaValue (const TMVA::Event const ev, Double_t err=0, Double_t *errUpper=0)

const char *	GetName () const

UInt_t	GetNEvents () const

UInt_t	GetNTargets () const

UInt_t	GetNvar () const

UInt_t	GetNVariables () const

virtual Double_t	GetProba (const Event *ev)

virtual Double_t	GetProba (Double_t mvaVal, Double_t ap_sig)
	compute likelihood ratio

const TString	GetProbaName () const

virtual void	GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const

virtual const std::vector< Float_t > &	GetRegressionValues ()

const std::vector< Float_t > &	GetRegressionValues (const TMVA::Event *const ev)

Double_t	GetRMS (Int_t ivar) const

virtual Double_t	GetROCIntegral (PDF pdfS=0, PDF pdfB=0) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification

virtual Double_t	GetROCIntegral (TH1D histS, TH1D histB) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification

Double_t	GetSignalReferenceCut () const

Double_t	GetSignalReferenceCutOrientation () const

const Event *	GetTestingEvent (Long64_t ievt) const

Double_t	GetTestTime () const

const TString &	GetTestvarName () const

const Event *	GetTrainingEvent (Long64_t ievt) const

virtual const std::vector< Float_t > &	GetTrainingHistory (const char *)

UInt_t	GetTrainingROOTVersionCode () const

TString	GetTrainingROOTVersionString () const
	calculates the ROOT version string from the training version code on the fly

UInt_t	GetTrainingTMVAVersionCode () const

TString	GetTrainingTMVAVersionString () const
	calculates the TMVA version string from the training version code on the fly

Double_t	GetTrainTime () const

TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true)

const TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const

TString	GetWeightFileName () const
	retrieve weight file name

Double_t	GetXmax (Int_t ivar) const

Double_t	GetXmin (Int_t ivar) const

Bool_t	HasMVAPdfs () const

void	InitIPythonInteractive ()

Bool_t	IsModelPersistence () const

virtual Bool_t	IsSignalLike ()
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event would be selected as signal or background

virtual Bool_t	IsSignalLike (Double_t mvaVal)
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event with this mva output value would be selected as signal or background

Bool_t	IsSilentFile () const

virtual void	MakeClass (const TString &classFileName=TString("")) const
	create reader class for method (classification only at present)

TDirectory *	MethodBaseDir () const
	returns the ROOT directory where all instances of the corresponding MVA method are stored

virtual std::map< TString, Double_t >	OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA")
	call the Optimizer with the set of parameters and ranges that are meant to be tuned.

void	PrintHelpMessage () const
	prints out method-specific help method

void	ProcessSetup ()
	process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)

void	ReadStateFromFile ()
	Function to write options and weights to file.

void	ReadStateFromStream (std::istream &tf)
	read the header from the weight files of the different MVA methods

void	ReadStateFromStream (TFile &rf)
	write reference MVA distributions (and other information) to a ROOT type weight file

void	ReadStateFromXMLString (const char *xmlstr)
	for reading from memory

void	RerouteTransformationHandler (TransformationHandler *fTargetTransformation)

virtual void	Reset ()

virtual void	SetAnalysisType (Types::EAnalysisType type)

void	SetBaseDir (TDirectory *methodDir)

void	SetFile (TFile *file)

void	SetMethodBaseDir (TDirectory *methodDir)

void	SetMethodDir (TDirectory *methodDir)

void	SetModelPersistence (Bool_t status)

void	SetSignalReferenceCut (Double_t cut)

void	SetSignalReferenceCutOrientation (Double_t cutOrientation)

void	SetSilentFile (Bool_t status)

void	SetTestTime (Double_t testTime)

void	SetTestvarName (const TString &v="")

void	SetTrainTime (Double_t trainTime)

virtual void	SetTuneParameters (std::map< TString, Double_t > tuneParameters)
	set the tuning parameters according to the argument This is just a dummy .

void	SetupMethod ()
	setup of methods

virtual void	TestMulticlass ()
	test multiclass classification

virtual void	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, Types::ETreeType type)
	calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample

bool	TrainingEnded ()

void	TrainMethod ()

virtual void	WriteEvaluationHistosToFile (Types::ETreeType treetype)
	writes all MVA evaluation histograms to file

void	WriteStateToFile () const
	write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created

Public Member Functions inherited from TMVA::IMethod
	IMethod ()

virtual	~IMethod ()

Public Member Functions inherited from TMVA::Configurable
	Configurable (const TString &theOption="")
	constructor

virtual	~Configurable ()
	default destructor

void	AddOptionsXMLTo (void *parent) const
	write options to XML file

template<class T >
void	AddPreDefVal (const T &)

template<class T >
void	AddPreDefVal (const TString &optname, const T &)

void	CheckForUnusedOptions () const
	checks for unused options in option string

template<class T >
TMVA::OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc)

template<class T >
OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc="")

template<class T >
TMVA::OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc)

template<class T >
OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="")

const char *	GetConfigDescription () const

const char *	GetConfigName () const

const TString &	GetOptions () const

MsgLogger &	Log () const

virtual void	ParseOptions ()
	options parser

void	PrintOptions () const
	prints out the options set in the options string and the defaults

void	ReadOptionsFromStream (std::istream &istr)
	read option back from the weight file

void	ReadOptionsFromXML (void *node)

void	SetConfigDescription (const char *d)

void	SetConfigName (const char *n)

void	SetMsgType (EMsgType t)

void	SetOptions (const TString &s)

void	WriteOptionsToStream (std::ostream &o, const TString &prefix) const
	write options to output stream (e.g. in writing the MVA weight files

Public Member Functions inherited from TNamed
	TNamed ()

	TNamed (const char name, const char title)

	TNamed (const TNamed &named)
	TNamed copy ctor.

	TNamed (const TString &name, const TString &title)

virtual	~TNamed ()
	TNamed destructor.

virtual void	Clear (Option_t *option="")
	Set name and title to empty strings ("").

virtual TObject *	Clone (const char *newname="") const
	Make a clone of an object using the Streamer facility.

virtual Int_t	Compare (const TObject *obj) const
	Compare two TNamed objects.

virtual void	Copy (TObject &named) const
	Copy this to obj.

virtual void	FillBuffer (char *&buffer)
	Encode TNamed into output buffer.

virtual const char *	GetTitle () const
	Returns title of object.

virtual ULong_t	Hash () const
	Return hash value for this object.

virtual Bool_t	IsSortable () const

virtual void	ls (Option_t *option="") const
	List TNamed name and title.

TNamed &	operator= (const TNamed &rhs)
	TNamed assignment operator.

virtual void	Print (Option_t *option="") const
	Print TNamed name and title.

virtual void	SetName (const char *name)
	Set the name of the TNamed.

virtual void	SetNameTitle (const char name, const char title)
	Set all the TNamed parameters (name and title).

virtual void	SetTitle (const char *title="")
	Set the title of the TNamed.

virtual Int_t	Sizeof () const
	Return size of the TNamed part of the TObject.

Public Member Functions inherited from TObject
	TObject ()
	TObject constructor.

	TObject (const TObject &object)
	TObject copy ctor.

virtual	~TObject ()
	TObject destructor.

void	AbstractMethod (const char *method) const
	Use this method to implement an "abstract" method that you don't want to leave purely abstract.

virtual void	AppendPad (Option_t *option="")
	Append graphics object to current pad.

virtual void	Browse (TBrowser *b)
	Browse object. May be overridden for another default action.

ULong_t	CheckedHash ()
	Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.

virtual const char *	ClassName () const
	Returns name of class to which the object belongs.

virtual void	Delete (Option_t *option="")
	Delete this object.

virtual Int_t	DistancetoPrimitive (Int_t px, Int_t py)
	Computes distance from point (px,py) to the object.

virtual void	Draw (Option_t *option="")
	Default Draw method for all objects.

virtual void	DrawClass () const
	Draw class inheritance tree of the class to which this object belongs.

virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current selected pad for instance with: `gROOT->SetSelectedPad(gPad)`.

virtual void	Dump () const
	Dump contents of object on stdout.

virtual void	Error (const char method, const char msgfmt,...) const
	Issue error message.

virtual void	Execute (const char method, const char params, Int_t *error=0)
	Execute method on this object with the given parameter string, e.g.

virtual void	Execute (TMethod method, TObjArray params, Int_t *error=0)
	Execute method on this object with parameters stored in the TObjArray.

virtual void	ExecuteEvent (Int_t event, Int_t px, Int_t py)
	Execute action corresponding to an event at (px,py).

virtual void	Fatal (const char method, const char msgfmt,...) const
	Issue fatal error message.

virtual TObject *	FindObject (const char *name) const
	Must be redefined in derived classes.

virtual TObject *	FindObject (const TObject *obj) const
	Must be redefined in derived classes.

virtual Option_t *	GetDrawOption () const
	Get option used by the graphics system to draw this object.

virtual const char *	GetIconName () const
	Returns mime type name of object.

virtual char *	GetObjectInfo (Int_t px, Int_t py) const
	Returns string containing info about the object at position (px,py).

virtual Option_t *	GetOption () const

virtual UInt_t	GetUniqueID () const
	Return the unique object id.

virtual Bool_t	HandleTimer (TTimer *timer)
	Execute action in response of a timer timing out.

Bool_t	HasInconsistentHash () const
	Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e.

virtual void	Info (const char method, const char msgfmt,...) const
	Issue info message.

virtual Bool_t	InheritsFrom (const char *classname) const
	Returns kTRUE if object inherits from class "classname".

virtual Bool_t	InheritsFrom (const TClass *cl) const
	Returns kTRUE if object inherits from TClass cl.

virtual void	Inspect () const
	Dump contents of this object in a graphics canvas.

void	InvertBit (UInt_t f)

Bool_t	IsDestructed () const
	IsDestructed.

virtual Bool_t	IsEqual (const TObject *obj) const
	Default equal comparison (objects are equal if they have the same address in memory).

virtual Bool_t	IsFolder () const
	Returns kTRUE in case object contains browsable objects (like containers or lists of other objects).

R__ALWAYS_INLINE Bool_t	IsOnHeap () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

void	MayNotUse (const char *method) const
	Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary).

virtual Bool_t	Notify ()
	This method must be overridden to handle object notification.

void	Obsolete (const char method, const char asOfVers, const char *removedFromVers) const
	Use this method to declare a method obsolete.

void	operator delete (void *ptr)
	Operator delete.

void	operator delete[] (void *ptr)
	Operator delete [].

void *	operator new (size_t sz)

void *	operator new (size_t sz, void *vp)

void *	operator new[] (size_t sz)

void *	operator new[] (size_t sz, void *vp)

TObject &	operator= (const TObject &rhs)
	TObject assignment operator.

virtual void	Paint (Option_t *option="")
	This method must be overridden if a class wants to paint itself.

virtual void	Pop ()
	Pop on object drawn in a pad to the top of the display list.

virtual Int_t	Read (const char *name)
	Read contents of object with specified name from the current directory.

virtual void	RecursiveRemove (TObject *obj)
	Recursively remove this object from a list.

void	ResetBit (UInt_t f)

virtual void	SaveAs (const char filename="", Option_t option="") const
	Save this object in the file specified by filename.

virtual void	SavePrimitive (std::ostream &out, Option_t *option="")
	Save a primitive as a C++ statement(s) on output stream "out".

void	SetBit (UInt_t f)

void	SetBit (UInt_t f, Bool_t set)
	Set or unset the user status bits as specified in f.

virtual void	SetDrawOption (Option_t *option="")
	Set drawing option for object.

virtual void	SetUniqueID (UInt_t uid)
	Set the unique object id.

virtual void	SysError (const char method, const char msgfmt,...) const
	Issue system error message.

R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

virtual void	UseCurrentStyle ()
	Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked.

virtual void	Warning (const char method, const char msgfmt,...) const
	Issue warning message.

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory.

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
	Write this object to the current directory.

Public Member Functions inherited from TMVA::IFitterTarget
	IFitterTarget ()
	constructor

virtual	~IFitterTarget ()

virtual void	ProgressNotifier (TString, TString)

Static Public Member Functions
static MethodCuts *	DynamicCast (IMethod *method)

Static Public Member Functions inherited from TObject
static Longptr_t	GetDtorOnly ()
	Return destructor only flag.

static Bool_t	GetObjectStat ()
	Get status of object stat flag.

static void	SetDtorOnly (void *obj)
	Set destructor only flag.

static void	SetObjectStat (Bool_t stat)
	Turn on/off tracking of objects in the TObjectTable.

Static Public Attributes
static const Double_t	fgMaxAbsCutVal = 1.0e30

Protected Member Functions
void	GetHelpMessage () const
	get help message text

void	MakeClassSpecific (std::ostream &, const TString &) const
	write specific classifier response

Protected Member Functions inherited from TMVA::MethodBase
virtual std::vector< Double_t >	GetDataMvaValues (DataSet *data=nullptr, Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
	get all the MVA values for the events of the given Data type

const TString &	GetInternalVarName (Int_t ivar) const

virtual std::vector< Double_t >	GetMvaValues (Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
	get all the MVA values for the events of the current Data type

const TString &	GetOriginalVarName (Int_t ivar) const

const TString &	GetWeightFileDir () const

Bool_t	HasTrainingTree () const

Bool_t	Help () const

Bool_t	IgnoreEventsWithNegWeightsInTraining () const

Bool_t	IsConstructedFromWeightFile () const

Bool_t	IsNormalised () const

virtual void	MakeClassSpecificHeader (std::ostream &, const TString &="") const

void	NoErrorCalc (Double_t const err, Double_t const errUpper)

void	SetNormalised (Bool_t norm)

void	SetWeightFileDir (TString fileDir)
	set directory of weight file

void	SetWeightFileName (TString)
	set the weight file name (depreciated)

void	Statistics (Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &)
	calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE

Bool_t	TxtWeightsOnly () const

Bool_t	Verbose () const

Protected Member Functions inherited from TMVA::Configurable
void	EnableLooseOptions (Bool_t b=kTRUE)

const TString &	GetReferenceFile () const

Bool_t	LooseOptionCheckingEnabled () const

void	ResetSetFlag ()
	resets the IsSet flag for all declare options to be called before options are read from stream

void	WriteOptionsReferenceToFile ()
	write complete options to output stream

Protected Member Functions inherited from TObject
virtual void	DoError (int level, const char location, const char fmt, va_list va) const
	Interface to ErrorHandler (protected).

void	MakeZombie ()

Private Types
enum	EEffMethod { kUseEventSelection = 0 , kUsePDFs }

enum	EFitMethodType { kUseMonteCarlo = 0 , kUseGeneticAlgorithm , kUseSimulatedAnnealing , kUseMinuit , kUseEventScan , kUseMonteCarloEvents }

enum	EFitParameters { kNotEnforced = 0 , kForceMin , kForceMax , kForceSmart }

Private Member Functions
void	CreateVariablePDFs (void)
	for PDF method: create efficiency reference histograms and PDFs

void	GetEffsfromPDFs (Double_t cutMin, Double_t cutMax, Double_t &effS, Double_t &effB)
	compute signal and background efficiencies from PDFs for given cut sample

void	GetEffsfromSelection (Double_t cutMin, Double_t cutMax, Double_t &effS, Double_t &effB)
	compute signal and background efficiencies from event counting for given cut sample

void	Init (void)
	default initialisation called by all constructors

void	MatchCutsToPars (std::vector< Double_t > &, Double_t , Double_t , Int_t ibin)
	translate the cuts into parameters (obsolete function)

void	MatchCutsToPars (std::vector< Double_t > &, Double_t , Double_t )
	translates cuts into parameters

void	MatchParsToCuts (const std::vector< Double_t > &, Double_t , Double_t )
	translates parameters into cuts

void	MatchParsToCuts (Double_t , Double_t , Double_t *)

Private Attributes
TString *	fAllVarsI

BinarySearchTree *	fBinaryTreeB

BinarySearchTree *	fBinaryTreeS

Double_t **	fCutMax

Double_t **	fCutMin

std::vector< Interval * >	fCutRange

Double_t *	fCutRangeMax

Double_t *	fCutRangeMin

TH1 *	fEffBvsSLocal

EEffMethod	fEffMethod

TString	fEffMethodS

Double_t	fEffRef

Double_t	fEffSMax

Double_t	fEffSMin

EFitMethodType	fFitMethod

TString	fFitMethodS

std::vector< EFitParameters > *	fFitParams

std::vector< Double_t > *	fMeanB

std::vector< Double_t > *	fMeanS

Bool_t	fNegEffWarning

Int_t	fNpar

TRandom *	fRandom

std::vector< Int_t > *	fRangeSign

std::vector< Double_t > *	fRmsB

std::vector< Double_t > *	fRmsS

Double_t	fTestSignalEff

Double_t *	fTmpCutMax

Double_t *	fTmpCutMin

std::vector< TH1 * > *	fVarHistB

std::vector< TH1 * > *	fVarHistB_smooth

std::vector< TH1 * > *	fVarHistS

std::vector< TH1 * > *	fVarHistS_smooth

std::vector< PDF * > *	fVarPdfB

std::vector< PDF * > *	fVarPdfS

Additional Inherited Members
Public Types inherited from TMVA::MethodBase
enum	EWeightFileType { kROOT =0 , kTEXT }

Public Types inherited from TObject
enum	{ kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff }

enum	{ kSingleKey = BIT(0) , kOverwrite = BIT(1) , kWriteDelete = BIT(2) }

enum	EDeprecatedStatusBits { kObjInCanvas = BIT(3) }

enum	EStatusBits { kCanDelete = BIT(0) , kMustCleanup = BIT(3) , kIsReferenced = BIT(4) , kHasUUID = BIT(5) , kCannotPick = BIT(6) , kNoContextMenu = BIT(8) , kInvalidObject = BIT(13) }

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

TrainingHistory	fTrainHistory

Protected Types inherited from TObject
enum	{ kOnlyPrepStep = BIT(3) }

Protected Attributes inherited from TMVA::MethodBase
Types::EAnalysisType	fAnalysisType

UInt_t	fBackgroundClass

bool	fExitFromTraining = false

std::vector< TString > *	fInputVars

IPythonInteractive *	fInteractive = nullptr
	temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues)

UInt_t	fIPyCurrentIter = 0

UInt_t	fIPyMaxIter = 0

std::vector< Float_t > *	fMulticlassReturnVal

Int_t	fNbins

Int_t	fNbinsH

Int_t	fNbinsMVAoutput

Ranking *	fRanking

std::vector< Float_t > *	fRegressionReturnVal

Results *	fResults

UInt_t	fSignalClass

DataSet *	fTmpData = nullptr
	temporary event when testing on a different DataSet than the own one

const Event *	fTmpEvent

Protected Attributes inherited from TMVA::Configurable
MsgLogger *	fLogger

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

#include <TMVA/MethodCuts.h>

Inheritance diagram for TMVA::MethodCuts:

[legend]

Member Enumeration Documentation

◆ EEffMethod

enum TMVA::MethodCuts::EEffMethod

private

Enumerator
kUseEventSelection
kUsePDFs

Definition at line 157 of file MethodCuts.h.

◆ EFitMethodType

enum TMVA::MethodCuts::EFitMethodType

private

Enumerator
kUseMonteCarlo
kUseGeneticAlgorithm
kUseSimulatedAnnealing
kUseMinuit
kUseEventScan
kUseMonteCarloEvents

Definition at line 146 of file MethodCuts.h.

◆ EFitParameters

enum TMVA::MethodCuts::EFitParameters

private

Enumerator
kNotEnforced
kForceMin
kForceMax
kForceSmart

Definition at line 161 of file MethodCuts.h.

Constructor & Destructor Documentation

◆ MethodCuts() [1/2]

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

standard constructor

Definition at line 129 of file MethodCuts.cxx.

◆ MethodCuts() [2/2]

TMVA::MethodCuts::MethodCuts	(	DataSetInfo &	theData,
		const TString &	theWeightFile
	)

construction from weight file

Definition at line 171 of file MethodCuts.cxx.

◆ ~MethodCuts()

TMVA::MethodCuts::~MethodCuts ( void )

virtual

destructor

Definition at line 270 of file MethodCuts.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

void TMVA::MethodCuts::AddWeightsXMLTo ( void * parent ) const

virtual

create XML description for LD classification and regression (for arbitrary number of output classes/targets)

Implements TMVA::MethodBase.

Definition at line 1287 of file MethodCuts.cxx.

◆ CheckSetup()

void TMVA::MethodCuts::CheckSetup ( )

inlinevirtual

check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)

Reimplemented from TMVA::MethodBase.

Definition at line 133 of file MethodCuts.h.

◆ ComputeEstimator()

Double_t TMVA::MethodCuts::ComputeEstimator ( std::vector< Double_t > & pars )

returns estimator for "cut fitness" used by GA.

there are two requirements:

the signal efficiency must be equal to the required one in the efficiency scan
the background efficiency must be as small as possible

the requirement 1. has priority over 2.

Definition at line 893 of file MethodCuts.cxx.

◆ CreateRanking()

const Ranking * TMVA::MethodCuts::CreateRanking ( )

inlinevirtual

Implements TMVA::MethodBase.

Definition at line 124 of file MethodCuts.h.

◆ CreateVariablePDFs()

void TMVA::MethodCuts::CreateVariablePDFs ( void )

private

for PDF method: create efficiency reference histograms and PDFs

Definition at line 1106 of file MethodCuts.cxx.

◆ DeclareOptions()

void TMVA::MethodCuts::DeclareOptions ( )

virtual

define the options (their key words) that can be set in the option string.

know options:

Method <string> Minimisation method. Available values are:
- MC Monte Carlo <default>
- GA Genetic Algorithm
- SA Simulated annealing
EffMethod <string> Efficiency selection method. Available values are:
- EffSel <default>
- EffPDF
VarProp <string> Property of variable 1 for the MC method (taking precedence over the globale setting. The same values as for the global option are available. Variables 1..10 can be set this way
CutRangeMin/Max <float> user-defined ranges in which cuts are varied

Implements TMVA::MethodBase.

Definition at line 319 of file MethodCuts.cxx.

◆ DynamicCast()

static MethodCuts * TMVA::MethodCuts::DynamicCast ( IMethod * method )

inlinestatic

Definition at line 74 of file MethodCuts.h.

◆ EstimatorFunction() [1/2]

Double_t TMVA::MethodCuts::EstimatorFunction	(	Int_t	ievt1,
		Int_t	ievt2
	)

for full event scan

Definition at line 834 of file MethodCuts.cxx.

◆ EstimatorFunction() [2/2]

Double_t TMVA::MethodCuts::EstimatorFunction ( std::vector< Double_t > & pars )

virtual

returns estimator for "cut fitness" used by GA

Implements TMVA::IFitterTarget.

Definition at line 878 of file MethodCuts.cxx.

◆ GetCuts() [1/2]

Double_t TMVA::MethodCuts::GetCuts	(	Double_t	effS,
		Double_t *	cutMin,
		Double_t *	cutMax
	)		const

retrieve cut values for given signal efficiency assume vector of correct size !!

Definition at line 536 of file MethodCuts.cxx.

◆ GetCuts() [2/2]

Double_t TMVA::MethodCuts::GetCuts	(	Double_t	effS,
		std::vector< Double_t > &	cutMin,
		std::vector< Double_t > &	cutMax
	)		const

retrieve cut values for given signal efficiency

Definition at line 551 of file MethodCuts.cxx.

◆ GetEfficiency()

Double_t TMVA::MethodCuts::GetEfficiency	(	const TString &	theString,
		Types::ETreeType	type,
		Double_t &	effSerr
	)

virtual

Overloaded function to create background efficiency (rejection) versus signal efficiency plot (first call of this function).

The function returns the signal efficiency at background efficiency indicated in theString

"theString" must have two entries:

[0]: "Efficiency"
[1]: the value of background efficiency at which the signal efficiency is to be returned

Reimplemented from TMVA::MethodBase.

Definition at line 1555 of file MethodCuts.cxx.

◆ GetEffsfromPDFs()

void TMVA::MethodCuts::GetEffsfromPDFs	(	Double_t *	cutMin,
		Double_t *	cutMax,
		Double_t &	effS,
		Double_t &	effB
	)

private

compute signal and background efficiencies from PDFs for given cut sample

Definition at line 1023 of file MethodCuts.cxx.

◆ GetEffsfromSelection()

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

private

compute signal and background efficiencies from event counting for given cut sample

Definition at line 1050 of file MethodCuts.cxx.

◆ GetHelpMessage()

void TMVA::MethodCuts::GetHelpMessage ( ) const

protectedvirtual

get help message text

typical length of text line: "|--------------------------------------------------------------|"

Implements TMVA::IMethod.

Definition at line 1719 of file MethodCuts.cxx.

◆ GetmuTransform()

Double_t TMVA::MethodCuts::GetmuTransform ( TTree * )

inline

Definition at line 103 of file MethodCuts.h.

◆ GetMvaValue()

Double_t TMVA::MethodCuts::GetMvaValue	(	Double_t *	err = `0`,
		Double_t *	errUpper = `0`
	)

virtual

cut evaluation: returns 1.0 if event passed, 0.0 otherwise

Implements TMVA::MethodBase.

Definition at line 432 of file MethodCuts.cxx.

◆ GetRarity()

Double_t TMVA::MethodCuts::GetRarity	(	Double_t	mvaVal,
		Types::ESBType	reftype
	)		const

inlinevirtual

compute rarity:

\[ R(x) = \int_{[-\infty..x]} { PDF(x') dx' } \]

where PDF(x) is the PDF of the classifier's signal or background distribution

Reimplemented from TMVA::MethodBase.

Definition at line 108 of file MethodCuts.h.

◆ GetSeparation() [1/2]

Double_t TMVA::MethodCuts::GetSeparation	(	PDF *	pdfS = `0`,
		PDF *	pdfB = `0`
	)		const

inlinevirtual

compute "separation" defined as

\[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \]

Reimplemented from TMVA::MethodBase.

Definition at line 101 of file MethodCuts.h.

◆ GetSeparation() [2/2]

Double_t TMVA::MethodCuts::GetSeparation	(	TH1 *	histoS,
		TH1 *	histoB
	)		const

inlinevirtual

compute "separation" defined as

\[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \]

Reimplemented from TMVA::MethodBase.

Definition at line 100 of file MethodCuts.h.

◆ GetSignificance()

Double_t TMVA::MethodCuts::GetSignificance ( void ) const

inlinevirtual

compute significance of mean difference

\[ significance = \frac{|<S> - <B>|}{\sqrt{RMS_{S2} + RMS_{B2}}} \]

Reimplemented from TMVA::MethodBase.

Definition at line 102 of file MethodCuts.h.

◆ GetTrainingEfficiency()

Double_t TMVA::MethodCuts::GetTrainingEfficiency ( const TString & theString )

virtual

Overloaded function to create background efficiency (rejection) versus signal efficiency plot (first call of this function).

The function returns the signal efficiency at background efficiency indicated in theString

"theString" must have two entries:

[0]: "Efficiency"
[1]: the value of background efficiency at which the signal efficiency is to be returned

Reimplemented from TMVA::MethodBase.

Definition at line 1442 of file MethodCuts.cxx.

◆ HasAnalysisType()

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

virtual

Cuts can only handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 211 of file MethodCuts.cxx.

◆ Init()

void TMVA::MethodCuts::Init ( void )

privatevirtual

default initialisation called by all constructors

Implements TMVA::MethodBase.

Definition at line 220 of file MethodCuts.cxx.

◆ MakeClassSpecific()

void TMVA::MethodCuts::MakeClassSpecific	(	std::ostream &	fout,
		const TString &	className
	)		const

protectedvirtual

write specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 1707 of file MethodCuts.cxx.

◆ MatchCutsToPars() [1/2]

void TMVA::MethodCuts::MatchCutsToPars	(	std::vector< Double_t > &	pars,
		Double_t **	cutMinAll,
		Double_t **	cutMaxAll,
		Int_t	ibin
	)

private

translate the cuts into parameters (obsolete function)

Definition at line 987 of file MethodCuts.cxx.

◆ MatchCutsToPars() [2/2]

void TMVA::MethodCuts::MatchCutsToPars	(	std::vector< Double_t > &	pars,
		Double_t *	cutMin,
		Double_t *	cutMax
	)

private

translates cuts into parameters

Definition at line 1009 of file MethodCuts.cxx.

◆ MatchParsToCuts() [1/2]

void TMVA::MethodCuts::MatchParsToCuts	(	const std::vector< Double_t > &	pars,
		Double_t *	cutMin,
		Double_t *	cutMax
	)

private

translates parameters into cuts

Definition at line 974 of file MethodCuts.cxx.

◆ MatchParsToCuts() [2/2]

void TMVA::MethodCuts::MatchParsToCuts	(	Double_t *	,
		Double_t *	,
		Double_t *
	)

private

◆ PrintCuts()

void TMVA::MethodCuts::PrintCuts ( Double_t effS ) const

print cuts

Definition at line 465 of file MethodCuts.cxx.

◆ ProcessOptions()

void TMVA::MethodCuts::ProcessOptions ( )

virtual

process user options.

sanity check, do not allow the input variables to be normalised, because this only creates problems when interpreting the cuts

Implements TMVA::MethodBase.

Definition at line 363 of file MethodCuts.cxx.

◆ ReadWeightsFromStream() [1/3]

virtual void TMVA::MethodBase::ReadWeightsFromStream ( std::istream & )

virtual

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/3]

void TMVA::MethodCuts::ReadWeightsFromStream ( std::istream & i )

virtual

read the cuts from stream

Implements TMVA::MethodBase.

Definition at line 1217 of file MethodCuts.cxx.

◆ ReadWeightsFromStream() [3/3]

virtual void TMVA::MethodBase::ReadWeightsFromStream ( TFile & )

inlinevirtual

Reimplemented from TMVA::MethodBase.

Definition at line 266 of file MethodBase.h.

◆ ReadWeightsFromXML()

void TMVA::MethodCuts::ReadWeightsFromXML ( void * wghtnode )

virtual

read coefficients from xml weight file

Implements TMVA::MethodBase.

Definition at line 1327 of file MethodCuts.cxx.

◆ SetTestSignalEfficiency()

void TMVA::MethodCuts::SetTestSignalEfficiency ( Double_t effS )

inline

Definition at line 116 of file MethodCuts.h.

◆ TestClassification()

void TMVA::MethodCuts::TestClassification ( )

virtual

nothing to test

Reimplemented from TMVA::MethodBase.

Definition at line 827 of file MethodCuts.cxx.

◆ Train()

void TMVA::MethodCuts::Train ( void )

virtual

training method: here the cuts are optimised for the training sample

Implements TMVA::MethodBase.

Definition at line 578 of file MethodCuts.cxx.

◆ WriteMonitoringHistosToFile()

void TMVA::MethodCuts::WriteMonitoringHistosToFile ( void ) const

virtual

write histograms and PDFs to file for monitoring purposes

Reimplemented from TMVA::MethodBase.

Definition at line 1411 of file MethodCuts.cxx.

Member Data Documentation

◆ fAllVarsI

TString* TMVA::MethodCuts::fAllVarsI

private

Definition at line 188 of file MethodCuts.h.

◆ fBinaryTreeB

BinarySearchTree* TMVA::MethodCuts::fBinaryTreeB

private

Definition at line 181 of file MethodCuts.h.

◆ fBinaryTreeS

BinarySearchTree* TMVA::MethodCuts::fBinaryTreeS

private

Definition at line 180 of file MethodCuts.h.

◆ fCutMax

Double_t** TMVA::MethodCuts::fCutMax

private

Definition at line 185 of file MethodCuts.h.

◆ fCutMin

Double_t** TMVA::MethodCuts::fCutMin

private

Definition at line 184 of file MethodCuts.h.

◆ fCutRange

std::vector<Interval*> TMVA::MethodCuts::fCutRange

private

Definition at line 177 of file MethodCuts.h.

◆ fCutRangeMax

Double_t* TMVA::MethodCuts::fCutRangeMax

private

Definition at line 176 of file MethodCuts.h.

◆ fCutRangeMin

Double_t* TMVA::MethodCuts::fCutRangeMin

private

Definition at line 175 of file MethodCuts.h.

◆ fEffBvsSLocal

TH1* TMVA::MethodCuts::fEffBvsSLocal

private

Definition at line 202 of file MethodCuts.h.

◆ fEffMethod

EEffMethod TMVA::MethodCuts::fEffMethod

private

Definition at line 170 of file MethodCuts.h.

◆ fEffMethodS

TString TMVA::MethodCuts::fEffMethodS

private

Definition at line 169 of file MethodCuts.h.

◆ fEffRef

Double_t TMVA::MethodCuts::fEffRef

private

Definition at line 192 of file MethodCuts.h.

◆ fEffSMax

Double_t TMVA::MethodCuts::fEffSMax

private

Definition at line 174 of file MethodCuts.h.

◆ fEffSMin

Double_t TMVA::MethodCuts::fEffSMin

private

Definition at line 173 of file MethodCuts.h.

◆ fFitMethod

EFitMethodType TMVA::MethodCuts::fFitMethod

private

Definition at line 168 of file MethodCuts.h.

◆ fFitMethodS

TString TMVA::MethodCuts::fFitMethodS

private

Definition at line 167 of file MethodCuts.h.

◆ fFitParams

std::vector<EFitParameters>* TMVA::MethodCuts::fFitParams

private

Definition at line 171 of file MethodCuts.h.

◆ fgMaxAbsCutVal

const Double_t TMVA::MethodCuts::fgMaxAbsCutVal = 1.0e30

static

Definition at line 130 of file MethodCuts.h.

◆ fMeanB

std::vector<Double_t>* TMVA::MethodCuts::fMeanB

private

Definition at line 198 of file MethodCuts.h.

◆ fMeanS

std::vector<Double_t>* TMVA::MethodCuts::fMeanS

private

Definition at line 197 of file MethodCuts.h.

◆ fNegEffWarning

Bool_t TMVA::MethodCuts::fNegEffWarning

private

Definition at line 213 of file MethodCuts.h.

◆ fNpar

Int_t TMVA::MethodCuts::fNpar

private

Definition at line 191 of file MethodCuts.h.

◆ fRandom

TRandom* TMVA::MethodCuts::fRandom

private

Definition at line 194 of file MethodCuts.h.

◆ fRangeSign

std::vector<Int_t>* TMVA::MethodCuts::fRangeSign

private

Definition at line 193 of file MethodCuts.h.

◆ fRmsB

std::vector<Double_t>* TMVA::MethodCuts::fRmsB

private

Definition at line 200 of file MethodCuts.h.

◆ fRmsS

std::vector<Double_t>* TMVA::MethodCuts::fRmsS

private

Definition at line 199 of file MethodCuts.h.

◆ fTestSignalEff

Double_t TMVA::MethodCuts::fTestSignalEff

private

Definition at line 172 of file MethodCuts.h.

◆ fTmpCutMax

Double_t* TMVA::MethodCuts::fTmpCutMax

private

Definition at line 187 of file MethodCuts.h.

◆ fTmpCutMin

Double_t* TMVA::MethodCuts::fTmpCutMin

private

Definition at line 186 of file MethodCuts.h.

◆ fVarHistB

std::vector<TH1*>* TMVA::MethodCuts::fVarHistB

private

Definition at line 206 of file MethodCuts.h.

◆ fVarHistB_smooth

std::vector<TH1*>* TMVA::MethodCuts::fVarHistB_smooth

private

Definition at line 208 of file MethodCuts.h.

◆ fVarHistS

std::vector<TH1*>* TMVA::MethodCuts::fVarHistS

private

Definition at line 205 of file MethodCuts.h.

◆ fVarHistS_smooth

std::vector<TH1*>* TMVA::MethodCuts::fVarHistS_smooth

private

Definition at line 207 of file MethodCuts.h.

◆ fVarPdfB

std::vector<PDF*>* TMVA::MethodCuts::fVarPdfB

private

Definition at line 210 of file MethodCuts.h.

◆ fVarPdfS

std::vector<PDF*>* TMVA::MethodCuts::fVarPdfS

private

Definition at line 209 of file MethodCuts.h.

Libraries for TMVA::MethodCuts:

[legend]

The documentation for this class was generated from the following files:

tmva/tmva/inc/TMVA/MethodCuts.h
tmva/tmva/src/MethodCuts.cxx

Public Member Functions

Static Public Member Functions

Static Public Attributes

Protected Member Functions

Private Types

Private Member Functions

Private Attributes

Additional Inherited Members

Member Enumeration Documentation

◆ EEffMethod

◆ EFitMethodType

◆ EFitParameters

Constructor & Destructor Documentation

◆ MethodCuts() [1/2]

◆ MethodCuts() [2/2]

◆ ~MethodCuts()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ CheckSetup()

◆ ComputeEstimator()

◆ CreateRanking()

◆ CreateVariablePDFs()

◆ DeclareOptions()

◆ DynamicCast()

◆ EstimatorFunction() [1/2]

◆ EstimatorFunction() [2/2]

◆ GetCuts() [1/2]

◆ GetCuts() [2/2]

◆ GetEfficiency()

◆ GetEffsfromPDFs()

◆ GetEffsfromSelection()

◆ GetHelpMessage()

◆ GetmuTransform()

◆ GetMvaValue()

◆ GetRarity()

◆ GetSeparation() [1/2]

◆ GetSeparation() [2/2]

◆ GetSignificance()

◆ GetTrainingEfficiency()

◆ HasAnalysisType()

◆ Init()

◆ MakeClassSpecific()

◆ MatchCutsToPars() [1/2]

◆ MatchCutsToPars() [2/2]

◆ MatchParsToCuts() [1/2]

◆ MatchParsToCuts() [2/2]

◆ PrintCuts()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/3]

◆ ReadWeightsFromStream() [2/3]

◆ ReadWeightsFromStream() [3/3]

◆ ReadWeightsFromXML()

◆ SetTestSignalEfficiency()

◆ TestClassification()

◆ Train()

◆ WriteMonitoringHistosToFile()

Member Data Documentation

◆ fAllVarsI

◆ fBinaryTreeB

◆ fBinaryTreeS

◆ fCutMax

◆ fCutMin

◆ fCutRange

◆ fCutRangeMax

◆ fCutRangeMin

◆ fEffBvsSLocal

◆ fEffMethod

◆ fEffMethodS

◆ fEffRef

◆ fEffSMax

◆ fEffSMin

◆ fFitMethod

◆ fFitMethodS

◆ fFitParams

◆ fgMaxAbsCutVal

◆ fMeanB

◆ fMeanS

◆ fNegEffWarning

◆ fNpar

◆ fRandom