This is a generalization of the above Likelihood methods to \( N_{var} \) dimensions, where \( N_{var} \) is the number of input variables used in the MVA.

If the multi-dimensional probability density functions (PDFs) for signal and background were known, this method contains the entire physical information, and is therefore optimal. Usually, kernel estimation methods are used to approximate the PDFs using the events from the training sample.

A very simple probability density estimator (PDE) has been suggested in hep-ex/0211019. The PDE for a given test event is obtained from counting the (normalized) number of signal and background (training) events that occur in the "vicinity" of the test event. The volume that describes "vicinity" is user-defined. A search method based on binary-trees is used to effectively reduce the selection time for the range search. Three different volume definitions are optional:

MinMax: the volume is defined in each dimension with respect to the full variable range found in the training sample.
RMS: the volume is defined in each dimensions with respect to the RMS estimated from the training sample.
Adaptive: a volume element is defined in each dimensions with respect to the RMS estimated from the training sample. The overall scale of the volume element is then determined for each event so that the total number of events confined in the volume be within a user-defined range.

The adaptive range search is used by default.

Definition at line 61 of file MethodPDERS.h.

Public Member Functions
	MethodPDERS (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption)
	standard constructor for the PDERS method

	MethodPDERS (DataSetInfo &theData, const TString &theWeightFile)
	construct MethodPDERS through from file

virtual	~MethodPDERS (void)
	destructor

void	AddWeightsXMLTo (void *parent) const
	write weights to xml file

Double_t	GetMvaValue (Double_t err=nullptr, Double_t errUpper=nullptr)
	init the size of a volume element using a defined fraction of the volume containing the entire events

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

Double_t	GetVolumeContentForRoot (Double_t)
	count number of events in rescaled volume

virtual Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
	PDERS can handle classification with 2 classes and regression with one or more regression-targets.

virtual TClass *	IsA () const

void	ReadWeightsFromStream (std::istream &istr)
	read weight info from file

void	ReadWeightsFromStream (TFile &istr)
	read training sample from file

void	ReadWeightsFromXML (void *wghtnode)

virtual void	Streamer (TBuffer &)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

void	Train (void)
	this is a dummy training: the preparation work to do is the construction of the binary tree as a pointer chain.

void	WriteWeightsToStream (TFile &rf) const
	write training sample (TTree) to file

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

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

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 ()

virtual Double_t	GetEfficiency (const TString &, Types::ETreeType, Double_t &err)
	fill background efficiency (resp.

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=nullptr, Double_t *errUpper=nullptr)

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 Double_t	GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
	compute rarity:

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

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

Double_t	GetRMS (Int_t ivar) const

virtual Double_t	GetROCIntegral (PDF pdfS=nullptr, PDF pdfB=nullptr) 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

virtual Double_t	GetSeparation (PDF pdfS=nullptr, PDF pdfB=nullptr) const
	compute "separation" defined as

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

Double_t	GetSignalReferenceCut () const

Double_t	GetSignalReferenceCutOrientation () const

virtual Double_t	GetSignificance () const
	compute significance of mean difference

const Event *	GetTestingEvent (Long64_t ievt) const

Double_t	GetTestTime () const

const TString &	GetTestvarName () const

virtual Double_t	GetTrainingEfficiency (const TString &)

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

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

virtual void	TestClassification ()
	initialization

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

virtual void	WriteMonitoringHistosToFile () const
	write special monitoring histograms to file dummy implementation here --------------—

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 ()

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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.

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

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

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

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

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

const char *	GetName () const override
	Returns name of object.

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

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

TClass *	IsA () const override

Bool_t	IsSortable () const override

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

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

void	Print (Option_t *option="") const override
	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.

void	Streamer (TBuffer &) override
	Stream an object of class TObject.

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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 with: `gROOT->SetSelectedPad(c1)`.

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=nullptr)
	Execute method on this object with the given parameter string, e.g.

virtual void	Execute (TMethod method, TObjArray params, Int_t *error=nullptr)
	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 (the base implementation is no-op).

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, void vp)
	Only called by placement new when throwing an exception.

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

void	operator delete[] (void ptr, void vp)
	Only called by placement new[] when throwing an exception.

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.

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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=nullptr, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory.

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

Static Public Member Functions
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

static Double_t	IGetVolumeContentForRoot (Double_t)
	Interface to RootFinder.

static MethodPDERS *	ThisPDERS (void)
	static pointer to this object

Static Public Member Functions inherited from TMVA::MethodBase
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TMVA::IMethod
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TMVA::Configurable
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TNamed
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TObject
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

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.

Protected Member Functions
Double_t	ApplyKernelFunction (Double_t normalized_distance)
	from the normalized euclidean distance calculate the distance for a certain kernel

Double_t	CKernelEstimate (const Event &, std::vector< const BinarySearchTreeNode * > &, Volume &)
	normalization factors so we can work with radius 1 hyperspheres

const Ranking *	CreateRanking ()

BinarySearchTree *	GetBinaryTree (void) const

void	GetHelpMessage () const
	get help message text

Double_t	GetNormalizedDistance (const TMVA::Event &base_event, const BinarySearchTreeNode &sample_event, Double_t *dim_normalization)
	We use Euclidian metric here. Might not be best or most efficient.

Double_t	KernelNormalization (Double_t pdf)
	Calculating the normalization factor only once (might need a reset at some point.

Double_t	LanczosFilter (Int_t level, Double_t x)
	Lanczos Filter.

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

Double_t	NormSinc (Double_t x)
	NormSinc.

void	RKernelEstimate (const Event &, std::vector< const BinarySearchTreeNode * > &, Volume &, std::vector< Float_t > *pdfSum)
	normalization factors so we can work with radius 1 hyperspheres

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 ()

Protected Attributes
Int_t	fFcnCall

Volume *	fHelpVolume

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
	! message logger

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

Private Types
enum	EKernelEstimator { kBox = 0 , kSphere , kTeepee , kGauss , kSinc3 , kSinc5 , kSinc7 , kSinc9 , kSinc11 , kLanczos2 , kLanczos3 , kLanczos5 , kLanczos8 , kTrim }

enum	EVolumeRangeMode { kUnsupported = 0 , kMinMax , kRMS , kAdaptive , kUnscaled , kkNN }

Private Member Functions
void	CalcAverages ()
	compute also average RMS values required for adaptive Gaussian

void	CreateBinarySearchTree (Types::ETreeType type)
	create binary search trees for signal and background

Double_t	CRScalc (const Event &)

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

Float_t	GetError (Float_t countS, Float_t countB, Float_t sumW2S, Float_t sumW2B) const
	statistical error estimate for RS estimator

void	GetSample (const Event &e, std::vector< const BinarySearchTreeNode * > &events, Volume *volume)

void	Init (void)
	default initialisation routine called by all constructors

void	ProcessOptions ()
	process the options specified by the user

void	RRScalc (const Event &, std::vector< Float_t > *count)

void	SetVolumeElement (void)
	defines volume dimensions

void	UpdateThis ()
	update static this pointer

Static Private Member Functions
static MethodPDERS *&	GetMethodPDERSThreadLocal ()

Private Attributes
std::vector< Float_t >	fAverageRMS
	average RMS of signal and background

BinarySearchTree *	fBinaryTree
	binary tree

std::vector< Float_t > *	fDelta
	size of volume

Float_t	fDeltaFrac
	fraction of RMS

Double_t	fGaussSigma
	size of Gauss in adaptive volume

Double_t	fGaussSigmaNorm
	size of Gauss in adaptive volume (normalised to dimensions)

Bool_t	fInitializedVolumeEle
	is volume element initialized ?

Float_t	fInitialScale
	initial scale for adaptive volume

enum TMVA::MethodPDERS::EKernelEstimator	fKernelEstimator

TString	fKernelString

Int_t	fkNNMax
	max number of events in kNN tree

Int_t	fkNNMin
	min number of events in kNN tree

Double_t	fMax_distance
	maximum distance

Float_t	fMaxVIterations
	maximum number of iterations to adapt volume size

Float_t	fNEventsMax
	maximum number of events in adaptive volume

Float_t	fNEventsMin
	minimum number of events in adaptive volume

Bool_t	fNormTree
	binary-search tree is normalised

Double_t	fNRegOut

Bool_t	fPrinted
	print

Float_t	fScaleB
	weight for background events

Float_t	fScaleS
	weight for signal events

std::vector< Float_t > *	fShift
	volume center

TString	fVolumeRange

enum TMVA::MethodPDERS::EVolumeRangeMode	fVRangeMode

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 = (1ULL << ( 0 )) , kOverwrite = (1ULL << ( 1 )) , kWriteDelete = (1ULL << ( 2 )) }

enum	EDeprecatedStatusBits { kObjInCanvas = (1ULL << ( 3 )) }

enum	EStatusBits { kCanDelete = (1ULL << ( 0 )) , kMustCleanup = (1ULL << ( 3 )) , kIsReferenced = (1ULL << ( 4 )) , kHasUUID = (1ULL << ( 5 )) , kCannotPick = (1ULL << ( 6 )) , kNoContextMenu = (1ULL << ( 8 )) , kInvalidObject = (1ULL << ( 13 )) }

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

TrainingHistory	fTrainHistory

Protected Types inherited from TObject
enum	{ kOnlyPrepStep = (1ULL << ( 3 )) }

#include <TMVA/MethodPDERS.h>

Inheritance diagram for TMVA::MethodPDERS:

[legend]

Member Enumeration Documentation

◆ EKernelEstimator

enum TMVA::MethodPDERS::EKernelEstimator

private

Enumerator
kBox
kSphere
kTeepee
kGauss
kSinc3	the sinc enumerators must be consecutive and in order!
kSinc5
kSinc7
kSinc9
kSinc11
kLanczos2
kLanczos3
kLanczos5
kLanczos8
kTrim

Definition at line 160 of file MethodPDERS.h.

◆ EVolumeRangeMode

enum TMVA::MethodPDERS::EVolumeRangeMode

private

Enumerator
kUnsupported
kMinMax
kRMS
kAdaptive
kUnscaled
kkNN

Definition at line 151 of file MethodPDERS.h.

Constructor & Destructor Documentation

◆ MethodPDERS() [1/2]

TMVA::MethodPDERS::MethodPDERS	(	const TString &	jobName,
		const TString &	methodTitle,
		DataSetInfo &	theData,
		const TString &	theOption
	)

standard constructor for the PDERS method

Definition at line 100 of file MethodPDERS.cxx.

◆ MethodPDERS() [2/2]

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

construct MethodPDERS through from file

Definition at line 134 of file MethodPDERS.cxx.

◆ ~MethodPDERS()

TMVA::MethodPDERS::~MethodPDERS ( void )

virtual

destructor

Definition at line 208 of file MethodPDERS.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

virtual

write weights to xml file

Implements TMVA::MethodBase.

Definition at line 1099 of file MethodPDERS.cxx.

◆ ApplyKernelFunction()

Double_t TMVA::MethodPDERS::ApplyKernelFunction ( Double_t normalized_distance )

protected

from the normalized euclidean distance calculate the distance for a certain kernel

Definition at line 922 of file MethodPDERS.cxx.

◆ CalcAverages()

void TMVA::MethodPDERS::CalcAverages ( )

private

compute also average RMS values required for adaptive Gaussian

Definition at line 433 of file MethodPDERS.cxx.

◆ CKernelEstimate()

Double_t TMVA::MethodPDERS::CKernelEstimate	(	const Event &	event,
		std::vector< const BinarySearchTreeNode * > &	events,
		Volume &	v
	)

protected

normalization factors so we can work with radius 1 hyperspheres

Definition at line 834 of file MethodPDERS.cxx.

◆ Class()

static TClass * TMVA::MethodPDERS::Class ( )

static

Returns: TClass describing this class

◆ Class_Name()

static const char * TMVA::MethodPDERS::Class_Name ( )

static

Returns: Name of this class

◆ Class_Version()

static constexpr Version_t TMVA::MethodPDERS::Class_Version ( )

inlinestaticconstexpr

Returns: Version of this class

Definition at line 221 of file MethodPDERS.h.

◆ CreateBinarySearchTree()

void TMVA::MethodPDERS::CreateBinarySearchTree ( Types::ETreeType type )

private

create binary search trees for signal and background

Definition at line 455 of file MethodPDERS.cxx.

◆ CreateRanking()

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

inlineprotectedvirtual

Implements TMVA::MethodBase.

Definition at line 130 of file MethodPDERS.h.

◆ CRScalc()

Double_t TMVA::MethodPDERS::CRScalc ( const Event & e )

private

Definition at line 778 of file MethodPDERS.cxx.

◆ DeclareOptions()

void TMVA::MethodPDERS::DeclareOptions ( )

privatevirtual

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

know options:

VolumeRangeMode <string> Method to determine volume range available values are:
- MinMax
- Unscaled
- RMS
- kNN
- Adaptive <default>
KernelEstimator <string> Kernel estimation function available values are:
- Box <default>
- Sphere
- Teepee
- Gauss
- Sinc3
- Sinc5
- Sinc7
- Sinc9
- Sinc11
- Lanczos2
- Lanczos3
- Lanczos5
- Lanczos8
- Trim
DeltaFrac <float> Ratio of #EventsMin/#EventsMax for MinMax and RMS volume range
NEventsMin <int> Minimum number of events for adaptive volume range
NEventsMax <int> Maximum number of events for adaptive volume range
MaxVIterations <int> Maximum number of iterations for adaptive volume range
InitialScale <float> Initial scale for adaptive volume range
GaussSigma <float> Width with respect to the volume size of Gaussian kernel estimator

Implements TMVA::MethodBase.

Definition at line 252 of file MethodPDERS.cxx.

◆ DeclFileName()

static const char * TMVA::MethodPDERS::DeclFileName ( )

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 221 of file MethodPDERS.h.

◆ GetBinaryTree()

BinarySearchTree * TMVA::MethodPDERS::GetBinaryTree ( void ) const

inlineprotected

Definition at line 116 of file MethodPDERS.h.

◆ GetError()

Float_t TMVA::MethodPDERS::GetError	(	Float_t	countS,
		Float_t	countB,
		Float_t	sumW2S,
		Float_t	sumW2B
	)		const

private

statistical error estimate for RS estimator

Definition at line 1080 of file MethodPDERS.cxx.

◆ GetHelpMessage()

void TMVA::MethodPDERS::GetHelpMessage ( ) const

protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 1209 of file MethodPDERS.cxx.

◆ GetMethodPDERSThreadLocal()

static MethodPDERS *& TMVA::MethodPDERS::GetMethodPDERSThreadLocal ( )

inlinestaticprivate

Definition at line 216 of file MethodPDERS.h.

◆ GetMvaValue()

Double_t TMVA::MethodPDERS::GetMvaValue	(	Double_t *	err = `nullptr`,
		Double_t *	errUpper = `nullptr`
	)

virtual

init the size of a volume element using a defined fraction of the volume containing the entire events

Implements TMVA::MethodBase.

Definition at line 370 of file MethodPDERS.cxx.

◆ GetNormalizedDistance()

Double_t TMVA::MethodPDERS::GetNormalizedDistance	(	const TMVA::Event &	base_event,
		const BinarySearchTreeNode &	sample_event,
		Double_t *	dim_normalization
	)

protected

We use Euclidian metric here. Might not be best or most efficient.

Definition at line 1022 of file MethodPDERS.cxx.

◆ GetRegressionValues()

const std::vector< Float_t > & TMVA::MethodPDERS::GetRegressionValues ( )

virtual

Reimplemented from TMVA::MethodBase.

Definition at line 390 of file MethodPDERS.cxx.

◆ GetSample()

void TMVA::MethodPDERS::GetSample	(	const Event &	e,
		std::vector< const BinarySearchTreeNode * > &	events,
		Volume *	volume
	)

private

Definition at line 548 of file MethodPDERS.cxx.

◆ GetVolumeContentForRoot()

Double_t TMVA::MethodPDERS::GetVolumeContentForRoot ( Double_t scale )

count number of events in rescaled volume

Definition at line 537 of file MethodPDERS.cxx.

◆ HasAnalysisType()

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

virtual

PDERS can handle classification with 2 classes and regression with one or more regression-targets.

Implements TMVA::IMethod.

Definition at line 166 of file MethodPDERS.cxx.

◆ IGetVolumeContentForRoot()

Double_t TMVA::MethodPDERS::IGetVolumeContentForRoot ( Double_t scale )

static

Interface to RootFinder.

Definition at line 529 of file MethodPDERS.cxx.

◆ Init()

void TMVA::MethodPDERS::Init ( void )

privatevirtual

default initialisation routine called by all constructors

Implements TMVA::MethodBase.

Definition at line 176 of file MethodPDERS.cxx.

◆ IsA()

virtual TClass * TMVA::MethodPDERS::IsA ( ) const

inlinevirtual

Returns: TClass describing current object

Reimplemented from TMVA::MethodBase.

Definition at line 221 of file MethodPDERS.h.

◆ KernelNormalization()

Double_t TMVA::MethodPDERS::KernelNormalization ( Double_t pdf )

protected

Calculating the normalization factor only once (might need a reset at some point.

Can the method be restarted with different params?)

Definition at line 974 of file MethodPDERS.cxx.

◆ LanczosFilter()

Double_t TMVA::MethodPDERS::LanczosFilter	(	Int_t	level,
		Double_t	x
	)

protected

Lanczos Filter.

Definition at line 1060 of file MethodPDERS.cxx.

◆ MakeClassSpecific()

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

protectedvirtual

write specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 1197 of file MethodPDERS.cxx.

◆ NormSinc()

Double_t TMVA::MethodPDERS::NormSinc ( Double_t x )

protected

NormSinc.

Definition at line 1039 of file MethodPDERS.cxx.

◆ ProcessOptions()

void TMVA::MethodPDERS::ProcessOptions ( )

privatevirtual

process the options specified by the user

Implements TMVA::MethodBase.

Definition at line 289 of file MethodPDERS.cxx.

◆ ReadWeightsFromStream() [1/2]

void TMVA::MethodPDERS::ReadWeightsFromStream ( std::istream & istr )

virtual

read weight info from file

Implements TMVA::MethodBase.

Definition at line 1138 of file MethodPDERS.cxx.

◆ ReadWeightsFromStream() [2/2]

void TMVA::MethodPDERS::ReadWeightsFromStream ( TFile & istr )

virtual

read training sample from file

Reimplemented from TMVA::MethodBase.

Definition at line 1175 of file MethodPDERS.cxx.

◆ ReadWeightsFromXML()

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

virtual

Implements TMVA::MethodBase.

Definition at line 1111 of file MethodPDERS.cxx.

◆ RKernelEstimate()

void TMVA::MethodPDERS::RKernelEstimate	(	const Event &	event,
		std::vector< const BinarySearchTreeNode * > &	events,
		Volume &	v,
		std::vector< Float_t > *	pdfSum
	)

protected

normalization factors so we can work with radius 1 hyperspheres

Definition at line 875 of file MethodPDERS.cxx.

◆ RRScalc()

void TMVA::MethodPDERS::RRScalc	(	const Event &	e,
		std::vector< Float_t > *	count
	)

private

Definition at line 807 of file MethodPDERS.cxx.

◆ SetVolumeElement()

void TMVA::MethodPDERS::SetVolumeElement ( void )

private

defines volume dimensions

Definition at line 481 of file MethodPDERS.cxx.

◆ Streamer()

virtual void TMVA::MethodPDERS::Streamer ( TBuffer & )

virtual

Reimplemented from TMVA::MethodBase.

◆ StreamerNVirtual()

void TMVA::MethodPDERS::StreamerNVirtual ( TBuffer & ClassDef_StreamerNVirtual_b )

inline

Definition at line 221 of file MethodPDERS.h.

◆ ThisPDERS()

TMVA::MethodPDERS * TMVA::MethodPDERS::ThisPDERS ( void )

static

static pointer to this object

Definition at line 1182 of file MethodPDERS.cxx.

◆ Train()

void TMVA::MethodPDERS::Train ( void )

virtual

this is a dummy training: the preparation work to do is the construction of the binary tree as a pointer chain.

It is easier to directly save the trainingTree in the weight file, and to rebuild the binary tree in the test phase from scratch

Implements TMVA::MethodBase.

Definition at line 350 of file MethodPDERS.cxx.

◆ UpdateThis()

void TMVA::MethodPDERS::UpdateThis ( void )

private

update static this pointer

Definition at line 1189 of file MethodPDERS.cxx.

◆ WriteWeightsToStream()

void TMVA::MethodPDERS::WriteWeightsToStream ( TFile & rf ) const

write training sample (TTree) to file

Definition at line 1168 of file MethodPDERS.cxx.

Member Data Documentation

◆ fAverageRMS

std::vector<Float_t> TMVA::MethodPDERS::fAverageRMS

private

average RMS of signal and background

Definition at line 181 of file MethodPDERS.h.

◆ fBinaryTree

BinarySearchTree* TMVA::MethodPDERS::fBinaryTree

private

binary tree

Definition at line 177 of file MethodPDERS.h.

◆ fDelta

std::vector<Float_t>* TMVA::MethodPDERS::fDelta

private

size of volume

Definition at line 179 of file MethodPDERS.h.

◆ fDeltaFrac

Float_t TMVA::MethodPDERS::fDeltaFrac

private

fraction of RMS

Definition at line 185 of file MethodPDERS.h.

◆ fFcnCall

Int_t TMVA::MethodPDERS::fFcnCall

protected

Definition at line 113 of file MethodPDERS.h.

◆ fGaussSigma

Double_t TMVA::MethodPDERS::fGaussSigma

private

size of Gauss in adaptive volume

Definition at line 186 of file MethodPDERS.h.

◆ fGaussSigmaNorm

Double_t TMVA::MethodPDERS::fGaussSigmaNorm

private

size of Gauss in adaptive volume (normalised to dimensions)

Definition at line 187 of file MethodPDERS.h.

◆ fHelpVolume

Volume* TMVA::MethodPDERS::fHelpVolume

protected

Definition at line 112 of file MethodPDERS.h.

◆ fInitializedVolumeEle

Bool_t TMVA::MethodPDERS::fInitializedVolumeEle

private

is volume element initialized ?

Definition at line 197 of file MethodPDERS.h.

◆ fInitialScale

Float_t TMVA::MethodPDERS::fInitialScale

private

initial scale for adaptive volume

Definition at line 195 of file MethodPDERS.h.

◆ fKernelEstimator

enum TMVA::MethodPDERS::EKernelEstimator TMVA::MethodPDERS::fKernelEstimator

private

◆ fKernelString

TString TMVA::MethodPDERS::fKernelString

private

Definition at line 149 of file MethodPDERS.h.

◆ fkNNMax

Int_t TMVA::MethodPDERS::fkNNMax

private

max number of events in kNN tree

Definition at line 200 of file MethodPDERS.h.

◆ fkNNMin

Int_t TMVA::MethodPDERS::fkNNMin

private

min number of events in kNN tree

Definition at line 199 of file MethodPDERS.h.

◆ fMax_distance

Double_t TMVA::MethodPDERS::fMax_distance

private

maximum distance

Definition at line 202 of file MethodPDERS.h.

◆ fMaxVIterations

Float_t TMVA::MethodPDERS::fMaxVIterations

private

maximum number of iterations to adapt volume size

Definition at line 194 of file MethodPDERS.h.

◆ fNEventsMax

Float_t TMVA::MethodPDERS::fNEventsMax

private

maximum number of events in adaptive volume

Definition at line 193 of file MethodPDERS.h.

◆ fNEventsMin

Float_t TMVA::MethodPDERS::fNEventsMin

private

minimum number of events in adaptive volume

Definition at line 192 of file MethodPDERS.h.

◆ fNormTree

Bool_t TMVA::MethodPDERS::fNormTree

private

binary-search tree is normalised

Definition at line 204 of file MethodPDERS.h.

◆ fNRegOut

Double_t TMVA::MethodPDERS::fNRegOut

private

Definition at line 189 of file MethodPDERS.h.

◆ fPrinted

Bool_t TMVA::MethodPDERS::fPrinted

private

print

Definition at line 203 of file MethodPDERS.h.

◆ fScaleB

Float_t TMVA::MethodPDERS::fScaleB

private

weight for background events

Definition at line 184 of file MethodPDERS.h.

◆ fScaleS

Float_t TMVA::MethodPDERS::fScaleS

private

weight for signal events

Definition at line 183 of file MethodPDERS.h.

◆ fShift

std::vector<Float_t>* TMVA::MethodPDERS::fShift

private

volume center

Definition at line 180 of file MethodPDERS.h.

◆ fVolumeRange

TString TMVA::MethodPDERS::fVolumeRange

private

Definition at line 148 of file MethodPDERS.h.

◆ fVRangeMode

enum TMVA::MethodPDERS::EVolumeRangeMode TMVA::MethodPDERS::fVRangeMode

private

Libraries for TMVA::MethodPDERS:

[legend]

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

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

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Private Types

Private Member Functions

Static Private Member Functions

Private Attributes

Additional Inherited Members

Member Enumeration Documentation

◆ EKernelEstimator

◆ EVolumeRangeMode

Constructor & Destructor Documentation

◆ MethodPDERS() [1/2]

◆ MethodPDERS() [2/2]

◆ ~MethodPDERS()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ ApplyKernelFunction()

◆ CalcAverages()

◆ CKernelEstimate()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ CreateBinarySearchTree()

◆ CreateRanking()

◆ CRScalc()

◆ DeclareOptions()

◆ DeclFileName()

◆ GetBinaryTree()

◆ GetError()

◆ GetHelpMessage()

◆ GetMethodPDERSThreadLocal()

◆ GetMvaValue()

◆ GetNormalizedDistance()

◆ GetRegressionValues()

◆ GetSample()

◆ GetVolumeContentForRoot()

◆ HasAnalysisType()

◆ IGetVolumeContentForRoot()

◆ Init()

◆ IsA()

◆ KernelNormalization()

◆ LanczosFilter()

◆ MakeClassSpecific()

◆ NormSinc()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/2]

◆ ReadWeightsFromStream() [2/2]

◆ ReadWeightsFromXML()

◆ RKernelEstimate()

◆ RRScalc()

◆ SetVolumeElement()

◆ Streamer()

◆ StreamerNVirtual()

◆ ThisPDERS()

◆ Train()

◆ UpdateThis()

◆ WriteWeightsToStream()

Member Data Documentation

◆ fAverageRMS

◆ fBinaryTree

◆ fDelta

◆ fDeltaFrac

◆ fFcnCall

◆ fGaussSigma

◆ fGaussSigmaNorm

◆ fHelpVolume

◆ fInitializedVolumeEle

◆ fInitialScale

◆ fKernelEstimator

◆ fKernelString

◆ fkNNMax

◆ fkNNMin

◆ fMax_distance

◆ fMaxVIterations

◆ fNEventsMax

◆ fNEventsMin

◆ fNormTree

◆ fNRegOut