Fisher and Mahalanobis Discriminants (Linear Discriminant Analysis)

In the method of Fisher discriminants event selection is performed in a transformed variable space with zero linear correlations, by distinguishing the mean values of the signal and background distributions.

The linear discriminant analysis determines an axis in the (correlated) hyperspace of the input variables such that, when projecting the output classes (signal and background) upon this axis, they are pushed as far as possible away from each other, while events of a same class are confined in a close vicinity. The linearity property of this method is reflected in the metric with which "far apart" and "close vicinity" are determined: the covariance matrix of the discriminant variable space.

The classification of the events in signal and background classes relies on the following characteristics (only): overall sample means, \( x_i \), for each input variable, \( i \), class-specific sample means, \( x_{S(B),i}\), and total covariance matrix \( T_{ij} \). The covariance matrix can be decomposed into the sum of a within ( \( W_{ij} \)) and a between-class ( \( B_{ij} \)) class matrix. They describe the dispersion of events relative to the means of their own class (within-class matrix), and relative to the overall sample means (between-class matrix). The Fisher coefficients, \( F_i \), are then given by

\[ F_i = \frac{\sqrt{N_s N_b}}{N_s + N_b} \sum_{j=1}^{N_{SB}} W_{ij}^{-1} (\bar{X}_{Sj} - \bar{X}_{Bj}) \]

where in TMVA is set \( N_S = N_B \), so that the factor in front of the sum simplifies to \( \frac{1}{2}\). The Fisher discriminant then reads

\[ X_{Fi} = F_0 + \sum_{i=1}^{N_{SB}} F_i X_i \]

The offset \( F_0 \) centers the sample mean of \( x_{Fi} \) at zero. Instead of using the within-class matrix, the Mahalanobis variant determines the Fisher coefficients as follows:

\[ F_i = \frac{\sqrt{N_s N_b}}{N_s + N_b} \sum_{j=1}^{N_{SB}} (W + B)_{ij}^{-1} (\bar{X}_{Sj} - \bar{X}_{Bj}) \]

with resulting \( x_{Ma} \) that are very similar to the \( x_{Fi} \).

TMVA provides two outputs for the ranking of the input variables:

Fisher test: the Fisher analysis aims at simultaneously maximising the between-class separation, while minimising the within-class dispersion. A useful measure of the discrimination power of a variable is hence given by the diagonal quantity: \( \frac{B_{ii}}{W_{ii}} \) .
Discrimination power: the value of the Fisher coefficient is a measure of the discriminating power of a variable. The discrimination power of set of input variables can therefore be measured by the scalar

\[ \lambda = \frac{\sqrt{N_s N_b}}{N_s + N_b} \sum_{j=1}^{N_{SB}} F_i (\bar{X}_{Sj} - \bar{X}_{Bj}) \]

The corresponding numbers are printed on standard output.

Definition at line 54 of file MethodFisher.h.

Public Types
enum	EFisherMethod { kFisher , kMahalanobis }

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 Member Functions
	MethodFisher (const TString &jobName, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="Fisher")
	standard constructor for the "Fisher"

	MethodFisher (DataSetInfo &dsi, const TString &theWeightFile)
	constructor from weight file

virtual	~MethodFisher (void)
	destructor

void	AddWeightsXMLTo (void *parent) const
	create XML description of Fisher classifier

const Ranking *	CreateRanking ()
	computes ranking of input variables

EFisherMethod	GetFisherMethod (void)

Double_t	GetMvaValue (Double_t err=0, Double_t errUpper=0)
	returns the Fisher value (no fixed range)

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

void	PrintCoefficients (void)
	display Fisher coefficients and discriminating power for each variable check maximum length of variable name

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

void	ReadWeightsFromStream (std::istream &i)
	read Fisher coefficients from weight file

virtual void	ReadWeightsFromStream (TFile &)

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

void	Train (void)
	computation of Fisher coefficients by series of matrix operations

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=0, Double_t *errUpper=0)

const char *	GetName () const

UInt_t	GetNEvents () const
	temporary event when testing on a different DataSet than the own one

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

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

virtual Double_t	GetSeparation (PDF pdfS=0, PDF pdfB=0) 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

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 ()

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)

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.

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

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

Protected Member Functions inherited from TMVA::MethodBase
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 Member Functions
void	DeclareOptions ()
	MethodFisher options: format and syntax of option string: "type" where type is "Fisher" or "Mahalanobis".

void	GetCov_BetweenClass (void)
	the matrix of covariance 'between class' reflects the dispersion of the events of a class relative to the global center of gravity of all the class hence the separation between classes

void	GetCov_Full (void)
	compute full covariance matrix from sum of within and between matrices

void	GetCov_WithinClass (void)
	the matrix of covariance 'within class' reflects the dispersion of the events relative to the center of gravity of their own class

void	GetDiscrimPower (void)
	computation of discrimination power indicator for each variable small values of "fWith" indicates little compactness of sig & of backgd big values of "fBetw" indicates large separation between sig & backgd

void	GetFisherCoeff (void)
	Fisher = Sum { [coeff]*[variables] }.

void	GetMean (void)
	compute mean values of variables in each sample, and the overall means

void	Init (void)
	default initialization called by all constructors

void	InitMatrices (void)
	initialization method; creates global matrices and vectors

void	ProcessOptions ()
	process user options

Private Attributes
TMatrixD *	fBetw

TMatrixD *	fCov

std::vector< Double_t > *	fDiscrimPow

Double_t	fF0

std::vector< Double_t > *	fFisherCoeff

EFisherMethod	fFisherMethod

TMatrixD *	fMeanMatx

Double_t	fSumOfWeightsB

Double_t	fSumOfWeightsS

TString	fTheMethod

TMatrixD *	fWith

Additional Inherited Members
Static Public Member Functions inherited from TObject
static Long_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.

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

const Event *	fTmpEvent

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

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

Protected Attributes inherited from TMVA::Configurable
MsgLogger *	fLogger

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

#include <TMVA/MethodFisher.h>

Inheritance diagram for TMVA::MethodFisher:

[legend]

Member Enumeration Documentation

◆ EFisherMethod

enum TMVA::MethodFisher::EFisherMethod

Enumerator
kFisher
kMahalanobis

Definition at line 86 of file MethodFisher.h.

Constructor & Destructor Documentation

◆ MethodFisher() [1/2]

TMVA::MethodFisher::MethodFisher	(	const TString &	jobName,
		const TString &	methodTitle,
		DataSetInfo &	dsi,
		const TString &	theOption = `"Fisher"`
	)

standard constructor for the "Fisher"

Definition at line 135 of file MethodFisher.cxx.

◆ MethodFisher() [2/2]

TMVA::MethodFisher::MethodFisher	(	DataSetInfo &	dsi,
		const TString &	theWeightFile
	)

constructor from weight file

Definition at line 157 of file MethodFisher.cxx.

◆ ~MethodFisher()

TMVA::MethodFisher::~MethodFisher ( void )

virtual

destructor

Definition at line 216 of file MethodFisher.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

virtual

create XML description of Fisher classifier

Implements TMVA::MethodBase.

Definition at line 618 of file MethodFisher.cxx.

◆ CreateRanking()

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

virtual

computes ranking of input variables

Implements TMVA::MethodBase.

Definition at line 541 of file MethodFisher.cxx.

◆ DeclareOptions()

void TMVA::MethodFisher::DeclareOptions ( )

privatevirtual

MethodFisher options: format and syntax of option string: "type" where type is "Fisher" or "Mahalanobis".

Implements TMVA::MethodBase.

Definition at line 194 of file MethodFisher.cxx.

◆ GetCov_BetweenClass()

void TMVA::MethodFisher::GetCov_BetweenClass ( void )

private

the matrix of covariance 'between class' reflects the dispersion of the events of a class relative to the global center of gravity of all the class hence the separation between classes

Definition at line 417 of file MethodFisher.cxx.

◆ GetCov_Full()

void TMVA::MethodFisher::GetCov_Full ( void )

private

compute full covariance matrix from sum of within and between matrices

Definition at line 440 of file MethodFisher.cxx.

◆ GetCov_WithinClass()

void TMVA::MethodFisher::GetCov_WithinClass ( void )

private

the matrix of covariance 'within class' reflects the dispersion of the events relative to the center of gravity of their own class

Definition at line 351 of file MethodFisher.cxx.

◆ GetDiscrimPower()

void TMVA::MethodFisher::GetDiscrimPower ( void )

private

computation of discrimination power indicator for each variable small values of "fWith" indicates little compactness of sig & of backgd big values of "fBetw" indicates large separation between sig & backgd

we want signal & backgd classes as compact and separated as possible the discriminating power is then defined as the ration "fBetw/fWith"

Definition at line 528 of file MethodFisher.cxx.

◆ GetFisherCoeff()

void TMVA::MethodFisher::GetFisherCoeff ( void )

private

Fisher = Sum { [coeff]*[variables] }.

let Xs be the array of the mean values of variables for signal evts let Xb be the array of the mean values of variables for backgd evts let InvWith be the inverse matrix of the 'within class' correlation matrix

then the array of Fisher coefficients is [coeff] =sqrt(fNsig*fNbgd)/fNevt*transpose{Xs-Xb}*InvWith

Definition at line 457 of file MethodFisher.cxx.

◆ GetFisherMethod()

EFisherMethod TMVA::MethodFisher::GetFisherMethod ( void )

inline

Definition at line 87 of file MethodFisher.h.

◆ GetHelpMessage()

void TMVA::MethodFisher::GetHelpMessage ( ) const

protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 702 of file MethodFisher.cxx.

◆ GetMean()

void TMVA::MethodFisher::GetMean ( void )

private

compute mean values of variables in each sample, and the overall means

Definition at line 302 of file MethodFisher.cxx.

◆ GetMvaValue()

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

virtual

returns the Fisher value (no fixed range)

Implements TMVA::MethodBase.

Definition at line 268 of file MethodFisher.cxx.

◆ HasAnalysisType()

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

virtual

Fisher can only handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 228 of file MethodFisher.cxx.

◆ Init()

void TMVA::MethodFisher::Init ( void )

privatevirtual

default initialization called by all constructors

Implements TMVA::MethodBase.

Definition at line 177 of file MethodFisher.cxx.

◆ InitMatrices()

void TMVA::MethodFisher::InitMatrices ( void )

private

initialization method; creates global matrices and vectors

Definition at line 285 of file MethodFisher.cxx.

◆ MakeClassSpecific()

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

protectedvirtual

write Fisher-specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 655 of file MethodFisher.cxx.

◆ PrintCoefficients()

void TMVA::MethodFisher::PrintCoefficients ( void )

display Fisher coefficients and discriminating power for each variable check maximum length of variable name

Definition at line 557 of file MethodFisher.cxx.

◆ ProcessOptions()

void TMVA::MethodFisher::ProcessOptions ( )

privatevirtual

process user options

Implements TMVA::MethodBase.

Definition at line 204 of file MethodFisher.cxx.

◆ ReadWeightsFromStream() [1/3]

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

virtual

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/3]

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

virtual

read Fisher coefficients from weight file

Implements TMVA::MethodBase.

Definition at line 609 of file MethodFisher.cxx.

◆ ReadWeightsFromStream() [3/3]

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

inlinevirtual

Reimplemented from TMVA::MethodBase.

Definition at line 265 of file MethodBase.h.

◆ ReadWeightsFromXML()

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

virtual

read Fisher coefficients from xml weight file

Implements TMVA::MethodBase.

Definition at line 635 of file MethodFisher.cxx.

◆ Train()

void TMVA::MethodFisher::Train ( void )

virtual

computation of Fisher coefficients by series of matrix operations

Implements TMVA::MethodBase.

Definition at line 237 of file MethodFisher.cxx.

Member Data Documentation

◆ fBetw

TMatrixD* TMVA::MethodFisher::fBetw

private

Definition at line 139 of file MethodFisher.h.

◆ fCov

TMatrixD* TMVA::MethodFisher::fCov

private

Definition at line 141 of file MethodFisher.h.

◆ fDiscrimPow

std::vector<Double_t>* TMVA::MethodFisher::fDiscrimPow

private

Definition at line 147 of file MethodFisher.h.

◆ fF0

Double_t TMVA::MethodFisher::fF0

private

Definition at line 149 of file MethodFisher.h.

◆ fFisherCoeff

std::vector<Double_t>* TMVA::MethodFisher::fFisherCoeff

private

Definition at line 148 of file MethodFisher.h.

◆ fFisherMethod

EFisherMethod TMVA::MethodFisher::fFisherMethod

private

Definition at line 136 of file MethodFisher.h.

◆ fMeanMatx

TMatrixD* TMVA::MethodFisher::fMeanMatx

private

Definition at line 132 of file MethodFisher.h.

◆ fSumOfWeightsB

Double_t TMVA::MethodFisher::fSumOfWeightsB

private

Definition at line 145 of file MethodFisher.h.

◆ fSumOfWeightsS

Double_t TMVA::MethodFisher::fSumOfWeightsS

private

Definition at line 144 of file MethodFisher.h.

◆ fTheMethod

TString TMVA::MethodFisher::fTheMethod

private

Definition at line 135 of file MethodFisher.h.

◆ fWith

TMatrixD* TMVA::MethodFisher::fWith

private

Definition at line 140 of file MethodFisher.h.

Libraries for TMVA::MethodFisher:

[legend]

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

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

Public Types

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Member Enumeration Documentation

◆ EFisherMethod

Constructor & Destructor Documentation

◆ MethodFisher() [1/2]

◆ MethodFisher() [2/2]

◆ ~MethodFisher()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ CreateRanking()

◆ DeclareOptions()

◆ GetCov_BetweenClass()

◆ GetCov_Full()

◆ GetCov_WithinClass()

◆ GetDiscrimPower()

◆ GetFisherCoeff()

◆ GetFisherMethod()

◆ GetHelpMessage()

◆ GetMean()

◆ GetMvaValue()

◆ HasAnalysisType()

◆ Init()

◆ InitMatrices()

◆ MakeClassSpecific()

◆ PrintCoefficients()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/3]

◆ ReadWeightsFromStream() [2/3]

◆ ReadWeightsFromStream() [3/3]

◆ ReadWeightsFromXML()

◆ Train()

Member Data Documentation

◆ fBetw

◆ fCov

◆ fDiscrimPow

◆ fF0

◆ fFisherCoeff

◆ fFisherMethod

◆ fMeanMatx

◆ fSumOfWeightsB

◆ fSumOfWeightsS

◆ fTheMethod

◆ fWith