SMO Platt's SVM classifier with Keerthi & Shavade improvements.

Definition at line 61 of file MethodSVM.h.

Public Member Functions
	MethodSVM (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="")
	standard constructor

	MethodSVM (DataSetInfo &theData, const TString &theWeightFile)
	constructor from weight file

virtual	~MethodSVM (void)
	destructor

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

const Ranking *	CreateRanking ()

void	GetMGamma (const std::vector< float > &gammas)
	Produces GammaList string for multigaussian kernel to be written to xml file.

Double_t	GetMvaValue (Double_t err=0, Double_t errUpper=0)
	returns MVA value for given event

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

std::map< TString, std::vector< Double_t > >	GetTuningOptions ()
	GetTuningOptions Function to allow for ranges and number of steps (for scan) when optimising kernel function parameters.

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

void	Init (void)
	default initialisation

std::vector< TMVA::SVKernelFunction::EKernelType >	MakeKernelList (std::string multiKernels, TString kernel)
	MakeKernelList Function providing string manipulation for product or sum of kernels functions to take list of kernels specified in the booking of the method and provide a vector of SV kernels to iterate over in SVKernelFunction.

virtual std::map< TString, Double_t >	OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="Minuit")
	Optimize Tuning Parameters This is used to optimise the kernel function parameters and cost.

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

void	ReadWeightsFromStream (std::istream &istr)

virtual void	ReadWeightsFromStream (TFile &)

void	ReadWeightsFromStream (TFile &fFin)
	TODO write IT.

void	ReadWeightsFromXML (void *wghtnode)

void	Reset (void)

void	SetCost (Double_t c)

void	SetGamma (Double_t g)

void	SetKappa (Double_t k)

void	SetMGamma (std::string &mg)
	Takes as input a string of values for multigaussian gammas and splits it, filling the gamma vector required by the SVKernelFunction.

void	SetMult (Double_t m)

void	SetOrder (Double_t o)

void	SetTheta (Double_t t)

virtual void	SetTuneParameters (std::map< TString, Double_t > tuneParameters)
	Set the tuning parameters according to the argument.

void	Train (void)
	Train SVM.

void	WriteWeightsToStream (TFile &fout) const
	TODO write IT 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

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

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=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

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	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)

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)

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.

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 Member Functions
void	DeclareCompatibilityOptions ()
	options that are used ONLY for the READER to ensure backward compatibility

void	DeclareOptions ()
	declare options available for this method

Double_t	getLoss (TString lossFunction)
	getLoss Calculates loss for testing dataset.

void	ProcessOptions ()
	option post processing (if necessary)

Private Attributes
Float_t	fBparm

Float_t	fCost

Int_t	fDataSize

Float_t	fDoubleSigmaSquared

Float_t	fGamma

std::string	fGammaList

std::string	fGammas

std::vector< TMVA::SVEvent * > *	fInputData

Float_t	fKappa

TString	fLoss

UInt_t	fMaxIter

TVectorD *	fMaxVars

std::vector< Float_t >	fmGamma

TVectorD *	fMinVars

Float_t	fMult

std::string	fMultiKernels

UShort_t	fNSubSets

Float_t	fNumVars

Int_t	fOrder

std::vector< TMVA::SVEvent * > *	fSupportVectors

SVKernelFunction *	fSVKernelFunction

TString	fTheKernel

Float_t	fTheta

Float_t	fTolerance

std::string	fTune

std::vector< TString >	fVarNames

SVWorkingSet *	fWgSet

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) }

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.

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/MethodSVM.h>

Inheritance diagram for TMVA::MethodSVM:

[legend]

Constructor & Destructor Documentation

◆ MethodSVM() [1/2]

TMVA::MethodSVM::MethodSVM	(	const TString &	jobName,
		const TString &	methodTitle,
		DataSetInfo &	theData,
		const TString &	theOption = `""`
	)

standard constructor

Definition at line 90 of file MethodSVM.cxx.

◆ MethodSVM() [2/2]

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

constructor from weight file

Definition at line 126 of file MethodSVM.cxx.

◆ ~MethodSVM()

TMVA::MethodSVM::~MethodSVM ( void )

virtual

destructor

Definition at line 161 of file MethodSVM.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

virtual

write configuration to xml file

Implements TMVA::MethodBase.

Definition at line 398 of file MethodSVM.cxx.

◆ CreateRanking()

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

inlinevirtual

Implements TMVA::MethodBase.

Definition at line 104 of file MethodSVM.h.

◆ DeclareCompatibilityOptions()

void TMVA::MethodSVM::DeclareCompatibilityOptions ( )

privatevirtual

options that are used ONLY for the READER to ensure backward compatibility

Reimplemented from TMVA::MethodBase.

Definition at line 251 of file MethodSVM.cxx.

◆ DeclareOptions()

void TMVA::MethodSVM::DeclareOptions ( )

privatevirtual

declare options available for this method

Implements TMVA::MethodBase.

Definition at line 220 of file MethodSVM.cxx.

◆ GetHelpMessage()

void TMVA::MethodSVM::GetHelpMessage ( ) const

protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 715 of file MethodSVM.cxx.

◆ getLoss()

Double_t TMVA::MethodSVM::getLoss ( TString lossFunction )

private

getLoss Calculates loss for testing dataset.

The loss function can be specified when booking the method, otherwise defaults to hinge loss. Currently not used however is accesible if required.

Definition at line 1163 of file MethodSVM.cxx.

◆ GetMGamma()

void TMVA::MethodSVM::GetMGamma ( const std::vector< float > & gammas )

Produces GammaList string for multigaussian kernel to be written to xml file.

Definition at line 1032 of file MethodSVM.cxx.

◆ GetMvaValue()

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

virtual

returns MVA value for given event

Implements TMVA::MethodBase.

Definition at line 577 of file MethodSVM.cxx.

◆ GetRegressionValues()

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

virtual

Reimplemented from TMVA::MethodBase.

Definition at line 602 of file MethodSVM.cxx.

◆ GetTuningOptions()

std::map< TString, std::vector< Double_t > > TMVA::MethodSVM::GetTuningOptions ( )

GetTuningOptions Function to allow for ranges and number of steps (for scan) when optimising kernel function parameters.

Specified when booking the method after the parameter to be optimised between square brackets with each value separated by ;, the first value is the lower limit, the second the upper limit and the third is the number of steps. Example: "Tune=Gamma[0.01;1.0;100]" would only tune the RBF Gamma between 0.01 and 100 steps.

Definition at line 1106 of file MethodSVM.cxx.

◆ HasAnalysisType()

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

virtual

SVM can handle classification with 2 classes and regression with one regression-target.

Implements TMVA::IMethod.

Definition at line 195 of file MethodSVM.cxx.

◆ Init()

void TMVA::MethodSVM::Init ( void )

virtual

default initialisation

Implements TMVA::MethodBase.

Definition at line 205 of file MethodSVM.cxx.

◆ MakeClassSpecific()

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

protectedvirtual

write specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 635 of file MethodSVM.cxx.

◆ MakeKernelList()

std::vector< TMVA::SVKernelFunction::EKernelType > TMVA::MethodSVM::MakeKernelList	(	std::string	multiKernels,
		TString	kernel
	)

MakeKernelList Function providing string manipulation for product or sum of kernels functions to take list of kernels specified in the booking of the method and provide a vector of SV kernels to iterate over in SVKernelFunction.

Example:

"KernelList=RBF*Polynomial" would use a product of the RBF and Polynomial kernels.

Definition at line 1054 of file MethodSVM.cxx.

◆ OptimizeTuningParameters()

std::map< TString, Double_t > TMVA::MethodSVM::OptimizeTuningParameters	(	TString	fomType = `"ROCIntegral"`,
		TString	fitType = `"Minuit"`
	)

virtual

Optimize Tuning Parameters This is used to optimise the kernel function parameters and cost.

All kernel parameters are optimised by default with default ranges, however the parameters to be optimised can be set when booking the method with the option Tune.

Example:

"Tune=Gamma[0.01;1.0;100]" would only tune the RBF Gamma between 0.01 and 1.0 with 100 steps.

Reimplemented from TMVA::MethodBase.

Definition at line 760 of file MethodSVM.cxx.

◆ ProcessOptions()

void TMVA::MethodSVM::ProcessOptions ( )

privatevirtual

option post processing (if necessary)

Implements TMVA::MethodBase.

Definition at line 268 of file MethodSVM.cxx.

◆ ReadWeightsFromStream() [1/4]

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

virtual

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/4]

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

virtual

Implements TMVA::MethodBase.

Definition at line 513 of file MethodSVM.cxx.

◆ ReadWeightsFromStream() [3/4]

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

inlinevirtual

Reimplemented from TMVA::MethodBase.

Definition at line 266 of file MethodBase.h.

◆ ReadWeightsFromStream() [4/4]

void TMVA::MethodSVM::ReadWeightsFromStream ( TFile & fFin )

virtual

TODO write IT.

Reimplemented from TMVA::MethodBase.

Definition at line 570 of file MethodSVM.cxx.

◆ ReadWeightsFromXML()

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

virtual

Implements TMVA::MethodBase.

Definition at line 430 of file MethodSVM.cxx.

◆ Reset()

void TMVA::MethodSVM::Reset ( void )

virtual

Reimplemented from TMVA::MethodBase.

Definition at line 174 of file MethodSVM.cxx.

◆ SetCost()

void TMVA::MethodSVM::SetCost ( Double_t c )

inline

Definition at line 108 of file MethodSVM.h.

◆ SetGamma()

void TMVA::MethodSVM::SetGamma ( Double_t g )

inline

Definition at line 107 of file MethodSVM.h.

◆ SetKappa()

void TMVA::MethodSVM::SetKappa ( Double_t k )

inline

Definition at line 112 of file MethodSVM.h.

◆ SetMGamma()

void TMVA::MethodSVM::SetMGamma ( std::string & mg )

Takes as input a string of values for multigaussian gammas and splits it, filling the gamma vector required by the SVKernelFunction.

Example: "GammaList=0.1,0.2,0.3" would make a vector with Gammas of 0.1,0.2 & 0.3 corresponding to input variables 1,2 & 3 respectively.

Definition at line 1018 of file MethodSVM.cxx.

◆ SetMult()

void TMVA::MethodSVM::SetMult ( Double_t m )

inline

Definition at line 113 of file MethodSVM.h.

◆ SetOrder()

void TMVA::MethodSVM::SetOrder ( Double_t o )

inline

Definition at line 110 of file MethodSVM.h.

◆ SetTheta()

void TMVA::MethodSVM::SetTheta ( Double_t t )

inline

Definition at line 111 of file MethodSVM.h.

◆ SetTuneParameters()

void TMVA::MethodSVM::SetTuneParameters ( std::map< TString, Double_t > tuneParameters )

virtual

Set the tuning parameters according to the argument.

Reimplemented from TMVA::MethodBase.

Definition at line 917 of file MethodSVM.cxx.

◆ Train()

void TMVA::MethodSVM::Train ( void )

virtual

Train SVM.

Implements TMVA::MethodBase.

Definition at line 281 of file MethodSVM.cxx.

◆ WriteWeightsToStream()

void TMVA::MethodSVM::WriteWeightsToStream ( TFile & fout ) const

TODO write IT write training sample (TTree) to file.

Definition at line 507 of file MethodSVM.cxx.

Member Data Documentation

◆ fBparm

Float_t TMVA::MethodSVM::fBparm

private

Definition at line 137 of file MethodSVM.h.

◆ fCost

Float_t TMVA::MethodSVM::fCost

private

Definition at line 133 of file MethodSVM.h.

◆ fDataSize

Int_t TMVA::MethodSVM::fDataSize

private

Definition at line 162 of file MethodSVM.h.

◆ fDoubleSigmaSquared

Float_t TMVA::MethodSVM::fDoubleSigmaSquared

private

Definition at line 149 of file MethodSVM.h.

◆ fGamma

Float_t TMVA::MethodSVM::fGamma

private

Definition at line 138 of file MethodSVM.h.

◆ fGammaList

std::string TMVA::MethodSVM::fGammaList

private

Definition at line 158 of file MethodSVM.h.

◆ fGammas

std::string TMVA::MethodSVM::fGammas

private

Definition at line 157 of file MethodSVM.h.

◆ fInputData

std::vector<TMVA::SVEvent*>* TMVA::MethodSVM::fInputData

private

Definition at line 140 of file MethodSVM.h.

◆ fKappa

Float_t TMVA::MethodSVM::fKappa

private

Definition at line 152 of file MethodSVM.h.

◆ fLoss

TString TMVA::MethodSVM::fLoss

private

Definition at line 163 of file MethodSVM.h.

◆ fMaxIter

UInt_t TMVA::MethodSVM::fMaxIter

private

Definition at line 135 of file MethodSVM.h.

◆ fMaxVars

TVectorD* TMVA::MethodSVM::fMaxVars

private

Definition at line 145 of file MethodSVM.h.

◆ fmGamma

std::vector<Float_t> TMVA::MethodSVM::fmGamma

private

Definition at line 154 of file MethodSVM.h.

◆ fMinVars

TVectorD* TMVA::MethodSVM::fMinVars

private

Definition at line 144 of file MethodSVM.h.

◆ fMult

Float_t TMVA::MethodSVM::fMult

private

Definition at line 153 of file MethodSVM.h.

◆ fMultiKernels

std::string TMVA::MethodSVM::fMultiKernels

private

Definition at line 160 of file MethodSVM.h.

◆ fNSubSets

UShort_t TMVA::MethodSVM::fNSubSets

private

Definition at line 136 of file MethodSVM.h.

◆ fNumVars

Float_t TMVA::MethodSVM::fNumVars

private

Definition at line 155 of file MethodSVM.h.

◆ fOrder

Int_t TMVA::MethodSVM::fOrder

private

Definition at line 150 of file MethodSVM.h.

◆ fSupportVectors

std::vector<TMVA::SVEvent*>* TMVA::MethodSVM::fSupportVectors

private

Definition at line 141 of file MethodSVM.h.

◆ fSVKernelFunction

SVKernelFunction* TMVA::MethodSVM::fSVKernelFunction

private

Definition at line 142 of file MethodSVM.h.

◆ fTheKernel

TString TMVA::MethodSVM::fTheKernel

private

Definition at line 148 of file MethodSVM.h.

◆ fTheta

Float_t TMVA::MethodSVM::fTheta

private

Definition at line 151 of file MethodSVM.h.

◆ fTolerance

Float_t TMVA::MethodSVM::fTolerance

private

Definition at line 134 of file MethodSVM.h.

◆ fTune

std::string TMVA::MethodSVM::fTune

private

Definition at line 159 of file MethodSVM.h.

◆ fVarNames

std::vector<TString> TMVA::MethodSVM::fVarNames

private

Definition at line 156 of file MethodSVM.h.

◆ fWgSet

SVWorkingSet* TMVA::MethodSVM::fWgSet

private

Definition at line 139 of file MethodSVM.h.

Libraries for TMVA::MethodSVM:

[legend]

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

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

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ MethodSVM() [1/2]

◆ MethodSVM() [2/2]

◆ ~MethodSVM()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ CreateRanking()

◆ DeclareCompatibilityOptions()

◆ DeclareOptions()

◆ GetHelpMessage()

◆ getLoss()

◆ GetMGamma()

◆ GetMvaValue()

◆ GetRegressionValues()

◆ GetTuningOptions()

◆ HasAnalysisType()

◆ Init()

◆ MakeClassSpecific()

◆ MakeKernelList()

◆ OptimizeTuningParameters()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/4]

◆ ReadWeightsFromStream() [2/4]

◆ ReadWeightsFromStream() [3/4]

◆ ReadWeightsFromStream() [4/4]

◆ ReadWeightsFromXML()

◆ Reset()

◆ SetCost()

◆ SetGamma()

◆ SetKappa()

◆ SetMGamma()

◆ SetMult()

◆ SetOrder()

◆ SetTheta()

◆ SetTuneParameters()

◆ Train()

◆ WriteWeightsToStream()

Member Data Documentation

◆ fBparm

◆ fCost

◆ fDataSize

◆ fDoubleSigmaSquared

◆ fGamma

◆ fGammaList

◆ fGammas

◆ fInputData

◆ fKappa

◆ fLoss

◆ fMaxIter

◆ fMaxVars

◆ fmGamma

◆ fMinVars

◆ fMult

◆ fMultiKernels

◆ fNSubSets

◆ fNumVars

◆ fOrder

◆ fSupportVectors

◆ fSVKernelFunction

◆ fTheKernel

◆ fTheta

◆ fTolerance

◆ fTune

◆ fVarNames

◆ fWgSet