Multilayer Perceptron class built off of MethodANNBase.

Definition at line 69 of file MethodMLP.h.

Public Types
enum	EBPTrainingMode { kSequential =0 , kBatch }

enum	ETrainingMethod { kBP =0 , kBFGS , kGA }

Public Types inherited from TMVA::MethodANNBase
enum	EEstimator { kMSE =0 , kCE }

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 Member Functions
	MethodMLP (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption)
	standard constructor

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

virtual	~MethodMLP ()
	destructor nothing to be done

Double_t	ComputeEstimator (std::vector< Double_t > &parameters)
	this function is called by GeneticANN for GA optimization

Double_t	EstimatorFunction (std::vector< Double_t > &parameters)
	interface to the estimate

Double_t	GetMvaValue (Double_t err=nullptr, Double_t errUpper=nullptr)
	get the mva value generated by the NN

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

bool	HasInverseHessian ()

virtual TClass *	IsA () const

virtual void	Streamer (TBuffer &)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

void	Train ()

Public Member Functions inherited from TMVA::MethodANNBase
	MethodANNBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &theData, const TString &theOption)
	standard constructor Note: Right now it is an option to choose the neuron input function, but only the input function "sum" leads to weight convergence – otherwise the weights go to nan and lead to an ABORT.

	MethodANNBase (Types::EMVA methodType, DataSetInfo &theData, const TString &theWeightFile)
	construct the Method from the weight file

virtual	~MethodANNBase ()
	destructor

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

const Ranking *	CreateRanking ()
	compute ranking of input variables by summing function of weights

Bool_t	Debug () const
	who the hell makes such strange Debug flags that even use "global pointers"..

template<typename WriteIterator >
void	GetLayerActivation (size_t layer, WriteIterator writeIterator)

virtual const std::vector< Float_t > &	GetMulticlassValues ()
	get the multiclass classification values generated by the NN

virtual const std::vector< Float_t > &	GetRegressionValues ()
	get the regression value generated by the NN

void	InitANNBase ()
	initialize ANNBase object

virtual void	PrintNetwork () const
	print network representation, for debugging

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

virtual void	ReadWeightsFromStream (std::istream &istr)
	destroy/clear the network then read it back in from the weights file

virtual void	ReadWeightsFromStream (TFile &)

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

void	SetActivation (TActivation *activation)

void	SetNeuronInputCalculator (TNeuronInput *inputCalculator)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

virtual void	WriteMonitoringHistosToFile () const
	write histograms 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)

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

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.

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

virtual	~IFitterTarget ()

virtual void	ProgressNotifier (TString, TString)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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

	~ConvergenceTest ()
	destructor

Float_t	GetCurrentValue ()

Bool_t	HasConverged (Bool_t withinConvergenceBand=kFALSE)
	gives back true if the last "steps" steps have lead to an improvement of the "fitness" of the "individuals" of at least "improvement"

Float_t	Progress ()
	returns a float from 0 (just started) to 1 (finished)

void	ResetConvergenceCounter ()

void	SetConvergenceParameters (Int_t steps, Double_t improvement)

void	SetCurrentValue (Float_t value)

Float_t	SpeedControl (UInt_t ofSteps)
	this function provides the ability to change the learning rate according to the success of the last generations.

Static Public Member Functions
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::MethodANNBase
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::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.

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

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

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::MethodANNBase
virtual void	BuildNetwork (std::vector< Int_t > layout, std::vector< Double_t > weights=nullptr, Bool_t fromFile=kFALSE)
	build network given a layout (number of neurons in each layer) and optional weights array

void	CreateWeightMonitoringHists (const TString &bulkname, std::vector< TH1 * > *hv=nullptr) const

void	ForceNetworkCalculations ()
	calculate input values to each neuron

void	ForceNetworkInputs (const Event *ev, Int_t ignoreIndex=-1)
	force the input values of the input neurons force the value for each input neuron

TNeuron *	GetInputNeuron (Int_t index)

Double_t	GetNetworkOutput ()

TNeuron *	GetOutputNeuron (Int_t index=0)

Int_t	NumCycles ()

std::vector< Int_t > *	ParseLayoutString (TString layerSpec)
	parse layout specification string and return a vector, each entry containing the number of neurons to go in each successive layer

void	PrintMessage (TString message, Bool_t force=kFALSE) const
	print messages, turn off printing by setting verbose and debug flag appropriately

void	WaitForKeyboard ()
	wait for keyboard input, for debugging

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	AdjustSynapseWeights ()
	just adjust the synapse weights (should be called in batch mode)

void	BackPropagationMinimize (Int_t nEpochs)
	minimize estimator / train network with back propagation algorithm

void	BFGSMinimize (Int_t nEpochs)
	train network with BFGS algorithm

Double_t	CalculateEstimator (Types::ETreeType treeType=Types::kTraining, Int_t iEpoch=-1)
	calculate the estimator that training is attempting to minimize

void	CalculateNeuronDeltas ()
	have each neuron calculate its delta by back propagation

void	ComputeDEDw ()

void	DecaySynapseWeights (Bool_t lateEpoch)
	decay synapse weights in last 10 epochs, lower learning rate even more to find a good minimum

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

Double_t	DerivDir (TMatrixD &Dir)

void	GeneticMinimize ()
	create genetics class similar to GeneticCut give it vector of parameter ranges (parameters = weights) link fitness function of this class to ComputeEstimator instantiate GA (see MethodCuts) run it then this should exist for GA, Minuit and random sampling

void	GetApproxInvHessian (TMatrixD &InvHessian, bool regulate=true)
	rank-1 approximation, neglect 2nd derivatives. //zjh

Double_t	GetCEErr (const Event *ev, UInt_t index=0)
	zjh

Double_t	GetDesiredOutput (const Event *ev)
	get the desired output of this event

Double_t	GetError ()

Bool_t	GetHessian (TMatrixD &Hessian, TMatrixD &Gamma, TMatrixD &Delta)

Double_t	GetMSEErr (const Event *ev, UInt_t index=0)
	zjh

void	Init ()
	default initializations

void	InitializeLearningRates ()
	initialize learning rates of synapses, used only by back propagation

Bool_t	LineSearch (TMatrixD &Dir, std::vector< Double_t > &Buffer, Double_t *dError=nullptr)
	zjh

void	ProcessOptions ()
	process user options

void	SetDir (TMatrixD &Hessian, TMatrixD &Dir)

void	SetDirWeights (std::vector< Double_t > &Origin, TMatrixD &Dir, Double_t alpha)

void	SetGammaDelta (TMatrixD &Gamma, TMatrixD &Delta, std::vector< Double_t > &Buffer)

void	Shuffle (Int_t *index, Int_t n)
	Input:

void	SimulateEvent (const Event *ev)

void	SteepestDir (TMatrixD &Dir)

void	Train (Int_t nEpochs)

void	TrainOneEpoch ()
	train network over a single epoch/cycle of events

void	TrainOneEvent (Int_t ievt)
	train network over a single event this uses the new event model

void	TrainOneEventFast (Int_t ievt, Float_t *&branchVar, Int_t &type)
	fast per-event training

void	UpdateNetwork (const std::vector< Float_t > &desired, Double_t eventWeight=1.0)
	update the network based on how closely the output matched the desired output

void	UpdateNetwork (Double_t desired, Double_t eventWeight=1.0)
	update the network based on how closely the output matched the desired output

void	UpdatePriors ()
	zjh

void	UpdateRegulators ()
	zjh

void	UpdateSynapses ()
	update synapse error fields and adjust the weights (if in sequential mode)

Private Attributes
Int_t	fBatchSize
	batch size, only matters if in batch learning mode

EBPTrainingMode	fBPMode
	backprop learning mode (sequential or batch)

TString	fBpModeS
	backprop learning mode option string (sequential or batch)

bool	fCalculateErrors
	compute inverse hessian matrix at the end of the training

Double_t	fDecayRate
	decay rate for above learning rate

std::vector< std::pair< Float_t, Float_t > > *	fDeviationsFromTargets
	deviation from the targets, event weight

Bool_t	fEpochMon
	create and fill epoch-wise monitoring histograms (makes outputfile big!)

Int_t	fGA_nsteps
	GA settings: number of steps.

Int_t	fGA_preCalc
	GA settings: number of pre-calc steps.

Double_t	fGA_SC_factor
	GA settings: SC_factor.

Int_t	fGA_SC_rate
	GA settings: SC_rate.

Int_t	fGA_SC_steps
	GA settings: SC_steps.

Double_t	fLastAlpha
	line search variable

Double_t	fLearnRate
	learning rate for synapse weight adjustments

Double_t	fPrior
	zjh

std::vector< Double_t >	fPriorDev
	zjh

Int_t	fResetStep
	reset time (how often we clear hessian matrix)

Float_t	fSamplingEpoch
	fraction of epochs where sampling is used

Float_t	fSamplingFraction
	fraction of events which is sampled for training

Bool_t	fSamplingTesting
	The testing sample is sampled.

Bool_t	fSamplingTraining
	The training sample is sampled.

Float_t	fSamplingWeight
	changing factor for event weights when sampling is turned on

Double_t	fTau
	line search variable

Int_t	fTestRate
	test for overtraining performed at each #th epochs

ETrainingMethod	fTrainingMethod
	method of training, BP or GA

TString	fTrainMethodS
	training method option param

Int_t	fUpdateLimit
	zjh

bool	fUseRegulator
	zjh

Float_t	fWeightRange
	suppress outliers for the estimator calculation

Static Private Attributes
static const Bool_t	fgPRINT_BATCH = kFALSE
	debug flags

static const Int_t	fgPRINT_ESTIMATOR_INC = 10
	debug flags

static const Bool_t	fgPRINT_SEQ = kFALSE
	debug flags

Additional Inherited Members
Public Attributes inherited from TMVA::MethodANNBase
TObjArray *	fNetwork

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

TrainingHistory	fTrainHistory

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

Protected Attributes inherited from TMVA::MethodANNBase
TActivation *	fActivation

std::vector< TH1 * >	fEpochMonHistB

std::vector< TH1 * >	fEpochMonHistS

std::vector< TH1 * >	fEpochMonHistW

EEstimator	fEstimator

TH1F *	fEstimatorHistTest

TH1F *	fEstimatorHistTrain

TString	fEstimatorS

TActivation *	fIdentity

TNeuronInput *	fInputCalculator

TMatrixD	fInvHessian
	zjh

Int_t	fNcycles
	number of epochs to train

TString	fNeuronInputType
	name of neuron input calculator class

TString	fNeuronType
	name of neuron activation function class

TActivation *	fOutput

Int_t	fRandomSeed
	random seed for initial synapse weights

std::vector< Int_t >	fRegulatorIdx

std::vector< Double_t >	fRegulators

TRandom3 *	frgen

TObjArray *	fSynapses

bool	fUseRegulator
	zjh

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

Protected Attributes inherited from TMVA::ConvergenceTest
Float_t	fCurrentValue
	! current value

Float_t	fImprovement
	! minimum improvement which counts as improvement

Int_t	fSteps
	! number of steps without improvement required for convergence

#include <TMVA/MethodMLP.h>

Inheritance diagram for TMVA::MethodMLP:

[legend]

Member Enumeration Documentation

◆ EBPTrainingMode

enum TMVA::MethodMLP::EBPTrainingMode

Enumerator
kSequential
kBatch

Definition at line 92 of file MethodMLP.h.

◆ ETrainingMethod

enum TMVA::MethodMLP::ETrainingMethod

Enumerator
kBP
kBFGS
kGA

Definition at line 91 of file MethodMLP.h.

Constructor & Destructor Documentation

◆ MethodMLP() [1/2]

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

standard constructor

Definition at line 90 of file MethodMLP.cxx.

◆ MethodMLP() [2/2]

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

constructor from a weight file

Definition at line 115 of file MethodMLP.cxx.

◆ ~MethodMLP()

TMVA::MethodMLP::~MethodMLP ( )

virtual

destructor nothing to be done

Definition at line 138 of file MethodMLP.cxx.

Member Function Documentation

◆ AdjustSynapseWeights()

void TMVA::MethodMLP::AdjustSynapseWeights ( )

private

just adjust the synapse weights (should be called in batch mode)

Definition at line 1434 of file MethodMLP.cxx.

◆ BackPropagationMinimize()

void TMVA::MethodMLP::BackPropagationMinimize ( Int_t nEpochs )

private

minimize estimator / train network with back propagation algorithm

Definition at line 1037 of file MethodMLP.cxx.

◆ BFGSMinimize()

void TMVA::MethodMLP::BFGSMinimize ( Int_t nEpochs )

private

train network with BFGS algorithm

Definition at line 487 of file MethodMLP.cxx.

◆ CalculateEstimator()

Double_t TMVA::MethodMLP::CalculateEstimator	(	Types::ETreeType	treeType = `Types::kTraining`,
		Int_t	iEpoch = `-1`
	)

private

calculate the estimator that training is attempting to minimize

Definition at line 292 of file MethodMLP.cxx.

◆ CalculateNeuronDeltas()

void TMVA::MethodMLP::CalculateNeuronDeltas ( )

private

have each neuron calculate its delta by back propagation

Definition at line 1328 of file MethodMLP.cxx.

◆ Class()

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

static

Returns: TClass describing this class

◆ Class_Name()

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

static

Returns: Name of this class

◆ Class_Version()

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

inlinestaticconstexpr

Returns: Version of this class

Definition at line 219 of file MethodMLP.h.

◆ ComputeDEDw()

void TMVA::MethodMLP::ComputeDEDw ( )

private

Definition at line 697 of file MethodMLP.cxx.

◆ ComputeEstimator()

Double_t TMVA::MethodMLP::ComputeEstimator ( std::vector< Double_t > & parameters )

this function is called by GeneticANN for GA optimization

Definition at line 1393 of file MethodMLP.cxx.

◆ DecaySynapseWeights()

void TMVA::MethodMLP::DecaySynapseWeights ( Bool_t lateEpoch )

private

decay synapse weights in last 10 epochs, lower learning rate even more to find a good minimum

Definition at line 1208 of file MethodMLP.cxx.

◆ DeclareOptions()

void TMVA::MethodMLP::DeclareOptions ( )

privatevirtual

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

know options:

TrainingMethod <string> Training method available values are:
- BP Back-Propagation <default>
- GA Genetic Algorithm (takes a LONG time)
LearningRate <float> NN learning rate parameter
DecayRate <float> Decay rate for learning parameter
TestRate <int> Test for overtraining performed at each #th epochs
BPMode <string> Back-propagation learning mode available values are:
- sequential <default>
- batch
BatchSize <int> Batch size: number of events/batch, only set if in Batch Mode,
- -1 for BatchSize=number_of_events

Reimplemented from TMVA::MethodANNBase.

Definition at line 195 of file MethodMLP.cxx.

◆ DeclFileName()

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

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 219 of file MethodMLP.h.

◆ DerivDir()

Double_t TMVA::MethodMLP::DerivDir ( TMatrixD & Dir )

private

Definition at line 825 of file MethodMLP.cxx.

◆ EstimatorFunction()

Double_t TMVA::MethodMLP::EstimatorFunction ( std::vector< Double_t > & parameters )

virtual

interface to the estimate

Implements TMVA::IFitterTarget.

Definition at line 1385 of file MethodMLP.cxx.

◆ GeneticMinimize()

void TMVA::MethodMLP::GeneticMinimize ( )

private

create genetics class similar to GeneticCut give it vector of parameter ranges (parameters = weights) link fitness function of this class to ComputeEstimator instantiate GA (see MethodCuts) run it then this should exist for GA, Minuit and random sampling

Definition at line 1356 of file MethodMLP.cxx.

◆ GetApproxInvHessian()

void TMVA::MethodMLP::GetApproxInvHessian	(	TMatrixD &	InvHessian,
		bool	regulate = `true`
	)

private

rank-1 approximation, neglect 2nd derivatives. //zjh

Definition at line 1508 of file MethodMLP.cxx.

◆ GetCEErr()

Double_t TMVA::MethodMLP::GetCEErr	(	const Event *	ev,
		UInt_t	index = `0`
	)

private

zjh

Definition at line 1020 of file MethodMLP.cxx.

◆ GetDesiredOutput()

Double_t TMVA::MethodMLP::GetDesiredOutput ( const Event * ev )

private

get the desired output of this event

Definition at line 1277 of file MethodMLP.cxx.

◆ GetError()

Double_t TMVA::MethodMLP::GetError ( )

private

Definition at line 966 of file MethodMLP.cxx.

◆ GetHelpMessage()

void TMVA::MethodMLP::GetHelpMessage ( ) const

protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 1715 of file MethodMLP.cxx.

◆ GetHessian()

Bool_t TMVA::MethodMLP::GetHessian	(	TMatrixD &	Hessian,
		TMatrixD &	Gamma,
		TMatrixD &	Delta
	)

private

Definition at line 787 of file MethodMLP.cxx.

◆ GetMSEErr()

Double_t TMVA::MethodMLP::GetMSEErr	(	const Event *	ev,
		UInt_t	index = `0`
	)

private

zjh

Definition at line 1003 of file MethodMLP.cxx.

◆ GetMvaValue()

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

virtual

get the mva value generated by the NN

Reimplemented from TMVA::MethodANNBase.

Definition at line 1549 of file MethodMLP.cxx.

◆ HasAnalysisType()

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

virtual

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

Implements TMVA::IMethod.

Definition at line 152 of file MethodMLP.cxx.

◆ HasInverseHessian()

bool TMVA::MethodMLP::HasInverseHessian ( )

inline

Definition at line 94 of file MethodMLP.h.

◆ Init()

void TMVA::MethodMLP::Init ( void )

privatevirtual

default initializations

Implements TMVA::MethodBase.

Definition at line 164 of file MethodMLP.cxx.

◆ InitializeLearningRates()

void TMVA::MethodMLP::InitializeLearningRates ( )

private

initialize learning rates of synapses, used only by back propagation

Definition at line 278 of file MethodMLP.cxx.

◆ IsA()

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

inlinevirtual

Returns: TClass describing current object

Reimplemented from TMVA::MethodANNBase.

Definition at line 219 of file MethodMLP.h.

◆ LineSearch()

Bool_t TMVA::MethodMLP::LineSearch	(	TMatrixD &	Dir,
		std::vector< Double_t > &	Buffer,
		Double_t *	dError = `nullptr`
	)

private

zjh

Definition at line 840 of file MethodMLP.cxx.

◆ MakeClassSpecific()

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

protectedvirtual

write specific classifier response

Reimplemented from TMVA::MethodANNBase.

Definition at line 1704 of file MethodMLP.cxx.

◆ ProcessOptions()

void TMVA::MethodMLP::ProcessOptions ( )

privatevirtual

process user options

Reimplemented from TMVA::MethodANNBase.

Definition at line 247 of file MethodMLP.cxx.

◆ SetDir()

void TMVA::MethodMLP::SetDir	(	TMatrixD &	Hessian,
		TMatrixD &	Dir
	)

private

Definition at line 808 of file MethodMLP.cxx.

◆ SetDirWeights()

void TMVA::MethodMLP::SetDirWeights	(	std::vector< Double_t > &	Origin,
		TMatrixD &	Dir,
		Double_t	alpha
	)

private

Definition at line 950 of file MethodMLP.cxx.

◆ SetGammaDelta()

void TMVA::MethodMLP::SetGammaDelta	(	TMatrixD &	Gamma,
		TMatrixD &	Delta,
		std::vector< Double_t > &	Buffer
	)

private

Definition at line 672 of file MethodMLP.cxx.

◆ Shuffle()

void TMVA::MethodMLP::Shuffle	(	Int_t *	index,
		Int_t	n
	)

private

Input:

index: the array to shuffle
n: the size of the array Output:
index: the shuffled indexes

This method is used for sequential training

Definition at line 1190 of file MethodMLP.cxx.

◆ SimulateEvent()

void TMVA::MethodMLP::SimulateEvent ( const Event * ev )

private

Definition at line 734 of file MethodMLP.cxx.

◆ SteepestDir()

void TMVA::MethodMLP::SteepestDir ( TMatrixD & Dir )

private

Definition at line 774 of file MethodMLP.cxx.

◆ Streamer()

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

virtual

Reimplemented from TMVA::MethodANNBase.

◆ StreamerNVirtual()

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

inline

Definition at line 219 of file MethodMLP.h.

◆ Train() [1/2]

void TMVA::MethodMLP::Train ( void )

virtual

Implements TMVA::MethodANNBase.

Definition at line 142 of file MethodMLP.cxx.

◆ Train() [2/2]

void TMVA::MethodMLP::Train ( Int_t nEpochs )

private

Definition at line 436 of file MethodMLP.cxx.

◆ TrainOneEpoch()

void TMVA::MethodMLP::TrainOneEpoch ( )

private

train network over a single epoch/cycle of events

Definition at line 1142 of file MethodMLP.cxx.

◆ TrainOneEvent()

void TMVA::MethodMLP::TrainOneEvent ( Int_t ievt )

private

train network over a single event this uses the new event model

Definition at line 1258 of file MethodMLP.cxx.

◆ TrainOneEventFast()

void TMVA::MethodMLP::TrainOneEventFast	(	Int_t	ievt,
		Float_t *&	branchVar,
		Int_t &	type
	)

private

fast per-event training

Definition at line 1222 of file MethodMLP.cxx.

◆ UpdateNetwork() [1/2]

void TMVA::MethodMLP::UpdateNetwork	(	const std::vector< Float_t > &	desired,
		Double_t	eventWeight = `1.0`
	)

private

update the network based on how closely the output matched the desired output

Definition at line 1302 of file MethodMLP.cxx.

◆ UpdateNetwork() [2/2]

void TMVA::MethodMLP::UpdateNetwork	(	Double_t	desired,
		Double_t	eventWeight = `1.0`
	)

private

update the network based on how closely the output matched the desired output

Definition at line 1286 of file MethodMLP.cxx.

◆ UpdatePriors()

void TMVA::MethodMLP::UpdatePriors ( )

private

zjh

Definition at line 1454 of file MethodMLP.cxx.

◆ UpdateRegulators()

void TMVA::MethodMLP::UpdateRegulators ( )

private

zjh

Definition at line 1468 of file MethodMLP.cxx.

◆ UpdateSynapses()

void TMVA::MethodMLP::UpdateSynapses ( )

private

update synapse error fields and adjust the weights (if in sequential mode)

Definition at line 1412 of file MethodMLP.cxx.

Member Data Documentation

◆ fBatchSize

Int_t TMVA::MethodMLP::fBatchSize

private

batch size, only matters if in batch learning mode

Definition at line 192 of file MethodMLP.h.

◆ fBPMode

EBPTrainingMode TMVA::MethodMLP::fBPMode

private

backprop learning mode (sequential or batch)

Definition at line 190 of file MethodMLP.h.

◆ fBpModeS

TString TMVA::MethodMLP::fBpModeS

private

backprop learning mode option string (sequential or batch)

Definition at line 191 of file MethodMLP.h.

◆ fCalculateErrors

bool TMVA::MethodMLP::fCalculateErrors

private

compute inverse hessian matrix at the end of the training

Definition at line 165 of file MethodMLP.h.

◆ fDecayRate

Double_t TMVA::MethodMLP::fDecayRate

private

decay rate for above learning rate

Definition at line 189 of file MethodMLP.h.

◆ fDeviationsFromTargets

std::vector<std::pair<Float_t,Float_t> >* TMVA::MethodMLP::fDeviationsFromTargets

private

deviation from the targets, event weight

Definition at line 204 of file MethodMLP.h.

◆ fEpochMon

Bool_t TMVA::MethodMLP::fEpochMon

private

create and fill epoch-wise monitoring histograms (makes outputfile big!)

Definition at line 194 of file MethodMLP.h.

◆ fGA_nsteps

Int_t TMVA::MethodMLP::fGA_nsteps

private

GA settings: number of steps.

Definition at line 197 of file MethodMLP.h.

◆ fGA_preCalc

Int_t TMVA::MethodMLP::fGA_preCalc

private

GA settings: number of pre-calc steps.

Definition at line 198 of file MethodMLP.h.

◆ fGA_SC_factor

Double_t TMVA::MethodMLP::fGA_SC_factor

private

GA settings: SC_factor.

Definition at line 201 of file MethodMLP.h.

◆ fGA_SC_rate

Int_t TMVA::MethodMLP::fGA_SC_rate

private

GA settings: SC_rate.

Definition at line 200 of file MethodMLP.h.

◆ fGA_SC_steps

Int_t TMVA::MethodMLP::fGA_SC_steps

private

GA settings: SC_steps.

Definition at line 199 of file MethodMLP.h.

◆ fgPRINT_BATCH

const Bool_t TMVA::MethodMLP::fgPRINT_BATCH = kFALSE

staticprivate

debug flags

Definition at line 217 of file MethodMLP.h.

◆ fgPRINT_ESTIMATOR_INC

const Int_t TMVA::MethodMLP::fgPRINT_ESTIMATOR_INC = 10

staticprivate

debug flags

Definition at line 215 of file MethodMLP.h.

◆ fgPRINT_SEQ

const Bool_t TMVA::MethodMLP::fgPRINT_SEQ = kFALSE

staticprivate

debug flags

Definition at line 216 of file MethodMLP.h.

◆ fLastAlpha

Double_t TMVA::MethodMLP::fLastAlpha

private

line search variable

Definition at line 183 of file MethodMLP.h.

◆ fLearnRate

Double_t TMVA::MethodMLP::fLearnRate

private

learning rate for synapse weight adjustments

Definition at line 188 of file MethodMLP.h.

◆ fPrior

Double_t TMVA::MethodMLP::fPrior

private

zjh

Definition at line 166 of file MethodMLP.h.

◆ fPriorDev

std::vector<Double_t> TMVA::MethodMLP::fPriorDev

private

zjh

Definition at line 167 of file MethodMLP.h.

◆ fResetStep

Int_t TMVA::MethodMLP::fResetStep

private

reset time (how often we clear hessian matrix)

Definition at line 185 of file MethodMLP.h.

◆ fSamplingEpoch

Float_t TMVA::MethodMLP::fSamplingEpoch

private

fraction of epochs where sampling is used

Definition at line 177 of file MethodMLP.h.

◆ fSamplingFraction

Float_t TMVA::MethodMLP::fSamplingFraction

private

fraction of events which is sampled for training

Definition at line 176 of file MethodMLP.h.

◆ fSamplingTesting

Bool_t TMVA::MethodMLP::fSamplingTesting

private

The testing sample is sampled.

Definition at line 180 of file MethodMLP.h.

◆ fSamplingTraining

Bool_t TMVA::MethodMLP::fSamplingTraining

private

The training sample is sampled.

Definition at line 179 of file MethodMLP.h.

◆ fSamplingWeight

Float_t TMVA::MethodMLP::fSamplingWeight

private

changing factor for event weights when sampling is turned on

Definition at line 178 of file MethodMLP.h.

◆ fTau

Double_t TMVA::MethodMLP::fTau

private

line search variable

Definition at line 184 of file MethodMLP.h.

◆ fTestRate

Int_t TMVA::MethodMLP::fTestRate

private

test for overtraining performed at each #th epochs

Definition at line 193 of file MethodMLP.h.

◆ fTrainingMethod

ETrainingMethod TMVA::MethodMLP::fTrainingMethod

private

method of training, BP or GA

Definition at line 173 of file MethodMLP.h.

◆ fTrainMethodS

TString TMVA::MethodMLP::fTrainMethodS

private

training method option param

Definition at line 174 of file MethodMLP.h.

◆ fUpdateLimit

Int_t TMVA::MethodMLP::fUpdateLimit

private

zjh

Definition at line 171 of file MethodMLP.h.

◆ fUseRegulator

bool TMVA::MethodMLP::fUseRegulator

private

zjh

Definition at line 164 of file MethodMLP.h.

◆ fWeightRange

Float_t TMVA::MethodMLP::fWeightRange

private

suppress outliers for the estimator calculation

Definition at line 206 of file MethodMLP.h.

Libraries for TMVA::MethodMLP:

[legend]

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

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Static Private Attributes

Additional Inherited Members

Member Enumeration Documentation

◆ EBPTrainingMode

◆ ETrainingMethod

Constructor & Destructor Documentation

◆ MethodMLP() [1/2]

◆ MethodMLP() [2/2]

◆ ~MethodMLP()

Member Function Documentation

◆ AdjustSynapseWeights()

◆ BackPropagationMinimize()

◆ BFGSMinimize()

◆ CalculateEstimator()

◆ CalculateNeuronDeltas()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ ComputeDEDw()

◆ ComputeEstimator()

◆ DecaySynapseWeights()

◆ DeclareOptions()

◆ DeclFileName()

◆ DerivDir()

◆ EstimatorFunction()

◆ GeneticMinimize()

◆ GetApproxInvHessian()

◆ GetCEErr()

◆ GetDesiredOutput()

◆ GetError()

◆ GetHelpMessage()

◆ GetHessian()

◆ GetMSEErr()

◆ GetMvaValue()

◆ HasAnalysisType()

◆ HasInverseHessian()

◆ Init()

◆ InitializeLearningRates()

◆ IsA()

◆ LineSearch()

◆ MakeClassSpecific()

◆ ProcessOptions()

◆ SetDir()

◆ SetDirWeights()

◆ SetGammaDelta()

◆ Shuffle()

◆ SimulateEvent()

◆ SteepestDir()

◆ Streamer()

◆ StreamerNVirtual()

◆ Train() [1/2]

◆ Train() [2/2]

◆ TrainOneEpoch()

◆ TrainOneEvent()

◆ TrainOneEventFast()

◆ UpdateNetwork() [1/2]

◆ UpdateNetwork() [2/2]

◆ UpdatePriors()

◆ UpdateRegulators()

◆ UpdateSynapses()

Member Data Documentation

◆ fBatchSize

◆ fBPMode

◆ fBpModeS

◆ fCalculateErrors

◆ fDecayRate

◆ fDeviationsFromTargets

◆ fEpochMon

◆ fGA_nsteps

◆ fGA_preCalc

◆ fGA_SC_factor

◆ fGA_SC_rate

◆ fGA_SC_steps

◆ fgPRINT_BATCH