Base class for all TMVA methods using artificial neural networks.

Definition at line 62 of file MethodANNBase.h.

Public Types
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 = 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
	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"..

virtual void	DeclareOptions ()
	define the options (their key words) that can be set in the option string here the options valid for ALL MVA methods are declared.

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 Double_t	GetMvaValue (Double_t err=0, Double_t errUpper=0)
	get the mva value 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	ProcessOptions ()
	do nothing specific at this moment

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)

virtual void	Train ()=0

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=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

virtual void	Init ()=0

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

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 ()

virtual Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)=0

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

virtual	~Configurable ()
	default destructor

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

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

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

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

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

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

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

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

const char *	GetConfigDescription () const

const char *	GetConfigName () const

const TString &	GetOptions () const

MsgLogger &	Log () const

virtual void	ParseOptions ()
	options parser

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

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

void	ReadOptionsFromXML (void *node)

void	SetConfigDescription (const char *d)

void	SetConfigName (const char *n)

void	SetMsgType (EMsgType t)

void	SetOptions (const TString &s)

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

Public Member Functions inherited from TNamed
	TNamed ()

	TNamed (const char name, const char title)

	TNamed (const TNamed &named)
	TNamed copy ctor.

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

virtual	~TNamed ()
	TNamed destructor.

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

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

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

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

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

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

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

virtual Bool_t	IsSortable () const

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

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

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

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

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

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

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

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

	TObject (const TObject &object)
	TObject copy ctor.

virtual	~TObject ()
	TObject destructor.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

virtual Option_t *	GetOption () const

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

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

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

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

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

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

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

void	InvertBit (UInt_t f)

Bool_t	IsDestructed () const
	IsDestructed.

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

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

R__ALWAYS_INLINE Bool_t	IsOnHeap () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

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

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

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

void	operator delete (void *ptr)
	Operator delete.

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

void *	operator new (size_t sz)

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

void *	operator new[] (size_t sz)

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

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

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

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

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

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

void	ResetBit (UInt_t f)

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

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

void	SetBit (UInt_t f)

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

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

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

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

R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

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

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

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

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

Public Attributes
TObjArray *	fNetwork

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

TrainingHistory	fTrainHistory

Protected Member Functions
virtual void	BuildNetwork (std::vector< Int_t > layout, std::vector< Double_t > weights=NULL, 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=0) 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)

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

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::IMethod
virtual void	GetHelpMessage () const =0

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

const TString &	GetReferenceFile () const

Bool_t	LooseOptionCheckingEnabled () const

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

void	WriteOptionsReferenceToFile ()
	write complete options to output stream

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

void	MakeZombie ()

Protected Attributes
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

Int_t	fNcycles

TString	fNeuronInputType

TString	fNeuronType

TActivation *	fOutput

Int_t	fRandomSeed

std::vector< Int_t >	fRegulatorIdx

std::vector< Double_t >	fRegulators

TRandom3 *	frgen

TObjArray *	fSynapses

bool	fUseRegulator

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

Private Member Functions
void	AddPreLinks (TNeuron neuron, TObjArray prevLayer)
	add synapses connecting a neuron to its preceding layer

void	BuildLayer (Int_t numNeurons, TObjArray curLayer, TObjArray prevLayer, Int_t layerIndex, Int_t numLayers, Bool_t from_file=false)
	build a single layer with neurons and synapses connecting this layer to the previous layer

void	BuildLayers (std::vector< Int_t > *layout, Bool_t from_file=false)
	build the network layers

void	DeleteNetwork ()
	delete/clear network

void	DeleteNetworkLayer (TObjArray *&layer)
	delete a network layer

void	ForceWeights (std::vector< Double_t > *weights)
	force the synapse weights

void	InitWeights ()
	initialize the synapse weights randomly

void	PrintLayer (TObjArray *layer) const
	print a single layer, for debugging

void	PrintNeuron (TNeuron *neuron) const
	print a neuron, for debugging

Private Attributes
TObjArray *	fInputLayer

TString	fLayerSpec

std::vector< TNeuron * >	fOutputNeurons

Static Private Attributes
static const Bool_t	fgDEBUG = kTRUE

Additional Inherited Members
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.

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

#include <TMVA/MethodANNBase.h>

Inheritance diagram for TMVA::MethodANNBase:

[legend]

Member Enumeration Documentation

◆ EEstimator

enum TMVA::MethodANNBase::EEstimator

Enumerator
kMSE
kCE

Definition at line 137 of file MethodANNBase.h.

Constructor & Destructor Documentation

◆ MethodANNBase() [1/2]

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.

Definition at line 82 of file MethodANNBase.cxx.

◆ MethodANNBase() [2/2]

TMVA::MethodANNBase::MethodANNBase	(	Types::EMVA	methodType,
		DataSetInfo &	theData,
		const TString &	theWeightFile
	)

construct the Method from the weight file

Definition at line 100 of file MethodANNBase.cxx.

◆ ~MethodANNBase()

TMVA::MethodANNBase::~MethodANNBase ( )

virtual

destructor

Definition at line 238 of file MethodANNBase.cxx.

Member Function Documentation

◆ AddPreLinks()

void TMVA::MethodANNBase::AddPreLinks	(	TNeuron *	neuron,
		TObjArray *	prevLayer
	)

private

add synapses connecting a neuron to its preceding layer

Definition at line 423 of file MethodANNBase.cxx.

◆ AddWeightsXMLTo()

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

virtual

create XML description of ANN classifier

Implements TMVA::MethodBase.

Definition at line 704 of file MethodANNBase.cxx.

◆ BuildLayer()

void TMVA::MethodANNBase::BuildLayer	(	Int_t	numNeurons,
		TObjArray *	curLayer,
		TObjArray *	prevLayer,
		Int_t	layerIndex,
		Int_t	numLayers,
		Bool_t	from_file = `false`
	)

private

build a single layer with neurons and synapses connecting this layer to the previous layer

Definition at line 371 of file MethodANNBase.cxx.

◆ BuildLayers()

void TMVA::MethodANNBase::BuildLayers	(	std::vector< Int_t > *	layout,
		Bool_t	from_file = `false`
	)

private

build the network layers

Definition at line 335 of file MethodANNBase.cxx.

◆ BuildNetwork()

void TMVA::MethodANNBase::BuildNetwork	(	std::vector< Int_t > *	layout,
		std::vector< Double_t > *	weights = `NULL`,
		Bool_t	fromFile = `kFALSE`
	)

protectedvirtual

build network given a layout (number of neurons in each layer) and optional weights array

Definition at line 293 of file MethodANNBase.cxx.

◆ CreateRanking()

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

virtual

compute ranking of input variables by summing function of weights

Implements TMVA::MethodBase.

Definition at line 915 of file MethodANNBase.cxx.

◆ CreateWeightMonitoringHists()

void TMVA::MethodANNBase::CreateWeightMonitoringHists	(	const TString &	bulkname,
		std::vector< TH1 * > *	hv = `0`
	)		const

protected

Definition at line 957 of file MethodANNBase.cxx.

◆ Debug()

Bool_t TMVA::MethodANNBase::Debug ( ) const

who the hell makes such strange Debug flags that even use "global pointers"..

Definition at line 1165 of file MethodANNBase.cxx.

◆ DeclareOptions()

void TMVA::MethodANNBase::DeclareOptions ( )

virtual

define the options (their key words) that can be set in the option string here the options valid for ALL MVA methods are declared.

know options:

NCycles=xx :the number of training cycles
Normalize=kTRUE,kFALSe :if normalised in put variables should be used
HiddenLayser="N-1,N-2" :the specification of the hidden layers
NeuronType=sigmoid,tanh,radial,linar : the type of activation function used at the neuron

Implements TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 125 of file MethodANNBase.cxx.

◆ DeleteNetwork()

void TMVA::MethodANNBase::DeleteNetwork ( )

private

delete/clear network

Definition at line 246 of file MethodANNBase.cxx.

◆ DeleteNetworkLayer()

void TMVA::MethodANNBase::DeleteNetworkLayer ( TObjArray *& layer )

private

delete a network layer

Definition at line 277 of file MethodANNBase.cxx.

◆ ForceNetworkCalculations()

void TMVA::MethodANNBase::ForceNetworkCalculations ( )

protected

calculate input values to each neuron

Definition at line 492 of file MethodANNBase.cxx.

◆ ForceNetworkInputs()

void TMVA::MethodANNBase::ForceNetworkInputs	(	const Event *	ev,
		Int_t	ignoreIndex = `-1`
	)

protected

force the input values of the input neurons force the value for each input neuron

Definition at line 474 of file MethodANNBase.cxx.

◆ ForceWeights()

void TMVA::MethodANNBase::ForceWeights ( std::vector< Double_t > * weights )

private

force the synapse weights

Definition at line 458 of file MethodANNBase.cxx.

◆ GetInputNeuron()

TNeuron * TMVA::MethodANNBase::GetInputNeuron ( Int_t index )

inlineprotected

Definition at line 158 of file MethodANNBase.h.

◆ GetLayerActivation()

template<typename WriteIterator >

void TMVA::MethodANNBase::GetLayerActivation	(	size_t	layer,
		WriteIterator	writeIterator
	)

inline

Definition at line 232 of file MethodANNBase.h.

◆ GetMulticlassValues()

const std::vector< Float_t > & TMVA::MethodANNBase::GetMulticlassValues ( )

virtual

get the multiclass classification values generated by the NN

Reimplemented from TMVA::MethodBase.

Definition at line 661 of file MethodANNBase.cxx.

◆ GetMvaValue()

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

virtual

get the mva value generated by the NN

Implements TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 595 of file MethodANNBase.cxx.

◆ GetNetworkOutput()

Double_t TMVA::MethodANNBase::GetNetworkOutput ( )

inlineprotected

Definition at line 149 of file MethodANNBase.h.

◆ GetOutputNeuron()

TNeuron * TMVA::MethodANNBase::GetOutputNeuron ( Int_t index = 0 )

inlineprotected

Definition at line 159 of file MethodANNBase.h.

◆ GetRegressionValues()

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

virtual

get the regression value generated by the NN

Reimplemented from TMVA::MethodBase.

Definition at line 622 of file MethodANNBase.cxx.

◆ InitANNBase()

void TMVA::MethodANNBase::InitANNBase ( )

initialize ANNBase object

Definition at line 209 of file MethodANNBase.cxx.

◆ InitWeights()

void TMVA::MethodANNBase::InitWeights ( )

private

initialize the synapse weights randomly

Definition at line 442 of file MethodANNBase.cxx.

◆ MakeClassSpecific()

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

protectedvirtual

write specific classifier response

Reimplemented from TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 1036 of file MethodANNBase.cxx.

◆ NumCycles()

Int_t TMVA::MethodANNBase::NumCycles ( )

inlineprotected

Definition at line 157 of file MethodANNBase.h.

◆ ParseLayoutString()

std::vector< Int_t > * TMVA::MethodANNBase::ParseLayoutString ( TString layerSpec )

protected

parse layout specification string and return a vector, each entry containing the number of neurons to go in each successive layer

Definition at line 172 of file MethodANNBase.cxx.

◆ PrintLayer()

void TMVA::MethodANNBase::PrintLayer ( TObjArray * layer ) const

private

print a single layer, for debugging

Definition at line 562 of file MethodANNBase.cxx.

◆ PrintMessage()

void TMVA::MethodANNBase::PrintMessage	(	TString	message,
		Bool_t	force = `kFALSE`
	)		const

protected

print messages, turn off printing by setting verbose and debug flag appropriately

Definition at line 515 of file MethodANNBase.cxx.

◆ PrintNetwork()

void TMVA::MethodANNBase::PrintNetwork ( ) const

virtual

print network representation, for debugging

Definition at line 538 of file MethodANNBase.cxx.

◆ PrintNeuron()

void TMVA::MethodANNBase::PrintNeuron ( TNeuron * neuron ) const

private

print a neuron, for debugging

Definition at line 578 of file MethodANNBase.cxx.

◆ ProcessOptions()

void TMVA::MethodANNBase::ProcessOptions ( )

virtual

do nothing specific at this moment

Implements TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 157 of file MethodANNBase.cxx.

◆ ReadWeightsFromStream() [1/3]

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

virtual

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/3]

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

virtual

destroy/clear the network then read it back in from the weights file

Implements TMVA::MethodBase.

Definition at line 894 of file MethodANNBase.cxx.

◆ ReadWeightsFromStream() [3/3]

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

inlinevirtual

Reimplemented from TMVA::MethodBase.

Definition at line 266 of file MethodBase.h.

◆ ReadWeightsFromXML()

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

virtual

read MLP from xml weight file

Implements TMVA::MethodBase.

Definition at line 772 of file MethodANNBase.cxx.

◆ SetActivation()

void TMVA::MethodANNBase::SetActivation ( TActivation * activation )

inline

Definition at line 83 of file MethodANNBase.h.

◆ SetNeuronInputCalculator()

void TMVA::MethodANNBase::SetNeuronInputCalculator ( TNeuronInput * inputCalculator )

inline

Definition at line 87 of file MethodANNBase.h.

◆ Train()

virtual void TMVA::MethodANNBase::Train ( )

pure virtual

Implements TMVA::MethodBase.

Implemented in TMVA::MethodMLP.

◆ WaitForKeyboard()

void TMVA::MethodANNBase::WaitForKeyboard ( )

protected

wait for keyboard input, for debugging

Definition at line 523 of file MethodANNBase.cxx.

◆ WriteMonitoringHistosToFile()

void TMVA::MethodANNBase::WriteMonitoringHistosToFile ( void ) const

virtual

write histograms to file

Reimplemented from TMVA::MethodBase.

Definition at line 998 of file MethodANNBase.cxx.

Member Data Documentation

◆ fActivation

TActivation* TMVA::MethodANNBase::fActivation

protected

Definition at line 163 of file MethodANNBase.h.

◆ fEpochMonHistB

std::vector<TH1*> TMVA::MethodANNBase::fEpochMonHistB

protected

Definition at line 181 of file MethodANNBase.h.

◆ fEpochMonHistS

std::vector<TH1*> TMVA::MethodANNBase::fEpochMonHistS

protected

Definition at line 180 of file MethodANNBase.h.

◆ fEpochMonHistW

std::vector<TH1*> TMVA::MethodANNBase::fEpochMonHistW

protected

Definition at line 182 of file MethodANNBase.h.

◆ fEstimator

EEstimator TMVA::MethodANNBase::fEstimator

protected

Definition at line 171 of file MethodANNBase.h.

◆ fEstimatorHistTest

TH1F* TMVA::MethodANNBase::fEstimatorHistTest

protected

Definition at line 176 of file MethodANNBase.h.

◆ fEstimatorHistTrain

TH1F* TMVA::MethodANNBase::fEstimatorHistTrain

protected

Definition at line 175 of file MethodANNBase.h.

◆ fEstimatorS

TString TMVA::MethodANNBase::fEstimatorS

protected

Definition at line 172 of file MethodANNBase.h.

◆ fgDEBUG

const Bool_t TMVA::MethodANNBase::fgDEBUG = kTRUE

staticprivate

Definition at line 224 of file MethodANNBase.h.

◆ fIdentity

TActivation* TMVA::MethodANNBase::fIdentity

protected

Definition at line 165 of file MethodANNBase.h.

◆ fInputCalculator

TNeuronInput* TMVA::MethodANNBase::fInputCalculator

protected

Definition at line 167 of file MethodANNBase.h.

◆ fInputLayer

TObjArray* TMVA::MethodANNBase::fInputLayer

private

Definition at line 219 of file MethodANNBase.h.

◆ fInvHessian

TMatrixD TMVA::MethodANNBase::fInvHessian

protected

Definition at line 186 of file MethodANNBase.h.

◆ fLayerSpec

TString TMVA::MethodANNBase::fLayerSpec

private

Definition at line 221 of file MethodANNBase.h.

◆ fNcycles

Int_t TMVA::MethodANNBase::fNcycles

protected

Definition at line 192 of file MethodANNBase.h.

◆ fNetwork

TObjArray* TMVA::MethodANNBase::fNetwork

Definition at line 139 of file MethodANNBase.h.

◆ fNeuronInputType

TString TMVA::MethodANNBase::fNeuronInputType

protected

Definition at line 195 of file MethodANNBase.h.

◆ fNeuronType

TString TMVA::MethodANNBase::fNeuronType

protected

Definition at line 194 of file MethodANNBase.h.

◆ fOutput

TActivation* TMVA::MethodANNBase::fOutput

protected

Definition at line 164 of file MethodANNBase.h.

◆ fOutputNeurons

std::vector<TNeuron*> TMVA::MethodANNBase::fOutputNeurons

private

Definition at line 220 of file MethodANNBase.h.

◆ fRandomSeed

Int_t TMVA::MethodANNBase::fRandomSeed

protected

Definition at line 190 of file MethodANNBase.h.

◆ fRegulatorIdx

std::vector<Int_t> TMVA::MethodANNBase::fRegulatorIdx

protected

Definition at line 169 of file MethodANNBase.h.

◆ fRegulators

std::vector<Double_t> TMVA::MethodANNBase::fRegulators

protected

Definition at line 170 of file MethodANNBase.h.

◆ frgen

TRandom3* TMVA::MethodANNBase::frgen

protected

Definition at line 166 of file MethodANNBase.h.

◆ fSynapses

TObjArray* TMVA::MethodANNBase::fSynapses

protected

Definition at line 162 of file MethodANNBase.h.

◆ fUseRegulator

bool TMVA::MethodANNBase::fUseRegulator

protected

Definition at line 187 of file MethodANNBase.h.

Libraries for TMVA::MethodANNBase:

[legend]

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

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

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Static Private Attributes

Additional Inherited Members

Member Enumeration Documentation

◆ EEstimator

Constructor & Destructor Documentation

◆ MethodANNBase() [1/2]

◆ MethodANNBase() [2/2]

◆ ~MethodANNBase()

Member Function Documentation

◆ AddPreLinks()

◆ AddWeightsXMLTo()

◆ BuildLayer()

◆ BuildLayers()

◆ BuildNetwork()

◆ CreateRanking()

◆ CreateWeightMonitoringHists()

◆ Debug()

◆ DeclareOptions()

◆ DeleteNetwork()

◆ DeleteNetworkLayer()

◆ ForceNetworkCalculations()

◆ ForceNetworkInputs()

◆ ForceWeights()

◆ GetInputNeuron()

◆ GetLayerActivation()

◆ GetMulticlassValues()

◆ GetMvaValue()

◆ GetNetworkOutput()

◆ GetOutputNeuron()

◆ GetRegressionValues()

◆ InitANNBase()

◆ InitWeights()

◆ MakeClassSpecific()

◆ NumCycles()

◆ ParseLayoutString()

◆ PrintLayer()

◆ PrintMessage()

◆ PrintNetwork()

◆ PrintNeuron()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/3]

◆ ReadWeightsFromStream() [2/3]

◆ ReadWeightsFromStream() [3/3]

◆ ReadWeightsFromXML()

◆ SetActivation()

◆ SetNeuronInputCalculator()

◆ Train()

◆ WaitForKeyboard()

◆ WriteMonitoringHistosToFile()

Member Data Documentation

◆ fActivation

◆ fEpochMonHistB

◆ fEpochMonHistS

◆ fEpochMonHistW

◆ fEstimator

◆ fEstimatorHistTest

◆ fEstimatorHistTrain

◆ fEstimatorS

◆ fgDEBUG

◆ fIdentity

◆ fInputCalculator

◆ fInputLayer

◆ fInvHessian

◆ fLayerSpec

◆ fNcycles

◆ fNetwork

◆ fNeuronInputType

◆ fNeuronType

◆ fOutput

◆ fOutputNeurons

◆ fRandomSeed

◆ fRegulatorIdx

◆ fRegulators