Definition at line 80 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	EStatusBits { kCanDelete = BIT(0), kMustCleanup = BIT(3), kObjInCanvas = BIT(3), kIsReferenced = BIT(4), kHasUUID = BIT(5), kCannotPick = BIT(6), kNoContextMenu = BIT(8), kInvalidObject = BIT(13) }

enum	{ kIsOnHeap = 0x01000000, kNotDeleted = 0x02000000, kZombie = 0x04000000, kBitMask = 0x00ffffff }

enum	{ kSingleKey = BIT(0), kOverwrite = BIT(1), kWriteDelete = BIT(2) }

Public Member Functions
	MethodANNBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &theData, const TString &theOption, TDirectory *theTargetDir)

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

virtual	~MethodANNBase ()
	destructor More...

void	InitANNBase ()
	initialize ANNBase object More...

void	SetActivation (TActivation *activation)

void	SetNeuronInputCalculator (TNeuronInput *inputCalculator)

virtual void	Train ()=0

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

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

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

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

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

virtual Double_t	GetMvaValue (Double_t err=0, Double_t errUpper=0)
	get the mva value generated by the NN More...

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

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

virtual void	WriteMonitoringHistosToFile () const
	write histograms to file More...

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

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. More...

virtual void	ProcessOptions ()
	do nothing specific at this moment More...

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

Public Member Functions inherited from TMVA::MethodBase
	MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="", TDirectory *theBaseDir=0)
	standard constructur More...

	MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile, TDirectory *theBaseDir=0)
	constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles More...

virtual	~MethodBase ()
	destructor More...

void	SetupMethod ()
	setup of methods More...

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) More...

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

void	AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType)

void	TrainMethod ()

virtual std::map< TString, Double_t >	OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA")
	call the Optimzier with the set of paremeters and ranges that are meant to be tuned. More...

virtual void	SetTuneParameters (std::map< TString, Double_t > tuneParameters)
	set the tuning parameters accoding to the argument This is just a dummy . More...

void	SetTrainTime (Double_t trainTime)

Double_t	GetTrainTime () const

void	SetTestTime (Double_t testTime)

Double_t	GetTestTime () const

virtual void	TestClassification ()
	initialization More...

virtual Double_t	GetKSTrainingVsTest (Char_t SorB, TString opt="X")

virtual void	TestMulticlass ()
	test multiclass classification More...

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 More...

virtual void	Init ()=0

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 weightfile at hand More...

virtual void	Reset ()

Double_t	GetMvaValue (const TMVA::Event const ev, Double_t err=0, Double_t *errUpper=0)

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

virtual Double_t	GetProba (const Event *ev)

virtual Double_t	GetProba (Double_t mvaVal, Double_t ap_sig)
	compute likelihood ratio More...

virtual Double_t	GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
	compute rarity: R(x) = Integrate_[-oo..x] { PDF(x') dx' } where PDF(x) is the PDF of the classifier's signal or background distribution More...

virtual void	MakeClass (const TString &classFileName=TString("")) const
	create reader class for method (classification only at present) More...

void	PrintHelpMessage () const
	prints out method-specific help method More...

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 More...

void	ReadStateFromFile ()
	Function to write options and weights to file. More...

void	ReadStateFromStream (std::istream &tf)
	read the header from the weight files of the different MVA methods More...

void	ReadStateFromStream (TFile &rf)
	write reference MVA distributions (and other information) to a ROOT type weight file More...

void	ReadStateFromXMLString (const char *xmlstr)
	for reading from memory More...

virtual void	WriteEvaluationHistosToFile (Types::ETreeType treetype)
	writes all MVA evaluation histograms to file More...

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

virtual Double_t	GetTrainingEfficiency (const TString &)

virtual std::vector< Float_t >	GetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity)

virtual std::vector< Float_t >	GetMulticlassTrainingEfficiency (std::vector< std::vector< Float_t > > &purity)

virtual Double_t	GetSignificance () const
	compute significance of mean difference significance = \|<S> - \|/Sqrt(RMS_S2 + RMS_B2) More...

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 More...

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 More...

virtual Double_t	GetMaximumSignificance (Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const
	plot significance, 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 More...

virtual Double_t	GetSeparation (TH1 , TH1 ) const
	compute "separation" defined as <s2> = (1/2) Int_-oo..+oo { (S(x) - B(x))^2/(S(x) + B(x)) dx } More...

virtual Double_t	GetSeparation (PDF pdfS=0, PDF pdfB=0) const
	compute "separation" defined as <s2> = (1/2) Int_-oo..+oo { (S(x) - B(x))^2/(S(x) + B(x)) dx } More...

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

const TString &	GetJobName () const

const TString &	GetMethodName () const

TString	GetMethodTypeName () const

Types::EMVA	GetMethodType () const

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

const TString &	GetTestvarName () const

const TString	GetProbaName () const

TString	GetWeightFileName () const
	retrieve weight file name More...

void	SetTestvarName (const TString &v="")

UInt_t	GetNvar () const

UInt_t	GetNVariables () const

UInt_t	GetNTargets () const

const TString &	GetInputVar (Int_t i) const

const TString &	GetInputLabel (Int_t i) const

const TString &	GetInputTitle (Int_t i) const

Double_t	GetMean (Int_t ivar) const

Double_t	GetRMS (Int_t ivar) const

Double_t	GetXmin (Int_t ivar) const

Double_t	GetXmax (Int_t ivar) const

Double_t	GetSignalReferenceCut () const

Double_t	GetSignalReferenceCutOrientation () const

void	SetSignalReferenceCut (Double_t cut)

void	SetSignalReferenceCutOrientation (Double_t cutOrientation)

TDirectory *	BaseDir () const
	returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored More...

TDirectory *	MethodBaseDir () const
	returns the ROOT directory where all instances of the corresponding MVA method are stored More...

void	SetMethodDir (TDirectory *methodDir)

void	SetBaseDir (TDirectory *methodDir)

void	SetMethodBaseDir (TDirectory *methodDir)

UInt_t	GetTrainingTMVAVersionCode () const

UInt_t	GetTrainingROOTVersionCode () const

TString	GetTrainingTMVAVersionString () const
	calculates the TMVA version string from the training version code on the fly More...

TString	GetTrainingROOTVersionString () const
	calculates the ROOT version string from the training version code on the fly More...

TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true)

const TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const

void	RerouteTransformationHandler (TransformationHandler *fTargetTransformation)

DataSet *	Data () const

DataSetInfo &	DataInfo () const

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

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 Event *	GetTrainingEvent (Long64_t ievt) const

const Event *	GetTestingEvent (Long64_t ievt) const

const std::vector< TMVA::Event * > &	GetEventCollection (Types::ETreeType type)
	returns the event collection (i.e. More...

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 More...

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 tbe selected as signal or background More...

Bool_t	HasMVAPdfs () const

virtual void	SetAnalysisType (Types::EAnalysisType type)

Types::EAnalysisType	GetAnalysisType () const

Bool_t	DoRegression () const

Bool_t	DoMulticlass () const

void	DisableWriting (Bool_t setter)

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="")

virtual	~Configurable ()
	default destructur More...

virtual void	ParseOptions ()
	options parser More...

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

const char *	GetConfigName () const

const char *	GetConfigDescription () const

void	SetConfigName (const char *n)

void	SetConfigDescription (const char *d)

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

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

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 More...

const TString &	GetOptions () const

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 More...

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

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

void	ReadOptionsFromXML (void *node)

void	SetMsgType (EMsgType t)

template<class T >
TMVA::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)

Public Member Functions inherited from TObject
	TObject ()

	TObject (const TObject &object)
	TObject copy ctor. More...

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

virtual	~TObject ()
	TObject destructor. More...

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

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

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

virtual void	Clear (Option_t *="")

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

virtual Int_t	Compare (const TObject *obj) const
	Compare abstract method. More...

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

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

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

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

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

virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current pad. More...

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

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. More...

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

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

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

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

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

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

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

virtual Option_t *	GetOption () const

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

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

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

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

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

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

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

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

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

virtual Bool_t	IsSortable () const

Bool_t	IsOnHeap () const

Bool_t	IsZombie () const

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

virtual void	ls (Option_t *option="") const
	The ls function lists the contents of a class on stdout. More...

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

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

virtual void	Print (Option_t *option="") const
	This method must be overridden when a class wants to print itself. More...

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

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

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

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

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

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

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

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

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

void *	operator new (size_t sz)

void *	operator new[] (size_t sz)

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

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

void	operator delete (void *ptr)
	Operator delete. More...

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

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

void	SetBit (UInt_t f)

void	ResetBit (UInt_t f)

Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

void	InvertBit (UInt_t f)

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

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

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

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

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

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

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). More...

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

Protected Member Functions
virtual void	MakeClassSpecific (std::ostream &, const TString &) const
	write specific classifier response More...

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 More...

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 More...

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

Double_t	GetNetworkOutput ()

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

void	ForceNetworkCalculations ()
	calculate input values to each neuron More...

void	WaitForKeyboard ()
	wait for keyboard input, for debugging More...

Int_t	NumCycles ()

TNeuron *	GetInputNeuron (Int_t index)

TNeuron *	GetOutputNeuron (Int_t index=0)

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

Protected Member Functions inherited from TMVA::MethodBase
void	NoErrorCalc (Double_t const err, Double_t const errUpper)

virtual void	ReadWeightsFromStream (TFile &)

void	SetWeightFileName (TString)
	set the weight file name (depreciated) More...

const TString &	GetWeightFileDir () const

void	SetWeightFileDir (TString fileDir)
	set directory of weight file More...

Bool_t	IsNormalised () const

void	SetNormalised (Bool_t norm)

Bool_t	Verbose () const

Bool_t	Help () const

const TString &	GetInternalVarName (Int_t ivar) const

const TString &	GetOriginalVarName (Int_t ivar) const

Bool_t	HasTrainingTree () const

virtual void	MakeClassSpecificHeader (std::ostream &, const TString &="") const

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 More...

Bool_t	TxtWeightsOnly () const

Bool_t	IsConstructedFromWeightFile () const

Bool_t	IgnoreEventsWithNegWeightsInTraining () const

Protected Member Functions inherited from TMVA::IMethod
virtual void	GetHelpMessage () const =0

Protected Member Functions inherited from TMVA::Configurable
Bool_t	LooseOptionCheckingEnabled () const

void	EnableLooseOptions (Bool_t b=kTRUE)

void	WriteOptionsReferenceToFile ()
	write complete options to output stream More...

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

const TString &	GetReferenceFile () const

MsgLogger &	Log () const

Protected Member Functions inherited from TObject
void	MakeZombie ()

virtual void	DoError (int level, const char location, const char fmt, va_list va) const
	Interface to ErrorHandler (protected). More...

Protected Attributes
TObjArray *	fNetwork

TObjArray *	fSynapses

TActivation *	fActivation

TActivation *	fOutput

TActivation *	fIdentity

TRandom3 *	frgen

TNeuronInput *	fInputCalculator

std::vector< Int_t >	fRegulatorIdx

std::vector< Double_t >	fRegulators

EEstimator	fEstimator

TString	fEstimatorS

TH1F *	fEstimatorHistTrain

TH1F *	fEstimatorHistTest

std::vector< TH1 * >	fEpochMonHistS

std::vector< TH1 * >	fEpochMonHistB

std::vector< TH1 * >	fEpochMonHistW

TMatrixD	fInvHessian

bool	fUseRegulator

Int_t	fRandomSeed

Int_t	fNcycles

TString	fNeuronType

TString	fNeuronInputType

Protected Attributes inherited from TMVA::MethodBase
Ranking *	fRanking

std::vector< TString > *	fInputVars

Int_t	fNbins

Int_t	fNbinsMVAoutput

Int_t	fNbinsH

Types::EAnalysisType	fAnalysisType

std::vector< Float_t > *	fRegressionReturnVal

std::vector< Float_t > *	fMulticlassReturnVal

UInt_t	fSignalClass

UInt_t	fBackgroundClass

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

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 More...

void	AddPreLinks (TNeuron neuron, TObjArray prevLayer)
	add synapses connecting a neuron to its preceding layer More...

void	InitWeights ()
	initialize the synapse weights randomly More...

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

void	DeleteNetwork ()
	delete/clear network More...

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

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

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

Private Attributes
TObjArray *	fInputLayer

std::vector< TNeuron * >	fOutputNeurons

TString	fLayerSpec

Static Private Attributes
static const Bool_t	fgDEBUG = kTRUE

Additional Inherited Members
Static Public Member Functions inherited from TObject
static Long_t	GetDtorOnly ()
	Return destructor only flag. More...

static void	SetDtorOnly (void *obj)
	Set destructor only flag. More...

static Bool_t	GetObjectStat ()
	Get status of object stat flag. More...

static void	SetObjectStat (Bool_t stat)
	Turn on/off tracking of objects in the TObjectTable. More...

Public Attributes inherited from TMVA::MethodBase
const Event *	fTmpEvent

Bool_t	fSetupCompleted

Static Protected Member Functions inherited from TMVA::MethodBase
static MethodBase *	GetThisBase ()
	return a pointer the base class of this method More...

#include <TMVA/MethodANNBase.h>

Inheritance diagram for TMVA::MethodANNBase:

[legend]

Member Enumeration Documentation

enum TMVA::MethodANNBase::EEstimator

Enumerator
kMSE
kCE

Definition at line 156 of file MethodANNBase.h.

Constructor & Destructor Documentation

TMVA::MethodANNBase::MethodANNBase	(	const TString &	jobName,
		Types::EMVA	methodType,
		const TString &	methodTitle,
		DataSetInfo &	theData,
		const TString &	theOption,
		TDirectory *	theTargetDir
	)

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

construct the Method from the weight file

Definition at line 99 of file MethodANNBase.cxx.

TMVA::MethodANNBase::~MethodANNBase ( )

virtual

destructor

Definition at line 236 of file MethodANNBase.cxx.

Member Function Documentation

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.

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

virtual

create XML description of ANN classifier

Implements TMVA::MethodBase.

Definition at line 712 of file MethodANNBase.cxx.

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.

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.

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 291 of file MethodANNBase.cxx.

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

virtual

compute ranking of input variables by summing function of weights

Implements TMVA::MethodBase.

Definition at line 924 of file MethodANNBase.cxx.

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

protected

Definition at line 966 of file MethodANNBase.cxx.

Bool_t TMVA::MethodANNBase::Debug ( ) const

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

Definition at line 1167 of file MethodANNBase.cxx.

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 neuronn

Implements TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 123 of file MethodANNBase.cxx.

Referenced by MethodANNBase().

void TMVA::MethodANNBase::DeleteNetwork ( )

private

delete/clear network

Definition at line 244 of file MethodANNBase.cxx.

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

private

delete a network layer

Definition at line 275 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::ForceNetworkCalculations ( )

protected

calculate input values to each neuron

Definition at line 492 of file MethodANNBase.cxx.

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.

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

private

force the synapse weights

Definition at line 458 of file MethodANNBase.cxx.

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

inlineprotected

Definition at line 176 of file MethodANNBase.h.

template<typename WriteIterator >

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

inline

Definition at line 251 of file MethodANNBase.h.

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 669 of file MethodANNBase.cxx.

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.

Referenced by TMVA::MethodMLP::GetMvaValue().

Double_t TMVA::MethodANNBase::GetNetworkOutput ( )

inlineprotected

Definition at line 167 of file MethodANNBase.h.

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

inlineprotected

Definition at line 177 of file MethodANNBase.h.

Referenced by GetNetworkOutput().

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.

void TMVA::MethodANNBase::InitANNBase ( )

initialize ANNBase object

Definition at line 207 of file MethodANNBase.cxx.

Referenced by MethodANNBase().

void TMVA::MethodANNBase::InitWeights ( )

private

initialize the synapse weights randomly

Definition at line 442 of file MethodANNBase.cxx.

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 1049 of file MethodANNBase.cxx.

Referenced by TMVA::MethodMLP::MakeClassSpecific().

Int_t TMVA::MethodANNBase::NumCycles ( )

inlineprotected

Definition at line 175 of file MethodANNBase.h.

Referenced by TMVA::MethodMLP::Train().

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 170 of file MethodANNBase.cxx.

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

private

print a single layer, for debugging

Definition at line 562 of file MethodANNBase.cxx.

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.

void TMVA::MethodANNBase::PrintNetwork ( ) const

virtual

print network representation, for debugging

Definition at line 538 of file MethodANNBase.cxx.

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

private

print a neuron, for debugging

Definition at line 578 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::ProcessOptions ( )

virtual

do nothing specific at this moment

Implements TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 155 of file MethodANNBase.cxx.

Referenced by TMVA::MethodMLP::ProcessOptions().

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 903 of file MethodANNBase.cxx.

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

virtual

read MLP from xml weight file

Implements TMVA::MethodBase.

Definition at line 780 of file MethodANNBase.cxx.

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

inline

Definition at line 103 of file MethodANNBase.h.

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

inline

Definition at line 106 of file MethodANNBase.h.

virtual void TMVA::MethodANNBase::Train ( )

pure virtual

Implements TMVA::MethodBase.

Implemented in TMVA::MethodMLP.

void TMVA::MethodANNBase::WaitForKeyboard ( )

protected

wait for keyboard input, for debugging

Definition at line 523 of file MethodANNBase.cxx.

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

virtual

write histograms to file

Reimplemented from TMVA::MethodBase.

Definition at line 1007 of file MethodANNBase.cxx.

Member Data Documentation

TActivation* TMVA::MethodANNBase::fActivation

protected

Definition at line 182 of file MethodANNBase.h.

Referenced by SetActivation().

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

protected

Definition at line 200 of file MethodANNBase.h.

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

protected

Definition at line 199 of file MethodANNBase.h.

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

protected

Definition at line 201 of file MethodANNBase.h.

EEstimator TMVA::MethodANNBase::fEstimator

protected

Definition at line 190 of file MethodANNBase.h.

TH1F* TMVA::MethodANNBase::fEstimatorHistTest

protected

Definition at line 195 of file MethodANNBase.h.

TH1F* TMVA::MethodANNBase::fEstimatorHistTrain

protected

Definition at line 194 of file MethodANNBase.h.

TString TMVA::MethodANNBase::fEstimatorS

protected

Definition at line 191 of file MethodANNBase.h.

const Bool_t TMVA::MethodANNBase::fgDEBUG = kTRUE

staticprivate

Definition at line 243 of file MethodANNBase.h.

TActivation* TMVA::MethodANNBase::fIdentity

protected

Definition at line 184 of file MethodANNBase.h.

TNeuronInput* TMVA::MethodANNBase::fInputCalculator

protected

Definition at line 186 of file MethodANNBase.h.

Referenced by SetNeuronInputCalculator().

TObjArray* TMVA::MethodANNBase::fInputLayer

private

Definition at line 238 of file MethodANNBase.h.

Referenced by GetInputNeuron().

TMatrixD TMVA::MethodANNBase::fInvHessian

protected

Definition at line 205 of file MethodANNBase.h.

TString TMVA::MethodANNBase::fLayerSpec

private

Definition at line 240 of file MethodANNBase.h.

Int_t TMVA::MethodANNBase::fNcycles

protected

Definition at line 211 of file MethodANNBase.h.

Referenced by NumCycles().

TObjArray* TMVA::MethodANNBase::fNetwork

protected

Definition at line 180 of file MethodANNBase.h.

Referenced by GetLayerActivation().

TString TMVA::MethodANNBase::fNeuronInputType

protected

Definition at line 214 of file MethodANNBase.h.

TString TMVA::MethodANNBase::fNeuronType

protected

Definition at line 213 of file MethodANNBase.h.

TActivation* TMVA::MethodANNBase::fOutput

protected

Definition at line 183 of file MethodANNBase.h.

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

private

Definition at line 239 of file MethodANNBase.h.

Referenced by GetOutputNeuron().

Int_t TMVA::MethodANNBase::fRandomSeed

protected

Definition at line 209 of file MethodANNBase.h.

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

protected

Definition at line 188 of file MethodANNBase.h.

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

protected

Definition at line 189 of file MethodANNBase.h.

TRandom3* TMVA::MethodANNBase::frgen

protected

Definition at line 185 of file MethodANNBase.h.

TObjArray* TMVA::MethodANNBase::fSynapses

protected

Definition at line 181 of file MethodANNBase.h.

bool TMVA::MethodANNBase::fUseRegulator

protected

Definition at line 206 of file MethodANNBase.h.

Collaboration diagram 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

Protected Member Functions

Protected Attributes

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