Definition at line 100 of file MethodCFMlpANN.h.

Public Member Functions
	MethodCFMlpANN (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="3000:N-1:N-2", TDirectory *theTargetDir=0)
	standard constructor option string: "n_training_cycles:n_hidden_layers" default is: n_training_cycles = 5000, n_layers = 4 More...

	MethodCFMlpANN (DataSetInfo &theData, const TString &theWeightFile, TDirectory *theTargetDir=NULL)
	constructor from weight file More...

virtual	~MethodCFMlpANN (void)
	destructor More...

virtual Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t)
	CFMlpANN can handle classification with 2 classes. More...

void	Train (void)
	training of the Clement-Ferrand NN classifier More...

void	AddWeightsXMLTo (void *parent) const
	write weights to xml file More...

void	ReadWeightsFromStream (std::istream &istr)
	read back the weight from the training from file (stream) More...

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

Double_t	GetMvaValue (Double_t err=0, Double_t errUpper=0)
	returns CFMlpANN output (normalised within [0,1]) More...

Double_t	GetData (Int_t isel, Int_t ivar) const

Int_t	GetClass (Int_t ivar) const

const Ranking *	CreateRanking ()

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

virtual const std::vector< Float_t > &	GetMulticlassValues ()

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 void	WriteMonitoringHistosToFile () const
	write special monitoring histograms to file dummy implementation here --------------— 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 ()

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...

Static Public Member Functions
static MethodCFMlpANN *	This (void)
	static pointer to this object (required for external functions More...

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...

Protected Member Functions
void	MakeClassSpecific (std::ostream &, const TString &) const

void	MakeClassSpecificHeader (std::ostream &, const TString &="") const
	write specific classifier response for header More...

void	GetHelpMessage () const
	get help message text More...

Int_t	DataInterface (Double_t , Double_t , Int_t , Int_t , Int_t , Int_t , Double_t , Int_t , Int_t *)
	data interface function More...

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

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::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...

Private Member Functions
void	PrintWeights (std::ostream &o) const
	write the weights of the neural net More...

void	DeclareOptions ()
	define the options (their key words) that can be set in the option string know options: NCycles=xx :the number of training cycles HiddenLayser="N-1,N-2" :the specification of the hidden layers More...

void	ProcessOptions ()
	decode the options in the option string More...

Double_t	EvalANN (std::vector< Double_t > &, Bool_t &isOK)
	evaluates NN value as function of input variables More...

void	NN_ava (Double_t *)
	auxiliary functions More...

Double_t	NN_fonc (Int_t, Double_t) const
	activation function More...

void	Init (void)
	default initialisation called by all constructors More...

Private Member Functions inherited from TMVA::MethodCFMlpANN_Utils
	MethodCFMlpANN_Utils ()

virtual	~MethodCFMlpANN_Utils ()

void	Train_nn (Double_t tin2, Double_t tout2, Int_t ntrain, Int_t ntest, Int_t nvar2, Int_t nlayer, Int_t nodes, Int_t ncycle)

void	Entree_new (Int_t , char , Int_t ntrain, Int_t ntest, Int_t numlayer, Int_t nodes, Int_t *numcycle, Int_t)

Double_t	Fdecroi (Int_t *i__)

Double_t	Sen3a (void)

void	Wini ()

void	En_avant (Int_t *ievent)

void	En_avant2 (Int_t *ievent)

void	En_arriere (Int_t *ievent)

void	Leclearn (Int_t ktest, Double_t tout2, Double_t *tin2)

void	Out (Int_t iii, Int_t maxcycle)

void	Cout (Int_t , Double_t xxx)

void	Innit (char det, Double_t tout2, Double_t *tin2, Int_t)

void	TestNN ()

void	Inl ()

void	GraphNN (Int_t ilearn, Double_t , Double_t , char , Int_t)

void	Foncf (Int_t i__, Double_t u, Double_t *f)

void	Cout2 (Int_t , Double_t yyy)

void	Lecev2 (Int_t ktest, Double_t tout2, Double_t *tin2)

void	Arret (const char *mot)

void	CollectVar (Int_t nvar, Int_t class__, Double_t *xpg)

Double_t	W_ref (const Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3) const

Double_t &	W_ref (Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3)

Double_t	Ww_ref (const Double_t wwNN[], Int_t a_1, Int_t a_2) const

Double_t &	Ww_ref (Double_t wwNN[], Int_t a_1, Int_t a_2)

void	SetLogger (MsgLogger *l)

Private Attributes
TMatrixF *	fData

std::vector< Int_t > *	fClass

Int_t	fNlayers

Int_t	fNcycles

Int_t *	fNodes

Double_t **	fYNN

TString	fLayerSpec

struct {
Double_t epsmin

Double_t epsmax

Double_t eeps

Double_t eta

Int_t layerm

Int_t lclass

Int_t nevl

Int_t nblearn

Int_t nunilec

Int_t nunisor

Int_t nunishort

Int_t nunap

Int_t nvar

Int_t itest

Int_t ndiv

Int_t ichoi

Int_t ndivis

Int_t nevt

}	fParam_1

struct {
Double_t xmax [max_nVar_]

Double_t xmin [max_nVar_]

Int_t nclass [max_Events_]

Int_t mclass [max_Events_]

Int_t iclass

}	fVarn_1

class TMVA::MethodCFMlpANN_Utils::VARn2	fVarn2_1

class TMVA::MethodCFMlpANN_Utils::VARn2	fVarn3_1

struct {
Double_t x [max_nLayers_ *max_nNodes_]

Double_t y [max_nLayers_ *max_nNodes_]

Double_t o [max_nNodes_]

Double_t w [max_nLayers_ max_nNodes_ max_nNodes_]

Double_t ww [max_nLayers_ *max_nNodes_]

Double_t cut [max_nNodes_]

Double_t deltaww [max_nLayers_ *max_nNodes_]

Int_t neuron [max_nLayers_]

}	fNeur_1

struct {
Double_t coef [max_nNodes_]

Double_t temp [max_nLayers_]

Double_t demin

Double_t demax

Double_t del [max_nLayers_ *max_nNodes_]

Double_t delw [max_nLayers_ max_nNodes_ max_nNodes_]

Double_t delta [max_nLayers_ max_nNodes_ max_nNodes_]

Double_t delww [max_nLayers_ *max_nNodes_]

Int_t idde

}	fDel_1

struct {
Double_t ancout

Double_t tolcou

Int_t ieps

}	fCost_1

Static Private Attributes
static MethodCFMlpANN *	fgThis = 0

Static Private Attributes inherited from TMVA::MethodCFMlpANN_Utils
static Int_t	fg_100

static Int_t	fg_0

static const Int_t	fg_max_nVar_ = max_nVar_

static const Int_t	fg_max_nNodes_ = max_nNodes_

static Int_t	fg_999 = 999

static const char *const	fg_MethodName = "--- CFMlpANN "

Additional Inherited Members
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 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...

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

#include <TMVA/MethodCFMlpANN.h>

Inheritance diagram for TMVA::MethodCFMlpANN:

Collaboration diagram for TMVA::MethodCFMlpANN:

Constructor & Destructor Documentation

TMVA::MethodCFMlpANN::MethodCFMlpANN	(	const TString &	jobName,
		const TString &	methodTitle,
		DataSetInfo &	theData,
		const TString &	theOption = `"3000:N-1:N-2"`,
		TDirectory *	theTargetDir = `0`
	)

standard constructor option string: "n_training_cycles:n_hidden_layers" default is: n_training_cycles = 5000, n_layers = 4

note that the number of hidden layers in the NN is: n_hidden_layers = n_layers - 2
since there is one input and one output layer. The number of nodes (neurons) is predefined to be: n_nodes[i] = nvars + 1 - i (where i=1..n_layers)

with nvars being the number of variables used in the NN.

Hence, the default case is: n_neurons(layer 1 (input)) : nvars n_neurons(layer 2 (hidden)): nvars-1 n_neurons(layer 3 (hidden)): nvars-1 n_neurons(layer 4 (out)) : 2

This artificial neural network usually needs a relatively large number of cycles to converge (8000 and more). Overtraining can be efficienctly tested by comparing the signal and background output of the NN for the events that were used for training and an independent data sample (with equal properties). If the separation performance is significantly better for the training sample, the NN interprets statistical effects, and is hence overtrained. In this case, the number of cycles should be reduced, or the size of the training sample increased.

Definition at line 124 of file MethodCFMlpANN.cxx.

TMVA::MethodCFMlpANN::MethodCFMlpANN	(	DataSetInfo &	theData,
		const TString &	theWeightFile,
		TDirectory *	theTargetDir = `NULL`
	)

constructor from weight file

Definition at line 144 of file MethodCFMlpANN.cxx.

TMVA::MethodCFMlpANN::~MethodCFMlpANN ( void )

virtual

destructor

Definition at line 267 of file MethodCFMlpANN.cxx.

Member Function Documentation

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

virtual

write weights to xml file

Implements TMVA::MethodBase.

Definition at line 535 of file MethodCFMlpANN.cxx.

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

inlinevirtual

Implements TMVA::MethodBase.

Definition at line 140 of file MethodCFMlpANN.h.

Int_t TMVA::MethodCFMlpANN::DataInterface	(	Double_t *	,
		Double_t *	,
		Int_t *	,
		Int_t *	,
		Int_t *	,
		Int_t *	nvar,
		Double_t *	xpg,
		Int_t *	iclass,
		Int_t *	ikend
	)

protectedvirtual

data interface function

Implements TMVA::MethodCFMlpANN_Utils.

Definition at line 502 of file MethodCFMlpANN.cxx.

void TMVA::MethodCFMlpANN::DeclareOptions ( )

privatevirtual

define the options (their key words) that can be set in the option string know options: NCycles=xx :the number of training cycles HiddenLayser="N-1,N-2" :the specification of the hidden layers

Implements TMVA::MethodBase.

Definition at line 171 of file MethodCFMlpANN.cxx.

Double_t TMVA::MethodCFMlpANN::EvalANN	(	std::vector< Double_t > &	inVar,
		Bool_t &	isOK
	)

private

evaluates NN value as function of input variables

Definition at line 339 of file MethodCFMlpANN.cxx.

Int_t TMVA::MethodCFMlpANN::GetClass ( Int_t ivar ) const

inline

Definition at line 134 of file MethodCFMlpANN.h.

Referenced by DataInterface().

Double_t TMVA::MethodCFMlpANN::GetData	(	Int_t	isel,
		Int_t	ivar
	)		const

inline

Definition at line 133 of file MethodCFMlpANN.h.

Referenced by DataInterface().

void TMVA::MethodCFMlpANN::GetHelpMessage ( ) const

protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 713 of file MethodCFMlpANN.cxx.

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

virtual

returns CFMlpANN output (normalised within [0,1])

Implements TMVA::MethodBase.

Definition at line 317 of file MethodCFMlpANN.cxx.

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

virtual

CFMlpANN can handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 160 of file MethodCFMlpANN.cxx.

void TMVA::MethodCFMlpANN::Init ( void )

privatevirtual

default initialisation called by all constructors

Implements TMVA::MethodBase.

Definition at line 252 of file MethodCFMlpANN.cxx.

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

protectedvirtual

Reimplemented from TMVA::MethodBase.

Definition at line 693 of file MethodCFMlpANN.cxx.

void TMVA::MethodCFMlpANN::MakeClassSpecificHeader	(	std::ostream &	,
		const TString &	= `""`
	)		const

protectedvirtual

write specific classifier response for header

Reimplemented from TMVA::MethodBase.

Definition at line 703 of file MethodCFMlpANN.cxx.

void TMVA::MethodCFMlpANN::NN_ava ( Double_t * xeev )

private

auxiliary functions

Definition at line 373 of file MethodCFMlpANN.cxx.

Double_t TMVA::MethodCFMlpANN::NN_fonc	(	Int_t	i,
		Double_t	u
	)		const

private

activation function

Definition at line 393 of file MethodCFMlpANN.cxx.

void TMVA::MethodCFMlpANN::PrintWeights ( std::ostream & o ) const

private

write the weights of the neural net

Definition at line 628 of file MethodCFMlpANN.cxx.

void TMVA::MethodCFMlpANN::ProcessOptions ( )

privatevirtual

decode the options in the option string

Implements TMVA::MethodBase.

Definition at line 180 of file MethodCFMlpANN.cxx.

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

virtual

read back the weight from the training from file (stream)

Implements TMVA::MethodBase.

Definition at line 410 of file MethodCFMlpANN.cxx.

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

virtual

read weights from xml file

Implements TMVA::MethodBase.

Definition at line 579 of file MethodCFMlpANN.cxx.

TMVA::MethodCFMlpANN * TMVA::MethodCFMlpANN::This ( void )

static

static pointer to this object (required for external functions

Definition at line 689 of file MethodCFMlpANN.cxx.

Referenced by DataInterface().

void TMVA::MethodCFMlpANN::Train ( void )

virtual

training of the Clement-Ferrand NN classifier

Implements TMVA::MethodBase.

Definition at line 283 of file MethodCFMlpANN.cxx.

Member Data Documentation

std::vector<Int_t>* TMVA::MethodCFMlpANN::fClass

private

Definition at line 168 of file MethodCFMlpANN.h.

Referenced by GetClass().

TMatrixF* TMVA::MethodCFMlpANN::fData

private

Definition at line 167 of file MethodCFMlpANN.h.

Referenced by GetData().

TMVA::MethodCFMlpANN * TMVA::MethodCFMlpANN::fgThis = 0

staticprivate

Definition at line 164 of file MethodCFMlpANN.h.

TString TMVA::MethodCFMlpANN::fLayerSpec

private

Definition at line 176 of file MethodCFMlpANN.h.

Int_t TMVA::MethodCFMlpANN::fNcycles

private

Definition at line 171 of file MethodCFMlpANN.h.

Int_t TMVA::MethodCFMlpANN::fNlayers

private

Definition at line 170 of file MethodCFMlpANN.h.

Int_t* TMVA::MethodCFMlpANN::fNodes

private

Definition at line 172 of file MethodCFMlpANN.h.

Double_t** TMVA::MethodCFMlpANN::fYNN

private

Definition at line 175 of file MethodCFMlpANN.h.

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

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

Public Member Functions

Static Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Static Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation