Interface to Clermond-Ferrand artificial neural network.

The CFMlpANN belong to the class of Multilayer Perceptrons (MLP), which are feed-forward networks according to the following propagation schema:

Schema for artificial neural network.

The input layer contains as many neurons as input variables used in the MVA. The output layer contains two neurons for the signal and background event classes. In between the input and output layers are a variable number of k hidden layers with arbitrary numbers of neurons. (While the structure of the input and output layers is determined by the problem, the hidden layers can be configured by the user through the option string of the method booking.)

As indicated in the sketch, all neuron inputs to a layer are linear combinations of the neuron output of the previous layer. The transfer from input to output within a neuron is performed by means of an "activation function". In general, the activation function of a neuron can be zero (deactivated), one (linear), or non-linear. The above example uses a sigmoid activation function. The transfer function of the output layer is usually linear. As a consequence: an ANN without hidden layer should give identical discrimination power as a linear discriminant analysis (Fisher). In case of one hidden layer, the ANN computes a linear combination of sigmoid.

The learning method used by the CFMlpANN is only stochastic.

Definition at line 95 of file MethodCFMlpANN.h.

Public Member Functions
	MethodCFMlpANN (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="3000:N-1:N-2")
	standard constructor

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

virtual	~MethodCFMlpANN (void)
	destructor

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

const Ranking *	CreateRanking ()

Int_t	GetClass (Int_t ivar) const

Double_t	GetData (Int_t isel, Int_t ivar) const

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

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

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

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

virtual void	ReadWeightsFromStream (TFile &)

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

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

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)

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

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

const char *	GetName () const

UInt_t	GetNEvents () const

UInt_t	GetNTargets () const

UInt_t	GetNvar () const

UInt_t	GetNVariables () const

virtual Double_t	GetProba (const Event *ev)

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

const TString	GetProbaName () const

virtual Double_t	GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
	compute rarity:

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

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

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

Double_t	GetRMS (Int_t ivar) const

virtual Double_t	GetROCIntegral (PDF pdfS=0, PDF pdfB=0) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification

virtual Double_t	GetROCIntegral (TH1D histS, TH1D histB) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification

virtual Double_t	GetSeparation (PDF pdfS=0, PDF pdfB=0) const
	compute "separation" defined as

virtual Double_t	GetSeparation (TH1 , TH1 ) const
	compute "separation" defined as

Double_t	GetSignalReferenceCut () const

Double_t	GetSignalReferenceCutOrientation () const

virtual Double_t	GetSignificance () const
	compute significance of mean difference

const Event *	GetTestingEvent (Long64_t ievt) const

Double_t	GetTestTime () const

const TString &	GetTestvarName () const

virtual Double_t	GetTrainingEfficiency (const TString &)

const Event *	GetTrainingEvent (Long64_t ievt) const

virtual const std::vector< Float_t > &	GetTrainingHistory (const char *)

UInt_t	GetTrainingROOTVersionCode () const

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

UInt_t	GetTrainingTMVAVersionCode () const

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

Double_t	GetTrainTime () const

TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true)

const TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const

TString	GetWeightFileName () const
	retrieve weight file name

Double_t	GetXmax (Int_t ivar) const

Double_t	GetXmin (Int_t ivar) const

Bool_t	HasMVAPdfs () const

void	InitIPythonInteractive ()

Bool_t	IsModelPersistence () const

virtual Bool_t	IsSignalLike ()
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event would be selected as signal or background

virtual Bool_t	IsSignalLike (Double_t mvaVal)
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event with this mva output value would be selected as signal or background

Bool_t	IsSilentFile () const

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

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

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

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

void	WriteStateToFile () const
	write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created

Public Member Functions inherited from TMVA::IMethod
	IMethod ()

virtual	~IMethod ()

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

virtual	~Configurable ()
	default destructor

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

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

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

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

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

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

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

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

const char *	GetConfigDescription () const

const char *	GetConfigName () const

const TString &	GetOptions () const

MsgLogger &	Log () const

virtual void	ParseOptions ()
	options parser

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

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

void	ReadOptionsFromXML (void *node)

void	SetConfigDescription (const char *d)

void	SetConfigName (const char *n)

void	SetMsgType (EMsgType t)

void	SetOptions (const TString &s)

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

Public Member Functions inherited from TNamed
	TNamed ()

	TNamed (const char name, const char title)

	TNamed (const TNamed &named)
	TNamed copy ctor.

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

virtual	~TNamed ()
	TNamed destructor.

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

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

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

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

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

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

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

virtual Bool_t	IsSortable () const

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

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

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

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

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

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

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

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

	TObject (const TObject &object)
	TObject copy ctor.

virtual	~TObject ()
	TObject destructor.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

virtual Option_t *	GetOption () const

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

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

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

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

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

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

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

void	InvertBit (UInt_t f)

Bool_t	IsDestructed () const
	IsDestructed.

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

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

R__ALWAYS_INLINE Bool_t	IsOnHeap () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

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

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

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

void	operator delete (void *ptr)
	Operator delete.

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

void *	operator new (size_t sz)

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

void *	operator new[] (size_t sz)

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

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

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

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

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

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

void	ResetBit (UInt_t f)

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

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

void	SetBit (UInt_t f)

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

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

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

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

R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

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

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

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

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

Protected Member Functions
Int_t	DataInterface (Double_t , Double_t , Int_t , Int_t , Int_t , Int_t , Double_t , Int_t , Int_t *)
	data interface function

void	GetHelpMessage () const
	get help message text

void	MakeClassSpecific (std::ostream &, const TString &) const

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

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

void	NoErrorCalc (Double_t const err, Double_t const errUpper)

void	SetNormalised (Bool_t norm)

void	SetWeightFileDir (TString fileDir)
	set directory of weight file

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

void	Statistics (Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &)
	calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE

Bool_t	TxtWeightsOnly () const

Bool_t	Verbose () const

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

const TString &	GetReferenceFile () const

Bool_t	LooseOptionCheckingEnabled () const

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

void	WriteOptionsReferenceToFile ()
	write complete options to output stream

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

void	MakeZombie ()

Private Member Functions
void	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

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

void	Init (void)
	default initialisation called by all constructors

void	NN_ava (Double_t *)
	auxiliary functions

Double_t	NN_fonc (Int_t, Double_t) const
	activation function

void	PrintWeights (std::ostream &o) const
	write the weights of the neural net

void	ProcessOptions ()
	decode the options in the option string

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

virtual	~MethodCFMlpANN_Utils ()
	Destructor.

void	Arret (const char *mot)

void	CollectVar (Int_t nvar, Int_t class__, Double_t *xpg)
	[smart comments to be added]

void	Cout (Int_t , Double_t xxx)
	[smart comments to be added]

void	Cout2 (Int_t , Double_t yyy)
	[smart comments to be added]

void	En_arriere (Int_t *ievent)
	[smart comments to be added]

void	En_avant (Int_t *ievent)
	[smart comments to be added]

void	En_avant2 (Int_t *ievent)
	[smart comments to be added]

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__)
	[smart comments to be added]

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

void	GraphNN (Int_t ilearn, Double_t , Double_t , char , Int_t)
	[smart comments to be added]

void	Inl ()
	[smart comments to be added]

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

void	Lecev2 (Int_t ktest, Double_t tout2, Double_t *tin2)
	[smart comments to be added]

void	Leclearn (Int_t ktest, Double_t tout2, Double_t *tin2)
	[smart comments to be added]

void	Out (Int_t iii, Int_t maxcycle)

Double_t	Sen3a (void)
	[smart comments to be added]

void	SetLogger (MsgLogger *l)

void	TestNN ()
	[smart comments to be added]

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)

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)

void	Wini ()
	[smart comments to be added]

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)

Private Attributes
std::vector< Int_t > *	fClass

TMatrixF *	fData

TString	fLayerSpec

Int_t	fNcycles

Int_t	fNlayers

Int_t *	fNodes

Double_t **	fYNN

Int_t	MethodCFMlpANN_nsel

struct {

Double_t ancout

Int_t ieps

Double_t tolcou

}	fCost_1

struct {

Double_t coef [max_nNodes_]

Double_t del [max_nLayers_ *max_nNodes_]

Double_t delta [max_nLayers_ max_nNodes_ max_nNodes_]

Double_t delw [max_nLayers_ max_nNodes_ max_nNodes_]

Double_t delww [max_nLayers_ *max_nNodes_]

Double_t demax

Double_t demin

Int_t idde

Double_t temp [max_nLayers_]

}	fDel_1

Int_t	fg_0

Int_t	fg_100

Int_t	fg_999

struct {

Double_t cut [max_nNodes_]

Double_t deltaww [max_nLayers_ *max_nNodes_]

Int_t neuron [max_nLayers_]

Double_t o [max_nNodes_]

Double_t w [max_nLayers_ max_nNodes_ max_nNodes_]

Double_t ww [max_nLayers_ *max_nNodes_]

Double_t x [max_nLayers_ *max_nNodes_]

Double_t y [max_nLayers_ *max_nNodes_]

}	fNeur_1

struct {

Double_t eeps

Double_t epsmax

Double_t epsmin

Double_t eta

Int_t ichoi

Int_t itest

Int_t layerm

Int_t lclass

Int_t nblearn

Int_t ndiv

Int_t ndivis

Int_t nevl

Int_t nevt

Int_t nunap

Int_t nunilec

Int_t nunishort

Int_t nunisor

Int_t nvar

}	fParam_1

class TMVA::MethodCFMlpANN_Utils::VARn2	fVarn2_1

class TMVA::MethodCFMlpANN_Utils::VARn2	fVarn3_1

struct {

Int_t iclass

Int_t mclass [max_Events_]

Int_t nclass [max_Events_]

Double_t xmax [max_nVar_]

Double_t xmin [max_nVar_]

}	fVarn_1

Additional Inherited Members
Public Types inherited from TMVA::MethodBase
enum	EWeightFileType { kROOT =0 , kTEXT }

Public Types inherited from TObject
enum	{ kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff }

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

enum	EDeprecatedStatusBits { kObjInCanvas = BIT(3) }

enum	EStatusBits { kCanDelete = BIT(0) , kMustCleanup = BIT(3) , kIsReferenced = BIT(4) , kHasUUID = BIT(5) , kCannotPick = BIT(6) , kNoContextMenu = BIT(8) , kInvalidObject = BIT(13) }

Static Public Member Functions inherited from TObject
static Longptr_t	GetDtorOnly ()
	Return destructor only flag.

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

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

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

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

TrainingHistory	fTrainHistory

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

Protected Attributes inherited from TMVA::MethodBase
Types::EAnalysisType	fAnalysisType

UInt_t	fBackgroundClass

bool	fExitFromTraining = false

std::vector< TString > *	fInputVars

IPythonInteractive *	fInteractive = nullptr
	temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues)

UInt_t	fIPyCurrentIter = 0

UInt_t	fIPyMaxIter = 0

std::vector< Float_t > *	fMulticlassReturnVal

Int_t	fNbins

Int_t	fNbinsH

Int_t	fNbinsMVAoutput

Ranking *	fRanking

std::vector< Float_t > *	fRegressionReturnVal

Results *	fResults

UInt_t	fSignalClass

DataSet *	fTmpData = nullptr
	temporary event when testing on a different DataSet than the own one

const Event *	fTmpEvent

Protected Attributes inherited from TMVA::Configurable
MsgLogger *	fLogger

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

Static Private Attributes inherited from TMVA::MethodCFMlpANN_Utils
static const Int_t	fg_max_nNodes_ = max_nNodes_

static const Int_t	fg_max_nVar_ = max_nVar_

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

#include <TMVA/MethodCFMlpANN.h>

Inheritance diagram for TMVA::MethodCFMlpANN:

[legend]

Constructor & Destructor Documentation

◆ MethodCFMlpANN() [1/2]

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

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 efficiently 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 130 of file MethodCFMlpANN.cxx.

◆ MethodCFMlpANN() [2/2]

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

constructor from weight file

Definition at line 149 of file MethodCFMlpANN.cxx.

◆ ~MethodCFMlpANN()

TMVA::MethodCFMlpANN::~MethodCFMlpANN ( void )

virtual

destructor

Definition at line 269 of file MethodCFMlpANN.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

virtual

write weights to xml file

Implements TMVA::MethodBase.

Definition at line 537 of file MethodCFMlpANN.cxx.

◆ CreateRanking()

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

inlinevirtual

Implements TMVA::MethodBase.

Definition at line 131 of file MethodCFMlpANN.h.

◆ DataInterface()

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 506 of file MethodCFMlpANN.cxx.

◆ DeclareOptions()

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 176 of file MethodCFMlpANN.cxx.

◆ EvalANN()

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 343 of file MethodCFMlpANN.cxx.

◆ GetClass()

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

inline

Definition at line 127 of file MethodCFMlpANN.h.

◆ GetData()

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

inline

Definition at line 126 of file MethodCFMlpANN.h.

◆ GetHelpMessage()

void TMVA::MethodCFMlpANN::GetHelpMessage ( ) const

protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 711 of file MethodCFMlpANN.cxx.

◆ GetMvaValue()

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 321 of file MethodCFMlpANN.cxx.

◆ HasAnalysisType()

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 165 of file MethodCFMlpANN.cxx.

◆ Init()

void TMVA::MethodCFMlpANN::Init ( void )

privatevirtual

default initialisation called by all constructors

Implements TMVA::MethodBase.

Definition at line 257 of file MethodCFMlpANN.cxx.

◆ MakeClassSpecific()

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

protectedvirtual

Reimplemented from TMVA::MethodBase.

Definition at line 691 of file MethodCFMlpANN.cxx.

◆ MakeClassSpecificHeader()

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

protectedvirtual

write specific classifier response for header

Reimplemented from TMVA::MethodBase.

Definition at line 701 of file MethodCFMlpANN.cxx.

◆ NN_ava()

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

private

auxiliary functions

Definition at line 377 of file MethodCFMlpANN.cxx.

◆ NN_fonc()

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

private

activation function

Definition at line 397 of file MethodCFMlpANN.cxx.

◆ PrintWeights()

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

private

write the weights of the neural net

Definition at line 630 of file MethodCFMlpANN.cxx.

◆ ProcessOptions()

void TMVA::MethodCFMlpANN::ProcessOptions ( )

privatevirtual

decode the options in the option string

Implements TMVA::MethodBase.

Definition at line 185 of file MethodCFMlpANN.cxx.

◆ ReadWeightsFromStream() [1/3]

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

virtual

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/3]

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

virtual

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

Implements TMVA::MethodBase.

Definition at line 414 of file MethodCFMlpANN.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::MethodCFMlpANN::ReadWeightsFromXML ( void * wghtnode )

virtual

read weights from xml file

Implements TMVA::MethodBase.

Definition at line 581 of file MethodCFMlpANN.cxx.

◆ Train()

void TMVA::MethodCFMlpANN::Train ( void )

virtual

training of the Clement-Ferrand NN classifier

Implements TMVA::MethodBase.

Definition at line 285 of file MethodCFMlpANN.cxx.

Member Data Documentation

◆ fClass

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

private

Definition at line 157 of file MethodCFMlpANN.h.

◆ fData

TMatrixF* TMVA::MethodCFMlpANN::fData

private

Definition at line 156 of file MethodCFMlpANN.h.

◆ fLayerSpec

TString TMVA::MethodCFMlpANN::fLayerSpec

private

Definition at line 165 of file MethodCFMlpANN.h.

◆ fNcycles

Int_t TMVA::MethodCFMlpANN::fNcycles

private

Definition at line 160 of file MethodCFMlpANN.h.

◆ fNlayers

Int_t TMVA::MethodCFMlpANN::fNlayers

private

Definition at line 159 of file MethodCFMlpANN.h.

◆ fNodes

Int_t* TMVA::MethodCFMlpANN::fNodes

private

Definition at line 161 of file MethodCFMlpANN.h.

◆ fYNN

Double_t** TMVA::MethodCFMlpANN::fYNN

private

Definition at line 164 of file MethodCFMlpANN.h.

◆ MethodCFMlpANN_nsel

Int_t TMVA::MethodCFMlpANN::MethodCFMlpANN_nsel

private

Definition at line 166 of file MethodCFMlpANN.h.

Libraries for TMVA::MethodCFMlpANN:

[legend]

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

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ MethodCFMlpANN() [1/2]

◆ MethodCFMlpANN() [2/2]

◆ ~MethodCFMlpANN()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ CreateRanking()

◆ DataInterface()

◆ DeclareOptions()

◆ EvalANN()

◆ GetClass()

◆ GetData()

◆ GetHelpMessage()

◆ GetMvaValue()

◆ HasAnalysisType()

◆ Init()

◆ MakeClassSpecific()

◆ MakeClassSpecificHeader()

◆ NN_ava()

◆ NN_fonc()

◆ PrintWeights()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/3]

◆ ReadWeightsFromStream() [2/3]

◆ ReadWeightsFromStream() [3/3]

◆ ReadWeightsFromXML()

◆ Train()

Member Data Documentation

◆ fClass

◆ fData

◆ fLayerSpec

◆ fNcycles

◆ fNlayers

◆ fNodes

◆ fYNN

◆ MethodCFMlpANN_nsel