Definition at line 51 of file MethodTMlpANN.h.

Public Member Functions
	MethodTMlpANN (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="3000:N-1:N-2", TDirectory *theTargetDir=0)

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

virtual	~MethodTMlpANN (void)
	destructor More...

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

void	Train (void)
	performs TMlpANN training available learning methods: More...

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

void	ReadWeightsFromStream (std::istream &istr)
	read weights from stream since the MLP can not read from the stream, we 1st: write the weights to temporary file More...

void	ReadWeightsFromXML (void *wghtnode)
	rebuild temporary textfile from xml weightfile and load this file into MLP More...

Double_t	GetMvaValue (Double_t err=0, Double_t errUpper=0)
	calculate the value of the neural net for the current event More...

void	SetHiddenLayer (TString hiddenlayer="")

const Ranking *	CreateRanking ()

void	MakeClass (const TString &classFileName=TString("")) const
	create reader class for classifier -> overwrites base class function create specific class for TMultiLayerPerceptron 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	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...

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...

Protected Member Functions
void	MakeClassSpecific (std::ostream &, const TString &) const
	write specific classifier response nothing to do here - all taken care of by TMultiLayerPerceptron More...

void	GetHelpMessage () const
	get help message text 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

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::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	DeclareOptions ()
	define the options (their key words) that can be set in the option string know options: NCycles <integer> Number of training cycles (too many cycles could overtrain the network) HiddenLayers <string> Layout of the hidden layers (nodes per layer) More...

void	ProcessOptions ()
	builds the neural network as specified by the user More...

void	CreateMLPOptions (TString)
	translates options from option string into TMlpANN language More...

void	Init (void)
	default initialisations More...

Private Attributes
TString	fLayerSpec

TMultiLayerPerceptron *	fMLP

TTree *	fLocalTrainingTree

TString	fHiddenLayer

Int_t	fNcycles

Double_t	fValidationFraction

TString	fMLPBuildOptions

TString	fLearningMethod

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) }

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...

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/MethodTMlpANN.h>

Inheritance diagram for TMVA::MethodTMlpANN:

Collaboration diagram for TMVA::MethodTMlpANN:

Constructor & Destructor Documentation

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

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

constructor from weight file

Definition at line 104 of file MethodTMlpANN.cxx.

TMVA::MethodTMlpANN::~MethodTMlpANN ( void )

virtual

destructor

Definition at line 137 of file MethodTMlpANN.cxx.

Member Function Documentation

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

virtual

write weights to xml file

Implements TMVA::MethodBase.

Definition at line 349 of file MethodTMlpANN.cxx.

void TMVA::MethodTMlpANN::CreateMLPOptions ( TString layerSpec )

private

translates options from option string into TMlpANN language

Definition at line 145 of file MethodTMlpANN.cxx.

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

inlinevirtual

Implements TMVA::MethodBase.

Definition at line 90 of file MethodTMlpANN.h.

void TMVA::MethodTMlpANN::DeclareOptions ( )

privatevirtual

define the options (their key words) that can be set in the option string know options: NCycles <integer> Number of training cycles (too many cycles could overtrain the network) HiddenLayers <string> Layout of the hidden layers (nodes per layer)

specifiactions for each hidden layer are separated by commata
for each layer the number of nodes can be either absolut (simply a number) or relative to the number of input nodes to the neural net (N)
there is always a single node in the output layer example: a net with 6 input nodes and "Hiddenlayers=N-1,N-2" has 6,5,4,1 nodes in the layers 1,2,3,4, repectively

Implements TMVA::MethodBase.

Definition at line 197 of file MethodTMlpANN.cxx.

void TMVA::MethodTMlpANN::GetHelpMessage ( ) const

protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 507 of file MethodTMlpANN.cxx.

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

virtual

calculate the value of the neural net for the current event

Implements TMVA::MethodBase.

Definition at line 232 of file MethodTMlpANN.cxx.

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

virtual

TMlpANN can handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 119 of file MethodTMlpANN.cxx.

void TMVA::MethodTMlpANN::Init ( void )

privatevirtual

default initialisations

Implements TMVA::MethodBase.

Definition at line 130 of file MethodTMlpANN.cxx.

void TMVA::MethodTMlpANN::MakeClass ( const TString & classFileName = TString("") ) const

virtual

create reader class for classifier -> overwrites base class function create specific class for TMultiLayerPerceptron

Reimplemented from TMVA::MethodBase.

Definition at line 479 of file MethodTMlpANN.cxx.

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

protectedvirtual

write specific classifier response nothing to do here - all taken care of by TMultiLayerPerceptron

Reimplemented from TMVA::MethodBase.

Definition at line 497 of file MethodTMlpANN.cxx.

void TMVA::MethodTMlpANN::ProcessOptions ( )

privatevirtual

builds the neural network as specified by the user

Implements TMVA::MethodBase.

Definition at line 217 of file MethodTMlpANN.cxx.

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

virtual

read weights from stream since the MLP can not read from the stream, we 1st: write the weights to temporary file

Implements TMVA::MethodBase.

Definition at line 447 of file MethodTMlpANN.cxx.

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

virtual

rebuild temporary textfile from xml weightfile and load this file into MLP

Implements TMVA::MethodBase.

Definition at line 384 of file MethodTMlpANN.cxx.

void TMVA::MethodTMlpANN::SetHiddenLayer ( TString hiddenlayer = "" )

inline

Definition at line 87 of file MethodTMlpANN.h.

void TMVA::MethodTMlpANN::Train ( void )

virtual

performs TMlpANN training available learning methods:

TMultiLayerPerceptron::kStochastic TMultiLayerPerceptron::kBatch TMultiLayerPerceptron::kSteepestDescent TMultiLayerPerceptron::kRibierePolak TMultiLayerPerceptron::kFletcherReeves TMultiLayerPerceptron::kBFGS

TMultiLayerPerceptron wants test and training tree at once so merge the training and testing trees from the MVA factory first:

Implements TMVA::MethodBase.

Definition at line 262 of file MethodTMlpANN.cxx.

Member Data Documentation

TString TMVA::MethodTMlpANN::fHiddenLayer

private

Definition at line 117 of file MethodTMlpANN.h.

Referenced by SetHiddenLayer().

TString TMVA::MethodTMlpANN::fLayerSpec

private

Definition at line 112 of file MethodTMlpANN.h.

TString TMVA::MethodTMlpANN::fLearningMethod

private

Definition at line 122 of file MethodTMlpANN.h.

TTree* TMVA::MethodTMlpANN::fLocalTrainingTree

private

Definition at line 115 of file MethodTMlpANN.h.

TMultiLayerPerceptron* TMVA::MethodTMlpANN::fMLP

private

Definition at line 114 of file MethodTMlpANN.h.

TString TMVA::MethodTMlpANN::fMLPBuildOptions

private

Definition at line 120 of file MethodTMlpANN.h.

Int_t TMVA::MethodTMlpANN::fNcycles

private

Definition at line 118 of file MethodTMlpANN.h.

Double_t TMVA::MethodTMlpANN::fValidationFraction

private

Definition at line 119 of file MethodTMlpANN.h.

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

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

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

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Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation