Definition at line 58 of file TMultiLayerPerceptron.h.

Public Types
enum	ELearningMethod { kStochastic, kBatch, kSteepestDescent, kRibierePolak, kFletcherReeves, kBFGS }

enum	EDataSet { kTraining, kTest }

Public Types inherited from TObject
enum	EStatusBits { kCanDelete = BIT(0), kMustCleanup = BIT(3), kObjInCanvas = BIT(3), kIsReferenced = BIT(4), kHasUUID = BIT(5), kCannotPick = BIT(6), kNoContextMenu = BIT(8), kInvalidObject = BIT(13) }

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

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

Public Member Functions
	TMultiLayerPerceptron ()

	TMultiLayerPerceptron (const char layout, TTree data=0, const char training="Entry$%2==0", const char test="", TNeuron::ENeuronType type=TNeuron::kSigmoid, const char extF="", const char extD="")
	The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas. More...

	TMultiLayerPerceptron (const char layout, const char weight, TTree data=0, const char training="Entry$%2==0", const char test="", TNeuron::ENeuronType type=TNeuron::kSigmoid, const char extF="", const char *extD="")
	The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas. More...

	TMultiLayerPerceptron (const char layout, TTree data, TEventList training, TEventList test, TNeuron::ENeuronType type=TNeuron::kSigmoid, const char extF="", const char extD="")
	The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas. More...

	TMultiLayerPerceptron (const char layout, const char weight, TTree data, TEventList training, TEventList test, TNeuron::ENeuronType type=TNeuron::kSigmoid, const char extF="", const char *extD="")
	The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas. More...

virtual	~TMultiLayerPerceptron ()
	Destructor. More...

void	SetData (TTree *)
	Set the data source. More...

void	SetTrainingDataSet (TEventList *train)
	Sets the Training dataset. More...

void	SetTestDataSet (TEventList *test)
	Sets the Test dataset. More...

void	SetTrainingDataSet (const char *train)
	Sets the Training dataset. More...

void	SetTestDataSet (const char *test)
	Sets the Test dataset. More...

void	SetLearningMethod (TMultiLayerPerceptron::ELearningMethod method)
	Sets the learning method. More...

void	SetEventWeight (const char *)
	Set the event weight. More...

void	Train (Int_t nEpoch, Option_t *option="text", Double_t minE=0)
	Train the network. More...

Double_t	Result (Int_t event, Int_t index=0) const
	Computes the output for a given event. More...

Double_t	GetError (Int_t event) const
	Error on the output for a given event. More...

Double_t	GetError (TMultiLayerPerceptron::EDataSet set) const
	Error on the whole dataset. More...

void	ComputeDEDw () const
	Compute the DEDw = sum on all training events of dedw for each weight normalized by the number of events. More...

void	Randomize () const
	Randomize the weights. More...

void	SetEta (Double_t eta)
	Sets Eta - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters) More...

void	SetEpsilon (Double_t eps)
	Sets Epsilon - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters) More...

void	SetDelta (Double_t delta)
	Sets Delta - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters) More...

void	SetEtaDecay (Double_t ed)
	Sets EtaDecay - Eta *= EtaDecay at each epoch (look at the constructor for the complete description of learning methods and parameters) More...

void	SetTau (Double_t tau)
	Sets Tau - used in line search (look at the constructor for the complete description of learning methods and parameters) More...

void	SetReset (Int_t reset)
	Sets number of epochs between two resets of the search direction to the steepest descent. More...

Double_t	GetEta () const

Double_t	GetEpsilon () const

Double_t	GetDelta () const

Double_t	GetEtaDecay () const

TMultiLayerPerceptron::ELearningMethod	GetLearningMethod () const

Double_t	GetTau () const

Int_t	GetReset () const

TString	GetStructure () const

TNeuron::ENeuronType	GetType () const

void	DrawResult (Int_t index=0, Option_t *option="test") const
	Draws the neural net output It produces an histogram with the output for the two datasets. More...

Bool_t	DumpWeights (Option_t *filename="-") const
	Dumps the weights to a text file. More...

Bool_t	LoadWeights (Option_t *filename="")
	Loads the weights from a text file conforming to the format defined by DumpWeights. More...

Double_t	Evaluate (Int_t index, Double_t *params) const
	Returns the Neural Net for a given set of input parameters #parameters must equal #input neurons. More...

void	Export (Option_t filename="NNfunction", Option_t language="C++") const
	Exports the NN as a function for any non-ROOT-dependant code Supported languages are: only C++ , FORTRAN and Python (yet) This feature is also usefull if you want to plot the NN as a function (TF1 or TF2). More...

virtual void	Draw (Option_t *option="")
	Draws the network structure. More...

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	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 *	GetName () const
	Returns name of object. 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	AttachData ()
	Connects the TTree to Neurons in input and output layers. More...

void	BuildNetwork ()
	Instanciates the network from the description. More...

void	GetEntry (Int_t) const
	Load an entry into the network. More...

void	MLP_Stochastic (Double_t *)
	One step for the stochastic method buffer should contain the previous dw vector and will be updated. More...

void	MLP_Batch (Double_t *)
	One step for the batch (stochastic) method. More...

Bool_t	LineSearch (Double_t , Double_t )
	Search along the line defined by direction. More...

void	SteepestDir (Double_t *)
	Sets the search direction to steepest descent. More...

void	ConjugateGradientsDir (Double_t *, Double_t)
	Sets the search direction to conjugate gradient direction beta should be: \|\|g_{(t+1)}\|\|^2 / \|\|g_{(t)}\|\|^2 (Fletcher-Reeves) g_{(t+1)} (g_{(t+1)}-g_{(t)}) / \|\|g_{(t)}\|\|^2 (Ribiere-Polak) More...

void	SetGammaDelta (TMatrixD &, TMatrixD &, Double_t *)
	Sets the gamma (g_{(t+1)}-g_{(t)}) and delta (w_{(t+1)}-w_{(t)}) vectors Gamma is computed here, so ComputeDEDw cannot have been called before, and delta is a direct translation of buffer into a TMatrixD. More...

bool	GetBFGSH (TMatrixD &, TMatrixD &, TMatrixD &)
	Computes the hessian matrix using the BFGS update algorithm. More...

void	BFGSDir (TMatrixD &, Double_t *)
	Computes the direction for the BFGS algorithm as the product between the Hessian estimate (bfgsh) and the dir. More...

Double_t	DerivDir (Double_t *)
	scalar product between gradient and direction = derivative along direction More...

Double_t	GetCrossEntropyBinary () const
	Cross entropy error for sigmoid output neurons, for a given event. More...

Double_t	GetCrossEntropy () const
	Cross entropy error for a softmax output neuron, for a given event. More...

Double_t	GetSumSquareError () const
	Error on the output for a given event. More...

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
	TMultiLayerPerceptron (const TMultiLayerPerceptron &)

TMultiLayerPerceptron &	operator= (const TMultiLayerPerceptron &)

void	ExpandStructure ()
	Expand the structure of the first layer. More...

void	BuildFirstLayer (TString &)
	Instanciates the neurons in input Inputs are normalised and the type is set to kOff (simple forward of the formula value) More...

void	BuildHiddenLayers (TString &)
	Builds hidden layers. More...

void	BuildOneHiddenLayer (const TString &sNumNodes, Int_t &layer, Int_t &prevStart, Int_t &prevStop, Bool_t lastLayer)
	Builds a hidden layer, updates the number of layers. More...

void	BuildLastLayer (TString &, Int_t)
	Builds the output layer Neurons are linear combinations of input, by defaul. More...

void	Shuffle (Int_t *, Int_t) const
	Shuffle the Int_t index[n] in input. More...

void	MLP_Line (Double_t , Double_t , Double_t)
	Sets the weights to a point along a line Weights are set to [origin + (dist * dir)]. More...

Private Attributes
TTree *	fData

Int_t	fCurrentTree
	pointer to the tree used as datasource More...

Double_t	fCurrentTreeWeight
	index of the current tree in a chain More...

TObjArray	fNetwork
	weight of the current tree in a chain More...

TObjArray	fFirstLayer

TObjArray	fLastLayer

TObjArray	fSynapses

TString	fStructure

TString	fWeight

TNeuron::ENeuronType	fType

TNeuron::ENeuronType	fOutType

TString	fextF

TString	fextD

TEventList *	fTraining

TEventList *	fTest
	EventList defining the events in the training dataset. More...

ELearningMethod	fLearningMethod
	EventList defining the events in the test dataset. More...

TTreeFormula *	fEventWeight
	The Learning Method. More...

TTreeFormulaManager *	fManager
	formula representing the event weight More...

Double_t	fEta
	TTreeFormulaManager for the weight and neurons. More...

Double_t	fEpsilon
	Eta - used in stochastic minimisation - Default=0.1. More...

Double_t	fDelta
	Epsilon - used in stochastic minimisation - Default=0. More...

Double_t	fEtaDecay
	Delta - used in stochastic minimisation - Default=0. More...

Double_t	fTau
	EtaDecay - Eta *= EtaDecay at each epoch - Default=1. More...

Double_t	fLastAlpha
	Tau - used in line search - Default=3. More...

Int_t	fReset
	internal parameter used in line search More...

Bool_t	fTrainingOwner
	number of epochs between two resets of the search direction to the steepest descent - Default=50 More...

Bool_t	fTestOwner
	internal flag whether one has to delete fTraining or not More...

Friends
class	TMLPAnalyzer

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

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

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

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

#include <TMultiLayerPerceptron.h>

Inheritance diagram for TMultiLayerPerceptron:

Collaboration diagram for TMultiLayerPerceptron:

Member Enumeration Documentation

enum TMultiLayerPerceptron::EDataSet

Enumerator
kTraining
kTest

Definition at line 64 of file TMultiLayerPerceptron.h.

enum TMultiLayerPerceptron::ELearningMethod

Enumerator
kStochastic
kBatch
kSteepestDescent
kRibierePolak
kFletcherReeves
kBFGS

Definition at line 62 of file TMultiLayerPerceptron.h.

Constructor & Destructor Documentation

TMultiLayerPerceptron::TMultiLayerPerceptron ( )

TMultiLayerPerceptron::TMultiLayerPerceptron	(	const char *	layout,
		TTree *	data = `0`,
		const char *	training = `"Entry$%2==0"`,
		const char *	test = `""`,
		TNeuron::ENeuronType	type = `TNeuron::kSigmoid`,
		const char *	extF = `""`,
		const char *	extD = `""`
	)

The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

Hidden layers are just described by the number of neurons. The layers are separated by colons. Ex: "x,y:10:5:f" The output can be prepended by '@' if the variable has to be normalized. The output can be followed by '!' to use Softmax neurons for the output layer only. Ex: "x,y:10:5:c1,c2,c3!" Input and outputs are taken from the TTree given as second argument. training and test are two cuts (see TTreeFormula) defining events to be used during the neural net training and testing. Example: "Entry$%2", "(Entry$+1)%2". Both the TTree and the cut can be defined in the constructor, or later with the suited setter method.

Definition at line 420 of file TMultiLayerPerceptron.cxx.

TMultiLayerPerceptron::TMultiLayerPerceptron	(	const char *	layout,
		const char *	weight,
		TTree *	data = `0`,
		const char *	training = `"Entry$%2==0"`,
		const char *	test = `""`,
		TNeuron::ENeuronType	type = `TNeuron::kSigmoid`,
		const char *	extF = `""`,
		const char *	extD = `""`
	)

The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

Hidden layers are just described by the number of neurons. The layers are separated by colons. Ex: "x,y:10:5:f" The output can be prepended by '@' if the variable has to be normalized. The output can be followed by '!' to use Softmax neurons for the output layer only. Ex: "x,y:10:5:c1,c2,c3!" Input and outputs are taken from the TTree given as second argument. training and test are two cuts (see TTreeFormula) defining events to be used during the neural net training and testing. Example: "Entry$%2", "(Entry$+1)%2". Both the TTree and the cut can be defined in the constructor, or later with the suited setter method.

Definition at line 486 of file TMultiLayerPerceptron.cxx.

TMultiLayerPerceptron::TMultiLayerPerceptron	(	const char *	layout,
		TTree *	data,
		TEventList *	training,
		TEventList *	test,
		TNeuron::ENeuronType	type = `TNeuron::kSigmoid`,
		const char *	extF = `""`,
		const char *	extD = `""`
	)

The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

Hidden layers are just described by the number of neurons. The layers are separated by colons. Ex: "x,y:10:5:f" The output can be prepended by '@' if the variable has to be normalized. The output can be followed by '!' to use Softmax neurons for the output layer only. Ex: "x,y:10:5:c1,c2,c3!" Input and outputs are taken from the TTree given as second argument. training and test are the two TEventLists defining events to be used during the neural net training. Both the TTree and the TEventLists can be defined in the constructor, or later with the suited setter method.

Definition at line 302 of file TMultiLayerPerceptron.cxx.

TMultiLayerPerceptron::TMultiLayerPerceptron	(	const char *	layout,
		const char *	weight,
		TTree *	data,
		TEventList *	training,
		TEventList *	test,
		TNeuron::ENeuronType	type = `TNeuron::kSigmoid`,
		const char *	extF = `""`,
		const char *	extD = `""`
	)

The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas.

Hidden layers are just described by the number of neurons. The layers are separated by colons. Ex: "x,y:10:5:f" The output can be prepended by '@' if the variable has to be normalized. The output can be followed by '!' to use Softmax neurons for the output layer only. Ex: "x,y:10:5:c1,c2,c3!" Input and outputs are taken from the TTree given as second argument. training and test are the two TEventLists defining events to be used during the neural net training. Both the TTree and the TEventLists can be defined in the constructor, or later with the suited setter method.

Definition at line 360 of file TMultiLayerPerceptron.cxx.

TMultiLayerPerceptron::~TMultiLayerPerceptron ( )

virtual

Destructor.

Definition at line 537 of file TMultiLayerPerceptron.cxx.

TMultiLayerPerceptron::TMultiLayerPerceptron ( const TMultiLayerPerceptron & )

private

Member Function Documentation

void TMultiLayerPerceptron::AttachData ( )

protected

Connects the TTree to Neurons in input and output layers.

The formulas associated to each neuron are created and reported to the network formula manager. By default, the branch is not normalised since this would degrade performance for classification jobs. Normalisation can be requested by putting '@' in front of the formula.

Definition at line 1216 of file TMultiLayerPerceptron.cxx.

Referenced by SetData(), and TMultiLayerPerceptron().

void TMultiLayerPerceptron::BFGSDir	(	TMatrixD &	bfgsh,
		Double_t *	dir
	)

protected

Computes the direction for the BFGS algorithm as the product between the Hessian estimate (bfgsh) and the dir.

Definition at line 2439 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

void TMultiLayerPerceptron::BuildFirstLayer ( TString & input )

private

Instanciates the neurons in input Inputs are normalised and the type is set to kOff (simple forward of the formula value)

Definition at line 1351 of file TMultiLayerPerceptron.cxx.

Referenced by BuildNetwork().

void TMultiLayerPerceptron::BuildHiddenLayers ( TString & hidden )

private

Builds hidden layers.

Definition at line 1369 of file TMultiLayerPerceptron.cxx.

Referenced by BuildNetwork().

void TMultiLayerPerceptron::BuildLastLayer	(	TString &	output,
		Int_t	prev
	)

private

Builds the output layer Neurons are linear combinations of input, by defaul.

If the structure ends with "!", neurons are set up for classification, ie. with a sigmoid (1 neuron) or softmax (more neurons) activation function.

Definition at line 1433 of file TMultiLayerPerceptron.cxx.

Referenced by BuildNetwork().

void TMultiLayerPerceptron::BuildNetwork ( )

protected

Instanciates the network from the description.

Definition at line 1320 of file TMultiLayerPerceptron.cxx.

Referenced by SetData(), and TMultiLayerPerceptron().

void TMultiLayerPerceptron::BuildOneHiddenLayer	(	const TString &	sNumNodes,
		Int_t &	layer,
		Int_t &	prevStart,
		Int_t &	prevStop,
		Bool_t	lastLayer
	)

private

Builds a hidden layer, updates the number of layers.

Definition at line 1388 of file TMultiLayerPerceptron.cxx.

Referenced by BuildHiddenLayers().

void TMultiLayerPerceptron::ComputeDEDw ( ) const

Compute the DEDw = sum on all training events of dedw for each weight normalized by the number of events.

Definition at line 1113 of file TMultiLayerPerceptron.cxx.

Referenced by SetGammaDelta(), and Train().

void TMultiLayerPerceptron::ConjugateGradientsDir	(	Double_t *	dir,
		Double_t	beta
	)

protected

Sets the search direction to conjugate gradient direction beta should be: ||g_{(t+1)}||^2 / ||g_{(t)}||^2 (Fletcher-Reeves) g_{(t+1)} (g_{(t+1)}-g_{(t)}) / ||g_{(t)}||^2 (Ribiere-Polak)

Definition at line 2324 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

Double_t TMultiLayerPerceptron::DerivDir ( Double_t * dir )

protected

scalar product between gradient and direction = derivative along direction

Definition at line 2415 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

void TMultiLayerPerceptron::Draw ( Option_t * option = "" )

virtual

Draws the network structure.

Neurons are depicted by a blue disk, and synapses by lines connecting neurons. The line width is proportionnal to the weight.

Reimplemented from TObject.

Definition at line 2469 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::DrawResult	(	Int_t	index = `0`,
		Option_t *	option = `"test"`
	)		const

Draws the neural net output It produces an histogram with the output for the two datasets.

Index is the number of the desired output neuron. "option" can contain:

test or train to select a dataset
comp to produce a X-Y comparison plot
nocanv to not create a new TCanvas for the plot

Definition at line 1483 of file TMultiLayerPerceptron.cxx.

Bool_t TMultiLayerPerceptron::DumpWeights ( Option_t * filename = "-" ) const

Dumps the weights to a text file.

Set filename to "-" (default) to dump to the standard output

Definition at line 1557 of file TMultiLayerPerceptron.cxx.

Double_t TMultiLayerPerceptron::Evaluate	(	Int_t	index,
		Double_t *	params
	)		const

Returns the Neural Net for a given set of input parameters #parameters must equal #input neurons.

Definition at line 1663 of file TMultiLayerPerceptron.cxx.

Referenced by TMLPAnalyzer::GatherInformations().

void TMultiLayerPerceptron::ExpandStructure ( )

private

Expand the structure of the first layer.

Definition at line 1274 of file TMultiLayerPerceptron.cxx.

Referenced by BuildNetwork().

void TMultiLayerPerceptron::Export	(	Option_t *	filename = `"NNfunction"`,
		Option_t *	language = `"C++"`
	)		const

Exports the NN as a function for any non-ROOT-dependant code Supported languages are: only C++ , FORTRAN and Python (yet) This feature is also usefull if you want to plot the NN as a function (TF1 or TF2).

Definition at line 1688 of file TMultiLayerPerceptron.cxx.

bool TMultiLayerPerceptron::GetBFGSH	(	TMatrixD &	bfgsh,
		TMatrixD &	gamma,
		TMatrixD &	delta
	)

protected

Computes the hessian matrix using the BFGS update algorithm.

from gamma (g_{(t+1)}-g_{(t)}) and delta (w_{(t+1)}-w_{(t)}). It returns true if such a direction could not be found (if gamma and delta are orthogonal).

Definition at line 2350 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

Double_t TMultiLayerPerceptron::GetCrossEntropy ( ) const

protected

Cross entropy error for a softmax output neuron, for a given event.

Definition at line 1092 of file TMultiLayerPerceptron.cxx.

Referenced by GetError().

Double_t TMultiLayerPerceptron::GetCrossEntropyBinary ( ) const

protected

Cross entropy error for sigmoid output neurons, for a given event.

Definition at line 1061 of file TMultiLayerPerceptron.cxx.

Referenced by GetError().

Double_t TMultiLayerPerceptron::GetDelta ( ) const

inline

Definition at line 110 of file TMultiLayerPerceptron.h.

void TMultiLayerPerceptron::GetEntry ( Int_t entry ) const

protected

Load an entry into the network.

Definition at line 709 of file TMultiLayerPerceptron.cxx.

Referenced by ComputeDEDw(), DrawResult(), TMLPAnalyzer::GatherInformations(), GetError(), MLP_Stochastic(), and Result().

Double_t TMultiLayerPerceptron::GetEpsilon ( ) const

inline

Definition at line 109 of file TMultiLayerPerceptron.h.

Double_t TMultiLayerPerceptron::GetError ( Int_t event ) const

Error on the output for a given event.

Definition at line 996 of file TMultiLayerPerceptron.cxx.

Referenced by GetError(), LineSearch(), and Train().

Double_t TMultiLayerPerceptron::GetError ( TMultiLayerPerceptron::EDataSet set ) const

Error on the whole dataset.

Definition at line 1025 of file TMultiLayerPerceptron.cxx.

Double_t TMultiLayerPerceptron::GetEta ( ) const

inline

Definition at line 108 of file TMultiLayerPerceptron.h.

Double_t TMultiLayerPerceptron::GetEtaDecay ( ) const

inline

Definition at line 111 of file TMultiLayerPerceptron.h.

TMultiLayerPerceptron::ELearningMethod TMultiLayerPerceptron::GetLearningMethod ( ) const

inline

Definition at line 112 of file TMultiLayerPerceptron.h.

Int_t TMultiLayerPerceptron::GetReset ( ) const

inline

Definition at line 114 of file TMultiLayerPerceptron.h.

TString TMultiLayerPerceptron::GetStructure ( ) const

inline

Definition at line 115 of file TMultiLayerPerceptron.h.

Referenced by TMLPAnalyzer::CheckNetwork(), TMLPAnalyzer::GetLayers(), TMLPAnalyzer::GetNeuronFormula(), and TMLPAnalyzer::GetNeurons().

Double_t TMultiLayerPerceptron::GetSumSquareError ( ) const

protected

Error on the output for a given event.

Definition at line 1048 of file TMultiLayerPerceptron.cxx.

Referenced by GetError().

Double_t TMultiLayerPerceptron::GetTau ( void ) const

inline

Definition at line 113 of file TMultiLayerPerceptron.h.

TNeuron::ENeuronType TMultiLayerPerceptron::GetType ( ) const

inline

Definition at line 116 of file TMultiLayerPerceptron.h.

Referenced by Export().

bool TMultiLayerPerceptron::LineSearch	(	Double_t *	direction,
		Double_t *	buffer
	)

protected

Search along the line defined by direction.

buffer is not used but is updated with the new dw so that it can be used by a later stochastic step. It returns true if the line search fails.

Definition at line 2221 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

Bool_t TMultiLayerPerceptron::LoadWeights ( Option_t * filename = "" )

Loads the weights from a text file conforming to the format defined by DumpWeights.

Definition at line 1607 of file TMultiLayerPerceptron.cxx.

Referenced by TMVA::MethodTMlpANN::ReadWeightsFromStream(), and TMVA::MethodTMlpANN::ReadWeightsFromXML().

void TMultiLayerPerceptron::MLP_Batch ( Double_t * buffer )

protected

One step for the batch (stochastic) method.

DEDw should have been updated before calling this.

Definition at line 2150 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

void TMultiLayerPerceptron::MLP_Line	(	Double_t *	origin,
		Double_t *	dir,
		Double_t	dist
	)

private

Sets the weights to a point along a line Weights are set to [origin + (dist * dir)].

Definition at line 2178 of file TMultiLayerPerceptron.cxx.

Referenced by LineSearch().

void TMultiLayerPerceptron::MLP_Stochastic ( Double_t * buffer )

protected

One step for the stochastic method buffer should contain the previous dw vector and will be updated.

Definition at line 2105 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

TMultiLayerPerceptron& TMultiLayerPerceptron::operator= ( const TMultiLayerPerceptron & )

private

void TMultiLayerPerceptron::Randomize ( ) const

Randomize the weights.

Definition at line 1189 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

Double_t TMultiLayerPerceptron::Result	(	Int_t	event,
		Int_t	index = `0`
	)		const

Computes the output for a given event.

Look at the output neuron designed by index.

Definition at line 983 of file TMultiLayerPerceptron.cxx.

Referenced by TMLPAnalyzer::DrawNetwork(), and DrawResult().

void TMultiLayerPerceptron::SetData ( TTree * data )

Set the data source.

Definition at line 546 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetDelta ( Double_t delta )

Sets Delta - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)

Definition at line 670 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetEpsilon ( Double_t eps )

Sets Epsilon - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)

Definition at line 660 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetEta ( Double_t eta )

Sets Eta - used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)

Definition at line 650 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetEtaDecay ( Double_t ed )

Sets EtaDecay - Eta *= EtaDecay at each epoch (look at the constructor for the complete description of learning methods and parameters)

Definition at line 680 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetEventWeight ( const char * branch )

Set the event weight.

Definition at line 562 of file TMultiLayerPerceptron.cxx.

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

void TMultiLayerPerceptron::SetGammaDelta	(	TMatrixD &	gamma,
		TMatrixD &	delta,
		Double_t *	buffer
	)

protected

Sets the gamma (g_{(t+1)}-g_{(t)}) and delta (w_{(t+1)}-w_{(t)}) vectors Gamma is computed here, so ComputeDEDw cannot have been called before, and delta is a direct translation of buffer into a TMatrixD.

Definition at line 2376 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

void TMultiLayerPerceptron::SetLearningMethod ( TMultiLayerPerceptron::ELearningMethod method )

Sets the learning method.

Available methods are: kStochastic, kBatch, kSteepestDescent, kRibierePolak, kFletcherReeves and kBFGS. (look at the constructor for the complete description of learning methods and parameters)

Definition at line 640 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetReset ( Int_t reset )

Sets number of epochs between two resets of the search direction to the steepest descent.

(look at the constructor for the complete description of learning methods and parameters)

Definition at line 701 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetTau ( Double_t tau )

Sets Tau - used in line search (look at the constructor for the complete description of learning methods and parameters)

Definition at line 690 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetTestDataSet ( TEventList * test )

Sets the Test dataset.

Those events will not be used for the minimization but for control

Definition at line 589 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetTestDataSet ( const char * test )

Sets the Test dataset.

Those events will not be used for the minimization but for control. Note that the tree must be already defined.

Definition at line 619 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetTrainingDataSet ( TEventList * train )

Sets the Training dataset.

Those events will be used for the minimization

Definition at line 578 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::SetTrainingDataSet ( const char * train )

Sets the Training dataset.

Those events will be used for the minimization. Note that the tree must be already defined.

Definition at line 601 of file TMultiLayerPerceptron.cxx.

void TMultiLayerPerceptron::Shuffle	(	Int_t *	index,
		Int_t	n
	)		const

private

Shuffle the Int_t index[n] in input.

Input: index: the array to shuffle n: the size of the array Output: index: the shuffled indexes This method is used for stochastic training

Definition at line 2086 of file TMultiLayerPerceptron.cxx.

Referenced by MLP_Stochastic().

void TMultiLayerPerceptron::SteepestDir ( Double_t * dir )

protected

Sets the search direction to steepest descent.

Definition at line 2200 of file TMultiLayerPerceptron.cxx.

Referenced by Train().

void TMultiLayerPerceptron::Train	(	Int_t	nEpoch,
		Option_t *	option = `"text"`,
		Double_t	minE = `0`
	)

Train the network.

nEpoch is the number of iterations. option can contain:

"text" (simple text output)
"graph" (evoluting graphical training curves)
"update=X" (step for the text/graph output update)
"+" will skip the randomisation and start from the previous values.
"current" (draw in the current canvas)
"minErrorTrain" (stop when NN error on the training sample gets below minE
"minErrorTest" (stop when NN error on the test sample gets below minE All combinations are available.

Definition at line 738 of file TMultiLayerPerceptron.cxx.