Definition at line 48 of file TMultiLayerPerceptron.h.

Public Types
enum	EDataSet { kTraining, kTest }

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

Public Member Functions
	TMultiLayerPerceptron ()
	Default constructor. More...

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

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

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...

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...

Double_t	GetDelta () const

Double_t	GetEpsilon () const

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...

Double_t	GetEta () const

Double_t	GetEtaDecay () const

TMultiLayerPerceptron::ELearningMethod	GetLearningMethod () const

Int_t	GetReset () const

TString	GetStructure () const

Double_t	GetTau () const

TNeuron::ENeuronType	GetType () const

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

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

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

void	SetData (TTree *)
	Set the data source. 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	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	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	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	SetEventWeight (const char *)
	Set the event weight. More...

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

void	SetReset (Int_t reset)
	Sets number of epochs between two resets of the search direction to the steepest descent. 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	SetTestDataSet (TEventList *test)
	Sets the Test dataset. More...

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

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

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

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

Protected Member Functions
void	AttachData ()
	Connects the TTree to Neurons in input and output layers. 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...

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

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

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

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

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

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

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

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

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

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

Private Member Functions
	TMultiLayerPerceptron (const TMultiLayerPerceptron &)

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	BuildLastLayer (TString &, Int_t)
	Builds the output layer Neurons are linear combinations of input, by defaul. 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	ExpandStructure ()
	Expand the structure of the first layer. 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...

TMultiLayerPerceptron &	operator= (const TMultiLayerPerceptron &)

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

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

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

TTree *	fData

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

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

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

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

TTreeFormula *	fEventWeight
	The Learning Method. More...

TString	fextD

TString	fextF

TObjArray	fFirstLayer

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

TObjArray	fLastLayer

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

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

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

TNeuron::ENeuronType	fOutType

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

TString	fStructure

TObjArray	fSynapses

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

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

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

TEventList *	fTraining

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

TNeuron::ENeuronType	fType

TString	fWeight

Friends
class	TMLPAnalyzer

#include <TMultiLayerPerceptron.h>

Inheritance diagram for TMultiLayerPerceptron:

[legend]

Member Enumeration Documentation

◆ EDataSet

enum TMultiLayerPerceptron::EDataSet

Enumerator
kTraining
kTest

Definition at line 54 of file TMultiLayerPerceptron.h.

◆ ELearningMethod

enum TMultiLayerPerceptron::ELearningMethod

Enumerator
kStochastic
kBatch
kSteepestDescent
kRibierePolak
kFletcherReeves
kBFGS

Definition at line 52 of file TMultiLayerPerceptron.h.

Constructor & Destructor Documentation

◆ TMultiLayerPerceptron() [1/6]

TMultiLayerPerceptron::TMultiLayerPerceptron ( )

Default constructor.

Definition at line 252 of file TMultiLayerPerceptron.cxx.

◆ TMultiLayerPerceptron() [2/6]

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() [3/6]

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() [4/6]

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() [5/6]

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::~TMultiLayerPerceptron ( )

virtual

Destructor.

Definition at line 537 of file TMultiLayerPerceptron.cxx.

◆ TMultiLayerPerceptron() [6/6]

TMultiLayerPerceptron::TMultiLayerPerceptron ( const TMultiLayerPerceptron & )

private

Member Function Documentation

◆ AttachData()

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.

◆ BFGSDir()

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.

◆ BuildFirstLayer()

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.

◆ BuildHiddenLayers()

void TMultiLayerPerceptron::BuildHiddenLayers ( TString & hidden )

private

Builds hidden layers.

Definition at line 1369 of file TMultiLayerPerceptron.cxx.

◆ BuildLastLayer()

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.

◆ BuildNetwork()

void TMultiLayerPerceptron::BuildNetwork ( )

protected

Instanciates the network from the description.

Definition at line 1320 of file TMultiLayerPerceptron.cxx.

◆ BuildOneHiddenLayer()

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.

◆ ComputeDEDw()

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.

◆ ConjugateGradientsDir()

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.

◆ DerivDir()

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.

◆ Draw()

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.

Definition at line 2469 of file TMultiLayerPerceptron.cxx.

◆ DrawResult()

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.

◆ DumpWeights()

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.

◆ Evaluate()

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.

◆ ExpandStructure()

void TMultiLayerPerceptron::ExpandStructure ( )

private

Expand the structure of the first layer.

Definition at line 1274 of file TMultiLayerPerceptron.cxx.

◆ Export()

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.

◆ GetBFGSH()

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.

◆ GetCrossEntropy()

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.

◆ GetCrossEntropyBinary()

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.

◆ GetDelta()

Double_t TMultiLayerPerceptron::GetDelta ( ) const

inline

Definition at line 100 of file TMultiLayerPerceptron.h.

◆ GetEntry()

void TMultiLayerPerceptron::GetEntry ( Int_t entry ) const

protected

Load an entry into the network.

Definition at line 709 of file TMultiLayerPerceptron.cxx.

◆ GetEpsilon()

Double_t TMultiLayerPerceptron::GetEpsilon ( ) const

inline

Definition at line 99 of file TMultiLayerPerceptron.h.

◆ GetError() [1/2]

Double_t TMultiLayerPerceptron::GetError ( Int_t event ) const

Error on the output for a given event.

Definition at line 996 of file TMultiLayerPerceptron.cxx.

◆ GetError() [2/2]

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

Error on the whole dataset.

Definition at line 1025 of file TMultiLayerPerceptron.cxx.

◆ GetEta()

Double_t TMultiLayerPerceptron::GetEta ( ) const

inline

Definition at line 98 of file TMultiLayerPerceptron.h.

◆ GetEtaDecay()

Double_t TMultiLayerPerceptron::GetEtaDecay ( ) const

inline

Definition at line 101 of file TMultiLayerPerceptron.h.

◆ GetLearningMethod()

TMultiLayerPerceptron::ELearningMethod TMultiLayerPerceptron::GetLearningMethod ( ) const

inline

Definition at line 102 of file TMultiLayerPerceptron.h.

◆ GetReset()

Int_t TMultiLayerPerceptron::GetReset ( ) const

inline

Definition at line 104 of file TMultiLayerPerceptron.h.

◆ GetStructure()

TString TMultiLayerPerceptron::GetStructure ( ) const

inline

Definition at line 105 of file TMultiLayerPerceptron.h.

◆ GetSumSquareError()

Double_t TMultiLayerPerceptron::GetSumSquareError ( ) const

protected

Error on the output for a given event.

Definition at line 1048 of file TMultiLayerPerceptron.cxx.

◆ GetTau()

Double_t TMultiLayerPerceptron::GetTau ( ) const

inline

Definition at line 103 of file TMultiLayerPerceptron.h.

◆ GetType()

TNeuron::ENeuronType TMultiLayerPerceptron::GetType ( ) const

inline

Definition at line 106 of file TMultiLayerPerceptron.h.

◆ LineSearch()

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.

◆ LoadWeights()

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.

◆ MLP_Batch()

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.

◆ MLP_Line()

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.

◆ MLP_Stochastic()

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.

◆ operator=()

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

private

◆ Randomize()

void TMultiLayerPerceptron::Randomize ( ) const

Randomize the weights.

Definition at line 1189 of file TMultiLayerPerceptron.cxx.

◆ Result()

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.

◆ SetData()

void TMultiLayerPerceptron::SetData ( TTree * data )

Set the data source.

Definition at line 546 of file TMultiLayerPerceptron.cxx.

◆ SetDelta()

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.

◆ SetEpsilon()

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.

◆ SetEta()

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.

◆ SetEtaDecay()

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.

◆ SetEventWeight()

void TMultiLayerPerceptron::SetEventWeight ( const char * branch )

Set the event weight.

Definition at line 562 of file TMultiLayerPerceptron.cxx.

◆ SetGammaDelta()

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.

◆ SetLearningMethod()

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.

◆ SetReset()

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.

◆ SetTau()

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.

◆ SetTestDataSet() [1/2]

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.

◆ SetTestDataSet() [2/2]

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.

◆ SetTrainingDataSet() [1/2]

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.

◆ SetTrainingDataSet() [2/2]

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.

◆ Shuffle()

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.

◆ SteepestDir()

void TMultiLayerPerceptron::SteepestDir ( Double_t * dir )

protected

Sets the search direction to steepest descent.

Definition at line 2200 of file TMultiLayerPerceptron.cxx.

◆ 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.

Friends And Related Function Documentation

◆ TMLPAnalyzer

friend class TMLPAnalyzer

friend

Definition at line 49 of file TMultiLayerPerceptron.h.

Member Data Documentation

◆ fCurrentTree

Int_t TMultiLayerPerceptron::fCurrentTree

private

pointer to the tree used as datasource

Definition at line 146 of file TMultiLayerPerceptron.h.

◆ fCurrentTreeWeight

Double_t TMultiLayerPerceptron::fCurrentTreeWeight

private

index of the current tree in a chain

Definition at line 147 of file TMultiLayerPerceptron.h.

◆ fData

TTree* TMultiLayerPerceptron::fData

private

Definition at line 145 of file TMultiLayerPerceptron.h.

◆ fDelta

Double_t TMultiLayerPerceptron::fDelta

private

Epsilon - used in stochastic minimisation - Default=0.

Definition at line 165 of file TMultiLayerPerceptron.h.

◆ fEpsilon

Double_t TMultiLayerPerceptron::fEpsilon

private

Eta - used in stochastic minimisation - Default=0.1.

Definition at line 164 of file TMultiLayerPerceptron.h.

◆ fEta

Double_t TMultiLayerPerceptron::fEta

private

TTreeFormulaManager for the weight and neurons.

Definition at line 163 of file TMultiLayerPerceptron.h.

◆ fEtaDecay

Double_t TMultiLayerPerceptron::fEtaDecay

private

Delta - used in stochastic minimisation - Default=0.

Definition at line 166 of file TMultiLayerPerceptron.h.

◆ fEventWeight

TTreeFormula* TMultiLayerPerceptron::fEventWeight

private

The Learning Method.

Definition at line 161 of file TMultiLayerPerceptron.h.

◆ fextD

TString TMultiLayerPerceptron::fextD

private

Definition at line 157 of file TMultiLayerPerceptron.h.

◆ fextF

TString TMultiLayerPerceptron::fextF

private

Definition at line 156 of file TMultiLayerPerceptron.h.

◆ fFirstLayer

TObjArray TMultiLayerPerceptron::fFirstLayer

private

Definition at line 149 of file TMultiLayerPerceptron.h.

◆ fLastAlpha

Double_t TMultiLayerPerceptron::fLastAlpha

private

Tau - used in line search - Default=3.

Definition at line 168 of file TMultiLayerPerceptron.h.

◆ fLastLayer

TObjArray TMultiLayerPerceptron::fLastLayer

private

Definition at line 150 of file TMultiLayerPerceptron.h.

◆ fLearningMethod

ELearningMethod TMultiLayerPerceptron::fLearningMethod

private

EventList defining the events in the test dataset.

Definition at line 160 of file TMultiLayerPerceptron.h.

◆ fManager

TTreeFormulaManager* TMultiLayerPerceptron::fManager

private

formula representing the event weight

Definition at line 162 of file TMultiLayerPerceptron.h.

◆ fNetwork

TObjArray TMultiLayerPerceptron::fNetwork

private

weight of the current tree in a chain

Definition at line 148 of file TMultiLayerPerceptron.h.

◆ fOutType

TNeuron::ENeuronType TMultiLayerPerceptron::fOutType

private

Definition at line 155 of file TMultiLayerPerceptron.h.

◆ fReset

Int_t TMultiLayerPerceptron::fReset

private

internal parameter used in line search

Definition at line 169 of file TMultiLayerPerceptron.h.

◆ fStructure

TString TMultiLayerPerceptron::fStructure

private

Definition at line 152 of file TMultiLayerPerceptron.h.

◆ fSynapses

TObjArray TMultiLayerPerceptron::fSynapses

private

Definition at line 151 of file TMultiLayerPerceptron.h.

◆ fTau

Double_t TMultiLayerPerceptron::fTau

private

EtaDecay - Eta *= EtaDecay at each epoch - Default=1.

Definition at line 167 of file TMultiLayerPerceptron.h.

◆ fTest

TEventList* TMultiLayerPerceptron::fTest

private

EventList defining the events in the training dataset.

Definition at line 159 of file TMultiLayerPerceptron.h.

◆ fTestOwner

Bool_t TMultiLayerPerceptron::fTestOwner

private

internal flag whether one has to delete fTraining or not

Definition at line 171 of file TMultiLayerPerceptron.h.

◆ fTraining

TEventList* TMultiLayerPerceptron::fTraining

private

Definition at line 158 of file TMultiLayerPerceptron.h.

◆ fTrainingOwner

Bool_t TMultiLayerPerceptron::fTrainingOwner

private

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

Definition at line 170 of file TMultiLayerPerceptron.h.

◆ fType

TNeuron::ENeuronType TMultiLayerPerceptron::fType

private

Definition at line 154 of file TMultiLayerPerceptron.h.

◆ fWeight

TString TMultiLayerPerceptron::fWeight

private

Definition at line 153 of file TMultiLayerPerceptron.h.

Libraries for TMultiLayerPerceptron:

[legend]

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

math/mlp/inc/TMultiLayerPerceptron.h
math/mlp/src/TMultiLayerPerceptron.cxx

Public Types

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Friends

Member Enumeration Documentation

◆ EDataSet

◆ ELearningMethod

Constructor & Destructor Documentation

◆ TMultiLayerPerceptron() [1/6]

◆ TMultiLayerPerceptron() [2/6]

◆ TMultiLayerPerceptron() [3/6]

◆ TMultiLayerPerceptron() [4/6]

◆ TMultiLayerPerceptron() [5/6]

◆ ~TMultiLayerPerceptron()

◆ TMultiLayerPerceptron() [6/6]

Member Function Documentation

◆ AttachData()

◆ BFGSDir()

◆ BuildFirstLayer()

◆ BuildHiddenLayers()

◆ BuildLastLayer()

◆ BuildNetwork()

◆ BuildOneHiddenLayer()

◆ ComputeDEDw()

◆ ConjugateGradientsDir()

◆ DerivDir()

◆ Draw()

◆ DrawResult()

◆ DumpWeights()

◆ Evaluate()

◆ ExpandStructure()

◆ Export()

◆ GetBFGSH()

◆ GetCrossEntropy()

◆ GetCrossEntropyBinary()

◆ GetDelta()

◆ GetEntry()

◆ GetEpsilon()

◆ GetError() [1/2]

◆ GetError() [2/2]

◆ GetEta()

◆ GetEtaDecay()

◆ GetLearningMethod()

◆ GetReset()

◆ GetStructure()

◆ GetSumSquareError()

◆ GetTau()

◆ GetType()

◆ LineSearch()

◆ LoadWeights()

◆ MLP_Batch()

◆ MLP_Line()

◆ MLP_Stochastic()

◆ operator=()

◆ Randomize()

◆ Result()

◆ SetData()

◆ SetDelta()

◆ SetEpsilon()

◆ SetEta()

◆ SetEtaDecay()

◆ SetEventWeight()

◆ SetGammaDelta()

◆ SetLearningMethod()

◆ SetReset()

◆ SetTau()

◆ SetTestDataSet() [1/2]

◆ SetTestDataSet() [2/2]

◆ SetTrainingDataSet() [1/2]

◆ SetTrainingDataSet() [2/2]

◆ Shuffle()

◆ SteepestDir()

◆ Train()

Friends And Related Function Documentation

◆ TMLPAnalyzer

Member Data Documentation

◆ fCurrentTree

◆ fCurrentTreeWeight