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class TMultiLayerPerceptron: public TObject


 TMultiLayerPerceptron

 This class decribes a Neural network.
 There are facilities to train the network and use the output.

 The input layer is made of inactive neurons (returning the
 normalized input), hidden layers are made of sigmoids and output
 neurons are linear.

 The basic input is a TTree and two (training and test) TEventLists.
 For classification jobs, a branch (maybe in a TFriend) must contain
 the expected output.
 6 learning methods are available: kStochastic, kBatch,
 kSteepestDescent, kRibierePolak, kFletcherReeves and kBFGS.

 This implementation is *inspired* from the mlpfit package from
 J.Schwindling et al.

Function Members (Methods)

public:

virtual	~TMultiLayerPerceptron()
void	TObject::AbstractMethod(const char* method) const
virtual void	TObject::AppendPad(Option_t* option = "")
virtual void	TObject::Browse(TBrowser* b)
static TClass*	Class()
virtual const char*	TObject::ClassName() const
virtual void	TObject::Clear(Option_t* = "")
virtual TObject*	TObject::Clone(const char* newname = "") const
virtual Int_t	TObject::Compare(const TObject* obj) const
void	ComputeDEDw() const
virtual void	TObject::Copy(TObject& object) const
virtual void	TObject::Delete(Option_t* option = "")MENU
virtual Int_t	TObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual void	Draw(Option_t* option = "")
virtual void	TObject::DrawClass() constMENU
virtual TObject*	TObject::DrawClone(Option_t* option = "") constMENU
void	DrawResult(Int_t index = 0, Option_t* option = "test") const
virtual void	TObject::Dump() constMENU
Bool_t	DumpWeights(Option_t* filename = "-") const
virtual void	TObject::Error(const char* method, const char* msgfmt) const
Double_t	Evaluate(Int_t index, Double_t* params) const
virtual void	TObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual void	TObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual void	TObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
void	Export(Option_t* filename = "NNfunction", Option_t* language = "C++") const
virtual void	TObject::Fatal(const char* method, const char* msgfmt) const
virtual TObject*	TObject::FindObject(const char* name) const
virtual TObject*	TObject::FindObject(const TObject* obj) const
Double_t	GetDelta() const
virtual Option_t*	TObject::GetDrawOption() const
static Long_t	TObject::GetDtorOnly()
Double_t	GetEpsilon() const
Double_t	GetError(Int_t event) const
Double_t	GetError(TMultiLayerPerceptron::EDataSet set) const
Double_t	GetEta() const
Double_t	GetEtaDecay() const
virtual const char*	TObject::GetIconName() const
TMultiLayerPerceptron::ELearningMethod	GetLearningMethod() const
virtual const char*	TObject::GetName() const
virtual char*	TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_t	TObject::GetObjectStat()
virtual Option_t*	TObject::GetOption() const
Int_t	GetReset() const
TString	GetStructure() const
Double_t	GetTau() const
virtual const char*	TObject::GetTitle() const
TNeuron::ENeuronType	GetType() const
virtual UInt_t	TObject::GetUniqueID() const
virtual Bool_t	TObject::HandleTimer(TTimer* timer)
virtual ULong_t	TObject::Hash() const
virtual void	TObject::Info(const char* method, const char* msgfmt) const
virtual Bool_t	TObject::InheritsFrom(const char* classname) const
virtual Bool_t	TObject::InheritsFrom(const TClass* cl) const
virtual void	TObject::Inspect() constMENU
void	TObject::InvertBit(UInt_t f)
virtual TClass*	IsA() const
virtual Bool_t	TObject::IsEqual(const TObject* obj) const
virtual Bool_t	TObject::IsFolder() const
Bool_t	TObject::IsOnHeap() const
virtual Bool_t	TObject::IsSortable() const
Bool_t	TObject::IsZombie() const
Bool_t	LoadWeights(Option_t* filename = "")
virtual void	TObject::ls(Option_t* option = "") const
void	TObject::MayNotUse(const char* method) const
virtual Bool_t	TObject::Notify()
void	TObject::Obsolete(const char* method, const char* asOfVers, const char* removedFromVers) const
void	TObject::operator delete(void* ptr)
void	TObject::operator delete(void* ptr, void* vp)
void	TObject::operator delete[](void* ptr)
void	TObject::operator delete[](void* ptr, void* vp)
void*	TObject::operator new(size_t sz)
void*	TObject::operator new(size_t sz, void* vp)
void*	TObject::operator new[](size_t sz)
void*	TObject::operator new[](size_t sz, void* vp)
virtual void	TObject::Paint(Option_t* option = "")
virtual void	TObject::Pop()
virtual void	TObject::Print(Option_t* option = "") const
void	Randomize() const
virtual Int_t	TObject::Read(const char* name)
virtual void	TObject::RecursiveRemove(TObject* obj)
void	TObject::ResetBit(UInt_t f)
Double_t	Result(Int_t event, Int_t index = 0) const
virtual void	TObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual void	TObject::SavePrimitive(ostream& out, Option_t* option = "")
void	TObject::SetBit(UInt_t f)
void	TObject::SetBit(UInt_t f, Bool_t set)
void	SetData(TTree*)
void	SetDelta(Double_t delta)
virtual void	TObject::SetDrawOption(Option_t* option = "")MENU
static void	TObject::SetDtorOnly(void* obj)
void	SetEpsilon(Double_t eps)
void	SetEta(Double_t eta)
void	SetEtaDecay(Double_t ed)
void	SetEventWeight(const char*)
void	SetLearningMethod(TMultiLayerPerceptron::ELearningMethod method)
static void	TObject::SetObjectStat(Bool_t stat)
void	SetReset(Int_t reset)
void	SetTau(Double_t tau)
void	SetTestDataSet(TEventList* test)
void	SetTestDataSet(const char* test)
void	SetTrainingDataSet(TEventList* train)
void	SetTrainingDataSet(const char* train)
virtual void	TObject::SetUniqueID(UInt_t uid)
virtual void	ShowMembers(TMemberInspector& insp) const
virtual void	Streamer(TBuffer&)
void	StreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b)
virtual void	TObject::SysError(const char* method, const char* msgfmt) const
Bool_t	TObject::TestBit(UInt_t f) const
Int_t	TObject::TestBits(UInt_t f) const
	TMultiLayerPerceptron()
	TMultiLayerPerceptron(const char* layout, TTree* data = 0, const char* training = "Entry$%2==0", const char* test = "", TNeuron::ENeuronType type = TNeuron::kSigmoid, const char* extF = "", const char* extD = "")
	TMultiLayerPerceptron(const char* layout, TTree* data, TEventList* training, TEventList* test, TNeuron::ENeuronType type = TNeuron::kSigmoid, const char* extF = "", const char* extD = "")
	TMultiLayerPerceptron(const char* layout, const char* weight, TTree* data = 0, const char* training = "Entry$%2==0", const char* test = "", TNeuron::ENeuronType type = TNeuron::kSigmoid, const char* extF = "", const char* extD = "")
	TMultiLayerPerceptron(const char* layout, const char* weight, TTree* data, TEventList* training, TEventList* test, TNeuron::ENeuronType type = TNeuron::kSigmoid, const char* extF = "", const char* extD = "")
void	Train(Int_t nEpoch, Option_t* option = "text", Double_t minE = 0)
virtual void	TObject::UseCurrentStyle()
virtual void	TObject::Warning(const char* method, const char* msgfmt) const
virtual Int_t	TObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_t	TObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const

protected:

void	AttachData()
void	BFGSDir(TMatrixD&, Double_t*)
void	BuildNetwork()
void	ConjugateGradientsDir(Double_t*, Double_t)
Double_t	DerivDir(Double_t*)
virtual void	TObject::DoError(int level, const char* location, const char* fmt, va_list va) const
bool	GetBFGSH(TMatrixD&, TMatrixD&, TMatrixD&)
Double_t	GetCrossEntropy() const
Double_t	GetCrossEntropyBinary() const
void	GetEntry(Int_t) const
Double_t	GetSumSquareError() const
Bool_t	LineSearch(Double_t, Double_t)
void	TObject::MakeZombie()
void	MLP_Batch(Double_t*)
void	MLP_Stochastic(Double_t*)
void	SetGammaDelta(TMatrixD&, TMatrixD&, Double_t*)
void	SteepestDir(Double_t*)

private:

void	BuildFirstLayer(TString&)
void	BuildHiddenLayers(TString&)
void	BuildLastLayer(TString&, Int_t)
void	BuildOneHiddenLayer(const TString& sNumNodes, Int_t& layer, Int_t& prevStart, Int_t& prevStop, Bool_t lastLayer)
void	ExpandStructure()
void	MLP_Line(Double_t, Double_t, Double_t)
TMultiLayerPerceptron&	operator=(const TMultiLayerPerceptron&)
void	Shuffle(Int_t*, Int_t) const
	TMultiLayerPerceptron(const TMultiLayerPerceptron&)

Data Members

public:

static TMultiLayerPerceptron::ELearningMethod	kBFGS
static TMultiLayerPerceptron::ELearningMethod	kBatch
static TObject::(anonymous)	TObject::kBitMask
static TObject::EStatusBits	TObject::kCanDelete
static TObject::EStatusBits	TObject::kCannotPick
static TMultiLayerPerceptron::ELearningMethod	kFletcherReeves
static TObject::EStatusBits	TObject::kHasUUID
static TObject::EStatusBits	TObject::kInvalidObject
static TObject::(anonymous)	TObject::kIsOnHeap
static TObject::EStatusBits	TObject::kIsReferenced
static TObject::EStatusBits	TObject::kMustCleanup
static TObject::EStatusBits	TObject::kNoContextMenu
static TObject::(anonymous)	TObject::kNotDeleted
static TObject::EStatusBits	TObject::kObjInCanvas
static TObject::(anonymous)	TObject::kOverwrite
static TMultiLayerPerceptron::ELearningMethod	kRibierePolak
static TObject::(anonymous)	TObject::kSingleKey
static TMultiLayerPerceptron::ELearningMethod	kSteepestDescent
static TMultiLayerPerceptron::ELearningMethod	kStochastic
static TMultiLayerPerceptron::EDataSet	kTest
static TMultiLayerPerceptron::EDataSet	kTraining
static TObject::(anonymous)	TObject::kWriteDelete
static TObject::(anonymous)	TObject::kZombie

private:

Int_t	fCurrentTree	! index of the current tree in a chain
Double_t	fCurrentTreeWeight	! weight of the current tree in a chain
TTree*	fData	! pointer to the tree used as datasource
Double_t	fDelta	! Delta - used in stochastic minimisation - Default=0.
Double_t	fEpsilon	! Epsilon - used in stochastic minimisation - Default=0.
Double_t	fEta	! Eta - used in stochastic minimisation - Default=0.1
Double_t	fEtaDecay	! EtaDecay - Eta *= EtaDecay at each epoch - Default=1.
TTreeFormula*	fEventWeight	! formula representing the event weight
TObjArray	fFirstLayer	Collection of the input neurons; subset of fNetwork
Double_t	fLastAlpha	! internal parameter used in line search
TObjArray	fLastLayer	Collection of the output neurons; subset of fNetwork
TMultiLayerPerceptron::ELearningMethod	fLearningMethod	! The Learning Method
TTreeFormulaManager*	fManager	! TTreeFormulaManager for the weight and neurons
TObjArray	fNetwork	Collection of all the neurons in the network
TNeuron::ENeuronType	fOutType	Type of output neurons
Int_t	fReset	! number of epochs between two resets of the search direction to the steepest descent - Default=50
TString	fStructure	String containing the network structure
TObjArray	fSynapses	Collection of all the synapses in the network
Double_t	fTau	! Tau - used in line search - Default=3.
TEventList*	fTest	! EventList defining the events in the test dataset
Bool_t	fTestOwner	! internal flag whether one has to delete fTest or not
TEventList*	fTraining	! EventList defining the events in the training dataset
Bool_t	fTrainingOwner	! internal flag whether one has to delete fTraining or not
TNeuron::ENeuronType	fType	Type of hidden neurons
TString	fWeight	String containing the event weight
TString	fextD	String containing the derivative name
TString	fextF	String containing the function name

Class Charts

Inheritance Chart:

TObject

←

TMultiLayerPerceptron

Function documentation

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 = "")

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

virtual ~TMultiLayerPerceptron()

void SetData(TTree* )

void SetTrainingDataSet(TEventList* train)

void SetTestDataSet(TEventList* test)

void SetTrainingDataSet(const char* train)

void SetTestDataSet(const char* test)

void SetLearningMethod(TMultiLayerPerceptron::ELearningMethod method)

void SetEventWeight(const char* )

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

Double_t Result(Int_t event, Int_t index = 0) const

Double_t GetError(Int_t event) const

Double_t GetError(TMultiLayerPerceptron::EDataSet set) const

void ComputeDEDw() const

void Randomize() const

void SetEta(Double_t eta)

void SetEpsilon(Double_t eps)

void SetDelta(Double_t delta)

void SetEtaDecay(Double_t ed)

void SetTau(Double_t tau)

void SetReset(Int_t reset)

Double_t GetEta() const

{ return fEta; }

Double_t GetEpsilon() const

{ return fEpsilon; }

Double_t GetDelta() const

{ return fDelta; }

Double_t GetEtaDecay() const

{ return fEtaDecay; }

TMultiLayerPerceptron::ELearningMethod GetLearningMethod() const

{ return fLearningMethod; }

Double_t GetTau() const

{ return fTau; }

Int_t GetReset() const

{ return fReset; }

TString GetStructure() const

{ return fStructure; }

TNeuron::ENeuronType GetType() const

{ return fType; }

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

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

Bool_t LoadWeights(Option_t* filename = "")

Double_t Evaluate(Int_t index, Double_t* params) const

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

void Draw(Option_t* option = "")

void AttachData()

void BuildNetwork()

void GetEntry(Int_t ) const

 it's a choice not to force learning function being const, even if possible

void MLP_Stochastic(Double_t* )

void MLP_Batch(Double_t* )

Bool_t LineSearch(Double_t* , Double_t* )

void SteepestDir(Double_t* )

void ConjugateGradientsDir(Double_t* , Double_t )

void SetGammaDelta(TMatrixD& , TMatrixD& , Double_t* )

bool GetBFGSH(TMatrixD& , TMatrixD& , TMatrixD& )

void BFGSDir(TMatrixD& , Double_t* )

Double_t DerivDir(Double_t* )

Double_t GetCrossEntropyBinary() const

Double_t GetCrossEntropy() const

Double_t GetSumSquareError() const

TMultiLayerPerceptron(const TMultiLayerPerceptron& )

TMultiLayerPerceptron& operator=(const TMultiLayerPerceptron& )

void ExpandStructure()

void BuildFirstLayer(TString& )

void BuildHiddenLayers(TString& )

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

void Shuffle(Int_t* , Int_t ) const

void MLP_Line(Double_t* , Double_t* , Double_t )