Quick Links: ROOT Homepage Class Index Class Hierarchy

Source: header file source file viewVC header viewVC source

Sections: class description function members data members class charts

class TNeuron: public TNamed


 TNeuron

 This class decribes an elementary neuron, which is the basic
 element for a Neural Network.
 A network is built connecting neurons by synapses.
 There are different types of neurons: linear (a+bx),
 sigmoid (1/(1+exp(-x)), tanh or gaussian.
 An external function can also be used, together with its derivative.
 In a Multi Layer Perceptron, the input layer is made of
 inactive neurons (returning the normalized input) and output neurons
 are linear. Hidden neurons may be anything, the default being sigmoids.

 This implementation contains several methods to compute the value,
 the derivative, the DeDw, ...
 Values are stored in local buffers. The SetNewEvent() method is
 there to inform buffered values are outdated.

Function Members (Methods)

public:

virtual	~TNeuron()
void	TObject::AbstractMethod(const char* method) const
void	AddInLayer(TNeuron*)
virtual void	TObject::AppendPad(Option_t* option = "")
virtual void	TObject::Browse(TBrowser* b)
static TClass*	Class()
virtual const char*	TObject::ClassName() const
virtual void	TNamed::Clear(Option_t* option = "")
virtual TObject*	TNamed::Clone(const char* newname = "") const
virtual Int_t	TNamed::Compare(const TObject* obj) const
virtual void	TNamed::Copy(TObject& named) const
virtual void	TObject::Delete(Option_t* option = "")MENU
virtual Int_t	TObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual void	TObject::Draw(Option_t* option = "")
virtual void	TObject::DrawClass() constMENU
virtual TObject*	TObject::DrawClone(Option_t* option = "") constMENU
virtual void	TObject::Dump() constMENU
virtual void	TObject::Error(const char* method, const char* msgfmt) 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)
virtual void	TObject::Fatal(const char* method, const char* msgfmt) const
virtual void	TNamed::FillBuffer(char*& buffer)
virtual TObject*	TObject::FindObject(const char* name) const
virtual TObject*	TObject::FindObject(const TObject* obj) const
void	ForceExternalValue(Double_t value)
Double_t	GetBranch() const
Double_t	GetDeDw() const
Double_t	GetDEDw() const
Double_t	GetDerivative() const
virtual Option_t*	TObject::GetDrawOption() const
static Long_t	TObject::GetDtorOnly()
Double_t	GetError() const
virtual const char*	TObject::GetIconName() const
TNeuron*	GetInLayer(Int_t n) const
Double_t	GetInput() const
virtual const char*	TNamed::GetName() const
const Double_t*	GetNormalisation() const
virtual char*	TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_t	TObject::GetObjectStat()
virtual Option_t*	TObject::GetOption() const
TSynapse*	GetPost(Int_t n) const
TSynapse*	GetPre(Int_t n) const
Double_t	GetTarget() const
virtual const char*	TNamed::GetTitle() const
TNeuron::ENeuronType	GetType() const
virtual UInt_t	TObject::GetUniqueID() const
Double_t	GetValue() const
Double_t	GetWeight() const
virtual Bool_t	TObject::HandleTimer(TTimer* timer)
virtual ULong_t	TNamed::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	TNamed::IsSortable() const
Bool_t	TObject::IsZombie() const
virtual void	TNamed::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	TNamed::Print(Option_t* option = "") const
virtual Int_t	TObject::Read(const char* name)
virtual void	TObject::RecursiveRemove(TObject* obj)
void	TObject::ResetBit(UInt_t f)
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	SetDEDw(Double_t in)
virtual void	TObject::SetDrawOption(Option_t* option = "")MENU
static void	TObject::SetDtorOnly(void* obj)
virtual void	TNamed::SetName(const char* name)MENU
virtual void	TNamed::SetNameTitle(const char* name, const char* title)
void	SetNewEvent() const
void	SetNormalisation(Double_t mean, Double_t RMS)
static void	TObject::SetObjectStat(Bool_t stat)
virtual void	TNamed::SetTitle(const char* title = "")MENU
virtual void	TObject::SetUniqueID(UInt_t uid)
void	SetWeight(Double_t w)
virtual void	ShowMembers(TMemberInspector& insp) const
virtual Int_t	TNamed::Sizeof() 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
	TNeuron(TNeuron::ENeuronType type = kSigmoid, const char* name = "", const char* title = "", const char* extF = "", const char* extD = "")
TTreeFormula*	UseBranch(TTree, const char)
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	AddPost(TSynapse*)
void	AddPre(TSynapse*)
virtual void	TObject::DoError(int level, const char* location, const char* fmt, va_list va) const
Double_t	DSigmoid(Double_t x) const
void	TObject::MakeZombie()
Double_t	Sigmoid(Double_t x) const

private:

TNeuron&	operator=(const TNeuron&)
	TNeuron(const TNeuron&)

Data Members

public:

static TObject::<anonymous>	TObject::kBitMask
static TObject::EStatusBits	TObject::kCanDelete
static TObject::EStatusBits	TObject::kCannotPick
static TNeuron::ENeuronType	kExternal
static TNeuron::ENeuronType	kGauss
static TObject::EStatusBits	TObject::kHasUUID
static TObject::EStatusBits	TObject::kInvalidObject
static TObject::<anonymous>	TObject::kIsOnHeap
static TObject::EStatusBits	TObject::kIsReferenced
static TNeuron::ENeuronType	kLinear
static TObject::EStatusBits	TObject::kMustCleanup
static TObject::EStatusBits	TObject::kNoContextMenu
static TObject::<anonymous>	TObject::kNotDeleted
static TObject::EStatusBits	TObject::kObjInCanvas
static TNeuron::ENeuronType	kOff
static TObject::<anonymous>	TObject::kOverwrite
static TNeuron::ENeuronType	kSigmoid
static TObject::<anonymous>	TObject::kSingleKey
static TNeuron::ENeuronType	kSoftmax
static TNeuron::ENeuronType	kTanh
static TObject::<anonymous>	TObject::kWriteDelete
static TObject::<anonymous>	TObject::kZombie

protected:

TString	TNamed::fName	object identifier
TString	TNamed::fTitle	object title

private:

Double_t	fDEDw	! buffer containing the sum over all examples of DeDw
Double_t	fDeDw	! buffer containing the last derivative of the error
Double_t	fDerivative	! buffer containing the last neuron derivative
TFormula*	fExtD	derivative (external mode)
TFormula*	fExtF	function (external mode)
TTreeFormula*	fFormula	! formula to be used for inputs and outputs
Int_t	fIndex	! index in the formula
Double_t	fInput	! buffer containing the last neuron input
Bool_t	fNewDeDw	! do we need to compute fDeDw again ?
Bool_t	fNewDeriv	! do we need to compute fDerivative again ?
Bool_t	fNewInput	! do we need to compute fInput again ?
Bool_t	fNewValue	! do we need to compute fValue again ?
Double_t	fNorm[2]	normalisation to mean=0, RMS=1.
TNeuron::ENeuronType	fType	neuron type
Double_t	fValue	! buffer containing the last neuron output
Double_t	fWeight	weight used for computation
TObjArray	flayer	pointers to the current level in a network (neurons, not synapses)
TObjArray	fpost	pointers to the next level in a network
TObjArray	fpre	pointers to the previous level in a network

Class Charts

Inheritance Inherited Members Includes Libraries

Function documentation

TNeuron(TNeuron::ENeuronType type = kSigmoid, const char* name = "", const char* title = "", const char* extF = "", const char* extD = "")

 Usual constructor

Double_t Sigmoid(Double_t x) const

 The Sigmoid.
 Fast computation of the values of the sigmoid function.
 Uses values of the function up  to the seventh order
 tabulated at 700 points.
 Values were computed in long double precision (16 bytes,
 precision to about 37 digits) on a hp computer.
 Some values were checked with Mathematica.
 Result should be correct to ~ 15 digits (about double
 precision)

 From the mlpfit package (J.Schwindling   20-Jul-1999)

Double_t DSigmoid(Double_t x) const

 The Derivative of the Sigmoid.

void AddPre(TSynapse* )

 Adds a synapse to the neuron as an input
 This method is used by the TSynapse while
 connecting two neurons.

void AddPost(TSynapse* )

 Adds a synapse to the neuron as an output
 This method is used by the TSynapse while
 connecting two neurons.

void AddInLayer(TNeuron* )

 Tells a neuron which neurons form its layer (including itself).
 This is needed for self-normalizing functions, like Softmax.

TTreeFormula* UseBranch(TTree* , const char* )

 Sets a formula that can be used to make the neuron an input.
 The formula is automatically normalized to mean=0, RMS=1.
 This normalisation is used by GetValue() (input neurons)
 and GetError() (output neurons)

Double_t GetBranch() const

 Returns the formula value.

Double_t GetInput() const

 Returns neuron input

Double_t GetValue() const

 Computes the output using the appropriate function and all
 the weighted inputs, or uses the branch as input.
 In that case, the branch normalisation is also used.

Double_t GetDerivative() const

 computes the derivative for the appropriate function
 at the working point

Double_t GetError() const

 Computes the error for output neurons.
 Returns 0 for other neurons.

Double_t GetTarget() const

 Computes the normalized target pattern for output neurons.
 Returns 0 for other neurons.

Double_t GetDeDw() const

 Computes the derivative of the error wrt the neuron weight.

void ForceExternalValue(Double_t value)

 Uses the branch type to force an external value.

void SetNormalisation(Double_t mean, Double_t RMS)

 Sets the normalization variables.
 Any input neuron will return (branch-mean)/RMS.
 When UseBranch is called, mean and RMS are automatically set
 to the actual branch mean and RMS.

void SetWeight(Double_t w)

 Sets the neuron weight to w.
 The neuron weight corresponds to the bias in the
 linear combination of the inputs.

void SetNewEvent() const

 Inform the neuron that inputs of the network have changed,
 so that the buffered values have to be recomputed.

void SetDEDw(Double_t in)

 Sets the derivative of the total error wrt the neuron weight.

TNeuron(TNeuron::ENeuronType type = kSigmoid, const char* name = "", const char* title = "", const char* extF = "", const char* extD = "")

{}

TSynapse* GetPre(Int_t n) const

{ return (TSynapse*) fpre.At(n); }

TSynapse* GetPost(Int_t n) const

{ return (TSynapse*) fpost.At(n); }

TNeuron* GetInLayer(Int_t n) const

{ return (TNeuron*) flayer.At(n); }

ENeuronType GetType() const

Double_t GetWeight() const

{ return fWeight; }

const Double_t* GetNormalisation() const

{ return fNorm; }

Double_t GetDEDw() const

{ return fDEDw; }

TNeuron& operator=(const TNeuron& )