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 The SetNewEvent() method is there to inform buffered values are outdated.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:
| TNeuron(TNeuron::ENeuronType type = kSigmoid, const char* name = "", const char* title = "", const char* extF = "", const char* extD = "") | |
| 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 |
| static void | TObject::operator delete(void* ptr) |
| static void | TObject::operator delete(void* ptr, void* vp) |
| static void | TObject::operator delete[](void* ptr) |
| static 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) |
| virtual Int_t | TNamed::Sizeof() const |
| virtual void | Streamer(TBuffer& b) |
| void | StreamerNVirtual(TBuffer& 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 |
| 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 |
Data Members
public:
| enum ENeuronType { | kOff | |
| kLinear | ||
| kSigmoid | ||
| kTanh | ||
| kGauss | ||
| kSoftmax | ||
| kExternal | ||
| }; | ||
| enum TObject::EStatusBits { | kCanDelete | |
| kMustCleanup | ||
| kObjInCanvas | ||
| kIsReferenced | ||
| kHasUUID | ||
| kCannotPick | ||
| kNoContextMenu | ||
| kInvalidObject | ||
| }; | ||
| enum TObject::[unnamed] { | kIsOnHeap | |
| kNotDeleted | ||
| kZombie | ||
| kBitMask | ||
| kSingleKey | ||
| kOverwrite | ||
| kWriteDelete | ||
| }; |
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
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)
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* )
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 GetTarget() const
Computes the normalized target pattern for output neurons. Returns 0 for other neurons.
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.
TNeuron(TNeuron::ENeuronType type = kSigmoid, const char* name = "", const char* title = "", const char* extF = "", const char* extD = "")
{}ENeuronType GetType() const