TNeuron.h

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// @(#)root/mlp:$Id$
// Author: Christophe.Delaere@cern.ch   20/07/03
 
/*************************************************************************
 * Copyright (C) 1995-2003, Rene Brun and Fons Rademakers.               *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/
 
#ifndef ROOT_TNeuron
#define ROOT_TNeuron
 
#include "TNamed.h"
#include "TObjArray.h"
 
class TTreeFormula;
class TSynapse;
class TBranch;
class TTree;
class TFormula;
 
 
class TNeuron : public TNamed {
   friend class TSynapse;
 
 public:
   enum ENeuronType { kOff, kLinear, kSigmoid, kTanh, kGauss, kSoftmax, kExternal };
 
   TNeuron(ENeuronType type = kSigmoid,
           const char* name = "", const char* title = "",
           const char* extF = "", const char* extD  = "" );
   ~TNeuron() override {}
   inline TSynapse* GetPre(Int_t n) const { return (TSynapse*) fpre.At(n); }
   inline TSynapse* GetPost(Int_t n) const { return (TSynapse*) fpost.At(n); }
   inline TNeuron* GetInLayer(Int_t n) const { return (TNeuron*) flayer.At(n); }
   TTreeFormula* UseBranch(TTree*, const char*);
   Double_t GetInput() const;
   Double_t GetValue() const;
   Double_t GetDerivative() const;
   Double_t GetError() const;
   Double_t GetTarget() const;
   Double_t GetDeDw() const;
   Double_t GetBranch() const;
   ENeuronType GetType() const;
   void SetWeight(Double_t w);
   inline Double_t GetWeight() const { return fWeight; }
   void SetNormalisation(Double_t mean, Double_t RMS);
   inline const Double_t* GetNormalisation() const { return fNorm; }
   void SetNewEvent() const;
   void SetDEDw(Double_t in);
   inline Double_t GetDEDw() const { return fDEDw; }
   void ForceExternalValue(Double_t value);
   void AddInLayer(TNeuron*);
 
 protected:
   Double_t Sigmoid(Double_t x) const;
   Double_t DSigmoid(Double_t x) const;
   void AddPre(TSynapse*);
   void AddPost(TSynapse*);
 
 private:
   TNeuron(const TNeuron&); // Not implemented
   TNeuron& operator=(const TNeuron&); // Not implemented
 
   TObjArray fpre;        ///< pointers to the previous level in a network
   TObjArray fpost;       ///< pointers to the next level in a network
   TObjArray flayer;      ///< pointers to the current level in a network (neurons, not synapses)
   Double_t fWeight;      ///< weight used for computation
   Double_t fNorm[2];     ///< normalisation to mean=0, RMS=1.
   ENeuronType fType;     ///< neuron type
   TFormula* fExtF;       ///< function   (external mode)
   TFormula* fExtD;       ///< derivative (external mode)
   TTreeFormula* fFormula;///<! formula to be used for inputs and outputs
   Int_t fIndex;          ///<! index in the formula
   Bool_t fNewInput;      ///<! do we need to compute fInput again ?
   Double_t fInput;       ///<! buffer containing the last neuron input
   Bool_t fNewValue;      ///<! do we need to compute fValue again ?
   Double_t fValue;       ///<! buffer containing the last neuron output
   Bool_t fNewDeriv;      ///<! do we need to compute fDerivative again ?
   Double_t fDerivative;  ///<! buffer containing the last neuron derivative
   Bool_t fNewDeDw;       ///<! do we need to compute fDeDw again ?
   Double_t fDeDw;        ///<! buffer containing the last derivative of the error
   Double_t fDEDw;        ///<! buffer containing the sum over all examples of DeDw
 
   ClassDefOverride(TNeuron, 4)   // Neuron for MultiLayerPerceptrons
};
 
#endif