// @(#)root/mlp:$Id: TMultiLayerPerceptron.h 43815 2012-04-18 10:13:05Z moneta $ // 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 $ROOTSYS/README/CREDITS. * *************************************************************************/ #ifndef ROOT_TMultiLayerPerceptron #define ROOT_TMultiLayerPerceptron #ifndef ROOT_TObject #include "TObject.h" #endif #ifndef ROOT_TString #include "TString.h" #endif #ifndef ROOT_TObjArray #include "TObjArray.h" #endif #ifndef ROOT_TMatrixD #include "TMatrixD.h" #endif #ifndef ROOT_TNeuron #include "TNeuron.h" #endif class TTree; class TEventList; class TTreeFormula; class TTreeFormulaManager; //____________________________________________________________________ // // TMultiLayerPerceptron // // This class decribes a Neural network. // There are facilities to train the network and use the output. // // The input layer is made of inactive neurons (returning the // normalized input), hidden layers are made of sigmoids and output // neurons are linear. // // The basic input is a TTree and two (training and test) TEventLists. // For classification jobs, a branch (maybe in a TFriend) must contain // the expected output. // 6 learning methods are available: kStochastic, kBatch, // kSteepestDescent, kRibierePolak, kFletcherReeves and kBFGS. // // This implementation is *inspired* from the mlpfit package from // J.Schwindling et al. // //____________________________________________________________________ class TMultiLayerPerceptron : public TObject { friend class TMLPAnalyzer; public: enum ELearningMethod { kStochastic, kBatch, kSteepestDescent, kRibierePolak, kFletcherReeves, kBFGS }; enum EDataSet { kTraining, kTest }; TMultiLayerPerceptron(); TMultiLayerPerceptron(const char* layout, TTree* data = 0, const char* training = "Entry$%2==0", const char* test = "", TNeuron::ENeuronType type = TNeuron::kSigmoid, const char* extF = "", const char* extD = ""); TMultiLayerPerceptron(const char* layout, 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, 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, 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); inline Double_t GetEta() const { return fEta; } inline Double_t GetEpsilon() const { return fEpsilon; } inline Double_t GetDelta() const { return fDelta; } inline Double_t GetEtaDecay() const { return fEtaDecay; } inline Double_t GetTau() const { return fTau; } inline Int_t GetReset() const { return fReset; } inline TString GetStructure() const { return fStructure; } inline 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; virtual void Draw(Option_t *option=""); protected: 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; private: TMultiLayerPerceptron(const TMultiLayerPerceptron&); // Not implemented TMultiLayerPerceptron& operator=(const TMultiLayerPerceptron&); // Not implemented 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 BuildLastLayer(TString&, Int_t); void Shuffle(Int_t*, Int_t) const; void MLP_Line(Double_t*, Double_t*, Double_t); TTree* fData; //! pointer to the tree used as datasource Int_t fCurrentTree; //! index of the current tree in a chain Double_t fCurrentTreeWeight; //! weight of the current tree in a chain TObjArray fNetwork; // Collection of all the neurons in the network TObjArray fFirstLayer; // Collection of the input neurons; subset of fNetwork TObjArray fLastLayer; // Collection of the output neurons; subset of fNetwork TObjArray fSynapses; // Collection of all the synapses in the network TString fStructure; // String containing the network structure TString fWeight; // String containing the event weight TNeuron::ENeuronType fType; // Type of hidden neurons TNeuron::ENeuronType fOutType; // Type of output neurons TString fextF; // String containing the function name TString fextD; // String containing the derivative name TEventList *fTraining; //! EventList defining the events in the training dataset TEventList *fTest; //! EventList defining the events in the test dataset ELearningMethod fLearningMethod; //! The Learning Method TTreeFormula* fEventWeight; //! formula representing the event weight TTreeFormulaManager* fManager; //! TTreeFormulaManager for the weight and neurons Double_t fEta; //! Eta - used in stochastic minimisation - Default=0.1 Double_t fEpsilon; //! Epsilon - used in stochastic minimisation - Default=0. Double_t fDelta; //! Delta - used in stochastic minimisation - Default=0. Double_t fEtaDecay; //! EtaDecay - Eta *= EtaDecay at each epoch - Default=1. Double_t fTau; //! Tau - used in line search - Default=3. Double_t fLastAlpha; //! internal parameter used in line search Int_t fReset; //! number of epochs between two resets of the search direction to the steepest descent - Default=50 Bool_t fTrainingOwner; //! internal flag whether one has to delete fTraining or not Bool_t fTestOwner; //! internal flag whether one has to delete fTest or not ClassDef(TMultiLayerPerceptron, 4) // a Neural Network }; #endif