/usr/include/root/TMultiLayerPerceptron.h is in libroot-math-mlp-dev 5.34.19+dfsg-1.2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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// 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
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