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TMultiLayerPerceptron.h
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1// @(#)root/mlp:$Id$
2// Author: Christophe.Delaere@cern.ch 20/07/03
3
4/*************************************************************************
5 * Copyright (C) 1995-2003, Rene Brun and Fons Rademakers. *
6 * All rights reserved. *
7 * *
8 * For the licensing terms see $ROOTSYS/LICENSE. *
9 * For the list of contributors see $ROOTSYS/README/CREDITS. *
10 *************************************************************************/
11
12#ifndef ROOT_TMultiLayerPerceptron
13#define ROOT_TMultiLayerPerceptron
14
15#include "TObject.h"
16#include "TString.h"
17#include "TObjArray.h"
18#include "TMatrixD.h"
19#include "TNeuron.h"
20
21class TTree;
22class TEventList;
23class TTreeFormula;
25
26//____________________________________________________________________
27//
28// TMultiLayerPerceptron
29//
30// This class decribes a Neural network.
31// There are facilities to train the network and use the output.
32//
33// The input layer is made of inactive neurons (returning the
34// normalized input), hidden layers are made of sigmoids and output
35// neurons are linear.
36//
37// The basic input is a TTree and two (training and test) TEventLists.
38// For classification jobs, a branch (maybe in a TFriend) must contain
39// the expected output.
40// 6 learning methods are available: kStochastic, kBatch,
41// kSteepestDescent, kRibierePolak, kFletcherReeves and kBFGS.
42//
43// This implementation is *inspired* from the mlpfit package from
44// J.Schwindling et al.
45//
46//____________________________________________________________________
47
49 friend class TMLPAnalyzer;
50
51 public:
56 TMultiLayerPerceptron(const char* layout, TTree* data = 0,
57 const char* training = "Entry$%2==0",
58 const char* test = "",
60 const char* extF = "", const char* extD = "");
61 TMultiLayerPerceptron(const char* layout,
62 const char* weight, TTree* data = 0,
63 const char* training = "Entry$%2==0",
64 const char* test = "",
66 const char* extF = "", const char* extD = "");
67 TMultiLayerPerceptron(const char* layout, TTree* data,
68 TEventList* training,
71 const char* extF = "", const char* extD = "");
72 TMultiLayerPerceptron(const char* layout,
73 const char* weight, TTree* data,
74 TEventList* training,
77 const char* extF = "", const char* extD = "");
78 virtual ~TMultiLayerPerceptron();
79 void SetData(TTree*);
80 void SetTrainingDataSet(TEventList* train);
82 void SetTrainingDataSet(const char* train);
83 void SetTestDataSet(const char* test);
85 void SetEventWeight(const char*);
86 void Train(Int_t nEpoch, Option_t* option = "text", Double_t minE=0);
87 Double_t Result(Int_t event, Int_t index = 0) const;
88 Double_t GetError(Int_t event) const;
90 void ComputeDEDw() const;
91 void Randomize() const;
92 void SetEta(Double_t eta);
93 void SetEpsilon(Double_t eps);
94 void SetDelta(Double_t delta);
95 void SetEtaDecay(Double_t ed);
96 void SetTau(Double_t tau);
97 void SetReset(Int_t reset);
98 inline Double_t GetEta() const { return fEta; }
99 inline Double_t GetEpsilon() const { return fEpsilon; }
100 inline Double_t GetDelta() const { return fDelta; }
101 inline Double_t GetEtaDecay() const { return fEtaDecay; }
103 inline Double_t GetTau() const { return fTau; }
104 inline Int_t GetReset() const { return fReset; }
105 inline TString GetStructure() const { return fStructure; }
106 inline TNeuron::ENeuronType GetType() const { return fType; }
107 void DrawResult(Int_t index = 0, Option_t* option = "test") const;
108 Bool_t DumpWeights(Option_t* filename = "-") const;
109 Bool_t LoadWeights(Option_t* filename = "");
110 Double_t Evaluate(Int_t index, Double_t* params) const;
111 void Export(Option_t* filename = "NNfunction", Option_t* language = "C++") const;
112 virtual void Draw(Option_t *option="");
113
114 protected:
115 void AttachData();
116 void BuildNetwork();
117 void GetEntry(Int_t) const;
118 // it's a choice not to force learning function being const, even if possible
120 void MLP_Batch(Double_t*);
122 void SteepestDir(Double_t*);
126 void BFGSDir(TMatrixD&, Double_t*);
131
132 private:
135 void ExpandStructure();
138 void BuildOneHiddenLayer(const TString& sNumNodes, Int_t& layer,
139 Int_t& prevStart, Int_t& prevStop,
140 Bool_t lastLayer);
142 void Shuffle(Int_t*, Int_t) const;
144
145 TTree* fData; //! pointer to the tree used as datasource
146 Int_t fCurrentTree; //! index of the current tree in a chain
147 Double_t fCurrentTreeWeight; //! weight of the current tree in a chain
148 TObjArray fNetwork; // Collection of all the neurons in the network
149 TObjArray fFirstLayer; // Collection of the input neurons; subset of fNetwork
150 TObjArray fLastLayer; // Collection of the output neurons; subset of fNetwork
151 TObjArray fSynapses; // Collection of all the synapses in the network
152 TString fStructure; // String containing the network structure
153 TString fWeight; // String containing the event weight
154 TNeuron::ENeuronType fType; // Type of hidden neurons
155 TNeuron::ENeuronType fOutType; // Type of output neurons
156 TString fextF; // String containing the function name
157 TString fextD; // String containing the derivative name
158 TEventList *fTraining; //! EventList defining the events in the training dataset
159 TEventList *fTest; //! EventList defining the events in the test dataset
160 ELearningMethod fLearningMethod; //! The Learning Method
161 TTreeFormula* fEventWeight; //! formula representing the event weight
162 TTreeFormulaManager* fManager; //! TTreeFormulaManager for the weight and neurons
163 Double_t fEta; //! Eta - used in stochastic minimisation - Default=0.1
164 Double_t fEpsilon; //! Epsilon - used in stochastic minimisation - Default=0.
165 Double_t fDelta; //! Delta - used in stochastic minimisation - Default=0.
166 Double_t fEtaDecay; //! EtaDecay - Eta *= EtaDecay at each epoch - Default=1.
167 Double_t fTau; //! Tau - used in line search - Default=3.
168 Double_t fLastAlpha; //! internal parameter used in line search
169 Int_t fReset; //! number of epochs between two resets of the search direction to the steepest descent - Default=50
170 Bool_t fTrainingOwner; //! internal flag whether one has to delete fTraining or not
171 Bool_t fTestOwner; //! internal flag whether one has to delete fTest or not
172 ClassDef(TMultiLayerPerceptron, 4) // a Neural Network
173};
174
175#endif
int Int_t
Definition: RtypesCore.h:41
bool Bool_t
Definition: RtypesCore.h:59
double Double_t
Definition: RtypesCore.h:55
const char Option_t
Definition: RtypesCore.h:62
#define ClassDef(name, id)
Definition: Rtypes.h:326
int type
Definition: TGX11.cxx:120
A TEventList object is a list of selected events (entries) in a TTree.
Definition: TEventList.h:31
TTreeFormula * fEventWeight
The Learning Method.
void BuildOneHiddenLayer(const TString &sNumNodes, Int_t &layer, Int_t &prevStart, Int_t &prevStop, Bool_t lastLayer)
Builds a hidden layer, updates the number of layers.
void SteepestDir(Double_t *)
Sets the search direction to steepest descent.
void BuildNetwork()
Instanciates the network from the description.
TObjArray fNetwork
weight of the current tree in a chain
Double_t Evaluate(Int_t index, Double_t *params) const
Returns the Neural Net for a given set of input parameters #parameters must equal #input neurons.
TEventList * fTest
EventList defining the events in the training dataset.
bool GetBFGSH(TMatrixD &, TMatrixD &, TMatrixD &)
Computes the hessian matrix using the BFGS update algorithm.
void BuildHiddenLayers(TString &)
Builds hidden layers.
void BuildFirstLayer(TString &)
Instanciates the neurons in input Inputs are normalised and the type is set to kOff (simple forward o...
void SetTau(Double_t tau)
Sets Tau - used in line search (look at the constructor for the complete description of learning meth...
TMultiLayerPerceptron()
Default constructor.
Double_t GetSumSquareError() const
Error on the output for a given event.
void ConjugateGradientsDir(Double_t *, Double_t)
Sets the search direction to conjugate gradient direction beta should be: ||g_{(t+1)}||^2 / ||g_{(t)}...
Double_t fTau
EtaDecay - Eta *= EtaDecay at each epoch - Default=1.
Double_t Result(Int_t event, Int_t index=0) const
Computes the output for a given event.
void SetGammaDelta(TMatrixD &, TMatrixD &, Double_t *)
Sets the gamma (g_{(t+1)}-g_{(t)}) and delta (w_{(t+1)}-w_{(t)}) vectors Gamma is computed here,...
Int_t fReset
internal parameter used in line search
Bool_t LoadWeights(Option_t *filename="")
Loads the weights from a text file conforming to the format defined by DumpWeights.
void MLP_Batch(Double_t *)
One step for the batch (stochastic) method.
TNeuron::ENeuronType fOutType
Double_t fCurrentTreeWeight
index of the current tree in a chain
ELearningMethod fLearningMethod
EventList defining the events in the test dataset.
Double_t fLastAlpha
Tau - used in line search - Default=3.
Int_t fCurrentTree
pointer to the tree used as datasource
void Export(Option_t *filename="NNfunction", Option_t *language="C++") const
Exports the NN as a function for any non-ROOT-dependant code Supported languages are: only C++ ,...
Double_t fEpsilon
Eta - used in stochastic minimisation - Default=0.1.
void Train(Int_t nEpoch, Option_t *option="text", Double_t minE=0)
Train the network.
TNeuron::ENeuronType GetType() const
void BFGSDir(TMatrixD &, Double_t *)
Computes the direction for the BFGS algorithm as the product between the Hessian estimate (bfgsh) and...
void SetTestDataSet(TEventList *test)
Sets the Test dataset.
Bool_t fTrainingOwner
number of epochs between two resets of the search direction to the steepest descent - Default=50
void SetLearningMethod(TMultiLayerPerceptron::ELearningMethod method)
Sets the learning method.
void SetTrainingDataSet(TEventList *train)
Sets the Training dataset.
TMultiLayerPerceptron & operator=(const TMultiLayerPerceptron &)
void BuildLastLayer(TString &, Int_t)
Builds the output layer Neurons are linear combinations of input, by defaul.
Double_t fDelta
Epsilon - used in stochastic minimisation - Default=0.
TTreeFormulaManager * fManager
formula representing the event weight
void Randomize() const
Randomize the weights.
Bool_t LineSearch(Double_t *, Double_t *)
Search along the line defined by direction.
virtual void Draw(Option_t *option="")
Draws the network structure.
void ExpandStructure()
Expand the structure of the first layer.
Double_t fEta
TTreeFormulaManager for the weight and neurons.
Double_t GetError(Int_t event) const
Error on the output for a given event.
TMultiLayerPerceptron::ELearningMethod GetLearningMethod() const
Double_t fEtaDecay
Delta - used in stochastic minimisation - Default=0.
void SetEtaDecay(Double_t ed)
Sets EtaDecay - Eta *= EtaDecay at each epoch (look at the constructor for the complete description o...
void AttachData()
Connects the TTree to Neurons in input and output layers.
TMultiLayerPerceptron(const TMultiLayerPerceptron &)
void SetData(TTree *)
Set the data source.
void SetEventWeight(const char *)
Set the event weight.
Bool_t DumpWeights(Option_t *filename="-") const
Dumps the weights to a text file.
void SetDelta(Double_t delta)
Sets Delta - used in stochastic minimisation (look at the constructor for the complete description of...
Double_t GetCrossEntropy() const
Cross entropy error for a softmax output neuron, for a given event.
void SetReset(Int_t reset)
Sets number of epochs between two resets of the search direction to the steepest descent.
Bool_t fTestOwner
internal flag whether one has to delete fTraining or not
void Shuffle(Int_t *, Int_t) const
Shuffle the Int_t index[n] in input.
virtual ~TMultiLayerPerceptron()
Destructor.
Double_t DerivDir(Double_t *)
scalar product between gradient and direction = derivative along direction
void MLP_Stochastic(Double_t *)
One step for the stochastic method buffer should contain the previous dw vector and will be updated.
void MLP_Line(Double_t *, Double_t *, Double_t)
Sets the weights to a point along a line Weights are set to [origin + (dist * dir)].
TNeuron::ENeuronType fType
void ComputeDEDw() const
Compute the DEDw = sum on all training events of dedw for each weight normalized by the number of eve...
Double_t GetCrossEntropyBinary() const
Cross entropy error for sigmoid output neurons, for a given event.
void DrawResult(Int_t index=0, Option_t *option="test") const
Draws the neural net output It produces an histogram with the output for the two datasets.
void SetEta(Double_t eta)
Sets Eta - used in stochastic minimisation (look at the constructor for the complete description of l...
void GetEntry(Int_t) const
Load an entry into the network.
void SetEpsilon(Double_t eps)
Sets Epsilon - used in stochastic minimisation (look at the constructor for the complete description ...
ENeuronType
Definition: TNeuron.h:48
@ kSigmoid
Definition: TNeuron.h:48
An array of TObjects.
Definition: TObjArray.h:37
Mother of all ROOT objects.
Definition: TObject.h:37
Basic string class.
Definition: TString.h:131
Used to coordinate one or more TTreeFormula objects.
Used to pass a selection expression to the Tree drawing routine.
Definition: TTreeFormula.h:58
A TTree represents a columnar dataset.
Definition: TTree.h:71
Definition: test.py:1