#include "TMultiLayerPerceptron.h" 
Inheritance Chart:  

private:
void BuildFirstLayer(TString&) void BuildHiddenLayers(TString&) void BuildLastLayer(TString&, Int_t) void MLP_Line(Double_t*, Double_t*, Double_t) void Shuffle(Int_t*, Int_t) const protected:
void AttachData() void BFGSDir(TMatrixD&, Double_t*) void BuildNetwork() void ConjugateGradientsDir(Double_t*, Double_t) Double_t DerivDir(Double_t*) bool GetBFGSH(TMatrixD&, TMatrixD&, TMatrixD&) void GetEntry(Int_t) const Bool_t LineSearch(Double_t*, Double_t*) void MLP_Batch(Double_t*) void MLP_Stochastic(Double_t*) void SetGammaDelta(TMatrixD&, TMatrixD&, Double_t*) void SteepestDir(Double_t*) public:
TMultiLayerPerceptron() TMultiLayerPerceptron(const char* layout, TTree* data = NULL, const char* training = "Entry$%2==0", const char* test) TMultiLayerPerceptron(const char* layout, const char* weight, TTree* data = NULL, const char* training = "Entry$%2==0", const char* test) TMultiLayerPerceptron(const char* layout, TTree* data, TEventList* training, TEventList* test) TMultiLayerPerceptron(const char* layout, const char* weight, TTree* data, TEventList* training, TEventList* test) virtual ~TMultiLayerPerceptron() static TClass* Class() void ComputeDEDw() const virtual void Draw(Option_t* option) void DrawResult(Int_t index = 0, Option_t* option) const void DumpWeights(Option_t* filename) const Double_t Evaluate(Int_t index, Double_t* params) const void Export(Option_t* filename = "NNfunction", Option_t* language = "C++") const Double_t GetDelta() const Double_t GetEpsilon() const Double_t GetError(Int_t event) const Double_t GetError(TMultiLayerPerceptron::DataSet set) const Double_t GetEta() const Double_t GetEtaDecay() const Int_t GetReset() const TString GetStructure() const Double_t GetTau() const virtual TClass* IsA() const void LoadWeights(Option_t* filename) void Randomize() const Double_t Result(Int_t event, Int_t index = 0) const void SetData(TTree*) void SetDelta(Double_t delta) void SetEpsilon(Double_t eps) void SetEta(Double_t eta) void SetEtaDecay(Double_t ed) void SetEventWeight(const char*) void SetLearningMethod(TMultiLayerPerceptron::LearningMethod method) void SetReset(Int_t reset) void SetTau(Double_t tau) void SetTestDataSet(TEventList* test) void SetTestDataSet(const char* test) void SetTrainingDataSet(TEventList* train) void SetTrainingDataSet(const char* train) virtual void ShowMembers(TMemberInspector& insp, char* parent) virtual void Streamer(TBuffer& b) void StreamerNVirtual(TBuffer& b) void Train(Int_t nEpoch, Option_t* option = "text")
private:
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 TEventList* fTraining ! EventList defining the events in the training dataset TEventList* fTest ! EventList defining the events in the test dataset TMultiLayerPerceptron::LearningMethod 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 public:
static const TMultiLayerPerceptron::LearningMethod kStochastic static const TMultiLayerPerceptron::LearningMethod kBatch static const TMultiLayerPerceptron::LearningMethod kSteepestDescent static const TMultiLayerPerceptron::LearningMethod kRibierePolak static const TMultiLayerPerceptron::LearningMethod kFletcherReeves static const TMultiLayerPerceptron::LearningMethod kBFGS static const TMultiLayerPerceptron::DataSet kTraining static const TMultiLayerPerceptron::DataSet kTest
TMultiLayerPerceptron This class describes 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. Input and output neurons are assigned a value computed for each event with the same possibilities as for TTree::Draw(). Events may be weighted individualy or via TTree::SetWeight(). 6 learning methods are available: kStochastic, kBatch, kSteepestDescent, kRibierePolak, kFletcherReeves and kBFGS. This implementation, written by C. Delaere, is *inspired* from the mlpfit package from J.Schwindling et al.
Neural Networks are more and more used in various fields for data analysis and classification, both for research and commercial institutions. Some randomly choosen examples are:
image analysis
financial movements predictions and analysis
sales forecast and product shipping optimisation
in particles physics: mainly for classification tasks (signal over background discrimination)
More than 50% of neural networks are multilayer perceptrons. This implementation of multilayer perceptrons is inspired from the MLPfit package originaly written by Jerome Schwindling. MLPfit remains one of the fastest tool for neural networks studies, and this ROOT addon will not try to compete on that. A clear and flexible Object Oriented implementation has been choosen over a faster but more difficult to maintain code. Nevertheless, the time penalty does not exceed a factor 2.
The multilayer perceptron is a simple feedforward network with the following structure:
It is made of neurons characterized by a bias and weighted links between them (let's call those links synapses). The input neurons receive the inputs, normalize them and forward them to the first hidden layer.
Each neuron in any subsequent layer first computes a linear combination of the outputs of the previous layer. The output of the neuron is then function of that combination with f being linear for output neurons or a sigmoid for hidden layers. This is useful because of two theorems:
A linear combination of sigmoids can approximate any continuous function.
Trained with output = 1 for the signal and 0 for the background, the approximated function of inputs X is the probability of signal, knowing X.
The aim of all learning methods is to minimize the total error on a set of weighted examples. The error is defined as the sum in quadrature, devided by two, of the error on each individual output neuron.
In all methods implemented, one needs to compute the first derivative of that error with respect to the weights. Exploiting the wellknown properties of the derivative, especialy the derivative of compound functions, one can write:
for a neuton: product of the local derivative with the weighted sum on the outputs of the derivatives.
for a synapse: product of the input with the local derivative of the output neuron.
This computation is called backpropagation of the errors. A loop over all examples is called an epoch.
Six learning methods are implemented.
Stochastic minimization: This is the most trivial learning method. This is the RobbinsMonro stochastic approximation applied to multilayer perceptrons. The weights are updated after each example according to the formula:
$w_{ij}(t+1) = w_{ij}(t) + \Delta w_{ij}(t)$
with
$\Delta w_{ij}(t) =  \eta(\d e_p / \d w_{ij} + \delta) + \epsilon \Deltaw_{ij}(t1)$
The parameters for this method are Eta, EtaDecay, Delta and Epsilon.
Steepest descent with fixed step size (batch learning): It is the same as the stochastic minimization, but the weights are updated after considering all the examples, with the total derivative dEdw. The parameters for this method are Eta, EtaDecay, Delta and Epsilon.
Steepest descent algorithm: Weights are set to the minimum along the line defined by the gradient. The only parameter for this method is Tau. Lower tau = higher precision = slower search. A value Tau = 3 seems reasonable.
Conjugate gradients with the PolakRibiere updating formula: Weights are set to the minimum along the line defined by the conjugate gradient. Parameters are Tau and Reset, which defines the epochs where the direction is reset to the steepes descent.
Conjugate gradients with the FletcherReeves updating formula: Weights are set to the minimum along the line defined by the conjugate gradient. Parameters are Tau and Reset, which defines the epochs where the direction is reset to the steepes descent.
Broyden, Fletcher, Goldfarb, Shanno (BFGS) method: Implies the computation of a NxN matrix computation, but seems more powerful at least for less than 300 weights. Parameters are Tau and Reset, which defines the epochs where the direction is reset to the steepes descent.
TMLP is build from 3 classes: TNeuron, TSynapse and TMultiLayerPerceptron. Only TMultiLayerPerceptron should be used explicitely by the user.
TMultiLayerPerceptron will take examples from a TTree given in the constructor. The network is described by a simple string: The input/output layers are defined by giving the expression for each neuron, separated by comas. Hidden layers are just described by the number of neurons. The layers are separated by semicolons. In addition, output layer formulas can be preceded by '@' (e.g "@out") if one wants to also normalize the output. Input and outputs are taken from the TTree given as second argument. Expressions are evaluated as for TTree::Draw(). One defines the training and test datasets by TEventLists.
Example: TMultiLayerPerceptron("x,y:10:5:f",inputTree);
Both the TTree and the TEventLists can be defined in the constructor, or later with the suited setter method.
The learning method is defined using the TMultiLayerPerceptron::SetLearningMethod() . Learning methods are :
TMultiLayerPerceptron::kStochastic,
TMultiLayerPerceptron::kBatch,
TMultiLayerPerceptron::kSteepestDescent,
TMultiLayerPerceptron::kRibierePolak,
TMultiLayerPerceptron::kFletcherReeves,
TMultiLayerPerceptron::kBFGS
A weight can be assigned to events, either in the constructor, either with TMultiLayerPerceptron::SetEventWeight(). In addition, the TTree weight is taken into account.
Finally, one starts the training with TMultiLayerPerceptron::Train(Int_t nepoch, Option_t* options). The
output neurons can be normalized, this is not the case for mlpfit
the neural net is exported in C++ (not in FORTRAN)
the drawResult() method allows a fast check of the learning procedure
Default constructor
The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas. Hidden layers are just described by the number of neurons. The layers are separated by semicolons. Ex: "x,y:10:5:f" The output can be prepended by '@' if the variable has to be normalized. Input and outputs are taken from the TTree given as second argument. training and test are the two TEventLists defining events to be used during the neural net training. Both the TTree and the TEventLists can be defined in the constructor, or later with the suited setter method.
The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas. Hidden layers are just described by the number of neurons. The layers are separated by semicolons. Ex: "x,y:10:5:f" The output can be prepended by '@' if the variable has to be normalized. Input and outputs are taken from the TTree given as second argument. training and test are the two TEventLists defining events to be used during the neural net training. Both the TTree and the TEventLists can be defined in the constructor, or later with the suited setter method.
The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas. Hidden layers are just described by the number of neurons. The layers are separated by semicolons. Ex: "x,y:10:5:f" The output can be prepended by '@' if the variable has to be normalized. Input and outputs are taken from the TTree given as second argument. training and test are two cuts (see TTreeFormula) defining events to be used during the neural net training and testing. Example: "Entry$%2", "(Entry$+1)%2". Both the TTree and the cut can be defined in the constructor, or later with the suited setter method.
The network is described by a simple string: The input/output layers are defined by giving the branch names separated by comas. Hidden layers are just described by the number of neurons. The layers are separated by semicolons. Ex: "x,y:10:5:f" The output can be prepended by '@' if the variable has to be normalized. Input and outputs are taken from the TTree given as second argument. training and test are two cuts (see TTreeFormula) defining events to be used during the neural net training and testing. Example: "Entry$%2", "(Entry$+1)%2". Both the TTree and the cut can be defined in the constructor, or later with the suited setter method.
Set the data source
Set the event weight
Sets the Training dataset. Those events will be used for the minimization
Sets the Test dataset. Those events will not be used for the minimization but for control
Sets the Training dataset. Those events will be used for the minimization. Note that the tree must be already defined.
Sets the Test dataset. Those events will not be used for the minimization but for control. Note that the tree must be already defined.
Sets the learning method. Available methods are: kStochastic, kBatch, kSteepestDescent, kRibierePolak, kFletcherReeves and kBFGS. (look at the constructor for the complete description of learning methods and parameters)
Sets Eta  used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)
Sets Epsilon  used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)
Sets Delta  used in stochastic minimisation (look at the constructor for the complete description of learning methods and parameters)
Sets EtaDecay  Eta *= EtaDecay at each epoch (look at the constructor for the complete description of learning methods and parameters)
Sets Tau  used in line search (look at the constructor for the complete description of learning methods and parameters)
Sets number of epochs between two resets of the search direction to the steepest descent. (look at the constructor for the complete description of learning methods and parameters)
Load an entry into the network
Train the network. nEpoch is the number of iterations. option can contain:  "text" (simple text output)  "graph" (evoluting graphical training curves)  "update=X" (step for the text/graph output update)  "+" will skip the randomisation and start from the previous values. All combinations are available.
Computes the output for a given event. Look at the output neuron designed by index.
Error on the output for a given event
Error on the whole dataset
Compute the DEDw = sum on all training events of dedw for each weight normalized by the number of events.
Randomize the weights
Connects the TTree to Neurons in input and output layers. The formulas associated to each neuron are created and reported to the network formula manager. By default, the branch is not normalised since this would degrade performance for classification jobs. Normalisation can be requested by putting '@' in front of the formula.
Instanciates the network from the description
Instanciates the neurons in input Inputs are normalised and the type is set to kOff (simple forward of the formula value)
Builds hidden layers. Neurons are Sigmoids.
Builds the output layer Neurons are linear combinations of input.
Draws the neural net output It produces an histogram with the output for the two datasets. Index is the number of the desired output neuron. "option" can contain:  test or train to select a dataset  comp to produce a XY comparison plot  nocanv to not create a new TCanvas for the plot
Dumps the weights to a text file. Set filename to "" (default) to dump to the standard output
Loads the weights from a text file conforming to the format defined by DumpWeights.
Returns the Neural Net for a given set of input parameters #parameters must equal #input neurons
Exports the NN as a function for any nonROOTdependant code Supported languages are: only C++ , FORTRAN and Python (yet) This feature is also usefull if you want to plot the NN as a function (TF1 or TF2).
Shuffle the Int_t index[n] in input. Input: index: the array to shuffle n: the size of the array Output: index: the shuffled indexes This method is used for stochastic training
One step for the stochastic method buffer should contain the previous dw vector and will be updated
One step for the batch (stochastic) method. DEDw should have been updated before calling this.
Sets the weights to a point along a line Weights are set to [origin + (dist * dir)].
Sets the search direction to steepest descent.
Search along the line defined by direction. buffer is not used but is updated with the new dw so that it can be used by a later stochastic step. It returns true if the line search fails.
Sets the search direction to conjugate gradient direction beta should be: g_{(t+1)}^2 / g_{(t)}^2 (FletcherReeves) g_{(t+1)} (g_{(t+1)}g_{(t)}) / g_{(t)}^2 (RibierePolak)
Computes the hessian matrix using the BFGS update algorithm. from gamma (g_{(t+1)}g_{(t)}) and delta (w_{(t+1)}w_{(t)}). It returns true if such a direction could not be found (if gamma and delta are orthogonal).
Sets the gamma (g_{(t+1)}g_{(t)}) and delta (w_{(t+1)}w_{(t)}) vectors Gamma is computed here, so ComputeDEDw cannot have been called before, and delta is a direct translation of buffer into a TMatrixD.
scalar product between gradient and direction = derivative along direction
Computes the direction for the BFGS algorithm as the product between the Hessian estimate (BFGSH) and the dir.
Draws the network structure. Neurons are depicted by a blue disk, and synapses by lines connecting neurons. The line width is proportionnal to the weight.
Double_t GetEta() const Double_t GetEpsilon() const Double_t GetDelta() const Double_t GetEtaDecay() const Double_t GetTau() const Int_t GetReset() const TString GetStructure() const TClass* Class() TClass* IsA() const void ShowMembers(TMemberInspector& insp, char* parent) void Streamer(TBuffer& b) void StreamerNVirtual(TBuffer& b)