library: libMLP #include "TMLPAnalyzer.h"

# TMLPAnalyzer

class description - source file - inheritance tree (.ps)

## class TMLPAnalyzer : public TObject

| Inheritance Chart:

`TObject`

<-

`TMLPAnalyzer`

        protected:

            Int_t GetLayers()
          TString GetNeuronFormula(Int_t idx)
            Int_t GetNeurons(Int_t layer)

        public:

                          TMLPAnalyzer(TMultiLayerPerceptron& net)
                          TMLPAnalyzer(TMultiLayerPerceptron* net)
                          TMLPAnalyzer(const TMLPAnalyzer&)
                  virtual ~TMLPAnalyzer()
                     void CheckNetwork()
           static TClass* Class()
                     void DrawDInput(Int_t i)
                     void DrawDInputs()
                     void DrawNetwork(Int_t neuron, const char* signal, const char* bg)
                     void GatherInformations()
          virtual TClass* IsA() const
            TMLPAnalyzer& operator=(const TMLPAnalyzer&)
             virtual void ShowMembers(TMemberInspector& insp, char* parent)
             virtual void Streamer(TBuffer& b)
                     void StreamerNVirtual(TBuffer& b)

### Data Members

        private:

          TMultiLayerPerceptron* network
                          TTree* analysisTree

## Class Description

     TMLPAnalyzer

     This utility class contains a set of tests usefull when developing
     a neural network.
     It allows you to check for unneeded variables, and to control
     the network structure.

~TMLPAnalyzer()

Int_t GetLayers()

     Returns the number of layers

Int_t GetNeurons(Int_t layer)

     Returns the number of neurons in given layer

TString GetNeuronFormula(Int_t idx)

     returns the formula used as input for neuron (idx) in
     the first layer.

void CheckNetwork()

     gives some information about the network in the terminal.

void GatherInformations()

     Collect informations about what is usefull in the network.
     This method as to be called first when analyzing a network.

void DrawDInput(Int_t i)

     Draws the distribution (on the test sample) of the
     impact on the network output of a small variation of
     the ith input.

void DrawDInputs()

     Draws the distribution (on the test sample) of the
     impact on the network output of a small variation of
     each input.

void DrawNetwork(Int_t neuron, const char* signal, const char* bg)

     Draws the distribution of the neural network (using ith neuron).
     Two distributions are drawn, for events passing respectively the "signal"
     and "background" cuts.
     Only the test sample is used.

### Inline Functions

             TMLPAnalyzer TMLPAnalyzer(TMultiLayerPerceptron& net)
             TMLPAnalyzer TMLPAnalyzer(TMultiLayerPerceptron* net)
                  TClass* Class()
                  TClass* IsA() const
                     void ShowMembers(TMemberInspector& insp, char* parent)
                     void Streamer(TBuffer& b)
                     void StreamerNVirtual(TBuffer& b)
             TMLPAnalyzer TMLPAnalyzer(const TMLPAnalyzer&)
            TMLPAnalyzer& operator=(const TMLPAnalyzer&)

Author: Christophe.Delaere@cern.ch 25/04/04
Last update: Thu Jun 10 08:35:12 2004