TMLPAnalyzer

class TMLPAnalyzer : public TObject

    protected:

      const char* GetInputNeuronTitle(Int_t in) const
            Int_t GetLayers()
          TString GetNeuronFormula(Int_t idx)
            Int_t GetNeurons(Int_t layer)
      const char* GetOutputNeuronTitle(Int_t out) const

    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)
            TProfile* DrawTruthDeviation(Int_t outnode = 0, Option_t* option = "")
            TProfile* DrawTruthDeviationInOut(Int_t innode, Int_t outnode = 0, Option_t* option = "")
             THStack* DrawTruthDeviationInsOut(Int_t outnode = 0, Option_t* option = "")
             THStack* DrawTruthDeviations(Option_t* option = "")
                 void GatherInformations()
               TTree* GetIOTree() const
      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* fNetwork       
                      TTree* fAnalysisTree  
                      TTree* fIOTree        

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.

 Returns the number of layers.

 Returns the number of neurons in given layer.

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

 Gives some information about the network in the terminal.

 Collect informations about what is usefull in the network.
 This method has to be called first when analyzing a network.
 Fills the two analysis trees.

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

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

 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.

 Create a profile of the difference of the MLP output minus the
 true value for a given output node outnode, vs the true value for
 outnode, for all test data events. This method is mainly useful
 when doing regression analysis with the MLP (i.e. not classification,
 but continuous truth values).
 The resulting TProfile histogram is returned.
 It is not drawn if option "goff" is specified.
 Options are passed to TProfile::Draw

 Creates TProfiles of the difference of the MLP output minus the
 true value vs the true value, one for each output, filled with the
 test data events. This method is mainly useful when doing regression
 analysis with the MLP (i.e. not classification, but continuous truth
 values).
 The returned THStack contains all the TProfiles. It is drawn unless
 the option "goff" is specified.
 Options are passed to TProfile::Draw.

 Creates a profile of the difference of the MLP output outnode minus
 the true value of outnode vs the input value innode, for all test
 data events.
 The resulting TProfile histogram is returned.
 It is not drawn if option "goff" is specified.
 Options are passed to TProfile::Draw

 Creates a profile of the difference of the MLP output outnode minus the
 true value of outnode vs the input value, stacked for all inputs, for
 all test data events.
 The returned THStack contains all the TProfiles. It is drawn unless
 the option "goff" is specified.
 Options are passed to TProfile::Draw.

Inline Functions

         TMLPAnalyzer TMLPAnalyzer(TMultiLayerPerceptron& net)
         TMLPAnalyzer TMLPAnalyzer(TMultiLayerPerceptron* net)
               TTree* GetIOTree() const
              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&)