TMLPAnalyzer

class TMLPAnalyzer : public TObject

    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)
            TProfile* DrawTruthDeviation(Int_t i, Option_t* option)
            TProfile* DrawTruthDeviationInOut(Int_t i, Int_t o, Option_t* option)
             THStack* DrawTruthDeviationInsOut(Int_t o, 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.

 Draws a profile of the difference of the MLP output minus the
 true value for a given output i, vs the true i, for all test data events.
 Options are passed to TProfile::Draw

 Draws a profile of the difference of the MLP output minus the
 true value vs the true value, stacked for all outputs, for all
 test data events.
 Options are passed to TProfile::Draw

 Draws a profile of the difference of the MLP output o minus the
 true value of o vs the input value i, for all test data events.
 Options are passed to TProfile::Draw

 Draws a profile of the difference of the MLP output o minus the
 true value of o vs the input value, stacked for all inputs, for
 all test data events.
 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&)