LEMNNpid.h

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//---------------------------------------------------------------------------
// Class: LEMNNpid
// November 2nd, 2010 - Ruth Toner
// Automatically generated by TMVA (MethodBase::MakeClass).
// Purpose: Neural net for processing the output of the LEM matching.  Defining
// object LEMNNpid and calling GetLEMpid() returns LEM discriminant.
// Description of Neural net: Multilayer Perceptron, trained in the TMVA root
// add-on package.  Four input variables: Mean Y, MFQM, Frac CC (all from 
// LEMStandard.cxx), Reconstructed Energy.  Neural net has one hidden layer
// with 9 nodes, tanh style neurons.  Net was trained on standard 
// f210/f213/f214 FD MC, with CHOOZ limit and deltam^2(32)=2.32e-3.
// Contact rt314@cam.ac.uk for questions or problems.
//---------------------------------------------------------------------------
#include "TObject.h"
#include <vector>
#include <cmath>
#include <string>
#include <iostream>
#include "MCNNAnalysis/NueAnaReader.h"
#include "MCNNAnalysis/LEMStandard.h"
#include "NueAna/NueRecord.h"

#ifndef LEMNNPID_H
#define LEMNNPID_H

class LEMNNpid {

 public:

  LEMNNpid();
   // destructor
  ~LEMNNpid() {
     //Clear(); // method-specific
  }

   // the classifier response
   // "inputValues" is a vector of input values in the same order as the 
   // variables given to the constructor
   double GetMvaValue( const std::vector<double>& inputValues ) const;

   //Call to get the PID value
  Double_t GetLEMpid(NueRecord *nr);
  Double_t GetLEMpid(NueAnaReader *nueana);

 private:

   // method-specific destructor
   void Clear();

   // input variable transformation

   double fMin_1[3][4];
   double fMax_1[3][4];
   void InitTransform_1();
   void Transform_1( std::vector<double> & iv, int sigOrBgd ) const;
   void InitTransform();
   void Transform( std::vector<double> & iv, int sigOrBgd ) const;

   // common member variables
   const char* fClassName;

   char   GetType( int ivar ) const { return fType[ivar]; }

   // normalisation of input variables
   //const Bool_t fIsNormalised;
   //Bool_t IsNormalised() const { return fIsNormalised; }
   Bool_t IsNormalised() const { return false; }
   double fVmin[4];
   double fVmax[4];
   double NormVariable( double x, double xmin, double xmax ) const {
      // normalise to output range: [-1, 1]
      return 2*(x - xmin)/(xmax - xmin) - 1.0;
   }

   // type of input variable: 'F' or 'I'
   char   fType[4];

   // initialize internal variables
   void Initialize();
   double GetMvaValue__( const std::vector<double>& inputValues ) const;

   // private members (method specific)

   double ActivationFnc(double x) const;

   int fLayers;
   int fLayerSize[3];
   double fWeightMatrix0to1[10][5];   // weight matrix from layer 0 to 1
   double fWeightMatrix1to2[1][10];   // weight matrix from layer 1 to 2

   double * fWeights[3];
};
/*
class LEMNNpid {

 public:

   // constructor
   LEMNNpid() : fStatusIsClean( true ) {}
   virtual ~LEMNNpid() {}

   // return classifier response
   virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;

   virtual double GetLEMpid(NueAnaReader *nueana);
   virtual double GetLEMpid(NueRecord *nr);

   // returns classifier status
   Bool_t IsStatusClean() const { return fStatusIsClean; }

 protected:

   Bool_t fStatusIsClean;
};
*/
#endif

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