TMVAClassification_LD.class.C

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// Class: ReadLD
// Automatically generated by MethodBase::MakeClass
//
 
/* configuration options =====================================================
 
#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
 
Method         : LD::LD
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.16/01       [397313]
Creator        : sftnight
Date           : Sun Dec 19 22:13:32 2021
Host           : Linux root-ubuntu-2004-3 5.4.0-73-generic #82-Ubuntu SMP Wed Apr 14 17:39:42 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
Dir            : /home/sftnight/build/workspace/root-makedoc-v616/rootspi/rdoc/src/v6-16-00-patches/documentation/doxygen
Training events: 2000
Analysis type  : [Classification]
 
 
#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-
 
# Set by User:
V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
VarTransform: "None" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
H: "True" [Print method-specific help message]
CreateMVAPdfs: "True" [Create PDFs for classifier outputs (signal and background)]
# Default:
VerbosityLevel: "Default" [Verbosity level]
IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
##
 
 
#VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-
 
NVar 4
var1+var2                     myvar1                        myvar1                        myvar1                                                          'F'    [-9.33803939819,7.69307804108]
var1-var2                     myvar2                        myvar2                        Expression 2                                                    'F'    [-3.25508260727,4.02912044525]
var3                          var3                          var3                          Variable 3                    units                             'F'    [-5.2777428627,4.64297914505]
var4                          var4                          var4                          Variable 4                    units                             'F'    [-5.6007027626,4.67435789108]
NSpec 2
var1*2                        spec1                         spec1                         Spectator 1                   units                             'F'    [-9.91655540466,8.7030172348]
var1*3                        spec2                         spec2                         Spectator 2                   units                             'F'    [-14.874833107,13.0545253754]
 
 
============================================================================ */
 
#include <array>
#include <vector>
#include <cmath>
#include <string>
#include <iostream>
 
#ifndef IClassifierReader__def
#define IClassifierReader__def
 
class IClassifierReader {
 
 public:
 
   // constructor
   IClassifierReader() : fStatusIsClean( true ) {}
   virtual ~IClassifierReader() {}
 
   // return classifier response
   virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
 
   // returns classifier status
   bool IsStatusClean() const { return fStatusIsClean; }
 
 protected:
 
   bool fStatusIsClean;
};
 
#endif
 
class ReadLD : public IClassifierReader {
 
 public:
 
   // constructor
   ReadLD( std::vector<std::string>& theInputVars )
      : IClassifierReader(),
        fClassName( "ReadLD" ),
        fNvars( 4 )
   {
      // the training input variables
      const char* inputVars[] = { "var1+var2", "var1-var2", "var3", "var4" };
 
      // sanity checks
      if (theInputVars.size() <= 0) {
         std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
         fStatusIsClean = false;
      }
 
      if (theInputVars.size() != fNvars) {
         std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
                   << theInputVars.size() << " != " << fNvars << std::endl;
         fStatusIsClean = false;
      }
 
      // validate input variables
      for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
         if (theInputVars[ivar] != inputVars[ivar]) {
            std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
                      << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
            fStatusIsClean = false;
         }
      }
 
      // initialize min and max vectors (for normalisation)
      fVmin[0] = 0;
      fVmax[0] = 0;
      fVmin[1] = 0;
      fVmax[1] = 0;
      fVmin[2] = 0;
      fVmax[2] = 0;
      fVmin[3] = 0;
      fVmax[3] = 0;
 
      // initialize input variable types
      fType[0] = 'F';
      fType[1] = 'F';
      fType[2] = 'F';
      fType[3] = 'F';
 
      // initialize constants
      Initialize();
 
   }
 
   // destructor
   virtual ~ReadLD() {
      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 override;
 
 private:
 
   // method-specific destructor
   void Clear();
 
   // common member variables
   const char* fClassName;
 
   const size_t fNvars;
   size_t GetNvar()           const { return fNvars; }
   char   GetType( int ivar ) const { return fType[ivar]; }
 
   // normalisation of input variables
   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)
   std::vector<double> fLDCoefficients;
};
 
inline void ReadLD::Initialize() 
{
   fLDCoefficients.push_back( -0.0548834585145 );
   fLDCoefficients.push_back( -0.309417182502 );
   fLDCoefficients.push_back( -0.101898689979 );
   fLDCoefficients.push_back( -0.142337150119 );
   fLDCoefficients.push_back( 0.705328504943 );
 
   // sanity check
   if (fLDCoefficients.size() != fNvars+1) {
      std::cout << "Problem in class \"" << fClassName << "\"::Initialize: mismatch in number of input values"
                << fLDCoefficients.size() << " != " << fNvars+1 << std::endl;
      fStatusIsClean = false;
   }         
}
 
inline double ReadLD::GetMvaValue__( const std::vector<double>& inputValues ) const
{
   double retval = fLDCoefficients[0];
   for (size_t ivar = 1; ivar < fNvars+1; ivar++) {
      retval += fLDCoefficients[ivar]*inputValues[ivar-1];
   }
 
   return retval;
}
 
// Clean up
inline void ReadLD::Clear() 
{
   // clear coefficients
   fLDCoefficients.clear(); 
}
   inline double ReadLD::GetMvaValue( const std::vector<double>& inputValues ) const
   {
      // classifier response value
      double retval = 0;
 
      // classifier response, sanity check first
      if (!IsStatusClean()) {
         std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
                   << " because status is dirty" << std::endl;
         retval = 0;
      }
      else {
            retval = GetMvaValue__( inputValues );
      }
 
      return retval;
   }