TMVAClassification_Cuts.class.C

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// Class: ReadCuts
// Automatically generated by MethodBase::MakeClass
//

/* configuration options =====================================================

#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-

Method         : Cuts::Cuts
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.22/02       [398850]
Creator        : cdean
Date           : Tue Apr 20 16:38:02 2021
Host           : Linux cvmfswrite02.sdcc.bnl.gov 3.10.0-957.12.2.el7.x86_64 #1 SMP Tue May 14 15:23:27 CDT 2019 x86_64 x86_64 x86_64 GNU/Linux
Dir            : /gpfs/mnt/gpfs02/sphenix/user/cdean/scripts/HF_trigger_ML/TMVA/Odd/wCalo
Training events: 31922
Analysis type  : [Classification]


#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-

# Set by User:
V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
H: "False" [Print method-specific help message]
FitMethod: "MC" [Minimisation Method (GA, SA, and MC are the primary methods to be used; the others have been introduced for testing purposes and are depreciated)]
EffMethod: "EffSel" [Selection Method]
# Default:
VerbosityLevel: "Default" [Verbosity level]
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)"]
CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
CutRangeMin[0]: "-1.000000e+00" [Minimum of allowed cut range (set per variable)]
    CutRangeMin[1]: "-1.000000e+00"
    CutRangeMin[2]: "-1.000000e+00"
    CutRangeMin[3]: "-1.000000e+00"
CutRangeMax[0]: "-1.000000e+00" [Maximum of allowed cut range (set per variable)]
    CutRangeMax[1]: "-1.000000e+00"
    CutRangeMax[2]: "-1.000000e+00"
    CutRangeMax[3]: "-1.000000e+00"
VarProp[0]: "FSmart" [Categorisation of cuts]
    VarProp[1]: "FSmart"
    VarProp[2]: "FSmart"
    VarProp[3]: "FSmart"
##


#VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-

NVar 4
max(track_1_IP,track_2_IP)    maxTrackDCA_3D                maxTrackDCA_3D                track-vertex 3D DCA, max      units                             'F'    [0.00383873376995,4.99581956863]
max(abs(track_1_IP_xy),abs(track_2_IP_xy)) maxTrackDCA_2D                             maxTrackDCA_2D                             track-vertex 2D DCA, max                   units                                          'F'    [9.33057162911e-05,4.8996257782]
track_1_track_2_DCA           track_1_track_2_DCA           track_1_track_2_DCA           track-track 3D DCA            units                             'F'    [1.57269468559e-07,0.0499997623265]
INTT_meanHits                 INTT_meanHits                 INTT_meanHits                 INTT avg. hits                units                             'F'    [0,93]
NSpec 0


============================================================================ */

#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 ReadCuts : public IClassifierReader {

 public:

   // constructor
   ReadCuts( std::vector<std::string>& theInputVars )
      : IClassifierReader(),
        fClassName( "ReadCuts" ),
        fNvars( 4 )
   {
      // the training input variables
      const char* inputVars[] = { "max(track_1_IP,track_2_IP)", "max(abs(track_1_IP_xy),abs(track_2_IP_xy))", "track_1_track_2_DCA", "INTT_meanHits" };

      // 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 ~ReadCuts() {
      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)
   // not implemented for class: "ReadCuts"
};
inline double ReadCuts::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;
   }
   else {
         retval = GetMvaValue__( inputValues );
   }

   return retval;
}