aback_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   : 6.16/00       [397312]
Creator        : vassalli
Date           : Mon Jul 22 19:11:42 2019
Host           : Linux cvmfswrite01.sdcc.bnl.gov 3.10.0-693.11.6.el7.x86_64 #1 SMP Wed Jan 3 18:09:42 CST 2018 x86_64 x86_64 x86_64 GNU/Linux
Dir            : /home/condor/local/sdcc/execute/dir_48382/training
Training events: 52985
Analysis type  : [Classification]


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

# Set by User:
# Default:
V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
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)"]
H: "False" [Print method-specific help message]
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)]
FitMethod: "GA" [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]
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"
    CutRangeMin[4]: "-1.000000e+00"
    CutRangeMin[5]: "-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"
    CutRangeMax[4]: "-1.000000e+00"
    CutRangeMax[5]: "-1.000000e+00"
VarProp[0]: "NotEnforced" [Categorisation of cuts]
    VarProp[1]: "NotEnforced"
    VarProp[2]: "NotEnforced"
    VarProp[3]: "NotEnforced"
    VarProp[4]: "NotEnforced"
    VarProp[5]: "NotEnforced"
##


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

NVar 6
track_deta                    track_deta                    track_deta                    track_deta                                                      'F'    [0,1.75113081932]
track_pT                      track_pT                      track_pT                      track_pT                                                        'F'    [0.201775103807,270.302978516]
approach_dist                 approach_dist                 approach_dist                 approach_dist                                                   'F'    [5.6726735238e-06,201.640762329]
vtx_radius                    vtx_radius                    vtx_radius                    vtx_radius                                                      'F'    [2.12119351505e-32,335.550384521]
vtx_chi2                      vtx_chi2                      vtx_chi2                      vtx_chi2                                                        'F'    [-0.0747850388288,305.772888184]
cluster_prob                  cluster_prob                  cluster_prob                  cluster_prob                                                    'F'    [0,0.999983727932]
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( 6 )
   {
      // the training input variables
      const char* inputVars[] = { "track_deta", "track_pT", "approach_dist", "vtx_radius", "vtx_chi2", "cluster_prob" };

      // 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;
      fVmin[4] = 0;
      fVmax[4] = 0;
      fVmin[5] = 0;
      fVmax[5] = 0;

      // initialize input variable types
      fType[0] = 'F';
      fType[1] = 'F';
      fType[2] = 'F';
      fType[3] = 'F';
      fType[4] = 'F';
      fType[5] = '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[6];
   double fVmax[6];
   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[6];

   // 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;
         retval = 0;
      }
      else {
            retval = GetMvaValue__( inputValues );
      }

      return retval;
   }