ReweighterExpert Class Reference

Expert for the Reweighter MVA method. More...

#include <Reweighter.h>

Inheritance diagram for ReweighterExpert:

Collaboration diagram for ReweighterExpert:

Public Member Functions
virtual void	load (Weightfile &weightfile) override
	Load the expert from a Weightfile. More...
virtual std::vector< float >	apply (Dataset &test_data) const override
	Apply this expert onto a dataset. More...
virtual std::vector< float >	apply (Dataset &test_data, const unsigned int classID) const
	Apply this expert onto a dataset. More...

Protected Attributes
GeneralOptions	m_general_options
	General options loaded from the weightfile.

Private Attributes
ReweighterOptions	m_specific_options
	Method specific options.
GeneralOptions	m_expert_options
	Method general options of the expert.
std::unique_ptr< Expert >	m_expert
	Experts used to reweight.
double	m_norm = 0.0
	Norm for the weights.

Detailed Description

Expert for the Reweighter MVA method.

Definition at line 86 of file Reweighter.h.

Member Function Documentation

apply() [1/2]

std::vector< float > apply ( Dataset &  test_data ) const

overridevirtual

Apply this expert onto a dataset.

Parameters

test_data

dataset

Implements Expert.

Definition at line 186 of file Reweighter.cc.

                                       {
            prediction[iEvent] = 1.0;
          } else {
            if (prediction[iEvent] > 0.995)
              prediction[iEvent] = 0.995;
            if (prediction[iEvent] < 0.005)
              prediction[iEvent] = 0.005;
 
            prediction[iEvent] = (prediction[iEvent]) / (1 - prediction[iEvent]);
            prediction[iEvent] /= m_norm;
          }
        }
      } else {
        for (unsigned int iEvent = 0; iEvent < test_data.getNumberOfEvents(); ++iEvent) {
          if (prediction[iEvent] > 0.995)
            prediction[iEvent] = 0.995;
          if (prediction[iEvent] < 0.005)
            prediction[iEvent] = 0.005;
 
          prediction[iEvent] = (prediction[iEvent]) / (1 - prediction[iEvent]);
          prediction[iEvent] /= m_norm;
        }
      }
 
      return prediction;
 
    }
  }
}

apply() [2/2]

virtual std::vector<float> apply ( Dataset &  test_data, const unsigned int  classID  ) const

inlinevirtualinherited

Apply this expert onto a dataset.

Multi-class mode signature. Not pure virtual, since not all derived classes need to re-implement this.

Parameters

test_data	dataset
classID	class identifier.

Reimplemented in TMVAExpertMulticlass.

Definition at line 59 of file Expert.h.

load()

void load ( Weightfile &  weightfile )

overridevirtual

Load the expert from a Weightfile.

Parameters

weightfile

containing all information necessary to build the expert

Implements Expert.

Definition at line 164 of file Reweighter.cc.

---

The documentation for this class was generated from the following files:

• mva/methods/include/Reweighter.h
• mva/methods/src/Reweighter.cc