FastBDTExpert Class Reference

Expert for the FastBDT MVA method. More...

#include <FastBDT.h>

Inheritance diagram for FastBDTExpert:

Collaboration diagram for FastBDTExpert:

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
FastBDTOptions	m_specific_options
	Method specific options.
FastBDT::Forest	m_expert_forest
	Forest Expert.
std::vector< FastBDT::FeatureBinning< float > >	m_expert_feature_binning
	Forest feature binning.

Detailed Description

Expert for the FastBDT MVA method.

Definition at line 124 of file FastBDT.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 414 of file FastBDT.cc.

    {
 
      std::vector<float> probabilities(test_data.getNumberOfEvents());
      for (unsigned int iEvent = 0; iEvent < test_data.getNumberOfEvents(); ++iEvent) {
        test_data.loadEvent(iEvent);
#if FastBDT_VERSION_MAJOR >= 3
#if FastBDT_VERSION_MAJOR >= 5
        if (m_use_simplified_interface)
          probabilities[iEvent] = m_classifier.predict(test_data.m_input);
        else
          probabilities[iEvent] = m_expert_forest.Analyse(test_data.m_input);
#else
        probabilities[iEvent] = m_expert_forest.Analyse(test_data.m_input);
#endif
#else
        std::vector<unsigned int> bins(m_expert_feature_binning.size());
        for (unsigned int iFeature = 0; iFeature < m_expert_feature_binning.size(); ++iFeature) {
          bins[iFeature] = m_expert_feature_binning[iFeature].ValueToBin(test_data.m_input[iFeature]);
        }
        probabilities[iEvent] = m_expert_forest.Analyse(bins);
#endif
      }
 
      return probabilities;
 
    }

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 321 of file FastBDT.cc.

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

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