PDFExpert Class Reference

Expert for the PDF MVA method. More...

#include <PDF.h>

Inheritance diagram for PDFExpert:

Collaboration diagram for PDFExpert:

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

Protected Attributes
GeneralOptions	m_general_options
	General options loaded from the weightfile.

Private Attributes
PDFOptions	m_specific_options
	Specific options of the PDF method.
Binning	m_binning
	used binning
std::vector< double >	m_value
	value returned by expert for each bin

Detailed Description

Expert for the PDF MVA method.

Definition at line 86 of file PDF.h.

Member Function Documentation

apply()

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

overridevirtual

Apply PDF expert onto a dataset.

Parameters

test_data

dataset

Implements Expert.

Definition at line 143 of file PDF.cc.

    {
      std::vector<float> probabilities(test_data.getNumberOfEvents(), 0);
      for (unsigned int iEvent = 0; iEvent < test_data.getNumberOfEvents(); ++iEvent) {
        test_data.loadEvent(iEvent);
        unsigned int bin = m_binning.getBin(test_data.m_input[0]);
        probabilities[iEvent] = m_value[bin];
      }
      return probabilities;
    }

applyMulticlass()

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

inlinevirtualinherited

Apply this m_expert onto a dataset.

Multiclass mode. Not pure virtual, since not all derived classes to re-implement this.

Parameters

test_data

dataset.

Returns

vector of size N=test_data.getNumberOfEvents() with N=m_classes.size() scores for each event in the dataset.

Reimplemented in PythonExpert, TMVAExpertMulticlass, and TrivialExpert.

Definition at line 56 of file Expert.h.

      {
 
        B2ERROR("Attempted to call applyMulticlass() of the abstract base class MVA::Expert. All methods that support multiclass classification should override this definition.");
        (void) test_data;
 
        return std::vector<std::vector<float>>();
      };

load()

void load ( Weightfile & weightfile )

overridevirtual

Load the PDF expert from a Weightfile.

Parameters

weightfile

containing all information necessary to build the expert

Implements Expert.

Definition at line 118 of file PDF.cc.

    {
 
      std::string custom_weightfile = weightfile.generateFileName();
      weightfile.getFile("PDF_Weightfile", custom_weightfile);
      std::fstream file(custom_weightfile, std::ios_base::in);
 
      unsigned int nBins = 0;
      file >> nBins;
      m_value.resize(nBins, 0);
 
      std::vector<float> boundaries(nBins + 1, 0);
 
      for (unsigned int iBin = 0; iBin < nBins; ++iBin) {
        file >> m_value[iBin] >> boundaries[iBin];
      }
      file >> boundaries[nBins];
 
      m_binning.m_boundaries = boundaries;
 
      file.close();
 
      weightfile.getOptions(m_specific_options);
    }

Member Data Documentation

m_binning

Binning m_binning

private

used binning

Definition at line 103 of file PDF.h.

m_general_options

GeneralOptions m_general_options

protectedinherited

General options loaded from the weightfile.

Definition at line 70 of file Expert.h.

m_specific_options

PDFOptions m_specific_options

private

Specific options of the PDF method.

Definition at line 102 of file PDF.h.

m_value

std::vector<double> m_value

private

value returned by expert for each bin

Definition at line 104 of file PDF.h.

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

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