Expert for the Combination MVA method. More...

Inheritance diagram for CombinationExpert:

Collaboration diagram for CombinationExpert:

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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< std::vector< float > >	applyMulticlass (Dataset &test_data) const
	Apply this m_expert onto a dataset. More...

Protected Attributes
GeneralOptions	m_general_options
	General options loaded from the weightfile.

Private Attributes
CombinationOptions	m_specific_options
	Method specific options.

std::vector< std::unique_ptr< Expert > >	m_experts
	Experts of the methods to combine.

std::vector< std::vector< std::string > >	m_expert_variables
	Results of the experts to combine.

Detailed Description

Expert for the Combination MVA method.

Definition at line 83 of file Combination.h.

Member Function Documentation

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

overridevirtual

Apply this expert onto a dataset.

Parameters

test_data dataset

Implements Expert.

Definition at line 105 of file Combination.cc.

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.

Definition at line 56 of file Expert.h.

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 77 of file Combination.cc.

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