RegressionExpert< BaseClassifierExpert, RegressionClassifierOptions > Class Template Reference

Generic expert for the regression applications. More...

#include <Regression.h>

Inheritance diagram for RegressionExpert< BaseClassifierExpert, RegressionClassifierOptions >:

Collaboration diagram for RegressionExpert< BaseClassifierExpert, RegressionClassifierOptions >:

Public Member Functions
void	load (Weightfile &weightfile) override
	Load the expert from the weightfile by loading each stored single classifier one ofter the other.
std::vector< float >	apply (Dataset &test_data) const override
	Apply the loaded experts by averaging over the single expert decisions.
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
std::vector< BaseClassifierExpert >	m_baseClassifierExperts
	The list of single experts.

Detailed Description

template<class BaseClassifierExpert, class RegressionClassifierOptions>
class Belle2::MVA::RegressionExpert< BaseClassifierExpert, RegressionClassifierOptions >

Generic expert for the regression applications.

For each trained base classifier (for each bin, see the teacher for how this is done), one weight file is written as part into the full weight file. This expert reads all of them, instantiates a base expert for each bin and applies all of them to the dataset.

The result of the calculation is given by

 probability = sum(expert probability for each expert) / # of experts

Template Parameters

BaseClassifierExpert	Another expert that is related to the teacher you used for training
RegressionClassifierOptions	The options of the classifier (probably inheriting from RegressionOptions)

Definition at line 189 of file Regression.h.

Member Function Documentation

apply()

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.

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

• mva/methods/include/Regression.h