Bagging Class Reference

Bagging (boostrap aggregating). More...

#include <bagging.h>

Inheritance diagram for Bagging:

[legend]Collaboration diagram for Bagging:

[legend]List of all members.

Public Member Functions
	Bagging (UINT max=0)
	Bagging (std::istream &is)
virtual const id_t &	id () const
virtual Bagging *	create () const
	Create a new object using the default constructor.
virtual Bagging *	clone () const
	Create a new object by replicating itself.
virtual bool	support_weighted_data () const
	Whether the learning model/algorithm supports unequally weighted data.
virtual REAL	train ()
	Train with preset data set and sample weight.
virtual Output	operator() (const Input &) const

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Detailed Description

Bagging (boostrap aggregating).

Bagging averages over all hypotheses.

Todo:

Documentation

Definition at line 20 of file bagging.h.

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Constructor & Destructor Documentation

Bagging (  UINT  max = 0  )  [explicit]

Definition at line 12 of file bagging.cpp. References Aggregating::set_max_models(). Referenced by Bagging::clone(), and Bagging::create().

Bagging (  std::istream &  is  )  [inline, explicit]

Definition at line 23 of file bagging.h.

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Member Function Documentation

virtual Bagging* clone (   )  const [inline, virtual]

Create a new object by replicating itself. Returns: A pointer to the new copy. The code for a derived class Derived is always return new Derived(*this); Though seemingly redundant, it helps to copy an object without knowing the real type of the object. See also: C++ FAQ Lite 20.6 Implements Aggregating. Definition at line 27 of file bagging.h. References Bagging::Bagging().

virtual Bagging* create (   )  const [inline, virtual]

Create a new object using the default constructor. The code for a derived class Derived is always return new Derived(); Implements Aggregating. Definition at line 26 of file bagging.h. References Bagging::Bagging().

virtual const id_t& id (   )  const [virtual]

Returns: Class ID string (class name) Implements Object.

Output operator() (  const Input &   )  const [virtual]

Implements LearnModel. Definition at line 16 of file bagging.cpp. References LearnModel::_n_out, Aggregating::lm, Aggregating::n_in_agg, and Aggregating::size().

virtual bool support_weighted_data (   )  const [inline, virtual]

Whether the learning model/algorithm supports unequally weighted data. Returns: true if supporting; false otherwise. The default is false, just for safety. See also: set_train_data() Reimplemented from LearnModel. Definition at line 29 of file bagging.h.

REAL train (   )  [virtual]

Train with preset data set and sample weight. Returns: Probably the training error. Todo: Make the return type void Implements LearnModel. Definition at line 33 of file bagging.cpp. References Aggregating::empty(), LearnModel::initialize(), Aggregating::lm, Aggregating::lm_base, Aggregating::max_n_model, Aggregating::n_in_agg, LearnModel::n_samples, LearnModel::ptd, LearnModel::ptw, LearnModel::set_train_data(), LearnModel::train(), and VERBOSE_OUTPUT.

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

• bagging.h
• bagging.cpp

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