gum::learning::AprioriBDeu< ALLOC > Class Template Reference

the internal apriori for the BDeu score (N' / (r_i * q_i)BDeu is a BD score with a N'/(r_i * q_i) apriori, where N' is an effective sample size and r_i is the domain size of the target variable and q_i is the domain size of the Cartesian product of its parents. More...

#include <agrum/tools/database/aprioriBDeu.h>

Inheritance diagram for gum::learning::AprioriBDeu< ALLOC >:

Collaboration diagram for gum::learning::AprioriBDeu< ALLOC >:

Public Member Functions
Constructors / Destructors
	AprioriBDeu (const DatabaseTable< ALLOC > &database, const Bijection< NodeId, std::size_t, ALLOC< std::size_t > > &nodeId2columns=Bijection< NodeId, std::size_t, ALLOC< std::size_t > >(), const allocator_type &alloc=allocator_type())
	default constructor More...
	AprioriBDeu (const AprioriBDeu< ALLOC > &from)
	copy constructor More...
	AprioriBDeu (const AprioriBDeu< ALLOC > &from, const allocator_type &alloc)
	copy constructor with a given allocator More...
	AprioriBDeu (AprioriBDeu< ALLOC > &&from)
	move constructor More...
	AprioriBDeu (AprioriBDeu< ALLOC > &&from, const allocator_type &alloc)
	move constructor with a given allocator More...
virtual AprioriBDeu< ALLOC > *	clone () const
	virtual copy constructor More...
virtual AprioriBDeu< ALLOC > *	clone (const allocator_type &alloc) const
	virtual copy constructor with a given allocator More...
virtual	~AprioriBDeu ()
	destructor More...
Operators
AprioriBDeu< ALLOC > &	operator= (const AprioriBDeu< ALLOC > &from)
	copy operator More...
AprioriBDeu< ALLOC > &	operator= (AprioriBDeu< ALLOC > &&from)
	move operator More...
Accessors / Modifiers
virtual void	setWeight (const double weight) final
	sets the effective sample size N' (alias of setEffectiveSampleSize ()) More...
void	setEffectiveSampleSize (const double weight)
	sets the effective sample size N' More...
virtual bool	isOfType (const std::string &type) final
	indicates whether an apriori is of a certain type More...
virtual const std::string &	getType () const final
	returns the type of the apriori More...
virtual bool	isInformative () const final
	indicates whether the apriori is potentially informative More...
virtual void	addAllApriori (const IdCondSet< ALLOC > &idset, std::vector< double, ALLOC< double > > &counts) final
	adds the apriori to a counting vector corresponding to the idset More...
virtual void	addConditioningApriori (const IdCondSet< ALLOC > &idset, std::vector< double, ALLOC< double > > &counts) final
	adds the apriori to a counting vectordefined over the right hand side of the idset More...
Accessors / Modifiers
double	weight () const
	returns the weight assigned to the apriori More...
allocator_type	getAllocator () const
	returns the allocator used by the internal apriori More...

Public Types
using	type = AprioriBDeuType
	the type of the a priori More...
using	allocator_type = ALLOC< NodeId >
	type for the allocators passed in arguments of methods More...

Protected Attributes
double	weight_ {1.0}
	the weight of the apriori More...
const DatabaseTable< ALLOC > *	database_
	a reference to the database in order to have access to its variables More...
Bijection< NodeId, std::size_t, ALLOC< std::size_t > >	nodeId2columns_
	a mapping from the NodeIds of the variables to the indices of the columns in the database More...

Detailed Description

template<template< typename > class ALLOC = std::allocator>
class gum::learning::AprioriBDeu< ALLOC >

the internal apriori for the BDeu score (N' / (r_i * q_i)

BDeu is a BD score with a N'/(r_i * q_i) apriori, where N' is an effective sample size and r_i is the domain size of the target variable and q_i is the domain size of the Cartesian product of its parents.

It is important to note that, to be meaningful a structure + parameter learning requires that the same aprioris are taken into account during structure learning and parameter learning.

Definition at line 53 of file aprioriBDeu.h.

Member Typedef Documentation

allocator_type

template<template< typename > class ALLOC = std::allocator>

using gum::learning::AprioriBDeu< ALLOC >::allocator_type = ALLOC< NodeId >

type for the allocators passed in arguments of methods

Definition at line 59 of file aprioriBDeu.h.

type

template<template< typename > class ALLOC = std::allocator>

using gum::learning::AprioriBDeu< ALLOC >::type = AprioriBDeuType

the type of the a priori

Definition at line 56 of file aprioriBDeu.h.

Constructor & Destructor Documentation

AprioriBDeu() [1/5]

template<template< typename > class ALLOC = std::allocator>

gum::learning::AprioriBDeu< ALLOC >::AprioriBDeu	(	const DatabaseTable< ALLOC > &	database,
		const Bijection< NodeId, std::size_t, ALLOC< std::size_t > > &	nodeId2columns = Bijection< NodeId, std::size_t, ALLOC< std::size_t > >(),
		const allocator_type &	alloc = allocator_type()
	)

default constructor

Parameters

database	the database from which learning is performed. This is useful to get access to the random variables
nodeId2Columns	a mapping from the ids of the nodes in the graphical model to the corresponding column in the DatabaseTable. This enables estimating from a database in which variable A corresponds to the 2nd column the parameters of a BN in which variable A has a NodeId of 5. An empty nodeId2Columns bijection means that the mapping is an identity, i.e., the value of a NodeId is equal to the index of the column in the DatabaseTable.
alloc	the allocator used to allocate the structures within the RecordCounter.

AprioriBDeu() [2/5]

template<template< typename > class ALLOC = std::allocator>

gum::learning::AprioriBDeu< ALLOC >::AprioriBDeu

(

const AprioriBDeu< ALLOC > &

from

)

copy constructor

AprioriBDeu() [3/5]

template<template< typename > class ALLOC = std::allocator>

gum::learning::AprioriBDeu< ALLOC >::AprioriBDeu	(	const AprioriBDeu< ALLOC > &	from,
		const allocator_type &	alloc
	)

copy constructor with a given allocator

AprioriBDeu() [4/5]

template<template< typename > class ALLOC = std::allocator>

gum::learning::AprioriBDeu< ALLOC >::AprioriBDeu

(

AprioriBDeu< ALLOC > &&

from

)

move constructor

AprioriBDeu() [5/5]

template<template< typename > class ALLOC = std::allocator>

gum::learning::AprioriBDeu< ALLOC >::AprioriBDeu	(	AprioriBDeu< ALLOC > &&	from,
		const allocator_type &	alloc
	)

move constructor with a given allocator

~AprioriBDeu()

template<template< typename > class ALLOC = std::allocator>

virtual gum::learning::AprioriBDeu< ALLOC >::~AprioriBDeu ( )

virtual

destructor

Member Function Documentation

addAllApriori()

template<template< typename > class ALLOC = std::allocator>

virtual void gum::learning::AprioriBDeu< ALLOC >::addAllApriori ( const IdCondSet< ALLOC > &  idset, std::vector< double, ALLOC< double > > &  counts  )

finalvirtual

adds the apriori to a counting vector corresponding to the idset

adds the apriori to an already created counting vector defined over the union of the variables on both the left and right hand side of the conditioning bar of the idset.

Warning

the method assumes that the size of the vector is exactly the domain size of the joint variables set.

Implements gum::learning::Apriori< ALLOC >.

addConditioningApriori()

template<template< typename > class ALLOC = std::allocator>

virtual void gum::learning::AprioriBDeu< ALLOC >::addConditioningApriori ( const IdCondSet< ALLOC > &  idset, std::vector< double, ALLOC< double > > &  counts  )

finalvirtual

adds the apriori to a counting vectordefined over the right hand side of the idset

Warning

the method assumes that the size of the vector is exactly the domain size of the joint RHS variables of the idset.

Implements gum::learning::Apriori< ALLOC >.

clone() [1/2]

template<template< typename > class ALLOC = std::allocator>

virtual AprioriBDeu< ALLOC >* gum::learning::AprioriBDeu< ALLOC >::clone ( ) const

virtual

virtual copy constructor

Implements gum::learning::Apriori< ALLOC >.

clone() [2/2]

template<template< typename > class ALLOC = std::allocator>

virtual AprioriBDeu< ALLOC >* gum::learning::AprioriBDeu< ALLOC >::clone ( const allocator_type &  alloc ) const

virtual

virtual copy constructor with a given allocator

Implements gum::learning::Apriori< ALLOC >.

getAllocator()

template<template< typename > class ALLOC = std::allocator>

allocator_type gum::learning::Apriori< ALLOC >::getAllocator ( ) const

inherited

returns the allocator used by the internal apriori

getType()

template<template< typename > class ALLOC = std::allocator>

virtual const std::string& gum::learning::AprioriBDeu< ALLOC >::getType ( ) const

finalvirtual

returns the type of the apriori

Implements gum::learning::Apriori< ALLOC >.

isInformative()

template<template< typename > class ALLOC = std::allocator>

virtual bool gum::learning::AprioriBDeu< ALLOC >::isInformative ( ) const

finalvirtual

indicates whether the apriori is potentially informative

Basically, only the NoApriori is uninformative. However, it may happen that, under some circonstances, an apriori, which is usually not equal to the NoApriori, becomes equal to it (e.g., when the weight is equal to zero). In this case, if the apriori can detect this case, it shall inform the classes that use it that it is temporarily uninformative. These classes will then be able to speed-up their code by avoiding to take into account the apriori in their computations.

Implements gum::learning::Apriori< ALLOC >.

isOfType()

template<template< typename > class ALLOC = std::allocator>

virtual bool gum::learning::AprioriBDeu< ALLOC >::isOfType ( const std::string &  type )

finalvirtual

indicates whether an apriori is of a certain type

Implements gum::learning::Apriori< ALLOC >.

operator=() [1/2]

template<template< typename > class ALLOC = std::allocator>

AprioriBDeu< ALLOC >& gum::learning::AprioriBDeu< ALLOC >::operator=

(

const AprioriBDeu< ALLOC > &

from

)

copy operator

operator=() [2/2]

template<template< typename > class ALLOC = std::allocator>

AprioriBDeu< ALLOC >& gum::learning::AprioriBDeu< ALLOC >::operator=

(

AprioriBDeu< ALLOC > &&

from

)

move operator

setEffectiveSampleSize()

template<template< typename > class ALLOC = std::allocator>

void gum::learning::AprioriBDeu< ALLOC >::setEffectiveSampleSize

(

const double

weight

)

sets the effective sample size N'

setWeight()

template<template< typename > class ALLOC = std::allocator>

virtual void gum::learning::AprioriBDeu< ALLOC >::setWeight ( const double  weight )

finalvirtual

sets the effective sample size N' (alias of setEffectiveSampleSize ())

Reimplemented from gum::learning::Apriori< ALLOC >.

weight()

template<template< typename > class ALLOC = std::allocator>

double gum::learning::Apriori< ALLOC >::weight ( ) const

inherited

returns the weight assigned to the apriori

Member Data Documentation

database_

template<template< typename > class ALLOC = std::allocator>

const DatabaseTable< ALLOC >* gum::learning::Apriori< ALLOC >::database_

protectedinherited

a reference to the database in order to have access to its variables

Definition at line 144 of file apriori.h.

nodeId2columns_

template<template< typename > class ALLOC = std::allocator>

Bijection< NodeId, std::size_t, ALLOC< std::size_t > > gum::learning::Apriori< ALLOC >::nodeId2columns_

protectedinherited

a mapping from the NodeIds of the variables to the indices of the columns in the database

Definition at line 148 of file apriori.h.

weight_

template<template< typename > class ALLOC = std::allocator>

double gum::learning::Apriori< ALLOC >::weight_ {1.0}

protectedinherited

the weight of the apriori

Definition at line 141 of file apriori.h.

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

• agrum/BN/learning/aprioris/aprioriBDeu.h