gum::learning::NoPrior Class Reference

the no a priorclass: corresponds to 0 weight-sample More...

#include <agrum/BN/learning/priors/noPrior.h>

Inheritance diagram for gum::learning::NoPrior:

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Public Member Functions
Constructors / Destructors
	NoPrior (const DatabaseTable &database, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
	default constructor
	NoPrior (const NoPrior &from)
	copy constructor
	NoPrior (NoPrior &&from)
	move constructor
virtual NoPrior *	clone () const
	virtual copy constructor
virtual	~NoPrior ()
	destructor
Operators
NoPrior &	operator= (const NoPrior &from)
	copy operator
NoPrior &	operator= (NoPrior &&from)
	move operator
Accessors / Modifiers
void	setWeight (const double weight) final
	sets the weight of the a prior(kind of effective sample size)
PriorType	getType () const final
	returns the type of the prior
bool	isInformative () const final
	indicates whether the prior is tensorly informative
void	addJointPseudoCount (const IdCondSet &idset, std::vector< double > &counts) final
	adds the prior to a counting vector corresponding to the idset
void	addConditioningPseudoCount (const IdCondSet &idset, std::vector< double > &counts) final
	adds the prior to a counting vectordefined over the right hand side of the idset
Accessors / Modifiers
double	weight () const
	returns the weight assigned to the prior

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

Detailed Description

the no a priorclass: corresponds to 0 weight-sample

Definition at line 65 of file noPrior.h.

Constructor & Destructor Documentation

NoPrior() [1/3]

gum::learning::NoPrior::NoPrior	(	const DatabaseTable &	database,
		const Bijection< NodeId, std::size_t > &	nodeId2columns = Bijection< NodeId, std::size_t >() )

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.

Referenced by NoPrior(), NoPrior(), clone(), operator=(), and operator=().

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NoPrior() [2/3]

gum::learning::NoPrior::NoPrior

(

const NoPrior &

from

)

copy constructor

References NoPrior().

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NoPrior() [3/3]

gum::learning::NoPrior::NoPrior

(

NoPrior &&

from

)

move constructor

References NoPrior().

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~NoPrior()

virtual gum::learning::NoPrior::~NoPrior ( )

virtual

destructor

Member Function Documentation

addConditioningPseudoCount()

void gum::learning::NoPrior::addConditioningPseudoCount ( const IdCondSet & idset, std::vector< double > & counts )

finalvirtual

adds the prior 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::Prior.

References addConditioningPseudoCount().

Referenced by addConditioningPseudoCount().

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addJointPseudoCount()

void gum::learning::NoPrior::addJointPseudoCount ( const IdCondSet & idset, std::vector< double > & counts )

finalvirtual

adds the prior to a counting vector corresponding to the idset

adds the prior 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::Prior.

References addJointPseudoCount().

Referenced by addJointPseudoCount().

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clone()

virtual NoPrior * gum::learning::NoPrior::clone ( ) const

virtual

virtual copy constructor

Implements gum::learning::Prior.

References NoPrior().

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getType()

PriorType gum::learning::NoPrior::getType ( ) const

finalvirtual

returns the type of the prior

Implements gum::learning::Prior.

isInformative()

bool gum::learning::NoPrior::isInformative ( ) const

finalvirtual

indicates whether the prior is tensorly informative

Basically, only the NoPrior is uninformative. However, it may happen that, under some circonstances, an prior, which is usually not equal to the NoPrior, becomes equal to it (e.g., when the weight is equal to zero). In this case, if the prior 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 prior in their computations.

Implements gum::learning::Prior.

References isInformative().

Referenced by isInformative().

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operator=() [1/2]

NoPrior & gum::learning::NoPrior::operator=

(

const NoPrior &

from

)

copy operator

References NoPrior().

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operator=() [2/2]

NoPrior & gum::learning::NoPrior::operator=

(

NoPrior &&

from

)

move operator

References NoPrior().

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setWeight()

void gum::learning::NoPrior::setWeight ( const double weight )

finalvirtual

sets the weight of the a prior(kind of effective sample size)

Reimplemented from gum::learning::Prior.

References gum::learning::Prior::weight().

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weight()

double gum::learning::Prior::weight ( ) const

inherited

returns the weight assigned to the prior

Referenced by gum::learning::BDeuPrior::setEffectiveSampleSize(), gum::learning::BDeuPrior::setWeight(), gum::learning::DirichletPriorFromBN< GUM_SCALAR >::setWeight(), gum::learning::DirichletPriorFromDatabase::setWeight(), gum::learning::K2Prior::setWeight(), gum::learning::NoPrior::setWeight(), and setWeight().

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Member Data Documentation

database_

const DatabaseTable* gum::learning::Prior::database_

protectedinherited

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

Definition at line 161 of file prior.h.

nodeId2columns_

Bijection< NodeId, std::size_t > gum::learning::Prior::nodeId2columns_

protectedinherited

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

Definition at line 165 of file prior.h.

weight_

double gum::learning::Prior::weight_ {1.0}

protectedinherited

the weight of the prior

Definition at line 158 of file prior.h.

{1.0};

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

• agrum/BN/learning/priors/noPrior.h