<agrum/BN/inference/lazyPropagation.h> More...

#include <lazyPropagation.h>

Inheritance diagram for gum::LazyPropagation< GUM_SCALAR >:

Collaboration diagram for gum::LazyPropagation< GUM_SCALAR >:

Public Member Functions
Potential< GUM_SCALAR >	evidenceJointImpact (const NodeSet &targets, const NodeSet &evs)
	Create a gum::Potential for P(joint targets\|evs) (for all instanciation of targets and evs) More...

Potential< GUM_SCALAR >	evidenceJointImpact (const std::vector< std::string > &targets, const std::vector< std::string > &evs)
	Create a gum::Potential for P(joint targets\|evs) (for all instanciation of targets and evs) More...

Potential< GUM_SCALAR >	evidenceImpact (NodeId target, const NodeSet &evs)
	Create a gum::Potential for P(target\|evs) (for all instanciation of target and evs) More...

Potential< GUM_SCALAR >	evidenceImpact (const std::string &target, const std::vector< std::string > &evs)
	Create a gum::Potential for P(target\|evs) (for all instanciation of target and evs) More...

Constructors / Destructors
	LazyPropagation (const IBayesNet< GUM_SCALAR > *BN, RelevantPotentialsFinderType=RelevantPotentialsFinderType::DSEP_BAYESBALL_POTENTIALS, FindBarrenNodesType=FindBarrenNodesType::FIND_BARREN_NODES, bool use_binary_join_tree=true)
	default constructor More...

	LazyPropagation (const LazyPropagation< GUM_SCALAR > &)=delete
	avoid copy constructors More...

LazyPropagation< GUM_SCALAR > &	operator= (const LazyPropagation< GUM_SCALAR > &)=delete
	avoid copy operators More...

	~LazyPropagation () final
	destructor More...

Accessors / Modifiers
void	setTriangulation (const Triangulation &new_triangulation)
	use a new triangulation algorithm More...

void	setRelevantPotentialsFinderType (RelevantPotentialsFinderType type)
	sets how we determine the relevant potentials to combine More...

void	setFindBarrenNodesType (FindBarrenNodesType type)
	sets how we determine barren nodes More...

const JoinTree *	joinTree ()
	returns the current join tree used More...

const JunctionTree *	junctionTree ()
	returns the current junction tree More...

GUM_SCALAR	evidenceProbability () final
	returns the probability of evidence More...

Probability computations
virtual const Potential< GUM_SCALAR > &	jointPosterior (const NodeSet &nodes) final
	Compute the joint posterior of a set of nodes. More...

virtual const Potential< GUM_SCALAR > &	posterior (NodeId node) final
	Computes and returns the posterior of a node. More...

virtual const Potential< GUM_SCALAR > &	posterior (const std::string &nodeName) final
	Computes and returns the posterior of a node. More...

Targets
virtual void	eraseAllTargets ()
	Clear all previously defined targets (marginal and joint targets) More...

virtual void	eraseAllJointTargets () final
	Clear all previously defined joint targets. More...

virtual void	eraseAllMarginalTargets () final
	Clear all the previously defined marginal targets. More...

virtual void	addJointTarget (const NodeSet &joint_target) final
	Add a set of nodes as a new joint target. As a collateral effect, every node is added as a marginal target. More...

virtual void	eraseJointTarget (const NodeSet &joint_target) final
	removes an existing joint target More...

virtual bool	isJointTarget (const NodeSet &vars) const final
	return true if target is a joint target. More...

virtual const Set< NodeSet > &	jointTargets () const noexcept final
	returns the list of joint targets More...

virtual Size	nbrJointTargets () const noexcept final
	returns the number of joint targets More...

Information Theory related functions
GUM_SCALAR	I (NodeId X, NodeId Y)
	Mutual information between X and Y. More...

GUM_SCALAR	VI (NodeId X, NodeId Y)
	Variation of information between X and Y. More...

GUM_SCALAR	jointMutualInformation (const NodeSet &targets)
	Mutual information between targets. More...

GUM_SCALAR	jointMutualInformation (const std::vector< std::string > &targets)
	Mutual information between targets. More...

Targets
virtual void	addAllTargets () final
	adds all nodes as targets More...

virtual void	addTarget (NodeId target) final
	Add a marginal target to the list of targets. More...

virtual void	addTarget (const std::string &nodeName) final
	Add a marginal target to the list of targets. More...

virtual void	eraseTarget (NodeId target) final
	removes an existing (marginal) target More...

virtual void	eraseTarget (const std::string &nodeName) final
	removes an existing (marginal) target More...

virtual bool	isTarget (NodeId node) const final
	return true if variable is a (marginal) target More...

virtual bool	isTarget (const std::string &nodeName) const final
	return true if variable is a (marginal) target More...

virtual const Size	nbrTargets () const noexcept final
	returns the number of marginal targets More...

virtual const NodeSet &	targets () const noexcept final
	returns the list of marginal targets More...

Information Theory related functions
virtual GUM_SCALAR	H (NodeId X) final
	Entropy Compute Shanon's entropy of a node given the observation. More...

virtual GUM_SCALAR	H (const std::string &nodeName) final
	Entropy Compute Shanon's entropy of a node given the observation. More...

Accessors / Modifiers
virtual void	setBN (const IBayesNet< GUM_SCALAR > *bn)
	assigns a new BN to the inference engine More...

virtual const IBayesNet< GUM_SCALAR > &	BN () const final
	Returns a constant reference over the IBayesNet referenced by this class. More...

virtual const NodeProperty< Size > &	domainSizes () const final
	get the domain sizes of the random variables of the BN More...

virtual bool	isInferenceReady () const noexcept final
	returns whether the inference object is in a ready state More...

virtual bool	isInferenceOutdatedBNStructure () const noexcept final
	returns whether the inference object is in a OutdatedBNStructure state More...

virtual bool	isInferenceOutdatedBNPotentials () const noexcept final
	returns whether the inference object is in a OutdatedBNPotential state More...

virtual bool	isInferenceDone () const noexcept final
	returns whether the inference object is in a InferenceDone state More...

virtual bool	isDone () const noexcept final
	returns whether the inference object is in a done state More...

virtual void	prepareInference () final
	prepare the internal inference structures for the next inference More...

virtual void	makeInference () final
	perform the heavy computations needed to compute the targets' posteriors More...

virtual void	clear ()
	clears all the data structures allocated for the last inference More...

virtual StateOfInference	state () const noexcept final
	returns the state of the inference engine More...

Evidence
virtual void	addEvidence (NodeId id, const Idx val) final
	adds a new hard evidence on node id More...

virtual void	addEvidence (const std::string &nodeName, const Idx val) final
	adds a new hard evidence on node named nodeName More...

virtual void	addEvidence (NodeId id, const std::string &label) final
	adds a new hard evidence on node id More...

virtual void	addEvidence (const std::string &nodeName, const std::string &label) final
	adds a new hard evidence on node named nodeName More...

virtual void	addEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node id (might be soft or hard) More...

virtual void	addEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node named nodeName (might be soft or hard) More...

virtual void	addEvidence (const Potential< GUM_SCALAR > &pot) final
	adds a new evidence on node id (might be soft or hard) More...

virtual void	addEvidence (Potential< GUM_SCALAR > &&pot) final
	adds a new evidence on node id (might be soft or hard) More...

virtual void	addSetOfEvidence (const Set< const Potential< GUM_SCALAR > * > &potset) final
	adds a new set of evidence More...

virtual void	addListOfEvidence (const List< const Potential< GUM_SCALAR > * > &potlist) final
	adds a new list of evidence More...

virtual void	chgEvidence (NodeId id, const Idx val) final
	change the value of an already existing hard evidence More...

virtual void	chgEvidence (const std::string &nodeName, const Idx val) final
	change the value of an already existing hard evidence More...

virtual void	chgEvidence (NodeId id, const std::string &label) final
	change the value of an already existing hard evidence More...

virtual void	chgEvidence (const std::string &nodeName, const std::string &label) final
	change the value of an already existing hard evidence More...

virtual void	chgEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard) More...

virtual void	chgEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard) More...

virtual void	chgEvidence (const Potential< GUM_SCALAR > &pot) final
	change the value of an already existing evidence (might be soft or hard) More...

virtual void	eraseAllEvidence () final
	removes all the evidence entered into the network More...

virtual void	eraseEvidence (NodeId id) final
	removed the evidence, if any, corresponding to node id More...

virtual void	eraseEvidence (const std::string &nodeName) final
	removed the evidence, if any, corresponding to node of name nodeName More...

virtual bool	hasEvidence () const final
	indicates whether some node(s) have received evidence More...

virtual bool	hasEvidence (NodeId id) const final
	indicates whether node id has received an evidence More...

virtual bool	hasEvidence (const std::string &nodeName) const final
	indicates whether node id has received an evidence More...

virtual bool	hasHardEvidence (NodeId id) const final
	indicates whether node id has received a hard evidence More...

virtual bool	hasHardEvidence (const std::string &nodeName) const final
	indicates whether node id has received a hard evidence More...

virtual bool	hasSoftEvidence (NodeId id) const final
	indicates whether node id has received a soft evidence More...

virtual bool	hasSoftEvidence (const std::string &nodeName) const final
	indicates whether node id has received a soft evidence More...

virtual Size	nbrEvidence () const final
	returns the number of evidence entered into the Bayesian network More...

virtual Size	nbrHardEvidence () const final
	returns the number of hard evidence entered into the Bayesian network More...

virtual Size	nbrSoftEvidence () const final
	returns the number of soft evidence entered into the Bayesian network More...

const NodeProperty< const Potential< GUM_SCALAR > *> &	evidence () const
	returns the set of evidence More...

const NodeSet &	softEvidenceNodes () const
	returns the set of nodes with soft evidence More...

const NodeSet &	hardEvidenceNodes () const
	returns the set of nodes with hard evidence More...

const NodeProperty< Idx > &	hardEvidence () const
	indicate for each node with hard evidence which value it took More...

Public Types
enum	StateOfInference { StateOfInference::OutdatedBNStructure, StateOfInference::OutdatedBNPotentials, StateOfInference::ReadyForInference, StateOfInference::Done }
	current state of the inference More...

Protected Member Functions
void	_onEvidenceAdded (const NodeId id, bool isHardEvidence) final
	fired after a new evidence is inserted More...

void	_onEvidenceErased (const NodeId id, bool isHardEvidence) final
	fired before an evidence is removed More...

void	_onAllEvidenceErased (bool has_hard_evidence) final
	fired before all the evidence are erased More...

void	_onEvidenceChanged (const NodeId id, bool hasChangedSoftHard) final
	fired after an evidence is changed, in particular when its status (soft/hard) changes More...

void	_onMarginalTargetAdded (const NodeId id) final
	fired after a new single target is inserted More...

void	_onMarginalTargetErased (const NodeId id) final
	fired before a single target is removed More...

virtual void	_onBayesNetChanged (const IBayesNet< GUM_SCALAR > *bn) final
	fired after a new Bayes net has been assigned to the engine More...

void	_onJointTargetAdded (const NodeSet &set) final
	fired after a new joint target is inserted More...

void	_onJointTargetErased (const NodeSet &set) final
	fired before a joint target is removed More...

void	_onAllMarginalTargetsAdded () final
	fired after all the nodes of the BN are added as single targets More...

void	_onAllMarginalTargetsErased () final
	fired before a all the single targets are removed More...

void	_onAllJointTargetsErased () final
	fired before a all the joint targets are removed More...

void	_onAllTargetsErased () final
	fired before a all single and joint_targets are removed More...

void	_onStateChanged () final
	fired when the stage is changed More...

void	_updateOutdatedBNStructure () final
	prepares inference when the latter is in OutdatedBNStructure state More...

void	_updateOutdatedBNPotentials () final
	prepares inference when the latter is in OutdatedBNPotentials state More...

void	_makeInference () final
	called when the inference has to be performed effectively More...

const Potential< GUM_SCALAR > &	_posterior (NodeId id) final
	returns the posterior of a given variable More...

const Potential< GUM_SCALAR > &	_jointPosterior (const NodeSet &set) final
	returns the posterior of a declared target set More...

const Potential< GUM_SCALAR > &	_jointPosterior (const NodeSet &wanted_target, const NodeSet &declared_target) final
	asks derived classes for the joint posterior of a set of variables not declared as a joint target More...

Potential< GUM_SCALAR > *	_unnormalizedJointPosterior (NodeId id) final
	returns a fresh potential equal to P(argument,evidence) More...

Potential< GUM_SCALAR > *	_unnormalizedJointPosterior (const NodeSet &set) final
	returns a fresh potential equal to P(argument,evidence) More...

void	_setTargetedMode ()

bool	_isTargetedMode () const

void	_setOutdatedBNStructureState ()
	put the inference into an outdated BN structure state More...

void	_setOutdatedBNPotentialsState ()
	puts the inference into an OutdatedBNPotentials state if it is not already in an OutdatedBNStructure state More...

Detailed Description

template<typename GUM_SCALAR>
class gum::LazyPropagation< GUM_SCALAR >

Implementation of a Shafer-Shenoy's-like version of lazy propagation for inference in Bayesian Networks

Definition at line 71 of file lazyPropagation.h.

Member Typedef Documentation

◆ __PotentialSet

template<typename GUM_SCALAR>

typedef Set< const Potential< GUM_SCALAR >* > gum::LazyPropagation< GUM_SCALAR >::__PotentialSet

private

Definition at line 245 of file lazyPropagation.h.

◆ __PotentialSetIterator

template<typename GUM_SCALAR>

typedef SetIteratorSafe< const Potential< GUM_SCALAR >* > gum::LazyPropagation< GUM_SCALAR >::__PotentialSetIterator

private

Definition at line 247 of file lazyPropagation.h.

Member Enumeration Documentation

◆ EvidenceChangeType

template<typename GUM_SCALAR>

enum gum::LazyPropagation::EvidenceChangeType

private

the possible types of evidence changes

Enumerator
EVIDENCE_ADDED
EVIDENCE_ERASED
EVIDENCE_MODIFIED

Definition at line 376 of file lazyPropagation.h.

376 { EVIDENCE_ADDED, EVIDENCE_ERASED, EVIDENCE_MODIFIED };

gum::LazyPropagation::EVIDENCE_MODIFIED

Definition: lazyPropagation.h:376

gum::LazyPropagation::EVIDENCE_ADDED

Definition: lazyPropagation.h:376

gum::LazyPropagation::EVIDENCE_ERASED

Definition: lazyPropagation.h:376

◆ StateOfInference

template<typename GUM_SCALAR >

enum gum::BayesNetInference::StateOfInference

stronginherited

current state of the inference

BayesNetInference can be in one of 4 different states:

OutdatedBNStructure: in this state, the inference is fully unprepared to be applied because some events changed the "logical" structure of the BN: for instance a node received a hard evidence, which implies that its outgoing arcs can be removed from the BN, hence involving a structural change in the BN. As a consequence, the (incremental) inference (probably) needs a significant amount of preparation to be ready for the next inference. In a Lazy propagation, for instance, this step amounts to compute a new join tree, hence a new structure in which inference will be applied. Note that classes that inherit from BayesNetInference may be smarter than BayesNetInference and may, in some situations, find out that their data structures are still ok for inference and, therefore, only resort to perform the actions related to the OutdatedBNPotentials state. As an example, consider a LazyPropagation inference in Bayes Net A->B->C->D->E in which C has received hard evidence e_C and E is the only target. In this case, A and B are not needed for inference, the only potentials that matter are P(D|e_C) and P(E|D). So the smallest join tree needed for inference contains only one clique DE. Now, adding new evidence e_A on A has no impact on E given hard evidence e_C. In this case, LazyPropagation can be smart and not update its join tree.
OutdatedBNPotentials: in this state, the structure of the BN remains unchanged, only some potentials stored in it have changed. Therefore, the inference probably just needs to invalidate some already computed potentials to be ready. Only a light amount of preparation is needed to be able to perform inference.
Ready4Inference: in this state, all the data structures are ready for inference. There just remains to perform the inference computations.
Done: the heavy computations of inference have been done. There might still remain a few light computations to perform to get the posterior potentials we need. Typically, in Lazy Propagation, all the messages in the join tree have been computed but, to get the potentials, we still need to perform the combinations of the potentials in the cliques with the messages sent to the cliques. In some inference algorithms, this step may even be empty.

Enumerator
OutdatedBNStructure
OutdatedBNPotentials
ReadyForInference
Done

Definition at line 183 of file BayesNetInference.h.

                                 {
       OutdatedBNStructure,
       OutdatedBNPotentials,
       ReadyForInference,
       Done
     };

Constructor & Destructor Documentation

◆ LazyPropagation() [1/2]

template<typename GUM_SCALAR>

gum::LazyPropagation< GUM_SCALAR >::LazyPropagation	(	const IBayesNet< GUM_SCALAR > *	BN,
		RelevantPotentialsFinderType	= `RelevantPotentialsFinderType::DSEP_BAYESBALL_POTENTIALS`,
		FindBarrenNodesType	= `FindBarrenNodesType::FIND_BARREN_NODES`,
		bool	use_binary_join_tree = `true`
	)

explicit

default constructor

◆ LazyPropagation() [2/2]

template<typename GUM_SCALAR>

gum::LazyPropagation< GUM_SCALAR >::LazyPropagation ( const LazyPropagation< GUM_SCALAR > & )

delete

avoid copy constructors

◆ ~LazyPropagation()

template<typename GUM_SCALAR>

gum::LazyPropagation< GUM_SCALAR >::~LazyPropagation ( )

final

destructor

Member Function Documentation

◆ __collectMessage()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__collectMessage	(	NodeId	id,
		NodeId	from
	)

private

actually perform the collect phase

◆ __computeJoinTreeRoots()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__computeJoinTreeRoots ( )

private

compute a root for each connected component of __JT

◆ __createNewJT()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__createNewJT ( )

private

create a new junction tree as well as its related data structures

◆ __diffuseMessageInvalidations()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__diffuseMessageInvalidations	(	NodeId	from_id,
		NodeId	to_id,
		NodeSet &	invalidated_cliques
	)

private

invalidate all the messages sent from a given clique

◆ __findRelevantPotentialsGetAll()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__findRelevantPotentialsGetAll	(	__PotentialSet &	pot_list,
		Set< const DiscreteVariable * > &	kept_vars
	)

private

update a set of potentials: the remaining are those to be combined to produce a message on a separator

◆ __findRelevantPotentialsWithdSeparation()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__findRelevantPotentialsWithdSeparation	(	__PotentialSet &	pot_list,
		Set< const DiscreteVariable * > &	kept_vars
	)

private

update a set of potentials: the remaining are those to be combined to produce a message on a separator

◆ __findRelevantPotentialsWithdSeparation2()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__findRelevantPotentialsWithdSeparation2	(	__PotentialSet &	pot_list,
		Set< const DiscreteVariable * > &	kept_vars
	)

private

update a set of potentials: the remaining are those to be combined to produce a message on a separator

◆ __findRelevantPotentialsWithdSeparation3()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__findRelevantPotentialsWithdSeparation3	(	__PotentialSet &	pot_list,
		Set< const DiscreteVariable * > &	kept_vars
	)

private

update a set of potentials: the remaining are those to be combined to produce a message on a separator

◆ __findRelevantPotentialsXX()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__findRelevantPotentialsXX	(	__PotentialSet &	pot_list,
		Set< const DiscreteVariable * > &	kept_vars
	)

private

update a set of potentials: the remaining are those to be combined to produce a message on a separator

◆ __invalidateAllMessages()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__invalidateAllMessages ( )

private

invalidate all messages, posteriors and created potentials

◆ __isNewJTNeeded()

template<typename GUM_SCALAR>

bool gum::LazyPropagation< GUM_SCALAR >::__isNewJTNeeded ( ) const

private

check whether a new join tree is really needed for the next inference

◆ __marginalizeOut()

template<typename GUM_SCALAR>

__PotentialSet gum::LazyPropagation< GUM_SCALAR >::__marginalizeOut	(	__PotentialSet	pot_list,
		Set< const DiscreteVariable * > &	del_vars,
		Set< const DiscreteVariable * > &	kept_vars
	)

private

removes variables del_vars from a list of potentials and returns the resulting list

◆ __produceMessage()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__produceMessage	(	NodeId	from_id,
		NodeId	to_id
	)

private

creates the message sent by clique from_id to clique to_id

◆ __removeBarrenVariables()

template<typename GUM_SCALAR>

__PotentialSet gum::LazyPropagation< GUM_SCALAR >::__removeBarrenVariables	(	__PotentialSet &	pot_list,
		Set< const DiscreteVariable * > &	del_vars
	)

private

◆ __setCombinationFunction()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__setCombinationFunction ( Potential< GUM_SCALAR > *(*)(const Potential< GUM_SCALAR > &, const Potential< GUM_SCALAR > &) comb )

private

sets the operator for performing the combinations

◆ __setProjectionFunction()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::__setProjectionFunction ( Potential< GUM_SCALAR > *(*)(const Potential< GUM_SCALAR > &, const Set< const DiscreteVariable * > &) proj )

private

sets the operator for performing the projections

◆ _isTargetedMode()

template<typename GUM_SCALAR >

INLINE bool gum::MarginalTargetedInference< GUM_SCALAR >::_isTargetedMode ( ) const

protectedinherited

Definition at line 338 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::__targeted_mode.

                                                                              {
     return __targeted_mode;
   }

◆ _jointPosterior() [1/2]

template<typename GUM_SCALAR>

const Potential< GUM_SCALAR >& gum::LazyPropagation< GUM_SCALAR >::_jointPosterior ( const NodeSet & set )

finalprotectedvirtual

returns the posterior of a declared target set

Parameters

set	The set of ids of the variables whose joint posterior is looked for.

Implements gum::JointTargetedInference< GUM_SCALAR >.

Referenced by gum::LazyPropagation< GUM_SCALAR >::_onStateChanged().

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◆ _jointPosterior() [2/2]

template<typename GUM_SCALAR>

const Potential< GUM_SCALAR >& gum::LazyPropagation< GUM_SCALAR >::_jointPosterior	(	const NodeSet &	wanted_target,
		const NodeSet &	declared_target
	)

finalprotectedvirtual

asks derived classes for the joint posterior of a set of variables not declared as a joint target

Parameters

wanted_target	The set of ids of the variables whose joint posterior is looked for.
declared_target	the joint target declared by the user that contains set

Implements gum::JointTargetedInference< GUM_SCALAR >.

◆ _makeInference()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_makeInference ( )

finalprotectedvirtual

called when the inference has to be performed effectively

Once the inference is done, _fillPosterior can be called.

Implements gum::BayesNetInference< GUM_SCALAR >.

Referenced by gum::LazyPropagation< GUM_SCALAR >::_onStateChanged().

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◆ _onAllEvidenceErased()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onAllEvidenceErased ( bool has_hard_evidence )

finalprotectedvirtual

fired before all the evidence are erased

Implements gum::BayesNetInference< GUM_SCALAR >.

◆ _onAllJointTargetsErased()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onAllJointTargetsErased ( )

finalprotectedvirtual

fired before a all the joint targets are removed

Implements gum::JointTargetedInference< GUM_SCALAR >.

◆ _onAllMarginalTargetsAdded()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onAllMarginalTargetsAdded ( )

finalprotectedvirtual

fired after all the nodes of the BN are added as single targets

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

◆ _onAllMarginalTargetsErased()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onAllMarginalTargetsErased ( )

finalprotectedvirtual

fired before a all the single targets are removed

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

◆ _onAllTargetsErased()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onAllTargetsErased ( )

finalprotectedvirtual

fired before a all single and joint_targets are removed

Implements gum::JointTargetedInference< GUM_SCALAR >.

◆ _onBayesNetChanged()

template<typename GUM_SCALAR>

virtual void gum::LazyPropagation< GUM_SCALAR >::_onBayesNetChanged ( const IBayesNet< GUM_SCALAR > * bn )

finalprotectedvirtual

fired after a new Bayes net has been assigned to the engine

Reimplemented from gum::JointTargetedInference< GUM_SCALAR >.

◆ _onEvidenceAdded()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onEvidenceAdded	(	const NodeId	id,
		bool	isHardEvidence
	)

finalprotectedvirtual

fired after a new evidence is inserted

Implements gum::BayesNetInference< GUM_SCALAR >.

◆ _onEvidenceChanged()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onEvidenceChanged	(	const NodeId	id,
		bool	hasChangedSoftHard
	)

finalprotectedvirtual

fired after an evidence is changed, in particular when its status (soft/hard) changes

Parameters

nodeId	the node of the changed evidence
hasChangedSoftHard	true if the evidence has changed from Soft to Hard or from Hard to Soft

Implements gum::BayesNetInference< GUM_SCALAR >.

◆ _onEvidenceErased()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onEvidenceErased	(	const NodeId	id,
		bool	isHardEvidence
	)

finalprotectedvirtual

fired before an evidence is removed

Implements gum::BayesNetInference< GUM_SCALAR >.

◆ _onJointTargetAdded()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onJointTargetAdded ( const NodeSet & set )

finalprotectedvirtual

fired after a new joint target is inserted

Parameters

set	The set of target variable's ids.

Implements gum::JointTargetedInference< GUM_SCALAR >.

◆ _onJointTargetErased()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onJointTargetErased ( const NodeSet & set )

finalprotectedvirtual

fired before a joint target is removed

Parameters

set	The set of target variable's ids.

Implements gum::JointTargetedInference< GUM_SCALAR >.

◆ _onMarginalTargetAdded()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onMarginalTargetAdded ( const NodeId id )

finalprotectedvirtual

fired after a new single target is inserted

Parameters

id	The target variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

◆ _onMarginalTargetErased()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onMarginalTargetErased ( const NodeId id )

finalprotectedvirtual

fired before a single target is removed

Parameters

id	The target variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

◆ _onStateChanged()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_onStateChanged ( )

inlinefinalprotectedvirtual

fired when the stage is changed

Implements gum::BayesNetInference< GUM_SCALAR >.

Definition at line 198 of file lazyPropagation.h.

References gum::LazyPropagation< GUM_SCALAR >::_jointPosterior(), gum::LazyPropagation< GUM_SCALAR >::_makeInference(), gum::LazyPropagation< GUM_SCALAR >::_posterior(), gum::LazyPropagation< GUM_SCALAR >::_unnormalizedJointPosterior(), gum::LazyPropagation< GUM_SCALAR >::_updateOutdatedBNPotentials(), and gum::LazyPropagation< GUM_SCALAR >::_updateOutdatedBNStructure().

198 {};

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◆ _posterior()

template<typename GUM_SCALAR>

const Potential< GUM_SCALAR >& gum::LazyPropagation< GUM_SCALAR >::_posterior ( NodeId id )

finalprotectedvirtual

returns the posterior of a given variable

Parameters

id	The variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Referenced by gum::LazyPropagation< GUM_SCALAR >::_onStateChanged().

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◆ _setOutdatedBNPotentialsState()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::_setOutdatedBNPotentialsState ( )

protectedinherited

puts the inference into an OutdatedBNPotentials state if it is not already in an OutdatedBNStructure state

OutdatedBNPotentials: in this state, the structure of the BN remains unchanged, only some potentials stored in it have changed. Therefore, the inference probably just needs to invalidate some already computed potentials to be ready. Only a light amount of preparation is needed to be able to perform inference.

Definition at line 685 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__setState(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNPotentials.

                                                                              {
     __setState(StateOfInference::OutdatedBNPotentials);
   }

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◆ _setOutdatedBNStructureState()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::_setOutdatedBNStructureState ( )

protectedinherited

put the inference into an outdated BN structure state

OutdatedBNStructure: in this state, the inference is fully unprepared to be applied because some events changed the "logical" structure of the BN: for instance a node received a hard evidence, which implies that its outgoing arcs can be removed from the BN, hence involving a structural change in the BN. As a consequence, the (incremental) inference (probably) needs a significant amount of preparation to be ready for the next inference. In a Lazy propagation, for instance, this step amounts to compute a new join tree, hence a new structure in which inference will be applied. Note that classes that inherit from BayesNetInference may be smarter than BayesNetInference and may, in some situations, find out that their data structures are still ok for inference and, therefore, only resort to perform the actions related to the OutdatedBNPotentials state. As an example, consider a LazyPropagation inference in Bayes Net A->B->C->D->E in which C has received hard evidence e_C and E is the only target. In this case, A and B are not needed for inference, the only potentials that matter are P(D|e_C) and P(E|D). So the smallest join tree needed for inference contains only one clique DE. Now, adding new evidence e_A on A has no impact on E given hard evidence e_C. In this case, LazyPropagation can be smart and not update its join tree.

Definition at line 677 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__setState(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

                                                                             {
     __setState(StateOfInference::OutdatedBNStructure);
   }

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◆ _setTargetedMode()

template<typename GUM_SCALAR >

INLINE void gum::MarginalTargetedInference< GUM_SCALAR >::_setTargetedMode ( )

protectedinherited

Definition at line 342 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::__targeted_mode, gum::MarginalTargetedInference< GUM_SCALAR >::__targets, and gum::Set< Key, Alloc >::clear().

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::addAllTargets(), gum::JointTargetedInference< GUM_SCALAR >::addJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), and gum::MarginalTargetedInference< GUM_SCALAR >::eraseAllTargets().

                                                                         {
     if (!__targeted_mode) {
       __targets.clear();
       __targeted_mode = true;
     }
   }

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◆ _unnormalizedJointPosterior() [1/2]

template<typename GUM_SCALAR>

Potential< GUM_SCALAR >* gum::LazyPropagation< GUM_SCALAR >::_unnormalizedJointPosterior ( NodeId id )

finalprotectedvirtual

returns a fresh potential equal to P(argument,evidence)

Implements gum::JointTargetedInference< GUM_SCALAR >.

Referenced by gum::LazyPropagation< GUM_SCALAR >::_onStateChanged().

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◆ _unnormalizedJointPosterior() [2/2]

template<typename GUM_SCALAR>

Potential< GUM_SCALAR >* gum::LazyPropagation< GUM_SCALAR >::_unnormalizedJointPosterior ( const NodeSet & set )

finalprotectedvirtual

returns a fresh potential equal to P(argument,evidence)

Implements gum::JointTargetedInference< GUM_SCALAR >.

◆ _updateOutdatedBNPotentials()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_updateOutdatedBNPotentials ( )

finalprotectedvirtual

prepares inference when the latter is in OutdatedBNPotentials state

Note that the values of evidence are not necessarily known and can be changed between _updateOutdatedBNPotentials and _makeInference.

Implements gum::BayesNetInference< GUM_SCALAR >.

Referenced by gum::LazyPropagation< GUM_SCALAR >::_onStateChanged().

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◆ _updateOutdatedBNStructure()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::_updateOutdatedBNStructure ( )

finalprotectedvirtual

prepares inference when the latter is in OutdatedBNStructure state

Note that the values of evidence are not necessarily known and can be changed between _updateOutdatedBNStructure and _makeInference.

Implements gum::BayesNetInference< GUM_SCALAR >.

Referenced by gum::LazyPropagation< GUM_SCALAR >::_onStateChanged().

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◆ addAllTargets()

template<typename GUM_SCALAR >

void gum::MarginalTargetedInference< GUM_SCALAR >::addAllTargets ( )

finalvirtualinherited

adds all nodes as targets

Definition at line 136 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::MarginalTargetedInference< GUM_SCALAR >::__targets, gum::MarginalTargetedInference< GUM_SCALAR >::_onMarginalTargetAdded(), gum::MarginalTargetedInference< GUM_SCALAR >::_setTargetedMode(), gum::Set< Key, Alloc >::contains(), GUM_ERROR, and gum::Set< Key, Alloc >::insert().

                                                               {
     // check if the node belongs to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
 
     _setTargetedMode();   // does nothing if already in targeted mode
     for (const auto target : this->__bn->dag()) {
       if (!__targets.contains(target)) {
         __targets.insert(target);
         _onMarginalTargetAdded(target);
         this->__setState(
            BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
       }
     }
   }

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◆ addEvidence() [1/8]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::addEvidence	(	NodeId	id,
		const Idx	val
	)

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

UndefinedElement	if id does not belong to the Bayesian network
InvalidArgument	if val is not a value for id
InvalidArgument	if id already has an evidence

Definition at line 247 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__createHardEvidence().

Referenced by gum::LoopySamplingInference< GUM_SCALAR, APPROX >::_makeInference(), gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::BayesNetInference< GUM_SCALAR >::addListOfEvidence(), gum::BayesNetInference< GUM_SCALAR >::addSetOfEvidence(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), and gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact().

                                                                           {
     addEvidence(__createHardEvidence(id, val));
   }

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◆ addEvidence() [2/8]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::addEvidence	(	const std::string &	nodeName,
		const Idx	val
	)

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

UndefinedElement	if nodeName does not belong to the Bayesian network
InvalidArgument	if val is not a value for id
InvalidArgument	if nodeName already has an evidence

Definition at line 255 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence(), and gum::BayesNetInference< GUM_SCALAR >::BN().

                                                                           {
     addEvidence(this->BN().idFromName(nodeName), val);
   }

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◆ addEvidence() [3/8]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::addEvidence	(	NodeId	id,
		const std::string &	label
	)

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

UndefinedElement	if id does not belong to the Bayesian network
InvalidArgument	if val is not a value for id
InvalidArgument	if id already has an evidence

Definition at line 263 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence(), and gum::BayesNetInference< GUM_SCALAR >::BN().

                                                                           {
     addEvidence(id, this->BN().variable(id)[label]);
   }

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◆ addEvidence() [4/8]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::addEvidence	(	const std::string &	nodeName,
		const std::string &	label
	)

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

UndefinedElement	if nodeName does not belong to the Bayesian network
InvalidArgument	if val is not a value for id
InvalidArgument	if nodeName already has an evidence

Definition at line 271 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence(), and gum::BayesNetInference< GUM_SCALAR >::BN().

                                                                           {
     NodeId id = this->BN().idFromName(nodeName);
     addEvidence(id, this->BN().variable(id)[label]);
   }

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◆ addEvidence() [5/8]

template<typename GUM_SCALAR >

void gum::BayesNetInference< GUM_SCALAR >::addEvidence	(	NodeId	id,
		const std::vector< GUM_SCALAR > &	vals
	)

finalvirtualinherited

adds a new evidence on node id (might be soft or hard)

Exceptions

UndefinedElement	if id does not belong to the Bayesian network
InvalidArgument	if id already has an evidence
FatalError	if vals=[0,0,...,0]
InvalidArgument	if the size of vals is different from the domain size of node id

Definition at line 279 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MultiDimDecorator< GUM_SCALAR >::add(), gum::BayesNetInference< GUM_SCALAR >::addEvidence(), and GUM_ERROR.

                                                      {
     // checks that the evidence is meaningful
     if (__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     if (!__bn->dag().exists(id)) {
       GUM_ERROR(UndefinedElement, id << " is not a NodeId in the bn");
     }
 
     if (__bn->variable(id).domainSize() != vals.size()) {
       GUM_ERROR(InvalidArgument,
                 "node " << __bn->variable(id)
                         << " and its evidence vector have different sizes.");
     }
 
     Potential< GUM_SCALAR > pot;
     pot.add(__bn->variable(id));
     pot.fillWith(vals);
     addEvidence(std::move(pot));
   }

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◆ addEvidence() [6/8]

template<typename GUM_SCALAR >

void gum::BayesNetInference< GUM_SCALAR >::addEvidence	(	const std::string &	nodeName,
		const std::vector< GUM_SCALAR > &	vals
	)

finalvirtualinherited

adds a new evidence on node named nodeName (might be soft or hard)

Exceptions

UndefinedElement	if id does not belong to the Bayesian network
InvalidArgument	if nodeName already has an evidence
FatalError	if vals=[0,0,...,0]
InvalidArgument	if the size of vals is different from the domain size of node nodeName

Definition at line 305 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence(), and gum::BayesNetInference< GUM_SCALAR >::BN().

                                                                      {
     addEvidence(this->BN().idFromName(nodeName), vals);
   }

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◆ addEvidence() [7/8]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::addEvidence ( const Potential< GUM_SCALAR > & pot )

finalvirtualinherited

adds a new evidence on node id (might be soft or hard)

Exceptions

UndefinedElement	if the potential is defined over several nodes
UndefinedElement	if the node on which the potential is defined does not belong to the Bayesian network
InvalidArgument	if the node of the potential already has an evidence
FatalError	if pot=[0,0,...,0]

Definition at line 354 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence().

                                          {
     Potential< GUM_SCALAR > new_pot(pot);
     addEvidence(std::move(new_pot));
   }

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◆ addEvidence() [8/8]

template<typename GUM_SCALAR >

void gum::BayesNetInference< GUM_SCALAR >::addEvidence ( Potential< GUM_SCALAR > && pot )

finalvirtualinherited

adds a new evidence on node id (might be soft or hard)

Exceptions

UndefinedElement	if the potential is defined over several nodes
UndefinedElement	if the node on which the potential is defined does not belong to the Bayesian network
InvalidArgument	if the node of the potential already has an evidence
FatalError	if pot=[0,0,...,0]

Definition at line 313 of file BayesNetInference_tpl.h.

                                                                                {
     // check if the potential corresponds to an evidence
     if (pot.nbrDim() != 1) {
       GUM_ERROR(InvalidArgument, pot << " is not mono-dimensional.");
     }
     if (__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     NodeId id = __bn->nodeId(pot.variable(0));
 
     if (hasEvidence(id)) {
       GUM_ERROR(InvalidArgument,
                 " node " << id
                          << " already has an evidence. Please use chgEvidence().");
     }
 
     // check whether we have a hard evidence (and also check whether the
     // potential only contains 0 (in this case, this will automatically raise
     // an exception) )
     Idx  val;
     bool is_hard_evidence = __isHardEvidence(pot, val);
 
     // insert the evidence
     __evidence.insert(
        id,
        new Potential< GUM_SCALAR >(std::forward< Potential< GUM_SCALAR > >(pot)));
     if (is_hard_evidence) {   // pot is deterministic
       __hard_evidence.insert(id, val);
       __hard_evidence_nodes.insert(id);
     } else {
       __soft_evidence_nodes.insert(id);
     }
     __setState(StateOfInference::OutdatedBNStructure);
     _onEvidenceAdded(id, is_hard_evidence);
   }

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◆ addJointTarget()

template<typename GUM_SCALAR >

void gum::JointTargetedInference< GUM_SCALAR >::addJointTarget ( const NodeSet & joint_target )

finalvirtualinherited

Add a set of nodes as a new joint target. As a collateral effect, every node is added as a marginal target.

Exceptions

UndefinedElement if some node(s) do not belong to the Bayes net

Definition at line 118 of file jointTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::JointTargetedInference< GUM_SCALAR >::__joint_targets, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::JointTargetedInference< GUM_SCALAR >::_onJointTargetAdded(), gum::MarginalTargetedInference< GUM_SCALAR >::_setTargetedMode(), gum::JointTargetedInference< GUM_SCALAR >::eraseJointTarget(), and GUM_ERROR.

Referenced by gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), and gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation().

                                   {
     // check if the nodes in the target belong to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     const auto& dag = this->__bn->dag();
     for (const auto node : joint_target) {
       if (!dag.exists(node)) {
         GUM_ERROR(UndefinedElement,
                   "at least one one in " << joint_target
                                          << " does not belong to the bn");
       }
     }
 
     // check that the joint_target set does not contain the new target
     if (__joint_targets.contains(joint_target)) return;
 
     // check if joint_target is a subset of an already existing target
     for (const auto& target : __joint_targets) {
       if (target.isSupersetOf(joint_target)) return;
     }
 
     // check if joint_target is not a superset of an already existing target
     // in this case, we need to remove old existing target
     for (auto iter = __joint_targets.beginSafe();
          iter != __joint_targets.endSafe();
          ++iter) {
       if (iter->isSubsetOf(joint_target)) eraseJointTarget(*iter);
     }
 
     this->_setTargetedMode();   // does nothing if already in targeted mode
     __joint_targets.insert(joint_target);
     _onJointTargetAdded(joint_target);
     this->__setState(
        BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
   }

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◆ addListOfEvidence()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::addListOfEvidence ( const List< const Potential< GUM_SCALAR > * > & potlist )

finalvirtualinherited

adds a new list of evidence

Exceptions

UndefinedElement	if some potential is defined over several nodes
UndefinedElement	if the node on which some potential is defined does not belong to the Bayesian network
InvalidArgument	if the node of some potential already has an evidence
FatalError	if pot=[0,0,...,0]

Definition at line 363 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence().

                                                             {
     for (const auto pot : potlist)
       addEvidence(*pot);
   }

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◆ addSetOfEvidence()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::addSetOfEvidence ( const Set< const Potential< GUM_SCALAR > * > & potset )

finalvirtualinherited

adds a new set of evidence

Exceptions

UndefinedElement	if some potential is defined over several nodes
UndefinedElement	if the node on which some potential is defined does not belong to the Bayesian network
InvalidArgument	if the node of some potential already has an evidence
FatalError	if pot=[0,0,...,0]

Definition at line 372 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence().

                                                           {
     for (const auto pot : potset)
       addEvidence(*pot);
   }

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◆ addTarget() [1/2]

template<typename GUM_SCALAR >

void gum::MarginalTargetedInference< GUM_SCALAR >::addTarget ( NodeId target )

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement if target is not a NodeId in the Bayes net

Definition at line 112 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::MarginalTargetedInference< GUM_SCALAR >::__targets, gum::MarginalTargetedInference< GUM_SCALAR >::_onMarginalTargetAdded(), gum::MarginalTargetedInference< GUM_SCALAR >::_setTargetedMode(), gum::Set< Key, Alloc >::contains(), GUM_ERROR, and gum::Set< Key, Alloc >::insert().

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), and gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact().

                                                                        {
     // check if the node belongs to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     if (!this->__bn->dag().exists(target)) {
       GUM_ERROR(UndefinedElement, target << " is not a NodeId in the bn");
     }
 
     _setTargetedMode();   // does nothing if already in targeted mode
     // add the new target
     if (!__targets.contains(target)) {
       __targets.insert(target);
       _onMarginalTargetAdded(target);
       this->__setState(
          BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
     }
   }

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◆ addTarget() [2/2]

template<typename GUM_SCALAR >

void gum::MarginalTargetedInference< GUM_SCALAR >::addTarget ( const std::string & nodeName )

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement if target is not a NodeId in the Bayes net

Definition at line 158 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), and GUM_ERROR.

                                 {
     // check if the node belongs to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     addTarget(this->__bn->idFromName(nodeName));
   }

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◆ BN()

template<typename GUM_SCALAR >

INLINE const IBayesNet< GUM_SCALAR > & gum::BayesNetInference< GUM_SCALAR >::BN ( ) const

finalvirtualinherited

Returns a constant reference over the IBayesNet referenced by this class.

Exceptions

UndefinedElement is raised if no Bayes net has been assigned to the inference.

Definition at line 121 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, and GUM_ERROR.

                                                          {
     if (__bn == nullptr)
       GUM_ERROR(UndefinedElement,
                 "No Bayes net has been assigned to "
                 "the inference algorithm.");
     return *__bn;
   }

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◆ chgEvidence() [1/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::chgEvidence	(	NodeId	id,
		const Idx	val
	)

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

UndefinedElement	if id does not belong to the Bayesian network
InvalidArgument	if val is not a value for id
InvalidArgument	if id does not already have an evidence

Definition at line 432 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__createHardEvidence().

Referenced by gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), and gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact().

                                                                           {
     chgEvidence(__createHardEvidence(id, val));
   }

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◆ chgEvidence() [2/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::chgEvidence	(	const std::string &	nodeName,
		const Idx	val
	)

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

UndefinedElement	if nodeName does not belong to the Bayesian network
InvalidArgument	if val is not a value for id
InvalidArgument	if id does not already have an evidence

Definition at line 440 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::BayesNetInference< GUM_SCALAR >::chgEvidence().

                                                                           {
     chgEvidence(this->BN().idFromName(nodeName), val);
   }

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◆ chgEvidence() [3/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::chgEvidence	(	NodeId	id,
		const std::string &	label
	)

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

UndefinedElement	if id does not belong to the Bayesian network
InvalidArgument	if val is not a value for id
InvalidArgument	if id does not already have an evidence

Definition at line 448 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::BayesNetInference< GUM_SCALAR >::chgEvidence().

                                                                           {
     chgEvidence(id, this->BN().variable(id)[label]);
   }

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◆ chgEvidence() [4/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::chgEvidence	(	const std::string &	nodeName,
		const std::string &	label
	)

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

UndefinedElement	if nodeName does not belong to the Bayesian network
InvalidArgument	if val is not a value for id
InvalidArgument	if id does not already have an evidence

Definition at line 456 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::BayesNetInference< GUM_SCALAR >::chgEvidence().

                                                                           {
     NodeId id = this->BN().idFromName(nodeName);
     chgEvidence(id, this->BN().variable(id)[label]);
   }

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◆ chgEvidence() [5/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::chgEvidence	(	NodeId	id,
		const std::vector< GUM_SCALAR > &	vals
	)

finalvirtualinherited

change the value of an already existing evidence (might be soft or hard)

Exceptions

UndefinedElement	if id does not belong to the Bayesian network
InvalidArgument	if the node does not already have an evidence
FatalError	if vals=[0,0,...,0]
InvalidArgument	if the size of vals is different from the domain size of node id

Definition at line 464 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MultiDimDecorator< GUM_SCALAR >::add(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), and GUM_ERROR.

                                                      {
     // check whether this corresponds to an evidence
     if (__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     if (!__bn->dag().exists(id)) {
       GUM_ERROR(UndefinedElement, id << " is not a NodeId in the bn");
     }
 
     if (__bn->variable(id).domainSize() != vals.size()) {
       GUM_ERROR(InvalidArgument,
                 "node " << __bn->variable(id)
                         << " and its evidence have different sizes.");
     }
 
     // create the potential corresponding to vals
     Potential< GUM_SCALAR > pot;
     pot.add(__bn->variable(id));
     pot.fillWith(vals);
     chgEvidence(pot);
   }

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◆ chgEvidence() [6/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::chgEvidence	(	const std::string &	nodeName,
		const std::vector< GUM_SCALAR > &	vals
	)

finalvirtualinherited

change the value of an already existing evidence (might be soft or hard)

Exceptions

UndefinedElement	if nodeName does not belong to the Bayesian network
InvalidArgument	if the node does not already have an evidence
FatalError	if vals=[0,0,...,0]
InvalidArgument	if the size of vals is different from the domain size of node id

Definition at line 491 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::BayesNetInference< GUM_SCALAR >::chgEvidence().

                                                                      {
     chgEvidence(this->BN().idFromName(nodeName), vals);
   }

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◆ chgEvidence() [7/7]

template<typename GUM_SCALAR >

void gum::BayesNetInference< GUM_SCALAR >::chgEvidence ( const Potential< GUM_SCALAR > & pot )

finalvirtualinherited

change the value of an already existing evidence (might be soft or hard)

Exceptions

UndefinedElement	if the potential is defined over several nodes
UndefinedElement	if the node on which the potential is defined does not belong to the Bayesian network
InvalidArgument	if the node of the potential does not already have an evidence
FatalError	if pot=[0,0,...,0]

Definition at line 499 of file BayesNetInference_tpl.h.

                                          {
     // check if the potential corresponds to an evidence
     if (pot.nbrDim() != 1) {
       GUM_ERROR(InvalidArgument, pot << " is not a mono-dimensional potential.");
     }
     if (__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     NodeId id = __bn->nodeId(pot.variable(0));
 
     if (!hasEvidence(id)) {
       GUM_ERROR(InvalidArgument,
                 id << " has no evidence. Please use addEvidence().");
     }
 
     // check whether we have a hard evidence (and also check whether the
     // potential only contains 0 (in this case, this will automatically raise
     // an exception) )
     Idx  val;
     bool is_hard_evidence = __isHardEvidence(pot, val);
 
     // modify the evidence already stored
     const Potential< GUM_SCALAR >* localPot = __evidence[id];
     Instantiation                  I(pot);
     for (I.setFirst(); !I.end(); I.inc()) {
       localPot->set(I, pot[I]);
     }
 
     // the inference state will be different
     // whether evidence change from Hard to Soft or not.
     bool hasChangedSoftHard = false;
 
     if (is_hard_evidence) {
       if (!hasHardEvidence(id)) {
         hasChangedSoftHard = true;
         __hard_evidence.insert(id, val);
         __hard_evidence_nodes.insert(id);
         __soft_evidence_nodes.erase(id);
       } else {
         __hard_evidence[id] = val;
       }
     } else {
       if (hasHardEvidence(id)) {   // evidence was hard
         __hard_evidence.erase(id);
         __hard_evidence_nodes.erase(id);
         __soft_evidence_nodes.insert(id);
         hasChangedSoftHard = true;
       }
     }
 
     if (hasChangedSoftHard) {
       __setState(StateOfInference::OutdatedBNStructure);
     } else {
       if (!isInferenceOutdatedBNStructure()) {
         __setState(StateOfInference::OutdatedBNPotentials);
       }
     }
 
     _onEvidenceChanged(id, hasChangedSoftHard);
   }

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◆ clear()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::clear ( )

virtualinherited

clears all the data structures allocated for the last inference

Definition at line 153 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

Referenced by gum::BayesNetInference< GUM_SCALAR >::setBN().

                                                      {
     eraseAllEvidence();
     __setState(StateOfInference::OutdatedBNStructure);
   }

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◆ domainSizes()

template<typename GUM_SCALAR >

INLINE const NodeProperty< Size > & gum::BayesNetInference< GUM_SCALAR >::domainSizes ( ) const

finalvirtualinherited

get the domain sizes of the random variables of the BN

Definition at line 174 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__domain_sizes.

                                                                   {
     return __domain_sizes;
   }

◆ eraseAllEvidence()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence ( )

finalvirtualinherited

removes all the evidence entered into the network

Definition at line 595 of file BayesNetInference_tpl.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::clear(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), and gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation().

                                                                 {
     bool has_hard_evidence = !__hard_evidence.empty();
     this->_onAllEvidenceErased(has_hard_evidence);
 
     for (const auto& pair : __evidence) {
       if (pair.second != nullptr) { delete (pair.second); }
     }
 
     __evidence.clear();
     __hard_evidence.clear();
     __hard_evidence_nodes.clear();
     __soft_evidence_nodes.clear();
 
     if (has_hard_evidence) {
       __setState(StateOfInference::OutdatedBNStructure);
     } else {
       if (!isInferenceOutdatedBNStructure()) {
         __setState(StateOfInference::OutdatedBNPotentials);
       }
     }
   }

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◆ eraseAllJointTargets()

template<typename GUM_SCALAR >

INLINE void gum::JointTargetedInference< GUM_SCALAR >::eraseAllJointTargets ( )

finalvirtualinherited

Clear all previously defined joint targets.

Definition at line 97 of file jointTargetedInference_tpl.h.

References gum::JointTargetedInference< GUM_SCALAR >::__joint_targets, gum::BayesNetInference< GUM_SCALAR >::__setState(), and gum::JointTargetedInference< GUM_SCALAR >::_onAllJointTargetsErased().

Referenced by gum::JointTargetedInference< GUM_SCALAR >::eraseAllTargets().

                                                                          {
     if (__joint_targets.size() > 0) {
       // we already are in target mode. So no this->_setTargetedMode();  is needed
       _onAllJointTargetsErased();
       __joint_targets.clear();
       this->__setState(
          BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
     }
   }

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◆ eraseAllMarginalTargets()

template<typename GUM_SCALAR >

INLINE void gum::JointTargetedInference< GUM_SCALAR >::eraseAllMarginalTargets ( )

finalvirtualinherited

Clear all the previously defined marginal targets.

Definition at line 90 of file jointTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::eraseAllTargets().

Referenced by gum::JointTargetedInference< GUM_SCALAR >::eraseAllTargets().

                                                                             {
     MarginalTargetedInference< GUM_SCALAR >::eraseAllTargets();
   }

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◆ eraseAllTargets()

template<typename GUM_SCALAR >

INLINE void gum::JointTargetedInference< GUM_SCALAR >::eraseAllTargets ( )

virtualinherited

Clear all previously defined targets (marginal and joint targets)

Clear all previously defined targets. As a result, no posterior can be computed (since we can only compute the posteriors of the marginal or joint targets that have been added by the user).

Reimplemented from gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 110 of file jointTargetedInference_tpl.h.

References gum::JointTargetedInference< GUM_SCALAR >::eraseAllJointTargets(), and gum::JointTargetedInference< GUM_SCALAR >::eraseAllMarginalTargets().

Referenced by gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), and gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation().

                                                                     {
     eraseAllMarginalTargets();
     eraseAllJointTargets();
   }

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◆ eraseEvidence() [1/2]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::eraseEvidence ( NodeId id )

finalvirtualinherited

removed the evidence, if any, corresponding to node id

Definition at line 566 of file BayesNetInference_tpl.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::eraseEvidence().

                                                                       {
     if (hasEvidence(id)) {
       if (hasHardEvidence(id)) {
         _onEvidenceErased(id, true);
         __hard_evidence.erase(id);
         __hard_evidence_nodes.erase(id);
         __setState(StateOfInference::OutdatedBNStructure);
       } else {
         _onEvidenceErased(id, false);
         __soft_evidence_nodes.erase(id);
         if (!isInferenceOutdatedBNStructure()) {
           __setState(StateOfInference::OutdatedBNPotentials);
         }
       }
 
       delete (__evidence[id]);
       __evidence.erase(id);
     }
   }

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◆ eraseEvidence() [2/2]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::eraseEvidence ( const std::string & nodeName )

finalvirtualinherited

removed the evidence, if any, corresponding to node of name nodeName

Definition at line 588 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::BayesNetInference< GUM_SCALAR >::eraseEvidence().

                                                                              {
     eraseEvidence(this->BN().idFromName(nodeName));
   }

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◆ eraseJointTarget()

template<typename GUM_SCALAR >

void gum::JointTargetedInference< GUM_SCALAR >::eraseJointTarget ( const NodeSet & joint_target )

finalvirtualinherited

removes an existing joint target

Warning: If the joint target does not already exist, the method does nothing. In particular, it does not raise any exception.

Definition at line 161 of file jointTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::JointTargetedInference< GUM_SCALAR >::__joint_targets, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::JointTargetedInference< GUM_SCALAR >::_onJointTargetErased(), and GUM_ERROR.

Referenced by gum::JointTargetedInference< GUM_SCALAR >::addJointTarget().

                                   {
     // check if the nodes in the target belong to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     const auto& dag = this->__bn->dag();
     for (const auto node : joint_target) {
       if (!dag.exists(node)) {
         GUM_ERROR(UndefinedElement,
                   "at least one one in " << joint_target
                                          << " does not belong to the bn");
       }
     }
 
     // check that the joint_target set does not contain the new target
     if (__joint_targets.contains(joint_target)) {
       // note that we have to be in target mode when we are here
       // so, no this->_setTargetedMode();  is necessary
       _onJointTargetErased(joint_target);
       __joint_targets.erase(joint_target);
       this->__setState(
          BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
     }
   }

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◆ eraseTarget() [1/2]

template<typename GUM_SCALAR >

void gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget ( NodeId target )

finalvirtualinherited

removes an existing (marginal) target

Warning: If the target does not already exist, the method does nothing. In particular, it does not raise any exception.

Definition at line 172 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::MarginalTargetedInference< GUM_SCALAR >::__targeted_mode, gum::MarginalTargetedInference< GUM_SCALAR >::__targets, gum::MarginalTargetedInference< GUM_SCALAR >::_onMarginalTargetErased(), gum::Set< Key, Alloc >::contains(), gum::Set< Key, Alloc >::erase(), and GUM_ERROR.

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget().

                                                                          {
     // check if the node belongs to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     if (!this->__bn->dag().exists(target)) {
       GUM_ERROR(UndefinedElement, target << " is not a NodeId in the bn");
     }
 
 
     if (__targets.contains(target)) {
       __targeted_mode = true;   // we do not use _setTargetedMode because we do not
                                 // want to clear the targets
       _onMarginalTargetErased(target);
       __targets.erase(target);
       this->__setState(
          BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
     }
   }

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◆ eraseTarget() [2/2]

template<typename GUM_SCALAR >

void gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget ( const std::string & nodeName )

finalvirtualinherited

removes an existing (marginal) target

Warning: If the target does not already exist, the method does nothing. In particular, it does not raise any exception.

Definition at line 197 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), and GUM_ERROR.

                                 {
     // check if the node belongs to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     eraseTarget(this->__bn->idFromName(nodeName));
   }

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◆ evidence()

template<typename GUM_SCALAR >

INLINE const NodeProperty< const Potential< GUM_SCALAR > *> & gum::BayesNetInference< GUM_SCALAR >::evidence ( ) const

inherited

returns the set of evidence

Definition at line 650 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence.

Referenced by gum::ImportanceSampling< GUM_SCALAR >::_onContextualize(), and gum::MarginalTargetedInference< GUM_SCALAR >::posterior().

                                                                {
     return __evidence;
   }

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◆ evidenceImpact() [1/2]

template<typename GUM_SCALAR >

Potential< GUM_SCALAR > gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact	(	NodeId	target,
		const NodeSet &	evs
	)

inherited

Create a gum::Potential for P(target|evs) (for all instanciation of target and evs)

Warning: If some evs are d-separated, they are not included in the Potential

Parameters

bn	the BayesNet
target	the nodeId of the targetted variable
evs	the vector of nodeId of the observed variables

Returns: a Potential

Definition at line 285 of file marginalTargetedInference_tpl.h.

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact().

                                                                                  {
     const auto& vtarget = this->BN().variable(target);
 
     if (evs.contains(target)) {
       GUM_ERROR(InvalidArgument,
                 "Target <" << vtarget.name() << "> (" << target
                            << ") can not be in evs (" << evs << ").");
     }
     auto condset = this->BN().minimalCondSet(target, evs);
 
     Potential< GUM_SCALAR > res;
     this->eraseAllTargets();
     this->eraseAllEvidence();
     res.add(this->BN().variable(target));
     this->addTarget(target);
     for (const auto& n : condset) {
       res.add(this->BN().variable(n));
       this->addEvidence(n, 0);
     }
 
     Instantiation inst(res);
     for (inst.setFirst(); !inst.end(); inst.incNotVar(vtarget)) {
       // inferring
       for (const auto& n : condset)
         this->chgEvidence(n, inst.val(this->BN().variable(n)));
       this->makeInference();
       // populate res
       for (inst.setFirstVar(vtarget); !inst.end(); inst.incVar(vtarget)) {
         res.set(inst, this->posterior(target)[inst]);
       }
       inst.setFirstVar(vtarget);   // remove inst.end() flag
     }
 
     return res;
   }

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◆ evidenceImpact() [2/2]

template<typename GUM_SCALAR >

Potential< GUM_SCALAR > gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact	(	const std::string &	target,
		const std::vector< std::string > &	evs
	)

inherited

Create a gum::Potential for P(target|evs) (for all instanciation of target and evs)

Warning: If some evs are d-separated, they are not included in the Potential

Parameters

target	the nodeId of the target variable
evs	the nodeId of the observed variable

Returns: a Potential

Definition at line 324 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), and gum::Set< Key, Alloc >::insert().

                                                                  {
     const auto& bn = this->BN();
 
     gum::NodeSet evsId;
     for (const auto& evname : evs) {
       evsId.insert(bn.idFromName(evname));
     }
 
     return evidenceImpact(bn.idFromName(target), evsId);
   }

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◆ evidenceJointImpact() [1/2]

template<typename GUM_SCALAR >

Potential< GUM_SCALAR > gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact	(	const NodeSet &	targets,
		const NodeSet &	evs
	)

inherited

Create a gum::Potential for P(joint targets|evs) (for all instanciation of targets and evs)

Warning: If some evs are d-separated, they are not included in the Potential

Parameters

targets	the NodeSet of the targeted variables
evs	the NodeSet of observed variables

Returns: a Potential

Definition at line 331 of file jointTargetedInference_tpl.h.

Referenced by gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact().

                                                     {
     if (!(evs * targets).empty()) {
       GUM_ERROR(InvalidArgument,
                 "Targets (" << targets << ") can not intersect evs (" << evs
                             << ").");
     }
     auto condset = this->BN().minimalCondSet(targets, evs);
 
     this->eraseAllTargets();
     this->eraseAllEvidence();
 
     Instantiation           iTarget;
     Potential< GUM_SCALAR > res;
     for (const auto& target : targets) {
       res.add(this->BN().variable(target));
       iTarget.add(this->BN().variable(target));
     }
     this->addJointTarget(targets);
 
     for (const auto& n : condset) {
       res.add(this->BN().variable(n));
       this->addEvidence(n, 0);
     }
 
     Instantiation inst(res);
     for (inst.setFirstOut(iTarget); !inst.end(); inst.incOut(iTarget)) {
       // inferring
       for (const auto& n : condset)
         this->chgEvidence(n, inst.val(this->BN().variable(n)));
       this->makeInference();
       // populate res
       for (inst.setFirstIn(iTarget); !inst.end(); inst.incIn(iTarget)) {
         res.set(inst, this->jointPosterior(targets)[inst]);
       }
       inst.setFirstIn(iTarget);   // remove inst.end() flag
     }
 
     return res;
   }

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◆ evidenceJointImpact() [2/2]

template<typename GUM_SCALAR >

Potential< GUM_SCALAR > gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact	(	const std::vector< std::string > &	targets,
		const std::vector< std::string > &	evs
	)

inherited

Create a gum::Potential for P(joint targets|evs) (for all instanciation of targets and evs)

Warning: If some evs are d-separated, they are not included in the Potential

Parameters

targets	the vector of std::string of the targeted variables
evs	the vector of std::string of observed variables

Returns: a Potential

Definition at line 374 of file jointTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), and gum::Set< Key, Alloc >::insert().

                                            {
     const auto& bn = this->BN();
 
     gum::NodeSet targetsId;
     for (const auto& targetname : targets) {
       targetsId.insert(bn.idFromName(targetname));
     }
 
     gum::NodeSet evsId;
     for (const auto& evname : evs) {
       evsId.insert(bn.idFromName(evname));
     }
 
     return evidenceJointImpact(targetsId, evsId);
   }

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◆ evidenceProbability()

template<typename GUM_SCALAR>

GUM_SCALAR gum::LazyPropagation< GUM_SCALAR >::evidenceProbability ( )

finalvirtual

returns the probability of evidence

Implements gum::EvidenceInference< GUM_SCALAR >.

◆ H() [1/2]

template<typename GUM_SCALAR >

INLINE GUM_SCALAR gum::MarginalTargetedInference< GUM_SCALAR >::H ( NodeId X )

finalvirtualinherited

Entropy Compute Shanon's entropy of a node given the observation.

See also: http://en.wikipedia.org/wiki/Information_entropy

Definition at line 269 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::posterior().

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::H(), and gum::JointTargetedInference< GUM_SCALAR >::VI().

                                                                        {
     return posterior(X).entropy();
   }

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◆ H() [2/2]

template<typename GUM_SCALAR >

INLINE GUM_SCALAR gum::MarginalTargetedInference< GUM_SCALAR >::H ( const std::string & nodeName )

finalvirtualinherited

Entropy Compute Shanon's entropy of a node given the observation.

See also: http://en.wikipedia.org/wiki/Information_entropy

Definition at line 278 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::MarginalTargetedInference< GUM_SCALAR >::H().

                                                                              {
     return H(this->BN().idFromName(nodeName));
   }

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◆ hardEvidence()

template<typename GUM_SCALAR >

INLINE const NodeProperty< Idx > & gum::BayesNetInference< GUM_SCALAR >::hardEvidence ( ) const

inherited

indicate for each node with hard evidence which value it took

Definition at line 642 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__hard_evidence.

Referenced by gum::WeightedSampling< GUM_SCALAR >::_draw(), gum::MonteCarloSampling< GUM_SCALAR >::_draw(), gum::ImportanceSampling< GUM_SCALAR >::_draw(), and gum::SamplingInference< GUM_SCALAR >::contextualize().

                                                                    {
     return __hard_evidence;
   }

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◆ hardEvidenceNodes()

template<typename GUM_SCALAR >

INLINE const NodeSet & gum::BayesNetInference< GUM_SCALAR >::hardEvidenceNodes ( ) const

inherited

returns the set of nodes with hard evidence

the set of nodes that received hard evidence

Definition at line 666 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes.

Referenced by gum::WeightedSampling< GUM_SCALAR >::_draw(), gum::MonteCarloSampling< GUM_SCALAR >::_draw(), gum::ImportanceSampling< GUM_SCALAR >::_draw(), gum::ImportanceSampling< GUM_SCALAR >::_onContextualize(), gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromBN(), gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromLBP(), gum::SamplingInference< GUM_SCALAR >::contextualize(), and gum::MarginalTargetedInference< GUM_SCALAR >::posterior().

                                                                         {
     return __hard_evidence_nodes;
   }

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◆ hasEvidence() [1/3]

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::hasEvidence ( ) const

finalvirtualinherited

indicates whether some node(s) have received evidence

Definition at line 381 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence.

Referenced by gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), and gum::BayesNetInference< GUM_SCALAR >::hasEvidence().

                                                                  {
     return !__evidence.empty();
   }

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◆ hasEvidence() [2/3]

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::hasEvidence ( NodeId id ) const

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 388 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence.

                                                                           {
     return __evidence.exists(id);
   }

◆ hasEvidence() [3/3]

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::hasEvidence ( const std::string & nodeName ) const

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 409 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::BayesNetInference< GUM_SCALAR >::hasEvidence().

                                       {
     return hasEvidence(this->BN().idFromName(nodeName));
   }

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◆ hasHardEvidence() [1/2]

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::hasHardEvidence ( NodeId id ) const

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 395 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes, and gum::Set< Key, Alloc >::exists().

Referenced by gum::ImportanceSampling< GUM_SCALAR >::_draw(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), and gum::BayesNetInference< GUM_SCALAR >::hasHardEvidence().

                                                                               {
     return __hard_evidence_nodes.exists(id);
   }

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◆ hasHardEvidence() [2/2]

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::hasHardEvidence ( const std::string & nodeName ) const

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 417 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::BayesNetInference< GUM_SCALAR >::hasHardEvidence().

                                       {
     return hasHardEvidence(this->BN().idFromName(nodeName));
   }

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◆ hasSoftEvidence() [1/2]

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::hasSoftEvidence ( NodeId id ) const

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 402 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes, and gum::Set< Key, Alloc >::exists().

Referenced by gum::BayesNetInference< GUM_SCALAR >::hasSoftEvidence().

                                                                               {
     return __soft_evidence_nodes.exists(id);
   }

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◆ hasSoftEvidence() [2/2]

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::hasSoftEvidence ( const std::string & nodeName ) const

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 425 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::BayesNetInference< GUM_SCALAR >::hasSoftEvidence().

                                       {
     return hasSoftEvidence(this->BN().idFromName(nodeName));
   }

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◆ I()

template<typename GUM_SCALAR >

GUM_SCALAR gum::JointTargetedInference< GUM_SCALAR >::I	(	NodeId	X,
		NodeId	Y
	)

inherited

Mutual information between X and Y.

See also: http://en.wikipedia.org/wiki/Mutual_information

Warning: Due to limitation of joint, may not be able to compute this value

Exceptions

OperationNotAllowed in these cases

Definition at line 273 of file jointTargetedInference_tpl.h.

References gum::JointTargetedInference< GUM_SCALAR >::_unnormalizedJointPosterior(), gum::BayesNetInference< GUM_SCALAR >::BN(), gum::Instantiation::end(), GUM_ERROR, gum::Potential< GUM_SCALAR >::margSumOut(), gum::Potential< GUM_SCALAR >::normalize(), and gum::Instantiation::setFirst().

Referenced by gum::JointTargetedInference< GUM_SCALAR >::VI().

                                                                        {
     Potential< GUM_SCALAR > pX, pY, *pXY = nullptr;
     if (X == Y) {
       GUM_ERROR(OperationNotAllowed, "Mutual Information I(X,Y) with X==Y");
     }
 
     try {
       // here use unnormalized joint posterior rather than just posterior
       // to avoid saving the posterior in the cache of the inference engines
       // like LazyPropagation or SahferShenoy.
       pXY = this->_unnormalizedJointPosterior({X, Y});
       pXY->normalize();
       pX = pXY->margSumOut({&(this->BN().variable(Y))});
       pY = pXY->margSumOut({&(this->BN().variable(X))});
     } catch (...) {
       if (pXY != nullptr) { delete pXY; }
       throw;
     }
 
     Instantiation i(*pXY);
     auto          res = (GUM_SCALAR)0;
 
     for (i.setFirst(); !i.end(); ++i) {
       GUM_SCALAR vXY = (*pXY)[i];
       GUM_SCALAR vX = pX[i];
       GUM_SCALAR vY = pY[i];
 
       if (vXY > (GUM_SCALAR)0) {
         if (vX == (GUM_SCALAR)0 || vY == (GUM_SCALAR)0) {
           GUM_ERROR(OperationNotAllowed,
                     "Mutual Information (X,Y) with P(X)=0 or P(Y)=0 "
                     "and P(X,Y)>0");
         }
 
         res += vXY * (log2(vXY) - log2(vX) - log2(vY));
       }
     }
 
     delete pXY;
 
     return res;
   }

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◆ isDone()

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::isDone ( ) const

finalvirtualnoexceptinherited

returns whether the inference object is in a done state

The inference object is in a done state when the posteriors can be retrieved without performing a new inference, i.e., all the heavy computations have already been performed. Typically, in a junction tree algorithm, this corresponds to a situation in which all the messages needed in the JT have been computed and sent.

Definition at line 96 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::Done.

Referenced by gum::JointTargetedInference< GUM_SCALAR >::jointPosterior(), gum::BayesNetInference< GUM_SCALAR >::makeInference(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), and gum::BayesNetInference< GUM_SCALAR >::prepareInference().

                                                                      {
     return (__state == StateOfInference::Done);
   }

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◆ isInferenceDone()

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::isInferenceDone ( ) const

finalvirtualnoexceptinherited

returns whether the inference object is in a InferenceDone state

Definition at line 89 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::Done.

                                                                               {
     return (__state == StateOfInference::Done);
   }

◆ isInferenceOutdatedBNPotentials()

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::isInferenceOutdatedBNPotentials ( ) const

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedBNPotential state

Definition at line 83 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNPotentials.

               {
     return (__state == StateOfInference::OutdatedBNPotentials);
   }

◆ isInferenceOutdatedBNStructure()

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::isInferenceOutdatedBNStructure ( ) const

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedBNStructure state

Definition at line 76 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

Referenced by gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), and gum::BayesNetInference< GUM_SCALAR >::eraseEvidence().

               {
     return (__state == StateOfInference::OutdatedBNStructure);
   }

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◆ isInferenceReady()

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::isInferenceReady ( ) const

finalvirtualnoexceptinherited

returns whether the inference object is in a ready state

Definition at line 70 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::ReadyForInference.

Referenced by gum::SamplingInference< GUM_SCALAR >::_onStateChanged(), gum::BayesNetInference< GUM_SCALAR >::makeInference(), and gum::BayesNetInference< GUM_SCALAR >::prepareInference().

                                                                                {
     return (__state == StateOfInference::ReadyForInference);
   }

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◆ isJointTarget()

template<typename GUM_SCALAR >

INLINE bool gum::JointTargetedInference< GUM_SCALAR >::isJointTarget ( const NodeSet & vars ) const

finalvirtualinherited

return true if target is a joint target.

Definition at line 70 of file jointTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::JointTargetedInference< GUM_SCALAR >::__joint_targets, and GUM_ERROR.

                                 {
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     const auto& dag = this->__bn->dag();
     for (const auto var : vars) {
       if (!dag.exists(var)) {
         GUM_ERROR(UndefinedElement, var << " is not a NodeId in the bn");
       }
     }
 
     return __joint_targets.contains(vars);
   }

◆ isTarget() [1/2]

template<typename GUM_SCALAR >

INLINE bool gum::MarginalTargetedInference< GUM_SCALAR >::isTarget ( NodeId node ) const

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 76 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MarginalTargetedInference< GUM_SCALAR >::__targets, gum::Set< Key, Alloc >::contains(), and GUM_ERROR.

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::isTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), and gum::JointTargetedInference< GUM_SCALAR >::posterior().

                                                                         {
     // check that the variable belongs to the bn
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
     if (!this->__bn->dag().exists(node)) {
       GUM_ERROR(UndefinedElement, node << " is not a NodeId in the bn");
     }
 
     return __targets.contains(node);
   }

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◆ isTarget() [2/2]

template<typename GUM_SCALAR >

INLINE bool gum::MarginalTargetedInference< GUM_SCALAR >::isTarget ( const std::string & nodeName ) const

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 91 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, and gum::MarginalTargetedInference< GUM_SCALAR >::isTarget().

                                       {
     return isTarget(this->__bn->idFromName(nodeName));
   }

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◆ jointMutualInformation() [1/2]

template<typename GUM_SCALAR >

GUM_SCALAR gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation ( const NodeSet & targets )

inherited

Mutual information between targets.

See also: https://en.wikipedia.org/wiki/Interaction_information

Parameters

targets the NodeSet of the targeted variables

Definition at line 394 of file jointTargetedInference_tpl.h.

References gum::Instantiation::add(), gum::JointTargetedInference< GUM_SCALAR >::addJointTarget(), gum::BayesNetInference< GUM_SCALAR >::BN(), gum::Set< Key, Alloc >::clear(), gum::Instantiation::end(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), gum::JointTargetedInference< GUM_SCALAR >::eraseAllTargets(), GUM_ERROR, gum::Instantiation::inc(), gum::Set< Key, Alloc >::insert(), gum::JointTargetedInference< GUM_SCALAR >::jointPosterior(), gum::BayesNetInference< GUM_SCALAR >::makeInference(), gum::Instantiation::nbrDim(), gum::Instantiation::setFirst(), gum::Set< Key, Alloc >::size(), gum::Instantiation::val(), and gum::Instantiation::variable().

Referenced by gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation().

                              {
     const auto& bn = this->BN();
     const Size  siz = targets.size();
     if (siz <= 1) {
       GUM_ERROR(InvalidArgument,
                 "jointMutualInformation needs at least 2 variables (targets="
                    << targets << ")");
     }
 
     this->eraseAllTargets();
     this->eraseAllEvidence();
     this->addJointTarget(targets);
     this->makeInference();
     const auto po = this->jointPosterior(targets);
 
     gum::Instantiation caracteristic;
     gum::Instantiation variables;
     for (const auto nod : targets) {
       const auto& var = bn.variable(nod);
       auto        pv = new gum::RangeVariable(var.name(), "", 0, 1);
       caracteristic.add(*pv);
       variables.add(var);
     }
 
     Set< const DiscreteVariable* > sov;
 
     const GUM_SCALAR start = (siz % 2 == 0) ? GUM_SCALAR(-1.0) : GUM_SCALAR(1.0);
     GUM_SCALAR       sign;
     GUM_SCALAR       res = GUM_SCALAR(0.0);
 
     caracteristic.setFirst();
     for (caracteristic.inc(); !caracteristic.end(); caracteristic.inc()) {
       sov.clear();
       sign = start;
       for (Idx i = 0; i < caracteristic.nbrDim(); i++) {
         if (caracteristic.val(i) == 1) {
           sign = -sign;
           sov.insert(&variables.variable(i));
         }
       }
       res += sign * po.margSumIn(sov).entropy();
     }
 
     for (Idx i = 0; i < caracteristic.nbrDim(); i++) {
       delete &caracteristic.variable(i);
     }
 
     return res;
   }

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◆ jointMutualInformation() [2/2]

template<typename GUM_SCALAR >

GUM_SCALAR gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation ( const std::vector< std::string > & targets )

inherited

Mutual information between targets.

See also: https://en.wikipedia.org/wiki/Interaction_information

Parameters

targets the vector of std::string of the targeted variables

Definition at line 446 of file jointTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), gum::Set< Key, Alloc >::insert(), and gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation().

                                             {
     const auto& bn = this->BN();
 
     gum::NodeSet targetsId;
     for (const auto& targetname : targets) {
       targetsId.insert(bn.idFromName(targetname));
     }
 
     return jointMutualInformation(targetsId);
   }

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◆ jointPosterior()

template<typename GUM_SCALAR >

const Potential< GUM_SCALAR > & gum::JointTargetedInference< GUM_SCALAR >::jointPosterior ( const NodeSet & nodes )

finalvirtualinherited

Compute the joint posterior of a set of nodes.

Returns: a const ref to the posterior joint probability of the set of nodes.

Parameters

nodes the set of nodes whose posterior joint probability is wanted

Warning: for efficiency reasons, the potential is stored into the inference engine and is returned by reference. In order to ensure that the potential may still exist even if the Inference object is destroyed, the user has to copy it explicitly.; prepareInference and makeInference may be applied if needed.

Exceptions

UndefinedElement if nodes is not in the targets

Definition at line 212 of file jointTargetedInference_tpl.h.

References gum::JointTargetedInference< GUM_SCALAR >::__joint_targets, gum::JointTargetedInference< GUM_SCALAR >::_jointPosterior(), GUM_ERROR, gum::BayesNetInference< GUM_SCALAR >::isDone(), gum::Set< Key, Alloc >::isSubsetOf(), and gum::BayesNetInference< GUM_SCALAR >::makeInference().

Referenced by gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), and gum::JointTargetedInference< GUM_SCALAR >::posterior().

                                                                               {
     // try to get the smallest set of targets that contains "nodes"
     NodeSet set;
     bool    found_exact_target = false;
 
     if (__joint_targets.contains(nodes)) {
       set = nodes;
       found_exact_target = true;
     } else {
       for (const auto& target : __joint_targets) {
         if (nodes.isSubsetOf(target)) {
           set = target;
           break;
         }
       }
     }
 
     if (set.empty()) {
       GUM_ERROR(UndefinedElement,
                 " no joint target containing " << nodes << " could be found among "
                                                << __joint_targets);
     }
 
     if (!this->isDone()) { this->makeInference(); }
 
     if (found_exact_target)
       return _jointPosterior(nodes);
     else
       return _jointPosterior(nodes, set);
   }

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◆ joinTree()

template<typename GUM_SCALAR>

const JoinTree* gum::LazyPropagation< GUM_SCALAR >::joinTree ( )

returns the current join tree used

Lazy Propagation does not use a junction tree but a binary join tree because this may enable faster inferences. So do not be surprised to see that somes cliques are contained into others in this tree.

◆ jointTargets()

template<typename GUM_SCALAR >

INLINE const Set< NodeSet > & gum::JointTargetedInference< GUM_SCALAR >::jointTargets ( ) const

finalvirtualnoexceptinherited

returns the list of joint targets

returns the list of target sets

Definition at line 193 of file jointTargetedInference_tpl.h.

References gum::JointTargetedInference< GUM_SCALAR >::__joint_targets.

                                                                                  {
     return __joint_targets;
   }

◆ junctionTree()

template<typename GUM_SCALAR>

const JunctionTree* gum::LazyPropagation< GUM_SCALAR >::junctionTree ( )

returns the current junction tree

Lazy Propagation does not use a junction tree but a binary join tree because this may enable faster inferences. This method return the junction tree, before optimizations

◆ makeInference()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::makeInference ( )

finalvirtualinherited

perform the heavy computations needed to compute the targets' posteriors

In a Junction tree propagation scheme, for instance, the heavy computations are those of the messages sent in the JT. This is precisely what makeInference should compute. Later, the computations of the posteriors can be done "lightly" by multiplying and projecting those messages.

Definition at line 711 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::_makeInference(), gum::BayesNetInference< GUM_SCALAR >::Done, gum::BayesNetInference< GUM_SCALAR >::isDone(), gum::BayesNetInference< GUM_SCALAR >::isInferenceReady(), and gum::BayesNetInference< GUM_SCALAR >::prepareInference().

Referenced by gum::LoopySamplingInference< GUM_SCALAR, APPROX >::_makeInference(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedInference< GUM_SCALAR >::jointPosterior(), and gum::MarginalTargetedInference< GUM_SCALAR >::posterior().

                                                              {
     if (isDone()) { return; }
 
     if (!isInferenceReady()) { prepareInference(); }
 
     _makeInference();
 
     __setState(StateOfInference::Done);
   }

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◆ nbrEvidence()

template<typename GUM_SCALAR >

INLINE Size gum::BayesNetInference< GUM_SCALAR >::nbrEvidence ( ) const

finalvirtualinherited

returns the number of evidence entered into the Bayesian network

Definition at line 620 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence.

                                                                  {
     return __evidence.size();
   }

◆ nbrHardEvidence()

template<typename GUM_SCALAR >

INLINE Size gum::BayesNetInference< GUM_SCALAR >::nbrHardEvidence ( ) const

finalvirtualinherited

returns the number of hard evidence entered into the Bayesian network

Definition at line 627 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes, and gum::Set< Key, Alloc >::size().

                                                                      {
     return __hard_evidence_nodes.size();
   }

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◆ nbrJointTargets()

template<typename GUM_SCALAR >

INLINE Size gum::JointTargetedInference< GUM_SCALAR >::nbrJointTargets ( ) const

finalvirtualnoexceptinherited

returns the number of joint targets

returns the number of target sets

Definition at line 199 of file jointTargetedInference_tpl.h.

References gum::JointTargetedInference< GUM_SCALAR >::__joint_targets.

               {
     return __joint_targets.size();
   }

◆ nbrSoftEvidence()

template<typename GUM_SCALAR >

INLINE Size gum::BayesNetInference< GUM_SCALAR >::nbrSoftEvidence ( ) const

finalvirtualinherited

returns the number of soft evidence entered into the Bayesian network

Definition at line 634 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes, and gum::Set< Key, Alloc >::size().

                                                                      {
     return __soft_evidence_nodes.size();
   }

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◆ nbrTargets()

template<typename GUM_SCALAR >

INLINE const Size gum::MarginalTargetedInference< GUM_SCALAR >::nbrTargets ( ) const

finalvirtualnoexceptinherited

returns the number of marginal targets

Definition at line 218 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::__targets, and gum::Set< Key, Alloc >::size().

               {
     return __targets.size();
   }

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◆ operator=()

template<typename GUM_SCALAR>

LazyPropagation< GUM_SCALAR >& gum::LazyPropagation< GUM_SCALAR >::operator= ( const LazyPropagation< GUM_SCALAR > & )

delete

avoid copy operators

◆ posterior() [1/2]

template<typename GUM_SCALAR >

const Potential< GUM_SCALAR > & gum::JointTargetedInference< GUM_SCALAR >::posterior ( NodeId node )

finalvirtualinherited

Computes and returns the posterior of a node.

Returns: a const ref to the posterior probability of the node.

Parameters

node	the node for which we need a posterior probability

Warning: for efficiency reasons, the potential is stored into the inference engine and is returned by reference. In order to ensure that the potential may still exist even if the Inference object is destroyed, the user has to copy it explicitly.; prepareInference and makeInference may be applied if needed by the posterior method.

Exceptions

UndefinedElement if node is not in the set of targets

Reimplemented from gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 247 of file jointTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::isTarget(), gum::JointTargetedInference< GUM_SCALAR >::jointPosterior(), and gum::MarginalTargetedInference< GUM_SCALAR >::posterior().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), and gum::JointTargetedInference< GUM_SCALAR >::posterior().

                                                                 {
     if (this->isTarget(node))
       return MarginalTargetedInference< GUM_SCALAR >::posterior(node);
     else
       return jointPosterior(NodeSet{node});
   }

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◆ posterior() [2/2]

template<typename GUM_SCALAR >

const Potential< GUM_SCALAR > & gum::JointTargetedInference< GUM_SCALAR >::posterior ( const std::string & nodeName )

finalvirtualinherited

Computes and returns the posterior of a node.

Returns: a const ref to the posterior probability of the node.

Parameters

node	the node for which we need a posterior probability

Warning: for efficiency reasons, the potential is stored into the inference engine and is returned by reference. In order to ensure that the potential may still exist even if the Inference object is destroyed, the user has to copy it explicitly.; prepareInference and makeInference may be applied if needed by the posterior method.

Exceptions

UndefinedElement if node is not in the set of targets

Reimplemented from gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 257 of file jointTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::JointTargetedInference< GUM_SCALAR >::posterior().

                                                                               {
     return posterior(this->BN().idFromName(nodeName));
   }

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◆ prepareInference()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::prepareInference ( )

finalvirtualinherited

prepare the internal inference structures for the next inference

Definition at line 692 of file BayesNetInference_tpl.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::makeInference(), and gum::SamplingInference< GUM_SCALAR >::samplingBN().

                                                                 {
     if (isInferenceReady() || isDone()) { return; }
 
     if (__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     if (__state == StateOfInference::OutdatedBNStructure)
       _updateOutdatedBNStructure();
     else
       _updateOutdatedBNPotentials();
 
     __setState(StateOfInference::ReadyForInference);
   }

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◆ setBN()

template<typename GUM_SCALAR >

void gum::BayesNetInference< GUM_SCALAR >::setBN ( const IBayesNet< GUM_SCALAR > * bn )

virtualinherited

assigns a new BN to the inference engine

Assigns a new BN to the BayesNetInference engine and sends messages to the descendants of BayesNetInference to inform them that the BN has changed.

Warning: By default, all the nodes of the Bayes net are targets.; note that, by aGrUM's rule, the bn is not copied into the inference engine but only referenced.

Definition at line 132 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__computeDomainSizes(), gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::_onBayesNetChanged(), gum::BayesNetInference< GUM_SCALAR >::clear(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

                                                                                {
     clear();
     __bn = bn;
     __computeDomainSizes();
     _onBayesNetChanged(bn);
     __setState(StateOfInference::OutdatedBNStructure);
   }

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◆ setFindBarrenNodesType()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::setFindBarrenNodesType ( FindBarrenNodesType type )

sets how we determine barren nodes

Barren nodes are unnecessary for probability inference, so they can be safely discarded in this case (type = FIND_BARREN_NODES). This speeds-up inference. However, there are some cases in which we do not want to remove barren nodes, typically when we want to answer queries such as Most Probable Explanations (MPE).

◆ setRelevantPotentialsFinderType()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::setRelevantPotentialsFinderType ( RelevantPotentialsFinderType type )

sets how we determine the relevant potentials to combine

When a clique sends a message to a separator, it first constitute the set of the potentials it contains and of the potentials contained in the messages it received. If RelevantPotentialsFinderType = FIND_ALL, all these potentials are combined and projected to produce the message sent to the separator. If RelevantPotentialsFinderType = DSEP_BAYESBALL_NODES, then only the set of potentials d-connected to the variables of the separator are kept for combination and projection.

◆ setTriangulation()

template<typename GUM_SCALAR>

void gum::LazyPropagation< GUM_SCALAR >::setTriangulation ( const Triangulation & new_triangulation )

use a new triangulation algorithm

◆ softEvidenceNodes()

template<typename GUM_SCALAR >

INLINE const NodeSet & gum::BayesNetInference< GUM_SCALAR >::softEvidenceNodes ( ) const

inherited

returns the set of nodes with soft evidence

the set of nodes that received soft evidence

Definition at line 658 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes.

Referenced by gum::SamplingInference< GUM_SCALAR >::contextualize().

                                                                         {
     return __soft_evidence_nodes;
   }

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◆ state()

template<typename GUM_SCALAR >

INLINE BayesNetInference< GUM_SCALAR >::StateOfInference gum::BayesNetInference< GUM_SCALAR >::state ( ) const

finalvirtualnoexceptinherited

returns the state of the inference engine

Definition at line 104 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state.

Referenced by gum::BayesNetInference< GUM_SCALAR >::__setState().

                                                            {
     return __state;
   }

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◆ targets()

template<typename GUM_SCALAR >

INLINE const NodeSet & gum::MarginalTargetedInference< GUM_SCALAR >::targets ( ) const

finalvirtualnoexceptinherited

returns the list of marginal targets

Definition at line 211 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::__targets.

Referenced by gum::SamplingInference< GUM_SCALAR >::contextualize().

               {
     return __targets;
   }

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◆ VI()

template<typename GUM_SCALAR >

INLINE GUM_SCALAR gum::JointTargetedInference< GUM_SCALAR >::VI	(	NodeId	X,
		NodeId	Y
	)

inherited

Variation of information between X and Y.

See also: http://en.wikipedia.org/wiki/Variation_of_information

Warning: Due to limitation of joint, may not be able to compute this value

Exceptions

OperationNotAllowed in these cases

See also: http://en.wikipedia.org/wiki/Variation_of_information

Warning: Due to limitation of , may not be able to compute this value

Exceptions

OperationNotAllowed in these cases

Definition at line 324 of file jointTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::H(), and gum::JointTargetedInference< GUM_SCALAR >::I().

                                                                                {
     return this->H(X) + this->H(Y) - 2 * I(X, Y);
   }

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Member Data Documentation

◆ __1_minus_epsilon

template<typename GUM_SCALAR>

const GUM_SCALAR gum::LazyPropagation< GUM_SCALAR >::__1_minus_epsilon {GUM_SCALAR(1.0 - 1e-6)}

private

for comparisons with 1 - epsilon

Definition at line 383 of file lazyPropagation.h.

◆ __barren_nodes_type

template<typename GUM_SCALAR>

FindBarrenNodesType gum::LazyPropagation< GUM_SCALAR >::__barren_nodes_type

private

the type of barren nodes computation we wish

Definition at line 260 of file lazyPropagation.h.

◆ __clique_potentials

template<typename GUM_SCALAR>

NodeProperty< __PotentialSet > gum::LazyPropagation< GUM_SCALAR >::__clique_potentials

private

the list of all potentials stored in the cliques

This structure contains a list for each clique in the join tree. If a clique did not received any potential, then its list is empty but the entry for the clique does exist. Note that clique potentials contain also soft evidence and the CPTs that were projected to remove their variables that received hard evidence.

Definition at line 320 of file lazyPropagation.h.

◆ __combination_op

template<typename GUM_SCALAR>

Potential< GUM_SCALAR >*(* gum::LazyPropagation< GUM_SCALAR >::__combination_op) (const Potential< GUM_SCALAR > &, const Potential< GUM_SCALAR > &)

inlineprivate

the operator for performing the combinations

Definition at line 268 of file lazyPropagation.h.

269 {

270 LPNewmultiPotential};

gum::LPNewmultiPotential

static INLINE Potential< GUM_SCALAR > * LPNewmultiPotential(const Potential< GUM_SCALAR > &t1, const Potential< GUM_SCALAR > &t2)

Definition: lazyPropagation.h:49

◆ __constants

template<typename GUM_SCALAR>

NodeProperty< GUM_SCALAR > gum::LazyPropagation< GUM_SCALAR >::__constants

private

the constants resulting from the projections of CPTs defined over only hard evidence nodes remove this constant and insert the notion of a constant into potentials/multidim arrays

Definition at line 347 of file lazyPropagation.h.

◆ __created_potentials

template<typename GUM_SCALAR>

ArcProperty< __PotentialSet > gum::LazyPropagation< GUM_SCALAR >::__created_potentials

private

the set of potentials created for the last inference messages

This structure contains only the arcs on which potentials have been created.

Warning: Note that the CPTs that were projected due to hard evidence do not belong to this structure, they are kept in __hard_ev_projected_CPTs.

Definition at line 333 of file lazyPropagation.h.

◆ __evidence_changes

template<typename GUM_SCALAR>

NodeProperty< EvidenceChangeType > gum::LazyPropagation< GUM_SCALAR >::__evidence_changes

private

indicates which nodes of the BN have evidence that changed since the last inference

Definition at line 380 of file lazyPropagation.h.

◆ __find_relevant_potential_type

template<typename GUM_SCALAR>

RelevantPotentialsFinderType gum::LazyPropagation< GUM_SCALAR >::__find_relevant_potential_type

private

the type of relevant potential finding algorithm to be used

Definition at line 251 of file lazyPropagation.h.

◆ __findRelevantPotentials

template<typename GUM_SCALAR>

void(LazyPropagation< GUM_SCALAR >::* gum::LazyPropagation< GUM_SCALAR >::__findRelevantPotentials) (Set< const Potential< GUM_SCALAR > *> &pot_list, Set< const DiscreteVariable *> &kept_vars)

private

update a set of potentials: the remaining are those to be combined to produce a message on a separator

Definition at line 255 of file lazyPropagation.h.

◆ __graph

template<typename GUM_SCALAR>

UndiGraph gum::LazyPropagation< GUM_SCALAR >::__graph

private

the undigraph extracted from the BN and used to construct the join tree

If all nodes are targets, this graph corresponds to the moral graph of the BN. Otherwise, it may be a subgraph of this moral graph. For instance if the BN is A->B->C and only B is a target, __graph will be equal to A-B if we exploit barren nodes (C is a barren node and, therefore, can be removed for inference).

Definition at line 285 of file lazyPropagation.h.

◆ __hard_ev_nodes

template<typename GUM_SCALAR>

NodeSet gum::LazyPropagation< GUM_SCALAR >::__hard_ev_nodes

private

the hard evidence nodes which were projected in CPTs

Definition at line 373 of file lazyPropagation.h.

◆ __hard_ev_projected_CPTs

template<typename GUM_SCALAR>

NodeProperty< const Potential< GUM_SCALAR >* > gum::LazyPropagation< GUM_SCALAR >::__hard_ev_projected_CPTs

private

the CPTs that were projected due to hard evidence nodes

For each node whose CPT is defined over some nodes that contain some hard evidence, assigns a new projected CPT that does not contain these nodes anymore.

Warning: These potentials are owned by LayPropagation.

Definition at line 370 of file lazyPropagation.h.

◆ __is_new_jt_needed

template<typename GUM_SCALAR>

bool gum::LazyPropagation< GUM_SCALAR >::__is_new_jt_needed {true}

private

indicates whether a new join tree is needed for the next inference

when modifying the set of hard evidence, we can determine that the current JT is no more appropriate for inference. This variable enables us to keep track of this.

Definition at line 297 of file lazyPropagation.h.

◆ __joint_target_posteriors

template<typename GUM_SCALAR>

HashTable< NodeSet, const Potential< GUM_SCALAR >* > gum::LazyPropagation< GUM_SCALAR >::__joint_target_posteriors

private

the set of set target posteriors computed during the last inference

the posteriors are owned by LazyPropagation.

Definition at line 341 of file lazyPropagation.h.

◆ __joint_target_to_clique

template<typename GUM_SCALAR>

HashTable< NodeSet, NodeId > gum::LazyPropagation< GUM_SCALAR >::__joint_target_to_clique

private

for each set target, assign a clique in the JT that contains it

Definition at line 312 of file lazyPropagation.h.

◆ __JT

template<typename GUM_SCALAR>

JoinTree* gum::LazyPropagation< GUM_SCALAR >::__JT {nullptr}

private

the join (or junction) tree used to answer the last inference query

Definition at line 288 of file lazyPropagation.h.

◆ __junctionTree

template<typename GUM_SCALAR>

JunctionTree* gum::LazyPropagation< GUM_SCALAR >::__junctionTree {nullptr}

private

the junction tree to answer the last inference query

Definition at line 291 of file lazyPropagation.h.

◆ __messages_computed

template<typename GUM_SCALAR>

ArcProperty< bool > gum::LazyPropagation< GUM_SCALAR >::__messages_computed

private

indicates whether a message (from one clique to another) has been computed

Here, all the messages, computed or not, are put into the property, only the Boolean makes the difference between messages computed and those that were not computed

Definition at line 354 of file lazyPropagation.h.

◆ __node_to_clique

template<typename GUM_SCALAR>

HashTable< NodeId, NodeId > gum::LazyPropagation< GUM_SCALAR >::__node_to_clique

private

for each node of __graph (~ in the Bayes net), associate an ID in the JT

Definition at line 309 of file lazyPropagation.h.

◆ __node_to_soft_evidence

template<typename GUM_SCALAR>

NodeProperty< const Potential< GUM_SCALAR >* > gum::LazyPropagation< GUM_SCALAR >::__node_to_soft_evidence

private

the soft evidence stored in the cliques per their assigned node in the BN

This variable is useful for method _updateOutdatedBNPotentials: it enables to know which soft evidence should be removed/added into the cliques of the join tree.

Warning: These potentials are not owned by LazyPropagation, they are only referenced by it. Only the cliques that contain evidence are filled in this structure.

Definition at line 363 of file lazyPropagation.h.

◆ __projection_op

template<typename GUM_SCALAR>

Potential< GUM_SCALAR >*(* gum::LazyPropagation< GUM_SCALAR >::__projection_op) (const Potential< GUM_SCALAR > &, const Set< const DiscreteVariable *> &)

inlineprivate

the operator for performing the projections

Definition at line 263 of file lazyPropagation.h.

265 {LPNewprojPotential};

gum::LPNewprojPotential

static INLINE Potential< GUM_SCALAR > * LPNewprojPotential(const Potential< GUM_SCALAR > &t1, const Set< const DiscreteVariable * > &del_vars)

Definition: lazyPropagation.h:57

◆ __roots

template<typename GUM_SCALAR>

NodeSet gum::LazyPropagation< GUM_SCALAR >::__roots

private

a clique node used as a root in each connected component of __JT

For usual probabilistic inference, roots is useless. This is useful when computing the probability of evidence. In this case, we need to compute this probability in every connected component and multiply them to get the overall probability of evidence.

Warning: __roots should be computed only when evidenceProbability is called.

Definition at line 306 of file lazyPropagation.h.

◆ __separator_potentials

template<typename GUM_SCALAR>

ArcProperty< __PotentialSet > gum::LazyPropagation< GUM_SCALAR >::__separator_potentials

private

the list of all potentials stored in the separators after inferences

This structure contains all the arcs of the join tree (edges in both directions) whether the arc received any potential or not.

Definition at line 325 of file lazyPropagation.h.

◆ __target_posteriors

template<typename GUM_SCALAR>

NodeProperty< const Potential< GUM_SCALAR >* > gum::LazyPropagation< GUM_SCALAR >::__target_posteriors

private

the set of single posteriors computed during the last inference

the posteriors are owned by LazyPropagation.

Definition at line 337 of file lazyPropagation.h.

◆ __triangulation

template<typename GUM_SCALAR>

Triangulation* gum::LazyPropagation< GUM_SCALAR >::__triangulation

private

the triangulation class creating the junction tree used for inference

Definition at line 270 of file lazyPropagation.h.

◆ __use_binary_join_tree

template<typename GUM_SCALAR>

bool gum::LazyPropagation< GUM_SCALAR >::__use_binary_join_tree {true}

private

indicates whether we should transform junction trees into binary join trees

Definition at line 277 of file lazyPropagation.h.

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

agrum/BN/inference/lazyPropagation.h

Public Member Functions

Public Types

Protected Member Functions

Detailed Description

template<typename GUM_SCALAR> class gum::LazyPropagation< GUM_SCALAR >

Member Typedef Documentation

◆ __PotentialSet

◆ __PotentialSetIterator

Member Enumeration Documentation

◆ EvidenceChangeType

◆ StateOfInference

Constructor & Destructor Documentation

◆ LazyPropagation() [1/2]

◆ LazyPropagation() [2/2]

◆ ~LazyPropagation()

Member Function Documentation

◆ __collectMessage()

◆ __computeJoinTreeRoots()

◆ __createNewJT()

◆ __diffuseMessageInvalidations()

◆ __findRelevantPotentialsGetAll()

◆ __findRelevantPotentialsWithdSeparation()

◆ __findRelevantPotentialsWithdSeparation2()

◆ __findRelevantPotentialsWithdSeparation3()

◆ __findRelevantPotentialsXX()

◆ __invalidateAllMessages()

◆ __isNewJTNeeded()

◆ __marginalizeOut()

◆ __produceMessage()

◆ __removeBarrenVariables()

◆ __setCombinationFunction()

◆ __setProjectionFunction()

◆ _isTargetedMode()

◆ _jointPosterior() [1/2]

◆ _jointPosterior() [2/2]

◆ _makeInference()

◆ _onAllEvidenceErased()

◆ _onAllJointTargetsErased()

◆ _onAllMarginalTargetsAdded()

◆ _onAllMarginalTargetsErased()

◆ _onAllTargetsErased()

◆ _onBayesNetChanged()

◆ _onEvidenceAdded()

◆ _onEvidenceChanged()

◆ _onEvidenceErased()

◆ _onJointTargetAdded()

◆ _onJointTargetErased()

◆ _onMarginalTargetAdded()

◆ _onMarginalTargetErased()

◆ _onStateChanged()

◆ _posterior()

◆ _setOutdatedBNPotentialsState()

◆ _setOutdatedBNStructureState()

◆ _setTargetedMode()

◆ _unnormalizedJointPosterior() [1/2]

◆ _unnormalizedJointPosterior() [2/2]

◆ _updateOutdatedBNPotentials()

◆ _updateOutdatedBNStructure()

◆ addAllTargets()

◆ addEvidence() [1/8]

◆ addEvidence() [2/8]

◆ addEvidence() [3/8]

◆ addEvidence() [4/8]

◆ addEvidence() [5/8]

◆ addEvidence() [6/8]

◆ addEvidence() [7/8]

◆ addEvidence() [8/8]

◆ addJointTarget()

◆ addListOfEvidence()

◆ addSetOfEvidence()

◆ addTarget() [1/2]

◆ addTarget() [2/2]

◆ BN()

◆ chgEvidence() [1/7]

◆ chgEvidence() [2/7]

◆ chgEvidence() [3/7]

◆ chgEvidence() [4/7]

◆ chgEvidence() [5/7]

◆ chgEvidence() [6/7]

◆ chgEvidence() [7/7]

template<typename GUM_SCALAR>
class gum::LazyPropagation< GUM_SCALAR >