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

#include <ShaferShenoyInference.h>

Inheritance diagram for gum::ShaferShenoyInference< GUM_SCALAR >:

Collaboration diagram for gum::ShaferShenoyInference< 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
	ShaferShenoyInference (const IBayesNet< GUM_SCALAR > *BN, FindBarrenNodesType barren_type=FindBarrenNodesType::FIND_BARREN_NODES, bool use_binary_join_tree=true)
	default constructor More...

	~ShaferShenoyInference () final
	destructor More...

Accessors / Modifiers
void	setTriangulation (const Triangulation &new_triangulation)
	use a new triangulation algorithm 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 ()
	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	I (const std::string &Xname, const std::string &Yname)
	Mutual information between X and Y. More...

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

GUM_SCALAR	VI (const std::string &Xname, const std::string &Yname)
	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...

Accessors / Modifiers
virtual const GraphicalModel &	model () 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 model More...

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

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

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

virtual bool	isInferenceDone () const noexcept final
	returns whether the inference object is in a InferenceDone 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::OutdatedStructure, StateOfInference::OutdatedPotentials, StateOfInference::ReadyForInference, StateOfInference::Done }
	current state of the inference More...

Protected Member Functions
void	onStateChanged_ () final
	fired when the stage is changed More...

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 contains_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	onModelChanged_ (const GraphicalModel *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	updateOutdatedStructure_ () final
	prepares inference when the latter is in OutdatedStructure state More...

void	updateOutdatedPotentials_ () final
	prepares inference when the latter is in OutdatedPotentials 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	setOutdatedStructureState_ ()
	put the inference into an outdated model structure state More...

void	setOutdatedPotentialsState_ ()
	puts the inference into an OutdatedPotentials state if it is not already in an OutdatedStructure state More...

virtual void	setState_ (const StateOfInference state) final
	set the state of the inference engine and call the notification onStateChanged_ when necessary (i.e. when the state has effectively changed). More...

void	setModel_ (const GraphicalModel *model)

void	setModelDuringConstruction_ (const GraphicalModel *model)
	assigns a model during the inference engine construction More...

bool	hasNoModel_ () const

Detailed Description

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

Implementation of Shafer-Shenoy's propagation algorithm for inference in Bayesian networks

Definition at line 66 of file ShaferShenoyInference.h.

Member Typedef Documentation

◆ _PotentialSet_

template<typename GUM_SCALAR>

typedef Set< const Potential< GUM_SCALAR >* > gum::ShaferShenoyInference< GUM_SCALAR >::_PotentialSet_

private

Definition at line 219 of file ShaferShenoyInference.h.

◆ _PotentialSetIterator_

template<typename GUM_SCALAR>

typedef SetIteratorSafe< const Potential< GUM_SCALAR >* > gum::ShaferShenoyInference< GUM_SCALAR >::_PotentialSetIterator_

private

Definition at line 220 of file ShaferShenoyInference.h.

Member Enumeration Documentation

◆ EvidenceChangeType

template<typename GUM_SCALAR>

enum gum::ShaferShenoyInference::EvidenceChangeType

private

the possible types of evidence changes

Enumerator
EVIDENCE_ADDED
EVIDENCE_ERASED
EVIDENCE_MODIFIED

Definition at line 349 of file ShaferShenoyInference.h.

     {
       EVIDENCE_ADDED,
       EVIDENCE_ERASED,
       EVIDENCE_MODIFIED
     };

◆ StateOfInference

template<typename GUM_SCALAR >

enum gum::GraphicalModelInference::StateOfInference

stronginherited

current state of the inference

graphicalModelInference can be in one of 4 different states:

OutdatedStructure: in this state, the inference is fully unprepared to be applied because some events changed the "logical" structure of the model: for instance a node received a hard evidence, which implies that its outgoing arcs can be removed from the model, hence involving a structural change in the model.
OutdatedPotentials: in this state, the structure of the model 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.

Enumerator
OutdatedStructure
OutdatedPotentials
ReadyForInference
Done

Definition at line 106 of file graphicalModelInference.h.

     {
       OutdatedStructure,
       OutdatedPotentials,
       ReadyForInference,
       Done
     };

Constructor & Destructor Documentation

◆ ShaferShenoyInference() [1/2]

template<typename GUM_SCALAR>

gum::ShaferShenoyInference< GUM_SCALAR >::ShaferShenoyInference	(	const IBayesNet< GUM_SCALAR > *	BN,
		FindBarrenNodesType	barren_type = `FindBarrenNodesType::FIND_BARREN_NODES`,
		bool	use_binary_join_tree = `true`
	)

explicit

default constructor

◆ ~ShaferShenoyInference()

template<typename GUM_SCALAR>

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

final

destructor

◆ ShaferShenoyInference() [2/2]

template<typename GUM_SCALAR>

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

private

avoid copy constructors

Member Function Documentation

◆ _collectMessage_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::_collectMessage_	(	NodeId	id,
		NodeId	from
	)

private

actually perform the collect phase

◆ _computeJoinTreeRoots_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::_computeJoinTreeRoots_ ( )

private

compute a root for each connected component of JT

◆ _createNewJT_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::_createNewJT_ ( )

private

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

◆ _diffuseMessageInvalidations_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::_diffuseMessageInvalidations_	(	NodeId	from,
		NodeId	to,
		NodeSet &	cliques_invalidated
	)

private

invalidate all the messages sent from a given clique

◆ _invalidateAllMessages_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::_invalidateAllMessages_ ( )

private

invalidate all messages, posteriors and created potentials

◆ _isNewJTNeeded_()

template<typename GUM_SCALAR>

bool gum::ShaferShenoyInference< 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::ShaferShenoyInference< 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::ShaferShenoyInference< 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::ShaferShenoyInference< GUM_SCALAR >::_removeBarrenVariables_	(	_PotentialSet_ &	pot_list,
		Set< const DiscreteVariable * > &	del_vars
	)

private

◆ _setCombinationFunction_()

template<typename GUM_SCALAR>

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

private

sets the operator for performing the projections

◆ addAllTargets()

template<typename GUM_SCALAR >

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

finalvirtualinherited

adds all nodes as targets

Definition at line 130 of file marginalTargetedInference_tpl.h.

                                                               {
     // check if the node belongs to the Bayesian network
     if (this->hasNoModel_())
       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_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
       }
     }
   }

◆ addEvidence() [1/8]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 228 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ addEvidence() [2/8]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 234 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ addEvidence() [3/8]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 241 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ addEvidence() [4/8]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 248 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ addEvidence() [5/8]

template<typename GUM_SCALAR >

void gum::GraphicalModelInference< 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 256 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ addEvidence() [6/8]

template<typename GUM_SCALAR >

void gum::GraphicalModelInference< 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 280 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ addEvidence() [7/8]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 325 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ addEvidence() [8/8]

template<typename GUM_SCALAR >

void gum::GraphicalModelInference< 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 287 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                        {
     // check if the potential corresponds to an evidence
     if (pot.nbrDim() != 1) { GUM_ERROR(InvalidArgument, pot << " is not mono-dimensional.") }
     if (_model_ == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     NodeId id = _model_->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::OutdatedStructure);
     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 111 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                        {
     // check if the nodes in the target belong to the Bayesian network
     if (this->hasNoModel_())
       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.isProperSupersetOf(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->isProperSubsetOf(joint_target)) eraseJointTarget(*iter);
     }
 
     this->setTargetedMode_();   // does nothing if already in targeted mode
     _joint_targets_.insert(joint_target);
     onJointTargetAdded_(joint_target);
     this->setState_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
   }

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◆ addListOfEvidence()

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 333 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ addSetOfEvidence()

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 342 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                           {
     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 107 of file marginalTargetedInference_tpl.h.

                                                                        {
     // check if the node belongs to the Bayesian network
     if (this->hasNoModel_())
       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_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
     }
   }

◆ 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 151 of file marginalTargetedInference_tpl.h.

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

◆ 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 57 of file BayesNetInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                   {
     return static_cast< const IBayesNet< GUM_SCALAR >& >(this->model());
   }

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◆ chgEvidence() [1/7]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 402 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ chgEvidence() [2/7]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 408 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ chgEvidence() [3/7]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 415 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ chgEvidence() [4/7]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 422 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ chgEvidence() [5/7]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 431 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ chgEvidence() [6/7]

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< 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 456 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ chgEvidence() [7/7]

template<typename GUM_SCALAR >

void gum::GraphicalModelInference< 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 464 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                             {
     // check if the potential corresponds to an evidence
     if (pot.nbrDim() != 1) {
       GUM_ERROR(InvalidArgument, pot << " is not a mono-dimensional potential.")
     }
     if (_model_ == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     NodeId id = _model_->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::OutdatedStructure);
     } else {
       if (!isInferenceOutdatedStructure()) { setState_(StateOfInference::OutdatedPotentials); }
     }
 
     onEvidenceChanged_(id, hasChangedSoftHard);
   }

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◆ clear()

template<typename GUM_SCALAR >

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

virtualinherited

clears all the data structures allocated for the last inference

Reimplemented in gum::ShaferShenoyLIMIDInference< GUM_SCALAR >.

Definition at line 139 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                            {
     eraseAllEvidence();
     setState_(StateOfInference::OutdatedStructure);
   }

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◆ domainSizes()

template<typename GUM_SCALAR >

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

finalvirtualinherited

get the domain sizes of the random variables of the model

Definition at line 159 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                               {
     return _domain_sizes_;
   }

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◆ eraseAllEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

removes all the evidence entered into the network

Definition at line 553 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                       {
     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::OutdatedStructure);
     } else {
       if (!isInferenceOutdatedStructure()) { setState_(StateOfInference::OutdatedPotentials); }
     }
   }

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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 91 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                          {
     if (_joint_targets_.size() > 0) {
       // we already are in target mode. So no this->setTargetedMode_();  is needed
       onAllJointTargetsErased_();
       _joint_targets_.clear();
       this->setState_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
     }
   }

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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 84 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                             {
     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 103 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                     {
     eraseAllMarginalTargets();
     eraseAllJointTargets();
   }

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◆ eraseEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

removed the evidence, if any, corresponding to node id

Definition at line 527 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                             {
     if (hasEvidence(id)) {
       if (hasHardEvidence(id)) {
         onEvidenceErased_(id, true);
         _hard_evidence_.erase(id);
         _hard_evidence_nodes_.erase(id);
         setState_(StateOfInference::OutdatedStructure);
       } else {
         onEvidenceErased_(id, false);
         _soft_evidence_nodes_.erase(id);
         if (!isInferenceOutdatedStructure()) { setState_(StateOfInference::OutdatedPotentials); }
       }
 
       delete (_evidence_[id]);
       _evidence_.erase(id);
     }
   }

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◆ eraseEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Definition at line 546 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                             {
     eraseEvidence(this->model().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 149 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                          {
     // check if the nodes in the target belong to the Bayesian network
     if (this->hasNoModel_())
       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_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
     }
   }

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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 164 of file marginalTargetedInference_tpl.h.

                                                                          {
     // check if the node belongs to the Bayesian network
     if (this->hasNoModel_())
       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_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
     }
   }

◆ 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 188 of file marginalTargetedInference_tpl.h.

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

◆ evidence()

template<typename GUM_SCALAR >

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

inherited

returns the set of evidence

Definition at line 605 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                      {
     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 268 of file marginalTargetedInference_tpl.h.

                                                                                               {
     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;
   }

◆ 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 306 of file marginalTargetedInference_tpl.h.

                                         {
     const auto& bn = this->BN();
     return evidenceImpact(bn.idFromName(target), bn.nodeset(evs));
   }

◆ 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 322 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                    {
     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 363 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                         {
     const auto& bn = this->BN();
     return evidenceJointImpact(bn.nodeset(targets), bn.nodeset(evs));
   }

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◆ evidenceProbability()

template<typename GUM_SCALAR>

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

virtual

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 253 of file marginalTargetedInference_tpl.h.

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

◆ 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 261 of file marginalTargetedInference_tpl.h.

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

◆ hardEvidence()

template<typename GUM_SCALAR >

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

inherited

indicate for each node with hard evidence which value it took

Definition at line 597 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                               {
     return _hard_evidence_;
   }

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◆ hardEvidenceNodes()

template<typename GUM_SCALAR >

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

inherited

returns the set of nodes with hard evidence

the set of nodes that received hard evidence

Definition at line 619 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                        {
     return _hard_evidence_nodes_;
   }

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◆ hasEvidence() [1/3]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether some node(s) have received evidence

Definition at line 351 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                        {
     return !_evidence_.empty();
   }

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◆ hasEvidence() [2/3]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 358 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                 {
     return _evidence_.exists(id);
   }

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◆ hasEvidence() [3/3]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 380 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ hasHardEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 365 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                     {
     return _hard_evidence_nodes_.exists(id);
   }

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◆ hasHardEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 388 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ hasNoModel_()

template<typename GUM_SCALAR >

bool gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_ ( ) const

inlineprotectedinherited

Definition at line 527 of file graphicalModelInference.h.

527 { return _model_ == nullptr; };

gum::GraphicalModelInference::_model_

const GraphicalModel * _model_

the Bayes net on which we perform inferences

Definition: graphicalModelInference.h:489

◆ hasSoftEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 372 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                     {
     return _soft_evidence_nodes_.exists(id);
   }

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◆ hasSoftEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 396 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ I() [1/2]

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 266 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                        {
     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 * (std::log2(vXY) - std::log2(vX) - std::log2(vY));
       }
     }
 
     delete pXY;
 
     return res;
   }

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◆ I() [2/2]

template<typename GUM_SCALAR >

GUM_SCALAR gum::JointTargetedInference< GUM_SCALAR >::I	(	const std::string &	Xname,
		const std::string &	Yname
	)

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 247 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                              {
     return I(this->BN().idFromName(Xname), this->BN().idFromName(Yname));
   }

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◆ isInferenceDone()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

returns whether the inference object is in a InferenceDone 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 84 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ isInferenceOutdatedPotentials()

template<typename GUM_SCALAR >

INLINE bool gum::GraphicalModelInference< GUM_SCALAR >::isInferenceOutdatedPotentials ( ) const

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedPotential state

Definition at line 79 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                          {
     return (_state_ == StateOfInference::OutdatedPotentials);
   }

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◆ isInferenceOutdatedStructure()

template<typename GUM_SCALAR >

INLINE bool gum::GraphicalModelInference< GUM_SCALAR >::isInferenceOutdatedStructure ( ) const

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedStructure state

Definition at line 73 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                                  {
     return (_state_ == StateOfInference::OutdatedStructure);
   }

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◆ isInferenceReady()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

returns whether the inference object is in a ready state

Definition at line 68 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                      {
     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 67 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                            {
     if (this->hasNoModel_())
       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);
   }

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◆ 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 73 of file marginalTargetedInference_tpl.h.

                                                                                  {
     // check that the variable belongs to the bn
     if (this->hasNoModel_())
       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);
   }

◆ 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 88 of file marginalTargetedInference_tpl.h.

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

◆ isTargetedMode_()

template<typename GUM_SCALAR >

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

protectedinherited

Definition at line 315 of file marginalTargetedInference_tpl.h.

                                                                              {
     return _targeted_mode_;
   }

◆ 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 372 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                                 {
     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 422 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                             {
     return jointMutualInformation(this->BN().ids(targets));
   }

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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 195 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                               {
     // 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.isProperSubsetOf(target)) {
           set = target;
           break;
         }
       }
     }
 
     if (set.empty()) {
       GUM_ERROR(UndefinedElement,
                 " no joint target containing " << nodes << " could be found among "
                                                << _joint_targets_);
     }
 
     if (!this->isInferenceDone()) { this->makeInference(); }
 
     if (found_exact_target)
       return jointPosterior_(nodes);
     else
       return jointPosterior_(nodes, set);
   }

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◆ jointPosterior_() [1/2]

template<typename GUM_SCALAR>

const Potential< GUM_SCALAR >& gum::ShaferShenoyInference< 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 >.

◆ jointPosterior_() [2/2]

template<typename GUM_SCALAR>

const Potential< GUM_SCALAR >& gum::ShaferShenoyInference< 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 >.

◆ joinTree()

template<typename GUM_SCALAR>

const JoinTree* gum::ShaferShenoyInference< 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 177 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                                  {
     return _joint_targets_;
   }

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◆ junctionTree()

template<typename GUM_SCALAR>

const JunctionTree* gum::ShaferShenoyInference< 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::GraphicalModelInference< 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 664 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                    {
     if (isInferenceDone()) { return; }
 
     if (!isInferenceReady()) { prepareInference(); }
 
     makeInference_();
 
     setState_(StateOfInference::Done);
   }

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◆ makeInference_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::makeInference_ ( )

finalprotectedvirtual

called when the inference has to be performed effectively

Once the inference is done, fillPosterior_ can be called.

Implements gum::GraphicalModelInference< GUM_SCALAR >.

◆ model()

template<typename GUM_SCALAR >

INLINE const GraphicalModel & gum::GraphicalModelInference< GUM_SCALAR >::model ( ) 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 107 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ nbrEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

returns the number of evidence entered into the Bayesian network

Definition at line 576 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                        {
     return _evidence_.size();
   }

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◆ nbrHardEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

returns the number of hard evidence entered into the Bayesian network

Definition at line 583 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                            {
     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 183 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                    {
     return _joint_targets_.size();
   }

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◆ nbrSoftEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

returns the number of soft evidence entered into the Bayesian network

Definition at line 590 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                            {
     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 207 of file marginalTargetedInference_tpl.h.

                                                                                        {
     return _targets_.size();
   }

◆ onAllEvidenceErased_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::onAllEvidenceErased_ ( bool contains_hard_evidence )

finalprotectedvirtual

fired before all the evidence are erased

Implements gum::GraphicalModelInference< GUM_SCALAR >.

◆ onAllJointTargetsErased_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< 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::ShaferShenoyInference< 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::ShaferShenoyInference< 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::ShaferShenoyInference< GUM_SCALAR >::onAllTargetsErased_ ( )

finalprotectedvirtual

fired before a all single and joint_targets are removed

Implements gum::JointTargetedInference< GUM_SCALAR >.

◆ onEvidenceAdded_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::onEvidenceAdded_	(	const NodeId	id,
		bool	isHardEvidence
	)

finalprotectedvirtual

fired after a new evidence is inserted

Implements gum::GraphicalModelInference< GUM_SCALAR >.

◆ onEvidenceChanged_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< 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::GraphicalModelInference< GUM_SCALAR >.

◆ onEvidenceErased_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::onEvidenceErased_	(	const NodeId	id,
		bool	isHardEvidence
	)

finalprotectedvirtual

fired before an evidence is removed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

◆ onJointTargetAdded_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< 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::ShaferShenoyInference< 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::ShaferShenoyInference< 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::ShaferShenoyInference< 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 >.

◆ onModelChanged_()

template<typename GUM_SCALAR>

virtual void gum::ShaferShenoyInference< GUM_SCALAR >::onModelChanged_ ( const GraphicalModel * bn )

finalprotectedvirtual

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

Reimplemented from gum::JointTargetedInference< GUM_SCALAR >.

◆ onStateChanged_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::onStateChanged_ ( )

inlinefinalprotectedvirtual

fired when the stage is changed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 124 of file ShaferShenoyInference.h.

124 {};

◆ operator=()

template<typename GUM_SCALAR>

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

private

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 229 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                             {
     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 239 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ posterior_()

template<typename GUM_SCALAR>

const Potential< GUM_SCALAR >& gum::ShaferShenoyInference< GUM_SCALAR >::posterior_ ( NodeId id )

finalprotectedvirtual

returns the posterior of a given variable

Parameters

id	The variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

◆ prepareInference()

template<typename GUM_SCALAR >

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

finalvirtualinherited

prepare the internal inference structures for the next inference

Definition at line 645 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                       {
     if (isInferenceReady() || isInferenceDone()) { return; }
 
     if (_model_ == nullptr)
       GUM_ERROR(NullElement,
                 "No model been assigned to the "
                 "inference algorithm");
 
     if (_state_ == StateOfInference::OutdatedStructure)
       updateOutdatedStructure_();
     else
       updateOutdatedPotentials_();
 
     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 64 of file BayesNetInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                {
     this->setModel_(bn);
   }

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◆ setFindBarrenNodesType()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< 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).

◆ setModel_()

template<typename GUM_SCALAR >

void gum::GraphicalModelInference< GUM_SCALAR >::setModel_ ( const GraphicalModel * model )

protectedinherited

Definition at line 118 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                    {
     clear();
     _model_ = model;
     _computeDomainSizes_();
     onModelChanged_(model);
     setState_(StateOfInference::OutdatedStructure);
   }

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◆ setModelDuringConstruction_()

template<typename GUM_SCALAR >

void gum::GraphicalModelInference< GUM_SCALAR >::setModelDuringConstruction_ ( const GraphicalModel * model )

protectedinherited

assigns a model during the inference engine construction

Definition at line 129 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                   {
     _model_ = model;
     _computeDomainSizes_();
     setState_(StateOfInference::OutdatedStructure);
   }

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◆ setOutdatedPotentialsState_()

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< GUM_SCALAR >::setOutdatedPotentialsState_ ( )

protectedinherited

puts the inference into an OutdatedPotentials state if it is not already in an OutdatedStructure state

OutdatedPotentials: in this state, the structure of the model 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 638 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                  {
     setState_(StateOfInference::OutdatedPotentials);
   }

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◆ setOutdatedStructureState_()

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< GUM_SCALAR >::setOutdatedStructureState_ ( )

protectedinherited

put the inference into an outdated model structure state

OutdatedStructure: in this state, the inference is fully unprepared to be applied because some events changed the "logical" structure of the model: for instance a node received a hard evidence, which implies that its outgoing arcs can be removed from the model, hence involving a structural change in the model. 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 graphicalModelInference may be smarter than graphicalModelInference 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 OutdatedPotentials 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 630 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                 {
     setState_(StateOfInference::OutdatedStructure);
   }

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◆ setState_()

template<typename GUM_SCALAR >

INLINE void gum::GraphicalModelInference< GUM_SCALAR >::setState_ ( const StateOfInference state )

finalprotectedvirtualinherited

set the state of the inference engine and call the notification onStateChanged_ when necessary (i.e. when the state has effectively changed).

Definition at line 98 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                            {
     if (_state_ != state) {
       _state_ = state;
       onStateChanged_();
     }
   }

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◆ setTargetedMode_()

template<typename GUM_SCALAR >

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

protectedinherited

Definition at line 319 of file marginalTargetedInference_tpl.h.

                                                                         {
     if (!_targeted_mode_) {
       _targets_.clear();
       _targeted_mode_ = true;
     }
   }

◆ setTriangulation()

template<typename GUM_SCALAR>

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

use a new triangulation algorithm

◆ softEvidenceNodes()

template<typename GUM_SCALAR >

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

inherited

returns the set of nodes with soft evidence

the set of nodes that received soft evidence

Definition at line 612 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                        {
     return _soft_evidence_nodes_;
   }

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◆ state()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

returns the state of the inference engine

Definition at line 92 of file graphicalModelInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                  {
     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 201 of file marginalTargetedInference_tpl.h.

                                                                                         {
     return _targets_;
   }

◆ unnormalizedJointPosterior_() [1/2]

template<typename GUM_SCALAR>

Potential< GUM_SCALAR >* gum::ShaferShenoyInference< GUM_SCALAR >::unnormalizedJointPosterior_ ( NodeId id )

finalprotectedvirtual

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

Implements gum::JointTargetedInference< GUM_SCALAR >.

◆ unnormalizedJointPosterior_() [2/2]

template<typename GUM_SCALAR>

Potential< GUM_SCALAR >* gum::ShaferShenoyInference< GUM_SCALAR >::unnormalizedJointPosterior_ ( const NodeSet & set )

finalprotectedvirtual

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

Implements gum::JointTargetedInference< GUM_SCALAR >.

◆ updateOutdatedPotentials_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::updateOutdatedPotentials_ ( )

finalprotectedvirtual

prepares inference when the latter is in OutdatedPotentials state

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

◆ updateOutdatedStructure_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyInference< GUM_SCALAR >::updateOutdatedStructure_ ( )

finalprotectedvirtual

prepares inference when the latter is in OutdatedStructure state

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

◆ VI() [1/2]

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 315 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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◆ VI() [2/2]

template<typename GUM_SCALAR >

GUM_SCALAR gum::JointTargetedInference< GUM_SCALAR >::VI	(	const std::string &	Xname,
		const std::string &	Yname
	)

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

Definition at line 253 of file jointTargetedInference_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                               {
     return VI(this->BN().idFromName(Xname), this->BN().idFromName(Yname));
   }

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Member Data Documentation

◆ _barren_nodes_type_

template<typename GUM_SCALAR>

FindBarrenNodesType gum::ShaferShenoyInference< GUM_SCALAR >::_barren_nodes_type_

private

the type of barren nodes computation we wish

Definition at line 224 of file ShaferShenoyInference.h.

◆ _clique_potentials_

template<typename GUM_SCALAR>

NodeProperty< _PotentialSet_ > gum::ShaferShenoyInference< 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. The product of all these potentials is precisely the potential stored into clique_ss_potential

Definition at line 286 of file ShaferShenoyInference.h.

◆ _clique_ss_potential_

template<typename GUM_SCALAR>

NodeProperty< const Potential< GUM_SCALAR >* > gum::ShaferShenoyInference< GUM_SCALAR >::_clique_ss_potential_

private

the potentials stored into the cliques by Shafer-Shenoy

For a given clique, there is an entry in clique_ss_potential if and only if the clique received some potential(s). In this case, the potential stored is the combination of all the corresponding list of potentials in clique_potentials.

Definition at line 293 of file ShaferShenoyInference.h.

◆ _combination_op_

template<typename GUM_SCALAR>

Potential< GUM_SCALAR >*(* gum::ShaferShenoyInference< GUM_SCALAR >::_combination_op_) (const Potential< GUM_SCALAR > &, const Potential< GUM_SCALAR > &)

inlineprivate

the operator for performing the combinations

Definition at line 232 of file ShaferShenoyInference.h.

233 {

234 SSNewmultiPotential};

gum::SSNewmultiPotential

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

Definition: ShaferShenoyInference.h:44

◆ _constants_

template<typename GUM_SCALAR>

NodeProperty< GUM_SCALAR > gum::ShaferShenoyInference< 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 320 of file ShaferShenoyInference.h.

◆ _created_potentials_

template<typename GUM_SCALAR>

ArcProperty< _PotentialSet_ > gum::ShaferShenoyInference< 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 306 of file ShaferShenoyInference.h.

◆ _evidence_changes_

template<typename GUM_SCALAR>

NodeProperty< EvidenceChangeType > gum::ShaferShenoyInference< GUM_SCALAR >::_evidence_changes_

private

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

Definition at line 358 of file ShaferShenoyInference.h.

◆ _graph_

template<typename GUM_SCALAR>

UndiGraph gum::ShaferShenoyInference< 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 249 of file ShaferShenoyInference.h.

◆ _hard_ev_nodes_

template<typename GUM_SCALAR>

NodeSet gum::ShaferShenoyInference< GUM_SCALAR >::_hard_ev_nodes_

private

the hard evidence nodes which were projected in CPTs

Definition at line 346 of file ShaferShenoyInference.h.

◆ _hard_ev_projected_CPTs_

template<typename GUM_SCALAR>

NodeProperty< const Potential< GUM_SCALAR >* > gum::ShaferShenoyInference< 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 343 of file ShaferShenoyInference.h.

◆ _is_new_jt_needed_

template<typename GUM_SCALAR>

bool gum::ShaferShenoyInference< 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 261 of file ShaferShenoyInference.h.

◆ _joint_target_posteriors_

template<typename GUM_SCALAR>

HashTable< NodeSet, const Potential< GUM_SCALAR >* > gum::ShaferShenoyInference< GUM_SCALAR >::_joint_target_posteriors_

private

the set of set target posteriors computed during the last inference

the posteriors are owned by ShaferShenoyInference.

Definition at line 314 of file ShaferShenoyInference.h.

◆ _joint_target_to_clique_

template<typename GUM_SCALAR>

HashTable< NodeSet, NodeId > gum::ShaferShenoyInference< GUM_SCALAR >::_joint_target_to_clique_

private

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

Definition at line 276 of file ShaferShenoyInference.h.

◆ _JT_

template<typename GUM_SCALAR>

JoinTree* gum::ShaferShenoyInference< GUM_SCALAR >::_JT_ {nullptr}

private

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

Definition at line 252 of file ShaferShenoyInference.h.

◆ _junctionTree_

template<typename GUM_SCALAR>

JunctionTree* gum::ShaferShenoyInference< GUM_SCALAR >::_junctionTree_ {nullptr}

private

the junction tree to answer the last inference query

Definition at line 255 of file ShaferShenoyInference.h.

◆ _messages_computed_

template<typename GUM_SCALAR>

ArcProperty< bool > gum::ShaferShenoyInference< 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 327 of file ShaferShenoyInference.h.

◆ _node_to_clique_

template<typename GUM_SCALAR>

HashTable< NodeId, NodeId > gum::ShaferShenoyInference< GUM_SCALAR >::_node_to_clique_

private

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

Definition at line 273 of file ShaferShenoyInference.h.

◆ _node_to_soft_evidence_

template<typename GUM_SCALAR>

NodeProperty< const Potential< GUM_SCALAR >* > gum::ShaferShenoyInference< 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 updateOutdatedPotentials_: 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 ShaferShenoyInference, they are only referenced by it. Only the cliques that contain evidence are filled in this structure.

Definition at line 336 of file ShaferShenoyInference.h.

◆ _one_minus_epsilon_

template<typename GUM_SCALAR>

const GUM_SCALAR gum::ShaferShenoyInference< GUM_SCALAR >::_one_minus_epsilon_ {GUM_SCALAR(1.0 - 1e-6)}

private

for comparisons with 1 - epsilon

Definition at line 361 of file ShaferShenoyInference.h.

◆ _projection_op_

template<typename GUM_SCALAR>

Potential< GUM_SCALAR >*(* gum::ShaferShenoyInference< GUM_SCALAR >::_projection_op_) (const Potential< GUM_SCALAR > &, const Set< const DiscreteVariable *> &)

inlineprivate

the operator for performing the projections

Definition at line 227 of file ShaferShenoyInference.h.

228 {

229 SSNewprojPotential};

gum::SSNewprojPotential

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

Definition: ShaferShenoyInference.h:52

◆ _roots_

template<typename GUM_SCALAR>

NodeSet gum::ShaferShenoyInference< 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 270 of file ShaferShenoyInference.h.

◆ _separator_potentials_

template<typename GUM_SCALAR>

ArcProperty< _PotentialSet_ > gum::ShaferShenoyInference< 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 298 of file ShaferShenoyInference.h.

◆ _target_posteriors_

template<typename GUM_SCALAR>

NodeProperty< const Potential< GUM_SCALAR >* > gum::ShaferShenoyInference< GUM_SCALAR >::_target_posteriors_

private

the set of single posteriors computed during the last inference

the posteriors are owned by ShaferShenoyInference.

Definition at line 310 of file ShaferShenoyInference.h.

◆ _triangulation_

template<typename GUM_SCALAR>

Triangulation* gum::ShaferShenoyInference< GUM_SCALAR >::_triangulation_

private

the triangulation class creating the junction tree used for inference

Definition at line 234 of file ShaferShenoyInference.h.

◆ _use_binary_join_tree_

template<typename GUM_SCALAR>

bool gum::ShaferShenoyInference< GUM_SCALAR >::_use_binary_join_tree_ {true}

private

indicates whether we should transform junction trees into binary join trees

Definition at line 241 of file ShaferShenoyInference.h.

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

agrum/BN/inference/ShaferShenoyInference.h

Public Member Functions

Public Types

Protected Member Functions

Detailed Description

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

Member Typedef Documentation

◆ _PotentialSet_

◆ _PotentialSetIterator_

Member Enumeration Documentation

◆ EvidenceChangeType

◆ StateOfInference

Constructor & Destructor Documentation

◆ ShaferShenoyInference() [1/2]

◆ ~ShaferShenoyInference()

◆ ShaferShenoyInference() [2/2]

Member Function Documentation

◆ _collectMessage_()

◆ _computeJoinTreeRoots_()

◆ _createNewJT_()

◆ _diffuseMessageInvalidations_()

◆ _invalidateAllMessages_()

◆ _isNewJTNeeded_()

◆ _marginalizeOut_()

◆ _produceMessage_()

◆ _removeBarrenVariables_()

◆ _setCombinationFunction_()

◆ _setProjectionFunction_()

◆ 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]

◆ clear()

◆ domainSizes()

◆ eraseAllEvidence()

◆ eraseAllJointTargets()

◆ eraseAllMarginalTargets()

◆ eraseAllTargets()

◆ eraseEvidence() [1/2]

◆ eraseEvidence() [2/2]

◆ eraseJointTarget()

◆ eraseTarget() [1/2]

◆ eraseTarget() [2/2]

◆ evidence()

◆ evidenceImpact() [1/2]

◆ evidenceImpact() [2/2]

◆ evidenceJointImpact() [1/2]

◆ evidenceJointImpact() [2/2]

◆ evidenceProbability()

◆ H() [1/2]

◆ H() [2/2]

◆ hardEvidence()

◆ hardEvidenceNodes()

◆ hasEvidence() [1/3]

◆ hasEvidence() [2/3]

◆ hasEvidence() [3/3]

◆ hasHardEvidence() [1/2]

◆ hasHardEvidence() [2/2]

◆ hasNoModel_()

◆ hasSoftEvidence() [1/2]

◆ hasSoftEvidence() [2/2]

◆ I() [1/2]

◆ I() [2/2]

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