gum::VariableElimination< GUM_SCALAR > Class Template Reference

Implementation of a Variable Elimination's-like version of lazy propagation for inference in Bayesian networks. More...

#include <agrum/BN/inference/variableElimination.h>

Inheritance diagram for gum::VariableElimination< GUM_SCALAR >:

Collaboration diagram for gum::VariableElimination< GUM_SCALAR >:

Public Types
enum class	StateOfInference { OutdatedStructure , OutdatedTensors , ReadyForInference , Done }
	current state of the inference More...

Public Member Functions
Tensor< GUM_SCALAR >	evidenceJointImpact (const NodeSet &targets, const NodeSet &evs)
	Create a gum::Tensor for P(joint targets|evs) (for all instanciation of targets and evs).
Tensor< GUM_SCALAR >	evidenceJointImpact (const std::vector< std::string > &targets, const std::vector< std::string > &evs)
	Create a gum::Tensor for P(joint targets|evs) (for all instanciation of targets and evs).
Tensor< GUM_SCALAR >	evidenceImpact (NodeId target, const NodeSet &evs)
	Create a gum::Tensor for P(target|evs) (for all instanciation of target and evs).
Tensor< GUM_SCALAR >	evidenceImpact (const std::string &target, const std::vector< std::string > &evs)
	Create a gum::Tensor for P(target|evs) (for all instanciation of target and evs).
Constructors / Destructors
	VariableElimination (const IBayesNet< GUM_SCALAR > *BN, RelevantTensorsFinderType=RelevantTensorsFinderType::DSEP_BAYESBALL_TENSORS, FindBarrenNodesType=FindBarrenNodesType::FIND_BARREN_NODES)
	default constructor
	VariableElimination (const VariableElimination< GUM_SCALAR > &)=delete
	avoid copy constructors
VariableElimination< GUM_SCALAR > &	operator= (const VariableElimination< GUM_SCALAR > &)=delete
	avoid copy operators
	~VariableElimination ()
	destructor
Accessors / Modifiers
void	setTriangulation (const Triangulation &new_triangulation)
	use a new triangulation algorithm
void	setRelevantTensorsFinderType (RelevantTensorsFinderType type)
	sets how we determine the relevant tensors to combine
void	setFindBarrenNodesType (FindBarrenNodesType type)
	sets how we determine barren nodes
const JunctionTree *	junctionTree (NodeId id)
	returns the join tree used for compute the posterior of node id
Probability computations
virtual const Tensor< GUM_SCALAR > &	jointPosterior (const NodeSet &nodes) final
	Compute the joint posterior of a set of nodes.
virtual const Tensor< GUM_SCALAR > &	posterior (NodeId node) final
	Computes and returns the posterior of a node.
virtual const Tensor< GUM_SCALAR > &	posterior (const std::string &nodeName) final
	Computes and returns the posterior of a node.
Targets
virtual void	eraseAllTargets ()
	Clear all previously defined targets (marginal and joint targets).
virtual void	eraseAllJointTargets () final
	Clear all previously defined joint targets.
virtual void	eraseAllMarginalTargets () final
	Clear all the previously defined marginal targets.
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.
virtual void	eraseJointTarget (const NodeSet &joint_target) final
	removes an existing joint target
virtual bool	isJointTarget (const NodeSet &vars) const final
	return true if target is a joint target.
virtual const Set< NodeSet > &	jointTargets () const noexcept final
	returns the list of joint targets
virtual Size	nbrJointTargets () const noexcept final
	returns the number of joint targets
Information Theory related functions
GUM_SCALAR	I (NodeId X, NodeId Y)
	Mutual information between X and Y.
GUM_SCALAR	I (const std::string &Xname, const std::string &Yname)
	Mutual information between X and Y.
GUM_SCALAR	VI (NodeId X, NodeId Y)
	Variation of information between X and Y.
GUM_SCALAR	VI (const std::string &Xname, const std::string &Yname)
	Variation of information between X and Y.
GUM_SCALAR	jointMutualInformation (const NodeSet &targets)
	Mutual information between targets.
GUM_SCALAR	jointMutualInformation (const std::vector< std::string > &targets)
	Mutual information between targets.
Targets
virtual void	addAllTargets () final
	adds all nodes as targets
virtual void	addTarget (NodeId target) final
	Add a marginal target to the list of targets.
virtual void	addTarget (const std::string &nodeName) final
	Add a marginal target to the list of targets.
virtual void	eraseTarget (NodeId target) final
	removes an existing (marginal) target
virtual void	eraseTarget (const std::string &nodeName) final
	removes an existing (marginal) target
virtual bool	isTarget (NodeId node) const final
	return true if variable is a (marginal) target
virtual bool	isTarget (const std::string &nodeName) const final
	return true if variable is a (marginal) target
virtual Size	nbrTargets () const noexcept final
	returns the number of marginal targets
virtual const NodeSet &	targets () const noexcept final
	returns the list of marginal targets
virtual bool	isInTargetMode () const noexcept final
	indicates whether the inference is in a target mode
Information Theory related functions
virtual GUM_SCALAR	H (NodeId X) final
	Entropy Compute Shanon's entropy of a node given the observation.
virtual GUM_SCALAR	H (const std::string &nodeName) final
	Entropy Compute Shanon's entropy of a node given the observation.
Accessors / Modifiers
virtual void	setBN (const IBayesNet< GUM_SCALAR > *bn)
	assigns a new BN to the inference engine
virtual const IBayesNet< GUM_SCALAR > &	BN () const final
	Returns a constant reference over the IBayesNet referenced by this class.
Accessors / Modifiers
virtual const GraphicalModel &	model () const final
	Returns a constant reference over the IBayesNet referenced by this class.
virtual const NodeProperty< Size > &	domainSizes () const final
	get the domain sizes of the random variables of the model
virtual bool	isInferenceReady () const noexcept final
	returns whether the inference object is in a ready state
virtual bool	isInferenceOutdatedStructure () const noexcept final
	returns whether the inference object is in a OutdatedStructure state
virtual bool	isInferenceOutdatedTensors () const noexcept final
	returns whether the inference object is in a OutdatedTensor state
virtual bool	isInferenceDone () const noexcept final
	returns whether the inference object is in a InferenceDone state
virtual void	prepareInference () final
	prepare the internal inference structures for the next inference
virtual void	makeInference () final
	perform the heavy computations needed to compute the targets' posteriors
virtual void	clear ()
	clears all the data structures allocated for the last inference
virtual StateOfInference	state () const noexcept final
	returns the state of the inference engine
Evidence
virtual void	addEvidence (NodeId id, const Idx val) final
	adds a new hard evidence on node id
virtual void	addEvidence (const std::string &nodeName, const Idx val) final
	adds a new hard evidence on node named nodeName
virtual void	addEvidence (NodeId id, const std::string &label) final
	adds a new hard evidence on node id
virtual void	addEvidence (const std::string &nodeName, const std::string &label) final
	adds a new hard evidence on node named nodeName
virtual void	addEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node id (might be soft or hard)
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)
virtual void	addEvidence (const Tensor< GUM_SCALAR > &pot) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addEvidence (Tensor< GUM_SCALAR > &&pot) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addSetOfEvidence (const Set< const Tensor< GUM_SCALAR > * > &potset) final
	adds a new set of evidence
virtual void	addListOfEvidence (const List< const Tensor< GUM_SCALAR > * > &potlist) final
	adds a new list of evidence
virtual void	chgEvidence (NodeId id, const Idx val) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (const std::string &nodeName, const Idx val) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (NodeId id, const std::string &label) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (const std::string &nodeName, const std::string &label) final
	change the value of an already existing hard evidence
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)
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)
virtual void	chgEvidence (const Tensor< GUM_SCALAR > &pot) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	eraseAllEvidence () final
	removes all the evidence entered into the network
virtual void	eraseEvidence (NodeId id) final
	removed the evidence, if any, corresponding to node id
virtual void	eraseEvidence (const std::string &nodeName) final
	removed the evidence, if any, corresponding to node of name nodeName
virtual bool	hasEvidence () const final
	indicates whether some node(s) have received evidence
virtual bool	hasEvidence (NodeId id) const final
	indicates whether node id has received an evidence
virtual bool	hasEvidence (const std::string &nodeName) const final
	indicates whether node id has received an evidence
virtual bool	hasHardEvidence (NodeId id) const final
	indicates whether node id has received a hard evidence
virtual bool	hasHardEvidence (const std::string &nodeName) const final
	indicates whether node id has received a hard evidence
virtual bool	hasSoftEvidence (NodeId id) const final
	indicates whether node id has received a soft evidence
virtual bool	hasSoftEvidence (const std::string &nodeName) const final
	indicates whether node id has received a soft evidence
virtual Size	nbrEvidence () const final
	returns the number of evidence entered into the Bayesian network
virtual Size	nbrHardEvidence () const final
	returns the number of hard evidence entered into the Bayesian network
virtual Size	nbrSoftEvidence () const final
	returns the number of soft evidence entered into the Bayesian network
const NodeProperty< const Tensor< GUM_SCALAR > * > &	evidence () const
	returns the set of evidence
const NodeSet &	softEvidenceNodes () const
	returns the set of nodes with soft evidence
const NodeSet &	hardEvidenceNodes () const
	returns the set of nodes with hard evidence
const NodeProperty< Idx > &	hardEvidence () const
	indicate for each node with hard evidence which value it took
Constructors / Destructors
virtual ScheduledInference *	clone () const
	virtual copy constructor
Accessors/Modifiers
Scheduler &	scheduler () const
	returns the current scheduler
void	setNumberOfThreads (Size nb) final
	sets the number max of threads that can be used
void	setMaxMemory (double megabytes)
	sets an upper bound on the memory consumption admissible
virtual double	maxMemory () const
	returns the Max memory (in megabytes) available for executing schedules
Accessors/Modifiers
virtual Size	getNumberOfThreads () const
	returns the current max number of threads used by the class containing this ThreadNumberManager
bool	isGumNumberOfThreadsOverriden () const
	indicates whether the class containing this ThreadNumberManager set its own number of threads

Protected Member Functions
void	onEvidenceAdded_ (const NodeId id, bool isHardEvidence) final
	fired after a new evidence is inserted
void	onEvidenceErased_ (const NodeId id, bool isHardEvidence) final
	fired before an evidence is removed
void	onAllEvidenceErased_ (bool has_hard_evidence) final
	fired before all the evidence are erased
void	onEvidenceChanged_ (const NodeId id, bool hasChangedSoftHard) final
	fired after an evidence is changed, in particular when its status (soft/hard) changes
void	onMarginalTargetAdded_ (const NodeId id) final
	fired after a new single target is inserted
void	onMarginalTargetErased_ (const NodeId id) final
	fired before a single target is removed
virtual void	onModelChanged_ (const GraphicalModel *bn) final
	fired after a new Bayes net has been assigned to the inference engine
void	onJointTargetAdded_ (const NodeSet &set) final
	fired after a new joint target is inserted
void	onJointTargetErased_ (const NodeSet &set) final
	fired before a joint target is removed
void	onAllMarginalTargetsAdded_ () final
	fired after all the nodes of the BN are added as single targets
void	onAllMarginalTargetsErased_ () final
	fired before all the single targets are removed
void	onAllJointTargetsErased_ () final
	fired before all the joint targets are removed
void	onAllTargetsErased_ () final
	fired before all single and joint targets are removed
void	onStateChanged_ () final
	fired when the state of the inference engine is changed
void	updateOutdatedStructure_ () final
	prepares inference when the latter is in OutdatedStructure state
void	updateOutdatedTensors_ () final
	prepares inference when the latter is in OutdatedTensors state
void	makeInference_ () final
	called when the inference has to be performed effectively
const Tensor< GUM_SCALAR > &	posterior_ (NodeId id) final
	returns the posterior of a given variable
const Tensor< GUM_SCALAR > &	jointPosterior_ (const NodeSet &set) final
	returns the posterior of a declared target set
const Tensor< 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
Tensor< GUM_SCALAR > *	unnormalizedJointPosterior_ (NodeId id) final
	returns a fresh tensor equal to P(argument,evidence)
Tensor< GUM_SCALAR > *	unnormalizedJointPosterior_ (const NodeSet &set) final
	returns a fresh tensor equal to P(argument,evidence)
void	setTargetedMode_ ()
bool	isTargetedMode_ () const
void	setOutdatedStructureState_ ()
	put the inference into an outdated model structure state
void	setOutdatedTensorsState_ ()
	puts the inference into an OutdatedTensors state if it is not already in an OutdatedStructure state
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).
void	setModel_ (const GraphicalModel *model)
void	setModelDuringConstruction_ (const GraphicalModel *model)
	assigns a model during the inference engine construction
bool	hasNoModel_ () const

Private Types
using	_TensorSet_ = Set< const Tensor< GUM_SCALAR >* >
using	_ScheduleMultiDimSet_ = Set< const IScheduleMultiDim* >

Private Member Functions
void	_createNewJT_ (const NodeSet &targets)
	create a new junction tree as well as its related data structures
void	_setProjectionFunction_ (Tensor< GUM_SCALAR >(*proj)(const Tensor< GUM_SCALAR > &, const gum::VariableSet &))
	sets the operator for performing the projections
void	_setCombinationFunction_ (Tensor< GUM_SCALAR >(*comb)(const Tensor< GUM_SCALAR > &, const Tensor< GUM_SCALAR > &))
	sets the operator for performing the combinations
void	_findRelevantTensorsWithdSeparation_ (_ScheduleMultiDimSet_ &pot_list, gum::VariableSet &kept_vars)
	update a set of tensors: the remaining are those to be combined to produce a message on a separator
void	_findRelevantTensorsWithdSeparation2_ (_ScheduleMultiDimSet_ &pot_list, gum::VariableSet &kept_vars)
	update a set of tensors: the remaining are those to be combined to produce a message on a separator
void	_findRelevantTensorsWithdSeparation3_ (_ScheduleMultiDimSet_ &pot_list, gum::VariableSet &kept_vars)
	update a set of tensors: the remaining are those to be combined to produce a message on a separator
void	_findRelevantTensorsGetAll_ (_ScheduleMultiDimSet_ &pot_list, gum::VariableSet &kept_vars)
	update a set of tensors: the remaining are those to be combined to produce a message on a separator
void	_findRelevantTensorsXX_ (_ScheduleMultiDimSet_ &pot_list, gum::VariableSet &kept_vars)
	update a set of tensors: the remaining are those to be combined to produce a message on a separator
_ScheduleMultiDimSet_	_removeBarrenVariables_ (Schedule &schedule, _ScheduleMultiDimSet_ &pot_list, gum::VariableSet &del_vars)
	remove barren variables using schedules and return the newly created projected tensors
_TensorSet_	_removeBarrenVariables_ (_TensorSet_ &pot_list, gum::VariableSet &del_vars)
	remove barren variables without schedules and return the newly created projected tensors
_ScheduleMultiDimSet_	_collectMessage_ (Schedule &schedule, NodeId id, NodeId from)
	perform the collect phase using schedules
std::pair< _TensorSet_, _TensorSet_ >	_collectMessage_ (NodeId id, NodeId from)
	perform the collect phase directly without schedules
std::pair< _TensorSet_, _TensorSet_ >	_NodeTensors_ (NodeId node)
	returns the CPT + evidence of a node projected w.r.t. hard evidence
_ScheduleMultiDimSet_	_NodeTensors_ (Schedule &schedule, NodeId node)
	returns the CPT + evidence of a node projected w.r.t. hard evidence
std::pair< _TensorSet_, _TensorSet_ >	_produceMessage_ (NodeId from_id, NodeId to_id, std::pair< _TensorSet_, _TensorSet_ > &&incoming_messages)
	creates the message sent by clique from_id to clique to_id without using schedules
_ScheduleMultiDimSet_	_produceMessage_ (Schedule &schedule, NodeId from_id, NodeId to_id, _ScheduleMultiDimSet_ &&incoming_messages)
	creates the message sent by clique from_id to clique to_id using schedules
_TensorSet_	_marginalizeOut_ (_TensorSet_ pot_list, gum::VariableSet &del_vars, gum::VariableSet &kept_vars)
	removes variables del_vars from a list of tensors and returns the resulting list directly without schedules
_ScheduleMultiDimSet_	_marginalizeOut_ (Schedule &schedule, _ScheduleMultiDimSet_ pot_list, gum::VariableSet &del_vars, gum::VariableSet &kept_vars)
	removes variables del_vars from a list of tensors and returns the resulting list using schedules
Tensor< GUM_SCALAR > *	_unnormalizedJointPosterior_ (NodeId id)
	returns a fresh tensor equal to P(1st arg,evidence) without using schedules
Tensor< GUM_SCALAR > *	_unnormalizedJointPosterior_ (Schedule &schedule, NodeId id)
	returns a fresh tensor equal to P(1st arg,evidence) without using schedules
Tensor< GUM_SCALAR > *	_unnormalizedJointPosterior_ (const NodeSet &set, const NodeSet &targets, const NodeSet &hard_evidence_nodes)
	returns a fresh tensor equal to P(1st arg,evidence) without using schedules
Tensor< GUM_SCALAR > *	_unnormalizedJointPosterior_ (Schedule &schedule, const NodeSet &set, const NodeSet &targets, const NodeSet &hard_evidence_nodes)
	returns a fresh tensor equal to P(1st arg,evidence) without using schedules
void	_setAllMarginalTargets_ ()
	sets all the nodes of the Bayes net as targets

Private Attributes
RelevantTensorsFinderType	_find_relevant_tensor_type_
	the type of relevant tensor finding algorithm to be used
void(VariableElimination< GUM_SCALAR >::*	_findRelevantTensors_ )(Set< const IScheduleMultiDim * > &pot_list, gum::VariableSet &kept_vars)
	update a set of tensors: the remaining are those to be combined to produce a message on a separator
FindBarrenNodesType	_barren_nodes_type_ {FindBarrenNodesType::FIND_BARREN_NODES}
	the type of barren nodes computation we wish
Tensor< GUM_SCALAR >(*	_projection_op_ )(const Tensor< GUM_SCALAR > &, const gum::VariableSet &)
	the operator for performing the projections
Tensor< GUM_SCALAR >(*	_combination_op_ )(const Tensor< GUM_SCALAR > &, const Tensor< GUM_SCALAR > &)
	the operator for performing the combinations
Triangulation *	_triangulation_
	the triangulation class creating the junction tree used for inference
UndiGraph	_graph_
	the undigraph extracted from the BN and used to construct the join tree
JunctionTree *	_JT_ {nullptr}
	the junction tree used to answer the last inference query
HashTable< NodeId, NodeId >	_node_to_clique_
	for each node of graph (~ in the Bayes net), associate an ID in the JT
HashTable< NodeId, NodeSet >	_clique_to_nodes_
	for each clique, indicate the set of nodes whose CPTs will be stored into it
NodeId	_targets2clique_
	indicate a clique that contains all the nodes of the target
Tensor< GUM_SCALAR > *	_target_posterior_ {nullptr}
	the posterior computed during the last inference
const GUM_SCALAR	_one_minus_epsilon_ {GUM_SCALAR(1.0 - 1e-6)}
	for comparisons with 1 - epsilon
Set< NodeSet >	_joint_targets_
	the set of joint targets
bool	_targeted_mode_
	whether the actual targets are default
NodeSet	_targets_
	the set of marginal targets
SchedulerParallel	_scheduler_parallel_
SchedulerSequential	_scheduler_sequential_
Size	_nb_threads_ {0}
	the max number of threads used by the class

Static Private Attributes
static constexpr double	_schedule_threshold_ {1000000.0}
	minimal number of operations to perform in the JT to use schedules

Detailed Description

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

Implementation of a Variable Elimination's-like version of lazy propagation for inference in Bayesian networks.

Definition at line 87 of file variableElimination.h.

Member Typedef Documentation

_ScheduleMultiDimSet_

template<typename GUM_SCALAR>

using gum::VariableElimination< GUM_SCALAR >::_ScheduleMultiDimSet_ = Set< const IScheduleMultiDim* >

private

Definition at line 245 of file variableElimination.h.

_TensorSet_

template<typename GUM_SCALAR>

using gum::VariableElimination< GUM_SCALAR >::_TensorSet_ = Set< const Tensor< GUM_SCALAR >* >

private

Definition at line 244 of file variableElimination.h.

Member Enumeration Documentation

StateOfInference

template<typename GUM_SCALAR>

enum class 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.
• OutdatedTensors: in this state, the structure of the model remains unchanged, only some tensors stored in it have changed. Therefore, the inference probably just needs to invalidate some already computed tensors 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 tensors we need.

Enumerator
OutdatedStructure
OutdatedTensors
ReadyForInference
Done

Definition at line 127 of file graphicalModelInference.h.

{ OutdatedStructure, OutdatedTensors, ReadyForInference, Done };

Constructor & Destructor Documentation

VariableElimination() [1/2]

template<typename GUM_SCALAR>

gum::VariableElimination< GUM_SCALAR >::VariableElimination ( const IBayesNet< GUM_SCALAR > * BN, RelevantTensorsFinderType = RelevantTensorsFinderType::DSEP_BAYESBALL_TENSORS, FindBarrenNodesType = FindBarrenNodesType::FIND_BARREN_NODES )

explicit

default constructor

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

Referenced by VariableElimination(), and operator=().

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VariableElimination() [2/2]

template<typename GUM_SCALAR>

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

delete

avoid copy constructors

References VariableElimination().

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~VariableElimination()

template<typename GUM_SCALAR>

gum::VariableElimination< GUM_SCALAR >::~VariableElimination

(

)

destructor

Member Function Documentation

_collectMessage_() [1/2]

template<typename GUM_SCALAR>

std::pair< _TensorSet_, _TensorSet_ > gum::VariableElimination< GUM_SCALAR >::_collectMessage_ ( NodeId id, NodeId from )

private

perform the collect phase directly without schedules

_collectMessage_() [2/2]

template<typename GUM_SCALAR>

_ScheduleMultiDimSet_ gum::VariableElimination< GUM_SCALAR >::_collectMessage_ ( Schedule & schedule, NodeId id, NodeId from )

private

perform the collect phase using schedules

_createNewJT_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::_createNewJT_ ( const NodeSet & targets )

private

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

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

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_findRelevantTensorsGetAll_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::_findRelevantTensorsGetAll_ ( _ScheduleMultiDimSet_ & pot_list, gum::VariableSet & kept_vars )

private

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

_findRelevantTensorsWithdSeparation2_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::_findRelevantTensorsWithdSeparation2_ ( _ScheduleMultiDimSet_ & pot_list, gum::VariableSet & kept_vars )

private

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

_findRelevantTensorsWithdSeparation3_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::_findRelevantTensorsWithdSeparation3_ ( _ScheduleMultiDimSet_ & pot_list, gum::VariableSet & kept_vars )

private

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

_findRelevantTensorsWithdSeparation_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::_findRelevantTensorsWithdSeparation_ ( _ScheduleMultiDimSet_ & pot_list, gum::VariableSet & kept_vars )

private

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

_findRelevantTensorsXX_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::_findRelevantTensorsXX_ ( _ScheduleMultiDimSet_ & pot_list, gum::VariableSet & kept_vars )

private

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

_marginalizeOut_() [1/2]

template<typename GUM_SCALAR>

_TensorSet_ gum::VariableElimination< GUM_SCALAR >::_marginalizeOut_ ( _TensorSet_ pot_list, gum::VariableSet & del_vars, gum::VariableSet & kept_vars )

private

removes variables del_vars from a list of tensors and returns the resulting list directly without schedules

_marginalizeOut_() [2/2]

template<typename GUM_SCALAR>

_ScheduleMultiDimSet_ gum::VariableElimination< GUM_SCALAR >::_marginalizeOut_ ( Schedule & schedule, _ScheduleMultiDimSet_ pot_list, gum::VariableSet & del_vars, gum::VariableSet & kept_vars )

private

removes variables del_vars from a list of tensors and returns the resulting list using schedules

_NodeTensors_() [1/2]

template<typename GUM_SCALAR>

std::pair< _TensorSet_, _TensorSet_ > gum::VariableElimination< GUM_SCALAR >::_NodeTensors_ ( NodeId node )

private

returns the CPT + evidence of a node projected w.r.t. hard evidence

_NodeTensors_() [2/2]

template<typename GUM_SCALAR>

_ScheduleMultiDimSet_ gum::VariableElimination< GUM_SCALAR >::_NodeTensors_ ( Schedule & schedule, NodeId node )

private

returns the CPT + evidence of a node projected w.r.t. hard evidence

_produceMessage_() [1/2]

template<typename GUM_SCALAR>

std::pair< _TensorSet_, _TensorSet_ > gum::VariableElimination< GUM_SCALAR >::_produceMessage_ ( NodeId from_id, NodeId to_id, std::pair< _TensorSet_, _TensorSet_ > && incoming_messages )

private

creates the message sent by clique from_id to clique to_id without using schedules

_produceMessage_() [2/2]

template<typename GUM_SCALAR>

_ScheduleMultiDimSet_ gum::VariableElimination< GUM_SCALAR >::_produceMessage_ ( Schedule & schedule, NodeId from_id, NodeId to_id, _ScheduleMultiDimSet_ && incoming_messages )

private

creates the message sent by clique from_id to clique to_id using schedules

_removeBarrenVariables_() [1/2]

template<typename GUM_SCALAR>

_TensorSet_ gum::VariableElimination< GUM_SCALAR >::_removeBarrenVariables_ ( _TensorSet_ & pot_list, gum::VariableSet & del_vars )

private

remove barren variables without schedules and return the newly created projected tensors

_removeBarrenVariables_() [2/2]

template<typename GUM_SCALAR>

_ScheduleMultiDimSet_ gum::VariableElimination< GUM_SCALAR >::_removeBarrenVariables_ ( Schedule & schedule, _ScheduleMultiDimSet_ & pot_list, gum::VariableSet & del_vars )

private

remove barren variables using schedules and return the newly created projected tensors

_setAllMarginalTargets_()

template<typename GUM_SCALAR>

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

privateinherited

sets all the nodes of the Bayes net as targets

Definition at line 229 of file marginalTargetedInference_tpl.h.

                                                                        {
    _targets_.clear();
    if (!this->hasNoModel_()) {
      _targets_ = this->BN().dag().asNodeSet();
      onAllMarginalTargetsAdded_();
    }
  }

References _targets_, gum::BayesNetInference< GUM_SCALAR >::BN(), gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), and onAllMarginalTargetsAdded_().

Referenced by onModelChanged_().

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_setCombinationFunction_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::_setCombinationFunction_ ( Tensor< GUM_SCALAR >(* comb )(const Tensor< GUM_SCALAR > &, const Tensor< GUM_SCALAR > &) )

private

sets the operator for performing the combinations

_setProjectionFunction_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::_setProjectionFunction_ ( Tensor< GUM_SCALAR >(* proj )(const Tensor< GUM_SCALAR > &, const gum::VariableSet &) )

private

sets the operator for performing the projections

_unnormalizedJointPosterior_() [1/4]

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > * gum::VariableElimination< GUM_SCALAR >::_unnormalizedJointPosterior_ ( const NodeSet & set, const NodeSet & targets, const NodeSet & hard_evidence_nodes )

private

returns a fresh tensor equal to P(1st arg,evidence) without using schedules

This function is used by unnormalizedJointPosterior_

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

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_unnormalizedJointPosterior_() [2/4]

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > * gum::VariableElimination< GUM_SCALAR >::_unnormalizedJointPosterior_ ( NodeId id )

private

returns a fresh tensor equal to P(1st arg,evidence) without using schedules

This function is used by unnormalizedJointPosterior_

_unnormalizedJointPosterior_() [3/4]

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > * gum::VariableElimination< GUM_SCALAR >::_unnormalizedJointPosterior_ ( Schedule & schedule, const NodeSet & set, const NodeSet & targets, const NodeSet & hard_evidence_nodes )

private

returns a fresh tensor equal to P(1st arg,evidence) without using schedules

This function is used by unnormalizedJointPosterior_

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

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_unnormalizedJointPosterior_() [4/4]

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > * gum::VariableElimination< GUM_SCALAR >::_unnormalizedJointPosterior_ ( Schedule & schedule, NodeId id )

private

returns a fresh tensor equal to P(1st arg,evidence) without using schedules

This function is used by unnormalizedJointPosterior_

addAllTargets()

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds all nodes as targets

Definition at line 144 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);
      }
    }
  }

References _targets_, gum::BayesNetInference< GUM_SCALAR >::BN(), GUM_ERROR, gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), onMarginalTargetAdded_(), gum::GraphicalModelInference< GUM_SCALAR >::OutdatedStructure, gum::GraphicalModelInference< GUM_SCALAR >::setState_(), and setTargetedMode_().

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addEvidence() [1/8]

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 235 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [2/8]

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 249 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [3/8]

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 281 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [4/8]

template<typename GUM_SCALAR>

INLINE void gum::GraphicalModelInference< GUM_SCALAR >::addEvidence ( const Tensor< GUM_SCALAR > & pot )

finalvirtualinherited

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

Exceptions

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

Definition at line 323 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addEvidence() [5/8]

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 229 of file graphicalModelInference_tpl.h.

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

References _createHardEvidence_(), and addEvidence().

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

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addEvidence() [6/8]

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 242 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [7/8]

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 257 of file graphicalModelInference_tpl.h.

                                                                                               {
    // 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.");
    }
 
    Tensor< GUM_SCALAR > pot;
    pot.add(_model_->variable(id));
    pot.fillWith(vals);
    addEvidence(std::move(pot));
  }

References _model_, addEvidence(), and GUM_ERROR.

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addEvidence() [8/8]

template<typename GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::addEvidence ( Tensor< GUM_SCALAR > && pot )

finalvirtualinherited

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

Exceptions

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

Definition at line 288 of file graphicalModelInference_tpl.h.

                                                                                    {
    // check if the tensor 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
    // tensor only contains 0 (in this case, this will automatically raise
    // an exception) )
    Idx  val              = 0;
    bool is_hard_evidence = _isHardEvidence_(pot, val);
 
    // insert the evidence
    _evidence_.insert(id, new Tensor< GUM_SCALAR >(std::forward< Tensor< 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);
  }

References _evidence_, _hard_evidence_, _hard_evidence_nodes_, _isHardEvidence_(), _model_, _soft_evidence_nodes_, GUM_ERROR, hasEvidence(), onEvidenceAdded_(), OutdatedStructure, and setState_().

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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 126 of file jointTargetedInference_tpl.h.

                                                                                       {
    // 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.isStrictSupersetOf(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->isStrictSubsetOf(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);
  }

References _joint_targets_, gum::BayesNetInference< GUM_SCALAR >::BN(), eraseJointTarget(), GUM_ERROR, gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), onJointTargetAdded_(), gum::GraphicalModelInference< GUM_SCALAR >::OutdatedStructure, gum::GraphicalModelInference< GUM_SCALAR >::setState_(), and gum::MarginalTargetedInference< GUM_SCALAR >::setTargetedMode_().

Referenced by evidenceJointImpact(), and jointMutualInformation().

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addListOfEvidence()

template<typename GUM_SCALAR>

INLINE void gum::GraphicalModelInference< GUM_SCALAR >::addListOfEvidence ( const List< const Tensor< GUM_SCALAR > * > & potlist )

finalvirtualinherited

adds a new list of evidence

Exceptions

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

Definition at line 330 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addSetOfEvidence()

template<typename GUM_SCALAR>

INLINE void gum::GraphicalModelInference< GUM_SCALAR >::addSetOfEvidence ( const Set< const Tensor< GUM_SCALAR > * > & potset )

finalvirtualinherited

adds a new set of evidence

Exceptions

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

Definition at line 338 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addTarget() [1/2]

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 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");
 
    addTarget(this->BN().idFromName(nodeName));
  }

References addTarget(), gum::BayesNetInference< GUM_SCALAR >::BN(), GUM_ERROR, and gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_().

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addTarget() [2/2]

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 122 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);
    }
  }

References _targets_, gum::BayesNetInference< GUM_SCALAR >::BN(), GUM_ERROR, gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), onMarginalTargetAdded_(), gum::GraphicalModelInference< GUM_SCALAR >::OutdatedStructure, gum::GraphicalModelInference< GUM_SCALAR >::setState_(), and setTargetedMode_().

Referenced by addTarget(), and evidenceImpact().

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BN()

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

UndefinedElement

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

Definition at line 75 of file BayesNetInference_tpl.h.

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

References gum::GraphicalModelInference< GUM_SCALAR >::model().

Referenced by gum::LazyPropagation< GUM_SCALAR >::LazyPropagation(), gum::ShaferShenoyInference< GUM_SCALAR >::ShaferShenoyInference(), gum::VariableElimination< GUM_SCALAR >::VariableElimination(), gum::MarginalTargetedInference< GUM_SCALAR >::_setAllMarginalTargets_(), gum::MarginalTargetedInference< GUM_SCALAR >::addAllTargets(), gum::JointTargetedInference< GUM_SCALAR >::addJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), gum::SamplingInference< GUM_SCALAR >::contextualize(), gum::SamplingInference< GUM_SCALAR >::currentPosterior(), gum::SamplingInference< GUM_SCALAR >::currentPosterior(), gum::ImportanceSampling< GUM_SCALAR >::draw_(), gum::MonteCarloSampling< GUM_SCALAR >::draw_(), gum::WeightedSampling< GUM_SCALAR >::draw_(), gum::JointTargetedInference< GUM_SCALAR >::eraseJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::MarginalTargetedInference< GUM_SCALAR >::H(), gum::JointTargetedInference< GUM_SCALAR >::isJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::isTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::isTarget(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedInference< GUM_SCALAR >::posterior(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), gum::SamplingInference< GUM_SCALAR >::posterior_(), gum::SamplingInference< GUM_SCALAR >::samplingBN(), and gum::Estimator< GUM_SCALAR >::setFromLBP().

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chgEvidence() [1/7]

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 397 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [2/7]

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 411 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [3/7]

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 445 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [4/7]

template<typename GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::chgEvidence ( const Tensor< GUM_SCALAR > & pot )

finalvirtualinherited

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

Exceptions

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

Definition at line 452 of file graphicalModelInference_tpl.h.

                                                                                         {
    // check if the tensor corresponds to an evidence
    if (pot.nbrDim() != 1) {
      GUM_ERROR(InvalidArgument, pot << " is not a mono-dimensional tensor.")
    }
    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
    // tensor 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 Tensor< 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::OutdatedTensors); }
    }
 
    onEvidenceChanged_(id, hasChangedSoftHard);
  }

References _evidence_, _hard_evidence_, _hard_evidence_nodes_, _isHardEvidence_(), _model_, _soft_evidence_nodes_, gum::Instantiation::end(), GUM_ERROR, hasEvidence(), hasHardEvidence(), gum::Instantiation::inc(), isInferenceOutdatedStructure(), onEvidenceChanged_(), OutdatedStructure, OutdatedTensors, gum::Instantiation::setFirst(), and setState_().

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chgEvidence() [5/7]

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 391 of file graphicalModelInference_tpl.h.

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

References _createHardEvidence_(), and chgEvidence().

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

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chgEvidence() [6/7]

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 404 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [7/7]

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 420 of file graphicalModelInference_tpl.h.

                                                                                              {
    // 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 tensor corresponding to vals
    Tensor< GUM_SCALAR > pot;
    pot.add(_model_->variable(id));
    pot.fillWith(vals);
    chgEvidence(pot);
  }

References _model_, chgEvidence(), and GUM_ERROR.

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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 155 of file graphicalModelInference_tpl.h.

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

References eraseAllEvidence(), OutdatedStructure, and setState_().

Referenced by setModel_().

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clone()

ScheduledInference * gum::ScheduledInference::clone ( ) const

virtualinherited

virtual copy constructor

Definition at line 104 of file scheduledInference.cpp.

{ return new ScheduledInference(*this); }

References ScheduledInference().

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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 173 of file graphicalModelInference_tpl.h.

                                                                                              {
    return _domain_sizes_;
  }

References _domain_sizes_.

eraseAllEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

removes all the evidence entered into the network

Definition at line 540 of file graphicalModelInference_tpl.h.

                                                                      {
    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::OutdatedTensors); }
    }
  }

References _evidence_, _hard_evidence_, _hard_evidence_nodes_, _soft_evidence_nodes_, isInferenceOutdatedStructure(), onAllEvidenceErased_(), OutdatedStructure, OutdatedTensors, and setState_().

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

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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 108 of file jointTargetedInference_tpl.h.

                                                                         {
    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);
    }
  }

References _joint_targets_, onAllJointTargetsErased_(), gum::GraphicalModelInference< GUM_SCALAR >::OutdatedStructure, and gum::GraphicalModelInference< GUM_SCALAR >::setState_().

Referenced by eraseAllTargets().

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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 102 of file jointTargetedInference_tpl.h.

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

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

Referenced by 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 119 of file jointTargetedInference_tpl.h.

                                                                    {
    eraseAllMarginalTargets();
    eraseAllJointTargets();
  }

References eraseAllJointTargets(), and eraseAllMarginalTargets().

Referenced by evidenceJointImpact(), and jointMutualInformation().

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eraseEvidence() [1/2]

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 534 of file graphicalModelInference_tpl.h.

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

References eraseEvidence(), and model().

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eraseEvidence() [2/2]

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 514 of file graphicalModelInference_tpl.h.

                                                                            {
    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::OutdatedTensors); }
      }
 
      delete (_evidence_[id]);
      _evidence_.erase(id);
    }
  }

References _evidence_, _hard_evidence_, _hard_evidence_nodes_, _soft_evidence_nodes_, hasEvidence(), hasHardEvidence(), isInferenceOutdatedStructure(), onEvidenceErased_(), OutdatedStructure, OutdatedTensors, and setState_().

Referenced by eraseEvidence().

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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 163 of file jointTargetedInference_tpl.h.

                                                                                         {
    // 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);
    }
  }

References _joint_targets_, gum::BayesNetInference< GUM_SCALAR >::BN(), GUM_ERROR, gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), onJointTargetErased_(), gum::GraphicalModelInference< GUM_SCALAR >::OutdatedStructure, and gum::GraphicalModelInference< GUM_SCALAR >::setState_().

Referenced by addJointTarget().

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eraseTarget() [1/2]

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 199 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));
  }

References gum::BayesNetInference< GUM_SCALAR >::BN(), eraseTarget(), GUM_ERROR, and gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_().

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eraseTarget() [2/2]

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 176 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);
    }
  }

References _targeted_mode_, _targets_, gum::BayesNetInference< GUM_SCALAR >::BN(), GUM_ERROR, gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), onMarginalTargetErased_(), gum::GraphicalModelInference< GUM_SCALAR >::OutdatedStructure, and gum::GraphicalModelInference< GUM_SCALAR >::setState_().

Referenced by eraseTarget().

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evidence()

template<typename GUM_SCALAR>

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

inherited

returns the set of evidence

Definition at line 587 of file graphicalModelInference_tpl.h.

                                                                     {
    return _evidence_;
  }

References _evidence_.

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

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evidenceImpact() [1/2]

template<typename GUM_SCALAR>

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

inherited

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

Warning

If some evs are d-separated, they are not included in the Tensor

Parameters

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

Returns

a Tensor

Definition at line 319 of file marginalTargetedInference_tpl.h.

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

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

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evidenceImpact() [2/2]

template<typename GUM_SCALAR>

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

inherited

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

Warning

If some evs are d-separated, they are not included in the Tensor

Parameters

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

Returns

a Tensor

Definition at line 280 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);
 
    Tensor< 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
      const auto& pot = this->posterior(target);
      for (inst.setFirstVar(vtarget); !inst.end(); inst.incVar(vtarget)) {
        res.set(inst, pot[inst]);
      }
      inst.setFirstVar(vtarget);   // remove inst.end() flag
    }
 
    return res;
  }

References gum::GraphicalModelInference< GUM_SCALAR >::addEvidence(), addTarget(), gum::BayesNetInference< GUM_SCALAR >::BN(), gum::GraphicalModelInference< GUM_SCALAR >::chgEvidence(), gum::Set< Key >::contains(), gum::Instantiation::end(), gum::GraphicalModelInference< GUM_SCALAR >::eraseAllEvidence(), eraseAllTargets(), GUM_ERROR, gum::Instantiation::incNotVar(), gum::Instantiation::incVar(), gum::GraphicalModelInference< GUM_SCALAR >::makeInference(), posterior(), gum::Instantiation::setFirst(), gum::Instantiation::setFirstVar(), and gum::Instantiation::val().

Referenced by evidenceImpact().

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evidenceJointImpact() [1/2]

template<typename GUM_SCALAR>

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

inherited

Create a gum::Tensor 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 Tensor

Parameters

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

Returns

a Tensor

Definition at line 252 of file jointTargetedInference_tpl.h.

                                                                                    {
    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;
    Tensor< 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;
  }

References gum::Instantiation::add(), gum::GraphicalModelInference< GUM_SCALAR >::addEvidence(), addJointTarget(), gum::BayesNetInference< GUM_SCALAR >::BN(), gum::GraphicalModelInference< GUM_SCALAR >::chgEvidence(), gum::Instantiation::end(), gum::GraphicalModelInference< GUM_SCALAR >::eraseAllEvidence(), eraseAllTargets(), GUM_ERROR, gum::Instantiation::incIn(), gum::Instantiation::incOut(), jointPosterior(), gum::GraphicalModelInference< GUM_SCALAR >::makeInference(), gum::Instantiation::setFirstIn(), gum::Instantiation::setFirstOut(), gum::MarginalTargetedInference< GUM_SCALAR >::targets(), and gum::Instantiation::val().

Referenced by evidenceJointImpact().

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evidenceJointImpact() [2/2]

template<typename GUM_SCALAR>

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

inherited

Create a gum::Tensor 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 Tensor

Parameters

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

Returns

a Tensor

Definition at line 293 of file jointTargetedInference_tpl.h.

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

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

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getNumberOfThreads()

virtual Size gum::ThreadNumberManager::getNumberOfThreads ( ) const

virtualinherited

returns the current max number of threads used by the class containing this ThreadNumberManager

Implements gum::IThreadNumberManager.

Referenced by gum::learning::IBNLearner::createParamEstimator_(), gum::learning::IBNLearner::createScore_(), gum::credal::InferenceEngine< GUM_SCALAR >::displatchMarginalsToThreads_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::expFusion_(), gum::ScheduledInference::scheduler(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::verticesFusion_().

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H() [1/2]

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 275 of file marginalTargetedInference_tpl.h.

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

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

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H() [2/2]

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 267 of file marginalTargetedInference_tpl.h.

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

References posterior().

Referenced by H().

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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 580 of file graphicalModelInference_tpl.h.

                                                                                              {
    return _hard_evidence_;
  }

References _hard_evidence_.

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

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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 599 of file graphicalModelInference_tpl.h.

                                                                                       {
    return _hard_evidence_nodes_;
  }

References _hard_evidence_nodes_.

Referenced by gum::SamplingInference< GUM_SCALAR >::contextualize(), gum::ImportanceSampling< GUM_SCALAR >::draw_(), gum::MonteCarloSampling< GUM_SCALAR >::draw_(), gum::WeightedSampling< GUM_SCALAR >::draw_(), gum::ImportanceSampling< GUM_SCALAR >::onContextualize_(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::posterior(), gum::SamplingInference< GUM_SCALAR >::setEstimatorFromBN_(), and gum::SamplingInference< GUM_SCALAR >::setEstimatorFromLBP_().

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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 346 of file graphicalModelInference_tpl.h.

                                                                       {
    return !_evidence_.empty();
  }

References _evidence_.

Referenced by addEvidence(), chgEvidence(), eraseEvidence(), and hasEvidence().

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hasEvidence() [2/3]

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 371 of file graphicalModelInference_tpl.h.

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

References hasEvidence(), and model().

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hasEvidence() [3/3]

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 352 of file graphicalModelInference_tpl.h.

                                                                                {
    return _evidence_.exists(id);
  }

References _evidence_.

hasHardEvidence() [1/2]

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 378 of file graphicalModelInference_tpl.h.

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

References hasHardEvidence(), and model().

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hasHardEvidence() [2/2]

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 358 of file graphicalModelInference_tpl.h.

                                                                                    {
    return _hard_evidence_nodes_.exists(id);
  }

References _hard_evidence_nodes_.

Referenced by chgEvidence(), gum::ImportanceSampling< GUM_SCALAR >::draw_(), eraseEvidence(), hasHardEvidence(), and gum::JointTargetedMRFInference< GUM_SCALAR >::jointPosterior().

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hasNoModel_()

template<typename GUM_SCALAR>

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

inlineprotectedinherited

Definition at line 542 of file graphicalModelInference.h.

{ return _model_ == nullptr; };

References _model_.

Referenced by gum::EvidenceInference< GUM_SCALAR >::EvidenceInference(), gum::EvidenceMRFInference< GUM_SCALAR >::EvidenceMRFInference(), gum::JointTargetedInference< GUM_SCALAR >::JointTargetedInference(), gum::JointTargetedMRFInference< GUM_SCALAR >::JointTargetedMRFInference(), gum::MarginalTargetedInference< GUM_SCALAR >::MarginalTargetedInference(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::MarginalTargetedMRFInference(), _computeDomainSizes_(), gum::MarginalTargetedInference< GUM_SCALAR >::_setAllMarginalTargets_(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::_setAllMarginalTargets_(), gum::MarginalTargetedInference< GUM_SCALAR >::addAllTargets(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::addAllTargets(), gum::JointTargetedInference< GUM_SCALAR >::addJointTarget(), gum::JointTargetedMRFInference< GUM_SCALAR >::addJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::addTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::addTarget(), gum::JointTargetedInference< GUM_SCALAR >::eraseJointTarget(), gum::JointTargetedMRFInference< GUM_SCALAR >::eraseJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::eraseTarget(), gum::JointTargetedInference< GUM_SCALAR >::isJointTarget(), gum::JointTargetedMRFInference< GUM_SCALAR >::isJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::isTarget(), and gum::MarginalTargetedMRFInference< GUM_SCALAR >::isTarget().

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hasSoftEvidence() [1/2]

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 385 of file graphicalModelInference_tpl.h.

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

References hasSoftEvidence(), and model().

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hasSoftEvidence() [2/2]

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 364 of file graphicalModelInference_tpl.h.

                                                                                    {
    return _soft_evidence_nodes_.exists(id);
  }

References _soft_evidence_nodes_.

Referenced by hasSoftEvidence().

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I() [1/2]

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

I() [2/2]

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

isGumNumberOfThreadsOverriden()

bool gum::ThreadNumberManager::isGumNumberOfThreadsOverriden ( ) const

virtualinherited

indicates whether the class containing this ThreadNumberManager set its own number of threads

Implements gum::IThreadNumberManager.

Referenced by gum::learning::IBNLearner::createParamEstimator_(), and gum::learning::IBNLearner::createScore_().

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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 104 of file graphicalModelInference_tpl.h.

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

References _state_, and Done.

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

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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 92 of file graphicalModelInference_tpl.h.

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

References _state_, and OutdatedStructure.

Referenced by chgEvidence(), eraseAllEvidence(), and eraseEvidence().

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isInferenceOutdatedTensors()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedTensor state

Definition at line 98 of file graphicalModelInference_tpl.h.

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

References _state_, and OutdatedTensors.

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 86 of file graphicalModelInference_tpl.h.

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

References _state_, and ReadyForInference.

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

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isInTargetMode()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

indicates whether the inference is in a target mode

Definition at line 223 of file marginalTargetedInference_tpl.h.

                                                                                     {
    return _targeted_mode_;
  }

References _targeted_mode_.

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 86 of file jointTargetedInference_tpl.h.

                                                                                           {
    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);
  }

References _joint_targets_, gum::BayesNetInference< GUM_SCALAR >::BN(), GUM_ERROR, and gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_().

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isTarget() [1/2]

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 105 of file marginalTargetedInference_tpl.h.

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

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

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isTarget() [2/2]

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 90 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);
  }

References _targets_, gum::BayesNetInference< GUM_SCALAR >::BN(), GUM_ERROR, and gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_().

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

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isTargetedMode_()

template<typename GUM_SCALAR>

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

protectedinherited

Definition at line 327 of file marginalTargetedInference_tpl.h.

                                                                             {
    return _targeted_mode_;
  }

References _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 301 of file jointTargetedInference_tpl.h.

                                                                                                {
    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);
    }
 
    gum::VariableSet 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.sumIn(sov).entropy();
    }
 
    for (Idx i = 0; i < caracteristic.nbrDim(); i++) {
      delete &caracteristic.variable(i);
    }
 
    return res;
  }

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

Referenced by jointMutualInformation().

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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 351 of file jointTargetedInference_tpl.h.

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

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

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jointPosterior()

template<typename GUM_SCALAR>

const Tensor< 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 tensor is stored into the inference engine and is returned by reference. In order to ensure that the tensor 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 207 of file jointTargetedInference_tpl.h.

                                                                               {
    // 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.isStrictSubsetOf(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 || set.empty()) return jointPosterior_(nodes);
    else return jointPosterior_(nodes, set);
  }

References _joint_targets_, gum::Set< Key >::empty(), gum::GraphicalModelInference< GUM_SCALAR >::isInferenceDone(), gum::Set< Key >::isStrictSubsetOf(), jointPosterior_(), and gum::GraphicalModelInference< GUM_SCALAR >::makeInference().

Referenced by evidenceJointImpact(), jointMutualInformation(), and posterior().

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jointPosterior_() [1/2]

template<typename GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::VariableElimination< 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 >.

References jointPosterior_().

Referenced by jointPosterior_(), and jointPosterior_().

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jointPosterior_() [2/2]

template<typename GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::VariableElimination< 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 >.

References jointPosterior_().

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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 190 of file jointTargetedInference_tpl.h.

                                                                                                 {
    return _joint_targets_;
  }

References _joint_targets_.

junctionTree()

template<typename GUM_SCALAR>

const JunctionTree * gum::VariableElimination< GUM_SCALAR >::junctionTree

(

NodeId

id

)

returns the join tree used for compute the posterior of node id

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 638 of file graphicalModelInference_tpl.h.

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

References Done, isInferenceDone(), isInferenceReady(), makeInference_(), prepareInference(), and setState_().

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::evidenceImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedMRFInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedMRFInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedInference< GUM_SCALAR >::jointPosterior(), gum::JointTargetedMRFInference< GUM_SCALAR >::jointPosterior(), gum::LoopySamplingInference< GUM_SCALAR, APPROX >::makeInference_(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), and gum::MarginalTargetedMRFInference< GUM_SCALAR >::posterior().

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makeInference_()

template<typename GUM_SCALAR>

void gum::VariableElimination< 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 >.

References makeInference_().

Referenced by makeInference_().

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maxMemory()

INLINE double gum::ScheduledInference::maxMemory ( ) const

virtualinherited

returns the Max memory (in megabytes) available for executing schedules

Returns

the Max memory (in megabytes) available for executing schedules. 0 means an unlimited amount of memory

Definition at line 68 of file scheduledInference_inl.h.

{ return _scheduler_parallel_.maxMemory(); }

References _scheduler_parallel_.

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 126 of file graphicalModelInference_tpl.h.

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

References _model_, and GUM_ERROR.

Referenced by GraphicalModelInference(), addEvidence(), addEvidence(), addEvidence(), addEvidence(), gum::BayesNetInference< GUM_SCALAR >::BN(), chgEvidence(), chgEvidence(), chgEvidence(), chgEvidence(), eraseEvidence(), hasEvidence(), hasHardEvidence(), hasSoftEvidence(), gum::MRFInference< GUM_SCALAR >::MRF(), onModelChanged_(), gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::onModelChanged_(), setModel_(), and setModelDuringConstruction_().

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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 562 of file graphicalModelInference_tpl.h.

                                                                       {
    return _evidence_.size();
  }

References _evidence_.

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 568 of file graphicalModelInference_tpl.h.

                                                                           {
    return _hard_evidence_nodes_.size();
  }

References _hard_evidence_nodes_.

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 196 of file jointTargetedInference_tpl.h.

                                                                                   {
    return _joint_targets_.size();
  }

References _joint_targets_.

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 574 of file graphicalModelInference_tpl.h.

                                                                           {
    return _soft_evidence_nodes_.size();
  }

References _soft_evidence_nodes_.

nbrTargets()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the number of marginal targets

Definition at line 217 of file marginalTargetedInference_tpl.h.

                                                                                 {
    return _targets_.size();
  }

References _targets_.

onAllEvidenceErased_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::onAllEvidenceErased_ ( bool has_hard_evidence )

finalprotectedvirtual

fired before all the evidence are erased

Implements gum::GraphicalModelInference< GUM_SCALAR >.

onAllJointTargetsErased_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::onAllJointTargetsErased_ ( )

finalprotectedvirtual

fired before all the joint targets are removed

Implements gum::JointTargetedInference< GUM_SCALAR >.

References onAllJointTargetsErased_().

Referenced by onAllJointTargetsErased_().

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onAllMarginalTargetsAdded_()

template<typename GUM_SCALAR>

void gum::VariableElimination< 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::VariableElimination< GUM_SCALAR >::onAllMarginalTargetsErased_ ( )

finalprotectedvirtual

fired before all the single targets are removed

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

References onAllMarginalTargetsErased_().

Referenced by onAllMarginalTargetsErased_().

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onAllTargetsErased_()

template<typename GUM_SCALAR>

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

finalprotectedvirtual

fired before all single and joint targets are removed

Implements gum::JointTargetedInference< GUM_SCALAR >.

References onAllTargetsErased_().

Referenced by onAllTargetsErased_().

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onEvidenceAdded_()

template<typename GUM_SCALAR>

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

finalprotectedvirtual

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

Reimplemented from gum::JointTargetedInference< GUM_SCALAR >.

onStateChanged_()

template<typename GUM_SCALAR>

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

inlinefinalprotectedvirtual

fired when the state of the inference engine is changed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 198 of file variableElimination.h.

{};

References onStateChanged_().

Referenced by onStateChanged_().

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operator=()

template<typename GUM_SCALAR>

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

delete

avoid copy operators

References VariableElimination().

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posterior() [1/2]

template<typename GUM_SCALAR>

const Tensor< 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 tensor is stored into the inference engine and is returned by reference. In order to ensure that the tensor 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 246 of file jointTargetedInference_tpl.h.

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

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

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posterior() [2/2]

template<typename GUM_SCALAR>

const Tensor< 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 tensor is stored into the inference engine and is returned by reference. In order to ensure that the tensor 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 238 of file jointTargetedInference_tpl.h.

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

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

Referenced by posterior().

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posterior_()

template<typename GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::VariableElimination< GUM_SCALAR >::posterior_ ( NodeId id )

finalprotectedvirtual

returns the posterior of a given variable

Parameters

id	The variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

References posterior_().

Referenced by posterior_().

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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 622 of file graphicalModelInference_tpl.h.

                                                                      {
    if (isInferenceReady() || isInferenceDone()) { return; }
 
    if (_model_ == nullptr)
      GUM_ERROR(NullElement,
                "No model been assigned to the "
                "inference algorithm");
 
    if (_state_ == StateOfInference::OutdatedStructure) updateOutdatedStructure_();
    else updateOutdatedTensors_();
 
    setState_(StateOfInference::ReadyForInference);
  }

References _model_, _state_, GUM_ERROR, isInferenceDone(), isInferenceReady(), OutdatedStructure, ReadyForInference, setState_(), updateOutdatedStructure_(), and updateOutdatedTensors_().

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

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scheduler()

INLINE Scheduler & gum::ScheduledInference::scheduler ( ) const

inherited

returns the current scheduler

Definition at line 48 of file scheduledInference_inl.h.

                                                        {
    if (ThreadNumberManager::getNumberOfThreads() != 1)
      return const_cast< SchedulerParallel& >(_scheduler_parallel_);
    else return const_cast< SchedulerSequential& >(_scheduler_sequential_);
  }

References _scheduler_parallel_, _scheduler_sequential_, and gum::ThreadNumberManager::getNumberOfThreads().

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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 81 of file BayesNetInference_tpl.h.

                                                                               {
    this->setModel_(bn);
  }

References gum::GraphicalModelInference< GUM_SCALAR >::setModel_().

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setFindBarrenNodesType()

template<typename GUM_SCALAR>

void gum::VariableElimination< 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).

setMaxMemory()

INLINE void gum::ScheduledInference::setMaxMemory ( double megabytes )

inherited

sets an upper bound on the memory consumption admissible

Parameters

megabytes

the number of megabytes that can be used for the execution of the schedule. If this number is equal to 0, then no memory limit is applied.

Definition at line 61 of file scheduledInference_inl.h.

                                                               {
    if (megabytes < 0.0) megabytes = 0.0;
    _scheduler_parallel_.setMaxMemory(megabytes);
    _scheduler_sequential_.setMaxMemory(megabytes);
  }

References _scheduler_parallel_, and _scheduler_sequential_.

Referenced by ScheduledInference().

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setModel_()

template<typename GUM_SCALAR>

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

protectedinherited

Definition at line 136 of file graphicalModelInference_tpl.h.

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

References _computeDomainSizes_(), _model_, clear(), model(), onModelChanged_(), OutdatedStructure, and setState_().

Referenced by gum::BayesNetInference< GUM_SCALAR >::setBN(), and gum::MRFInference< GUM_SCALAR >::setMRF().

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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 146 of file graphicalModelInference_tpl.h.

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

References _computeDomainSizes_(), _model_, model(), OutdatedStructure, and setState_().

Referenced by gum::BayesNetInference< GUM_SCALAR >::_setBayesNetDuringConstruction_(), and gum::MRFInference< GUM_SCALAR >::_setMRFDuringConstruction_().

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setNumberOfThreads()

INLINE void gum::ScheduledInference::setNumberOfThreads ( Size nb )

finalvirtualinherited

sets the number max of threads that can be used

Parameters

nb	the number max of threads used for executing schedules. If this number is set to 0, then it is defaulted to aGrUM's number of threads

Reimplemented from gum::ThreadNumberManager.

Definition at line 55 of file scheduledInference_inl.h.

                                                            {
    ThreadNumberManager::setNumberOfThreads(nb);
    _scheduler_parallel_.setNumberOfThreads(nb);
  }

References _scheduler_parallel_, and gum::ThreadNumberManager::setNumberOfThreads().

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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 OutdatedTensors 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 tensors 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 609 of file graphicalModelInference_tpl.h.

                                                                                {
    setState_(StateOfInference::OutdatedStructure);
  }

References OutdatedStructure, and setState_().

Referenced by makeInference_().

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setOutdatedTensorsState_()

template<typename GUM_SCALAR>

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

protectedinherited

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

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

Definition at line 616 of file graphicalModelInference_tpl.h.

                                                                              {
    setState_(StateOfInference::OutdatedTensors);
  }

References OutdatedTensors, and setState_().

Referenced by makeInference_().

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setRelevantTensorsFinderType()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::setRelevantTensorsFinderType

(

RelevantTensorsFinderType

type

)

sets how we determine the relevant tensors to combine

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

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 117 of file graphicalModelInference_tpl.h.

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

References _state_, onStateChanged_(), and state().

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::addAllTargets(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::addAllTargets(), addEvidence(), gum::JointTargetedInference< GUM_SCALAR >::addJointTarget(), gum::JointTargetedMRFInference< GUM_SCALAR >::addJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::addTarget(), chgEvidence(), clear(), eraseAllEvidence(), gum::JointTargetedInference< GUM_SCALAR >::eraseAllJointTargets(), gum::JointTargetedMRFInference< GUM_SCALAR >::eraseAllJointTargets(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseAllTargets(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::eraseAllTargets(), eraseEvidence(), gum::JointTargetedInference< GUM_SCALAR >::eraseJointTarget(), gum::JointTargetedMRFInference< GUM_SCALAR >::eraseJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::eraseTarget(), makeInference(), prepareInference(), setModel_(), setModelDuringConstruction_(), setOutdatedStructureState_(), and setOutdatedTensorsState_().

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setTargetedMode_()

template<typename GUM_SCALAR>

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

protectedinherited

Definition at line 332 of file marginalTargetedInference_tpl.h.

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

References _targeted_mode_, and _targets_.

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

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setTriangulation()

template<typename GUM_SCALAR>

void gum::VariableElimination< 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 593 of file graphicalModelInference_tpl.h.

                                                                                       {
    return _soft_evidence_nodes_;
  }

References _soft_evidence_nodes_.

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

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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 111 of file graphicalModelInference_tpl.h.

                                                                  {
    return _state_;
  }

References _state_.

Referenced by setState_().

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targets()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the list of marginal targets

Definition at line 211 of file marginalTargetedInference_tpl.h.

                                                                                        {
    return _targets_;
  }

References _targets_.

Referenced by gum::VariableElimination< GUM_SCALAR >::_createNewJT_(), gum::VariableElimination< GUM_SCALAR >::_unnormalizedJointPosterior_(), gum::VariableElimination< GUM_SCALAR >::_unnormalizedJointPosterior_(), gum::SamplingInference< GUM_SCALAR >::contextualize(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), and gum::JointTargetedInference< GUM_SCALAR >::VI().

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unnormalizedJointPosterior_() [1/2]

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > * gum::VariableElimination< GUM_SCALAR >::unnormalizedJointPosterior_ ( const NodeSet & set )

finalprotectedvirtual

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

Implements gum::JointTargetedInference< GUM_SCALAR >.

References unnormalizedJointPosterior_().

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unnormalizedJointPosterior_() [2/2]

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > * gum::VariableElimination< GUM_SCALAR >::unnormalizedJointPosterior_ ( NodeId id )

finalprotectedvirtual

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

Implements gum::JointTargetedInference< GUM_SCALAR >.

References unnormalizedJointPosterior_().

Referenced by unnormalizedJointPosterior_(), and unnormalizedJointPosterior_().

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updateOutdatedStructure_()

template<typename GUM_SCALAR>

void gum::VariableElimination< 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 >.

updateOutdatedTensors_()

template<typename GUM_SCALAR>

void gum::VariableElimination< GUM_SCALAR >::updateOutdatedTensors_ ( )

finalprotectedvirtual

prepares inference when the latter is in OutdatedTensors state

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

References updateOutdatedTensors_().

Referenced by updateOutdatedTensors_().

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VI() [1/2]

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

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

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VI() [2/2]

template<typename GUM_SCALAR>

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

Member Data Documentation

_barren_nodes_type_

template<typename GUM_SCALAR>

FindBarrenNodesType gum::VariableElimination< GUM_SCALAR >::_barren_nodes_type_ {FindBarrenNodesType::FIND_BARREN_NODES}

private

the type of barren nodes computation we wish

Definition at line 259 of file variableElimination.h.

{FindBarrenNodesType::FIND_BARREN_NODES};

_clique_to_nodes_

template<typename GUM_SCALAR>

HashTable< NodeId, NodeSet > gum::VariableElimination< GUM_SCALAR >::_clique_to_nodes_

private

for each clique, indicate the set of nodes whose CPTs will be stored into it

Definition at line 287 of file variableElimination.h.

_combination_op_

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR >(* gum::VariableElimination< GUM_SCALAR >::_combination_op_) (const Tensor< GUM_SCALAR > &, const Tensor< GUM_SCALAR > &)

inlineprivate

the operator for performing the combinations

Definition at line 259 of file variableElimination.h.

                                                                         {VENewmultiTensor};

_find_relevant_tensor_type_

template<typename GUM_SCALAR>

RelevantTensorsFinderType gum::VariableElimination< GUM_SCALAR >::_find_relevant_tensor_type_

private

Initial value:

{
        RelevantTensorsFinderType::DSEP_BAYESBALL_TENSORS}

the type of relevant tensor finding algorithm to be used

Definition at line 249 of file variableElimination.h.

                                                         {
        RelevantTensorsFinderType::DSEP_BAYESBALL_TENSORS};

_findRelevantTensors_

template<typename GUM_SCALAR>

void(VariableElimination< GUM_SCALAR >::* gum::VariableElimination< GUM_SCALAR >::_findRelevantTensors_) (Set< const IScheduleMultiDim * > &pot_list, gum::VariableSet &kept_vars)

private

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

Definition at line 254 of file variableElimination.h.

_graph_

template<typename GUM_SCALAR>

UndiGraph gum::VariableElimination< 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 278 of file variableElimination.h.

_joint_targets_

template<typename GUM_SCALAR>

Set< NodeSet > gum::JointTargetedInference< GUM_SCALAR >::_joint_targets_

privateinherited

the set of joint targets

Definition at line 323 of file jointTargetedInference.h.

Referenced by addJointTarget(), eraseAllJointTargets(), eraseJointTarget(), isJointTarget(), jointPosterior(), jointTargets(), nbrJointTargets(), and onModelChanged_().

_JT_

template<typename GUM_SCALAR>

JunctionTree* gum::VariableElimination< GUM_SCALAR >::_JT_ {nullptr}

private

the junction tree used to answer the last inference query

Definition at line 281 of file variableElimination.h.

{nullptr};

_nb_threads_

Size gum::ThreadNumberManager::_nb_threads_ {0}

privateinherited

the max number of threads used by the class

Definition at line 126 of file threadNumberManager.h.

{0};

_node_to_clique_

template<typename GUM_SCALAR>

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

private

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

Definition at line 284 of file variableElimination.h.

_one_minus_epsilon_

template<typename GUM_SCALAR>

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

private

for comparisons with 1 - epsilon

Definition at line 300 of file variableElimination.h.

{GUM_SCALAR(1.0 - 1e-6)};

_projection_op_

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR >(* gum::VariableElimination< GUM_SCALAR >::_projection_op_) (const Tensor< GUM_SCALAR > &, const gum::VariableSet &)

inlineprivate

the operator for performing the projections

Definition at line 259 of file variableElimination.h.

                                                                  {VENewprojTensor};

_schedule_threshold_

template<typename GUM_SCALAR>

double gum::VariableElimination< GUM_SCALAR >::_schedule_threshold_ {1000000.0}

staticconstexprprivate

minimal number of operations to perform in the JT to use schedules

Definition at line 297 of file variableElimination.h.

{1000000.0};

_scheduler_parallel_

SchedulerParallel gum::ScheduledInference::_scheduler_parallel_

privateinherited

Definition at line 137 of file scheduledInference.h.

Referenced by ScheduledInference(), ScheduledInference(), ScheduledInference(), maxMemory(), operator=(), operator=(), scheduler(), setMaxMemory(), and setNumberOfThreads().

_scheduler_sequential_

SchedulerSequential gum::ScheduledInference::_scheduler_sequential_

privateinherited

Definition at line 140 of file scheduledInference.h.

Referenced by ScheduledInference(), ScheduledInference(), ScheduledInference(), operator=(), operator=(), scheduler(), and setMaxMemory().

_target_posterior_

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR >* gum::VariableElimination< GUM_SCALAR >::_target_posterior_ {nullptr}

private

the posterior computed during the last inference

the posterior is owned by VariableElimination.

Definition at line 294 of file variableElimination.h.

{nullptr};

_targeted_mode_

template<typename GUM_SCALAR>

bool gum::MarginalTargetedInference< GUM_SCALAR >::_targeted_mode_

privateinherited

whether the actual targets are default

Definition at line 260 of file marginalTargetedInference.h.

Referenced by MarginalTargetedInference(), eraseTarget(), isInTargetMode(), isTargetedMode_(), onModelChanged_(), and setTargetedMode_().

_targets2clique_

template<typename GUM_SCALAR>

NodeId gum::VariableElimination< GUM_SCALAR >::_targets2clique_

private

indicate a clique that contains all the nodes of the target

Definition at line 290 of file variableElimination.h.

_targets_

template<typename GUM_SCALAR>

NodeSet gum::MarginalTargetedInference< GUM_SCALAR >::_targets_

privateinherited

the set of marginal targets

Definition at line 263 of file marginalTargetedInference.h.

Referenced by MarginalTargetedInference(), _setAllMarginalTargets_(), addAllTargets(), addTarget(), eraseAllTargets(), eraseTarget(), isTarget(), nbrTargets(), setTargetedMode_(), and targets().

_triangulation_

template<typename GUM_SCALAR>

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

private

the triangulation class creating the junction tree used for inference

Definition at line 270 of file variableElimination.h.

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

• agrum/BN/inference/variableElimination.h