#include <ShaferShenoyLIMIDInference.h>

Inheritance diagram for gum::ShaferShenoyLIMIDInference< GUM_SCALAR >:

Collaboration diagram for gum::ShaferShenoyLIMIDInference< GUM_SCALAR >:

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

Public Member Functions
DAG	reducedGraph () const
std::vector< NodeSet >	reversePartialOrder () const
InfluenceDiagram< GUM_SCALAR >	reducedLIMID () const
bool	isSolvable () const
gum::Tensor< GUM_SCALAR >	optimalDecision (NodeId decisionId) final
gum::Tensor< GUM_SCALAR >	optimalDecision (const std::string &decisionName) final
virtual const Tensor< GUM_SCALAR > &	posterior (NodeId node) final
	Return the posterior probability of a node.
const Tensor< GUM_SCALAR > &	posterior (const std::string &name) final
virtual const Tensor< GUM_SCALAR > &	posteriorUtility (NodeId node) final
	Return the posterior utility of a node.
virtual const Tensor< GUM_SCALAR > &	posteriorUtility (const std::string &name) final
virtual std::pair< GUM_SCALAR, GUM_SCALAR >	meanVar (NodeId node) final
	Return the pair (mean,variance) for a node.
std::pair< GUM_SCALAR, GUM_SCALAR >	meanVar (const std::string &name) final
std::pair< GUM_SCALAR, GUM_SCALAR >	MEU () final
	Return the pair (mean,variance) for the total utility (MEU).
virtual void	setInfluenceDiagram (const InfluenceDiagram< GUM_SCALAR > *infdiag)
	assigns a new influence diagram to the inference engine
virtual const InfluenceDiagram< GUM_SCALAR > &	influenceDiagram () const final
	Returns a constant reference over the IBayesNet referenced by this class.
Constructor & destructor
	ShaferShenoyLIMIDInference (const InfluenceDiagram< GUM_SCALAR > *infDiag)
	Default constructor.
virtual	~ShaferShenoyLIMIDInference ()
	Destructor.
const JunctionTree *	junctionTree () const
	Default constructor.
void	clear () override
	Default constructor.
void	addNoForgettingAssumption (const std::vector< NodeId > &ids)
	No forgetting rule assumption.
void	addNoForgettingAssumption (const std::vector< std::string > &names)
	Default constructor.
bool	hasNoForgettingAssumption () const
	Default constructor.
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 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

Protected Member Functions
void	onStateChanged_ () override
	fired when the stage is changed
void	onEvidenceAdded_ (NodeId id, bool isHardEvidence) override
	fired after a new evidence is inserted
void	onEvidenceErased_ (NodeId id, bool isHardEvidence) override
	fired before an evidence is removed
void	onAllEvidenceErased_ (bool contains_hard_evidence) override
	fired before all the evidence are erased
void	onEvidenceChanged_ (NodeId id, bool hasChangedSoftHard) override
	fired after an evidence is changed, in particular when its status (soft/hard) changes
void	onModelChanged_ (const GraphicalModel *model) override
	fired after a new Bayes net has been assigned to the engine
void	updateOutdatedStructure_ () override
	prepares inference when the latter is in OutdatedStructure state
void	updateOutdatedTensors_ () override
	prepares inference when the latter is in OutdatedTensors state
void	makeInference_ () override
	called when the inference has to be performed effectively
NodeSet	nonRequisiteNodes_ (NodeId d) const
	Returns the set of non-requisite for node d.
void	createReduced_ ()
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

Protected Attributes
DAG	reduced_
CliqueGraph	reducedJunctionTree_
NodeProperty< NodeId >	node_to_clique_
EdgeProperty< SetOfVars >	varsSeparator_
NodeProperty< Tensor< GUM_SCALAR > >	strategies_
NodeProperty< DecisionTensor< GUM_SCALAR > >	posteriors_
NodeProperty< DecisionTensor< GUM_SCALAR > >	unconditionalDecisions_
std::vector< NodeSet >	reversePartialOrder_
std::vector< NodeId >	solvabilityOrder_
std::vector< NodeId >	noForgettingOrder_

Private Types
using	PhiNodeProperty = NodeProperty< DecisionTensor< GUM_SCALAR > >
using	PsiArcProperty = ArcProperty< DecisionTensor< GUM_SCALAR > >
using	SetOfVars = gum::VariableSet

Private Member Functions
void	_completingNoForgettingAssumption_ ()
void	_reducingLIMID_ ()
void	_creatingPartialOrder_ (const NodeSet &utilities)
void	_checkingSolvability_ (const NodeSet &utilities)
void	_creatingJunctionTree_ ()
void	_findingCliqueForEachNode_ (DefaultTriangulation &triangulation)
void	initializingInference_ (PhiNodeProperty &phi, PsiArcProperty &psi)
void	collectingMessage_ (PhiNodeProperty &phi, PsiArcProperty &psi, NodeId rootClique)
void	collectingToFollowingRoot_ (PhiNodeProperty &phi, PsiArcProperty &psi, NodeId fromClique, NodeId toClique)
void	deciding_ (PhiNodeProperty &phi, PsiArcProperty &psi, NodeId decisionNode)
void	transmittingMessage_ (PhiNodeProperty &phi, PsiArcProperty &psi, NodeId fromClique, NodeId toClique)
void	transmittingFinalMessage_ (PhiNodeProperty &phi, PsiArcProperty &psi, NodeId fromClique, NodeId toClique)
void	distributingMessage_ (PhiNodeProperty &phi, PsiArcProperty &psi, NodeId rootClique)
void	computingPosteriors_ (const PhiNodeProperty &phi, const PsiArcProperty &psi)
DecisionTensor< double >	integrating_ (const PhiNodeProperty &phi, const PsiArcProperty &psi, NodeId clique, NodeId except) const
DecisionTensor< double >	integrating_ (const PhiNodeProperty &phi, const PsiArcProperty &psi, NodeId clique) const
void	binarizingMax_ (const Tensor< GUM_SCALAR > &decision, const Tensor< GUM_SCALAR > &proba) const
void	_setIDDuringConstruction_ (const InfluenceDiagram< GUM_SCALAR > *infdiag)
	assigns a influence diagram during the inference engine construction
Tensor< GUM_SCALAR >	_createHardEvidence_ (NodeId id, Idx val) const
	create the internal structure for a hard evidence
bool	_isHardEvidence_ (const Tensor< GUM_SCALAR > &pot, Idx &val) const
	checks whether a tensor corresponds to a hard evidence or not
void	_computeDomainSizes_ ()
	computes the domain sizes of the random variables

Private Attributes
StateOfInference	_state_ {StateOfInference::OutdatedStructure}
	the current state of the inference (outdated/ready/done)
const GraphicalModel *	_model_ {nullptr}
	the Bayes net on which we perform inferences
NodeProperty< Size >	_domain_sizes_
	the domain sizes of the random variables
NodeProperty< const Tensor< GUM_SCALAR > * >	_evidence_
	the set of evidence entered into the network
NodeProperty< Idx >	_hard_evidence_
	assign to each node with a hard evidence the index of its observed value
NodeSet	_soft_evidence_nodes_
	the set of nodes that received soft evidence
NodeSet	_hard_evidence_nodes_
	the set of nodes that received hard evidence

Detailed Description

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

Definition at line 79 of file ShaferShenoyLIMIDInference.h.

Member Typedef Documentation

◆ PhiNodeProperty

template<typename GUM_SCALAR>

using gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::PhiNodeProperty = NodeProperty< DecisionTensor< GUM_SCALAR > >

private

Definition at line 80 of file ShaferShenoyLIMIDInference.h.

◆ PsiArcProperty

template<typename GUM_SCALAR>

using gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::PsiArcProperty = ArcProperty< DecisionTensor< GUM_SCALAR > >

private

Definition at line 81 of file ShaferShenoyLIMIDInference.h.

◆ SetOfVars

template<typename GUM_SCALAR>

using gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::SetOfVars = gum::VariableSet

private

Definition at line 82 of file ShaferShenoyLIMIDInference.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.

127{ OutdatedStructure, OutdatedTensors, ReadyForInference, Done };

Constructor & Destructor Documentation

◆ ShaferShenoyLIMIDInference()

template<typename GUM_SCALAR>

gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::ShaferShenoyLIMIDInference ( const InfluenceDiagram< GUM_SCALAR > * infDiag )

explicit

Default constructor.

Parameters

infDiag the influence diagram we want to perform inference upon

◆ ~ShaferShenoyLIMIDInference()

template<typename GUM_SCALAR>

virtual gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::~ShaferShenoyLIMIDInference ( )

virtual

Destructor.

Member Function Documentation

◆ _checkingSolvability_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::_checkingSolvability_ ( const NodeSet & utilities )

private

References _checkingSolvability_().

Referenced by _checkingSolvability_().

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◆ _completingNoForgettingAssumption_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::_completingNoForgettingAssumption_ ( )

private

References _completingNoForgettingAssumption_().

Referenced by _completingNoForgettingAssumption_().

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◆ _computeDomainSizes_()

template<typename GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::_computeDomainSizes_ ( )

privateinherited

computes the domain sizes of the random variables

Definition at line 162 of file graphicalModelInference_tpl.h.

                                                                   {
    _domain_sizes_.clear();
    if (!hasNoModel_()) {
      for (auto node: _model_->nodes()) {
        _domain_sizes_.insert(node, _model_->variable(node).domainSize());
      }
    }
  }

References _domain_sizes_, _model_, and hasNoModel_().

Referenced by GraphicalModelInference(), setModel_(), and setModelDuringConstruction_().

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◆ _createHardEvidence_()

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > gum::GraphicalModelInference< GUM_SCALAR >::_createHardEvidence_	(	NodeId	id,
		Idx	val ) const

privateinherited

create the internal structure for a hard evidence

Definition at line 184 of file graphicalModelInference_tpl.h.

                                                                                                {
    // check that it is possible to create the 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() <= val) {
      GUM_ERROR(InvalidArgument,
                "node " << _model_->variable(id) << " has fewer possible values than " << val);
    }
 
    return Tensor< GUM_SCALAR >::deterministicTensor(_model_->variable(id), val);
  }

References _model_, gum::Tensor< GUM_SCALAR >::deterministicTensor(), and GUM_ERROR.

Referenced by addEvidence(), and chgEvidence().

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◆ _creatingJunctionTree_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::_creatingJunctionTree_ ( )

private

References _creatingJunctionTree_().

Referenced by _creatingJunctionTree_().

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◆ _creatingPartialOrder_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::_creatingPartialOrder_ ( const NodeSet & utilities )

private

References _creatingPartialOrder_().

Referenced by _creatingPartialOrder_().

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◆ _findingCliqueForEachNode_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::_findingCliqueForEachNode_ ( DefaultTriangulation & triangulation )

private

References _findingCliqueForEachNode_().

Referenced by _findingCliqueForEachNode_().

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◆ _isHardEvidence_()

template<typename GUM_SCALAR>

bool gum::GraphicalModelInference< GUM_SCALAR >::_isHardEvidence_	(	const Tensor< GUM_SCALAR > &	pot,
		Idx &	val ) const

privateinherited

checks whether a tensor corresponds to a hard evidence or not

Definition at line 203 of file graphicalModelInference_tpl.h.

                                                                               {
    // checking if pot is determininstic
    bool          notZero = false;
    Instantiation I(pot);
 
    for (I.setFirst(); !I.end(); I.inc()) {
      if (pot[I] != 0.0) {
        if (notZero) {   // we already met a non-zero value
          return false;
        } else {
          val     = I.val(0);
          notZero = true;   // this is the first met non-zero value
        }
      }
    }
 
    if (!notZero) {   // we met no non-zero value
      GUM_ERROR(FatalError, "Evidence of impossibility (vector of 0s)")
    }
 
    return true;   // pot is deterministic
  }

References gum::Instantiation::end(), GUM_ERROR, gum::Instantiation::inc(), gum::Instantiation::setFirst(), and gum::Instantiation::val().

Referenced by addEvidence(), and chgEvidence().

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◆ _reducingLIMID_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::_reducingLIMID_ ( )

private

References _reducingLIMID_().

Referenced by _reducingLIMID_().

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◆ _setIDDuringConstruction_()

template<typename GUM_SCALAR>

void gum::InfluenceDiagramInference< GUM_SCALAR >::_setIDDuringConstruction_ ( const InfluenceDiagram< GUM_SCALAR > * infdiag )

privateinherited

assigns a influence diagram during the inference engine construction

References _setIDDuringConstruction_().

Referenced by _setIDDuringConstruction_().

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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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◆ 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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◆ addNoForgettingAssumption() [1/2]

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::addNoForgettingAssumption ( const std::vector< NodeId > & ids )

No forgetting rule assumption.

◆ addNoForgettingAssumption() [2/2]

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::addNoForgettingAssumption ( const std::vector< std::string > & names )

Default constructor.

Parameters

infDiag the influence diagram we want to perform inference upon

◆ 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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◆ binarizingMax_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::binarizingMax_	(	const Tensor< GUM_SCALAR > &	decision,
		const Tensor< GUM_SCALAR > &	proba ) const

private

References binarizingMax_().

Referenced by binarizingMax_().

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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>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::clear ( )

overridevirtual

Default constructor.

Parameters

infDiag the influence diagram we want to perform inference upon

Reimplemented from gum::GraphicalModelInference< GUM_SCALAR >.

◆ collectingMessage_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::collectingMessage_	(	PhiNodeProperty &	phi,
		PsiArcProperty &	psi,
		NodeId	rootClique )

private

References collectingMessage_().

Referenced by collectingMessage_().

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◆ collectingToFollowingRoot_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::collectingToFollowingRoot_	(	PhiNodeProperty &	phi,
		PsiArcProperty &	psi,
		NodeId	fromClique,
		NodeId	toClique )

private

References collectingToFollowingRoot_().

Referenced by collectingToFollowingRoot_().

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◆ computingPosteriors_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::computingPosteriors_	(	const PhiNodeProperty &	phi,
		const PsiArcProperty &	psi )

private

References computingPosteriors_().

Referenced by computingPosteriors_().

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◆ createReduced_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::createReduced_ ( )

protected

References createReduced_().

Referenced by createReduced_().

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◆ deciding_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::deciding_	(	PhiNodeProperty &	phi,
		PsiArcProperty &	psi,
		NodeId	decisionNode )

private

References deciding_().

Referenced by deciding_().

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◆ distributingMessage_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::distributingMessage_	(	PhiNodeProperty &	phi,
		PsiArcProperty &	psi,
		NodeId	rootClique )

private

References distributingMessage_().

Referenced by distributingMessage_().

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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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◆ 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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◆ 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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◆ 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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◆ hasNoForgettingAssumption()

template<typename GUM_SCALAR>

bool gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::hasNoForgettingAssumption ( ) const

Default constructor.

Parameters

infDiag the influence diagram we want to perform inference upon

◆ hasNoModel_()

template<typename GUM_SCALAR>

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

inlineprotectedinherited

Definition at line 542 of file graphicalModelInference.h.

542{ return _model_ == nullptr; };

References _model_.

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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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◆ influenceDiagram()

template<typename GUM_SCALAR>

virtual const InfluenceDiagram< GUM_SCALAR > & gum::InfluenceDiagramInference< GUM_SCALAR >::influenceDiagram ( ) 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.

Referenced by gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::meanVar(), gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::optimalDecision(), gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::posterior(), and gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::posteriorUtility().

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◆ initializingInference_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::initializingInference_	(	PhiNodeProperty &	phi,
		PsiArcProperty &	psi )

private

References initializingInference_().

Referenced by initializingInference_().

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◆ integrating_() [1/2]

template<typename GUM_SCALAR>

DecisionTensor< double > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::integrating_	(	const PhiNodeProperty &	phi,
		const PsiArcProperty &	psi,
		NodeId	clique ) const

private

References integrating_().

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◆ integrating_() [2/2]

template<typename GUM_SCALAR>

DecisionTensor< double > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::integrating_	(	const PhiNodeProperty &	phi,
		const PsiArcProperty &	psi,
		NodeId	clique,
		NodeId	except ) const

private

References integrating_().

Referenced by integrating_(), and integrating_().

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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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◆ isSolvable()

template<typename GUM_SCALAR>

bool gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::isSolvable ( ) const

◆ junctionTree()

template<typename GUM_SCALAR>

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

Default constructor.

Parameters

infDiag the influence diagram we want to perform inference upon

◆ 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::ShaferShenoyLIMIDInference< GUM_SCALAR >::makeInference_ ( )

overrideprotectedvirtual

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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◆ meanVar() [1/2]

template<typename GUM_SCALAR>

std::pair< GUM_SCALAR, GUM_SCALAR > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::meanVar ( const std::string & name )

inlinefinalvirtual

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Definition at line 161 of file ShaferShenoyLIMIDInference.h.

                                                                           {
      return meanVar(this->influenceDiagram().idFromName(name));
    };

References gum::InfluenceDiagramInference< GUM_SCALAR >::influenceDiagram(), and meanVar().

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◆ meanVar() [2/2]

template<typename GUM_SCALAR>

virtual std::pair< GUM_SCALAR, GUM_SCALAR > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::meanVar ( NodeId node )

finalvirtual

Return the pair (mean,variance) for a node.

Parameters

node

Returns: the pair (mean,variance) for a node

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Referenced by meanVar().

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◆ MEU()

template<typename GUM_SCALAR>

std::pair< GUM_SCALAR, GUM_SCALAR > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::MEU ( )

finalvirtual

Return the pair (mean,variance) for the total utility (MEU).

Returns: the pair (mean,variance) for MEU

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

◆ model()

template<typename GUM_SCALAR>

INLINE const GraphicalModel & gum::GraphicalModelInference< GUM_SCALAR >::model ( ) const

finalvirtualinherited

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

Exceptions

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

Definition at line 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_.

◆ 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_.

◆ nonRequisiteNodes_()

template<typename GUM_SCALAR>

NodeSet gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::nonRequisiteNodes_ ( NodeId d ) const

protected

Returns the set of non-requisite for node d.

References nonRequisiteNodes_().

Referenced by nonRequisiteNodes_().

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◆ onAllEvidenceErased_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired before all the evidence are erased

Implements gum::GraphicalModelInference< GUM_SCALAR >.

References onAllEvidenceErased_().

Referenced by onAllEvidenceErased_().

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◆ onEvidenceAdded_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired after a new evidence is inserted

Implements gum::GraphicalModelInference< GUM_SCALAR >.

References onEvidenceAdded_().

Referenced by onEvidenceAdded_().

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◆ onEvidenceChanged_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::onEvidenceChanged_	(	NodeId	id,
		bool	hasChangedSoftHard )

overrideprotectedvirtual

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 >.

References onEvidenceChanged_().

Referenced by onEvidenceChanged_().

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◆ onEvidenceErased_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired before an evidence is removed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

References onEvidenceErased_().

Referenced by onEvidenceErased_().

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◆ onModelChanged_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::onModelChanged_ ( const GraphicalModel * model )

overrideprotectedvirtual

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

References gum::GraphicalModelInference< GUM_SCALAR >::model(), and onModelChanged_().

Referenced by onModelChanged_().

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◆ onStateChanged_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired when the stage is changed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

References onStateChanged_().

Referenced by onStateChanged_().

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◆ optimalDecision() [1/2]

template<typename GUM_SCALAR>

gum::Tensor< GUM_SCALAR > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::optimalDecision ( const std::string & decisionName )

inlinefinalvirtual

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Definition at line 125 of file ShaferShenoyLIMIDInference.h.

                                                                                 {
      return optimalDecision(this->influenceDiagram().idFromName(decisionName));
    };

References gum::InfluenceDiagramInference< GUM_SCALAR >::influenceDiagram(), and optimalDecision().

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◆ optimalDecision() [2/2]

template<typename GUM_SCALAR>

gum::Tensor< GUM_SCALAR > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::optimalDecision ( NodeId decisionId )

finalvirtual

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Referenced by optimalDecision().

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◆ posterior() [1/2]

template<typename GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::posterior ( const std::string & name )

inlinefinalvirtual

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Definition at line 137 of file ShaferShenoyLIMIDInference.h.

                                                                       {
      return posterior(this->influenceDiagram().idFromName(name));
    };

References gum::InfluenceDiagramInference< GUM_SCALAR >::influenceDiagram(), and posterior().

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◆ posterior() [2/2]

template<typename GUM_SCALAR>

virtual const Tensor< GUM_SCALAR > & gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::posterior ( NodeId node )

finalvirtual

Return the posterior probability of a node.

Parameters

node

Returns: the posterior probability

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Referenced by posterior().

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◆ posteriorUtility() [1/2]

template<typename GUM_SCALAR>

virtual const Tensor< GUM_SCALAR > & gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::posteriorUtility ( const std::string & name )

inlinefinalvirtual

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Definition at line 149 of file ShaferShenoyLIMIDInference.h.

                                                                                      {
      return posteriorUtility(this->influenceDiagram().idFromName(name));
    };

References gum::InfluenceDiagramInference< GUM_SCALAR >::influenceDiagram(), and posteriorUtility().

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◆ posteriorUtility() [2/2]

template<typename GUM_SCALAR>

virtual const Tensor< GUM_SCALAR > & gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::posteriorUtility ( NodeId node )

finalvirtual

Return the posterior utility of a node.

Parameters

node

Returns: the posterior utility of a node

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Referenced by posteriorUtility().

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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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◆ reducedGraph()

template<typename GUM_SCALAR>

DAG gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::reducedGraph ( ) const

inline

Definition at line 114 of file ShaferShenoyLIMIDInference.h.

114{ return reduced_; };

gum::ShaferShenoyLIMIDInference::reduced_

DAG reduced_

Definition ShaferShenoyLIMIDInference.h:186

References reduced_.

◆ reducedLIMID()

template<typename GUM_SCALAR>

InfluenceDiagram< GUM_SCALAR > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::reducedLIMID ( ) const

◆ reversePartialOrder()

template<typename GUM_SCALAR>

std::vector< NodeSet > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::reversePartialOrder ( ) const

◆ setInfluenceDiagram()

template<typename GUM_SCALAR>

virtual void gum::InfluenceDiagramInference< GUM_SCALAR >::setInfluenceDiagram ( const InfluenceDiagram< GUM_SCALAR > * infdiag )

virtualinherited

assigns a new influence diagram to the inference engine

Assigns a new influence diagram to the o,fere,ce e,go,e engine and sends messages to the descendants of ShaferShenoyLIMIDInference to inform them that the ID has changed.

Warning: note that, by aGrUM's rule, the influence Diagram is not copied into the inference engine but only referenced.

◆ 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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◆ 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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◆ 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().

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◆ 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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◆ transmittingFinalMessage_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::transmittingFinalMessage_	(	PhiNodeProperty &	phi,
		PsiArcProperty &	psi,
		NodeId	fromClique,
		NodeId	toClique )

private

References transmittingFinalMessage_().

Referenced by transmittingFinalMessage_().

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◆ transmittingMessage_()

template<typename GUM_SCALAR>

void gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::transmittingMessage_	(	PhiNodeProperty &	phi,
		PsiArcProperty &	psi,
		NodeId	fromClique,
		NodeId	toClique )

private

References transmittingMessage_().

Referenced by transmittingMessage_().

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◆ updateOutdatedStructure_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

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 >.

References updateOutdatedStructure_().

Referenced by updateOutdatedStructure_().

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◆ updateOutdatedTensors_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

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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Member Data Documentation

◆ _domain_sizes_

template<typename GUM_SCALAR>

NodeProperty< Size > gum::GraphicalModelInference< GUM_SCALAR >::_domain_sizes_

privateinherited

the domain sizes of the random variables

Definition at line 507 of file graphicalModelInference.h.

Referenced by _computeDomainSizes_(), and domainSizes().

◆ _evidence_

template<typename GUM_SCALAR>

NodeProperty< const Tensor< GUM_SCALAR >* > gum::GraphicalModelInference< GUM_SCALAR >::_evidence_

privateinherited

the set of evidence entered into the network

Definition at line 510 of file graphicalModelInference.h.

Referenced by ~GraphicalModelInference(), addEvidence(), chgEvidence(), eraseAllEvidence(), eraseEvidence(), evidence(), hasEvidence(), hasEvidence(), and nbrEvidence().

◆ _hard_evidence_

template<typename GUM_SCALAR>

NodeProperty< Idx > gum::GraphicalModelInference< GUM_SCALAR >::_hard_evidence_

privateinherited

assign to each node with a hard evidence the index of its observed value

Definition at line 513 of file graphicalModelInference.h.

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

◆ _hard_evidence_nodes_

template<typename GUM_SCALAR>

NodeSet gum::GraphicalModelInference< GUM_SCALAR >::_hard_evidence_nodes_

privateinherited

the set of nodes that received hard evidence

Definition at line 519 of file graphicalModelInference.h.

Referenced by addEvidence(), chgEvidence(), eraseAllEvidence(), eraseEvidence(), hardEvidenceNodes(), hasHardEvidence(), and nbrHardEvidence().

◆ _model_

template<typename GUM_SCALAR>

const GraphicalModel* gum::GraphicalModelInference< GUM_SCALAR >::_model_ {nullptr}

privateinherited

the Bayes net on which we perform inferences

Definition at line 504 of file graphicalModelInference.h.

504{nullptr};

Referenced by GraphicalModelInference(), _computeDomainSizes_(), _createHardEvidence_(), addEvidence(), addEvidence(), chgEvidence(), chgEvidence(), hasNoModel_(), model(), prepareInference(), setModel_(), and setModelDuringConstruction_().

◆ _soft_evidence_nodes_

template<typename GUM_SCALAR>

NodeSet gum::GraphicalModelInference< GUM_SCALAR >::_soft_evidence_nodes_

privateinherited

the set of nodes that received soft evidence

Definition at line 516 of file graphicalModelInference.h.

Referenced by addEvidence(), chgEvidence(), eraseAllEvidence(), eraseEvidence(), hasSoftEvidence(), nbrSoftEvidence(), and softEvidenceNodes().

◆ _state_

template<typename GUM_SCALAR>

StateOfInference gum::GraphicalModelInference< GUM_SCALAR >::_state_ {StateOfInference::OutdatedStructure}

privateinherited

the current state of the inference (outdated/ready/done)

Definition at line 501 of file graphicalModelInference.h.

501{StateOfInference::OutdatedStructure};

Referenced by isInferenceDone(), isInferenceOutdatedStructure(), isInferenceOutdatedTensors(), isInferenceReady(), prepareInference(), setState_(), and state().

◆ node_to_clique_

template<typename GUM_SCALAR>

NodeProperty< NodeId > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::node_to_clique_

protected

Definition at line 188 of file ShaferShenoyLIMIDInference.h.

◆ noForgettingOrder_

template<typename GUM_SCALAR>

std::vector< NodeId > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::noForgettingOrder_

protected

Definition at line 197 of file ShaferShenoyLIMIDInference.h.

◆ posteriors_

template<typename GUM_SCALAR>

NodeProperty< DecisionTensor< GUM_SCALAR > > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::posteriors_

protected

Definition at line 191 of file ShaferShenoyLIMIDInference.h.

◆ reduced_

template<typename GUM_SCALAR>

DAG gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::reduced_

protected

Definition at line 186 of file ShaferShenoyLIMIDInference.h.

Referenced by reducedGraph().

◆ reducedJunctionTree_

template<typename GUM_SCALAR>

CliqueGraph gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::reducedJunctionTree_

protected

Definition at line 187 of file ShaferShenoyLIMIDInference.h.

◆ reversePartialOrder_

template<typename GUM_SCALAR>

std::vector< NodeSet > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::reversePartialOrder_

protected

Definition at line 195 of file ShaferShenoyLIMIDInference.h.

◆ solvabilityOrder_

template<typename GUM_SCALAR>

std::vector< NodeId > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::solvabilityOrder_

protected

Definition at line 196 of file ShaferShenoyLIMIDInference.h.

◆ strategies_

template<typename GUM_SCALAR>

NodeProperty< Tensor< GUM_SCALAR > > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::strategies_

protected

Definition at line 190 of file ShaferShenoyLIMIDInference.h.

◆ unconditionalDecisions_

template<typename GUM_SCALAR>

NodeProperty< DecisionTensor< GUM_SCALAR > > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::unconditionalDecisions_

protected

Definition at line 192 of file ShaferShenoyLIMIDInference.h.

◆ varsSeparator_

template<typename GUM_SCALAR>

EdgeProperty< SetOfVars > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::varsSeparator_

protected

Definition at line 189 of file ShaferShenoyLIMIDInference.h.

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

agrum/ID/inference/ShaferShenoyLIMIDInference.h

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Private Types

Private Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

◆ PhiNodeProperty

◆ PsiArcProperty

◆ SetOfVars

Member Enumeration Documentation

◆ StateOfInference

Constructor & Destructor Documentation

◆ ShaferShenoyLIMIDInference()

◆ ~ShaferShenoyLIMIDInference()

Member Function Documentation

◆ _checkingSolvability_()

◆ _completingNoForgettingAssumption_()

◆ _computeDomainSizes_()

◆ _createHardEvidence_()

◆ _creatingJunctionTree_()

◆ _creatingPartialOrder_()

◆ _findingCliqueForEachNode_()

◆ _isHardEvidence_()

◆ _reducingLIMID_()

◆ _setIDDuringConstruction_()

◆ addEvidence() [1/8]

◆ addEvidence() [2/8]

◆ addEvidence() [3/8]

◆ addEvidence() [4/8]

◆ addEvidence() [5/8]

◆ addEvidence() [6/8]

◆ addEvidence() [7/8]

◆ addEvidence() [8/8]

◆ addListOfEvidence()

◆ addNoForgettingAssumption() [1/2]

◆ addNoForgettingAssumption() [2/2]

◆ addSetOfEvidence()

◆ binarizingMax_()

◆ chgEvidence() [1/7]

◆ chgEvidence() [2/7]

◆ chgEvidence() [3/7]

◆ chgEvidence() [4/7]

◆ chgEvidence() [5/7]

◆ chgEvidence() [6/7]

◆ chgEvidence() [7/7]

◆ clear()

◆ collectingMessage_()

◆ collectingToFollowingRoot_()

◆ computingPosteriors_()

◆ createReduced_()

◆ deciding_()

◆ distributingMessage_()

◆ domainSizes()

◆ eraseAllEvidence()

◆ eraseEvidence() [1/2]

◆ eraseEvidence() [2/2]

◆ evidence()

◆ hardEvidence()

◆ hardEvidenceNodes()

◆ hasEvidence() [1/3]

◆ hasEvidence() [2/3]

◆ hasEvidence() [3/3]

◆ hasHardEvidence() [1/2]

◆ hasHardEvidence() [2/2]

◆ hasNoForgettingAssumption()

◆ hasNoModel_()

◆ hasSoftEvidence() [1/2]

◆ hasSoftEvidence() [2/2]

◆ influenceDiagram()

◆ initializingInference_()

◆ integrating_() [1/2]

◆ integrating_() [2/2]

◆ isInferenceDone()

◆ isInferenceOutdatedStructure()

◆ isInferenceOutdatedTensors()

◆ isInferenceReady()

◆ isSolvable()