gum::ShaferShenoyLIMIDInference< GUM_SCALAR > Class Template Reference

#include <ShaferShenoyLIMIDInference.h>

Inheritance diagram for gum::ShaferShenoyLIMIDInference< GUM_SCALAR >:

Collaboration diagram for gum::ShaferShenoyLIMIDInference< GUM_SCALAR >:

Public Member Functions
DAG	reducedGraph () const
std::vector< NodeSet >	reversePartialOrder () const
InfluenceDiagram< GUM_SCALAR >	reducedLIMID () const
bool	isSolvable () const
gum::Potential< GUM_SCALAR >	optimalDecision (NodeId decisionId) final
gum::Potential< GUM_SCALAR >	optimalDecision (const std::string &decisionName) final
virtual const Potential< GUM_SCALAR > &	posterior (NodeId node) final
	Return the posterior probability of a node. More...
const Potential< GUM_SCALAR > &	posterior (const std::string &name) final
virtual const Potential< GUM_SCALAR > &	posteriorUtility (NodeId node) final
	Return the posterior utility of a node. More...
virtual const Potential< 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. More...
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) More...
virtual void	setInfluenceDiagram (const InfluenceDiagram< GUM_SCALAR > *infdiag)
	assigns a new influence diagram to the inference engine More...
virtual const InfluenceDiagram< GUM_SCALAR > &	influenceDiagram () const final
	Returns a constant reference over the IBayesNet referenced by this class. More...
Constructor & destructor
	ShaferShenoyLIMIDInference (const InfluenceDiagram< GUM_SCALAR > *infDiag)
	Default constructor. More...
virtual	~ShaferShenoyLIMIDInference ()
	Destructor. More...
const JunctionTree *	junctionTree () const
	Default constructor. More...
void	clear () override
	Default constructor. More...
void	addNoForgettingAssumption (const std::vector< NodeId > &ids)
	No forgetting rule assumption. More...
void	addNoForgettingAssumption (const std::vector< std::string > &names)
	Default constructor. More...
bool	hasNoForgettingAssumption () const
	Default constructor. More...
Accessors / Modifiers
virtual const GraphicalModel &	model () const final
	Returns a constant reference over the IBayesNet referenced by this class. More...
virtual const NodeProperty< Size > &	domainSizes () const final
	get the domain sizes of the random variables of the model More...
virtual bool	isInferenceReady () const noexcept final
	returns whether the inference object is in a ready state More...
virtual bool	isInferenceOutdatedStructure () const noexcept final
	returns whether the inference object is in a OutdatedStructure state More...
virtual bool	isInferenceOutdatedPotentials () const noexcept final
	returns whether the inference object is in a OutdatedPotential state More...
virtual bool	isInferenceDone () const noexcept final
	returns whether the inference object is in a InferenceDone state More...
virtual void	prepareInference () final
	prepare the internal inference structures for the next inference More...
virtual void	makeInference () final
	perform the heavy computations needed to compute the targets' posteriors More...
virtual StateOfInference	state () const noexcept final
	returns the state of the inference engine More...
Evidence
virtual void	addEvidence (NodeId id, const Idx val) final
	adds a new hard evidence on node id More...
virtual void	addEvidence (const std::string &nodeName, const Idx val) final
	adds a new hard evidence on node named nodeName More...
virtual void	addEvidence (NodeId id, const std::string &label) final
	adds a new hard evidence on node id More...
virtual void	addEvidence (const std::string &nodeName, const std::string &label) final
	adds a new hard evidence on node named nodeName More...
virtual void	addEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node id (might be soft or hard) More...
virtual void	addEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node named nodeName (might be soft or hard) More...
virtual void	addEvidence (const Potential< GUM_SCALAR > &pot) final
	adds a new evidence on node id (might be soft or hard) More...
virtual void	addEvidence (Potential< GUM_SCALAR > &&pot) final
	adds a new evidence on node id (might be soft or hard) More...
virtual void	addSetOfEvidence (const Set< const Potential< GUM_SCALAR > * > &potset) final
	adds a new set of evidence More...
virtual void	addListOfEvidence (const List< const Potential< GUM_SCALAR > * > &potlist) final
	adds a new list of evidence More...
virtual void	chgEvidence (NodeId id, const Idx val) final
	change the value of an already existing hard evidence More...
virtual void	chgEvidence (const std::string &nodeName, const Idx val) final
	change the value of an already existing hard evidence More...
virtual void	chgEvidence (NodeId id, const std::string &label) final
	change the value of an already existing hard evidence More...
virtual void	chgEvidence (const std::string &nodeName, const std::string &label) final
	change the value of an already existing hard evidence More...
virtual void	chgEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard) More...
virtual void	chgEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard) More...
virtual void	chgEvidence (const Potential< GUM_SCALAR > &pot) final
	change the value of an already existing evidence (might be soft or hard) More...
virtual void	eraseAllEvidence () final
	removes all the evidence entered into the network More...
virtual void	eraseEvidence (NodeId id) final
	removed the evidence, if any, corresponding to node id More...
virtual void	eraseEvidence (const std::string &nodeName) final
	removed the evidence, if any, corresponding to node of name nodeName More...
virtual bool	hasEvidence () const final
	indicates whether some node(s) have received evidence More...
virtual bool	hasEvidence (NodeId id) const final
	indicates whether node id has received an evidence More...
virtual bool	hasEvidence (const std::string &nodeName) const final
	indicates whether node id has received an evidence More...
virtual bool	hasHardEvidence (NodeId id) const final
	indicates whether node id has received a hard evidence More...
virtual bool	hasHardEvidence (const std::string &nodeName) const final
	indicates whether node id has received a hard evidence More...
virtual bool	hasSoftEvidence (NodeId id) const final
	indicates whether node id has received a soft evidence More...
virtual bool	hasSoftEvidence (const std::string &nodeName) const final
	indicates whether node id has received a soft evidence More...
virtual Size	nbrEvidence () const final
	returns the number of evidence entered into the Bayesian network More...
virtual Size	nbrHardEvidence () const final
	returns the number of hard evidence entered into the Bayesian network More...
virtual Size	nbrSoftEvidence () const final
	returns the number of soft evidence entered into the Bayesian network More...
const NodeProperty< const Potential< GUM_SCALAR > *> &	evidence () const
	returns the set of evidence More...
const NodeSet &	softEvidenceNodes () const
	returns the set of nodes with soft evidence More...
const NodeSet &	hardEvidenceNodes () const
	returns the set of nodes with hard evidence More...
const NodeProperty< Idx > &	hardEvidence () const
	indicate for each node with hard evidence which value it took More...

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

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

Protected Member Functions
void	onStateChanged_ () override
	fired when the stage is changed More...
void	onEvidenceAdded_ (NodeId id, bool isHardEvidence) override
	fired after a new evidence is inserted More...
void	onEvidenceErased_ (NodeId id, bool isHardEvidence) override
	fired before an evidence is removed More...
void	onAllEvidenceErased_ (bool contains_hard_evidence) override
	fired before all the evidence are erased More...
void	onEvidenceChanged_ (NodeId id, bool hasChangedSoftHard) override
	fired after an evidence is changed, in particular when its status (soft/hard) changes More...
void	onModelChanged_ (const GraphicalModel *model) override
	fired after a new Bayes net has been assigned to the engine More...
void	updateOutdatedStructure_ () override
	prepares inference when the latter is in OutdatedStructure state More...
void	updateOutdatedPotentials_ () override
	prepares inference when the latter is in OutdatedPotentials state More...
void	makeInference_ () override
	called when the inference has to be performed effectively More...
NodeSet	nonRequisiteNodes_ (NodeId d) const
	Returns the set of non-requisite for node d. More...
void	createReduced_ ()
void	setOutdatedStructureState_ ()
	put the inference into an outdated model structure state More...
void	setOutdatedPotentialsState_ ()
	puts the inference into an OutdatedPotentials state if it is not already in an OutdatedStructure state More...
virtual void	setState_ (const StateOfInference state) final
	set the state of the inference engine and call the notification onStateChanged_ when necessary (i.e. when the state has effectively changed). More...
void	setModel_ (const GraphicalModel *model)
void	setModelDuringConstruction_ (const GraphicalModel *model)
	assigns a model during the inference engine construction More...
bool	hasNoModel_ () const

Detailed Description

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

Definition at line 65 of file ShaferShenoyLIMIDInference.h.

Member Typedef Documentation

PhiNodeProperty

template<typename GUM_SCALAR >

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

private

Definition at line 67 of file ShaferShenoyLIMIDInference.h.

PsiArcProperty

template<typename GUM_SCALAR >

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

private

Definition at line 68 of file ShaferShenoyLIMIDInference.h.

SetOfVars

template<typename GUM_SCALAR >

using gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::SetOfVars = Set< const DiscreteVariable* >

private

Definition at line 69 of file ShaferShenoyLIMIDInference.h.

Member Enumeration Documentation

StateOfInference

template<typename GUM_SCALAR >

enum gum::GraphicalModelInference::StateOfInference

stronginherited

current state of the inference

graphicalModelInference can be in one of 4 different states:

• OutdatedStructure: in this state, the inference is fully unprepared to be applied because some events changed the "logical" structure of the model: for instance a node received a hard evidence, which implies that its outgoing arcs can be removed from the model, hence involving a structural change in the model.
• OutdatedPotentials: in this state, the structure of the model remains unchanged, only some potentials stored in it have changed. Therefore, the inference probably just needs to invalidate some already computed potentials to be ready. Only a light amount of preparation is needed to be able to perform inference.
• Ready4Inference: in this state, all the data structures are ready for inference. There just remains to perform the inference computations.
• Done: the heavy computations of inference have been done. There might still remain a few light computations to perform to get the posterior potentials we need.

Enumerator
OutdatedStructure
OutdatedPotentials
ReadyForInference
Done

Definition at line 106 of file graphicalModelInference.h.

    {
      OutdatedStructure,
      OutdatedPotentials,
      ReadyForInference,
      Done
    };

Constructor & Destructor Documentation

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

addEvidence() [1/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

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

Definition at line 243 of file graphicalModelInference_tpl.h.

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

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

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addEvidence() [2/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

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

Definition at line 250 of file graphicalModelInference_tpl.h.

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

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

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addEvidence() [3/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

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

Definition at line 259 of file graphicalModelInference_tpl.h.

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

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

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addEvidence() [4/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

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

Definition at line 266 of file graphicalModelInference_tpl.h.

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

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

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addEvidence() [5/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 275 of file graphicalModelInference_tpl.h.

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

                                          {
    // checks that the evidence is meaningful
    if (model__ == nullptr)
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");

    if (!model__->exists(id)) {
      GUM_ERROR(UndefinedElement, id << " is not a NodeId in the model");
    }

    if (model__->variable(id).domainSize() != vals.size()) {
      GUM_ERROR(InvalidArgument,
                "node " << model__->variable(id)
                        << " and its evidence vector have different sizes.");
    }

    Potential< GUM_SCALAR > pot;
    pot.add(model__->variable(id));
    pot.fillWith(vals);
    addEvidence(std::move(pot));
  }

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addEvidence() [6/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 302 of file graphicalModelInference_tpl.h.

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

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

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addEvidence() [7/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 352 of file graphicalModelInference_tpl.h.

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

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

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addEvidence() [8/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 310 of file graphicalModelInference_tpl.h.

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

                                    {
    // check if the potential corresponds to an evidence
    if (pot.nbrDim() != 1) {
      GUM_ERROR(InvalidArgument, pot << " is not mono-dimensional.");
    }
    if (model__ == nullptr)
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");

    NodeId id = model__->nodeId(pot.variable(0));

    if (hasEvidence(id)) {
      GUM_ERROR(InvalidArgument,
                " node " << id
                         << " already has an evidence. Please use chgEvidence().");
    }

    // check whether we have a hard evidence (and also check whether the
    // potential only contains 0 (in this case, this will automatically raise
    // an exception) )
    Idx  val;
    bool is_hard_evidence = isHardEvidence__(pot, val);

    // insert the evidence
    evidence__.insert(
       id,
       new Potential< GUM_SCALAR >(std::forward< Potential< GUM_SCALAR > >(pot)));
    if (is_hard_evidence) {   // pot is deterministic
      hard_evidence__.insert(id, val);
      hard_evidence_nodes__.insert(id);
    } else {
      soft_evidence_nodes__.insert(id);
    }
    setState_(StateOfInference::OutdatedStructure);
    onEvidenceAdded_(id, is_hard_evidence);
  }

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addListOfEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

adds a new list of evidence

Exceptions

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

Definition at line 361 of file graphicalModelInference_tpl.h.

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

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

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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 Potential< GUM_SCALAR > * > &  potset )

finalvirtualinherited

adds a new set of evidence

Exceptions

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

Definition at line 370 of file graphicalModelInference_tpl.h.

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

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

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binarizingMax_()

template<typename GUM_SCALAR >

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

private

checkingSolvability__()

template<typename GUM_SCALAR >

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

private

chgEvidence() [1/7]

template<typename GUM_SCALAR >

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 432 of file graphicalModelInference_tpl.h.

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

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

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chgEvidence() [2/7]

template<typename GUM_SCALAR >

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 439 of file graphicalModelInference_tpl.h.

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

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

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chgEvidence() [3/7]

template<typename GUM_SCALAR >

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 448 of file graphicalModelInference_tpl.h.

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

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

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chgEvidence() [4/7]

template<typename GUM_SCALAR >

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 455 of file graphicalModelInference_tpl.h.

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

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

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chgEvidence() [5/7]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 464 of file graphicalModelInference_tpl.h.

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

                                          {
    // check whether this corresponds to an evidence
    if (model__ == nullptr)
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");

    if (!model__->exists(id)) {
      GUM_ERROR(UndefinedElement, id << " is not a NodeId in the model");
    }

    if (model__->variable(id).domainSize() != vals.size()) {
      GUM_ERROR(InvalidArgument,
                "node " << model__->variable(id)
                        << " and its evidence have different sizes.");
    }

    // create the potential corresponding to vals
    Potential< GUM_SCALAR > pot;
    pot.add(model__->variable(id));
    pot.fillWith(vals);
    chgEvidence(pot);
  }

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chgEvidence() [6/7]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 492 of file graphicalModelInference_tpl.h.

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

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

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chgEvidence() [7/7]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 501 of file graphicalModelInference_tpl.h.

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

                                         {
    // check if the potential corresponds to an evidence
    if (pot.nbrDim() != 1) {
      GUM_ERROR(InvalidArgument, pot << " is not a mono-dimensional potential.");
    }
    if (model__ == nullptr)
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");

    NodeId id = model__->nodeId(pot.variable(0));

    if (!hasEvidence(id)) {
      GUM_ERROR(InvalidArgument,
                id << " has no evidence. Please use addEvidence().");
    }

    // check whether we have a hard evidence (and also check whether the
    // potential only contains 0 (in this case, this will automatically raise
    // an exception) )
    Idx  val;
    bool is_hard_evidence = isHardEvidence__(pot, val);

    // modify the evidence already stored
    const Potential< GUM_SCALAR >* localPot = evidence__[id];
    Instantiation                  I(pot);
    for (I.setFirst(); !I.end(); I.inc()) {
      localPot->set(I, pot[I]);
    }

    // the inference state will be different
    // whether evidence change from Hard to Soft or not.
    bool hasChangedSoftHard = false;

    if (is_hard_evidence) {
      if (!hasHardEvidence(id)) {
        hasChangedSoftHard = true;
        hard_evidence__.insert(id, val);
        hard_evidence_nodes__.insert(id);
        soft_evidence_nodes__.erase(id);
      } else {
        hard_evidence__[id] = val;
      }
    } else {
      if (hasHardEvidence(id)) {   // evidence was hard
        hard_evidence__.erase(id);
        hard_evidence_nodes__.erase(id);
        soft_evidence_nodes__.insert(id);
        hasChangedSoftHard = true;
      }
    }

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

    onEvidenceChanged_(id, hasChangedSoftHard);
  }

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clear()

template<typename GUM_SCALAR >

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

collectingToFollowingRoot_()

template<typename GUM_SCALAR >

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

private

completingNoForgettingAssumption__()

template<typename GUM_SCALAR >

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

private

computingPosteriors_()

template<typename GUM_SCALAR >

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

private

createReduced_()

template<typename GUM_SCALAR >

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

protected

creatingJunctionTree__()

template<typename GUM_SCALAR >

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

private

creatingPartialOrder__()

template<typename GUM_SCALAR >

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

private

deciding_()

template<typename GUM_SCALAR >

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

private

distributingMessage_()

template<typename GUM_SCALAR >

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

private

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 168 of file graphicalModelInference_tpl.h.

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

                                                                        {
    return domain_sizes__;
  }

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eraseAllEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

removes all the evidence entered into the network

Definition at line 597 of file graphicalModelInference_tpl.h.

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

                                                                      {
    bool has_hard_evidence = !hard_evidence__.empty();
    this->onAllEvidenceErased_(has_hard_evidence);

    for (const auto& pair: evidence__) {
      if (pair.second != nullptr) { delete (pair.second); }
    }

    evidence__.clear();
    hard_evidence__.clear();
    hard_evidence_nodes__.clear();
    soft_evidence_nodes__.clear();

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

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eraseEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

removed the evidence, if any, corresponding to node id

Definition at line 568 of file graphicalModelInference_tpl.h.

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

                                                                            {
    if (hasEvidence(id)) {
      if (hasHardEvidence(id)) {
        onEvidenceErased_(id, true);
        hard_evidence__.erase(id);
        hard_evidence_nodes__.erase(id);
        setState_(StateOfInference::OutdatedStructure);
      } else {
        onEvidenceErased_(id, false);
        soft_evidence_nodes__.erase(id);
        if (!isInferenceOutdatedStructure()) {
          setState_(StateOfInference::OutdatedPotentials);
        }
      }

      delete (evidence__[id]);
      evidence__.erase(id);
    }
  }

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eraseEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Definition at line 589 of file graphicalModelInference_tpl.h.

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

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

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evidence()

template<typename GUM_SCALAR >

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

inherited

returns the set of evidence

Definition at line 652 of file graphicalModelInference_tpl.h.

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

                                                                     {
    return evidence__;
  }

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findingCliqueForEachNode__()

template<typename GUM_SCALAR >

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

private

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 644 of file graphicalModelInference_tpl.h.

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

                                                                         {
    return hard_evidence__;
  }

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hardEvidenceNodes()

template<typename GUM_SCALAR >

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

inherited

returns the set of nodes with hard evidence

the set of nodes that received hard evidence

Definition at line 668 of file graphicalModelInference_tpl.h.

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

                                                                              {
    return hard_evidence_nodes__;
  }

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hasEvidence() [1/3]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether some node(s) have received evidence

Definition at line 379 of file graphicalModelInference_tpl.h.

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

                                                                       {
    return !evidence__.empty();
  }

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hasEvidence() [2/3]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 386 of file graphicalModelInference_tpl.h.

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

                                                                                {
    return evidence__.exists(id);
  }

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hasEvidence() [3/3]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 409 of file graphicalModelInference_tpl.h.

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

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

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hasHardEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 394 of file graphicalModelInference_tpl.h.

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

                                                                           {
    return hard_evidence_nodes__.exists(id);
  }

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hasHardEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 417 of file graphicalModelInference_tpl.h.

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

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

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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 533 of file graphicalModelInference.h.

{ return model__ == nullptr; };

hasSoftEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 402 of file graphicalModelInference_tpl.h.

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

                                                                           {
    return soft_evidence_nodes__.exists(id);
  }

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hasSoftEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 425 of file graphicalModelInference_tpl.h.

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

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

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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.

initializingInference_()

template<typename GUM_SCALAR >

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

private

integrating_() [1/2]

template<typename GUM_SCALAR >

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

private

integrating_() [2/2]

template<typename GUM_SCALAR >

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

private

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 89 of file graphicalModelInference_tpl.h.

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

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

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isInferenceOutdatedPotentials()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedPotential state

Definition at line 82 of file graphicalModelInference_tpl.h.

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

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

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isInferenceOutdatedStructure()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedStructure state

Definition at line 75 of file graphicalModelInference_tpl.h.

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

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

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isInferenceReady()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

returns whether the inference object is in a ready state

Definition at line 70 of file graphicalModelInference_tpl.h.

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

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

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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 714 of file graphicalModelInference_tpl.h.

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

                                                                   {
    if (isInferenceDone()) { return; }

    if (!isInferenceReady()) { prepareInference(); }

    makeInference_();

    setState_(StateOfInference::Done);
  }

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makeInference_()

template<typename GUM_SCALAR >

void gum::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 >.

meanVar() [1/2]

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 >.

meanVar() [2/2]

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 146 of file ShaferShenoyLIMIDInference.h.

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

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 114 of file graphicalModelInference_tpl.h.

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

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

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nbrEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

returns the number of evidence entered into the Bayesian network

Definition at line 622 of file graphicalModelInference_tpl.h.

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

                                                                       {
    return evidence__.size();
  }

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nbrHardEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

returns the number of hard evidence entered into the Bayesian network

Definition at line 629 of file graphicalModelInference_tpl.h.

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

                                                                           {
    return hard_evidence_nodes__.size();
  }

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nbrSoftEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

returns the number of soft evidence entered into the Bayesian network

Definition at line 636 of file graphicalModelInference_tpl.h.

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

                                                                           {
    return soft_evidence_nodes__.size();
  }

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nonRequisiteNodes_()

template<typename GUM_SCALAR >

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

protected

Returns the set of non-requisite for node d.

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 >.

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 >.

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 >.

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 >.

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 >.

onStateChanged_()

template<typename GUM_SCALAR >

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

overrideprotectedvirtual

fired when the stage is changed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

optimalDecision() [1/2]

template<typename GUM_SCALAR >

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

finalvirtual

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

optimalDecision() [2/2]

template<typename GUM_SCALAR >

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

inlinefinalvirtual

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Definition at line 112 of file ShaferShenoyLIMIDInference.h.

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

posterior() [1/2]

template<typename GUM_SCALAR >

virtual const Potential< 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 >.

posterior() [2/2]

template<typename GUM_SCALAR >

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

inlinefinalvirtual

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Definition at line 123 of file ShaferShenoyLIMIDInference.h.

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

posteriorUtility() [1/2]

template<typename GUM_SCALAR >

virtual const Potential< 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 >.

posteriorUtility() [2/2]

template<typename GUM_SCALAR >

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

inlinefinalvirtual

Implements gum::InfluenceDiagramInference< GUM_SCALAR >.

Definition at line 135 of file ShaferShenoyLIMIDInference.h.

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

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 695 of file graphicalModelInference_tpl.h.

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

                                                                      {
    if (isInferenceReady() || isInferenceDone()) { return; }

    if (model__ == nullptr)
      GUM_ERROR(NullElement,
                "No model been assigned to the "
                "inference algorithm");

    if (state__ == StateOfInference::OutdatedStructure)
      updateOutdatedStructure_();
    else
      updateOutdatedPotentials_();

    setState_(StateOfInference::ReadyForInference);
  }

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reducedGraph()

template<typename GUM_SCALAR >

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

inline

Definition at line 101 of file ShaferShenoyLIMIDInference.h.

{ return reduced_; };

reducedLIMID()

template<typename GUM_SCALAR >

InfluenceDiagram< GUM_SCALAR > gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::reducedLIMID

(

)

const

reducingLIMID__()

template<typename GUM_SCALAR >

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

private

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 125 of file graphicalModelInference_tpl.h.

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

                                  {
    clear();
    model__ = model;
    computeDomainSizes__();
    onModelChanged_(model);
    setState_(StateOfInference::OutdatedStructure);
  }

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setModelDuringConstruction_()

template<typename GUM_SCALAR >

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

protectedinherited

assigns a model during the inference engine construction

Definition at line 137 of file graphicalModelInference_tpl.h.

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

                                  {
    model__ = model;
    computeDomainSizes__();
    setState_(StateOfInference::OutdatedStructure);
  }

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setOutdatedPotentialsState_()

template<typename GUM_SCALAR >

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

protectedinherited

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

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

Definition at line 688 of file graphicalModelInference_tpl.h.

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

                                                                        {
    setState_(StateOfInference::OutdatedPotentials);
  }

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setOutdatedStructureState_()

template<typename GUM_SCALAR >

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

protectedinherited

put the inference into an outdated model structure state

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

Definition at line 679 of file graphicalModelInference_tpl.h.

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

                                                                                {
    setState_(StateOfInference::OutdatedStructure);
  }

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setState_()

template<typename GUM_SCALAR >

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

finalprotectedvirtualinherited

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

Definition at line 103 of file graphicalModelInference_tpl.h.

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

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

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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 660 of file graphicalModelInference_tpl.h.

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

                                                                              {
    return soft_evidence_nodes__;
  }

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state()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

returns the state of the inference engine

Definition at line 97 of file graphicalModelInference_tpl.h.

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

                                                                 {
    return state__;
  }

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transmittingFinalMessage_()

template<typename GUM_SCALAR >

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

private

transmittingMessage_()

template<typename GUM_SCALAR >

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

private

updateOutdatedPotentials_()

template<typename GUM_SCALAR >

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

overrideprotectedvirtual

prepares inference when the latter is in OutdatedPotentials state

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

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 >.

Member Data Documentation

node_to_clique_

template<typename GUM_SCALAR >

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

protected

Definition at line 173 of file ShaferShenoyLIMIDInference.h.

noForgettingOrder_

template<typename GUM_SCALAR >

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

protected

Definition at line 182 of file ShaferShenoyLIMIDInference.h.

posteriors_

template<typename GUM_SCALAR >

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

protected

Definition at line 176 of file ShaferShenoyLIMIDInference.h.

reduced_

template<typename GUM_SCALAR >

DAG gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::reduced_

protected

Definition at line 171 of file ShaferShenoyLIMIDInference.h.

reducedJunctionTree_

template<typename GUM_SCALAR >

CliqueGraph gum::ShaferShenoyLIMIDInference< GUM_SCALAR >::reducedJunctionTree_

protected

Definition at line 172 of file ShaferShenoyLIMIDInference.h.

reversePartialOrder_

template<typename GUM_SCALAR >

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

protected

Definition at line 180 of file ShaferShenoyLIMIDInference.h.

solvabilityOrder_

template<typename GUM_SCALAR >

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

protected

Definition at line 181 of file ShaferShenoyLIMIDInference.h.

strategies_

template<typename GUM_SCALAR >

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

protected

Definition at line 175 of file ShaferShenoyLIMIDInference.h.

unconditionalDecisions_

template<typename GUM_SCALAR >

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

protected

Definition at line 177 of file ShaferShenoyLIMIDInference.h.

varsSeparator_

template<typename GUM_SCALAR >

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

protected

Definition at line 174 of file ShaferShenoyLIMIDInference.h.

---

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

• agrum/ID/inference/ShaferShenoyLIMIDInference.h