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

#include <jointTargetedInference.h>

Inheritance diagram for gum::JointTargetedInference< GUM_SCALAR >:

Collaboration diagram for gum::JointTargetedInference< GUM_SCALAR >:

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

Public Member Functions
Tensor< GUM_SCALAR >	evidenceJointImpact (const NodeSet &targets, const NodeSet &evs)
	Create a gum::Tensor for P(joint targets\|evs) (for all instantiation of targets and evs).
Tensor< GUM_SCALAR >	evidenceJointImpact (const std::vector< std::string > &targets, const std::vector< std::string > &evs)
	Create a gum::Tensor for P(joint targets\|evs) (for all instantiation of targets and evs).
Tensor< GUM_SCALAR >	evidenceImpact (NodeId target, const NodeSet &evs)
	Create a gum::Tensor for P(target\|evs) (for all instantiation of target and evs).
Tensor< GUM_SCALAR >	evidenceImpact (std::string_view target, const std::vector< std::string > &evs)
	Create a gum::Tensor for P(target\|evs) (for all instantiation of target and evs).
Constructors / Destructors
	JointTargetedInference (const IBayesNet< GUM_SCALAR > *bn)
	default constructor
	~JointTargetedInference () override
	destructor
Probability computations
virtual const Tensor< GUM_SCALAR > &	jointPosterior (const NodeSet &nodes) final
	Compute the joint posterior of a set of nodes.
const Tensor< GUM_SCALAR > &	posterior (NodeId node) final
	Computes and returns the posterior of a node.
const Tensor< GUM_SCALAR > &	posterior (std::string_view nodeName) final
	Computes and returns the posterior of a node.
Targets
void	eraseAllTargets () override
	Clear all previously defined targets (marginal and joint targets).
virtual void	eraseAllJointTargets () final
	Clear all previously defined joint targets.
virtual void	eraseAllMarginalTargets () final
	Clear all the previously defined marginal targets.
virtual void	addJointTarget (const NodeSet &joint_target) final
	Add a set of nodes as a new joint target. As a collateral effect, every node is added as a marginal target.
virtual void	eraseJointTarget (const NodeSet &joint_target) final
	removes an existing joint target
virtual bool	isJointTarget (const NodeSet &vars) const final
	return true if target is a joint target.
virtual const Set< NodeSet > &	jointTargets () const noexcept final
	returns the list of joint targets
virtual Size	nbrJointTargets () const noexcept final
	returns the number of joint targets
Information Theory related functions
GUM_SCALAR	I (NodeId X, NodeId Y)
	Mutual information between X and Y.
GUM_SCALAR	I (std::string_view Xname, std::string_view Yname)
	Mutual information between X and Y.
GUM_SCALAR	VI (NodeId X, NodeId Y)
	Variation of information between X and Y.
GUM_SCALAR	VI (std::string_view Xname, std::string_view Yname)
	Variation of information between X and Y.
GUM_SCALAR	jointMutualInformation (const NodeSet &targets)
	Mutual information between targets.
GUM_SCALAR	jointMutualInformation (const std::vector< std::string > &targets)
	Mutual information between targets.
Targets
virtual void	addAllTargets () final
	adds all nodes as targets
virtual void	addTarget (NodeId target) final
	Add a marginal target to the list of targets.
virtual void	addTarget (std::string_view nodeName) final
	Add a marginal target to the list of targets.
virtual void	eraseTarget (NodeId target) final
	removes an existing (marginal) target
virtual void	eraseTarget (std::string_view nodeName) final
	removes an existing (marginal) target
virtual bool	isTarget (NodeId node) const final
	return true if variable is a (marginal) target
virtual bool	isTarget (std::string_view nodeName) const final
	return true if variable is a (marginal) target
virtual Size	nbrTargets () const noexcept final
	returns the number of marginal targets
virtual const NodeSet &	targets () const noexcept final
	returns the list of marginal targets
virtual bool	isInTargetMode () const noexcept final
	indicates whether the inference is in a target mode
Information Theory related functions
virtual GUM_SCALAR	H (NodeId X) final
	Entropy Compute Shanon's entropy of a node given the observation.
virtual GUM_SCALAR	H (std::string_view nodeName) final
	Entropy Compute Shanon's entropy of a node given the observation.
Accessors / Modifiers
virtual void	setBN (const IBayesNet< GUM_SCALAR > *bn)
	assigns a new BN to the inference engine
virtual const IBayesNet< GUM_SCALAR > &	BN () const final
	Returns a constant reference over the IBayesNet referenced by this class.
Accessors / Modifiers
virtual const GraphicalModel &	model () const final
	Returns a constant reference over the IBayesNet referenced by this class.
virtual const NodeProperty< Size > &	domainSizes () const final
	get the domain sizes of the random variables of the model
virtual bool	isInferenceReady () const noexcept final
	returns whether the inference object is in a ready state
virtual bool	isInferenceOutdatedStructure () const noexcept final
	returns whether the inference object is in a OutdatedStructure state
virtual bool	isInferenceOutdatedTensors () const noexcept final
	returns whether the inference object is in a OutdatedTensor state
virtual bool	isInferenceDone () const noexcept final
	returns whether the inference object is in a InferenceDone state
virtual void	prepareInference () final
	prepare the internal inference structures for the next inference
virtual void	makeInference () final
	perform the heavy computations needed to compute the targets' posteriors
virtual void	clear ()
	clears all the data structures allocated for the last inference
virtual StateOfInference	state () const noexcept final
	returns the state of the inference engine
Evidence
virtual void	addEvidence (NodeId id, const Idx val) final
	adds a new hard evidence on node id
virtual void	addEvidence (std::string_view nodeName, const Idx val) final
	adds a new hard evidence on node named nodeName
virtual void	addEvidence (NodeId id, std::string_view label) final
	adds a new hard evidence on node id
virtual void	addEvidence (std::string_view nodeName, std::string_view 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 (std::string_view 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 (std::string_view nodeName, const Idx val) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (NodeId id, std::string_view label) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (std::string_view nodeName, std::string_view 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 (std::string_view 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 (std::string_view 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 (std::string_view 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 (std::string_view 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 (std::string_view 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	onModelChanged_ (const GraphicalModel *bn) override
	fired after a new Bayes net has been assigned to the engine
virtual void	onJointTargetAdded_ (const NodeSet &set)=0
	fired after a new joint target is inserted
virtual void	onJointTargetErased_ (const NodeSet &set)=0
	fired before a joint target is removed
virtual void	onAllTargetsErased_ ()=0
	fired before a all the marginal and joint targets are removed
virtual void	onAllJointTargetsErased_ ()=0
	fired before a all the joint targets are removed
virtual const Tensor< GUM_SCALAR > &	jointPosterior_ (const NodeSet &set)=0
	asks derived classes for the joint posterior of a declared target set
virtual const Tensor< GUM_SCALAR > &	jointPosterior_ (const NodeSet &wanted_target, const NodeSet &declared_target)=0
	asks derived classes for the joint posterior of a set of variables not declared as a joint target
virtual Tensor< GUM_SCALAR > *	unnormalizedJointPosterior_ (const NodeSet &set)=0
	returns a fresh unnormalized joint posterior of a given set of variables
virtual Tensor< GUM_SCALAR > *	unnormalizedJointPosterior_ (NodeId id)=0
	returns a fresh tensor equal to P(argument,evidence)
virtual void	onMarginalTargetAdded_ (const NodeId id)=0
	fired after a new marginal target is inserted
virtual void	onMarginalTargetErased_ (const NodeId id)=0
	fired before a marginal target is removed
virtual void	onAllMarginalTargetsAdded_ ()=0
	fired after all the nodes of the BN are added as marginal targets
virtual void	onAllMarginalTargetsErased_ ()=0
	fired before a all marginal targets are removed
virtual const Tensor< GUM_SCALAR > &	posterior_ (NodeId id)=0
	asks derived classes for the posterior of a given variable
void	setTargetedMode_ ()
bool	isTargetedMode_ () const
virtual void	onStateChanged_ ()=0
	fired when the stage is changed
virtual void	onEvidenceAdded_ (const NodeId id, bool isHardEvidence)=0
	fired after a new evidence is inserted
virtual void	onEvidenceErased_ (const NodeId id, bool isHardEvidence)=0
	fired before an evidence is removed
virtual void	onAllEvidenceErased_ (bool contains_hard_evidence)=0
	fired before all the evidence are erased
virtual void	onEvidenceChanged_ (const NodeId id, bool hasChangedSoftHard)=0
	fired after an evidence is changed, in particular when its status (soft/hard) changes
virtual void	updateOutdatedStructure_ ()=0
	prepares inference when the latter is in OutdatedStructure state
virtual void	updateOutdatedTensors_ ()=0
	prepares inference when the latter is in OutdatedTensors state
virtual void	makeInference_ ()=0
	called when the inference has to be performed effectively
void	setOutdatedStructureState_ ()
	put the inference into an outdated model structure state
void	setOutdatedTensorsState_ ()
	puts the inference into an OutdatedTensors state if it is not already in an OutdatedStructure state
virtual void	setState_ (const StateOfInference state) final
	set the state of the inference engine and call the notification onStateChanged_ when necessary (i.e. when the state has effectively changed).
void	setModel_ (const GraphicalModel *model)
void	setModelDuringConstruction_ (const GraphicalModel *model)
	assigns a model during the inference engine construction
bool	hasNoModel_ () const

Private Member Functions
void	_setAllMarginalTargets_ ()
	sets all the nodes of the Bayes net as targets

Private Attributes
Set< NodeSet >	_joint_targets_
	the set of joint targets
bool	_targeted_mode_
	whether the actual targets are default
NodeSet	_targets_
	the set of marginal targets

Detailed Description

template<GUM_Numeric GUM_SCALAR>
class gum::JointTargetedInference< GUM_SCALAR >

A generic class for the computation of (possibly incrementally) joint posteriors

The goal of this class is to take care of the joint targets used for computing joint posteriors. The JointTargetedInference class inherits from Inference that takes care of handling both evidence and the current state of the inference and from MarginalTargetedInference for the handling of marginal targets. Note that the JointTargetedInference is designed to be used in incremental inference engines.

Definition at line 78 of file jointTargetedInference.h.

Member Enumeration Documentation

◆ StateOfInference

template<GUM_Numeric 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

◆ JointTargetedInference()

template<GUM_Numeric GUM_SCALAR>

gum::JointTargetedInference< GUM_SCALAR >::JointTargetedInference ( const IBayesNet< GUM_SCALAR > * bn )

explicit

default constructor

Warning: note that, by aGrUM's rule, the BN is not copied but only referenced by the inference algorithm.

Definition at line 58 of file jointTargetedInference_tpl.h.

                                                                                                :
      MarginalTargetedInference< GUM_SCALAR >(bn) {
    // assign a BN if this has not been done before (due to virtual inheritance)
    if (this->hasNoModel_()) {
      BayesNetInference< GUM_SCALAR >::_setBayesNetDuringConstruction_(bn);
    }
    GUM_CONSTRUCTOR(JointTargetedInference);
  }

References JointTargetedInference(), gum::MarginalTargetedInference< GUM_SCALAR >::MarginalTargetedInference(), gum::BayesNetInference< GUM_SCALAR >::_setBayesNetDuringConstruction_(), and gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_().

Referenced by JointTargetedInference(), and ~JointTargetedInference().

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◆ ~JointTargetedInference()

template<GUM_Numeric GUM_SCALAR>

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

override

destructor

Definition at line 69 of file jointTargetedInference_tpl.h.

                                                                {
    GUM_DESTRUCTOR(JointTargetedInference);
  }

References JointTargetedInference().

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Member Function Documentation

◆ _setAllMarginalTargets_()

template<GUM_Numeric GUM_SCALAR>

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

privateinherited

sets all the nodes of the Bayes net as targets

Definition at line 232 of file marginalTargetedInference_tpl.h.

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

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

Referenced by onModelChanged_().

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◆ addAllTargets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

adds all nodes as targets

Definition at line 147 of file marginalTargetedInference_tpl.h.

                                                              {
    // check if the node belongs to the Bayesian network
    if (this->hasNoModel_())
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");
 
 
    setTargetedMode_();   // does nothing if already in targeted mode
    for (const auto target: this->BN().internalDag()) {
      if (!_targets_.contains(target)) {
        _targets_.insert(target);
        onMarginalTargetAdded_(target);
        this->setState_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
      }
    }
  }

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

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◆ addEvidence() [1/8]

template<GUM_Numeric GUM_SCALAR>

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() [2/8]

template<GUM_Numeric GUM_SCALAR>

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 230 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::Counterfactual< GUM_ELEMENT >::counterFactualModel(), gum::Counterfactual< GUM_ELEMENT >::counterFactualModel(), gum::counterfactualModel(), 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() [3/8]

template<GUM_Numeric 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() [4/8]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::addEvidence	(	NodeId	id,
		std::string_view	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 243 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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◆ addEvidence() [5/8]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::addEvidence	(	std::string_view	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 236 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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◆ addEvidence() [6/8]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::addEvidence	(	std::string_view	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() [7/8]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::addEvidence	(	std::string_view	nodeName,
		std::string_view	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() [8/8]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 288 of file graphicalModelInference_tpl.h.

                                                                                    {
    // check if the tensor corresponds to an evidence
    if (pot.nbrDim() != 1) { GUM_ERROR(InvalidArgument, pot << " is not mono-dimensional.") }
    if (_model_ == nullptr)
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");
 
    NodeId id = _model_->nodeId(pot.variable(0));
 
    if (hasEvidence(id)) {
      GUM_ERROR(InvalidArgument,
                " node " << id << " already has an evidence. Please use chgEvidence().");
    }
 
    // check whether we have a hard evidence (and also check whether the
    // tensor only contains 0 (in this case, this will automatically raise
    // an exception) )
    Idx  val              = 0;
    bool is_hard_evidence = _isHardEvidence_(pot, val);
 
    // insert the evidence
    _evidence_.insert(id, new Tensor< GUM_SCALAR >(std::forward< Tensor< GUM_SCALAR > >(pot)));
    if (is_hard_evidence) {   // pot is deterministic
      _hard_evidence_.insert(id, val);
      _hard_evidence_nodes_.insert(id);
    } else {
      _soft_evidence_nodes_.insert(id);
    }
    setState_(StateOfInference::OutdatedStructure);
    onEvidenceAdded_(id, is_hard_evidence);
  }

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

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◆ addJointTarget()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtual

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

Exceptions

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

Definition at line 127 of file jointTargetedInference_tpl.h.

                                                                                       {
    // check if the nodes in the target belong to the Bayesian network
    if (this->hasNoModel_())
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");
 
    const auto& dag = this->BN().internalDag();
    for (const auto node: joint_target) {
      if (!dag.exists(node)) {
        GUM_ERROR(UndefinedElement,
                  "at least one one in " << joint_target << " does not belong to the bn");
      }
    }
 
    // check that the joint_target set does not contain the new target
    if (_joint_targets_.contains(joint_target)) return;
 
    // check if joint_target is a subset of an already existing target
    for (const auto& target: _joint_targets_) {
      if (target.isStrictSupersetOf(joint_target)) return;
    }
 
    // check if joint_target is not a superset of an already existing target
    // in this case, we need to remove old existing target
    for (auto iter = _joint_targets_.beginSafe(); iter != _joint_targets_.endSafe(); ++iter) {
      if (iter->isStrictSubsetOf(joint_target)) eraseJointTarget(*iter);
    }
 
    this->setTargetedMode_();   // does nothing if already in targeted mode
    _joint_targets_.insert(joint_target);
    onJointTargetAdded_(joint_target);
    this->setState_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
  }

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

Referenced by gum::ASTjointProba< GUM_SCALAR >::eval(), gum::ASTposteriorProba< GUM_SCALAR >::eval(), evidenceJointImpact(), and jointMutualInformation().

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◆ addListOfEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

adds a new list of evidence

Exceptions

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

Definition at line 330 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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◆ addSetOfEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

adds a new set of evidence

Exceptions

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

Definition at line 338 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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◆ addTarget() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement if target is not a NodeId in the Bayes net

Definition at line 125 of file marginalTargetedInference_tpl.h.

                                                                       {
    // check if the node belongs to the Bayesian network
    if (this->hasNoModel_())
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");
 
    if (!this->BN().internalDag().exists(target)) {
      GUM_ERROR(UndefinedElement, target << " is not a NodeId in the bn")
    }
 
    setTargetedMode_();   // does nothing if already in targeted mode
    // add the new target
    if (!_targets_.contains(target)) {
      _targets_.insert(target);
      onMarginalTargetAdded_(target);
      this->setState_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
    }
  }

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

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

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◆ addTarget() [2/2]

template<GUM_Numeric GUM_SCALAR>

void gum::MarginalTargetedInference< GUM_SCALAR >::addTarget ( std::string_view nodeName )

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement if target is not a NodeId in the Bayes net

Definition at line 167 of file marginalTargetedInference_tpl.h.

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

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

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◆ BN()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 76 of file BayesNetInference_tpl.h.

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

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

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◆ chgEvidence() [1/7]

template<GUM_Numeric 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 446 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() [2/7]

template<GUM_Numeric GUM_SCALAR>

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 388 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() [3/7]

template<GUM_Numeric GUM_SCALAR>

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 415 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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◆ chgEvidence() [4/7]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::chgEvidence	(	NodeId	id,
		std::string_view	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 401 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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◆ chgEvidence() [5/7]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::chgEvidence	(	std::string_view	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 394 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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◆ chgEvidence() [6/7]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::chgEvidence	(	std::string_view	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 439 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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◆ chgEvidence() [7/7]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::chgEvidence	(	std::string_view	nodeName,
		std::string_view	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 407 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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◆ clear()

template<GUM_Numeric GUM_SCALAR>

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

virtualinherited

clears all the data structures allocated for the last inference

Reimplemented in gum::ShaferShenoyLIMIDInference< GUM_SCALAR >.

Definition at line 156 of file graphicalModelInference_tpl.h.

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

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

Referenced by setModel_().

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◆ domainSizes()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

get the domain sizes of the random variables of the model

Definition at line 174 of file graphicalModelInference_tpl.h.

                                                                                       {
    return _domain_sizes_;
  }

References _domain_sizes_.

◆ eraseAllEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

removes all the evidence entered into the network

Definition at line 534 of file graphicalModelInference_tpl.h.

                                                               {
    bool has_hard_evidence = !_hard_evidence_.empty();
    this->onAllEvidenceErased_(has_hard_evidence);
 
    for (const auto& pair: _evidence_) {
      if (pair.second != nullptr) { delete (pair.second); }
    }
 
    _evidence_.clear();
    _hard_evidence_.clear();
    _hard_evidence_nodes_.clear();
    _soft_evidence_nodes_.clear();
 
    if (has_hard_evidence) {
      setState_(StateOfInference::OutdatedStructure);
    } else {
      if (!isInferenceOutdatedStructure()) { setState_(StateOfInference::OutdatedTensors); }
    }
  }

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

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

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◆ eraseAllJointTargets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtual

Clear all previously defined joint targets.

Definition at line 109 of file jointTargetedInference_tpl.h.

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

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

Referenced by eraseAllTargets().

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◆ eraseAllMarginalTargets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtual

Clear all the previously defined marginal targets.

Definition at line 103 of file jointTargetedInference_tpl.h.

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

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

Referenced by eraseAllTargets().

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◆ eraseAllTargets()

template<GUM_Numeric GUM_SCALAR>

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

overridevirtual

Clear all previously defined targets (marginal and joint targets).

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

Reimplemented from gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 120 of file jointTargetedInference_tpl.h.

                                                             {
    eraseAllMarginalTargets();
    eraseAllJointTargets();
  }

References eraseAllJointTargets(), and eraseAllMarginalTargets().

Referenced by evidenceJointImpact(), and jointMutualInformation().

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◆ eraseEvidence() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

removed the evidence, if any, corresponding to node id

Definition at line 508 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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◆ eraseEvidence() [2/2]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::eraseEvidence ( std::string_view nodeName )

finalvirtualinherited

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

Definition at line 528 of file graphicalModelInference_tpl.h.

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

References eraseEvidence(), and model().

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◆ eraseJointTarget()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtual

removes an existing joint target

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

Definition at line 164 of file jointTargetedInference_tpl.h.

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

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

Referenced by addJointTarget().

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◆ eraseTarget() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

removes an existing (marginal) target

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

Definition at line 179 of file marginalTargetedInference_tpl.h.

                                                                         {
    // check if the node belongs to the Bayesian network
    if (this->hasNoModel_())
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");
 
    if (!this->BN().internalDag().exists(target)) {
      GUM_ERROR(UndefinedElement, target << " is not a NodeId in the bn")
    }
 
 
    if (_targets_.contains(target)) {
      _targeted_mode_ = true;   // we do not use setTargetedMode_ because we do not
                                // want to clear the targets
      onMarginalTargetErased_(target);
      _targets_.erase(target);
      this->setState_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
    }
  }

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

Referenced by eraseTarget().

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◆ eraseTarget() [2/2]

template<GUM_Numeric GUM_SCALAR>

void gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget ( std::string_view nodeName )

finalvirtualinherited

removes an existing (marginal) target

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

Definition at line 202 of file marginalTargetedInference_tpl.h.

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

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

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◆ evidence()

template<GUM_Numeric GUM_SCALAR>

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

inherited

returns the set of evidence

Definition at line 581 of file graphicalModelInference_tpl.h.

                                                            {
    return _evidence_;
  }

References _evidence_.

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

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◆ evidenceImpact() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

inherited

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

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

Parameters

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

Returns: a Tensor

Definition at line 283 of file marginalTargetedInference_tpl.h.

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

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

Referenced by evidenceImpact().

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◆ evidenceImpact() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

inherited

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

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

Parameters

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

Returns: a Tensor

Definition at line 322 of file marginalTargetedInference_tpl.h.

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

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

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◆ evidenceJointImpact() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

Create a gum::Tensor for P(joint targets|evs) (for all instantiation of targets and evs).

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

Parameters

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

Returns: a Tensor

Definition at line 253 of file jointTargetedInference_tpl.h.

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

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

Referenced by gum::ASTposteriorProba< GUM_SCALAR >::eval(), and evidenceJointImpact().

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◆ evidenceJointImpact() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

Create a gum::Tensor for P(joint targets|evs) (for all instantiation of targets and evs).

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

Parameters

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

Returns: a Tensor

Definition at line 294 of file jointTargetedInference_tpl.h.

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

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

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◆ H() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

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

Definition at line 270 of file marginalTargetedInference_tpl.h.

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

References posterior().

Referenced by H().

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◆ H() [2/2]

template<GUM_Numeric GUM_SCALAR>

GUM_SCALAR gum::MarginalTargetedInference< GUM_SCALAR >::H ( std::string_view nodeName )

finalvirtualinherited

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

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

Definition at line 278 of file marginalTargetedInference_tpl.h.

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

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

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◆ hardEvidence()

template<GUM_Numeric GUM_SCALAR>

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

inherited

indicate for each node with hard evidence which value it took

Definition at line 574 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<GUM_Numeric GUM_SCALAR>

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 593 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<GUM_Numeric GUM_SCALAR>

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<GUM_Numeric GUM_SCALAR>

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_.

◆ hasEvidence() [3/3]

template<GUM_Numeric GUM_SCALAR>

bool gum::GraphicalModelInference< GUM_SCALAR >::hasEvidence ( std::string_view nodeName ) const

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 370 of file graphicalModelInference_tpl.h.

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

References hasEvidence(), and model().

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◆ hasHardEvidence() [1/2]

template<GUM_Numeric GUM_SCALAR>

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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◆ hasHardEvidence() [2/2]

template<GUM_Numeric GUM_SCALAR>

bool gum::GraphicalModelInference< GUM_SCALAR >::hasHardEvidence ( std::string_view nodeName ) const

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 376 of file graphicalModelInference_tpl.h.

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

References hasHardEvidence(), and model().

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◆ hasNoModel_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 643 of file graphicalModelInference_tpl.h.

                                                                {
    return _model_ == nullptr;
  }

References _model_.

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◆ hasSoftEvidence() [1/2]

template<GUM_Numeric GUM_SCALAR>

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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◆ hasSoftEvidence() [2/2]

template<GUM_Numeric GUM_SCALAR>

bool gum::GraphicalModelInference< GUM_SCALAR >::hasSoftEvidence ( std::string_view nodeName ) const

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 382 of file graphicalModelInference_tpl.h.

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

References hasSoftEvidence(), and model().

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◆ I() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

Mutual information between X and Y.

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

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

Exceptions

OperationNotAllowed in these cases

◆ I() [2/2]

template<GUM_Numeric GUM_SCALAR>

GUM_SCALAR gum::JointTargetedInference< GUM_SCALAR >::I	(	std::string_view	Xname,
		std::string_view	Yname )

Mutual information between X and Y.

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

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

Exceptions

OperationNotAllowed in these cases

◆ isInferenceDone()

template<GUM_Numeric GUM_SCALAR>

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 105 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<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedStructure state

Definition at line 93 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<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedTensor state

Definition at line 99 of file graphicalModelInference_tpl.h.

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

References _state_, and OutdatedTensors.

◆ isInferenceReady()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a ready state

Definition at line 87 of file graphicalModelInference_tpl.h.

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

References _state_, and ReadyForInference.

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

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◆ isInTargetMode()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

indicates whether the inference is in a target mode

Definition at line 226 of file marginalTargetedInference_tpl.h.

                                                                              {
    return _targeted_mode_;
  }

References _targeted_mode_.

◆ isJointTarget()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtual

return true if target is a joint target.

Definition at line 87 of file jointTargetedInference_tpl.h.

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

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

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◆ isTarget() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 93 of file marginalTargetedInference_tpl.h.

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

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

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

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◆ isTarget() [2/2]

template<GUM_Numeric GUM_SCALAR>

bool gum::MarginalTargetedInference< GUM_SCALAR >::isTarget ( std::string_view nodeName ) const

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 108 of file marginalTargetedInference_tpl.h.

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

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

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◆ isTargetedMode_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 330 of file marginalTargetedInference_tpl.h.

                                                                      {
    return _targeted_mode_;
  }

References _targeted_mode_.

◆ jointMutualInformation() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

Mutual information between targets.

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

Parameters

targets the NodeSet of the targeted variables

Definition at line 302 of file jointTargetedInference_tpl.h.

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

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

Referenced by jointMutualInformation().

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◆ jointMutualInformation() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

Mutual information between targets.

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

Parameters

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

Definition at line 352 of file jointTargetedInference_tpl.h.

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

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

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◆ jointPosterior()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtual

Compute the joint posterior of a set of nodes.

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

Parameters

nodes the set of nodes whose posterior joint probability is wanted

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

Exceptions

UndefinedElement if nodes is not in the targets

Definition at line 208 of file jointTargetedInference_tpl.h.

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

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

Referenced by gum::ASTjointProba< GUM_SCALAR >::eval(), gum::ASTposteriorProba< GUM_SCALAR >::eval(), evidenceJointImpact(), jointMutualInformation(), and posterior().

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◆ jointPosterior_() [1/2]

template<GUM_Numeric GUM_SCALAR>

virtual const Tensor< GUM_SCALAR > & gum::JointTargetedInference< GUM_SCALAR >::jointPosterior_ ( const NodeSet & set )

protectedpure virtual

asks derived classes for the joint posterior of a declared target set

Parameters

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

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Referenced by jointPosterior().

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◆ jointPosterior_() [2/2]

template<GUM_Numeric GUM_SCALAR>

virtual const Tensor< GUM_SCALAR > & gum::JointTargetedInference< GUM_SCALAR >::jointPosterior_	(	const NodeSet &	wanted_target,
		const NodeSet &	declared_target )

protectedpure virtual

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

Parameters

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

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

◆ jointTargets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexcept

returns the list of joint targets

returns the list of target sets

Definition at line 191 of file jointTargetedInference_tpl.h.

                                                                                          {
    return _joint_targets_;
  }

References _joint_targets_.

◆ makeInference()

template<GUM_Numeric GUM_SCALAR>

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 632 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::Counterfactual< GUM_ELEMENT >::counterFactualModel(), gum::Counterfactual< GUM_ELEMENT >::counterFactualModel(), gum::counterfactualModel(), gum::ASTjointProba< GUM_SCALAR >::eval(), gum::ASTposteriorProba< GUM_SCALAR >::eval(), 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<GUM_Numeric GUM_SCALAR>

virtual void gum::GraphicalModelInference< GUM_SCALAR >::makeInference_ ( )

protectedpure virtualinherited

called when the inference has to be performed effectively

Once the inference is done, fillPosterior_ can be called.

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::LoopySamplingInference< GUM_SCALAR, GibbsSampling >, gum::LoopySamplingInference< GUM_SCALAR, ImportanceSampling >, gum::LoopySamplingInference< GUM_SCALAR, MonteCarloSampling >, gum::LoopySamplingInference< GUM_SCALAR, WeightedSampling >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, gum::ShaferShenoyLIMIDInference< GUM_SCALAR >, gum::ShaferShenoyMRFInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

References makeInference_(), setOutdatedStructureState_(), and setOutdatedTensorsState_().

Referenced by makeInference(), and makeInference_().

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◆ model()

template<GUM_Numeric GUM_SCALAR>

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 127 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<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

returns the number of evidence entered into the Bayesian network

Definition at line 556 of file graphicalModelInference_tpl.h.

                                                                {
    return _evidence_.size();
  }

References _evidence_.

◆ nbrHardEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

returns the number of hard evidence entered into the Bayesian network

Definition at line 562 of file graphicalModelInference_tpl.h.

                                                                    {
    return _hard_evidence_nodes_.size();
  }

References _hard_evidence_nodes_.

◆ nbrJointTargets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexcept

returns the number of joint targets

returns the number of target sets

Definition at line 197 of file jointTargetedInference_tpl.h.

                                                                            {
    return _joint_targets_.size();
  }

References _joint_targets_.

◆ nbrSoftEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

returns the number of soft evidence entered into the Bayesian network

Definition at line 568 of file graphicalModelInference_tpl.h.

                                                                    {
    return _soft_evidence_nodes_.size();
  }

References _soft_evidence_nodes_.

◆ nbrTargets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the number of marginal targets

Definition at line 220 of file marginalTargetedInference_tpl.h.

                                                                          {
    return _targets_.size();
  }

References _targets_.

◆ onAllEvidenceErased_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::GraphicalModelInference< GUM_SCALAR >::onAllEvidenceErased_ ( bool contains_hard_evidence )

protectedpure virtualinherited

fired before all the evidence are erased

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, gum::ShaferShenoyLIMIDInference< GUM_SCALAR >, gum::ShaferShenoyMRFInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

References onAllEvidenceErased_().

Referenced by eraseAllEvidence(), and onAllEvidenceErased_().

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◆ onAllJointTargetsErased_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::JointTargetedInference< GUM_SCALAR >::onAllJointTargetsErased_ ( )

protectedpure virtual

fired before a all the joint targets are removed

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Referenced by eraseAllJointTargets(), and onModelChanged_().

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◆ onAllMarginalTargetsAdded_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::MarginalTargetedInference< GUM_SCALAR >::onAllMarginalTargetsAdded_ ( )

protectedpure virtualinherited

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

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Referenced by _setAllMarginalTargets_().

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◆ onAllMarginalTargetsErased_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::MarginalTargetedInference< GUM_SCALAR >::onAllMarginalTargetsErased_ ( )

protectedpure virtualinherited

fired before a all marginal targets are removed

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Referenced by eraseAllTargets().

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◆ onAllTargetsErased_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::JointTargetedInference< GUM_SCALAR >::onAllTargetsErased_ ( )

protectedpure virtual

fired before a all the marginal and joint targets are removed

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

◆ onEvidenceAdded_()

template<GUM_Numeric GUM_SCALAR>

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

protectedpure virtualinherited

fired after a new evidence is inserted

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, gum::ShaferShenoyLIMIDInference< GUM_SCALAR >, gum::ShaferShenoyMRFInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

References onEvidenceAdded_().

Referenced by addEvidence(), and onEvidenceAdded_().

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◆ onEvidenceChanged_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::GraphicalModelInference< GUM_SCALAR >::onEvidenceChanged_	(	const NodeId	id,
		bool	hasChangedSoftHard )

protectedpure virtualinherited

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

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, gum::ShaferShenoyLIMIDInference< GUM_SCALAR >, gum::ShaferShenoyMRFInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

References onEvidenceChanged_().

Referenced by chgEvidence(), and onEvidenceChanged_().

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◆ onEvidenceErased_()

template<GUM_Numeric GUM_SCALAR>

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

protectedpure virtualinherited

fired before an evidence is removed

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, gum::ShaferShenoyLIMIDInference< GUM_SCALAR >, gum::ShaferShenoyMRFInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

References onEvidenceErased_().

Referenced by eraseEvidence(), and onEvidenceErased_().

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◆ onJointTargetAdded_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::JointTargetedInference< GUM_SCALAR >::onJointTargetAdded_ ( const NodeSet & set )

protectedpure virtual

fired after a new joint target is inserted

Parameters

set	The set of target variable's ids.

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Referenced by addJointTarget().

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◆ onJointTargetErased_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::JointTargetedInference< GUM_SCALAR >::onJointTargetErased_ ( const NodeSet & set )

protectedpure virtual

fired before a joint target is removed

Parameters

set	The set of target variable's ids.

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Referenced by eraseJointTarget().

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◆ onMarginalTargetAdded_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::MarginalTargetedInference< GUM_SCALAR >::onMarginalTargetAdded_ ( const NodeId id )

protectedpure virtualinherited

fired after a new marginal target is inserted

Parameters

id	The target variable's id.

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Referenced by addAllTargets(), and addTarget().

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◆ onMarginalTargetErased_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::MarginalTargetedInference< GUM_SCALAR >::onMarginalTargetErased_ ( const NodeId id )

protectedpure virtualinherited

fired before a marginal target is removed

Parameters

id	The target variable's id.

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Referenced by eraseTarget().

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◆ onModelChanged_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtual

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Reimplemented in gum::LazyPropagation< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Definition at line 75 of file jointTargetedInference_tpl.h.

                                                                                     {
    MarginalTargetedInference< GUM_SCALAR >::onModelChanged_(bn);
    onAllJointTargetsErased_();
    _joint_targets_.clear();
  }

References _joint_targets_, onAllJointTargetsErased_(), and gum::MarginalTargetedInference< GUM_SCALAR >::onModelChanged_().

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◆ onStateChanged_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::GraphicalModelInference< GUM_SCALAR >::onStateChanged_ ( )

protectedpure virtualinherited

fired when the stage is changed

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, gum::ShaferShenoyLIMIDInference< GUM_SCALAR >, gum::ShaferShenoyMRFInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

References onStateChanged_().

Referenced by onStateChanged_(), and setState_().

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◆ posterior() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtual

Computes and returns the posterior of a node.

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

Parameters

node	the node for which we need a posterior probability

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

Exceptions

UndefinedElement if node is not in the set of targets

Reimplemented from gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 239 of file jointTargetedInference_tpl.h.

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

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

Referenced by gum::Counterfactual< GUM_ELEMENT >::counterFactualModel(), gum::Counterfactual< GUM_ELEMENT >::counterFactualModel(), gum::counterfactualModel(), gum::ASTjointProba< GUM_SCALAR >::eval(), and posterior().

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◆ posterior() [2/2]

template<GUM_Numeric GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::JointTargetedInference< GUM_SCALAR >::posterior ( std::string_view nodeName )

finalvirtual

Computes and returns the posterior of a node.

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

Parameters

node	the node for which we need a posterior probability

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

Exceptions

UndefinedElement if node is not in the set of targets

Reimplemented from gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 247 of file jointTargetedInference_tpl.h.

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

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

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◆ posterior_()

template<GUM_Numeric GUM_SCALAR>

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

protectedpure virtualinherited

asks derived classes for the posterior of a given variable

Parameters

id	The variable's id.

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

Referenced by posterior().

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◆ prepareInference()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

prepare the internal inference structures for the next inference

Definition at line 616 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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◆ setBN()

template<GUM_Numeric GUM_SCALAR>

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

virtualinherited

assigns a new BN to the inference engine

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

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

Definition at line 82 of file BayesNetInference_tpl.h.

                                                                               {
    this->setModel_(bn);
  }

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

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◆ setModel_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 137 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<GUM_Numeric GUM_SCALAR>

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

protectedinherited

assigns a model during the inference engine construction

Definition at line 147 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<GUM_Numeric GUM_SCALAR>

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 603 of file graphicalModelInference_tpl.h.

                                                                         {
    setState_(StateOfInference::OutdatedStructure);
  }

References OutdatedStructure, and setState_().

Referenced by makeInference_().

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◆ setOutdatedTensorsState_()

template<GUM_Numeric GUM_SCALAR>

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 610 of file graphicalModelInference_tpl.h.

                                                                       {
    setState_(StateOfInference::OutdatedTensors);
  }

References OutdatedTensors, and setState_().

Referenced by makeInference_().

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◆ setState_()

template<GUM_Numeric GUM_SCALAR>

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 118 of file graphicalModelInference_tpl.h.

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

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

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◆ setTargetedMode_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 335 of file marginalTargetedInference_tpl.h.

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

References _targeted_mode_, and _targets_.

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

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◆ softEvidenceNodes()

template<GUM_Numeric GUM_SCALAR>

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 587 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<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the state of the inference engine

Definition at line 112 of file graphicalModelInference_tpl.h.

                                                                  {
    return _state_;
  }

References _state_.

Referenced by setState_().

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◆ targets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the list of marginal targets

Definition at line 214 of file marginalTargetedInference_tpl.h.

                                                                                 {
    return _targets_;
  }

References _targets_.

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

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◆ unnormalizedJointPosterior_() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

protectedpure virtual

returns a fresh unnormalized joint posterior of a given set of variables

Parameters

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

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

◆ unnormalizedJointPosterior_() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

protectedpure virtual

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

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

◆ updateOutdatedStructure_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::GraphicalModelInference< GUM_SCALAR >::updateOutdatedStructure_ ( )

protectedpure virtualinherited

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_.

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, gum::ShaferShenoyLIMIDInference< GUM_SCALAR >, gum::ShaferShenoyMRFInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

References updateOutdatedStructure_().

Referenced by prepareInference(), and updateOutdatedStructure_().

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◆ updateOutdatedTensors_()

template<GUM_Numeric GUM_SCALAR>

virtual void gum::GraphicalModelInference< GUM_SCALAR >::updateOutdatedTensors_ ( )

protectedpure virtualinherited

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_.

Implemented in gum::LazyPropagation< GUM_SCALAR >, gum::LoopyBeliefPropagation< GUM_SCALAR >, gum::SamplingInference< GUM_SCALAR >, gum::ShaferShenoyInference< GUM_SCALAR >, gum::ShaferShenoyLIMIDInference< GUM_SCALAR >, gum::ShaferShenoyMRFInference< GUM_SCALAR >, and gum::VariableElimination< GUM_SCALAR >.

References updateOutdatedTensors_().

Referenced by prepareInference(), and updateOutdatedTensors_().

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◆ VI() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

Variation of information between X and Y.

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

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

Exceptions

OperationNotAllowed in these cases

◆ VI() [2/2]

template<GUM_Numeric GUM_SCALAR>

GUM_SCALAR gum::JointTargetedInference< GUM_SCALAR >::VI	(	std::string_view	Xname,
		std::string_view	Yname )

Variation of information between X and Y.

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

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

Exceptions

OperationNotAllowed in these cases

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

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Member Data Documentation

◆ _joint_targets_

template<GUM_Numeric GUM_SCALAR>

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

private

the set of joint targets

Definition at line 324 of file jointTargetedInference.h.

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

◆ _targeted_mode_

template<GUM_Numeric GUM_SCALAR>

bool gum::MarginalTargetedInference< GUM_SCALAR >::_targeted_mode_

privateinherited

whether the actual targets are default

Definition at line 262 of file marginalTargetedInference.h.

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

◆ _targets_

template<GUM_Numeric GUM_SCALAR>

NodeSet gum::MarginalTargetedInference< GUM_SCALAR >::_targets_

privateinherited

the set of marginal targets

Definition at line 265 of file marginalTargetedInference.h.

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

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

agrum/BN/inference/tools/BayesNetInference.h
agrum/BN/inference/tools/jointTargetedInference.h
agrum/BN/inference/tools/jointTargetedInference_tpl.h

Public Types

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Detailed Description

Member Enumeration Documentation

◆ StateOfInference

Constructor & Destructor Documentation

◆ JointTargetedInference()

◆ ~JointTargetedInference()

Member Function Documentation

◆ _setAllMarginalTargets_()

◆ addAllTargets()

◆ addEvidence() [1/8]

◆ addEvidence() [2/8]

◆ addEvidence() [3/8]

◆ addEvidence() [4/8]

◆ addEvidence() [5/8]

◆ addEvidence() [6/8]

◆ addEvidence() [7/8]

◆ addEvidence() [8/8]

◆ addJointTarget()

◆ addListOfEvidence()

◆ addSetOfEvidence()

◆ addTarget() [1/2]

◆ addTarget() [2/2]

◆ BN()

◆ chgEvidence() [1/7]

◆ chgEvidence() [2/7]

◆ chgEvidence() [3/7]

◆ chgEvidence() [4/7]

◆ chgEvidence() [5/7]

◆ chgEvidence() [6/7]

◆ chgEvidence() [7/7]

◆ clear()

◆ domainSizes()

◆ eraseAllEvidence()

◆ eraseAllJointTargets()

◆ eraseAllMarginalTargets()

◆ eraseAllTargets()

◆ eraseEvidence() [1/2]

◆ eraseEvidence() [2/2]

◆ eraseJointTarget()

◆ eraseTarget() [1/2]

◆ eraseTarget() [2/2]

◆ evidence()

◆ evidenceImpact() [1/2]

◆ evidenceImpact() [2/2]

◆ evidenceJointImpact() [1/2]

◆ evidenceJointImpact() [2/2]

◆ H() [1/2]

◆ H() [2/2]

◆ hardEvidence()

◆ hardEvidenceNodes()

◆ hasEvidence() [1/3]

◆ hasEvidence() [2/3]

◆ hasEvidence() [3/3]

◆ hasHardEvidence() [1/2]

◆ hasHardEvidence() [2/2]

◆ hasNoModel_()

◆ hasSoftEvidence() [1/2]

◆ hasSoftEvidence() [2/2]

◆ I() [1/2]

◆ I() [2/2]

◆ isInferenceDone()

◆ isInferenceOutdatedStructure()

◆ isInferenceOutdatedTensors()

◆ isInferenceReady()

◆ isInTargetMode()

◆ isJointTarget()

◆ isTarget() [1/2]

◆ isTarget() [2/2]

◆ isTargetedMode_()

◆ jointMutualInformation() [1/2]

◆ jointMutualInformation() [2/2]

◆ jointPosterior()

◆ jointPosterior_() [1/2]

◆ jointPosterior_() [2/2]

◆ jointTargets()