gum::MarginalTargetedMRFInference< GUM_SCALAR > Class Template Reference

<agrum/MRF/inference/marginalTargetedMRFInference.h> More...

#include <marginalTargetedMRFInference.h>

Inheritance diagram for gum::MarginalTargetedMRFInference< GUM_SCALAR >:

Collaboration diagram for gum::MarginalTargetedMRFInference< GUM_SCALAR >:

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

Public Member Functions
Tensor< GUM_SCALAR >	evidenceImpact (NodeId target, const NodeSet &evs)
	Create a gum::Tensor for P(target|evs) (for all instanciation of target and evs).
Tensor< GUM_SCALAR >	evidenceImpact (const std::string &target, const std::vector< std::string > &evs)
	Create a gum::Tensor for P(target|evs) (for all instanciation of target and evs).
Constructors / Destructors
	MarginalTargetedMRFInference (const IMarkovRandomField< GUM_SCALAR > *mn)
	default constructor
virtual	~MarginalTargetedMRFInference ()
	destructor
Probability computations
virtual const Tensor< GUM_SCALAR > &	posterior (NodeId node)
	Computes and returns the posterior of a node.
virtual const Tensor< GUM_SCALAR > &	posterior (const std::string &nodeName)
	Computes and returns the posterior of a node.
Targets
virtual void	eraseAllTargets ()
	Clear all previously defined targets.
virtual void	addAllTargets () final
	adds all nodes as targets
virtual void	addTarget (NodeId target) final
	Add a marginal target to the list of targets.
virtual void	addTarget (const std::string &nodeName) final
	Add a marginal target to the list of targets.
virtual void	eraseTarget (NodeId target) final
	removes an existing (marginal) target
virtual void	eraseTarget (const std::string &nodeName) final
	removes an existing (marginal) target
virtual bool	isTarget (NodeId node) const final
	return true if variable is a (marginal) target
virtual bool	isTarget (const std::string &nodeName) const final
	return true if variable is a (marginal) target
virtual Size	nbrTargets () const noexcept final
	returns the number of marginal targets
virtual const NodeSet &	targets () const noexcept final
	returns the list of marginal targets
Information Theory related functions
virtual GUM_SCALAR	H (NodeId X) final
	Entropy Compute Shanon's entropy of a node given the observation.
virtual GUM_SCALAR	H (const std::string &nodeName) final
	Entropy Compute Shanon's entropy of a node given the observation.
Accessors / Modifiers
virtual const GraphicalModel &	model () const final
	Returns a constant reference over the IBayesNet referenced by this class.
virtual const NodeProperty< Size > &	domainSizes () const final
	get the domain sizes of the random variables of the model
virtual bool	isInferenceReady () const noexcept final
	returns whether the inference object is in a ready state
virtual bool	isInferenceOutdatedStructure () const noexcept final
	returns whether the inference object is in a OutdatedStructure state
virtual bool	isInferenceOutdatedTensors () const noexcept final
	returns whether the inference object is in a OutdatedTensor state
virtual bool	isInferenceDone () const noexcept final
	returns whether the inference object is in a InferenceDone state
virtual void	prepareInference () final
	prepare the internal inference structures for the next inference
virtual void	makeInference () final
	perform the heavy computations needed to compute the targets' posteriors
virtual void	clear ()
	clears all the data structures allocated for the last inference
virtual StateOfInference	state () const noexcept final
	returns the state of the inference engine
Evidence
virtual void	addEvidence (NodeId id, const Idx val) final
	adds a new hard evidence on node id
virtual void	addEvidence (const std::string &nodeName, const Idx val) final
	adds a new hard evidence on node named nodeName
virtual void	addEvidence (NodeId id, const std::string &label) final
	adds a new hard evidence on node id
virtual void	addEvidence (const std::string &nodeName, const std::string &label) final
	adds a new hard evidence on node named nodeName
virtual void	addEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node named nodeName (might be soft or hard)
virtual void	addEvidence (const Tensor< GUM_SCALAR > &pot) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addEvidence (Tensor< GUM_SCALAR > &&pot) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addSetOfEvidence (const Set< const Tensor< GUM_SCALAR > * > &potset) final
	adds a new set of evidence
virtual void	addListOfEvidence (const List< const Tensor< GUM_SCALAR > * > &potlist) final
	adds a new list of evidence
virtual void	chgEvidence (NodeId id, const Idx val) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (const std::string &nodeName, const Idx val) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (NodeId id, const std::string &label) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (const std::string &nodeName, const std::string &label) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	chgEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	chgEvidence (const Tensor< GUM_SCALAR > &pot) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	eraseAllEvidence () final
	removes all the evidence entered into the network
virtual void	eraseEvidence (NodeId id) final
	removed the evidence, if any, corresponding to node id
virtual void	eraseEvidence (const std::string &nodeName) final
	removed the evidence, if any, corresponding to node of name nodeName
virtual bool	hasEvidence () const final
	indicates whether some node(s) have received evidence
virtual bool	hasEvidence (NodeId id) const final
	indicates whether node id has received an evidence
virtual bool	hasEvidence (const std::string &nodeName) const final
	indicates whether node id has received an evidence
virtual bool	hasHardEvidence (NodeId id) const final
	indicates whether node id has received a hard evidence
virtual bool	hasHardEvidence (const std::string &nodeName) const final
	indicates whether node id has received a hard evidence
virtual bool	hasSoftEvidence (NodeId id) const final
	indicates whether node id has received a soft evidence
virtual bool	hasSoftEvidence (const std::string &nodeName) const final
	indicates whether node id has received a soft evidence
virtual Size	nbrEvidence () const final
	returns the number of evidence entered into the Bayesian network
virtual Size	nbrHardEvidence () const final
	returns the number of hard evidence entered into the Bayesian network
virtual Size	nbrSoftEvidence () const final
	returns the number of soft evidence entered into the Bayesian network
const NodeProperty< const Tensor< GUM_SCALAR > * > &	evidence () const
	returns the set of evidence
const NodeSet &	softEvidenceNodes () const
	returns the set of nodes with soft evidence
const NodeSet &	hardEvidenceNodes () const
	returns the set of nodes with hard evidence
const NodeProperty< Idx > &	hardEvidence () const
	indicate for each node with hard evidence which value it took

Protected Member Functions
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 MRF are added as marginal targets
virtual void	onAllMarginalTargetsErased_ ()=0
	fired before a all marginal targets are removed
virtual void	onModelChanged_ (const GraphicalModel *mn)
	fired after a new Markov net has been assigned to the engine
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 Markov net as targets
Tensor< GUM_SCALAR >	_createHardEvidence_ (NodeId id, Idx val) const
	create the internal structure for a hard evidence
bool	_isHardEvidence_ (const Tensor< GUM_SCALAR > &pot, Idx &val) const
	checks whether a tensor corresponds to a hard evidence or not
void	_computeDomainSizes_ ()
	computes the domain sizes of the random variables

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

Accessors / Modifiers
friend	MarginalTargetedMRFInference< GUM_SCALAR >
	allow JointInference to access the single targets and inference states
friend	JointTargetedMRFInference< GUM_SCALAR >
	assigns a new MRF to the inference engine
friend	EvidenceMRFInference< GUM_SCALAR >
	assigns a new MRF to the inference engine
virtual void	setMRF (const IMarkovRandomField< GUM_SCALAR > *mrf)
	assigns a new MRF to the inference engine
virtual const IMarkovRandomField< GUM_SCALAR > &	MRF () const final
	Returns a constant reference over the IMarkovRandomField referenced by this class.
void	_setMRFDuringConstruction_ (const IMarkovRandomField< GUM_SCALAR > *mn)
	assigns a MRF during the inference engine construction

Detailed Description

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

<agrum/MRF/inference/marginalTargetedMRFInference.h>

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

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

Definition at line 76 of file marginalTargetedMRFInference.h.

Member Enumeration Documentation

StateOfInference

template<typename GUM_SCALAR>

enum class gum::GraphicalModelInference::StateOfInference

stronginherited

current state of the inference

graphicalModelInference can be in one of 4 different states:

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

Enumerator
OutdatedStructure
OutdatedTensors
ReadyForInference
Done

Definition at line 127 of file graphicalModelInference.h.

{ OutdatedStructure, OutdatedTensors, ReadyForInference, Done };

Constructor & Destructor Documentation

MarginalTargetedMRFInference()

template<typename GUM_SCALAR>

gum::MarginalTargetedMRFInference< GUM_SCALAR >::MarginalTargetedMRFInference ( const IMarkovRandomField< GUM_SCALAR > * mn )

explicit

default constructor

Warning

By default, all the nodes of the Markov net are targets.

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

Definition at line 55 of file marginalTargetedMRFInference_tpl.h.

                                                  : MRFInference< GUM_SCALAR >(mn) {
    // assign a MRF if this has not been done before (due to virtual inheritance)
    if (this->hasNoModel_()) { MRFInference< GUM_SCALAR >::_setMRFDuringConstruction_(mn); }
 
    // sets all the nodes as targets
    if (mn != nullptr) {
      _targeted_mode_ = false;
      _targets_       = mn->graph().asNodeSet();
    }
 
    GUM_CONSTRUCTOR(MarginalTargetedMRFInference);
  }

References MarginalTargetedMRFInference(), gum::MRFInference< GUM_SCALAR >::MRFInference(), gum::MRFInference< GUM_SCALAR >::_setMRFDuringConstruction_(), _targeted_mode_, _targets_, gum::NodeGraphPart::asNodeSet(), gum::UGmodel::graph(), and gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_().

Referenced by gum::JointTargetedMRFInference< GUM_SCALAR >::JointTargetedMRFInference(), MarginalTargetedMRFInference(), and ~MarginalTargetedMRFInference().

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~MarginalTargetedMRFInference()

template<typename GUM_SCALAR>

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

virtual

destructor

Definition at line 71 of file marginalTargetedMRFInference_tpl.h.

                                                                            {
    GUM_DESTRUCTOR(MarginalTargetedMRFInference);
  }

References MarginalTargetedMRFInference().

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Member Function Documentation

_computeDomainSizes_()

template<typename GUM_SCALAR>

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

privateinherited

computes the domain sizes of the random variables

Definition at line 162 of file graphicalModelInference_tpl.h.

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

References _domain_sizes_, _model_, and hasNoModel_().

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

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_createHardEvidence_()

template<typename GUM_SCALAR>

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

privateinherited

create the internal structure for a hard evidence

Definition at line 184 of file graphicalModelInference_tpl.h.

                                                                                                {
    // check that it is possible to create the evidence
    if (_model_ == nullptr)
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");
 
    if (!_model_->exists(id)) { GUM_ERROR(UndefinedElement, id << " is not a NodeId in the model") }
 
    if (_model_->variable(id).domainSize() <= val) {
      GUM_ERROR(InvalidArgument,
                "node " << _model_->variable(id) << " has fewer possible values than " << val);
    }
 
    return Tensor< GUM_SCALAR >::deterministicTensor(_model_->variable(id), val);
  }

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

Referenced by addEvidence(), and chgEvidence().

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_isHardEvidence_()

template<typename GUM_SCALAR>

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

privateinherited

checks whether a tensor corresponds to a hard evidence or not

Definition at line 203 of file graphicalModelInference_tpl.h.

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

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

Referenced by addEvidence(), and chgEvidence().

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_setAllMarginalTargets_()

template<typename GUM_SCALAR>

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

private

sets all the nodes of the Markov net as targets

Definition at line 222 of file marginalTargetedMRFInference_tpl.h.

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

References _targets_, gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), gum::MRFInference< GUM_SCALAR >::MRF(), and onAllMarginalTargetsAdded_().

Referenced by onModelChanged_().

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_setMRFDuringConstruction_()

template<typename GUM_SCALAR>

void gum::MRFInference< GUM_SCALAR >::_setMRFDuringConstruction_ ( const IMarkovRandomField< GUM_SCALAR > * mn )

privateinherited

assigns a MRF during the inference engine construction

Definition at line 87 of file MRFInference_tpl.h.

                                                  {
    this->setModelDuringConstruction_(mn);
  }

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

Referenced by gum::EvidenceMRFInference< GUM_SCALAR >::EvidenceMRFInference(), gum::JointTargetedMRFInference< GUM_SCALAR >::JointTargetedMRFInference(), and gum::MarginalTargetedMRFInference< GUM_SCALAR >::MarginalTargetedMRFInference().

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addAllTargets()

template<typename GUM_SCALAR>

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

finalvirtual

adds all nodes as targets

Definition at line 143 of file marginalTargetedMRFInference_tpl.h.

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

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

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addEvidence() [1/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

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

Definition at line 235 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [2/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

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

Definition at line 249 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [3/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 281 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [4/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 323 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addEvidence() [5/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

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

Definition at line 229 of file graphicalModelInference_tpl.h.

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

References _createHardEvidence_(), and addEvidence().

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

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addEvidence() [6/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

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

Definition at line 242 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [7/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 257 of file graphicalModelInference_tpl.h.

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

References _model_, addEvidence(), and GUM_ERROR.

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addEvidence() [8/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 288 of file graphicalModelInference_tpl.h.

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

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

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addListOfEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new list of evidence

Exceptions

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

Definition at line 330 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addSetOfEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new set of evidence

Exceptions

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

Definition at line 338 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addTarget() [1/2]

template<typename GUM_SCALAR>

void gum::MarginalTargetedMRFInference< GUM_SCALAR >::addTarget ( const std::string & nodeName )

finalvirtual

Add a marginal target to the list of targets.

Exceptions

UndefinedElement

if target is not a NodeId in the Markov net

Definition at line 163 of file marginalTargetedMRFInference_tpl.h.

                                                                                      {
    // check if the node belongs to the Markov random field
    if (this->hasNoModel_())
      GUM_ERROR(NullElement,
                "No Markov net has been assigned to the "
                "inference algorithm");
 
    addTarget(this->MRF().idFromName(nodeName));
  }

References addTarget(), GUM_ERROR, gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), and gum::MRFInference< GUM_SCALAR >::MRF().

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addTarget() [2/2]

template<typename GUM_SCALAR>

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

finalvirtual

Add a marginal target to the list of targets.

Exceptions

UndefinedElement

if target is not a NodeId in the Markov net

Definition at line 121 of file marginalTargetedMRFInference_tpl.h.

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

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

Referenced by addTarget(), and evidenceImpact().

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chgEvidence() [1/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 397 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [2/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 411 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [3/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 445 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [4/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 452 of file graphicalModelInference_tpl.h.

                                                                                         {
    // check if the tensor corresponds to an evidence
    if (pot.nbrDim() != 1) {
      GUM_ERROR(InvalidArgument, pot << " is not a mono-dimensional tensor.")
    }
    if (_model_ == nullptr)
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");
 
    NodeId id = _model_->nodeId(pot.variable(0));
 
    if (!hasEvidence(id)) {
      GUM_ERROR(InvalidArgument, id << " has no evidence. Please use addEvidence().")
    }
 
    // check whether we have a hard evidence (and also check whether the
    // tensor only contains 0 (in this case, this will automatically raise
    // an exception) )
    Idx  val;
    bool is_hard_evidence = _isHardEvidence_(pot, val);
 
    // modify the evidence already stored
    const Tensor< GUM_SCALAR >* localPot = _evidence_[id];
    Instantiation               I(pot);
    for (I.setFirst(); !I.end(); I.inc()) {
      localPot->set(I, pot[I]);
    }
 
    // the inference state will be different
    // whether evidence change from Hard to Soft or not.
    bool hasChangedSoftHard = false;
 
    if (is_hard_evidence) {
      if (!hasHardEvidence(id)) {
        hasChangedSoftHard = true;
        _hard_evidence_.insert(id, val);
        _hard_evidence_nodes_.insert(id);
        _soft_evidence_nodes_.erase(id);
      } else {
        _hard_evidence_[id] = val;
      }
    } else {
      if (hasHardEvidence(id)) {   // evidence was hard
        _hard_evidence_.erase(id);
        _hard_evidence_nodes_.erase(id);
        _soft_evidence_nodes_.insert(id);
        hasChangedSoftHard = true;
      }
    }
 
    if (hasChangedSoftHard) {
      setState_(StateOfInference::OutdatedStructure);
    } else {
      if (!isInferenceOutdatedStructure()) { setState_(StateOfInference::OutdatedTensors); }
    }
 
    onEvidenceChanged_(id, hasChangedSoftHard);
  }

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

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chgEvidence() [5/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 391 of file graphicalModelInference_tpl.h.

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

References _createHardEvidence_(), and chgEvidence().

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

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chgEvidence() [6/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 404 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [7/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 420 of file graphicalModelInference_tpl.h.

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

References _model_, chgEvidence(), and GUM_ERROR.

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clear()

template<typename GUM_SCALAR>

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

virtualinherited

clears all the data structures allocated for the last inference

Reimplemented in gum::ShaferShenoyLIMIDInference< GUM_SCALAR >.

Definition at line 155 of file graphicalModelInference_tpl.h.

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

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

Referenced by setModel_().

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domainSizes()

template<typename GUM_SCALAR>

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

finalvirtualinherited

get the domain sizes of the random variables of the model

Definition at line 173 of file graphicalModelInference_tpl.h.

                                                                                              {
    return _domain_sizes_;
  }

References _domain_sizes_.

eraseAllEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

removes all the evidence entered into the network

Definition at line 540 of file graphicalModelInference_tpl.h.

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

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

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

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eraseAllTargets()

template<typename GUM_SCALAR>

INLINE void gum::MarginalTargetedMRFInference< GUM_SCALAR >::eraseAllTargets ( )

virtual

Clear all previously defined targets.

Reimplemented in gum::JointTargetedMRFInference< GUM_SCALAR >.

Definition at line 110 of file marginalTargetedMRFInference_tpl.h.

                                                                          {
    onAllMarginalTargetsErased_();
 
    _targets_.clear();
    setTargetedMode_();   // does nothing if already in targeted mode
 
    this->setState_(MRFInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
  }

References _targets_, onAllMarginalTargetsErased_(), gum::GraphicalModelInference< GUM_SCALAR >::OutdatedStructure, gum::GraphicalModelInference< GUM_SCALAR >::setState_(), and setTargetedMode_().

Referenced by gum::JointTargetedMRFInference< GUM_SCALAR >::eraseAllMarginalTargets(), and evidenceImpact().

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eraseEvidence() [1/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Definition at line 534 of file graphicalModelInference_tpl.h.

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

References eraseEvidence(), and model().

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eraseEvidence() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

removed the evidence, if any, corresponding to node id

Definition at line 514 of file graphicalModelInference_tpl.h.

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

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

Referenced by eraseEvidence().

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eraseTarget() [1/2]

template<typename GUM_SCALAR>

void gum::MarginalTargetedMRFInference< GUM_SCALAR >::eraseTarget ( const std::string & nodeName )

finalvirtual

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 198 of file marginalTargetedMRFInference_tpl.h.

                                                                                        {
    // check if the node belongs to the Markov random field
    if (this->hasNoModel_())
      GUM_ERROR(NullElement,
                "No Markov net has been assigned to the "
                "inference algorithm");
 
    eraseTarget(this->MRF().idFromName(nodeName));
  }

References eraseTarget(), GUM_ERROR, gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), and gum::MRFInference< GUM_SCALAR >::MRF().

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eraseTarget() [2/2]

template<typename GUM_SCALAR>

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

finalvirtual

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 175 of file marginalTargetedMRFInference_tpl.h.

                                                                            {
    // check if the node belongs to the Markov random field
    if (this->hasNoModel_())
      GUM_ERROR(NullElement,
                "No Markov net has been assigned to the "
                "inference algorithm");
 
    if (!this->MRF().graph().exists(target)) {
      GUM_ERROR(UndefinedElement, target << " is not a NodeId in the Markov random field")
    }
 
 
    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_(MRFInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
    }
  }

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

Referenced by eraseTarget().

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evidence()

template<typename GUM_SCALAR>

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

inherited

returns the set of evidence

Definition at line 587 of file graphicalModelInference_tpl.h.

                                                                     {
    return _evidence_;
  }

References _evidence_.

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

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evidenceImpact() [1/2]

template<typename GUM_SCALAR>

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

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

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 312 of file marginalTargetedMRFInference_tpl.h.

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

References evidenceImpact(), and gum::MRFInference< GUM_SCALAR >::MRF().

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evidenceImpact() [2/2]

template<typename GUM_SCALAR>

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

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

Warning

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

Parameters

mn	the MarkovRandomField
target	the nodeId of the targetted variable
evs	the vector of nodeId of the observed variables

Returns

a Tensor

Definition at line 274 of file marginalTargetedMRFInference_tpl.h.

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

References gum::GraphicalModelInference< GUM_SCALAR >::addEvidence(), addTarget(), 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(), gum::MRFInference< GUM_SCALAR >::MRF(), posterior(), gum::Instantiation::setFirst(), gum::Instantiation::setFirstVar(), and gum::Instantiation::val().

Referenced by evidenceImpact().

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H() [1/2]

template<typename GUM_SCALAR>

INLINE GUM_SCALAR gum::MarginalTargetedMRFInference< GUM_SCALAR >::H ( const std::string & nodeName )

finalvirtual

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

See also

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

Definition at line 268 of file marginalTargetedMRFInference_tpl.h.

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

References H(), and gum::MRFInference< GUM_SCALAR >::MRF().

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H() [2/2]

template<typename GUM_SCALAR>

INLINE GUM_SCALAR gum::MarginalTargetedMRFInference< GUM_SCALAR >::H ( NodeId X )

finalvirtual

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

See also

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

Definition at line 260 of file marginalTargetedMRFInference_tpl.h.

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

References posterior().

Referenced by H().

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hardEvidence()

template<typename GUM_SCALAR>

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

inherited

indicate for each node with hard evidence which value it took

Definition at line 580 of file graphicalModelInference_tpl.h.

                                                                                              {
    return _hard_evidence_;
  }

References _hard_evidence_.

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

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hardEvidenceNodes()

template<typename GUM_SCALAR>

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

inherited

returns the set of nodes with hard evidence

the set of nodes that received hard evidence

Definition at line 599 of file graphicalModelInference_tpl.h.

                                                                                       {
    return _hard_evidence_nodes_;
  }

References _hard_evidence_nodes_.

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

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hasEvidence() [1/3]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether some node(s) have received evidence

Definition at line 346 of file graphicalModelInference_tpl.h.

                                                                       {
    return !_evidence_.empty();
  }

References _evidence_.

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

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hasEvidence() [2/3]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 371 of file graphicalModelInference_tpl.h.

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

References hasEvidence(), and model().

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hasEvidence() [3/3]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 352 of file graphicalModelInference_tpl.h.

                                                                                {
    return _evidence_.exists(id);
  }

References _evidence_.

hasHardEvidence() [1/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 378 of file graphicalModelInference_tpl.h.

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

References hasHardEvidence(), and model().

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hasHardEvidence() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 358 of file graphicalModelInference_tpl.h.

                                                                                    {
    return _hard_evidence_nodes_.exists(id);
  }

References _hard_evidence_nodes_.

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

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hasNoModel_()

template<typename GUM_SCALAR>

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

inlineprotectedinherited

Definition at line 542 of file graphicalModelInference.h.

{ return _model_ == nullptr; };

References _model_.

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

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hasSoftEvidence() [1/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 385 of file graphicalModelInference_tpl.h.

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

References hasSoftEvidence(), and model().

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hasSoftEvidence() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 364 of file graphicalModelInference_tpl.h.

                                                                                    {
    return _soft_evidence_nodes_.exists(id);
  }

References _soft_evidence_nodes_.

Referenced by hasSoftEvidence().

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isInferenceDone()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a InferenceDone state

The inference object is in a done state when the posteriors can be retrieved without performing a new inference, i.e., all the heavy computations have already been performed. Typically, in a junction tree algorithm, this corresponds to a situation in which all the messages needed in the JT have been computed and sent.

Definition at line 104 of file graphicalModelInference_tpl.h.

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

References _state_, and Done.

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

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isInferenceOutdatedStructure()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedStructure state

Definition at line 92 of file graphicalModelInference_tpl.h.

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

References _state_, and OutdatedStructure.

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

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isInferenceOutdatedTensors()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedTensor state

Definition at line 98 of file graphicalModelInference_tpl.h.

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

References _state_, and OutdatedTensors.

isInferenceReady()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a ready state

Definition at line 86 of file graphicalModelInference_tpl.h.

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

References _state_, and ReadyForInference.

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

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isTarget() [1/2]

template<typename GUM_SCALAR>

INLINE bool gum::MarginalTargetedMRFInference< GUM_SCALAR >::isTarget ( const std::string & nodeName ) const

finalvirtual

return true if variable is a (marginal) target

Definition at line 104 of file marginalTargetedMRFInference_tpl.h.

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

References isTarget(), and gum::MRFInference< GUM_SCALAR >::MRF().

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isTarget() [2/2]

template<typename GUM_SCALAR>

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

finalvirtual

return true if variable is a (marginal) target

Definition at line 88 of file marginalTargetedMRFInference_tpl.h.

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

References _targets_, GUM_ERROR, gum::GraphicalModelInference< GUM_SCALAR >::hasNoModel_(), and gum::MRFInference< GUM_SCALAR >::MRF().

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

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isTargetedMode_()

template<typename GUM_SCALAR>

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

protected

Definition at line 320 of file marginalTargetedMRFInference_tpl.h.

                                                                                {
    return _targeted_mode_;
  }

References _targeted_mode_.

makeInference()

template<typename GUM_SCALAR>

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

finalvirtualinherited

perform the heavy computations needed to compute the targets' posteriors

In a Junction tree propagation scheme, for instance, the heavy computations are those of the messages sent in the JT. This is precisely what makeInference should compute. Later, the computations of the posteriors can be done "lightly" by multiplying and projecting those messages.

Definition at line 638 of file graphicalModelInference_tpl.h.

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

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

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

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makeInference_()

template<typename GUM_SCALAR>

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<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

UndefinedElement

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

Definition at line 126 of file graphicalModelInference_tpl.h.

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

References _model_, and GUM_ERROR.

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

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MRF()

template<typename GUM_SCALAR>

INLINE const IMarkovRandomField< GUM_SCALAR > & gum::MRFInference< GUM_SCALAR >::MRF ( ) const

finalvirtualinherited

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

Exceptions

UndefinedElement

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

Definition at line 75 of file MRFInference_tpl.h.

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

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

Referenced by gum::MarginalTargetedMRFInference< GUM_SCALAR >::_setAllMarginalTargets_(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::addAllTargets(), gum::JointTargetedMRFInference< GUM_SCALAR >::addJointTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::addTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::addTarget(), gum::JointTargetedMRFInference< GUM_SCALAR >::eraseJointTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::evidenceImpact(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::evidenceImpact(), gum::JointTargetedMRFInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedMRFInference< GUM_SCALAR >::evidenceJointImpact(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::H(), gum::JointTargetedMRFInference< GUM_SCALAR >::isJointTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::isTarget(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::isTarget(), gum::JointTargetedMRFInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedMRFInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedMRFInference< GUM_SCALAR >::posterior(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::posterior(), and gum::JointTargetedMRFInference< GUM_SCALAR >::superForJointComputable_().

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nbrEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

returns the number of evidence entered into the Bayesian network

Definition at line 562 of file graphicalModelInference_tpl.h.

                                                                       {
    return _evidence_.size();
  }

References _evidence_.

nbrHardEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

returns the number of hard evidence entered into the Bayesian network

Definition at line 568 of file graphicalModelInference_tpl.h.

                                                                           {
    return _hard_evidence_nodes_.size();
  }

References _hard_evidence_nodes_.

nbrSoftEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

returns the number of soft evidence entered into the Bayesian network

Definition at line 574 of file graphicalModelInference_tpl.h.

                                                                           {
    return _soft_evidence_nodes_.size();
  }

References _soft_evidence_nodes_.

nbrTargets()

template<typename GUM_SCALAR>

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

finalvirtualnoexcept

returns the number of marginal targets

Definition at line 216 of file marginalTargetedMRFInference_tpl.h.

                                                                                    {
    return _targets_.size();
  }

References _targets_.

onAllEvidenceErased_()

template<typename 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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onAllMarginalTargetsAdded_()

template<typename GUM_SCALAR>

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

protectedpure virtual

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

Implemented in gum::ShaferShenoyMRFInference< GUM_SCALAR >.

Referenced by _setAllMarginalTargets_().

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onAllMarginalTargetsErased_()

template<typename GUM_SCALAR>

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

protectedpure virtual

fired before a all marginal targets are removed

Implemented in gum::ShaferShenoyMRFInference< GUM_SCALAR >.

Referenced by eraseAllTargets().

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onEvidenceAdded_()

template<typename 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<typename 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<typename 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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onMarginalTargetAdded_()

template<typename GUM_SCALAR>

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

protectedpure virtual

fired after a new marginal target is inserted

Parameters

id	The target variable's id.

Implemented in gum::ShaferShenoyMRFInference< GUM_SCALAR >.

Referenced by addAllTargets(), and addTarget().

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onMarginalTargetErased_()

template<typename GUM_SCALAR>

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

protectedpure virtual

fired before a marginal target is removed

Parameters

id	The target variable's id.

Implemented in gum::ShaferShenoyMRFInference< GUM_SCALAR >.

Referenced by eraseTarget().

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onModelChanged_()

template<typename GUM_SCALAR>

void gum::MarginalTargetedMRFInference< GUM_SCALAR >::onModelChanged_ ( const GraphicalModel * mn )

protectedvirtual

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Reimplemented in gum::JointTargetedMRFInference< GUM_SCALAR >, and gum::ShaferShenoyMRFInference< GUM_SCALAR >.

Definition at line 77 of file marginalTargetedMRFInference_tpl.h.

                                                                                           {
    _targeted_mode_ = true;
    _setAllMarginalTargets_();
  }

References _setAllMarginalTargets_(), and _targeted_mode_.

Referenced by gum::JointTargetedMRFInference< GUM_SCALAR >::onModelChanged_().

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onStateChanged_()

template<typename 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<typename GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::MarginalTargetedMRFInference< GUM_SCALAR >::posterior ( const std::string & nodeName )

virtual

Computes and returns the posterior of a node.

Returns

a const ref to the posterior probability of the node.

Parameters

nodeName

the anme of 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 in gum::JointTargetedMRFInference< GUM_SCALAR >.

Definition at line 252 of file marginalTargetedMRFInference_tpl.h.

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

References gum::MRFInference< GUM_SCALAR >::MRF(), and posterior().

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posterior() [2/2]

template<typename GUM_SCALAR>

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

virtual

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 in gum::JointTargetedMRFInference< GUM_SCALAR >.

Definition at line 236 of file marginalTargetedMRFInference_tpl.h.

                                                                                               {
    if (this->hardEvidenceNodes().contains(node)) { return *(this->evidence()[node]); }
 
    if (!isTarget(node)) {
      // throws UndefinedElement if var is not a target
      GUM_ERROR(UndefinedElement, node << " is not a target node")
    }
 
    if (!this->isInferenceDone()) { this->makeInference(); }
 
    return posterior_(node);
  }

References gum::GraphicalModelInference< GUM_SCALAR >::evidence(), GUM_ERROR, gum::GraphicalModelInference< GUM_SCALAR >::hardEvidenceNodes(), gum::GraphicalModelInference< GUM_SCALAR >::isInferenceDone(), isTarget(), gum::GraphicalModelInference< GUM_SCALAR >::makeInference(), and posterior_().

Referenced by evidenceImpact(), H(), gum::JointTargetedMRFInference< GUM_SCALAR >::posterior(), and posterior().

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posterior_()

template<typename GUM_SCALAR>

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

protectedpure virtual

asks derived classes for the posterior of a given variable

Parameters

id	The variable's id.

Implemented in gum::ShaferShenoyMRFInference< GUM_SCALAR >.

Referenced by posterior().

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prepareInference()

template<typename GUM_SCALAR>

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

finalvirtualinherited

prepare the internal inference structures for the next inference

Definition at line 622 of file graphicalModelInference_tpl.h.

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

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

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

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setModel_()

template<typename GUM_SCALAR>

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

protectedinherited

Definition at line 136 of file graphicalModelInference_tpl.h.

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

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

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

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setModelDuringConstruction_()

template<typename GUM_SCALAR>

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

protectedinherited

assigns a model during the inference engine construction

Definition at line 146 of file graphicalModelInference_tpl.h.

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

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

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

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setMRF()

template<typename GUM_SCALAR>

void gum::MRFInference< GUM_SCALAR >::setMRF ( const IMarkovRandomField< GUM_SCALAR > * mrf )

virtualinherited

assigns a new MRF to the inference engine

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

Warning

By default, all the nodes of the Markov net are targets.

note that, by aGrUM's rule, the mn is not copied into the inference engine but only referenced.

Definition at line 81 of file MRFInference_tpl.h.

                                                                                     {
    this->setModel_(mrf);
  }

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

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setOutdatedStructureState_()

template<typename GUM_SCALAR>

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

protectedinherited

put the inference into an outdated model structure state

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

Definition at line 609 of file graphicalModelInference_tpl.h.

                                                                                {
    setState_(StateOfInference::OutdatedStructure);
  }

References OutdatedStructure, and setState_().

Referenced by makeInference_().

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setOutdatedTensorsState_()

template<typename GUM_SCALAR>

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

protectedinherited

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

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

Definition at line 616 of file graphicalModelInference_tpl.h.

                                                                              {
    setState_(StateOfInference::OutdatedTensors);
  }

References OutdatedTensors, and setState_().

Referenced by makeInference_().

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setState_()

template<typename GUM_SCALAR>

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

finalprotectedvirtualinherited

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

Definition at line 117 of file graphicalModelInference_tpl.h.

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

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

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

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setTargetedMode_()

template<typename GUM_SCALAR>

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

protected

Definition at line 325 of file marginalTargetedMRFInference_tpl.h.

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

References _targeted_mode_, and _targets_.

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

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softEvidenceNodes()

template<typename GUM_SCALAR>

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

inherited

returns the set of nodes with soft evidence

the set of nodes that received soft evidence

Definition at line 593 of file graphicalModelInference_tpl.h.

                                                                                       {
    return _soft_evidence_nodes_;
  }

References _soft_evidence_nodes_.

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

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state()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the state of the inference engine

Definition at line 111 of file graphicalModelInference_tpl.h.

                                                                  {
    return _state_;
  }

References _state_.

Referenced by setState_().

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targets()

template<typename GUM_SCALAR>

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

finalvirtualnoexcept

returns the list of marginal targets

Definition at line 210 of file marginalTargetedMRFInference_tpl.h.

                                                                                           {
    return _targets_;
  }

References _targets_.

Referenced by gum::JointTargetedMRFInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedMRFInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedMRFInference< GUM_SCALAR >::jointMutualInformation(), and gum::JointTargetedMRFInference< GUM_SCALAR >::jointMutualInformation().

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updateOutdatedStructure_()

template<typename 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<typename 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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Member Data Documentation

_domain_sizes_

template<typename GUM_SCALAR>

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

privateinherited

the domain sizes of the random variables

Definition at line 507 of file graphicalModelInference.h.

Referenced by _computeDomainSizes_(), and domainSizes().

_evidence_

template<typename GUM_SCALAR>

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

privateinherited

the set of evidence entered into the network

Definition at line 510 of file graphicalModelInference.h.

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

_hard_evidence_

template<typename GUM_SCALAR>

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

privateinherited

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

Definition at line 513 of file graphicalModelInference.h.

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

_hard_evidence_nodes_

template<typename GUM_SCALAR>

NodeSet gum::GraphicalModelInference< GUM_SCALAR >::_hard_evidence_nodes_

privateinherited

the set of nodes that received hard evidence

Definition at line 519 of file graphicalModelInference.h.

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

_model_

template<typename GUM_SCALAR>

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

privateinherited

the Bayes net on which we perform inferences

Definition at line 504 of file graphicalModelInference.h.

{nullptr};

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

_soft_evidence_nodes_

template<typename GUM_SCALAR>

NodeSet gum::GraphicalModelInference< GUM_SCALAR >::_soft_evidence_nodes_

privateinherited

the set of nodes that received soft evidence

Definition at line 516 of file graphicalModelInference.h.

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

_state_

template<typename GUM_SCALAR>

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

privateinherited

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

Definition at line 501 of file graphicalModelInference.h.

{StateOfInference::OutdatedStructure};

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

_targeted_mode_

template<typename GUM_SCALAR>

bool gum::MarginalTargetedMRFInference< GUM_SCALAR >::_targeted_mode_

private

whether the actual targets are default

Definition at line 259 of file marginalTargetedMRFInference.h.

Referenced by MarginalTargetedMRFInference(), eraseTarget(), isTargetedMode_(), onModelChanged_(), and setTargetedMode_().

_targets_

template<typename GUM_SCALAR>

NodeSet gum::MarginalTargetedMRFInference< GUM_SCALAR >::_targets_

private

the set of marginal targets

Definition at line 262 of file marginalTargetedMRFInference.h.

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

EvidenceMRFInference< GUM_SCALAR >

template<typename GUM_SCALAR>

friend gum::MRFInference< GUM_SCALAR >::EvidenceMRFInference< GUM_SCALAR >

privateinherited

assigns a new MRF to the inference engine

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

Warning

By default, all the nodes of the Markov net are targets.

note that, by aGrUM's rule, the mn is not copied into the inference engine but only referenced.

Definition at line 223 of file MRFInference.h.

JointTargetedMRFInference< GUM_SCALAR >

template<typename GUM_SCALAR>

friend gum::MRFInference< GUM_SCALAR >::JointTargetedMRFInference< GUM_SCALAR >

privateinherited

assigns a new MRF to the inference engine

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

Warning

By default, all the nodes of the Markov net are targets.

note that, by aGrUM's rule, the mn is not copied into the inference engine but only referenced.

Definition at line 222 of file MRFInference.h.

MarginalTargetedMRFInference< GUM_SCALAR >

template<typename GUM_SCALAR>

friend gum::MRFInference< GUM_SCALAR >::MarginalTargetedMRFInference< GUM_SCALAR >

privateinherited

allow JointInference to access the single targets and inference states

Definition at line 221 of file MRFInference.h.

---

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

• agrum/MRF/inference/tools/marginalTargetedMRFInference.h
• agrum/MRF/inference/tools/marginalTargetedMRFInference_tpl.h