gum::BayesNetInference< GUM_SCALAR > Class Template Reference

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

#include <BayesNetInference.h>

Inheritance diagram for gum::BayesNetInference< GUM_SCALAR >:

Collaboration diagram for gum::BayesNetInference< GUM_SCALAR >:

Public Member Functions
Constructors / Destructors
	BayesNetInference (const IBayesNet< GUM_SCALAR > *bn)
	default constructor More...
	BayesNetInference ()
	default constructor with a null BN (useful for virtual inheritance) More...
virtual	~BayesNetInference ()
	destructor More...
Accessors / Modifiers
virtual void	setBN (const IBayesNet< GUM_SCALAR > *bn)
	assigns a new BN to the inference engine More...
virtual const IBayesNet< GUM_SCALAR > &	BN () const final
	Returns a constant reference over the IBayesNet referenced by this class. More...
virtual const NodeProperty< Size > &	domainSizes () const final
	get the domain sizes of the random variables of the BN More...
virtual bool	isInferenceReady () const noexcept final
	returns whether the inference object is in a ready state More...
virtual bool	isInferenceOutdatedBNStructure () const noexcept final
	returns whether the inference object is in a OutdatedBNStructure state More...
virtual bool	isInferenceOutdatedBNPotentials () const noexcept final
	returns whether the inference object is in a OutdatedBNPotential state More...
virtual bool	isInferenceDone () const noexcept final
	returns whether the inference object is in a InferenceDone state More...
virtual bool	isDone () const noexcept final
	returns whether the inference object is in a done state More...
virtual void	prepareInference () final
	prepare the internal inference structures for the next inference More...
virtual void	makeInference () final
	perform the heavy computations needed to compute the targets' posteriors More...
virtual void	clear ()
	clears all the data structures allocated for the last inference More...
virtual StateOfInference	state () const noexcept final
	returns the state of the inference engine More...
Evidence
virtual void	addEvidence (NodeId id, const Idx val) final
	adds a new hard evidence on node id More...
virtual void	addEvidence (const std::string &nodeName, const Idx val) final
	adds a new hard evidence on node named nodeName More...
virtual void	addEvidence (NodeId id, const std::string &label) final
	adds a new hard evidence on node id More...
virtual void	addEvidence (const std::string &nodeName, const std::string &label) final
	adds a new hard evidence on node named nodeName More...
virtual void	addEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node id (might be soft or hard) More...
virtual void	addEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node named nodeName (might be soft or hard) More...
virtual void	addEvidence (const Potential< GUM_SCALAR > &pot) final
	adds a new evidence on node id (might be soft or hard) More...
virtual void	addEvidence (Potential< GUM_SCALAR > &&pot) final
	adds a new evidence on node id (might be soft or hard) More...
virtual void	addSetOfEvidence (const Set< const Potential< GUM_SCALAR > * > &potset) final
	adds a new set of evidence More...
virtual void	addListOfEvidence (const List< const Potential< GUM_SCALAR > * > &potlist) final
	adds a new list of evidence More...
virtual void	chgEvidence (NodeId id, const Idx val) final
	change the value of an already existing hard evidence More...
virtual void	chgEvidence (const std::string &nodeName, const Idx val) final
	change the value of an already existing hard evidence More...
virtual void	chgEvidence (NodeId id, const std::string &label) final
	change the value of an already existing hard evidence More...
virtual void	chgEvidence (const std::string &nodeName, const std::string &label) final
	change the value of an already existing hard evidence More...
virtual void	chgEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard) More...
virtual void	chgEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard) More...
virtual void	chgEvidence (const Potential< GUM_SCALAR > &pot) final
	change the value of an already existing evidence (might be soft or hard) More...
virtual void	eraseAllEvidence () final
	removes all the evidence entered into the network More...
virtual void	eraseEvidence (NodeId id) final
	removed the evidence, if any, corresponding to node id More...
virtual void	eraseEvidence (const std::string &nodeName) final
	removed the evidence, if any, corresponding to node of name nodeName More...
virtual bool	hasEvidence () const final
	indicates whether some node(s) have received evidence More...
virtual bool	hasEvidence (NodeId id) const final
	indicates whether node id has received an evidence More...
virtual bool	hasHardEvidence (NodeId id) const final
	indicates whether node id has received a hard evidence More...
virtual bool	hasSoftEvidence (NodeId id) const final
	indicates whether node id has received a soft evidence More...
virtual bool	hasEvidence (const std::string &nodeName) const final
	indicates whether node id has received an evidence More...
virtual bool	hasHardEvidence (const std::string &nodeName) const final
	indicates whether node id has received a hard evidence More...
virtual bool	hasSoftEvidence (const std::string &nodeName) const final
	indicates whether node id has received a soft evidence More...
virtual Size	nbrEvidence () const final
	returns the number of evidence entered into the Bayesian network More...
virtual Size	nbrHardEvidence () const final
	returns the number of hard evidence entered into the Bayesian network More...
virtual Size	nbrSoftEvidence () const final
	returns the number of soft evidence entered into the Bayesian network More...
const NodeProperty< const Potential< GUM_SCALAR > *> &	evidence () const
	returns the set of evidence More...
const NodeSet &	softEvidenceNodes () const
	returns the set of nodes with soft evidence More...
const NodeSet &	hardEvidenceNodes () const
	returns the set of nodes with hard evidence More...
const NodeProperty< Idx > &	hardEvidence () const
	indicate for each node with hard evidence which value it took More...

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

Protected Member Functions
virtual void	_onStateChanged ()=0
	fired when the stage is changed More...
virtual void	_onEvidenceAdded (const NodeId id, bool isHardEvidence)=0
	fired after a new evidence is inserted More...
virtual void	_onEvidenceErased (const NodeId id, bool isHardEvidence)=0
	fired before an evidence is removed More...
virtual void	_onAllEvidenceErased (bool contains_hard_evidence)=0
	fired before all the evidence are erased More...
virtual void	_onEvidenceChanged (const NodeId id, bool hasChangedSoftHard)=0
	fired after an evidence is changed, in particular when its status (soft/hard) changes More...
virtual void	_onBayesNetChanged (const IBayesNet< GUM_SCALAR > *bn)=0
	fired after a new Bayes net has been assigned to the engine More...
virtual void	_updateOutdatedBNStructure ()=0
	prepares inference when the latter is in OutdatedBNStructure state More...
virtual void	_updateOutdatedBNPotentials ()=0
	prepares inference when the latter is in OutdatedBNPotentials state More...
virtual void	_makeInference ()=0
	called when the inference has to be performed effectively More...
void	_setOutdatedBNStructureState ()
	put the inference into an outdated BN structure state More...
void	_setOutdatedBNPotentialsState ()
	puts the inference into an OutdatedBNPotentials state if it is not already in an OutdatedBNStructure state More...

Detailed Description

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

<agrum/BN/inference/BayesNetInference.h>

A generic class for Bayes net inference: handles evidence and the current state of the (incremental) inference

The goal of the BayesNetInference class is twofold: i) handling the common resources of BN inference (bn, soft/hard evidence); ii) propose a general high-level scheme for all the inference methods.

A specialized inference just has to specify how to prepare inference, how to make inference and how to get the posteriors for nodes and set of nodes. The scheme for every inference derived from BayesNetInference will be the same:

1- ie=SpecificInference(bn); // state <- OutdatedBNStructure 2- set targets and evidence in ie 3- ie.prepareInference(); // state <- Ready4Inference 4.a- change values of evidence in ie // state <- OutdatedBNPotentials 4.b- change some hard evidence or targets // state <- OutdatedBNStructure 5- ie.makeInference(); // state <- Done 6- get posteriors 7- goto 2 or 4

BayesNetInference can be in one of 4 different states:

• OutdatedBNStructure: in this state, the inference is fully unprepared to be applied because some events changed the "logical" structure of the BN: for instance a node received a hard evidence, which implies that its outgoing arcs can be removed from the BN, hence involving a structural change in the BN. 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 BayesNetInference may be smarter than BayesNetInference 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 OutdatedBNPotentials state. As an example, consider a LazyPropagation inference in Bayes Net A->B->C->D->E in which C has received hard evidence e_C and E is the only target. In this case, A and B are not needed for inference, the only potentials that matter are P(D|e_C) and P(E|D). So the smallest join tree needed for inference contains only one clique DE. Now, adding new evidence e_A on A has no impact on E given hard evidence e_C. In this case, LazyPropagation can be smart and not update its join tree.
• OutdatedBNPotentials: in this state, the structure of the BN remains unchanged, only some potentials stored in it have changed. Therefore, the inference probably just needs to invalidate some already computed potentials to be ready. Only a light amount of preparation is needed to be able to perform inference.
• Ready4Inference: in this state, all the data structures are ready for inference. There just remains to perform the inference computations.
• Done: the heavy computations of inference have been done. There might still remain a few light computations to perform to get the posterior potentials we need. Typically, in Lazy Propagation, all the messages in the join tree have been computed but, to get the potentials, we still need to perform the combinations of the potentials in the cliques with the messages sent to the cliques. In some inference algorithms, this step may even be empty.

Definition at line 139 of file BayesNetInference.h.

Member Enumeration Documentation

StateOfInference

template<typename GUM_SCALAR >

enum gum::BayesNetInference::StateOfInference

strong

current state of the inference

BayesNetInference can be in one of 4 different states:

• OutdatedBNStructure: in this state, the inference is fully unprepared to be applied because some events changed the "logical" structure of the BN: for instance a node received a hard evidence, which implies that its outgoing arcs can be removed from the BN, hence involving a structural change in the BN. 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 BayesNetInference may be smarter than BayesNetInference 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 OutdatedBNPotentials state. As an example, consider a LazyPropagation inference in Bayes Net A->B->C->D->E in which C has received hard evidence e_C and E is the only target. In this case, A and B are not needed for inference, the only potentials that matter are P(D|e_C) and P(E|D). So the smallest join tree needed for inference contains only one clique DE. Now, adding new evidence e_A on A has no impact on E given hard evidence e_C. In this case, LazyPropagation can be smart and not update its join tree.
• OutdatedBNPotentials: in this state, the structure of the BN remains unchanged, only some potentials stored in it have changed. Therefore, the inference probably just needs to invalidate some already computed potentials to be ready. Only a light amount of preparation is needed to be able to perform inference.
• Ready4Inference: in this state, all the data structures are ready for inference. There just remains to perform the inference computations.
• Done: the heavy computations of inference have been done. There might still remain a few light computations to perform to get the posterior potentials we need. Typically, in Lazy Propagation, all the messages in the join tree have been computed but, to get the potentials, we still need to perform the combinations of the potentials in the cliques with the messages sent to the cliques. In some inference algorithms, this step may even be empty.

Enumerator
OutdatedBNStructure
OutdatedBNPotentials
ReadyForInference
Done

Definition at line 180 of file BayesNetInference.h.

                                {
      OutdatedBNStructure,
      OutdatedBNPotentials,
      ReadyForInference,
      Done
    };

Constructor & Destructor Documentation

BayesNetInference() [1/2]

template<typename GUM_SCALAR >

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

explicit

default constructor

Warning

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

Definition at line 33 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__computeDomainSizes().

                                        :
      __bn(bn) {
    __computeDomainSizes();

    GUM_CONSTRUCTOR(BayesNetInference);
  }

Here is the call graph for this function:

BayesNetInference() [2/2]

template<typename GUM_SCALAR >

gum::BayesNetInference< GUM_SCALAR >::BayesNetInference

(

)

default constructor with a null BN (useful for virtual inheritance)

Warning

BayesNetInference is virtually inherited by MarginalTargetedInference. As a result, the lowest descendant of BayesNetInference will create the latter. To avoid requiring developpers to add in the constructors of their inference algorithms a call to BayesNetInference( bn ), we added constructor BayesNetInference(), which will be called automatically by the lowest descendant. Then, MarginalTargetedInference and JointTargetedInference will take care of setting the appropriate bn into BayesNetInference.

Definition at line 44 of file BayesNetInference_tpl.h.

                                                     {
    GUM_CONSTRUCTOR(BayesNetInference);
  }

~BayesNetInference()

template<typename GUM_SCALAR >

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

virtual

destructor

Definition at line 51 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence.

                                                      {
    // clear all evidence.
    // Warning: Do not use method eraseAllEvidence () because it contains a call
    // to pure virtual method _onAllEvidenceErased which belongs to an inherited
    // instance and, therefore, does not exist anymore when ~BayesNetInference ()
    // is called
    for (const auto& pair : __evidence) {
      if (pair.second != nullptr) { delete (pair.second); }
    }

    GUM_DESTRUCTOR(BayesNetInference);
  }

Member Function Documentation

__computeDomainSizes()

template<typename GUM_SCALAR >

void gum::BayesNetInference< GUM_SCALAR >::__computeDomainSizes ( )

private

computes the domain sizes of the random variables

Definition at line 158 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, and gum::BayesNetInference< GUM_SCALAR >::__domain_sizes.

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

                                                             {
    __domain_sizes.clear();
    if (__bn != nullptr) {
      for (const auto node : __bn->dag()) {
        __domain_sizes.insert(node, __bn->variable(node).domainSize());
      }
    }
  }

Here is the caller graph for this function:

__createHardEvidence()

template<typename GUM_SCALAR >

Potential< GUM_SCALAR > gum::BayesNetInference< GUM_SCALAR >::__createHardEvidence ( NodeId  id, Idx  val  ) const

private

create the internal structure for a hard evidence

Definition at line 183 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MultiDimDecorator< GUM_SCALAR >::beginMultipleChanges(), gum::Instantiation::chgVal(), and GUM_ERROR.

Referenced by gum::BayesNetInference< GUM_SCALAR >::addEvidence(), and gum::BayesNetInference< GUM_SCALAR >::chgEvidence().

                                                                                {
    // check that it is possible to create the evidence
    if (__bn == nullptr)
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");

    if (!__bn->dag().exists(id)) {
      GUM_ERROR(UndefinedElement, id << " is not a NodeId in the bn");
    }

    if (__bn->variable(id).domainSize() <= val) {
      GUM_ERROR(InvalidArgument,
                "node " << __bn->variable(id) << " has fewer possible values than "
                        << val);
    }

    // create the deterministic potential
    Potential< GUM_SCALAR > pot;
    pot.beginMultipleChanges();
    pot << __bn->variable(id);
    pot.endMultipleChanges(0.0);

    Instantiation I(pot);
    I.chgVal(__bn->variable(id), val);
    pot.set(I, 1.0);

    return pot;
  }

Here is the call graph for this function:

Here is the caller graph for this function:

__isHardEvidence()

template<typename GUM_SCALAR >

bool gum::BayesNetInference< GUM_SCALAR >::__isHardEvidence ( const Potential< GUM_SCALAR > &  pot, Idx &  val  ) const

private

checks whether a potential corresponds to a hard evidence or not

Definition at line 217 of file BayesNetInference_tpl.h.

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::addEvidence(), and gum::BayesNetInference< GUM_SCALAR >::chgEvidence().

                                                         {
    // 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
  }

Here is the call graph for this function:

Here is the caller graph for this function:

__setBayesNetDuringConstruction()

template<typename GUM_SCALAR >

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

private

assigns a BN during the inference engine construction

Definition at line 140 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__computeDomainSizes(), gum::BayesNetInference< GUM_SCALAR >::__setState(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

Referenced by gum::EvidenceInference< GUM_SCALAR >::EvidenceInference(), gum::JointTargetedInference< GUM_SCALAR >::JointTargetedInference(), and gum::MarginalTargetedInference< GUM_SCALAR >::MarginalTargetedInference().

                                        {
    __bn = bn;
    __computeDomainSizes();
    __setState(StateOfInference::OutdatedBNStructure);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

__setState()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::__setState ( const StateOfInference  state )

finalprivatevirtual

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 108 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, gum::BayesNetInference< GUM_SCALAR >::_onStateChanged(), and gum::BayesNetInference< GUM_SCALAR >::state().

Referenced by gum::BayesNetInference< GUM_SCALAR >::__setBayesNetDuringConstruction(), gum::BayesNetInference< GUM_SCALAR >::_setOutdatedBNPotentialsState(), gum::BayesNetInference< GUM_SCALAR >::_setOutdatedBNStructureState(), gum::MarginalTargetedInference< GUM_SCALAR >::addAllTargets(), gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::JointTargetedInference< GUM_SCALAR >::addJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::clear(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), gum::JointTargetedInference< GUM_SCALAR >::eraseAllJointTargets(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseAllTargets(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), gum::JointTargetedInference< GUM_SCALAR >::eraseJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), gum::BayesNetInference< GUM_SCALAR >::makeInference(), gum::BayesNetInference< GUM_SCALAR >::prepareInference(), and gum::BayesNetInference< GUM_SCALAR >::setBN().

                                                                             {
    if (__state != state) {
      __state = state;
      _onStateChanged();
    }
  }

Here is the call graph for this function:

Here is the caller graph for this function:

_makeInference()

template<typename GUM_SCALAR >

virtual void gum::BayesNetInference< GUM_SCALAR >::_makeInference ( )

protectedpure virtual

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::makeInference().

Here is the caller graph for this function:

_onAllEvidenceErased()

template<typename GUM_SCALAR >

virtual void gum::BayesNetInference< GUM_SCALAR >::_onAllEvidenceErased ( bool  contains_hard_evidence )

protectedpure virtual

fired before all the evidence are erased

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence().

Here is the caller graph for this function:

_onBayesNetChanged()

template<typename GUM_SCALAR >

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

protectedpure virtual

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

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::setBN().

Here is the caller graph for this function:

_onEvidenceAdded()

template<typename GUM_SCALAR >

virtual void gum::BayesNetInference< GUM_SCALAR >::_onEvidenceAdded ( const NodeId  id, bool  isHardEvidence  )

protectedpure virtual

fired after a new evidence is inserted

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::addEvidence().

Here is the caller graph for this function:

_onEvidenceChanged()

template<typename GUM_SCALAR >

virtual void gum::BayesNetInference< GUM_SCALAR >::_onEvidenceChanged ( const NodeId  id, bool  hasChangedSoftHard  )

protectedpure virtual

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::chgEvidence().

Here is the caller graph for this function:

_onEvidenceErased()

template<typename GUM_SCALAR >

virtual void gum::BayesNetInference< GUM_SCALAR >::_onEvidenceErased ( const NodeId  id, bool  isHardEvidence  )

protectedpure virtual

fired before an evidence is removed

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::eraseEvidence().

Here is the caller graph for this function:

_onStateChanged()

template<typename GUM_SCALAR >

virtual void gum::BayesNetInference< GUM_SCALAR >::_onStateChanged ( )

protectedpure virtual

fired when the stage is changed

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::__setState().

Here is the caller graph for this function:

_setOutdatedBNPotentialsState()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::_setOutdatedBNPotentialsState ( )

protected

puts the inference into an OutdatedBNPotentials state if it is not already in an OutdatedBNStructure state

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

Definition at line 682 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__setState(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNPotentials.

                                                                             {
    __setState(StateOfInference::OutdatedBNPotentials);
  }

Here is the call graph for this function:

_setOutdatedBNStructureState()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::_setOutdatedBNStructureState ( )

protected

put the inference into an outdated BN structure state

OutdatedBNStructure: in this state, the inference is fully unprepared to be applied because some events changed the "logical" structure of the BN: for instance a node received a hard evidence, which implies that its outgoing arcs can be removed from the BN, hence involving a structural change in the BN. 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 BayesNetInference may be smarter than BayesNetInference 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 OutdatedBNPotentials state. As an example, consider a LazyPropagation inference in Bayes Net A->B->C->D->E in which C has received hard evidence e_C and E is the only target. In this case, A and B are not needed for inference, the only potentials that matter are P(D|e_C) and P(E|D). So the smallest join tree needed for inference contains only one clique DE. Now, adding new evidence e_A on A has no impact on E given hard evidence e_C. In this case, LazyPropagation can be smart and not update its join tree.

Definition at line 674 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__setState(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

                                                                            {
    __setState(StateOfInference::OutdatedBNStructure);
  }

Here is the call graph for this function:

_updateOutdatedBNPotentials()

template<typename GUM_SCALAR >

virtual void gum::BayesNetInference< GUM_SCALAR >::_updateOutdatedBNPotentials ( )

protectedpure virtual

prepares inference when the latter is in OutdatedBNPotentials state

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

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::prepareInference().

Here is the caller graph for this function:

_updateOutdatedBNStructure()

template<typename GUM_SCALAR >

virtual void gum::BayesNetInference< GUM_SCALAR >::_updateOutdatedBNStructure ( )

protectedpure virtual

prepares inference when the latter is in OutdatedBNStructure state

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

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

Referenced by gum::BayesNetInference< GUM_SCALAR >::prepareInference().

Here is the caller graph for this function:

addEvidence() [1/8]

template<typename GUM_SCALAR >

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

finalvirtual

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 244 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__createHardEvidence().

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

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

Here is the call graph for this function:

Here is the caller graph for this function:

addEvidence() [2/8]

template<typename GUM_SCALAR >

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

finalvirtual

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 252 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

addEvidence() [3/8]

template<typename GUM_SCALAR >

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

finalvirtual

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 260 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

addEvidence() [4/8]

template<typename GUM_SCALAR >

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

finalvirtual

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 268 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

addEvidence() [5/8]

template<typename GUM_SCALAR >

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

finalvirtual

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 276 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MultiDimDecorator< GUM_SCALAR >::add(), gum::BayesNetInference< GUM_SCALAR >::addEvidence(), and GUM_ERROR.

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

    if (!__bn->dag().exists(id)) {
      GUM_ERROR(UndefinedElement, id << " is not a NodeId in the bn");
    }

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

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

Here is the call graph for this function:

addEvidence() [6/8]

template<typename GUM_SCALAR >

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

finalvirtual

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

Exceptions

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

Definition at line 302 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

addEvidence() [7/8]

template<typename GUM_SCALAR >

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

finalvirtual

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

Exceptions

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

Definition at line 351 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence().

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

Here is the call graph for this function:

addEvidence() [8/8]

template<typename GUM_SCALAR >

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

finalvirtual

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

Exceptions

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

Definition at line 310 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__evidence, gum::BayesNetInference< GUM_SCALAR >::__hard_evidence, gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes, gum::BayesNetInference< GUM_SCALAR >::__isHardEvidence(), gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes, gum::BayesNetInference< GUM_SCALAR >::_onEvidenceAdded(), GUM_ERROR, gum::BayesNetInference< GUM_SCALAR >::hasEvidence(), gum::Set< Key, Alloc >::insert(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

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

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

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

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

    // insert the evidence
    __evidence.insert(
       id,
       new Potential< GUM_SCALAR >(std::forward< Potential< GUM_SCALAR > >(pot)));
    if (is_hard_evidence) {   // pot is deterministic
      __hard_evidence.insert(id, val);
      __hard_evidence_nodes.insert(id);
    } else {
      __soft_evidence_nodes.insert(id);
    }
    __setState(StateOfInference::OutdatedBNStructure);
    _onEvidenceAdded(id, is_hard_evidence);
  }

Here is the call graph for this function:

addListOfEvidence()

template<typename GUM_SCALAR >

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

finalvirtual

adds a new list of evidence

Exceptions

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

Definition at line 360 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence().

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

Here is the call graph for this function:

addSetOfEvidence()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< GUM_SCALAR >::addSetOfEvidence ( const Set< const Potential< GUM_SCALAR > * > &  potset )

finalvirtual

adds a new set of evidence

Exceptions

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

Definition at line 369 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::addEvidence().

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

Here is the call graph for this function:

BN()

template<typename GUM_SCALAR >

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

finalvirtual

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 118 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, and GUM_ERROR.

Referenced by gum::WeightedSampling< GUM_SCALAR >::_draw(), gum::MonteCarloSampling< GUM_SCALAR >::_draw(), gum::ImportanceSampling< GUM_SCALAR >::_draw(), gum::SamplingInference< GUM_SCALAR >::_posterior(), gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::SamplingInference< GUM_SCALAR >::contextualize(), gum::SamplingInference< GUM_SCALAR >::currentPosterior(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::MarginalTargetedInference< GUM_SCALAR >::H(), gum::BayesNetInference< GUM_SCALAR >::hasEvidence(), gum::BayesNetInference< GUM_SCALAR >::hasHardEvidence(), gum::BayesNetInference< GUM_SCALAR >::hasSoftEvidence(), gum::JointTargetedInference< GUM_SCALAR >::I(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), gum::JointTargetedInference< GUM_SCALAR >::posterior(), gum::SamplingInference< GUM_SCALAR >::samplingBN(), and gum::Estimator< GUM_SCALAR >::setFromLBP().

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

Here is the caller graph for this function:

chgEvidence() [1/7]

template<typename GUM_SCALAR >

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

finalvirtual

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 429 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__createHardEvidence().

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

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

Here is the call graph for this function:

Here is the caller graph for this function:

chgEvidence() [2/7]

template<typename GUM_SCALAR >

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

finalvirtual

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 437 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

chgEvidence() [3/7]

template<typename GUM_SCALAR >

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

finalvirtual

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 445 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

chgEvidence() [4/7]

template<typename GUM_SCALAR >

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

finalvirtual

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 453 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

chgEvidence() [5/7]

template<typename GUM_SCALAR >

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

finalvirtual

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 461 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MultiDimDecorator< GUM_SCALAR >::add(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), and GUM_ERROR.

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

    if (!__bn->dag().exists(id)) {
      GUM_ERROR(UndefinedElement, id << " is not a NodeId in the bn");
    }

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

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

Here is the call graph for this function:

chgEvidence() [6/7]

template<typename GUM_SCALAR >

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

finalvirtual

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 488 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

chgEvidence() [7/7]

template<typename GUM_SCALAR >

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

finalvirtual

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

Exceptions

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

Definition at line 496 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__evidence, gum::BayesNetInference< GUM_SCALAR >::__hard_evidence, gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes, gum::BayesNetInference< GUM_SCALAR >::__isHardEvidence(), gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes, gum::BayesNetInference< GUM_SCALAR >::_onEvidenceChanged(), gum::Instantiation::end(), gum::Set< Key, Alloc >::erase(), GUM_ERROR, gum::BayesNetInference< GUM_SCALAR >::hasEvidence(), gum::BayesNetInference< GUM_SCALAR >::hasHardEvidence(), gum::Instantiation::inc(), gum::Set< Key, Alloc >::insert(), gum::BayesNetInference< GUM_SCALAR >::isInferenceOutdatedBNStructure(), gum::MultiDimDecorator< GUM_SCALAR >::nbrDim(), gum::BayesNetInference< GUM_SCALAR >::OutdatedBNPotentials, gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure, gum::MultiDimDecorator< GUM_SCALAR >::set(), gum::Instantiation::setFirst(), and gum::MultiDimDecorator< GUM_SCALAR >::variable().

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

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

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

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

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

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

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

    if (hasChangedSoftHard) {
      __setState(StateOfInference::OutdatedBNStructure);
    } else {
      if (!isInferenceOutdatedBNStructure()) {
        __setState(StateOfInference::OutdatedBNPotentials);
      }
    }

    _onEvidenceChanged(id, hasChangedSoftHard);
  }

Here is the call graph for this function:

clear()

template<typename GUM_SCALAR >

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

virtual

clears all the data structures allocated for the last inference

Definition at line 150 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

Referenced by gum::BayesNetInference< GUM_SCALAR >::setBN().

                                                     {
    eraseAllEvidence();
    __setState(StateOfInference::OutdatedBNStructure);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

domainSizes()

template<typename GUM_SCALAR >

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

finalvirtual

get the domain sizes of the random variables of the BN

Definition at line 171 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__domain_sizes.

                                                                  {
    return __domain_sizes;
  }

eraseAllEvidence()

template<typename GUM_SCALAR >

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

finalvirtual

removes all the evidence entered into the network

Definition at line 592 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence, gum::BayesNetInference< GUM_SCALAR >::__hard_evidence, gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes, gum::BayesNetInference< GUM_SCALAR >::_onAllEvidenceErased(), gum::Set< Key, Alloc >::clear(), gum::BayesNetInference< GUM_SCALAR >::isInferenceOutdatedBNStructure(), gum::BayesNetInference< GUM_SCALAR >::OutdatedBNPotentials, and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

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

                                                                {
    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::OutdatedBNStructure);
    } else {
      if (!isInferenceOutdatedBNStructure()) {
        __setState(StateOfInference::OutdatedBNPotentials);
      }
    }
  }

Here is the call graph for this function:

Here is the caller graph for this function:

eraseEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtual

removed the evidence, if any, corresponding to node id

Definition at line 563 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence, gum::BayesNetInference< GUM_SCALAR >::__hard_evidence, gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes, gum::BayesNetInference< GUM_SCALAR >::_onEvidenceErased(), gum::Set< Key, Alloc >::erase(), gum::BayesNetInference< GUM_SCALAR >::hasEvidence(), gum::BayesNetInference< GUM_SCALAR >::hasHardEvidence(), gum::BayesNetInference< GUM_SCALAR >::isInferenceOutdatedBNStructure(), gum::BayesNetInference< GUM_SCALAR >::OutdatedBNPotentials, and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

Referenced by gum::BayesNetInference< GUM_SCALAR >::eraseEvidence().

                                                                      {
    if (hasEvidence(id)) {
      if (hasHardEvidence(id)) {
        _onEvidenceErased(id, true);
        __hard_evidence.erase(id);
        __hard_evidence_nodes.erase(id);
        __setState(StateOfInference::OutdatedBNStructure);
      } else {
        _onEvidenceErased(id, false);
        __soft_evidence_nodes.erase(id);
        if (!isInferenceOutdatedBNStructure()) {
          __setState(StateOfInference::OutdatedBNPotentials);
        }
      }

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

Here is the call graph for this function:

Here is the caller graph for this function:

eraseEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtual

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

Definition at line 585 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

evidence()

template<typename GUM_SCALAR >

INLINE const NodeProperty< const Potential< GUM_SCALAR > *> & gum::BayesNetInference< GUM_SCALAR >::evidence

(

)

const

returns the set of evidence

Definition at line 647 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence.

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

                                                               {
    return __evidence;
  }

Here is the caller graph for this function:

hardEvidence()

template<typename GUM_SCALAR >

INLINE const NodeProperty< Idx > & gum::BayesNetInference< GUM_SCALAR >::hardEvidence

(

)

const

indicate for each node with hard evidence which value it took

Definition at line 639 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__hard_evidence.

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

                                                                   {
    return __hard_evidence;
  }

Here is the caller graph for this function:

hardEvidenceNodes()

template<typename GUM_SCALAR >

INLINE const NodeSet & gum::BayesNetInference< GUM_SCALAR >::hardEvidenceNodes

(

)

const

returns the set of nodes with hard evidence

the set of nodes that received hard evidence

Definition at line 663 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes.

Referenced by gum::WeightedSampling< GUM_SCALAR >::_draw(), gum::MonteCarloSampling< GUM_SCALAR >::_draw(), gum::ImportanceSampling< GUM_SCALAR >::_draw(), gum::ImportanceSampling< GUM_SCALAR >::_onContextualize(), gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromBN(), gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromLBP(), gum::SamplingInference< GUM_SCALAR >::contextualize(), and gum::MarginalTargetedInference< GUM_SCALAR >::posterior().

                                                                        {
    return __hard_evidence_nodes;
  }

Here is the caller graph for this function:

hasEvidence() [1/3]

template<typename GUM_SCALAR >

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

finalvirtual

indicates whether some node(s) have received evidence

Definition at line 378 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence.

Referenced by gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), and gum::BayesNetInference< GUM_SCALAR >::hasEvidence().

                                                                 {
    return !__evidence.empty();
  }

Here is the caller graph for this function:

hasEvidence() [2/3]

template<typename GUM_SCALAR >

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

finalvirtual

indicates whether node id has received an evidence

Definition at line 385 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence.

                                                                          {
    return __evidence.exists(id);
  }

hasEvidence() [3/3]

template<typename GUM_SCALAR >

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

finalvirtual

indicates whether node id has received an evidence

Definition at line 406 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

hasHardEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtual

indicates whether node id has received a hard evidence

Definition at line 392 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes, and gum::Set< Key, Alloc >::exists().

Referenced by gum::ImportanceSampling< GUM_SCALAR >::_draw(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), and gum::BayesNetInference< GUM_SCALAR >::hasHardEvidence().

                                                                              {
    return __hard_evidence_nodes.exists(id);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

hasHardEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtual

indicates whether node id has received a hard evidence

Definition at line 414 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

hasSoftEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtual

indicates whether node id has received a soft evidence

Definition at line 399 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes, and gum::Set< Key, Alloc >::exists().

Referenced by gum::BayesNetInference< GUM_SCALAR >::hasSoftEvidence().

                                                                              {
    return __soft_evidence_nodes.exists(id);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

hasSoftEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtual

indicates whether node id has received a soft evidence

Definition at line 422 of file BayesNetInference_tpl.h.

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

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

Here is the call graph for this function:

isDone()

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::isDone ( ) const

finalvirtualnoexcept

returns whether the inference object is in a done 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 93 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::Done.

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

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

Here is the caller graph for this function:

isInferenceDone()

template<typename GUM_SCALAR >

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

finalvirtualnoexcept

returns whether the inference object is in a InferenceDone state

Definition at line 86 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::Done.

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

isInferenceOutdatedBNPotentials()

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::isInferenceOutdatedBNPotentials ( ) const

finalvirtualnoexcept

returns whether the inference object is in a OutdatedBNPotential state

Definition at line 80 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNPotentials.

              {
    return (__state == StateOfInference::OutdatedBNPotentials);
  }

isInferenceOutdatedBNStructure()

template<typename GUM_SCALAR >

INLINE bool gum::BayesNetInference< GUM_SCALAR >::isInferenceOutdatedBNStructure ( ) const

finalvirtualnoexcept

returns whether the inference object is in a OutdatedBNStructure state

Definition at line 73 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

Referenced by gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), and gum::BayesNetInference< GUM_SCALAR >::eraseEvidence().

              {
    return (__state == StateOfInference::OutdatedBNStructure);
  }

Here is the caller graph for this function:

isInferenceReady()

template<typename GUM_SCALAR >

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

finalvirtualnoexcept

returns whether the inference object is in a ready state

Definition at line 67 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state, and gum::BayesNetInference< GUM_SCALAR >::ReadyForInference.

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

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

Here is the caller graph for this function:

makeInference()

template<typename GUM_SCALAR >

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

finalvirtual

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 708 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::_makeInference(), gum::BayesNetInference< GUM_SCALAR >::Done, gum::BayesNetInference< GUM_SCALAR >::isDone(), gum::BayesNetInference< GUM_SCALAR >::isInferenceReady(), and gum::BayesNetInference< GUM_SCALAR >::prepareInference().

Referenced by gum::LoopySamplingInference< GUM_SCALAR, APPROX >::_makeInference(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedInference< GUM_SCALAR >::jointPosterior(), and gum::MarginalTargetedInference< GUM_SCALAR >::posterior().

                                                             {
    if (isDone()) { return; }

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

    _makeInference();

    __setState(StateOfInference::Done);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

nbrEvidence()

template<typename GUM_SCALAR >

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

finalvirtual

returns the number of evidence entered into the Bayesian network

Definition at line 617 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__evidence.

                                                                 {
    return __evidence.size();
  }

nbrHardEvidence()

template<typename GUM_SCALAR >

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

finalvirtual

returns the number of hard evidence entered into the Bayesian network

Definition at line 624 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes, and gum::Set< Key, Alloc >::size().

                                                                     {
    return __hard_evidence_nodes.size();
  }

Here is the call graph for this function:

nbrSoftEvidence()

template<typename GUM_SCALAR >

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

finalvirtual

returns the number of soft evidence entered into the Bayesian network

Definition at line 631 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes, and gum::Set< Key, Alloc >::size().

                                                                     {
    return __soft_evidence_nodes.size();
  }

Here is the call graph for this function:

prepareInference()

template<typename GUM_SCALAR >

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

finalvirtual

prepare the internal inference structures for the next inference

Definition at line 689 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::__state, gum::BayesNetInference< GUM_SCALAR >::_updateOutdatedBNPotentials(), gum::BayesNetInference< GUM_SCALAR >::_updateOutdatedBNStructure(), GUM_ERROR, gum::BayesNetInference< GUM_SCALAR >::isDone(), gum::BayesNetInference< GUM_SCALAR >::isInferenceReady(), gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure, and gum::BayesNetInference< GUM_SCALAR >::ReadyForInference.

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

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

    if (__bn == nullptr)
      GUM_ERROR(NullElement,
                "No Bayes net has been assigned to the "
                "inference algorithm");

    if (__state == StateOfInference::OutdatedBNStructure)
      _updateOutdatedBNStructure();
    else
      _updateOutdatedBNPotentials();

    __setState(StateOfInference::ReadyForInference);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

setBN()

template<typename GUM_SCALAR >

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

virtual

assigns a new BN to the inference engine

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

Warning

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

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

Definition at line 129 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__computeDomainSizes(), gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::_onBayesNetChanged(), gum::BayesNetInference< GUM_SCALAR >::clear(), and gum::BayesNetInference< GUM_SCALAR >::OutdatedBNStructure.

                                                                               {
    clear();
    __bn = bn;
    __computeDomainSizes();
    _onBayesNetChanged(bn);
    __setState(StateOfInference::OutdatedBNStructure);
  }

Here is the call graph for this function:

softEvidenceNodes()

template<typename GUM_SCALAR >

INLINE const NodeSet & gum::BayesNetInference< GUM_SCALAR >::softEvidenceNodes

(

)

const

returns the set of nodes with soft evidence

the set of nodes that received soft evidence

Definition at line 655 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes.

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

                                                                        {
    return __soft_evidence_nodes;
  }

Here is the caller graph for this function:

state()

template<typename GUM_SCALAR >

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

finalvirtualnoexcept

returns the state of the inference engine

Definition at line 101 of file BayesNetInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__state.

Referenced by gum::BayesNetInference< GUM_SCALAR >::__setState().

                                                           {
    return __state;
  }

Here is the caller graph for this function:

Member Data Documentation

__bn

template<typename GUM_SCALAR >

const IBayesNet< GUM_SCALAR >* gum::BayesNetInference< GUM_SCALAR >::__bn {nullptr}

private

the Bayes net on which we perform inferences

Definition at line 580 of file BayesNetInference.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::__computeDomainSizes(), gum::BayesNetInference< GUM_SCALAR >::__createHardEvidence(), gum::MarginalTargetedInference< GUM_SCALAR >::__setAllMarginalTargets(), gum::BayesNetInference< GUM_SCALAR >::__setBayesNetDuringConstruction(), gum::MarginalTargetedInference< GUM_SCALAR >::addAllTargets(), gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::JointTargetedInference< GUM_SCALAR >::addJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), gum::BayesNetInference< GUM_SCALAR >::BN(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::JointTargetedInference< GUM_SCALAR >::eraseJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), gum::EvidenceInference< GUM_SCALAR >::EvidenceInference(), gum::JointTargetedInference< GUM_SCALAR >::isJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::isTarget(), gum::JointTargetedInference< GUM_SCALAR >::JointTargetedInference(), gum::MarginalTargetedInference< GUM_SCALAR >::MarginalTargetedInference(), gum::BayesNetInference< GUM_SCALAR >::prepareInference(), and gum::BayesNetInference< GUM_SCALAR >::setBN().

__domain_sizes

template<typename GUM_SCALAR >

NodeProperty< Size > gum::BayesNetInference< GUM_SCALAR >::__domain_sizes

private

the domain sizes of the random variables

Definition at line 583 of file BayesNetInference.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::__computeDomainSizes(), and gum::BayesNetInference< GUM_SCALAR >::domainSizes().

__evidence

template<typename GUM_SCALAR >

NodeProperty< const Potential< GUM_SCALAR >* > gum::BayesNetInference< GUM_SCALAR >::__evidence

private

the set of evidence entered into the network

Definition at line 586 of file BayesNetInference.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), gum::BayesNetInference< GUM_SCALAR >::evidence(), gum::BayesNetInference< GUM_SCALAR >::hasEvidence(), gum::BayesNetInference< GUM_SCALAR >::nbrEvidence(), and gum::BayesNetInference< GUM_SCALAR >::~BayesNetInference().

__hard_evidence

template<typename GUM_SCALAR >

NodeProperty< Idx > gum::BayesNetInference< GUM_SCALAR >::__hard_evidence

private

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

Definition at line 589 of file BayesNetInference.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), and gum::BayesNetInference< GUM_SCALAR >::hardEvidence().

__hard_evidence_nodes

template<typename GUM_SCALAR >

NodeSet gum::BayesNetInference< GUM_SCALAR >::__hard_evidence_nodes

private

the set of nodes that received hard evidence

Definition at line 595 of file BayesNetInference.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), gum::BayesNetInference< GUM_SCALAR >::hardEvidenceNodes(), gum::BayesNetInference< GUM_SCALAR >::hasHardEvidence(), and gum::BayesNetInference< GUM_SCALAR >::nbrHardEvidence().

__soft_evidence_nodes

template<typename GUM_SCALAR >

NodeSet gum::BayesNetInference< GUM_SCALAR >::__soft_evidence_nodes

private

the set of nodes that received soft evidence

Definition at line 592 of file BayesNetInference.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::addEvidence(), gum::BayesNetInference< GUM_SCALAR >::chgEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseAllEvidence(), gum::BayesNetInference< GUM_SCALAR >::eraseEvidence(), gum::BayesNetInference< GUM_SCALAR >::hasSoftEvidence(), gum::BayesNetInference< GUM_SCALAR >::nbrSoftEvidence(), and gum::BayesNetInference< GUM_SCALAR >::softEvidenceNodes().

__state

template<typename GUM_SCALAR >

StateOfInference gum::BayesNetInference< GUM_SCALAR >::__state {StateOfInference::OutdatedBNStructure}

private

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

Definition at line 577 of file BayesNetInference.h.

Referenced by gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::BayesNetInference< GUM_SCALAR >::isDone(), gum::BayesNetInference< GUM_SCALAR >::isInferenceDone(), gum::BayesNetInference< GUM_SCALAR >::isInferenceOutdatedBNPotentials(), gum::BayesNetInference< GUM_SCALAR >::isInferenceOutdatedBNStructure(), gum::BayesNetInference< GUM_SCALAR >::isInferenceReady(), gum::BayesNetInference< GUM_SCALAR >::prepareInference(), and gum::BayesNetInference< GUM_SCALAR >::state().

EvidenceInference< GUM_SCALAR >

template<typename GUM_SCALAR >

friend gum::BayesNetInference< GUM_SCALAR >::EvidenceInference< GUM_SCALAR >

private

Definition at line 618 of file BayesNetInference.h.

JointTargetedInference< GUM_SCALAR >

template<typename GUM_SCALAR >

friend gum::BayesNetInference< GUM_SCALAR >::JointTargetedInference< GUM_SCALAR >

private

Definition at line 617 of file BayesNetInference.h.

MarginalTargetedInference< GUM_SCALAR >

template<typename GUM_SCALAR >

friend gum::BayesNetInference< GUM_SCALAR >::MarginalTargetedInference< GUM_SCALAR >

private

allow JointInference to access the single targets and inference states

Definition at line 616 of file BayesNetInference.h.

---

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

• agrum/BN/inference/tools/BayesNetInference.h
• agrum/BN/inference/tools/BayesNetInference_tpl.h