#include <samplingInference.h>

Inheritance diagram for gum::SamplingInference< GUM_SCALAR >:

Collaboration diagram for gum::SamplingInference< GUM_SCALAR >:

Public Attributes
Signaler3< Size, double, double >	onProgress
	Progression, error and time. More...

Signaler1< std::string >	onStop
	Criteria messageApproximationScheme. More...

Public Member Functions
virtual void	contextualize ()
	Simplifying the bayesian network with relevance reasonning to lighten the computational charge. More...

Potential< GUM_SCALAR >	evidenceImpact (NodeId target, const NodeSet &evs)
	Create a gum::Potential for P(target\|evs) (for all instanciation of target and evs) More...

Potential< GUM_SCALAR >	evidenceImpact (const std::string &target, const std::vector< std::string > &evs)
	Create a gum::Potential for P(target\|evs) (for all instanciation of target and evs) More...

Constructors / Destructors
	SamplingInference (const IBayesNet< GUM_SCALAR > *bn)
	default constructor More...

	~SamplingInference () override
	destructor More...

const Potential< GUM_SCALAR > &	currentPosterior (NodeId id)
	Computes and returns the actual estimation of the posterior of a node. More...

const Potential< GUM_SCALAR > &	currentPosterior (const std::string &name)
	Computes and returns the actual estimation of the posterior of a node by its name. More...

Probability computations
const IBayesNet< GUM_SCALAR > &	samplingBN ()
	get the BayesNet which is used to really perform the sampling More...

const Potential< GUM_SCALAR > &	_posterior (NodeId id) override
	Computes and returns the posterior of a node. More...

Estimator objects initializing
virtual void	_setEstimatorFromBN ()
	Initializes the estimators object linked to the simulation. More...

virtual void	_setEstimatorFromLBP (LoopyBeliefPropagation< GUM_SCALAR > *lbp, GUM_SCALAR virtualLBPSize)
	Initializes the estimators object linked to the simulation. More...

Probability computations
virtual const Potential< GUM_SCALAR > &	posterior (NodeId node)
	Computes and returns the posterior of a node. More...

virtual const Potential< GUM_SCALAR > &	posterior (const std::string &nodeName)
	Computes and returns the posterior of a node. More...

Targets
virtual void	eraseAllTargets ()
	Clear all previously defined targets. More...

virtual void	addAllTargets () final
	adds all nodes as targets More...

virtual void	addTarget (NodeId target) final
	Add a marginal target to the list of targets. More...

virtual void	addTarget (const std::string &nodeName) final
	Add a marginal target to the list of targets. More...

virtual void	eraseTarget (NodeId target) final
	removes an existing (marginal) target More...

virtual void	eraseTarget (const std::string &nodeName) final
	removes an existing (marginal) target More...

virtual bool	isTarget (NodeId node) const final
	return true if variable is a (marginal) target More...

virtual bool	isTarget (const std::string &nodeName) const final
	return true if variable is a (marginal) target More...

virtual const Size	nbrTargets () const noexcept final
	returns the number of marginal targets More...

virtual const NodeSet &	targets () const noexcept final
	returns the list of marginal targets More...

Information Theory related functions
virtual GUM_SCALAR	H (NodeId X) final
	Entropy Compute Shanon's entropy of a node given the observation. More...

virtual GUM_SCALAR	H (const std::string &nodeName) final
	Entropy Compute Shanon's entropy of a node given the observation. 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	hasEvidence (const std::string &nodeName) const final
	indicates whether node id has received an evidence More...

virtual bool	hasHardEvidence (NodeId id) const final
	indicates whether node id has received a hard evidence More...

virtual bool	hasHardEvidence (const std::string &nodeName) const final
	indicates whether node id has received a hard evidence More...

virtual bool	hasSoftEvidence (NodeId id) const final
	indicates whether node id has received a soft evidence More...

virtual bool	hasSoftEvidence (const std::string &nodeName) const final
	indicates whether node id has received a soft evidence More...

virtual Size	nbrEvidence () const final
	returns the number of evidence entered into the Bayesian network More...

virtual Size	nbrHardEvidence () const final
	returns the number of hard evidence entered into the Bayesian network More...

virtual Size	nbrSoftEvidence () const final
	returns the number of soft evidence entered into the Bayesian network More...

const NodeProperty< const Potential< GUM_SCALAR > *> &	evidence () const
	returns the set of evidence More...

const NodeSet &	softEvidenceNodes () const
	returns the set of nodes with soft evidence More...

const NodeSet &	hardEvidenceNodes () const
	returns the set of nodes with hard evidence More...

const NodeProperty< Idx > &	hardEvidence () const
	indicate for each node with hard evidence which value it took More...

Getters and setters
void	setEpsilon (double eps)
	Given that we approximate f(t), stopping criterion on \|f(t+1)-f(t)\|. More...

double	epsilon () const
	Returns the value of epsilon. More...

void	disableEpsilon ()
	Disable stopping criterion on epsilon. More...

void	enableEpsilon ()
	Enable stopping criterion on epsilon. More...

bool	isEnabledEpsilon () const
	Returns true if stopping criterion on epsilon is enabled, false otherwise. More...

void	setMinEpsilonRate (double rate)
	Given that we approximate f(t), stopping criterion on d/dt(\|f(t+1)-f(t)\|). More...

double	minEpsilonRate () const
	Returns the value of the minimal epsilon rate. More...

void	disableMinEpsilonRate ()
	Disable stopping criterion on epsilon rate. More...

void	enableMinEpsilonRate ()
	Enable stopping criterion on epsilon rate. More...

bool	isEnabledMinEpsilonRate () const
	Returns true if stopping criterion on epsilon rate is enabled, false otherwise. More...

void	setMaxIter (Size max)
	Stopping criterion on number of iterations. More...

Size	maxIter () const
	Returns the criterion on number of iterations. More...

void	disableMaxIter ()
	Disable stopping criterion on max iterations. More...

void	enableMaxIter ()
	Enable stopping criterion on max iterations. More...

bool	isEnabledMaxIter () const
	Returns true if stopping criterion on max iterations is enabled, false otherwise. More...

void	setMaxTime (double timeout)
	Stopping criterion on timeout. More...

double	maxTime () const
	Returns the timeout (in seconds). More...

double	currentTime () const
	Returns the current running time in second. More...

void	disableMaxTime ()
	Disable stopping criterion on timeout. More...

void	enableMaxTime ()
	Enable stopping criterion on timeout. More...

bool	isEnabledMaxTime () const
	Returns true if stopping criterion on timeout is enabled, false otherwise. More...

void	setPeriodSize (Size p)
	How many samples between two stopping is enable. More...

Size	periodSize () const
	Returns the period size. More...

void	setVerbosity (bool v)
	Set the verbosity on (true) or off (false). More...

bool	verbosity () const
	Returns true if verbosity is enabled. More...

ApproximationSchemeSTATE	stateApproximationScheme () const
	Returns the approximation scheme state. More...

Size	nbrIterations () const
	Returns the number of iterations. More...

const std::vector< double > &	history () const
	Returns the scheme history. More...

void	initApproximationScheme ()
	Initialise the scheme. More...

bool	startOfPeriod ()
	Returns true if we are at the beginning of a period (compute error is mandatory). More...

void	updateApproximationScheme (unsigned int incr=1)
	Update the scheme w.r.t the new error and increment steps. More...

Size	remainingBurnIn ()
	Returns the remaining burn in. More...

void	stopApproximationScheme ()
	Stop the approximation scheme. More...

bool	continueApproximationScheme (double error)
	Update the scheme w.r.t the new error. More...

Getters and setters
std::string	messageApproximationScheme () const
	Returns the approximation scheme message. More...

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

enum	ApproximationSchemeSTATE : char { ApproximationSchemeSTATE::Undefined, ApproximationSchemeSTATE::Continue, ApproximationSchemeSTATE::Epsilon, ApproximationSchemeSTATE::Rate, ApproximationSchemeSTATE::Limit, ApproximationSchemeSTATE::TimeLimit, ApproximationSchemeSTATE::Stopped }
	The different state of an approximation scheme. More...

Protected Attributes
Estimator< GUM_SCALAR >	__estimator
	Estimator object designed to approximate target posteriors. More...

bool	isSetEstimator = false
	whether the Estimator object has been initialized More...

bool	isContextualized = false
	whether the referenced Bayesian Network has been "contextualized" More...

double	_current_epsilon
	Current epsilon. More...

double	_last_epsilon
	Last epsilon value. More...

double	_current_rate
	Current rate. More...

Size	_current_step
	The current step. More...

Timer	_timer
	The timer. More...

ApproximationSchemeSTATE	_current_state
	The current state. More...

std::vector< double >	_history
	The scheme history, used only if verbosity == true. More...

double	_eps
	Threshold for convergence. More...

bool	_enabled_eps
	If true, the threshold convergence is enabled. More...

double	_min_rate_eps
	Threshold for the epsilon rate. More...

bool	_enabled_min_rate_eps
	If true, the minimal threshold for epsilon rate is enabled. More...

double	_max_time
	The timeout. More...

bool	_enabled_max_time
	If true, the timeout is enabled. More...

Size	_max_iter
	The maximum iterations. More...

bool	_enabled_max_iter
	If true, the maximum iterations stopping criterion is enabled. More...

Size	_burn_in
	Number of iterations before checking stopping criteria. More...

Size	_period_size
	Checking criteria frequency. More...

bool	_verbosity
	If true, verbosity is enabled. More...

Protected Member Functions
virtual Instantiation	_burnIn ()=0
	draws samples without updating the estimators More...

virtual Instantiation	_draw (GUM_SCALAR *w, Instantiation prev)=0
	draws a sample in the bayesian network given a previous one More...

void	_makeInference () override
	makes the inference by generating samples More...

void	_loopApproxInference ()

virtual void	_addVarSample (NodeId nod, Instantiation *I)
	adds a node to current instantiation More...

virtual void	_onContextualize (BayesNetFragment< GUM_SCALAR > *bn)
	fired when Bayesian network is contextualized More...

void	_onEvidenceAdded (const NodeId id, bool isHardEvidence) override
	fired after a new evidence is inserted More...

void	_onEvidenceErased (const NodeId id, bool isHardEvidence) override
	fired before an evidence is removed More...

void	_onAllEvidenceErased (bool contains_hard_evidence) override
	fired before all the evidence are erased More...

void	_onEvidenceChanged (const NodeId id, bool hasChangedSoftHard) override
	fired after an evidence is changed, in particular when its status (soft/hard) changes More...

void	_onBayesNetChanged (const IBayesNet< GUM_SCALAR > *bn) override
	fired after a new Bayes net has been assigned to the engine More...

void	_updateOutdatedBNStructure () override
	prepares inference when the latter is in OutdatedBNStructure state More...

void	_updateOutdatedBNPotentials () override
	prepares inference when the latter is in OutdatedBNPotentials state More...

void	_onMarginalTargetAdded (const NodeId id) override
	fired after a new marginal target is inserted More...

void	_onMarginalTargetErased (const NodeId id) override
	fired before a marginal target is removed More...

void	_onAllMarginalTargetsAdded () override
	fired after all the nodes of the BN are added as marginal targets More...

void	_onAllMarginalTargetsErased () override
	fired before a all marginal targets are removed More...

void	_onStateChanged () override
	fired when the stage is changed More...

void	_setTargetedMode ()

bool	_isTargetedMode () const

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::SamplingInference< GUM_SCALAR >

Definition at line 57 of file samplingInference.h.

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

enum gum::IApproximationSchemeConfiguration::ApproximationSchemeSTATE : char

stronginherited

The different state of an approximation scheme.

Enumerator
Undefined
Continue
Epsilon
Rate
Limit
TimeLimit
Stopped

Definition at line 63 of file IApproximationSchemeConfiguration.h.

                                         : char {
       Undefined,
       Continue,
       Epsilon,
       Rate,
       Limit,
       TimeLimit,
       Stopped
     };

◆ StateOfInference

template<typename GUM_SCALAR >

enum gum::BayesNetInference::StateOfInference

stronginherited

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

◆ SamplingInference()

template<typename GUM_SCALAR >

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

explicit

default constructor

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

Definition at line 45 of file samplingInference_tpl.h.

References DEFAULT_EPSILON, DEFAULT_MAXITER, DEFAULT_MIN_EPSILON_RATE, DEFAULT_PERIOD_SIZE, DEFAULT_TIMEOUT, DEFAULT_VERBOSITY, gum::ApproximationScheme::setEpsilon(), gum::ApproximationScheme::setMaxIter(), gum::ApproximationScheme::setMaxTime(), gum::ApproximationScheme::setMinEpsilonRate(), gum::ApproximationScheme::setPeriodSize(), and gum::ApproximationScheme::setVerbosity().

                                         :
       ApproximateInference< GUM_SCALAR >(bn),
       __estimator(), __samplingBN(nullptr) {
     this->setEpsilon(DEFAULT_EPSILON);
     this->setMinEpsilonRate(DEFAULT_MIN_EPSILON_RATE);
     this->setMaxIter(DEFAULT_MAXITER);
     this->setVerbosity(DEFAULT_VERBOSITY);
     this->setPeriodSize(DEFAULT_PERIOD_SIZE);
     this->setMaxTime(DEFAULT_TIMEOUT);
     GUM_CONSTRUCTOR(SamplingInference);
   }

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◆ ~SamplingInference()

template<typename GUM_SCALAR >

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

override

destructor

Definition at line 60 of file samplingInference_tpl.h.

References gum::SamplingInference< GUM_SCALAR >::__samplingBN, and gum::SamplingInference< GUM_SCALAR >::isContextualized.

                                                       {
     GUM_DESTRUCTOR(SamplingInference);
     if (__samplingBN != nullptr) {
       if (isContextualized) {   // otherwise __samplingBN==&BN()
         delete __samplingBN;
       }
     }
   }

Member Function Documentation

◆ _addVarSample()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_addVarSample	(	NodeId	nod,
		Instantiation *	I
	)

protectedvirtual

adds a node to current instantiation

Parameters

nod	the node to add to the sample
I	the current sample

generates random value based on the BN's CPT's and adds the node to the Instantiation with that value

Definition at line 185 of file samplingInference_tpl.h.

References gum::Instantiation::add(), gum::Instantiation::chgVal(), and gum::SamplingInference< GUM_SCALAR >::samplingBN().

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

                                                                         {
     gum::Instantiation Itop = gum::Instantiation(*I);
 
     I->add(samplingBN().variable(nod));
     I->chgVal(samplingBN().variable(nod),
               samplingBN().cpt(nod).extract(Itop).draw());
   }

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◆ _burnIn()

template<typename GUM_SCALAR >

virtual Instantiation gum::SamplingInference< GUM_SCALAR >::_burnIn ( )

protectedpure virtual

draws samples without updating the estimators

Implemented in gum::GibbsSampling< GUM_SCALAR >, gum::ImportanceSampling< GUM_SCALAR >, gum::MonteCarloSampling< GUM_SCALAR >, and gum::WeightedSampling< GUM_SCALAR >.

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

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◆ _draw()

template<typename GUM_SCALAR >

virtual Instantiation gum::SamplingInference< GUM_SCALAR >::_draw	(	GUM_SCALAR *	w,
		Instantiation	prev
	)

protectedpure virtual

draws a sample in the bayesian network given a previous one

Parameters

w	the weight of sample being generated
prev	the previous sample generated

Implemented in gum::GibbsSampling< GUM_SCALAR >, gum::ImportanceSampling< GUM_SCALAR >, gum::MonteCarloSampling< GUM_SCALAR >, and gum::WeightedSampling< GUM_SCALAR >.

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

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◆ _isTargetedMode()

template<typename GUM_SCALAR >

INLINE bool gum::MarginalTargetedInference< GUM_SCALAR >::_isTargetedMode ( ) const

protectedinherited

Definition at line 335 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::__targeted_mode.

                                                                              {
     return __targeted_mode;
   }

◆ _loopApproxInference()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_loopApproxInference ( )

protected

Definition at line 164 of file samplingInference_tpl.h.

References gum::SamplingInference< GUM_SCALAR >::__estimator, gum::SamplingInference< GUM_SCALAR >::_burnIn(), gum::SamplingInference< GUM_SCALAR >::_draw(), gum::SamplingInference< GUM_SCALAR >::contextualize(), gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::initApproximationScheme(), gum::SamplingInference< GUM_SCALAR >::isContextualized, gum::SamplingInference< GUM_SCALAR >::isSetEstimator, and gum::ApproximationScheme::updateApproximationScheme().

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

                                                              {
     //@todo This should be in __prepareInference
     if (!isContextualized) { this->contextualize(); }
 
     this->initApproximationScheme();
     gum::Instantiation Ip;
     GUM_SCALAR         w = .0;   //
 
     // Burn in
     Ip = this->_burnIn();
     do {
       Ip = this->_draw(&w, Ip);
       __estimator.update(Ip, w);
       this->updateApproximationScheme();
     } while (this->continueApproximationScheme(__estimator.confidence()));
 
     this->isSetEstimator = false;
   }

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◆ _makeInference()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_makeInference ( )

overrideprotectedvirtual

makes the inference by generating samples

Implements gum::BayesNetInference< GUM_SCALAR >.

Definition at line 158 of file samplingInference_tpl.h.

References gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromBN(), and gum::SamplingInference< GUM_SCALAR >::isSetEstimator.

                                                        {
     if (!isSetEstimator) this->_setEstimatorFromBN();
     _loopApproxInference();
   }

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◆ _onAllEvidenceErased()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_onAllEvidenceErased ( bool contains_hard_evidence )

overrideprotectedvirtual

fired before all the evidence are erased

Implements gum::BayesNetInference< GUM_SCALAR >.

Definition at line 212 of file samplingInference_tpl.h.

213 {}

◆ _onAllMarginalTargetsAdded()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_onAllMarginalTargetsAdded ( )

overrideprotectedvirtual

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

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 241 of file samplingInference_tpl.h.

241 {}

◆ _onAllMarginalTargetsErased()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_onAllMarginalTargetsErased ( )

overrideprotectedvirtual

fired before a all marginal targets are removed

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 244 of file samplingInference_tpl.h.

244 {}

◆ _onBayesNetChanged()

template<typename GUM_SCALAR >

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

overrideprotectedvirtual

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

Reimplemented from gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 225 of file samplingInference_tpl.h.

226 {}

◆ _onContextualize()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_onContextualize ( BayesNetFragment< GUM_SCALAR > * bn )

protectedvirtual

fired when Bayesian network is contextualized

Parameters

bn	the contextualized BayesNetFragment
targets	inference target variables
hardEvNodes	hard evidence nodes
hardEv	hard evidences values

Reimplemented in gum::ImportanceSampling< GUM_SCALAR >.

Definition at line 195 of file samplingInference_tpl.h.

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

196 {}

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◆ _onEvidenceAdded()

template<typename GUM_SCALAR >

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

overrideprotectedvirtual

fired after a new evidence is inserted

Implements gum::BayesNetInference< GUM_SCALAR >.

Definition at line 200 of file samplingInference_tpl.h.

References GUM_ERROR.

                                                                               {
     if (!isHardEvidence) {
       GUM_ERROR(FatalError, "Approximated inference only accept hard evidence");
     }
   }

◆ _onEvidenceChanged()

template<typename GUM_SCALAR >

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

overrideprotectedvirtual

fired after an evidence is changed, in particular when its status (soft/hard) changes

Parameters

nodeId	the node of the changed evidence
hasChangedSoftHard	true if the evidence has changed from Soft to Hard or from Hard to Soft

Implements gum::BayesNetInference< GUM_SCALAR >.

Definition at line 217 of file samplingInference_tpl.h.

References GUM_ERROR.

                                                                                   {
     if (hasChangedSoftHard) {
       GUM_ERROR(FatalError, "Approximated inference only accept hard evidence");
     }
   }

◆ _onEvidenceErased()

template<typename GUM_SCALAR >

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

overrideprotectedvirtual

fired before an evidence is removed

Implements gum::BayesNetInference< GUM_SCALAR >.

Definition at line 208 of file samplingInference_tpl.h.

209 {}

◆ _onMarginalTargetAdded()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_onMarginalTargetAdded ( const NodeId id )

overrideprotectedvirtual

fired after a new marginal target is inserted

Parameters

id	The target variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 235 of file samplingInference_tpl.h.

235 {}

◆ _onMarginalTargetErased()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_onMarginalTargetErased ( const NodeId id )

overrideprotectedvirtual

fired before a marginal target is removed

Parameters

id	The target variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 238 of file samplingInference_tpl.h.

238 {}

◆ _onStateChanged()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_onStateChanged ( )

overrideprotectedvirtual

fired when the stage is changed

Implements gum::BayesNetInference< GUM_SCALAR >.

Definition at line 247 of file samplingInference_tpl.h.

References gum::SamplingInference< GUM_SCALAR >::__estimator, gum::ApproximationScheme::initApproximationScheme(), and gum::BayesNetInference< GUM_SCALAR >::isInferenceReady().

                                                         {
     if (this->isInferenceReady()) {
       __estimator.clear();
       this->initApproximationScheme();
     }
   }

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◆ _posterior()

template<typename GUM_SCALAR >

const Potential< GUM_SCALAR > & gum::SamplingInference< GUM_SCALAR >::_posterior ( NodeId id )

overridevirtual

Computes and returns the posterior of a node.

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

Parameters

id	the node for which we need a posterior probability

Warning: for efficiency reasons, the potential is returned by reference. In order to ensure that the potential may still exist even if the Inference object is destroyed, the user has to copy it explicitly.

Exceptions

UndefinedElement	if node is not in the set of targets.
NotFound	if node is not in the BN.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 106 of file samplingInference_tpl.h.

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

                                                           {
     return __estimator.posterior(this->BN().variable(id));
   }

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◆ _setEstimatorFromBN()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromBN ( )

virtual

Initializes the estimators object linked to the simulation.

Initializes the estimator object by creating a hashtable between non evidence nodes and a 0-filled potential which will approximate the node's posterior

Definition at line 79 of file samplingInference_tpl.h.

References gum::SamplingInference< GUM_SCALAR >::__estimator, gum::BayesNetInference< GUM_SCALAR >::hardEvidenceNodes(), gum::SamplingInference< GUM_SCALAR >::isSetEstimator, and gum::SamplingInference< GUM_SCALAR >::samplingBN().

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

                                                             {
     __estimator.setFromBN(&samplingBN(), this->hardEvidenceNodes());
     this->isSetEstimator = true;
   }

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◆ _setEstimatorFromLBP()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromLBP	(	LoopyBeliefPropagation< GUM_SCALAR > *	lbp,
		GUM_SCALAR	virtualLBPSize
	)

virtual

Initializes the estimators object linked to the simulation.

Parameters

lbp	a LoopyBeliefPropagation object
virtualLBPSize	the size of the equivalent sampling by LBP

Initializes the estimator object by creating a hashtable between non evidence nodes and the current approximation of the node's posterior obtained by running LoopyBeliefPropagation algorithm

Definition at line 85 of file samplingInference_tpl.h.

References gum::SamplingInference< GUM_SCALAR >::__estimator, gum::BayesNetInference< GUM_SCALAR >::hardEvidenceNodes(), and gum::SamplingInference< GUM_SCALAR >::isSetEstimator.

                                                                            {
     __estimator.setFromLBP(lbp, this->hardEvidenceNodes(), virtualLBPSize);
     this->isSetEstimator = true;
   }

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◆ _setOutdatedBNPotentialsState()

template<typename GUM_SCALAR >

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

protectedinherited

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);
   }

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◆ _setOutdatedBNStructureState()

template<typename GUM_SCALAR >

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

protectedinherited

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);
   }

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◆ _setTargetedMode()

template<typename GUM_SCALAR >

INLINE void gum::MarginalTargetedInference< GUM_SCALAR >::_setTargetedMode ( )

protectedinherited

Definition at line 339 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::__targeted_mode, gum::MarginalTargetedInference< GUM_SCALAR >::__targets, and gum::Set< Key, Alloc >::clear().

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

                                                                         {
     if (!__targeted_mode) {
       __targets.clear();
       __targeted_mode = true;
     }
   }

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◆ _updateOutdatedBNPotentials()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_updateOutdatedBNPotentials ( )

overrideprotectedvirtual

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.

Implements gum::BayesNetInference< GUM_SCALAR >.

Definition at line 232 of file samplingInference_tpl.h.

232 {}

◆ _updateOutdatedBNStructure()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::_updateOutdatedBNStructure ( )

overrideprotectedvirtual

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.

Implements gum::BayesNetInference< GUM_SCALAR >.

Definition at line 229 of file samplingInference_tpl.h.

229 {}

◆ addAllTargets()

template<typename GUM_SCALAR >

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

finalvirtualinherited

adds all nodes as targets

Definition at line 133 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::MarginalTargetedInference< GUM_SCALAR >::__targets, gum::MarginalTargetedInference< GUM_SCALAR >::_onMarginalTargetAdded(), gum::MarginalTargetedInference< GUM_SCALAR >::_setTargetedMode(), gum::Set< Key, Alloc >::contains(), GUM_ERROR, and gum::Set< Key, Alloc >::insert().

                                                               {
     // check if the node belongs to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
 
     _setTargetedMode();   // does nothing if already in targeted mode
     for (const auto target : this->__bn->dag()) {
       if (!__targets.contains(target)) {
         __targets.insert(target);
         _onMarginalTargetAdded(target);
         this->__setState(
            BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
       }
     }
   }

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◆ addEvidence() [1/8]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 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));
   }

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◆ addEvidence() [2/8]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 252 of file BayesNetInference_tpl.h.

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

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

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◆ addEvidence() [3/8]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 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]);
   }

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◆ addEvidence() [4/8]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 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]);
   }

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◆ addEvidence() [5/8]

template<typename GUM_SCALAR >

void gum::BayesNetInference< 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 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));
   }

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◆ addEvidence() [6/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 302 of file BayesNetInference_tpl.h.

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

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

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◆ addEvidence() [7/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 351 of file BayesNetInference_tpl.h.

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

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

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◆ addEvidence() [8/8]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 310 of file BayesNetInference_tpl.h.

                                                                                {
     // 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);
   }

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◆ addListOfEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

adds a new list of evidence

Exceptions

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

Definition at line 360 of file BayesNetInference_tpl.h.

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

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

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◆ addSetOfEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

adds a new set of evidence

Exceptions

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

Definition at line 369 of file BayesNetInference_tpl.h.

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

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

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◆ addTarget() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement if target is not a NodeId in the Bayes net

Definition at line 109 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::MarginalTargetedInference< GUM_SCALAR >::__targets, gum::MarginalTargetedInference< GUM_SCALAR >::_onMarginalTargetAdded(), gum::MarginalTargetedInference< GUM_SCALAR >::_setTargetedMode(), gum::Set< Key, Alloc >::contains(), GUM_ERROR, and gum::Set< Key, Alloc >::insert().

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

                                                                        {
     // check if the node belongs to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     if (!this->__bn->dag().exists(target)) {
       GUM_ERROR(UndefinedElement, target << " is not a NodeId in the bn");
     }
 
     _setTargetedMode();   // does nothing if already in targeted mode
     // add the new target
     if (!__targets.contains(target)) {
       __targets.insert(target);
       _onMarginalTargetAdded(target);
       this->__setState(
          BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
     }
   }

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◆ addTarget() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement if target is not a NodeId in the Bayes net

Definition at line 155 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), and GUM_ERROR.

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

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◆ BN()

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 118 of file BayesNetInference_tpl.h.

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

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

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◆ chgEvidence() [1/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 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));
   }

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◆ chgEvidence() [2/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 437 of file BayesNetInference_tpl.h.

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

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

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◆ chgEvidence() [3/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 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]);
   }

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◆ chgEvidence() [4/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 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]);
   }

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◆ chgEvidence() [5/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 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);
   }

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◆ chgEvidence() [6/7]

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 488 of file BayesNetInference_tpl.h.

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

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

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◆ chgEvidence() [7/7]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

Exceptions

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

Definition at line 496 of file BayesNetInference_tpl.h.

                                          {
     // 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);
   }

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◆ clear()

template<typename GUM_SCALAR >

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

virtualinherited

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);
   }

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◆ contextualize()

template<typename GUM_SCALAR >

void gum::SamplingInference< GUM_SCALAR >::contextualize ( )

virtual

Simplifying the bayesian network with relevance reasonning to lighten the computational charge.

Sets the reference Bayesian Network as a BayesNetFragment after having eliminated nodes that are idle for simulation and computation, such as barren or d-separated nodes. Eliminates the arcs from evidence nodes to it's children, after setting new CPT's for them.

Definition at line 111 of file samplingInference_tpl.h.

References gum::SamplingInference< GUM_SCALAR >::__samplingBN, gum::SamplingInference< GUM_SCALAR >::_onContextualize(), gum::Instantiation::add(), gum::BarrenNodesFinder::barrenNodes(), gum::BayesNetInference< GUM_SCALAR >::BN(), gum::Instantiation::chgVal(), gum::BayesNetInference< GUM_SCALAR >::hardEvidence(), gum::BayesNetInference< GUM_SCALAR >::hardEvidenceNodes(), gum::SamplingInference< GUM_SCALAR >::isContextualized, gum::dSeparation::requisiteNodes(), gum::BarrenNodesFinder::setEvidence(), gum::BarrenNodesFinder::setTargets(), gum::BayesNetInference< GUM_SCALAR >::softEvidenceNodes(), and gum::MarginalTargetedInference< GUM_SCALAR >::targets().

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

                                                       {
     // Finding Barren nodes
 
     BarrenNodesFinder barr_nodes = BarrenNodesFinder(&this->BN().dag());
     barr_nodes.setTargets(&this->targets());
     barr_nodes.setEvidence(&this->hardEvidenceNodes());
     const NodeSet& barren = barr_nodes.barrenNodes();
 
     // creating BN fragment
     __samplingBN = new BayesNetFragment< GUM_SCALAR >(this->BN());
     for (const auto elmt : this->BN().dag().asNodeSet() - barren)
       __samplingBN->installNode(elmt);
 
     // D-separated nodes
 
     dSeparation dsep = gum::dSeparation();
     NodeSet     requisite;
     dsep.requisiteNodes(
        this->BN().dag(),
        this->BN().nodes().asNodeSet(),   // no target for approximateInference
        this->hardEvidenceNodes(),
        this->softEvidenceNodes(),   // should be empty
        requisite);
     requisite += this->hardEvidenceNodes();
 
     auto nonRequisite = this->BN().dag().asNodeSet() - requisite;
 
     for (const auto elmt : nonRequisite)
       __samplingBN->uninstallNode(elmt);
     for (const auto hard : this->hardEvidenceNodes()) {
       gum::Instantiation I;
       I.add(this->BN().variable(hard));
       I.chgVal(this->BN().variable(hard), this->hardEvidence()[hard]);
 
       for (const auto& child : this->BN().children(hard)) {
         auto p = new gum::Potential< GUM_SCALAR >();
         *p = this->BN().cpt(child).extract(I);
         __samplingBN->installCPT(child, p);
       }
     }
 
     this->isContextualized = true;
     this->_onContextualize(__samplingBN);
   }

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◆ continueApproximationScheme()

INLINE bool gum::ApproximationScheme::continueApproximationScheme ( double error )

inherited

Update the scheme w.r.t the new error.

Test the stopping criterion that are enabled.

Parameters

error The new error value.

Returns: false if state become != ApproximationSchemeSTATE::Continue

Exceptions

OperationNotAllowed Raised if state != ApproximationSchemeSTATE::Continue.

Definition at line 225 of file approximationScheme_inl.h.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                            {
     // For coherence, we fix the time used in the method
 
     double timer_step = _timer.step();
 
     if (_enabled_max_time) {
       if (timer_step > _max_time) {
         _stopScheme(ApproximationSchemeSTATE::TimeLimit);
         return false;
       }
     }
 
     if (!startOfPeriod()) { return true; }
 
     if (_current_state != ApproximationSchemeSTATE::Continue) {
       GUM_ERROR(OperationNotAllowed,
                 "state of the approximation scheme is not correct : "
                    + messageApproximationScheme());
     }
 
     if (verbosity()) { _history.push_back(error); }
 
     if (_enabled_max_iter) {
       if (_current_step > _max_iter) {
         _stopScheme(ApproximationSchemeSTATE::Limit);
         return false;
       }
     }
 
     _last_epsilon = _current_epsilon;
     _current_epsilon = error;   // eps rate isEnabled needs it so affectation was
     // moved from eps isEnabled below
 
     if (_enabled_eps) {
       if (_current_epsilon <= _eps) {
         _stopScheme(ApproximationSchemeSTATE::Epsilon);
         return false;
       }
     }
 
     if (_last_epsilon >= 0.) {
       if (_current_epsilon > .0) {
         // ! _current_epsilon can be 0. AND epsilon
         // isEnabled can be disabled !
         _current_rate =
            std::fabs((_current_epsilon - _last_epsilon) / _current_epsilon);
       }
       // limit with current eps ---> 0 is | 1 - ( last_eps / 0 ) | --->
       // infinity the else means a return false if we isEnabled the rate below,
       // as we would have returned false if epsilon isEnabled was enabled
       else {
         _current_rate = _min_rate_eps;
       }
 
       if (_enabled_min_rate_eps) {
         if (_current_rate <= _min_rate_eps) {
           _stopScheme(ApproximationSchemeSTATE::Rate);
           return false;
         }
       }
     }
 
     if (stateApproximationScheme() == ApproximationSchemeSTATE::Continue) {
       if (onProgress.hasListener()) {
         GUM_EMIT3(onProgress, _current_step, _current_epsilon, timer_step);
       }
 
       return true;
     } else {
       return false;
     }
   }

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◆ currentPosterior() [1/2]

template<typename GUM_SCALAR >

const Potential< GUM_SCALAR > & gum::SamplingInference< GUM_SCALAR >::currentPosterior ( NodeId id )

Computes and returns the actual estimation of the posterior of a node.

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

Parameters

id	the node for which we need a posterior probability

Warning: for efficiency reasons, the potential is returned by reference. In order to ensure that the potential may still exist even if the Inference object is destroyed, the user has to copy it explicitly.

Exceptions

UndefinedElement	if node is not in the set of targets.
NotFound	if node is not in the BN.

Definition at line 94 of file samplingInference_tpl.h.

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

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

                                                                 {
     return __estimator.posterior(this->BN().variable(id));
   }

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◆ currentPosterior() [2/2]

template<typename GUM_SCALAR >

const Potential< GUM_SCALAR > & gum::SamplingInference< GUM_SCALAR >::currentPosterior ( const std::string & name )

Computes and returns the actual estimation of the posterior of a node by its name.

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

Parameters

name	the name of the node for which we need a posterior probability

Warning: for efficiency reasons, the potential is returned by reference. In order to ensure that the potential may still exist even if the Inference object is destroyed, the user has to copy it explicitly.

Exceptions

UndefinedElement	if node corresponding to name is not in the set of targets.
NotFound	if node corresponding to name is not in the BN.

Definition at line 100 of file samplingInference_tpl.h.

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

                                                                             {
     return currentPosterior(this->BN().idFromName(name));
   }

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◆ currentTime()

INLINE double gum::ApproximationScheme::currentTime ( ) const

virtualinherited

Returns the current running time in second.

Returns: Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 126 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_timer, and gum::Timer::step().

Referenced by gum::learning::genericBNLearner::currentTime().

126 { return _timer.step(); }

gum::Timer::step

double step() const

Returns the delta time between now and the last reset() call (or the constructor).

Definition: timer_inl.h:39

gum::ApproximationScheme::_timer

Timer _timer

The timer.

Definition: approximationScheme.h:379

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◆ disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

virtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 52 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

Referenced by gum::learning::genericBNLearner::disableEpsilon().

52 { _enabled_eps = false; }

gum::ApproximationScheme::_enabled_eps

bool _enabled_eps

If true, the threshold convergence is enabled.

Definition: approximationScheme.h:391

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◆ disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

virtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 103 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::learning::genericBNLearner::disableMaxIter(), and gum::learning::GreedyHillClimbing::GreedyHillClimbing().

103 { _enabled_max_iter = false; }

gum::ApproximationScheme::_enabled_max_iter

bool _enabled_max_iter

If true, the maximum iterations stopping criterion is enabled.

Definition: approximationScheme.h:409

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◆ disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

virtualinherited

Disable stopping criterion on timeout.

Returns: Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 129 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

Referenced by gum::learning::genericBNLearner::disableMaxTime(), and gum::learning::GreedyHillClimbing::GreedyHillClimbing().

129 { _enabled_max_time = false; }

gum::ApproximationScheme::_enabled_max_time

bool _enabled_max_time

If true, the timeout is enabled.

Definition: approximationScheme.h:403

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◆ disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

virtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 77 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::learning::genericBNLearner::disableMinEpsilonRate(), and gum::learning::GreedyHillClimbing::GreedyHillClimbing().

                                                          {
     _enabled_min_rate_eps = false;
   }

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◆ domainSizes()

template<typename GUM_SCALAR >

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

finalvirtualinherited

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;
   }

◆ enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

virtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 55 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), and gum::learning::genericBNLearner::enableEpsilon().

55 { _enabled_eps = true; }

gum::ApproximationScheme::_enabled_eps

bool _enabled_eps

If true, the threshold convergence is enabled.

Definition: approximationScheme.h:391

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◆ enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

virtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 106 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

Referenced by gum::learning::genericBNLearner::enableMaxIter().

106 { _enabled_max_iter = true; }

gum::ApproximationScheme::_enabled_max_iter

bool _enabled_max_iter

If true, the maximum iterations stopping criterion is enabled.

Definition: approximationScheme.h:409

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◆ enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

virtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 132 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), and gum::learning::genericBNLearner::enableMaxTime().

132 { _enabled_max_time = true; }

gum::ApproximationScheme::_enabled_max_time

bool _enabled_max_time

If true, the timeout is enabled.

Definition: approximationScheme.h:403

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◆ enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

virtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 82 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::enableMinEpsilonRate().

                                                         {
     _enabled_min_rate_eps = true;
   }

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◆ epsilon()

INLINE double gum::ApproximationScheme::epsilon ( ) const

virtualinherited

Returns the value of epsilon.

Returns: Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 49 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_eps.

Referenced by gum::ImportanceSampling< GUM_SCALAR >::_onContextualize(), and gum::learning::genericBNLearner::epsilon().

49 { return _eps; }

gum::ApproximationScheme::_eps

double _eps

Threshold for convergence.

Definition: approximationScheme.h:388

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◆ eraseAllEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

removes all the evidence entered into the network

Definition at line 592 of file BayesNetInference_tpl.h.

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);
       }
     }
   }

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◆ eraseAllTargets()

template<typename GUM_SCALAR >

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

virtualinherited

Clear all previously defined targets.

Reimplemented in gum::JointTargetedInference< GUM_SCALAR >.

Definition at line 96 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::MarginalTargetedInference< GUM_SCALAR >::__targets, gum::MarginalTargetedInference< GUM_SCALAR >::_onAllMarginalTargetsErased(), gum::MarginalTargetedInference< GUM_SCALAR >::_setTargetedMode(), and gum::Set< Key, Alloc >::clear().

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

                                                                        {
     _onAllMarginalTargetsErased();
 
     __targets.clear();
     _setTargetedMode();   // does nothing if already in targeted mode
 
     this->__setState(
        BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
   }

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◆ eraseEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

removed the evidence, if any, corresponding to node id

Definition at line 563 of file BayesNetInference_tpl.h.

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);
     }
   }

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◆ eraseEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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));
   }

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◆ eraseTarget() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

removes an existing (marginal) target

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

Definition at line 169 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::BayesNetInference< GUM_SCALAR >::__setState(), gum::MarginalTargetedInference< GUM_SCALAR >::__targeted_mode, gum::MarginalTargetedInference< GUM_SCALAR >::__targets, gum::MarginalTargetedInference< GUM_SCALAR >::_onMarginalTargetErased(), gum::Set< Key, Alloc >::contains(), gum::Set< Key, Alloc >::erase(), and GUM_ERROR.

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget().

                                                                          {
     // check if the node belongs to the Bayesian network
     if (this->__bn == nullptr)
       GUM_ERROR(NullElement,
                 "No Bayes net has been assigned to the "
                 "inference algorithm");
 
     if (!this->__bn->dag().exists(target)) {
       GUM_ERROR(UndefinedElement, target << " is not a NodeId in the bn");
     }
 
 
     if (__targets.contains(target)) {
       __targeted_mode = true;   // we do not use _setTargetedMode because we do not
                                 // want to clear the targets
       _onMarginalTargetErased(target);
       __targets.erase(target);
       this->__setState(
          BayesNetInference< GUM_SCALAR >::StateOfInference::OutdatedBNStructure);
     }
   }

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◆ eraseTarget() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

removes an existing (marginal) target

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

Definition at line 194 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), and GUM_ERROR.

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

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◆ evidence()

template<typename GUM_SCALAR >

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

inherited

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;
   }

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◆ evidenceImpact() [1/2]

template<typename GUM_SCALAR >

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

inherited

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

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

Parameters

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

Returns: a Potential

Definition at line 282 of file marginalTargetedInference_tpl.h.

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact().

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

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◆ evidenceImpact() [2/2]

template<typename GUM_SCALAR >

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

inherited

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

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

Parameters

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

Returns: a Potential

Definition at line 321 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::BN(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), and gum::Set< Key, Alloc >::insert().

                                                                  {
     const auto& bn = this->BN();
 
     gum::NodeSet evsId;
     for (const auto& evname : evs) {
       evsId.insert(bn.idFromName(evname));
     }
 
     return evidenceImpact(bn.idFromName(target), evsId);
   }

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◆ H() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

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

Definition at line 266 of file marginalTargetedInference_tpl.h.

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

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

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

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◆ H() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

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

Definition at line 275 of file marginalTargetedInference_tpl.h.

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

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

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◆ hardEvidence()

template<typename GUM_SCALAR >

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

inherited

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;
   }

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◆ hardEvidenceNodes()

template<typename GUM_SCALAR >

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

inherited

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;
   }

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◆ hasEvidence() [1/3]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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();
   }

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◆ hasEvidence() [2/3]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

finalvirtualinherited

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));
   }

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◆ hasHardEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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);
   }

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◆ hasHardEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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));
   }

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◆ hasSoftEvidence() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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);
   }

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◆ hasSoftEvidence() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

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));
   }

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◆ history()

INLINE const std::vector< double > & gum::ApproximationScheme::history ( ) const

virtualinherited

Returns the scheme history.

Returns: Returns the scheme history.

Exceptions

OperationNotAllowed Raised if the scheme did not performed or if verbosity is set to false.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 171 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_history, GUM_ERROR, gum::ApproximationScheme::stateApproximationScheme(), gum::IApproximationSchemeConfiguration::Undefined, and gum::ApproximationScheme::verbosity().

Referenced by gum::learning::genericBNLearner::history().

                                                                        {
     if (stateApproximationScheme() == ApproximationSchemeSTATE::Undefined) {
       GUM_ERROR(OperationNotAllowed,
                 "state of the approximation scheme is udefined");
     }
 
     if (verbosity() == false) {
       GUM_ERROR(OperationNotAllowed, "No history when verbosity=false");
     }
 
     return _history;
   }

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◆ initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 185 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_epsilon, gum::ApproximationScheme::_current_rate, gum::ApproximationScheme::_current_state, gum::ApproximationScheme::_current_step, gum::ApproximationScheme::_history, gum::ApproximationScheme::_timer, gum::IApproximationSchemeConfiguration::Continue, and gum::Timer::reset().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::SamplingInference< GUM_SCALAR >::_onStateChanged(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::GreedyHillClimbing::learnStructure(), and gum::learning::LocalSearchWithTabuList::learnStructure().

                                                            {
     _current_state = ApproximationSchemeSTATE::Continue;
     _current_step = 0;
     _current_epsilon = _current_rate = -1.0;
     _history.clear();
     _timer.reset();
   }

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◆ isDone()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

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);
   }

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◆ isEnabledEpsilon()

INLINE bool gum::ApproximationScheme::isEnabledEpsilon ( ) const

virtualinherited

Returns true if stopping criterion on epsilon is enabled, false otherwise.

Returns: Returns true if stopping criterion on epsilon is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 59 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

Referenced by gum::learning::genericBNLearner::isEnabledEpsilon().

                                                           {
     return _enabled_eps;
   }

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◆ isEnabledMaxIter()

INLINE bool gum::ApproximationScheme::isEnabledMaxIter ( ) const

virtualinherited

Returns true if stopping criterion on max iterations is enabled, false otherwise.

Returns: Returns true if stopping criterion on max iterations is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 110 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

Referenced by gum::learning::genericBNLearner::isEnabledMaxIter().

                                                           {
     return _enabled_max_iter;
   }

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◆ isEnabledMaxTime()

INLINE bool gum::ApproximationScheme::isEnabledMaxTime ( ) const

virtualinherited

Returns true if stopping criterion on timeout is enabled, false otherwise.

Returns: Returns true if stopping criterion on timeout is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 136 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

Referenced by gum::learning::genericBNLearner::isEnabledMaxTime().

                                                           {
     return _enabled_max_time;
   }

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◆ isEnabledMinEpsilonRate()

INLINE bool gum::ApproximationScheme::isEnabledMinEpsilonRate ( ) const

virtualinherited

Returns true if stopping criterion on epsilon rate is enabled, false otherwise.

Returns: Returns true if stopping criterion on epsilon rate is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 88 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::isEnabledMinEpsilonRate().

                                                                  {
     return _enabled_min_rate_eps;
   }

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◆ isInferenceDone()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

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

finalvirtualnoexceptinherited

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

finalvirtualnoexceptinherited

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);
   }

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◆ isInferenceReady()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

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);
   }

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◆ isTarget() [1/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 73 of file marginalTargetedInference_tpl.h.

References gum::BayesNetInference< GUM_SCALAR >::__bn, gum::MarginalTargetedInference< GUM_SCALAR >::__targets, gum::Set< Key, Alloc >::contains(), and GUM_ERROR.

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

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

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◆ isTarget() [2/2]

template<typename GUM_SCALAR >

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

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 88 of file marginalTargetedInference_tpl.h.

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

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

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◆ makeInference()

template<typename GUM_SCALAR >

INLINE void gum::BayesNetInference< 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 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);
   }

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◆ maxIter()

INLINE Size gum::ApproximationScheme::maxIter ( ) const

virtualinherited

Returns the criterion on number of iterations.

Returns: Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 100 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_iter.

Referenced by gum::learning::genericBNLearner::maxIter().

100 { return _max_iter; }

gum::ApproximationScheme::_max_iter

Size _max_iter

The maximum iterations.

Definition: approximationScheme.h:406

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◆ maxTime()

INLINE double gum::ApproximationScheme::maxTime ( ) const

virtualinherited

Returns the timeout (in seconds).

Returns: Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 123 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_time.

Referenced by gum::learning::genericBNLearner::maxTime().

123 { return _max_time; }

gum::ApproximationScheme::_max_time

double _max_time

The timeout.

Definition: approximationScheme.h:400

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◆ messageApproximationScheme()

INLINE std::string gum::IApproximationSchemeConfiguration::messageApproximationScheme ( ) const

inherited

Returns the approximation scheme message.

Returns: Returns the approximation scheme message.

Definition at line 38 of file IApproximationSchemeConfiguration_inl.h.

Referenced by gum::ApproximationScheme::_stopScheme(), gum::ApproximationScheme::continueApproximationScheme(), and gum::credal::InferenceEngine< GUM_SCALAR >::getApproximationSchemeMsg().

                                                                          {
     std::stringstream s;
 
     switch (stateApproximationScheme()) {
       case ApproximationSchemeSTATE::Continue: s << "in progress"; break;
 
       case ApproximationSchemeSTATE::Epsilon:
         s << "stopped with epsilon=" << epsilon();
         break;
 
       case ApproximationSchemeSTATE::Rate:
         s << "stopped with rate=" << minEpsilonRate();
         break;
 
       case ApproximationSchemeSTATE::Limit:
         s << "stopped with max iteration=" << maxIter();
         break;
 
       case ApproximationSchemeSTATE::TimeLimit:
         s << "stopped with timeout=" << maxTime();
         break;
 
       case ApproximationSchemeSTATE::Stopped: s << "stopped on request"; break;
 
       case ApproximationSchemeSTATE::Undefined: s << "undefined state"; break;
     };
 
     return s.str();
   }

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◆ minEpsilonRate()

INLINE double gum::ApproximationScheme::minEpsilonRate ( ) const

virtualinherited

Returns the value of the minimal epsilon rate.

Returns: Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 72 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_min_rate_eps.

Referenced by gum::learning::genericBNLearner::minEpsilonRate().

                                                           {
     return _min_rate_eps;
   }

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◆ nbrEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

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

finalvirtualinherited

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();
   }

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◆ nbrIterations()

INLINE Size gum::ApproximationScheme::nbrIterations ( ) const

virtualinherited

Returns the number of iterations.

Returns: Returns the number of iterations.

Exceptions

OperationNotAllowed Raised if the scheme did not perform.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 161 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_step, GUM_ERROR, gum::ApproximationScheme::stateApproximationScheme(), and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::nbrIterations().

                                                        {
     if (stateApproximationScheme() == ApproximationSchemeSTATE::Undefined) {
       GUM_ERROR(OperationNotAllowed,
                 "state of the approximation scheme is undefined");
     }
 
     return _current_step;
   }

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◆ nbrSoftEvidence()

template<typename GUM_SCALAR >

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

finalvirtualinherited

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();
   }

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◆ nbrTargets()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

returns the number of marginal targets

Definition at line 215 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::__targets, and gum::Set< Key, Alloc >::size().

               {
     return __targets.size();
   }

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◆ periodSize()

INLINE Size gum::ApproximationScheme::periodSize ( ) const

virtualinherited

Returns the period size.

Returns: Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 147 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_period_size.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference(), and gum::learning::genericBNLearner::periodSize().

147 { return _period_size; }

gum::ApproximationScheme::_period_size

Size _period_size

Checking criteria frequency.

Definition: approximationScheme.h:415

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◆ posterior() [1/2]

template<typename GUM_SCALAR >

const Potential< GUM_SCALAR > & gum::MarginalTargetedInference< GUM_SCALAR >::posterior ( NodeId node )

virtualinherited

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 potential is stored into the inference engine and is returned by reference. In order to ensure that the potential 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::JointTargetedInference< GUM_SCALAR >.

Definition at line 239 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::_posterior(), gum::BayesNetInference< GUM_SCALAR >::evidence(), GUM_ERROR, gum::BayesNetInference< GUM_SCALAR >::hardEvidenceNodes(), gum::BayesNetInference< GUM_SCALAR >::isDone(), gum::MarginalTargetedInference< GUM_SCALAR >::isTarget(), and gum::BayesNetInference< GUM_SCALAR >::makeInference().

Referenced by gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::MarginalTargetedInference< GUM_SCALAR >::H(), gum::JointTargetedInference< GUM_SCALAR >::posterior(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), and gum::Estimator< GUM_SCALAR >::setFromLBP().

                                                                    {
     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->isDone()) { this->makeInference(); }
 
     return _posterior(node);
   }

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◆ posterior() [2/2]

template<typename GUM_SCALAR >

const Potential< GUM_SCALAR > & gum::MarginalTargetedInference< GUM_SCALAR >::posterior ( const std::string & nodeName )

virtualinherited

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 potential is stored into the inference engine and is returned by reference. In order to ensure that the potential 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::JointTargetedInference< GUM_SCALAR >.

Definition at line 257 of file marginalTargetedInference_tpl.h.

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

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

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◆ prepareInference()

template<typename GUM_SCALAR >

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

finalvirtualinherited

prepare the internal inference structures for the next inference

Definition at line 689 of file BayesNetInference_tpl.h.

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);
   }

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◆ remainingBurnIn()

INLINE Size gum::ApproximationScheme::remainingBurnIn ( )

inherited

Returns the remaining burn in.

Returns: Returns the remaining burn in.

Definition at line 208 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_burn_in, and gum::ApproximationScheme::_current_step.

                                                    {
     if (_burn_in > _current_step) {
       return _burn_in - _current_step;
     } else {
       return 0;
     }
   }

◆ samplingBN()

template<typename GUM_SCALAR >

INLINE const IBayesNet< GUM_SCALAR > & gum::SamplingInference< GUM_SCALAR >::samplingBN ( )

get the BayesNet which is used to really perform the sampling

Definition at line 71 of file samplingInference_tpl.h.

References gum::SamplingInference< GUM_SCALAR >::__samplingBN, gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::BayesNetInference< GUM_SCALAR >::prepareInference().

Referenced by gum::SamplingInference< GUM_SCALAR >::_addVarSample(), gum::ImportanceSampling< GUM_SCALAR >::_draw(), and gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromBN().

                                                            {
     this->prepareInference();
     if (__samplingBN == nullptr)
       return this->BN();
     else
       return *__samplingBN;
   }

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◆ setBN()

template<typename GUM_SCALAR >

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

virtualinherited

assigns a new BN to the inference engine

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

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

Definition at line 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);
   }

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◆ setEpsilon()

INLINE void gum::ApproximationScheme::setEpsilon ( double eps )

virtualinherited

Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|.

If the criterion was disabled it will be enabled.

Parameters

eps	The new epsilon value.

Exceptions

OutOfLowerBound Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 41 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps, gum::ApproximationScheme::_eps, and GUM_ERROR.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setEpsilon().

                                                         {
     if (eps < 0.) { GUM_ERROR(OutOfLowerBound, "eps should be >=0"); }
 
     _eps = eps;
     _enabled_eps = true;
   }

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◆ setMaxIter()

INLINE void gum::ApproximationScheme::setMaxIter ( Size max )

virtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfLowerBound Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 93 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter, gum::ApproximationScheme::_max_iter, and GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMaxIter().

                                                       {
     if (max < 1) { GUM_ERROR(OutOfLowerBound, "max should be >=1"); }
     _max_iter = max;
     _enabled_max_iter = true;
   }

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◆ setMaxTime()

INLINE void gum::ApproximationScheme::setMaxTime ( double timeout )

virtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout The timeout value in seconds.

Exceptions

OutOfLowerBound Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 116 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time, gum::ApproximationScheme::_max_time, and GUM_ERROR.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMaxTime().

                                                             {
     if (timeout <= 0.) { GUM_ERROR(OutOfLowerBound, "timeout should be >0."); }
     _max_time = timeout;
     _enabled_max_time = true;
   }

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◆ setMinEpsilonRate()

INLINE void gum::ApproximationScheme::setMinEpsilonRate ( double rate )

virtualinherited

Given that we approximate f(t), stopping criterion on d/dt(|f(t+1)-f(t)|).

If the criterion was disabled it will be enabled

Parameters

rate	The minimal epsilon rate.

Exceptions

OutOfLowerBound if rate<0

Implements gum::IApproximationSchemeConfiguration.

Definition at line 64 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps, gum::ApproximationScheme::_min_rate_eps, and GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMinEpsilonRate().

                                                                 {
     if (rate < 0) { GUM_ERROR(OutOfLowerBound, "rate should be >=0"); }
 
     _min_rate_eps = rate;
     _enabled_min_rate_eps = true;
   }

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◆ setPeriodSize()

INLINE void gum::ApproximationScheme::setPeriodSize ( Size p )

virtualinherited

How many samples between two stopping is enable.

Parameters

p	The new period value.

Exceptions

OutOfLowerBound Raised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 141 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_period_size, and GUM_ERROR.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setPeriodSize().

                                                        {
     if (p < 1) { GUM_ERROR(OutOfLowerBound, "p should be >=1"); }
 
     _period_size = p;
   }

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◆ setVerbosity()

INLINE void gum::ApproximationScheme::setVerbosity ( bool v )

virtualinherited

Set the verbosity on (true) or off (false).

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 150 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setVerbosity().

150 { _verbosity = v; }

gum::ApproximationScheme::_verbosity

bool _verbosity

If true, verbosity is enabled.

Definition: approximationScheme.h:418

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◆ softEvidenceNodes()

template<typename GUM_SCALAR >

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

inherited

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;
   }

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◆ startOfPeriod()

INLINE bool gum::ApproximationScheme::startOfPeriod ( )

inherited

Returns true if we are at the beginning of a period (compute error is mandatory).

Returns: Returns true if we are at the beginning of a period (compute error is mandatory).

Definition at line 195 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_burn_in, gum::ApproximationScheme::_current_step, and gum::ApproximationScheme::_period_size.

Referenced by gum::ApproximationScheme::continueApproximationScheme().

                                                  {
     if (_current_step < _burn_in) { return false; }
 
     if (_period_size == 1) { return true; }
 
     return ((_current_step - _burn_in) % _period_size == 0);
   }

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◆ state()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

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;
   }

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◆ stateApproximationScheme()

INLINE IApproximationSchemeConfiguration::ApproximationSchemeSTATE gum::ApproximationScheme::stateApproximationScheme ( ) const

virtualinherited

Returns the approximation scheme state.

Returns: Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 156 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_state.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::history(), gum::ApproximationScheme::nbrIterations(), and gum::learning::genericBNLearner::stateApproximationScheme().

                                                              {
     return _current_state;
   }

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◆ stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 217 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_state, gum::ApproximationScheme::_stopScheme(), gum::IApproximationSchemeConfiguration::Continue, and gum::IApproximationSchemeConfiguration::Stopped.

Referenced by gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::GreedyHillClimbing::learnStructure(), and gum::learning::LocalSearchWithTabuList::learnStructure().

                                                            {
     if (_current_state == ApproximationSchemeSTATE::Continue) {
       _stopScheme(ApproximationSchemeSTATE::Stopped);
     }
   }

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◆ targets()

template<typename GUM_SCALAR >

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

finalvirtualnoexceptinherited

returns the list of marginal targets

Definition at line 208 of file marginalTargetedInference_tpl.h.

References gum::MarginalTargetedInference< GUM_SCALAR >::__targets.

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

               {
     return __targets;
   }

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◆ updateApproximationScheme()

INLINE void gum::ApproximationScheme::updateApproximationScheme ( unsigned int incr = 1 )

inherited

Update the scheme w.r.t the new error and increment steps.

Parameters

incr	The new increment steps.

Definition at line 204 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_step.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                               {
     _current_step += incr;
   }

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◆ verbosity()

INLINE bool gum::ApproximationScheme::verbosity ( ) const

virtualinherited

Returns true if verbosity is enabled.

Returns: Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 152 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::history(), and gum::learning::genericBNLearner::verbosity().

152 { return _verbosity; }

gum::ApproximationScheme::_verbosity

bool _verbosity

If true, verbosity is enabled.

Definition: approximationScheme.h:418

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Member Data Documentation

◆ __estimator

template<typename GUM_SCALAR >

Estimator< GUM_SCALAR > gum::SamplingInference< GUM_SCALAR >::__estimator

protected

Estimator object designed to approximate target posteriors.

Definition at line 176 of file samplingInference.h.

Referenced by gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::SamplingInference< GUM_SCALAR >::_onStateChanged(), gum::SamplingInference< GUM_SCALAR >::_posterior(), gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromBN(), gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromLBP(), and gum::SamplingInference< GUM_SCALAR >::currentPosterior().

◆ __samplingBN

template<typename GUM_SCALAR >

BayesNetFragment< GUM_SCALAR >* gum::SamplingInference< GUM_SCALAR >::__samplingBN

private

Definition at line 245 of file samplingInference.h.

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

◆ _burn_in

Size gum::ApproximationScheme::_burn_in

protectedinherited

Number of iterations before checking stopping criteria.

Definition at line 412 of file approximationScheme.h.

Referenced by gum::GibbsSampling< GUM_SCALAR >::burnIn(), gum::GibbsBNdistance< GUM_SCALAR >::burnIn(), gum::ApproximationScheme::remainingBurnIn(), gum::GibbsSampling< GUM_SCALAR >::setBurnIn(), gum::GibbsBNdistance< GUM_SCALAR >::setBurnIn(), and gum::ApproximationScheme::startOfPeriod().

◆ _current_epsilon

double gum::ApproximationScheme::_current_epsilon

protectedinherited

Current epsilon.

Definition at line 367 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), and gum::ApproximationScheme::initApproximationScheme().

◆ _current_rate

double gum::ApproximationScheme::_current_rate

protectedinherited

Current rate.

Definition at line 373 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), and gum::ApproximationScheme::initApproximationScheme().

◆ _current_state

ApproximationSchemeSTATE gum::ApproximationScheme::_current_state

protectedinherited

The current state.

Definition at line 382 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::_stopScheme(), gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::initApproximationScheme(), gum::ApproximationScheme::stateApproximationScheme(), and gum::ApproximationScheme::stopApproximationScheme().

◆ _current_step

Size gum::ApproximationScheme::_current_step

protectedinherited

The current step.

Definition at line 376 of file approximationScheme.h.

Referenced by gum::learning::Miic::_initiation(), gum::learning::Miic::_iteration(), gum::learning::Miic::_orientation_3off2(), gum::learning::Miic::_orientation_latents(), gum::learning::Miic::_orientation_miic(), gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::initApproximationScheme(), gum::learning::Miic::learnMixedStructure(), gum::ApproximationScheme::nbrIterations(), gum::ApproximationScheme::remainingBurnIn(), gum::ApproximationScheme::startOfPeriod(), and gum::ApproximationScheme::updateApproximationScheme().

◆ _enabled_eps

bool gum::ApproximationScheme::_enabled_eps

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 391 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::disableEpsilon(), gum::ApproximationScheme::enableEpsilon(), gum::ApproximationScheme::isEnabledEpsilon(), and gum::ApproximationScheme::setEpsilon().

◆ _enabled_max_iter

bool gum::ApproximationScheme::_enabled_max_iter

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 409 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::disableMaxIter(), gum::ApproximationScheme::enableMaxIter(), gum::ApproximationScheme::isEnabledMaxIter(), and gum::ApproximationScheme::setMaxIter().

◆ _enabled_max_time

bool gum::ApproximationScheme::_enabled_max_time

protectedinherited

If true, the timeout is enabled.

Definition at line 403 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::disableMaxTime(), gum::ApproximationScheme::enableMaxTime(), gum::ApproximationScheme::isEnabledMaxTime(), and gum::ApproximationScheme::setMaxTime().

◆ _enabled_min_rate_eps

bool gum::ApproximationScheme::_enabled_min_rate_eps

protectedinherited

If true, the minimal threshold for epsilon rate is enabled.

Definition at line 397 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::disableMinEpsilonRate(), gum::ApproximationScheme::enableMinEpsilonRate(), gum::ApproximationScheme::isEnabledMinEpsilonRate(), and gum::ApproximationScheme::setMinEpsilonRate().

◆ _eps

double gum::ApproximationScheme::_eps

protectedinherited

Threshold for convergence.

Definition at line 388 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::epsilon(), and gum::ApproximationScheme::setEpsilon().

◆ _history

std::vector< double > gum::ApproximationScheme::_history

protectedinherited

The scheme history, used only if verbosity == true.

Definition at line 385 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::history(), and gum::ApproximationScheme::initApproximationScheme().

◆ _last_epsilon

double gum::ApproximationScheme::_last_epsilon

protectedinherited

Last epsilon value.

Definition at line 370 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme().

◆ _max_iter

Size gum::ApproximationScheme::_max_iter

protectedinherited

The maximum iterations.

Definition at line 406 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::maxIter(), and gum::ApproximationScheme::setMaxIter().

◆ _max_time

double gum::ApproximationScheme::_max_time

protectedinherited

The timeout.

Definition at line 400 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::maxTime(), and gum::ApproximationScheme::setMaxTime().

◆ _min_rate_eps

double gum::ApproximationScheme::_min_rate_eps

protectedinherited

Threshold for the epsilon rate.

Definition at line 394 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::minEpsilonRate(), and gum::ApproximationScheme::setMinEpsilonRate().

◆ _period_size

Size gum::ApproximationScheme::_period_size

protectedinherited

Checking criteria frequency.

Definition at line 415 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::periodSize(), gum::ApproximationScheme::setPeriodSize(), and gum::ApproximationScheme::startOfPeriod().

◆ _timer

Timer gum::ApproximationScheme::_timer

protectedinherited

The timer.

Definition at line 379 of file approximationScheme.h.

Referenced by gum::learning::Miic::_initiation(), gum::learning::Miic::_iteration(), gum::learning::Miic::_orientation_3off2(), gum::learning::Miic::_orientation_latents(), gum::learning::Miic::_orientation_miic(), gum::ApproximationScheme::_stopScheme(), gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::currentTime(), gum::ApproximationScheme::initApproximationScheme(), and gum::learning::Miic::learnMixedStructure().

◆ _verbosity

bool gum::ApproximationScheme::_verbosity

protectedinherited

If true, verbosity is enabled.

Definition at line 418 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::setVerbosity(), and gum::ApproximationScheme::verbosity().

◆ isContextualized

template<typename GUM_SCALAR >

bool gum::SamplingInference< GUM_SCALAR >::isContextualized = false

protected

whether the referenced Bayesian Network has been "contextualized"

Definition at line 182 of file samplingInference.h.

Referenced by gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::SamplingInference< GUM_SCALAR >::contextualize(), and gum::SamplingInference< GUM_SCALAR >::~SamplingInference().

◆ isSetEstimator

template<typename GUM_SCALAR >

bool gum::SamplingInference< GUM_SCALAR >::isSetEstimator = false

protected

whether the Estimator object has been initialized

Definition at line 179 of file samplingInference.h.

Referenced by gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::SamplingInference< GUM_SCALAR >::_makeInference(), gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromBN(), and gum::SamplingInference< GUM_SCALAR >::_setEstimatorFromLBP().

◆ onProgress

Signaler3< Size, double, double > gum::IApproximationSchemeConfiguration::onProgress

inherited

Progression, error and time.

Definition at line 57 of file IApproximationSchemeConfiguration.h.

Referenced by gum::learning::Miic::_initiation(), gum::learning::Miic::_iteration(), gum::learning::Miic::_orientation_3off2(), gum::learning::Miic::_orientation_latents(), gum::learning::Miic::_orientation_miic(), gum::ApproximationScheme::continueApproximationScheme(), and gum::learning::genericBNLearner::distributeProgress().

◆ onStop

Signaler1< std::string > gum::IApproximationSchemeConfiguration::onStop

inherited

Criteria messageApproximationScheme.

Definition at line 60 of file IApproximationSchemeConfiguration.h.

Referenced by gum::ApproximationScheme::_stopScheme(), and gum::learning::genericBNLearner::distributeStop().

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

agrum/BN/inference/tools/samplingInference.h
agrum/BN/inference/tools/samplingInference_tpl.h

Public Attributes

Public Member Functions

Public Types

Protected Attributes

Protected Member Functions

Detailed Description

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

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

◆ StateOfInference

Constructor & Destructor Documentation

◆ SamplingInference()

◆ ~SamplingInference()

Member Function Documentation

◆ _addVarSample()

◆ _burnIn()

◆ _draw()

◆ _isTargetedMode()

◆ _loopApproxInference()

◆ _makeInference()

◆ _onAllEvidenceErased()

◆ _onAllMarginalTargetsAdded()

◆ _onAllMarginalTargetsErased()

◆ _onBayesNetChanged()

◆ _onContextualize()

◆ _onEvidenceAdded()

◆ _onEvidenceChanged()

◆ _onEvidenceErased()

◆ _onMarginalTargetAdded()

◆ _onMarginalTargetErased()

◆ _onStateChanged()

◆ _posterior()

◆ _setEstimatorFromBN()

◆ _setEstimatorFromLBP()

◆ _setOutdatedBNPotentialsState()

◆ _setOutdatedBNStructureState()

◆ _setTargetedMode()

◆ _updateOutdatedBNPotentials()

◆ _updateOutdatedBNStructure()

◆ addAllTargets()

◆ addEvidence() [1/8]

◆ addEvidence() [2/8]

◆ addEvidence() [3/8]

◆ addEvidence() [4/8]

◆ addEvidence() [5/8]

◆ addEvidence() [6/8]

◆ addEvidence() [7/8]

◆ addEvidence() [8/8]

◆ addListOfEvidence()

◆ addSetOfEvidence()

◆ addTarget() [1/2]

◆ addTarget() [2/2]

◆ BN()

◆ chgEvidence() [1/7]

◆ chgEvidence() [2/7]

◆ chgEvidence() [3/7]

◆ chgEvidence() [4/7]

◆ chgEvidence() [5/7]

◆ chgEvidence() [6/7]

◆ chgEvidence() [7/7]

◆ clear()

◆ contextualize()

◆ continueApproximationScheme()

◆ currentPosterior() [1/2]

◆ currentPosterior() [2/2]

◆ currentTime()

◆ disableEpsilon()

◆ disableMaxIter()

◆ disableMaxTime()

◆ disableMinEpsilonRate()

◆ domainSizes()

◆ enableEpsilon()

◆ enableMaxIter()

◆ enableMaxTime()

◆ enableMinEpsilonRate()

◆ epsilon()

◆ eraseAllEvidence()

◆ eraseAllTargets()

◆ eraseEvidence() [1/2]

◆ eraseEvidence() [2/2]

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