gum::GibbsSampling< GUM_SCALAR > Class Template Reference

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

#include <GibbsSampling.h>

Inheritance diagram for gum::GibbsSampling< GUM_SCALAR >:

Collaboration diagram for gum::GibbsSampling< GUM_SCALAR >:

Public Types
enum class	StateOfInference { OutdatedStructure , OutdatedTensors , ReadyForInference , Done }
	current state of the inference More...
enum class	ApproximationSchemeSTATE : char {   Undefined , Continue , Epsilon , Rate ,   Limit , TimeLimit , Stopped }
	The different state of an approximation scheme. More...

Public Member Functions
	GibbsSampling (const IBayesNet< GUM_SCALAR > *bn)
	Default constructor.
	~GibbsSampling () override
	Destructor.
void	setBurnIn (Size b)
	Number of burn in for one iteration.
Size	burnIn () const
	Returns the number of burn in.
virtual void	contextualize ()
	Simplifying the Bayesian network with relevance reasonning to lighten the computational charge.
Tensor< GUM_SCALAR >	evidenceImpact (NodeId target, const NodeSet &evs)
	Create a gum::Tensor for P(target|evs) (for all instantiation of target and evs).
Tensor< GUM_SCALAR >	evidenceImpact (std::string_view target, const std::vector< std::string > &evs)
	Create a gum::Tensor for P(target|evs) (for all instantiation of target and evs).
Size	nbrDrawnVar () const
	Getters and setters.
void	setNbrDrawnVar (Size nbr)
bool	isDrawnAtRandom () const
void	setDrawnAtRandom (bool atRandom)
Instantiation	monteCarloSample ()
	draws a Monte Carlo sample
Instantiation	nextSample (Instantiation prev)
	draws next sample of Gibbs sampling
Constructors / Destructors
const Tensor< GUM_SCALAR > &	currentPosterior (NodeId id)
	Computes and returns the actual estimation of the posterior of a node.
const Tensor< GUM_SCALAR > &	currentPosterior (std::string_view name)
	Computes and returns the actual estimation of the posterior of a node by its name.
Probability computations
const IBayesNet< GUM_SCALAR > &	samplingBN ()
	get the BayesNet which is used to really perform the sampling
const Tensor< GUM_SCALAR > &	posterior_ (NodeId id) override
	Computes and returns the posterior of a node.
Estimator objects initializing
virtual void	setEstimatorFromBN_ ()
	Initializes the estimators object linked to the simulation.
virtual void	setEstimatorFromLBP_ (LoopyBeliefPropagation< GUM_SCALAR > *lbp, GUM_SCALAR virtualLBPSize)
	Initializes the estimators object linked to the simulation.
Probability computations
virtual const Tensor< GUM_SCALAR > &	posterior (NodeId node)
	Computes and returns the posterior of a node.
virtual const Tensor< GUM_SCALAR > &	posterior (std::string_view nodeName)
	Computes and returns the posterior of a node.
Targets
virtual void	eraseAllTargets ()
	Clear all previously defined targets.
virtual void	addAllTargets () final
	adds all nodes as targets
virtual void	addTarget (NodeId target) final
	Add a marginal target to the list of targets.
virtual void	addTarget (std::string_view nodeName) final
	Add a marginal target to the list of targets.
virtual void	eraseTarget (NodeId target) final
	removes an existing (marginal) target
virtual void	eraseTarget (std::string_view nodeName) final
	removes an existing (marginal) target
virtual bool	isTarget (NodeId node) const final
	return true if variable is a (marginal) target
virtual bool	isTarget (std::string_view nodeName) const final
	return true if variable is a (marginal) target
virtual Size	nbrTargets () const noexcept final
	returns the number of marginal targets
virtual const NodeSet &	targets () const noexcept final
	returns the list of marginal targets
virtual bool	isInTargetMode () const noexcept final
	indicates whether the inference is in a target mode
Information Theory related functions
virtual GUM_SCALAR	H (NodeId X) final
	Entropy Compute Shanon's entropy of a node given the observation.
virtual GUM_SCALAR	H (std::string_view nodeName) final
	Entropy Compute Shanon's entropy of a node given the observation.
Accessors / Modifiers
virtual void	setBN (const IBayesNet< GUM_SCALAR > *bn)
	assigns a new BN to the inference engine
virtual const IBayesNet< GUM_SCALAR > &	BN () const final
	Returns a constant reference over the IBayesNet referenced by this class.
Accessors / Modifiers
virtual const GraphicalModel &	model () const final
	Returns a constant reference over the IBayesNet referenced by this class.
virtual const NodeProperty< Size > &	domainSizes () const final
	get the domain sizes of the random variables of the model
virtual bool	isInferenceReady () const noexcept final
	returns whether the inference object is in a ready state
virtual bool	isInferenceOutdatedStructure () const noexcept final
	returns whether the inference object is in a OutdatedStructure state
virtual bool	isInferenceOutdatedTensors () const noexcept final
	returns whether the inference object is in a OutdatedTensor state
virtual bool	isInferenceDone () const noexcept final
	returns whether the inference object is in a InferenceDone state
virtual void	prepareInference () final
	prepare the internal inference structures for the next inference
virtual void	makeInference () final
	perform the heavy computations needed to compute the targets' posteriors
virtual void	clear ()
	clears all the data structures allocated for the last inference
virtual StateOfInference	state () const noexcept final
	returns the state of the inference engine
Evidence
virtual void	addEvidence (NodeId id, const Idx val) final
	adds a new hard evidence on node id
virtual void	addEvidence (std::string_view nodeName, const Idx val) final
	adds a new hard evidence on node named nodeName
virtual void	addEvidence (NodeId id, std::string_view label) final
	adds a new hard evidence on node id
virtual void	addEvidence (std::string_view nodeName, std::string_view label) final
	adds a new hard evidence on node named nodeName
virtual void	addEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addEvidence (std::string_view nodeName, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node named nodeName (might be soft or hard)
virtual void	addEvidence (const Tensor< GUM_SCALAR > &pot) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addEvidence (Tensor< GUM_SCALAR > &&pot) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addSetOfEvidence (const Set< const Tensor< GUM_SCALAR > * > &potset) final
	adds a new set of evidence
virtual void	addListOfEvidence (const List< const Tensor< GUM_SCALAR > * > &potlist) final
	adds a new list of evidence
virtual void	chgEvidence (NodeId id, const Idx val) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (std::string_view nodeName, const Idx val) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (NodeId id, std::string_view label) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (std::string_view nodeName, std::string_view label) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	chgEvidence (std::string_view nodeName, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	chgEvidence (const Tensor< GUM_SCALAR > &pot) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	eraseAllEvidence () final
	removes all the evidence entered into the network
virtual void	eraseEvidence (NodeId id) final
	removed the evidence, if any, corresponding to node id
virtual void	eraseEvidence (std::string_view nodeName) final
	removed the evidence, if any, corresponding to node of name nodeName
virtual bool	hasEvidence () const final
	indicates whether some node(s) have received evidence
virtual bool	hasEvidence (NodeId id) const final
	indicates whether node id has received an evidence
virtual bool	hasEvidence (std::string_view nodeName) const final
	indicates whether node id has received an evidence
virtual bool	hasHardEvidence (NodeId id) const final
	indicates whether node id has received a hard evidence
virtual bool	hasHardEvidence (std::string_view nodeName) const final
	indicates whether node id has received a hard evidence
virtual bool	hasSoftEvidence (NodeId id) const final
	indicates whether node id has received a soft evidence
virtual bool	hasSoftEvidence (std::string_view nodeName) const final
	indicates whether node id has received a soft evidence
virtual Size	nbrEvidence () const final
	returns the number of evidence entered into the Bayesian network
virtual Size	nbrHardEvidence () const final
	returns the number of hard evidence entered into the Bayesian network
virtual Size	nbrSoftEvidence () const final
	returns the number of soft evidence entered into the Bayesian network
const NodeProperty< const Tensor< GUM_SCALAR > * > &	evidence () const
	returns the set of evidence
const NodeSet &	softEvidenceNodes () const
	returns the set of nodes with soft evidence
const NodeSet &	hardEvidenceNodes () const
	returns the set of nodes with hard evidence
const NodeProperty< Idx > &	hardEvidence () const
	indicate for each node with hard evidence which value it took
Getters and setters
void	setEpsilon (double eps) override
	Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|.
double	epsilon () const override
	Returns the value of epsilon.
void	disableEpsilon () override
	Disable stopping criterion on epsilon.
void	enableEpsilon () override
	Enable stopping criterion on epsilon.
bool	isEnabledEpsilon () const override
	Returns true if stopping criterion on epsilon is enabled, false otherwise.
void	setMinEpsilonRate (double rate) override
	Given that we approximate f(t), stopping criterion on d/dt(|f(t+1)-f(t)|).
double	minEpsilonRate () const override
	Returns the value of the minimal epsilon rate.
void	disableMinEpsilonRate () override
	Disable stopping criterion on epsilon rate.
void	enableMinEpsilonRate () override
	Enable stopping criterion on epsilon rate.
bool	isEnabledMinEpsilonRate () const override
	Returns true if stopping criterion on epsilon rate is enabled, false otherwise.
void	setMaxIter (Size max) override
	Stopping criterion on number of iterations.
Size	maxIter () const override
	Returns the criterion on number of iterations.
void	disableMaxIter () override
	Disable stopping criterion on max iterations.
void	enableMaxIter () override
	Enable stopping criterion on max iterations.
bool	isEnabledMaxIter () const override
	Returns true if stopping criterion on max iterations is enabled, false otherwise.
void	setMaxTime (double timeout) override
	Stopping criterion on timeout.
double	maxTime () const override
	Returns the timeout (in seconds).
double	currentTime () const override
	Returns the current running time in second.
void	disableMaxTime () override
	Disable stopping criterion on timeout.
void	enableMaxTime () override
	Enable stopping criterion on timeout.
bool	isEnabledMaxTime () const override
	Returns true if stopping criterion on timeout is enabled, false otherwise.
void	setPeriodSize (Size p) override
	How many samples between two stopping is enable.
Size	periodSize () const override
	Returns the period size.
void	setVerbosity (bool v) override
	Set the verbosity on (true) or off (false).
bool	verbosity () const override
	Returns true if verbosity is enabled.
ApproximationSchemeSTATE	stateApproximationScheme () const override
	Returns the approximation scheme state.
Size	nbrIterations () const override
	Returns the number of iterations.
const std::vector< double > &	history () const override
	Returns the scheme history.
void	initApproximationScheme ()
	Initialise the scheme.
bool	startOfPeriod () const
	Returns true if we are at the beginning of a period (compute error is mandatory).
void	updateApproximationScheme (unsigned int incr=1)
	Update the scheme w.r.t the new error and increment steps.
Size	remainingBurnIn () const
	Returns the remaining burn in.
void	stopApproximationScheme ()
	Stop the approximation scheme.
bool	continueApproximationScheme (double error)
	Update the scheme w.r.t the new error.
Getters and setters
std::string	messageApproximationScheme () const
	Returns the approximation scheme message.

Public Attributes
Signaler< Size, double, double >	onProgress
	Progression, error and time.
Signaler< std::string_view >	onStop
	Criteria messageApproximationScheme.

Protected Member Functions
Instantiation	burnIn_ () override
	draws a defined number of samples without updating the estimators
Instantiation	draw_ (GUM_SCALAR *w, Instantiation prev) override
	draws a sample given previous one according to Gibbs sampling
Instantiation	monteCarloSample_ ()
	draws a Monte Carlo sample
void	makeInference_ () override
	makes the inference by generating samples
void	loopApproxInference_ ()
virtual void	addVarSample_ (NodeId nod, Instantiation *I)
	adds a node to current instantiation
virtual void	onContextualize_ (BayesNetFragment< GUM_SCALAR > *bn)
	fired when Bayesian network is contextualized
void	onEvidenceAdded_ (const NodeId id, bool isHardEvidence) override
	fired after a new evidence is inserted
void	onEvidenceErased_ (const NodeId id, bool isHardEvidence) override
	fired before an evidence is removed
void	onAllEvidenceErased_ (bool contains_hard_evidence) override
	fired before all the evidence are erased
void	onEvidenceChanged_ (const NodeId id, bool hasChangedSoftHard) override
	fired after an evidence is changed, in particular when its status (soft/hard) changes
void	onModelChanged_ (const GraphicalModel *bn) override
	fired after a new Bayes net has been assigned to the engine
void	updateOutdatedStructure_ () override
	prepares inference when the latter is in OutdatedStructure state
void	updateOutdatedTensors_ () override
	prepares inference when the latter is in OutdatedTensors state
void	onMarginalTargetAdded_ (const NodeId id) override
	fired after a new marginal target is inserted
void	onMarginalTargetErased_ (const NodeId id) override
	fired before a marginal target is removed
void	onAllMarginalTargetsAdded_ () override
	fired after all the nodes of the BN are added as marginal targets
void	onAllMarginalTargetsErased_ () override
	fired before a all marginal targets are removed
void	onStateChanged_ () override
	fired when the stage is changed
void	setTargetedMode_ ()
bool	isTargetedMode_ () const
void	setOutdatedStructureState_ ()
	put the inference into an outdated model structure state
void	setOutdatedTensorsState_ ()
	puts the inference into an OutdatedTensors state if it is not already in an OutdatedStructure state
virtual void	setState_ (const StateOfInference state) final
	set the state of the inference engine and call the notification onStateChanged_ when necessary (i.e. when the state has effectively changed).
void	setModel_ (const GraphicalModel *model)
void	setModelDuringConstruction_ (const GraphicalModel *model)
	assigns a model during the inference engine construction
bool	hasNoModel_ () const
void	updateSamplingNodes_ ()

Protected Attributes
Estimator< GUM_SCALAR >	_estimator_
	Estimator object designed to approximate target posteriors.
bool	isSetEstimator = false
	whether the Estimator object has been initialized
bool	isContextualized = false
	whether the referenced Bayesian network has been "contextualized"
double	current_epsilon_
	Current epsilon.
double	last_epsilon_
	Last epsilon value.
double	current_rate_
	Current rate.
Size	current_step_
	The current step.
Timer	timer_
	The timer.
ApproximationSchemeSTATE	current_state_
	The current state.
std::vector< double >	history_
	The scheme history, used only if verbosity == true.
double	eps_
	Threshold for convergence.
bool	enabled_eps_
	If true, the threshold convergence is enabled.
double	min_rate_eps_
	Threshold for the epsilon rate.
bool	enabled_min_rate_eps_
	If true, the minimal threshold for epsilon rate is enabled.
double	max_time_
	The timeout.
bool	enabled_max_time_
	If true, the timeout is enabled.
Size	max_iter_
	The maximum iterations.
bool	enabled_max_iter_
	If true, the maximum iterations stopping criterion is enabled.
Size	burn_in_
	Number of iterations before checking stopping criteria.
Size	period_size_
	Checking criteria frequency.
bool	verbosity_
	If true, verbosity is enabled.
Size	counting_
const IBayesNet< GUM_SCALAR > &	samplingBn_
const NodeProperty< Idx > *	hardEv_
Sequence< NodeId >	samplingNodes_
Size	nbr_
bool	atRandom_

Private Member Functions
void	_setAllMarginalTargets_ ()
	sets all the nodes of the Bayes net as targets
void	stopScheme_ (ApproximationSchemeSTATE new_state)
	Stop the scheme given a new state.
void	_drawVarMonteCarlo_ (NodeId nod, Instantiation *I)
void	_GibbsSample_ (NodeId id, Instantiation *I)
	change in Instantiation I a new drawn value for id

Private Attributes
BayesNetFragment< GUM_SCALAR > *	_samplingBN_
bool	_targeted_mode_
	whether the actual targets are default
NodeSet	_targets_
	the set of marginal targets

Detailed Description

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

<agrum/BN/inference/gibbsSampling.h>

class for making Gibbs sampling inference in Bayesian networks.

This class overrides pure function declared in the inherited class ApproximateInference. It defines the way Gibbs sampling draws a sample. It also inherits GibbsOperator which contains Gibbs sampling methods.

Definition at line 72 of file GibbsSampling.h.

Member Enumeration Documentation

ApproximationSchemeSTATE

enum class gum::IApproximationSchemeConfiguration::ApproximationSchemeSTATE : char

stronginherited

The different state of an approximation scheme.

Enumerator
Undefined
Continue
Epsilon
Rate
Limit
TimeLimit
Stopped

Definition at line 87 of file IApproximationSchemeConfiguration.h.

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

StateOfInference

template<GUM_Numeric GUM_SCALAR>

enum class gum::GraphicalModelInference::StateOfInference

stronginherited

current state of the inference

graphicalModelInference can be in one of 4 different states:

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

Enumerator
OutdatedStructure
OutdatedTensors
ReadyForInference
Done

Definition at line 127 of file graphicalModelInference.h.

{ OutdatedStructure, OutdatedTensors, ReadyForInference, Done };

Constructor & Destructor Documentation

GibbsSampling()

template<GUM_Numeric GUM_SCALAR>

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

explicit

Default constructor.

default constructor

Definition at line 64 of file GibbsSampling_tpl.h.

                                                                              :
      SamplingInference< GUM_SCALAR >(bn),
      GibbsOperator< GUM_SCALAR >(*bn,
                                  &this->hardEvidence(),
                                  1 + (bn->size() * GIBBS_SAMPLING_POURCENT_DRAWN_SAMPLE / 100),
                                  GIBBS_SAMPLING_DRAWN_AT_RANDOM) {
    GUM_CONSTRUCTOR(GibbsSampling);
 
    this->setEpsilon(GIBBS_SAMPLING_DEFAULT_EPSILON);
    this->setMinEpsilonRate(GIBBS_SAMPLING_DEFAULT_MIN_EPSILON_RATE);
    this->setBurnIn(GIBBS_SAMPLING_DEFAULT_BURNIN);
  }

References gum::GibbsOperator< GUM_SCALAR >::GibbsOperator(), GibbsSampling(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), GIBBS_SAMPLING_DEFAULT_BURNIN, GIBBS_SAMPLING_DEFAULT_EPSILON, GIBBS_SAMPLING_DEFAULT_MIN_EPSILON_RATE, GIBBS_SAMPLING_DRAWN_AT_RANDOM, GIBBS_SAMPLING_POURCENT_DRAWN_SAMPLE, gum::GraphicalModelInference< GUM_SCALAR >::hardEvidence(), setBurnIn(), gum::ApproximationScheme::setEpsilon(), and gum::ApproximationScheme::setMinEpsilonRate().

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

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~GibbsSampling()

template<GUM_Numeric GUM_SCALAR>

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

override

Destructor.

destructor

Definition at line 79 of file GibbsSampling_tpl.h.

                                              {
    GUM_DESTRUCTOR(GibbsSampling);
  }

References GibbsSampling().

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Member Function Documentation

_drawVarMonteCarlo_()

template<GUM_Numeric GUM_SCALAR>

void gum::GibbsOperator< GUM_SCALAR >::_drawVarMonteCarlo_ ( NodeId nod, Instantiation * I )

privateinherited

Definition at line 103 of file gibbsOperator_tpl.h.

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

References gum::Instantiation::chgVal(), gum::Instantiation::erase(), and samplingBn_.

Referenced by monteCarloSample().

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_GibbsSample_()

template<GUM_Numeric GUM_SCALAR>

void gum::GibbsOperator< GUM_SCALAR >::_GibbsSample_ ( NodeId id, Instantiation * I )

privateinherited

change in Instantiation I a new drawn value for id

Definition at line 123 of file gibbsOperator_tpl.h.

                                                                             {
    gum::Instantiation Itop(*I);
    Itop.erase(samplingBn_.variable(id));
    gum::Tensor< GUM_SCALAR > p = samplingBn_.cpt(id).extract(Itop);
    for (const auto nod: samplingBn_.children(id))
      p *= samplingBn_.cpt(nod).extract(Itop);
    GUM_ASSERT(p.nbrDim() == 1);
    if (p.sum() != 0) {
      p.normalize();
      I->chgVal(samplingBn_.variable(id), p.draw());
    }
  }

References gum::Instantiation::chgVal(), gum::Tensor< GUM_SCALAR >::draw(), gum::Instantiation::erase(), gum::MultiDimDecorator< GUM_ELEMENT >::nbrDim(), gum::Tensor< GUM_SCALAR >::normalize(), samplingBn_, and gum::Tensor< GUM_SCALAR >::sum().

Referenced by nextSample().

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_setAllMarginalTargets_()

template<GUM_Numeric GUM_SCALAR>

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

privateinherited

sets all the nodes of the Bayes net as targets

Definition at line 232 of file marginalTargetedInference_tpl.h.

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

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

Referenced by onModelChanged_().

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addAllTargets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

adds all nodes as targets

Definition at line 147 of file marginalTargetedInference_tpl.h.

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

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

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addEvidence() [1/8]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 323 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addEvidence() [2/8]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

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

Definition at line 230 of file graphicalModelInference_tpl.h.

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

References _createHardEvidence_(), and addEvidence().

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

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addEvidence() [3/8]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 257 of file graphicalModelInference_tpl.h.

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

References _model_, addEvidence(), and GUM_ERROR.

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addEvidence() [4/8]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::addEvidence ( NodeId id, std::string_view label )

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

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

Definition at line 243 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [5/8]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

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

Definition at line 236 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [6/8]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 281 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [7/8]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::addEvidence ( std::string_view nodeName, std::string_view label )

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

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

Definition at line 249 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [8/8]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 288 of file graphicalModelInference_tpl.h.

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

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

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addListOfEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

adds a new list of evidence

Exceptions

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

Definition at line 330 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addSetOfEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

adds a new set of evidence

Exceptions

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

Definition at line 338 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addTarget() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement

if target is not a NodeId in the Bayes net

Definition at line 125 of file marginalTargetedInference_tpl.h.

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

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

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

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addTarget() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement

if target is not a NodeId in the Bayes net

Definition at line 167 of file marginalTargetedInference_tpl.h.

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

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

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addVarSample_()

template<GUM_Numeric GUM_SCALAR>

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

protectedvirtualinherited

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 196 of file samplingInference_tpl.h.

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

References gum::Instantiation::add(), gum::Instantiation::chgVal(), and samplingBN().

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

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BN()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

UndefinedElement

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

Definition at line 76 of file BayesNetInference_tpl.h.

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

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

Referenced by gum::LazyPropagation< GUM_SCALAR >::LazyPropagation(), gum::ShaferShenoyInference< GUM_SCALAR >::ShaferShenoyInference(), gum::VariableElimination< GUM_SCALAR >::VariableElimination(), gum::MarginalTargetedInference< GUM_SCALAR >::_setAllMarginalTargets_(), gum::MarginalTargetedInference< GUM_SCALAR >::addAllTargets(), gum::JointTargetedInference< GUM_SCALAR >::addJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::addTarget(), gum::SamplingInference< GUM_SCALAR >::contextualize(), gum::SamplingInference< GUM_SCALAR >::currentPosterior(), gum::SamplingInference< GUM_SCALAR >::currentPosterior(), gum::ImportanceSampling< GUM_SCALAR >::draw_(), gum::MonteCarloSampling< GUM_SCALAR >::draw_(), gum::WeightedSampling< GUM_SCALAR >::draw_(), gum::JointTargetedInference< GUM_SCALAR >::eraseJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::eraseTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::MarginalTargetedInference< GUM_SCALAR >::H(), gum::JointTargetedInference< GUM_SCALAR >::isJointTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::isTarget(), gum::MarginalTargetedInference< GUM_SCALAR >::isTarget(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedInference< GUM_SCALAR >::posterior(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), gum::SamplingInference< GUM_SCALAR >::posterior_(), gum::SamplingInference< GUM_SCALAR >::samplingBN(), and gum::Estimator< GUM_SCALAR >::setFromLBP().

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burnIn()

template<GUM_Numeric GUM_SCALAR>

Size gum::GibbsSampling< GUM_SCALAR >::burnIn

(

)

const

Returns the number of burn in.

Returns

Returns the number of burn in.

Definition at line 119 of file GibbsSampling_tpl.h.

                                                 {
    return this->burn_in_;
  }

References gum::ApproximationScheme::burn_in_.

Referenced by burnIn_().

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burnIn_()

template<GUM_Numeric GUM_SCALAR>

Instantiation gum::GibbsSampling< GUM_SCALAR >::burnIn_ ( )

overrideprotectedvirtual

draws a defined number of samples without updating the estimators

Implements gum::SamplingInference< GUM_SCALAR >.

Definition at line 89 of file GibbsSampling_tpl.h.

                                                     {
    // we initialize the nodes with hard evidence
    // hypothesis : burnIn_ is called at the beginning of makeInference
    this->updateSamplingNodes_();
 
    gum::Instantiation Ip;
    if (this->burnIn() == 0) return Ip;
 
    GUM_SCALAR w = 1.0f;
    Ip           = monteCarloSample_();
    for (Size i = 1; i < this->burnIn(); i++)
      Ip = this->draw_(&w, Ip);
 
    return Ip;
  }

References burnIn(), draw_(), monteCarloSample_(), and gum::GibbsOperator< GUM_SCALAR >::updateSamplingNodes_().

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chgEvidence() [1/7]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 446 of file graphicalModelInference_tpl.h.

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

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

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chgEvidence() [2/7]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 388 of file graphicalModelInference_tpl.h.

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

References _createHardEvidence_(), and chgEvidence().

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

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chgEvidence() [3/7]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 415 of file graphicalModelInference_tpl.h.

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

References _model_, chgEvidence(), and GUM_ERROR.

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chgEvidence() [4/7]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::chgEvidence ( NodeId id, std::string_view label )

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 401 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [5/7]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 394 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [6/7]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 439 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [7/7]

template<GUM_Numeric GUM_SCALAR>

void gum::GraphicalModelInference< GUM_SCALAR >::chgEvidence ( std::string_view nodeName, std::string_view label )

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 407 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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clear()

template<GUM_Numeric GUM_SCALAR>

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

virtualinherited

clears all the data structures allocated for the last inference

Reimplemented in gum::ShaferShenoyLIMIDInference< GUM_SCALAR >.

Definition at line 156 of file graphicalModelInference_tpl.h.

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

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

Referenced by setModel_().

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contextualize()

template<GUM_Numeric GUM_SCALAR>

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

virtualinherited

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 127 of file samplingInference_tpl.h.

                                                      {
    // Finding Barren nodes
 
    BarrenNodesFinder barr_nodes = BarrenNodesFinder(&this->BN().internalDag());
    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().internalDag().asNodeSet() - barren)
      _samplingBN_->installNode(elmt);
 
    // D-separated nodes
 
    dSeparationAlgorithm dsep = gum::dSeparationAlgorithm();
    NodeSet              requisite;
    dsep.requisiteNodes(this->BN().internalDag(),
                        this->BN().nodes().asNodeSet(),   // no target for approximateInference
                        this->hardEvidenceNodes(),
                        this->softEvidenceNodes(),        // should be empty
                        requisite);
    requisite += this->hardEvidenceNodes();
 
    auto nonRequisite = this->BN().internalDag().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)) {
        _samplingBN_->installCPT(child, this->BN().cpt(child).extract(I));
      }
    }
 
    this->isContextualized = true;
    this->onContextualize_(_samplingBN_);
  }

References _samplingBN_, gum::Instantiation::add(), gum::BarrenNodesFinder::barrenNodes(), gum::BayesNetInference< GUM_SCALAR >::BN(), gum::Instantiation::chgVal(), gum::GraphicalModelInference< GUM_SCALAR >::hardEvidence(), gum::GraphicalModelInference< GUM_SCALAR >::hardEvidenceNodes(), isContextualized, onContextualize_(), gum::dSeparationAlgorithm::requisiteNodes(), gum::BarrenNodesFinder::setEvidence(), gum::BarrenNodesFinder::setTargets(), gum::GraphicalModelInference< GUM_SCALAR >::softEvidenceNodes(), and gum::MarginalTargetedInference< GUM_SCALAR >::targets().

Referenced by loopApproxInference_().

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continueApproximationScheme()

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 69 of file approximationScheme.cpp.

                                                                    {
    // 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;
    }
  }

References gum::IApproximationSchemeConfiguration::Continue, current_epsilon_, current_rate_, current_state_, current_step_, enabled_eps_, enabled_max_iter_, enabled_max_time_, enabled_min_rate_eps_, eps_, gum::IApproximationSchemeConfiguration::Epsilon, GUM_EMIT3, GUM_ERROR, history_, last_epsilon_, gum::IApproximationSchemeConfiguration::Limit, max_iter_, max_time_, gum::IApproximationSchemeConfiguration::messageApproximationScheme(), min_rate_eps_, gum::IApproximationSchemeConfiguration::onProgress, gum::IApproximationSchemeConfiguration::Rate, startOfPeriod(), stateApproximationScheme(), stopScheme_(), gum::IApproximationSchemeConfiguration::TimeLimit, timer_, and verbosity().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByOrderedArcs_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByRandomOrder_(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

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currentPosterior() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

inherited

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 tensor is returned by reference. In order to ensure that the tensor may still exist even if the Inference object is destroyed, the user has to copy it explicitly.

Exceptions

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

Definition at line 111 of file samplingInference_tpl.h.

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

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

Referenced by currentPosterior().

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currentPosterior() [2/2]

template<GUM_Numeric GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::SamplingInference< GUM_SCALAR >::currentPosterior ( std::string_view name )

inherited

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 tensor is returned by reference. In order to ensure that the tensor may still exist even if the Inference object is destroyed, the user has to copy it explicitly.

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 117 of file samplingInference_tpl.h.

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

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

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currentTime()

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

overridevirtualinherited

Returns the current running time in second.

Returns

Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 137 of file approximationScheme_inl.h.

{ return timer_.step(); }

References timer_.

disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

overridevirtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 75 of file approximationScheme_inl.h.

{ enabled_eps_ = false; }

References enabled_eps_.

Referenced by gum::learning::EMApproximationScheme::EMApproximationScheme(), and gum::learning::EMApproximationScheme::setMinEpsilonRate().

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disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

overridevirtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 116 of file approximationScheme_inl.h.

{ enabled_max_iter_ = false; }

References enabled_max_iter_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), and gum::learning::GreedyThickThinning::GreedyThickThinning().

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disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

overridevirtualinherited

Disable stopping criterion on timeout.

Returns

Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 140 of file approximationScheme_inl.h.

{ enabled_max_time_ = false; }

References enabled_max_time_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), and gum::learning::GreedyThickThinning::GreedyThickThinning().

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disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

overridevirtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 96 of file approximationScheme_inl.h.

{ enabled_min_rate_eps_ = false; }

References enabled_min_rate_eps_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::learning::GreedyThickThinning::GreedyThickThinning(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::learning::EMApproximationScheme::setEpsilon().

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domainSizes()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

get the domain sizes of the random variables of the model

Definition at line 174 of file graphicalModelInference_tpl.h.

                                                                                       {
    return _domain_sizes_;
  }

References _domain_sizes_.

draw_()

template<GUM_Numeric GUM_SCALAR>

Instantiation gum::GibbsSampling< GUM_SCALAR >::draw_ ( GUM_SCALAR * w, Instantiation prev )

overrideprotectedvirtual

draws a sample given previous one according to Gibbs sampling

draws next sample for gibbs sampling

Parameters

w	the weight of sample being generated
prev	the previous sample generated
bn	the Bayesian network containing the evidence
hardEvNodes	hard evidence nodes
hardEv	hard evidences values

Uses the Gibbs sampling method to generate a new sample given the previous one. The method is implemented in the inherited class GibbsOperator. This function only makes the call to it. It consists of choosing one node x to sample, given the instantiation of all other nodes. It requires computing of P( x \given instantiation_markovblanket(x)).

Implements gum::SamplingInference< GUM_SCALAR >.

Definition at line 108 of file GibbsSampling_tpl.h.

                                                                                    {
    *w = 1.0;
    return GibbsOperator< GUM_SCALAR >::nextSample(prev);
  }

References gum::GibbsOperator< GUM_SCALAR >::nextSample().

Referenced by burnIn_().

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enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

overridevirtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 78 of file approximationScheme_inl.h.

{ enabled_eps_ = true; }

References enabled_eps_.

enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

overridevirtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 119 of file approximationScheme_inl.h.

{ enabled_max_iter_ = true; }

References enabled_max_iter_.

enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

overridevirtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 143 of file approximationScheme_inl.h.

{ enabled_max_time_ = true; }

References enabled_max_time_.

enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

overridevirtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 99 of file approximationScheme_inl.h.

{ enabled_min_rate_eps_ = true; }

References enabled_min_rate_eps_.

Referenced by gum::learning::EMApproximationScheme::EMApproximationScheme(), and gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

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epsilon()

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

overridevirtualinherited

Returns the value of epsilon.

Returns

Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 72 of file approximationScheme_inl.h.

{ return eps_; }

References eps_.

Referenced by gum::ImportanceSampling< GUM_SCALAR >::onContextualize_(), and gum::ImportanceSampling< GUM_SCALAR >::unsharpenBN_().

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eraseAllEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

removes all the evidence entered into the network

Definition at line 534 of file graphicalModelInference_tpl.h.

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

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

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

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eraseAllTargets()

template<GUM_Numeric GUM_SCALAR>

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

virtualinherited

Clear all previously defined targets.

Reimplemented in gum::JointTargetedInference< GUM_SCALAR >.

Definition at line 114 of file marginalTargetedInference_tpl.h.

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

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

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

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eraseEvidence() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

removed the evidence, if any, corresponding to node id

Definition at line 508 of file graphicalModelInference_tpl.h.

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

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

Referenced by eraseEvidence().

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eraseEvidence() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Definition at line 528 of file graphicalModelInference_tpl.h.

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

References eraseEvidence(), and model().

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eraseTarget() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

removes an existing (marginal) target

Warning

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

Definition at line 179 of file marginalTargetedInference_tpl.h.

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

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

Referenced by eraseTarget().

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eraseTarget() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

removes an existing (marginal) target

Warning

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

Definition at line 202 of file marginalTargetedInference_tpl.h.

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

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

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evidence()

template<GUM_Numeric GUM_SCALAR>

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

inherited

returns the set of evidence

Definition at line 581 of file graphicalModelInference_tpl.h.

                                                            {
    return _evidence_;
  }

References _evidence_.

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

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evidenceImpact() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

inherited

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

Warning

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

Parameters

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

Returns

a Tensor

Definition at line 283 of file marginalTargetedInference_tpl.h.

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

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

Referenced by evidenceImpact().

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evidenceImpact() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

inherited

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

Warning

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

Parameters

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

Returns

a Tensor

Definition at line 322 of file marginalTargetedInference_tpl.h.

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

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

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H() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

See also

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

Definition at line 270 of file marginalTargetedInference_tpl.h.

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

References posterior().

Referenced by H().

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H() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

See also

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

Definition at line 278 of file marginalTargetedInference_tpl.h.

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

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

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hardEvidence()

template<GUM_Numeric GUM_SCALAR>

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

inherited

indicate for each node with hard evidence which value it took

Definition at line 574 of file graphicalModelInference_tpl.h.

                                                                                       {
    return _hard_evidence_;
  }

References _hard_evidence_.

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

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hardEvidenceNodes()

template<GUM_Numeric GUM_SCALAR>

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

inherited

returns the set of nodes with hard evidence

the set of nodes that received hard evidence

Definition at line 593 of file graphicalModelInference_tpl.h.

                                                                                {
    return _hard_evidence_nodes_;
  }

References _hard_evidence_nodes_.

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

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hasEvidence() [1/3]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

indicates whether some node(s) have received evidence

Definition at line 346 of file graphicalModelInference_tpl.h.

                                                                {
    return !_evidence_.empty();
  }

References _evidence_.

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

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hasEvidence() [2/3]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 352 of file graphicalModelInference_tpl.h.

                                                                         {
    return _evidence_.exists(id);
  }

References _evidence_.

hasEvidence() [3/3]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 370 of file graphicalModelInference_tpl.h.

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

References hasEvidence(), and model().

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hasHardEvidence() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 358 of file graphicalModelInference_tpl.h.

                                                                             {
    return _hard_evidence_nodes_.exists(id);
  }

References _hard_evidence_nodes_.

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

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hasHardEvidence() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 376 of file graphicalModelInference_tpl.h.

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

References hasHardEvidence(), and model().

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hasNoModel_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 643 of file graphicalModelInference_tpl.h.

                                                                {
    return _model_ == nullptr;
  }

References _model_.

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

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hasSoftEvidence() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 364 of file graphicalModelInference_tpl.h.

                                                                             {
    return _soft_evidence_nodes_.exists(id);
  }

References _soft_evidence_nodes_.

Referenced by hasSoftEvidence().

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hasSoftEvidence() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 382 of file graphicalModelInference_tpl.h.

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

References hasSoftEvidence(), and model().

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history()

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

overridevirtualinherited

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 179 of file approximationScheme_inl.h.

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

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

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initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 190 of file approximationScheme_inl.h.

                                                           {
    current_state_   = ApproximationSchemeSTATE::Continue;
    current_step_    = 0;
    current_epsilon_ = current_rate_ = -1.0;
    history_.clear();
    timer_.reset();
  }

References ApproximationScheme(), gum::IApproximationSchemeConfiguration::Continue, current_state_, current_step_, and initApproximationScheme().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), initApproximationScheme(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInference(), and gum::SamplingInference< GUM_SCALAR >::onStateChanged_().

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isDrawnAtRandom()

template<GUM_Numeric GUM_SCALAR>

bool gum::GibbsOperator< GUM_SCALAR >::isDrawnAtRandom ( ) const

inherited

Definition at line 147 of file gibbsOperator_tpl.h.

                                                          {
    return atRandom_;
  }

References atRandom_.

isEnabledEpsilon()

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

overridevirtualinherited

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 82 of file approximationScheme_inl.h.

{ return enabled_eps_; }

References enabled_eps_.

isEnabledMaxIter()

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

overridevirtualinherited

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 123 of file approximationScheme_inl.h.

{ return enabled_max_iter_; }

References enabled_max_iter_.

isEnabledMaxTime()

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

overridevirtualinherited

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 147 of file approximationScheme_inl.h.

{ return enabled_max_time_; }

References enabled_max_time_.

isEnabledMinEpsilonRate()

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

overridevirtualinherited

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 103 of file approximationScheme_inl.h.

{ return enabled_min_rate_eps_; }

References enabled_min_rate_eps_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

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isInferenceDone()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a InferenceDone state

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

Definition at line 105 of file graphicalModelInference_tpl.h.

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

References _state_, and Done.

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

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isInferenceOutdatedStructure()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedStructure state

Definition at line 93 of file graphicalModelInference_tpl.h.

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

References _state_, and OutdatedStructure.

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

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isInferenceOutdatedTensors()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedTensor state

Definition at line 99 of file graphicalModelInference_tpl.h.

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

References _state_, and OutdatedTensors.

isInferenceReady()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a ready state

Definition at line 87 of file graphicalModelInference_tpl.h.

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

References _state_, and ReadyForInference.

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

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isInTargetMode()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

indicates whether the inference is in a target mode

Definition at line 226 of file marginalTargetedInference_tpl.h.

                                                                              {
    return _targeted_mode_;
  }

References _targeted_mode_.

isTarget() [1/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 93 of file marginalTargetedInference_tpl.h.

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

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

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

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isTarget() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 108 of file marginalTargetedInference_tpl.h.

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

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

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isTargetedMode_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 330 of file marginalTargetedInference_tpl.h.

                                                                      {
    return _targeted_mode_;
  }

References _targeted_mode_.

loopApproxInference_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 176 of file samplingInference_tpl.h.

                                                             {
    //@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;
  }

References _estimator_, burnIn_(), contextualize(), gum::ApproximationScheme::continueApproximationScheme(), draw_(), gum::ApproximationScheme::initApproximationScheme(), isContextualized, isSetEstimator, and gum::ApproximationScheme::updateApproximationScheme().

Referenced by makeInference_().

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makeInference()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

perform the heavy computations needed to compute the targets' posteriors

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

Definition at line 632 of file graphicalModelInference_tpl.h.

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

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

Referenced by gum::Counterfactual< GUM_ELEMENT >::counterFactualModel(), gum::Counterfactual< GUM_ELEMENT >::counterFactualModel(), gum::counterfactualModel(), gum::ASTjointProba< GUM_SCALAR >::eval(), gum::ASTposteriorProba< GUM_SCALAR >::eval(), gum::MarginalTargetedInference< GUM_SCALAR >::evidenceImpact(), gum::MarginalTargetedMRFInference< GUM_SCALAR >::evidenceImpact(), gum::JointTargetedInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedMRFInference< GUM_SCALAR >::evidenceJointImpact(), gum::JointTargetedInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedMRFInference< GUM_SCALAR >::jointMutualInformation(), gum::JointTargetedInference< GUM_SCALAR >::jointPosterior(), gum::JointTargetedMRFInference< GUM_SCALAR >::jointPosterior(), gum::LoopySamplingInference< GUM_SCALAR, APPROX >::makeInference_(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), and gum::MarginalTargetedMRFInference< GUM_SCALAR >::posterior().

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makeInference_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

makes the inference by generating samples

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 170 of file samplingInference_tpl.h.

                                                       {
    if (!isSetEstimator) this->setEstimatorFromBN_();
    loopApproxInference_();
  }

References isSetEstimator, loopApproxInference_(), and setEstimatorFromBN_().

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maxIter()

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

overridevirtualinherited

Returns the criterion on number of iterations.

Returns

Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 113 of file approximationScheme_inl.h.

{ return max_iter_; }

References max_iter_.

maxTime()

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

overridevirtualinherited

Returns the timeout (in seconds).

Returns

Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 134 of file approximationScheme_inl.h.

{ return max_time_; }

References max_time_.

messageApproximationScheme()

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

inherited

Returns the approximation scheme message.

Returns

Returns the approximation scheme message.

Definition at line 64 of file IApproximationSchemeConfiguration.cpp.

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

References Continue, Epsilon, epsilon(), Limit, maxIter(), maxTime(), minEpsilonRate(), Rate, stateApproximationScheme(), Stopped, TimeLimit, and Undefined.

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

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minEpsilonRate()

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

overridevirtualinherited

Returns the value of the minimal epsilon rate.

Returns

Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 93 of file approximationScheme_inl.h.

{ return min_rate_eps_; }

References min_rate_eps_.

model()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

UndefinedElement

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

Definition at line 127 of file graphicalModelInference_tpl.h.

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

References _model_, and GUM_ERROR.

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

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monteCarloSample()

template<GUM_Numeric GUM_SCALAR>

Instantiation gum::GibbsOperator< GUM_SCALAR >::monteCarloSample ( )

inherited

draws a Monte Carlo sample

returns a MC sample This is not a really sample since we take into account evidence without care about parent of evidence, etc. This is just a not-so-bad first sample for GibbsSampler

Definition at line 88 of file gibbsOperator_tpl.h.

                                                              {
    gum::Instantiation I;
 
    for (const auto nod: samplingBn_.topologicalOrder()) {
      I.add(samplingBn_.variable(nod));
      if (hardEv_ != nullptr && hardEv_->exists(nod)) {
        I.chgVal(samplingBn_.variable(nod), (*hardEv_)[nod]);
      } else {
        _drawVarMonteCarlo_(nod, &I);
      }
    }
    return I;
  }

References _drawVarMonteCarlo_(), gum::Instantiation::add(), gum::Instantiation::chgVal(), hardEv_, and samplingBn_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::GibbsSampling< GUM_SCALAR >::monteCarloSample_().

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monteCarloSample_()

template<GUM_Numeric GUM_SCALAR>

Instantiation gum::GibbsSampling< GUM_SCALAR >::monteCarloSample_ ( )

protected

draws a Monte Carlo sample

Parameters

bn	the reference Bayesian network

This Monte Carlo sample generates a good starting point for Gibbs sampling, because it returns a sample consistent with the evidence, but it differs from the one implemented in the inherited class Approximate Inference because it also initializes attributes needed for Gibbs sampling.

Definition at line 84 of file GibbsSampling_tpl.h.

                                                               {
    return GibbsOperator< GUM_SCALAR >::monteCarloSample();
  }

References gum::GibbsOperator< GUM_SCALAR >::monteCarloSample().

Referenced by burnIn_().

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nbrDrawnVar()

template<GUM_Numeric GUM_SCALAR>

Size gum::GibbsOperator< GUM_SCALAR >::nbrDrawnVar ( ) const

inherited

Getters and setters.

Definition at line 137 of file gibbsOperator_tpl.h.

                                                      {
    return nbr_;
  }

References nbr_.

nbrEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

returns the number of evidence entered into the Bayesian network

Definition at line 556 of file graphicalModelInference_tpl.h.

                                                                {
    return _evidence_.size();
  }

References _evidence_.

nbrHardEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

returns the number of hard evidence entered into the Bayesian network

Definition at line 562 of file graphicalModelInference_tpl.h.

                                                                    {
    return _hard_evidence_nodes_.size();
  }

References _hard_evidence_nodes_.

nbrIterations()

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

overridevirtualinherited

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 170 of file approximationScheme_inl.h.

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

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

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

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nbrSoftEvidence()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

returns the number of soft evidence entered into the Bayesian network

Definition at line 568 of file graphicalModelInference_tpl.h.

                                                                    {
    return _soft_evidence_nodes_.size();
  }

References _soft_evidence_nodes_.

nbrTargets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the number of marginal targets

Definition at line 220 of file marginalTargetedInference_tpl.h.

                                                                          {
    return _targets_.size();
  }

References _targets_.

nextSample()

template<GUM_Numeric GUM_SCALAR>

Instantiation gum::GibbsOperator< GUM_SCALAR >::nextSample ( Instantiation prev )

inherited

draws next sample of Gibbs sampling

Definition at line 110 of file gibbsOperator_tpl.h.

                                                                          {
    for (Idx i = 0; i < nbr_; i++) {
      auto pos
          = atRandom_ ? randomValue(samplingNodes_.size()) : (counting_ % samplingNodes_.size());
      this->_GibbsSample_(samplingNodes_[pos], &prev);
      counting_++;
    }
    return prev;
  }

References _GibbsSample_(), atRandom_, counting_, nbr_, gum::randomValue(), and samplingNodes_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::GibbsSampling< GUM_SCALAR >::draw_().

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onAllEvidenceErased_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

fired before all the evidence are erased

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 217 of file samplingInference_tpl.h.

{}

onAllMarginalTargetsAdded_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

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

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 243 of file samplingInference_tpl.h.

{}

onAllMarginalTargetsErased_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

fired before a all marginal targets are removed

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 246 of file samplingInference_tpl.h.

{}

onContextualize_()

template<GUM_Numeric GUM_SCALAR>

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

protectedvirtualinherited

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 204 of file samplingInference_tpl.h.

{}

Referenced by contextualize().

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onEvidenceAdded_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

fired after a new evidence is inserted

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 207 of file samplingInference_tpl.h.

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

References GUM_ERROR.

onEvidenceChanged_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

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

Parameters

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 220 of file samplingInference_tpl.h.

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

References GUM_ERROR.

onEvidenceErased_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

fired before an evidence is removed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 214 of file samplingInference_tpl.h.

{}

onMarginalTargetAdded_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

fired after a new marginal target is inserted

Parameters

id	The target variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 237 of file samplingInference_tpl.h.

{}

onMarginalTargetErased_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

fired before a marginal target is removed

Parameters

id	The target variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 240 of file samplingInference_tpl.h.

{}

onModelChanged_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

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

Reimplemented from gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 228 of file samplingInference_tpl.h.

{}

onStateChanged_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

fired when the stage is changed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 249 of file samplingInference_tpl.h.

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

References _estimator_, gum::ApproximationScheme::initApproximationScheme(), and gum::GraphicalModelInference< GUM_SCALAR >::isInferenceReady().

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periodSize()

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

overridevirtualinherited

Returns the period size.

Returns

Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 156 of file approximationScheme_inl.h.

{ return period_size_; }

References period_size_.

posterior() [1/2]

template<GUM_Numeric GUM_SCALAR>

const Tensor< 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 tensor is stored into the inference engine and is returned by reference. In order to ensure that the tensor may still exist even if the Inference object is destroyed, the user has to copy it explicitly.

prepareInference and makeInference may be applied if needed by the posterior method.

Exceptions

UndefinedElement

if node is not in the set of targets

Reimplemented in gum::JointTargetedInference< GUM_SCALAR >.

Definition at line 246 of file marginalTargetedInference_tpl.h.

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

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

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

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posterior() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

virtualinherited

Computes and returns the posterior of a node.

Returns

a const ref to the posterior probability of the node.

Parameters

nodeName

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

Warning

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

prepareInference and makeInference may be applied if needed by the posterior method.

Exceptions

UndefinedElement

if node is not in the set of targets

Reimplemented in gum::JointTargetedInference< GUM_SCALAR >.

Definition at line 262 of file marginalTargetedInference_tpl.h.

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

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

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posterior_()

template<GUM_Numeric GUM_SCALAR>

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

overridevirtualinherited

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 tensor is returned by reference. In order to ensure that the tensor may still exist even if the Inference object is destroyed, the user has to copy it explicitly.

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 122 of file samplingInference_tpl.h.

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

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

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prepareInference()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualinherited

prepare the internal inference structures for the next inference

Definition at line 616 of file graphicalModelInference_tpl.h.

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

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

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

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remainingBurnIn()

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

inherited

Returns the remaining burn in.

Returns

Returns the remaining burn in.

Definition at line 213 of file approximationScheme_inl.h.

                                                         {
    if (burn_in_ > current_step_) {
      return burn_in_ - current_step_;
    } else {
      return 0;
    }
  }

References burn_in_, and current_step_.

samplingBN()

template<GUM_Numeric GUM_SCALAR>

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

inherited

get the BayesNet which is used to really perform the sampling

Definition at line 90 of file samplingInference_tpl.h.

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

References _samplingBN_, gum::BayesNetInference< GUM_SCALAR >::BN(), and gum::GraphicalModelInference< GUM_SCALAR >::prepareInference().

Referenced by addVarSample_(), gum::ImportanceSampling< GUM_SCALAR >::draw_(), and setEstimatorFromBN_().

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setBN()

template<GUM_Numeric GUM_SCALAR>

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

virtualinherited

assigns a new BN to the inference engine

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

Warning

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

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

Definition at line 82 of file BayesNetInference_tpl.h.

                                                                               {
    this->setModel_(bn);
  }

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

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setBurnIn()

template<GUM_Numeric GUM_SCALAR>

void gum::GibbsSampling< GUM_SCALAR >::setBurnIn

(

Size

b

)

Number of burn in for one iteration.

Parameters

b	The number of burn in.

Exceptions

OutOfBounds

Raised if b < 1.

Definition at line 114 of file GibbsSampling_tpl.h.

                                                    {
    this->burn_in_ = b;
  }

References gum::ApproximationScheme::burn_in_.

Referenced by GibbsSampling().

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setDrawnAtRandom()

template<GUM_Numeric GUM_SCALAR>

void gum::GibbsOperator< GUM_SCALAR >::setDrawnAtRandom ( bool atRandom )

inherited

Definition at line 152 of file gibbsOperator_tpl.h.

                                                                  {
    atRandom_ = atRandom;
  }

References atRandom_.

setEpsilon()

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

overridevirtualinherited

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

OutOfBounds

Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Reimplemented in gum::learning::EMApproximationScheme.

Definition at line 64 of file approximationScheme_inl.h.

                                                        {
    if (eps < 0.) { GUM_ERROR(OutOfBounds, "eps should be >=0") }
 
    eps_         = eps;
    enabled_eps_ = true;
  }

References enabled_eps_, eps_, and GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::learning::GreedyThickThinning::GreedyThickThinning(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::EMApproximationScheme::setEpsilon().

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setEstimatorFromBN_()

template<GUM_Numeric GUM_SCALAR>

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

virtualinherited

Initializes the estimators object linked to the simulation.

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

Definition at line 97 of file samplingInference_tpl.h.

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

References _estimator_, gum::GraphicalModelInference< GUM_SCALAR >::hardEvidenceNodes(), isSetEstimator, and samplingBN().

Referenced by makeInference_().

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setEstimatorFromLBP_()

template<GUM_Numeric GUM_SCALAR>

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

virtualinherited

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 103 of file samplingInference_tpl.h.

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

References _estimator_, gum::GraphicalModelInference< GUM_SCALAR >::hardEvidenceNodes(), and isSetEstimator.

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setMaxIter()

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

overridevirtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfBounds

Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 106 of file approximationScheme_inl.h.

                                                      {
    if (max < 1) { GUM_ERROR(OutOfBounds, "max should be >=1") }
    max_iter_         = max;
    enabled_max_iter_ = true;
  }

References enabled_max_iter_, GUM_ERROR, and max_iter_.

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

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setMaxTime()

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

overridevirtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout

The timeout value in seconds.

Exceptions

OutOfBounds

Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 127 of file approximationScheme_inl.h.

                                                            {
    if (timeout <= 0.) { GUM_ERROR(OutOfBounds, "timeout should be >0.") }
    max_time_         = timeout;
    enabled_max_time_ = true;
  }

References enabled_max_time_, GUM_ERROR, and max_time_.

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

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setMinEpsilonRate()

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

overridevirtualinherited

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

OutOfBounds

if rate<0

Implements gum::IApproximationSchemeConfiguration.

Reimplemented in gum::learning::EMApproximationScheme.

Definition at line 85 of file approximationScheme_inl.h.

                                                                {
    if (rate < 0) { GUM_ERROR(OutOfBounds, "rate should be >=0") }
 
    min_rate_eps_         = rate;
    enabled_min_rate_eps_ = true;
  }

References enabled_min_rate_eps_, GUM_ERROR, and min_rate_eps_.

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

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setModel_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 137 of file graphicalModelInference_tpl.h.

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

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

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

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setModelDuringConstruction_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

assigns a model during the inference engine construction

Definition at line 147 of file graphicalModelInference_tpl.h.

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

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

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

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setNbrDrawnVar()

template<GUM_Numeric GUM_SCALAR>

void gum::GibbsOperator< GUM_SCALAR >::setNbrDrawnVar ( Size nbr )

inherited

Definition at line 142 of file gibbsOperator_tpl.h.

                                                           {
    nbr_ = nbr;
  }

References nbr_.

setOutdatedStructureState_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

put the inference into an outdated model structure state

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

Definition at line 603 of file graphicalModelInference_tpl.h.

                                                                         {
    setState_(StateOfInference::OutdatedStructure);
  }

References OutdatedStructure, and setState_().

Referenced by makeInference_().

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setOutdatedTensorsState_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

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

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

Definition at line 610 of file graphicalModelInference_tpl.h.

                                                                       {
    setState_(StateOfInference::OutdatedTensors);
  }

References OutdatedTensors, and setState_().

Referenced by makeInference_().

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setPeriodSize()

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

overridevirtualinherited

How many samples between two stopping is enable.

Parameters

p	The new period value.

Exceptions

OutOfBounds

Raised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 150 of file approximationScheme_inl.h.

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

References GUM_ERROR.

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

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setState_()

template<GUM_Numeric GUM_SCALAR>

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

finalprotectedvirtualinherited

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

Definition at line 118 of file graphicalModelInference_tpl.h.

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

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

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

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setTargetedMode_()

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

Definition at line 335 of file marginalTargetedInference_tpl.h.

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

References _targeted_mode_, and _targets_.

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

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setVerbosity()

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

overridevirtualinherited

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

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 159 of file approximationScheme_inl.h.

{ verbosity_ = v; }

References verbosity_.

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

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softEvidenceNodes()

template<GUM_Numeric GUM_SCALAR>

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

inherited

returns the set of nodes with soft evidence

the set of nodes that received soft evidence

Definition at line 587 of file graphicalModelInference_tpl.h.

                                                                                {
    return _soft_evidence_nodes_;
  }

References _soft_evidence_nodes_.

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

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startOfPeriod()

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

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 200 of file approximationScheme_inl.h.

                                                       {
    if (current_step_ < burn_in_) { return false; }
 
    if (period_size_ == 1) { return true; }
 
    return ((current_step_ - burn_in_) % period_size_ == 0);
  }

Referenced by continueApproximationScheme().

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state()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the state of the inference engine

Definition at line 112 of file graphicalModelInference_tpl.h.

                                                                  {
    return _state_;
  }

References _state_.

Referenced by setState_().

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stateApproximationScheme()

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

overridevirtualinherited

Returns the approximation scheme state.

Returns

Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 165 of file approximationScheme_inl.h.

                                                             {
    return current_state_;
  }

Referenced by continueApproximationScheme(), history(), and nbrIterations().

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stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 222 of file approximationScheme_inl.h.

                                                           {
    if (current_state_ == ApproximationSchemeSTATE::Continue) {
      stopScheme_(ApproximationSchemeSTATE::Stopped);
    }
  }

Referenced by gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

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stopScheme_()

INLINE void gum::ApproximationScheme::stopScheme_ ( ApproximationSchemeSTATE new_state )

privateinherited

Stop the scheme given a new state.

Parameters

new_state

The scheme new state.

Definition at line 231 of file approximationScheme_inl.h.

                                                                                 {
    if (new_state == ApproximationSchemeSTATE::Continue) { return; }
 
    if (new_state == ApproximationSchemeSTATE::Undefined) { return; }
 
    current_state_ = new_state;
    timer_.pause();
 
    if (onStop.hasListener()) { GUM_EMIT1(onStop, messageApproximationScheme()); }
  }

References gum::IApproximationSchemeConfiguration::Continue, and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by continueApproximationScheme(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::disableMaxIter().

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targets()

template<GUM_Numeric GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the list of marginal targets

Definition at line 214 of file marginalTargetedInference_tpl.h.

                                                                                 {
    return _targets_;
  }

References _targets_.

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

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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 209 of file approximationScheme_inl.h.

                                                                              {
    current_step_ += incr;
  }

References current_step_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByOrderedArcs_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByRandomOrder_(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

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updateOutdatedStructure_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

prepares inference when the latter is in OutdatedStructure state

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 231 of file samplingInference_tpl.h.

{}

updateOutdatedTensors_()

template<GUM_Numeric GUM_SCALAR>

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

overrideprotectedvirtualinherited

prepares inference when the latter is in OutdatedTensors state

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 234 of file samplingInference_tpl.h.

{}

updateSamplingNodes_()

template<GUM_Numeric GUM_SCALAR>

void gum::GibbsOperator< GUM_SCALAR >::updateSamplingNodes_ ( )

protectedinherited

Definition at line 73 of file gibbsOperator_tpl.h.

                                                         {
    samplingNodes_.clear();
    for (const auto node: samplingBn_.nodes())
      if (hardEv_ == nullptr || !hardEv_->exists(node)) { samplingNodes_.insert(node); }
    if (samplingNodes_.size() == 0) {
      GUM_ERROR(InvalidArgument, "No node to sample (too many nodes or too much evidence)!")
    }
    if (nbr_ > samplingNodes_.size()) nbr_ = samplingNodes_.size();
  }

References GUM_ERROR, hardEv_, nbr_, samplingBn_, and samplingNodes_.

Referenced by gum::GibbsSampling< GUM_SCALAR >::burnIn_(), and gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

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verbosity()

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

overridevirtualinherited

Returns true if verbosity is enabled.

Returns

Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 161 of file approximationScheme_inl.h.

{ return verbosity_; }

References verbosity_.

Referenced by ApproximationScheme(), gum::learning::EMApproximationScheme::EMApproximationScheme(), and continueApproximationScheme().

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Member Data Documentation

_estimator_

template<GUM_Numeric GUM_SCALAR>

Estimator< GUM_SCALAR > gum::SamplingInference< GUM_SCALAR >::_estimator_

protectedinherited

Estimator object designed to approximate target posteriors.

Definition at line 196 of file samplingInference.h.

Referenced by SamplingInference(), currentPosterior(), loopApproxInference_(), onStateChanged_(), posterior_(), setEstimatorFromBN_(), and setEstimatorFromLBP_().

_samplingBN_

template<GUM_Numeric GUM_SCALAR>

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

privateinherited

Definition at line 265 of file samplingInference.h.

Referenced by SamplingInference(), ~SamplingInference(), contextualize(), and samplingBN().

_targeted_mode_

template<GUM_Numeric GUM_SCALAR>

bool gum::MarginalTargetedInference< GUM_SCALAR >::_targeted_mode_

privateinherited

whether the actual targets are default

Definition at line 262 of file marginalTargetedInference.h.

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

_targets_

template<GUM_Numeric GUM_SCALAR>

NodeSet gum::MarginalTargetedInference< GUM_SCALAR >::_targets_

privateinherited

the set of marginal targets

Definition at line 265 of file marginalTargetedInference.h.

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

atRandom_

template<GUM_Numeric GUM_SCALAR>

bool gum::GibbsOperator< GUM_SCALAR >::atRandom_

protectedinherited

Definition at line 104 of file gibbsOperator.h.

Referenced by GibbsOperator(), isDrawnAtRandom(), nextSample(), and setDrawnAtRandom().

burn_in_

Size gum::ApproximationScheme::burn_in_

protectedinherited

Number of iterations before checking stopping criteria.

Definition at line 423 of file approximationScheme.h.

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

counting_

template<GUM_Numeric GUM_SCALAR>

Size gum::GibbsOperator< GUM_SCALAR >::counting_

protectedinherited

Definition at line 98 of file gibbsOperator.h.

Referenced by GibbsOperator(), and nextSample().

current_epsilon_

double gum::ApproximationScheme::current_epsilon_

protectedinherited

Current epsilon.

Definition at line 378 of file approximationScheme.h.

Referenced by continueApproximationScheme().

current_rate_

double gum::ApproximationScheme::current_rate_

protectedinherited

Current rate.

Definition at line 384 of file approximationScheme.h.

Referenced by continueApproximationScheme().

current_state_

ApproximationSchemeSTATE gum::ApproximationScheme::current_state_

protectedinherited

The current state.

Definition at line 393 of file approximationScheme.h.

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

current_step_

Size gum::ApproximationScheme::current_step_

protectedinherited

The current step.

Definition at line 387 of file approximationScheme.h.

Referenced by continueApproximationScheme(), initApproximationScheme(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::Miic::learnMixedStructure(), gum::learning::SimpleMiic::learnMixedStructure(), gum::learning::Miic::learnSkeleton(), nbrIterations(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), gum::learning::SimpleMiic::orientationMiic_(), remainingBurnIn(), and updateApproximationScheme().

enabled_eps_

bool gum::ApproximationScheme::enabled_eps_

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 402 of file approximationScheme.h.

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

enabled_max_iter_

bool gum::ApproximationScheme::enabled_max_iter_

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 420 of file approximationScheme.h.

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

enabled_max_time_

bool gum::ApproximationScheme::enabled_max_time_

protectedinherited

If true, the timeout is enabled.

Definition at line 414 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), disableMaxTime(), enableMaxTime(), isEnabledMaxTime(), and 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 408 of file approximationScheme.h.

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

eps_

double gum::ApproximationScheme::eps_

protectedinherited

Threshold for convergence.

Definition at line 399 of file approximationScheme.h.

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

hardEv_

template<GUM_Numeric GUM_SCALAR>

const NodeProperty< Idx >* gum::GibbsOperator< GUM_SCALAR >::hardEv_

protectedinherited

Definition at line 100 of file gibbsOperator.h.

Referenced by GibbsOperator(), monteCarloSample(), and updateSamplingNodes_().

history_

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

protectedinherited

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

Definition at line 396 of file approximationScheme.h.

Referenced by continueApproximationScheme().

isContextualized

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

whether the referenced Bayesian network has been "contextualized"

Definition at line 202 of file samplingInference.h.

Referenced by ~SamplingInference(), contextualize(), and loopApproxInference_().

isSetEstimator

template<GUM_Numeric GUM_SCALAR>

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

protectedinherited

whether the Estimator object has been initialized

Definition at line 199 of file samplingInference.h.

Referenced by loopApproxInference_(), makeInference_(), setEstimatorFromBN_(), and setEstimatorFromLBP_().

last_epsilon_

double gum::ApproximationScheme::last_epsilon_

protectedinherited

Last epsilon value.

Definition at line 381 of file approximationScheme.h.

Referenced by continueApproximationScheme().

max_iter_

Size gum::ApproximationScheme::max_iter_

protectedinherited

The maximum iterations.

Definition at line 417 of file approximationScheme.h.

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

max_time_

double gum::ApproximationScheme::max_time_

protectedinherited

The timeout.

Definition at line 411 of file approximationScheme.h.

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

min_rate_eps_

double gum::ApproximationScheme::min_rate_eps_

protectedinherited

Threshold for the epsilon rate.

Definition at line 405 of file approximationScheme.h.

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

nbr_

template<GUM_Numeric GUM_SCALAR>

Size gum::GibbsOperator< GUM_SCALAR >::nbr_

protectedinherited

Definition at line 103 of file gibbsOperator.h.

Referenced by GibbsOperator(), nbrDrawnVar(), nextSample(), setNbrDrawnVar(), and updateSamplingNodes_().

onProgress

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

inherited

Progression, error and time.

Definition at line 81 of file IApproximationSchemeConfiguration.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::learning::IBNLearner::distributeProgress(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), and gum::learning::SimpleMiic::orientationMiic_().

onStop

Signaler< std::string_view > gum::IApproximationSchemeConfiguration::onStop

inherited

Criteria messageApproximationScheme.

Definition at line 84 of file IApproximationSchemeConfiguration.h.

Referenced by gum::learning::IBNLearner::distributeStop().

period_size_

Size gum::ApproximationScheme::period_size_

protectedinherited

Checking criteria frequency.

Definition at line 426 of file approximationScheme.h.

Referenced by ApproximationScheme(), and periodSize().

samplingBn_

template<GUM_Numeric GUM_SCALAR>

const IBayesNet< GUM_SCALAR >& gum::GibbsOperator< GUM_SCALAR >::samplingBn_

protectedinherited

Definition at line 99 of file gibbsOperator.h.

Referenced by GibbsOperator(), _drawVarMonteCarlo_(), _GibbsSample_(), monteCarloSample(), and updateSamplingNodes_().

samplingNodes_

template<GUM_Numeric GUM_SCALAR>

Sequence< NodeId > gum::GibbsOperator< GUM_SCALAR >::samplingNodes_

protectedinherited

Definition at line 101 of file gibbsOperator.h.

Referenced by nextSample(), and updateSamplingNodes_().

timer_

Timer gum::ApproximationScheme::timer_

protectedinherited

The timer.

Definition at line 390 of file approximationScheme.h.

Referenced by continueApproximationScheme(), currentTime(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::Miic::learnMixedStructure(), gum::learning::SimpleMiic::learnMixedStructure(), gum::learning::Miic::learnSkeleton(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), and gum::learning::SimpleMiic::orientationMiic_().

verbosity_

bool gum::ApproximationScheme::verbosity_

protectedinherited

If true, verbosity is enabled.

Definition at line 429 of file approximationScheme.h.

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

---

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

• agrum/BN/inference/GibbsSampling.h
• agrum/BN/inference/GibbsSampling_tpl.h