gum::SamplingInference< GUM_SCALAR > Class Template Reference

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

#include <samplingInference.h>

Inheritance diagram for gum::SamplingInference< GUM_SCALAR >:

Collaboration diagram for gum::SamplingInference< 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
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 instanciation of target and evs).
Tensor< GUM_SCALAR >	evidenceImpact (const std::string &target, const std::vector< std::string > &evs)
	Create a gum::Tensor for P(target|evs) (for all instanciation of target and evs).
Constructors / Destructors
	SamplingInference (const IBayesNet< GUM_SCALAR > *bn)
	default constructor
	~SamplingInference () override
	destructor
const Tensor< GUM_SCALAR > &	currentPosterior (NodeId id)
	Computes and returns the actual estimation of the posterior of a node.
const Tensor< GUM_SCALAR > &	currentPosterior (const std::string &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 (const std::string &nodeName)
	Computes and returns the posterior of a node.
Targets
virtual void	eraseAllTargets ()
	Clear all previously defined targets.
virtual void	addAllTargets () final
	adds all nodes as targets
virtual void	addTarget (NodeId target) final
	Add a marginal target to the list of targets.
virtual void	addTarget (const std::string &nodeName) final
	Add a marginal target to the list of targets.
virtual void	eraseTarget (NodeId target) final
	removes an existing (marginal) target
virtual void	eraseTarget (const std::string &nodeName) final
	removes an existing (marginal) target
virtual bool	isTarget (NodeId node) const final
	return true if variable is a (marginal) target
virtual bool	isTarget (const std::string &nodeName) const final
	return true if variable is a (marginal) target
virtual Size	nbrTargets () const noexcept final
	returns the number of marginal targets
virtual const NodeSet &	targets () const noexcept final
	returns the list of marginal targets
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 (const std::string &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 (const std::string &nodeName, const Idx val) final
	adds a new hard evidence on node named nodeName
virtual void	addEvidence (NodeId id, const std::string &label) final
	adds a new hard evidence on node id
virtual void	addEvidence (const std::string &nodeName, const std::string &label) final
	adds a new hard evidence on node named nodeName
virtual void	addEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	adds a new evidence on node named nodeName (might be soft or hard)
virtual void	addEvidence (const Tensor< GUM_SCALAR > &pot) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addEvidence (Tensor< GUM_SCALAR > &&pot) final
	adds a new evidence on node id (might be soft or hard)
virtual void	addSetOfEvidence (const Set< const Tensor< GUM_SCALAR > * > &potset) final
	adds a new set of evidence
virtual void	addListOfEvidence (const List< const Tensor< GUM_SCALAR > * > &potlist) final
	adds a new list of evidence
virtual void	chgEvidence (NodeId id, const Idx val) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (const std::string &nodeName, const Idx val) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (NodeId id, const std::string &label) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (const std::string &nodeName, const std::string &label) final
	change the value of an already existing hard evidence
virtual void	chgEvidence (NodeId id, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	chgEvidence (const std::string &nodeName, const std::vector< GUM_SCALAR > &vals) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	chgEvidence (const Tensor< GUM_SCALAR > &pot) final
	change the value of an already existing evidence (might be soft or hard)
virtual void	eraseAllEvidence () final
	removes all the evidence entered into the network
virtual void	eraseEvidence (NodeId id) final
	removed the evidence, if any, corresponding to node id
virtual void	eraseEvidence (const std::string &nodeName) final
	removed the evidence, if any, corresponding to node of name nodeName
virtual bool	hasEvidence () const final
	indicates whether some node(s) have received evidence
virtual bool	hasEvidence (NodeId id) const final
	indicates whether node id has received an evidence
virtual bool	hasEvidence (const std::string &nodeName) const final
	indicates whether node id has received an evidence
virtual bool	hasHardEvidence (NodeId id) const final
	indicates whether node id has received a hard evidence
virtual bool	hasHardEvidence (const std::string &nodeName) const final
	indicates whether node id has received a hard evidence
virtual bool	hasSoftEvidence (NodeId id) const final
	indicates whether node id has received a soft evidence
virtual bool	hasSoftEvidence (const std::string &nodeName) const final
	indicates whether node id has received a soft evidence
virtual Size	nbrEvidence () const final
	returns the number of evidence entered into the Bayesian network
virtual Size	nbrHardEvidence () const final
	returns the number of hard evidence entered into the Bayesian network
virtual Size	nbrSoftEvidence () const final
	returns the number of soft evidence entered into the Bayesian network
const NodeProperty< const Tensor< GUM_SCALAR > * > &	evidence () const
	returns the set of evidence
const NodeSet &	softEvidenceNodes () const
	returns the set of nodes with soft evidence
const NodeSet &	hardEvidenceNodes () const
	returns the set of nodes with hard evidence
const NodeProperty< Idx > &	hardEvidence () const
	indicate for each node with hard evidence which value it took
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
Signaler3< Size, double, double >	onProgress
	Progression, error and time.
Signaler1< const std::string & >	onStop
	Criteria messageApproximationScheme.

Protected Member Functions
virtual Instantiation	burnIn_ ()=0
	draws samples without updating the estimators
virtual Instantiation	draw_ (GUM_SCALAR *w, Instantiation prev)=0
	draws a sample in the Bayesian network given a previous one
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

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.

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.

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

<agrum/BN/inference/samplingInference.h>

A generic class for making sampling inference in Bayesian networks adapted

The goal of this class is to define the genereal scheme used by all sampling inference algorithms, which are implemented as derived classes of ApproximateInference. This class inherits from MarginalTargetedInference for the handling of marginal targets and from ApproximationScheme.

Definition at line 75 of file samplingInference.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 86 of file IApproximationSchemeConfiguration.h.

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

StateOfInference

template<typename GUM_SCALAR>

enum class gum::GraphicalModelInference::StateOfInference

stronginherited

current state of the inference

graphicalModelInference can be in one of 4 different states:

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

Enumerator
OutdatedStructure
OutdatedTensors
ReadyForInference
Done

Definition at line 127 of file graphicalModelInference.h.

{ OutdatedStructure, OutdatedTensors, ReadyForInference, Done };

Constructor & Destructor Documentation

SamplingInference()

template<typename GUM_SCALAR>

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

explicit

default constructor

Warning

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

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

Definition at line 67 of file samplingInference_tpl.h.

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

References gum::ApproximateInference< GUM_SCALAR >::ApproximateInference(), SamplingInference(), _estimator_, _samplingBN_, DEFAULT_EPSILON, DEFAULT_MAXITER, DEFAULT_MIN_EPSILON_RATE, DEFAULT_PERIOD_SIZE, DEFAULT_TIMEOUT, DEFAULT_VERBOSITY, gum::ApproximationScheme::setEpsilon(), gum::ApproximationScheme::setMaxIter(), gum::ApproximationScheme::setMaxTime(), gum::ApproximationScheme::setMinEpsilonRate(), gum::ApproximationScheme::setPeriodSize(), and gum::ApproximationScheme::setVerbosity().

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

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~SamplingInference()

template<typename GUM_SCALAR>

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

override

destructor

Definition at line 79 of file samplingInference_tpl.h.

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

References SamplingInference(), _samplingBN_, and isContextualized.

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Member Function Documentation

_setAllMarginalTargets_()

template<typename GUM_SCALAR>

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

privateinherited

sets all the nodes of the Bayes net as targets

Definition at line 229 of file marginalTargetedInference_tpl.h.

                                                                        {
    _targets_.clear();
    if (!this->hasNoModel_()) {
      _targets_ = this->BN().dag().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<typename GUM_SCALAR>

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

finalvirtualinherited

adds all nodes as targets

Definition at line 144 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().dag()) {
      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<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

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

Definition at line 235 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [2/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node named nodeName

Exceptions

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

Definition at line 249 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [3/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 281 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [4/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 323 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addEvidence() [5/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

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

Definition at line 229 of file graphicalModelInference_tpl.h.

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

References _createHardEvidence_(), and addEvidence().

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

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addEvidence() [6/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new hard evidence on node id

Exceptions

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

Definition at line 242 of file graphicalModelInference_tpl.h.

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

References addEvidence(), and model().

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addEvidence() [7/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 257 of file graphicalModelInference_tpl.h.

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

References _model_, addEvidence(), and GUM_ERROR.

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addEvidence() [8/8]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 288 of file graphicalModelInference_tpl.h.

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

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

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addListOfEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new list of evidence

Exceptions

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

Definition at line 330 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addSetOfEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

adds a new set of evidence

Exceptions

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

Definition at line 338 of file graphicalModelInference_tpl.h.

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

References addEvidence().

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addTarget() [1/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement

if target is not a NodeId in the Bayes net

Definition at line 164 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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addTarget() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

Add a marginal target to the list of targets.

Exceptions

UndefinedElement

if target is not a NodeId in the Bayes net

Definition at line 122 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().dag().exists(target)) {
      GUM_ERROR(UndefinedElement, target << " is not a NodeId in the bn")
    }
 
    setTargetedMode_();   // does nothing if already in targeted mode
    // add the new target
    if (!_targets_.contains(target)) {
      _targets_.insert(target);
      onMarginalTargetAdded_(target);
      this->setState_(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(), and evidenceImpact().

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addVarSample_()

template<typename GUM_SCALAR>

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

protectedvirtual

adds a node to current instantiation

Parameters

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

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

Definition at line 195 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<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

UndefinedElement

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

Definition at line 75 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<typename GUM_SCALAR>

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

protectedpure virtual

draws samples without updating the estimators

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

Referenced by loopApproxInference_().

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chgEvidence() [1/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 397 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [2/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 411 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [3/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 445 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [4/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 452 of file graphicalModelInference_tpl.h.

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

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

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chgEvidence() [5/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 391 of file graphicalModelInference_tpl.h.

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

References _createHardEvidence_(), and chgEvidence().

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

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chgEvidence() [6/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

change the value of an already existing hard evidence

Exceptions

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

Definition at line 404 of file graphicalModelInference_tpl.h.

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

References chgEvidence(), and model().

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chgEvidence() [7/7]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

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

Definition at line 420 of file graphicalModelInference_tpl.h.

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

References _model_, chgEvidence(), and GUM_ERROR.

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clear()

template<typename GUM_SCALAR>

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

virtualinherited

clears all the data structures allocated for the last inference

Reimplemented in gum::ShaferShenoyLIMIDInference< GUM_SCALAR >.

Definition at line 155 of file graphicalModelInference_tpl.h.

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

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

Referenced by setModel_().

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contextualize()

template<typename GUM_SCALAR>

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

virtual

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

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

Definition at line 126 of file samplingInference_tpl.h.

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

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

inherited

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

Test the stopping criterion that are enabled.

Parameters

error

The new error value.

Returns

false if state become != ApproximationSchemeSTATE::Continue

Exceptions

OperationNotAllowed

Raised if state != ApproximationSchemeSTATE::Continue.

Definition at line 229 of file approximationScheme_inl.h.

                                                                           {
    // 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 enabled_max_time_, and timer_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::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<typename GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::SamplingInference< GUM_SCALAR >::currentPosterior

(

const std::string &

name

)

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

Returns

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

Parameters

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

Warning

for efficiency reasons, the 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 116 of file samplingInference_tpl.h.

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

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

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currentPosterior() [2/2]

template<typename GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::SamplingInference< GUM_SCALAR >::currentPosterior

(

NodeId

id

)

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

Returns

a const ref to the posterior probability of the node.

Parameters

id	the node for which we need a posterior probability

Warning

for efficiency reasons, the 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 110 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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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 136 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 74 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 115 of file approximationScheme_inl.h.

{ enabled_max_iter_ = false; }

References enabled_max_iter_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing().

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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 139 of file approximationScheme_inl.h.

{ enabled_max_time_ = false; }

References enabled_max_time_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing().

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disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

overridevirtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 95 of file approximationScheme_inl.h.

{ enabled_min_rate_eps_ = false; }

References enabled_min_rate_eps_.

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

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domainSizes()

template<typename GUM_SCALAR>

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

finalvirtualinherited

get the domain sizes of the random variables of the model

Definition at line 173 of file graphicalModelInference_tpl.h.

                                                                                              {
    return _domain_sizes_;
  }

References _domain_sizes_.

draw_()

template<typename GUM_SCALAR>

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

protectedpure virtual

draws a sample in the Bayesian network given a previous one

Parameters

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

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

Referenced by loopApproxInference_().

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enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

overridevirtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 77 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 118 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 142 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 98 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 71 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<typename GUM_SCALAR>

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

finalvirtualinherited

removes all the evidence entered into the network

Definition at line 540 of file graphicalModelInference_tpl.h.

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

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

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

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eraseAllTargets()

template<typename GUM_SCALAR>

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

virtualinherited

Clear all previously defined targets.

Reimplemented in gum::JointTargetedInference< GUM_SCALAR >.

Definition at line 111 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<typename GUM_SCALAR>

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

finalvirtualinherited

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

Definition at line 534 of file graphicalModelInference_tpl.h.

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

References eraseEvidence(), and model().

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eraseEvidence() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

removed the evidence, if any, corresponding to node id

Definition at line 514 of file graphicalModelInference_tpl.h.

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

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

Referenced by eraseEvidence().

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eraseTarget() [1/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

removes an existing (marginal) target

Warning

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

Definition at line 199 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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eraseTarget() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

removes an existing (marginal) target

Warning

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

Definition at line 176 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().dag().exists(target)) {
      GUM_ERROR(UndefinedElement, target << " is not a NodeId in the bn")
    }
 
 
    if (_targets_.contains(target)) {
      _targeted_mode_ = true;   // we do not use setTargetedMode_ because we do not
                                // want to clear the targets
      onMarginalTargetErased_(target);
      _targets_.erase(target);
      this->setState_(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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evidence()

template<typename GUM_SCALAR>

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

inherited

returns the set of evidence

Definition at line 587 of file graphicalModelInference_tpl.h.

                                                                     {
    return _evidence_;
  }

References _evidence_.

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

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evidenceImpact() [1/2]

template<typename GUM_SCALAR>

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

inherited

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

Warning

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

Parameters

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

Returns

a Tensor

Definition at line 319 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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evidenceImpact() [2/2]

template<typename 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 instanciation 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 280 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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H() [1/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

See also

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

Definition at line 275 of file marginalTargetedInference_tpl.h.

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

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

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H() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

See also

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

Definition at line 267 of file marginalTargetedInference_tpl.h.

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

References posterior().

Referenced by H().

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hardEvidence()

template<typename GUM_SCALAR>

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

inherited

indicate for each node with hard evidence which value it took

Definition at line 580 of file graphicalModelInference_tpl.h.

                                                                                              {
    return _hard_evidence_;
  }

References _hard_evidence_.

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

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hardEvidenceNodes()

template<typename GUM_SCALAR>

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

inherited

returns the set of nodes with hard evidence

the set of nodes that received hard evidence

Definition at line 599 of file graphicalModelInference_tpl.h.

                                                                                       {
    return _hard_evidence_nodes_;
  }

References _hard_evidence_nodes_.

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

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hasEvidence() [1/3]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether some node(s) have received evidence

Definition at line 346 of file graphicalModelInference_tpl.h.

                                                                       {
    return !_evidence_.empty();
  }

References _evidence_.

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

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hasEvidence() [2/3]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 371 of file graphicalModelInference_tpl.h.

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

References hasEvidence(), and model().

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hasEvidence() [3/3]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received an evidence

Definition at line 352 of file graphicalModelInference_tpl.h.

                                                                                {
    return _evidence_.exists(id);
  }

References _evidence_.

hasHardEvidence() [1/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 378 of file graphicalModelInference_tpl.h.

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

References hasHardEvidence(), and model().

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hasHardEvidence() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a hard evidence

Definition at line 358 of file graphicalModelInference_tpl.h.

                                                                                    {
    return _hard_evidence_nodes_.exists(id);
  }

References _hard_evidence_nodes_.

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

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hasNoModel_()

template<typename GUM_SCALAR>

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

inlineprotectedinherited

Definition at line 542 of file graphicalModelInference.h.

{ return _model_ == nullptr; };

References _model_.

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

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hasSoftEvidence() [1/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 385 of file graphicalModelInference_tpl.h.

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

References hasSoftEvidence(), and model().

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hasSoftEvidence() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

indicates whether node id has received a soft evidence

Definition at line 364 of file graphicalModelInference_tpl.h.

                                                                                    {
    return _soft_evidence_nodes_.exists(id);
  }

References _soft_evidence_nodes_.

Referenced by hasSoftEvidence().

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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 178 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 189 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_epsilon_, current_rate_, current_state_, current_step_, and initApproximationScheme().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), initApproximationScheme(), gum::learning::GreedyHillClimbing::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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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 81 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 122 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 146 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 102 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<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a InferenceDone state

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

Definition at line 104 of file graphicalModelInference_tpl.h.

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

References _state_, and Done.

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

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isInferenceOutdatedStructure()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedStructure state

Definition at line 92 of file graphicalModelInference_tpl.h.

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

References _state_, and OutdatedStructure.

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

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isInferenceOutdatedTensors()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a OutdatedTensor state

Definition at line 98 of file graphicalModelInference_tpl.h.

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

References _state_, and OutdatedTensors.

isInferenceReady()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns whether the inference object is in a ready state

Definition at line 86 of file graphicalModelInference_tpl.h.

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

References _state_, and ReadyForInference.

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

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isInTargetMode()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

indicates whether the inference is in a target mode

Definition at line 223 of file marginalTargetedInference_tpl.h.

                                                                                     {
    return _targeted_mode_;
  }

References _targeted_mode_.

isTarget() [1/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 105 of file marginalTargetedInference_tpl.h.

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

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

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isTarget() [2/2]

template<typename GUM_SCALAR>

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

finalvirtualinherited

return true if variable is a (marginal) target

Definition at line 90 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().dag().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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isTargetedMode_()

template<typename GUM_SCALAR>

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

protectedinherited

Definition at line 327 of file marginalTargetedInference_tpl.h.

                                                                             {
    return _targeted_mode_;
  }

References _targeted_mode_.

loopApproxInference_()

template<typename GUM_SCALAR>

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

protected

Definition at line 175 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<typename GUM_SCALAR>

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

finalvirtualinherited

perform the heavy computations needed to compute the targets' posteriors

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

Definition at line 638 of file graphicalModelInference_tpl.h.

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

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

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

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makeInference_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

makes the inference by generating samples

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 169 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 112 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 133 of file approximationScheme_inl.h.

{ return max_time_; }

References max_time_.

messageApproximationScheme()

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

inherited

Returns the approximation scheme message.

Returns

Returns the approximation scheme message.

Definition at line 59 of file IApproximationSchemeConfiguration_inl.h.

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

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

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

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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 92 of file approximationScheme_inl.h.

{ return min_rate_eps_; }

References min_rate_eps_.

model()

template<typename GUM_SCALAR>

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

finalvirtualinherited

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

Exceptions

UndefinedElement

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

Definition at line 126 of file graphicalModelInference_tpl.h.

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

References _model_, and GUM_ERROR.

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

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nbrEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

returns the number of evidence entered into the Bayesian network

Definition at line 562 of file graphicalModelInference_tpl.h.

                                                                       {
    return _evidence_.size();
  }

References _evidence_.

nbrHardEvidence()

template<typename GUM_SCALAR>

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

finalvirtualinherited

returns the number of hard evidence entered into the Bayesian network

Definition at line 568 of file graphicalModelInference_tpl.h.

                                                                           {
    return _hard_evidence_nodes_.size();
  }

References _hard_evidence_nodes_.

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 169 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<typename GUM_SCALAR>

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

finalvirtualinherited

returns the number of soft evidence entered into the Bayesian network

Definition at line 574 of file graphicalModelInference_tpl.h.

                                                                           {
    return _soft_evidence_nodes_.size();
  }

References _soft_evidence_nodes_.

nbrTargets()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the number of marginal targets

Definition at line 217 of file marginalTargetedInference_tpl.h.

                                                                                 {
    return _targets_.size();
  }

References _targets_.

onAllEvidenceErased_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired before all the evidence are erased

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 216 of file samplingInference_tpl.h.

{}

onAllMarginalTargetsAdded_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

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

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 242 of file samplingInference_tpl.h.

{}

onAllMarginalTargetsErased_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired before a all marginal targets are removed

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 245 of file samplingInference_tpl.h.

{}

onContextualize_()

template<typename GUM_SCALAR>

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

protectedvirtual

fired when Bayesian network is contextualized

Parameters

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

Reimplemented in gum::ImportanceSampling< GUM_SCALAR >.

Definition at line 203 of file samplingInference_tpl.h.

{}

Referenced by contextualize().

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onEvidenceAdded_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired after a new evidence is inserted

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 206 of file samplingInference_tpl.h.

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

References GUM_ERROR.

onEvidenceChanged_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

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

Parameters

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

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 219 of file samplingInference_tpl.h.

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

References GUM_ERROR.

onEvidenceErased_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired before an evidence is removed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 213 of file samplingInference_tpl.h.

{}

onMarginalTargetAdded_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired after a new marginal target is inserted

Parameters

id	The target variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 236 of file samplingInference_tpl.h.

{}

onMarginalTargetErased_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired before a marginal target is removed

Parameters

id	The target variable's id.

Implements gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 239 of file samplingInference_tpl.h.

{}

onModelChanged_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

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

Reimplemented from gum::MarginalTargetedInference< GUM_SCALAR >.

Definition at line 227 of file samplingInference_tpl.h.

{}

onStateChanged_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

fired when the stage is changed

Implements gum::GraphicalModelInference< GUM_SCALAR >.

Definition at line 248 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 155 of file approximationScheme_inl.h.

{ return period_size_; }

References period_size_.

posterior() [1/2]

template<typename GUM_SCALAR>

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

virtualinherited

Computes and returns the posterior of a node.

Returns

a const ref to the posterior probability of the node.

Parameters

nodeName

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

Warning

for efficiency reasons, the 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 259 of file marginalTargetedInference_tpl.h.

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

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

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posterior() [2/2]

template<typename 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 243 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::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_()

template<typename GUM_SCALAR>

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

overridevirtual

Computes and returns the posterior of a node.

Returns

a const ref to the posterior probability of the node.

Parameters

id	the node for which we need a posterior probability

Warning

for efficiency reasons, the 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 121 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<typename GUM_SCALAR>

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

finalvirtualinherited

prepare the internal inference structures for the next inference

Definition at line 622 of file graphicalModelInference_tpl.h.

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

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

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

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remainingBurnIn()

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

inherited

Returns the remaining burn in.

Returns

Returns the remaining burn in.

Definition at line 212 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<typename GUM_SCALAR>

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

(

)

get the BayesNet which is used to really perform the sampling

Definition at line 89 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<typename GUM_SCALAR>

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

virtualinherited

assigns a new BN to the inference engine

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

Warning

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

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

Definition at line 81 of file BayesNetInference_tpl.h.

                                                                               {
    this->setModel_(bn);
  }

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

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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 63 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::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::EMApproximationScheme::setEpsilon().

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setEstimatorFromBN_()

template<typename GUM_SCALAR>

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

virtual

Initializes the estimators object linked to the simulation.

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

Definition at line 96 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<typename GUM_SCALAR>

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

virtual

Initializes the estimators object linked to the simulation.

Parameters

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

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

Definition at line 102 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 105 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 126 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 84 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<typename GUM_SCALAR>

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

protectedinherited

Definition at line 136 of file graphicalModelInference_tpl.h.

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

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

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

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setModelDuringConstruction_()

template<typename GUM_SCALAR>

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

protectedinherited

assigns a model during the inference engine construction

Definition at line 146 of file graphicalModelInference_tpl.h.

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

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

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

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setOutdatedStructureState_()

template<typename GUM_SCALAR>

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

protectedinherited

put the inference into an outdated model structure state

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

Definition at line 609 of file graphicalModelInference_tpl.h.

                                                                                {
    setState_(StateOfInference::OutdatedStructure);
  }

References OutdatedStructure, and setState_().

Referenced by makeInference_().

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setOutdatedTensorsState_()

template<typename GUM_SCALAR>

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

protectedinherited

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

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

Definition at line 616 of file graphicalModelInference_tpl.h.

                                                                              {
    setState_(StateOfInference::OutdatedTensors);
  }

References OutdatedTensors, and setState_().

Referenced by makeInference_().

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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 149 of file approximationScheme_inl.h.

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

References GUM_ERROR, and period_size_.

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

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setState_()

template<typename GUM_SCALAR>

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

finalprotectedvirtualinherited

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

Definition at line 117 of file graphicalModelInference_tpl.h.

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

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

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

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setTargetedMode_()

template<typename GUM_SCALAR>

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

protectedinherited

Definition at line 332 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 158 of file approximationScheme_inl.h.

{ verbosity_ = v; }

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

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softEvidenceNodes()

template<typename GUM_SCALAR>

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

inherited

returns the set of nodes with soft evidence

the set of nodes that received soft evidence

Definition at line 593 of file graphicalModelInference_tpl.h.

                                                                                       {
    return _soft_evidence_nodes_;
  }

References _soft_evidence_nodes_.

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

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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 199 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);
  }

References burn_in_, and current_step_.

state()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the state of the inference engine

Definition at line 111 of file graphicalModelInference_tpl.h.

                                                                  {
    return _state_;
  }

References _state_.

Referenced by setState_().

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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 164 of file approximationScheme_inl.h.

                                                             {
    return current_state_;
  }

References current_state_.

Referenced by history(), and nbrIterations().

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stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 221 of file approximationScheme_inl.h.

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

Referenced by gum::learning::GreedyHillClimbing::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 301 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, current_state_, and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::disableMaxIter(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::disableMaxTime(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::isEnabledMaxIter(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::maxTime(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::setPeriodSize().

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targets()

template<typename GUM_SCALAR>

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

finalvirtualnoexceptinherited

returns the list of marginal targets

Definition at line 211 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 208 of file approximationScheme_inl.h.

                                                                              {
    current_step_ += incr;
  }

References current_step_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::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<typename GUM_SCALAR>

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

overrideprotectedvirtual

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 230 of file samplingInference_tpl.h.

{}

updateOutdatedTensors_()

template<typename GUM_SCALAR>

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

overrideprotectedvirtual

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 233 of file samplingInference_tpl.h.

{}

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 160 of file approximationScheme_inl.h.

{ return verbosity_; }

References verbosity_.

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

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Member Data Documentation

_estimator_

template<typename GUM_SCALAR>

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

protected

Estimator object designed to approximate target posteriors.

Definition at line 194 of file samplingInference.h.

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

_samplingBN_

template<typename GUM_SCALAR>

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

private

Definition at line 263 of file samplingInference.h.

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

_targeted_mode_

template<typename GUM_SCALAR>

bool gum::MarginalTargetedInference< GUM_SCALAR >::_targeted_mode_

privateinherited

whether the actual targets are default

Definition at line 260 of file marginalTargetedInference.h.

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

_targets_

template<typename GUM_SCALAR>

NodeSet gum::MarginalTargetedInference< GUM_SCALAR >::_targets_

privateinherited

the set of marginal targets

Definition at line 263 of file marginalTargetedInference.h.

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

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(), gum::GibbsSampling< GUM_SCALAR >::setBurnIn(), and startOfPeriod().

current_epsilon_

double gum::ApproximationScheme::current_epsilon_

protectedinherited

Current epsilon.

Definition at line 378 of file approximationScheme.h.

Referenced by initApproximationScheme().

current_rate_

double gum::ApproximationScheme::current_rate_

protectedinherited

Current rate.

Definition at line 384 of file approximationScheme.h.

Referenced by initApproximationScheme().

current_state_

ApproximationSchemeSTATE gum::ApproximationScheme::current_state_

protectedinherited

The current state.

Definition at line 393 of file approximationScheme.h.

Referenced by ApproximationScheme(), initApproximationScheme(), stateApproximationScheme(), and stopScheme_().

current_step_

Size gum::ApproximationScheme::current_step_

protectedinherited

The current step.

Definition at line 387 of file approximationScheme.h.

Referenced by 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(), startOfPeriod(), 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(), 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(), 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(), 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(), epsilon(), and setEpsilon().

history_

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

protectedinherited

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

Definition at line 396 of file approximationScheme.h.

isContextualized

template<typename GUM_SCALAR>

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

protected

whether the referenced Bayesian network has been "contextualized"

Definition at line 200 of file samplingInference.h.

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

isSetEstimator

template<typename GUM_SCALAR>

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

protected

whether the Estimator object has been initialized

Definition at line 197 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.

max_iter_

Size gum::ApproximationScheme::max_iter_

protectedinherited

The maximum iterations.

Definition at line 417 of file approximationScheme.h.

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

max_time_

double gum::ApproximationScheme::max_time_

protectedinherited

The timeout.

Definition at line 411 of file approximationScheme.h.

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

onProgress

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

inherited

Progression, error and time.

Definition at line 80 of file IApproximationSchemeConfiguration.h.

Referenced by 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

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

inherited

Criteria messageApproximationScheme.

Definition at line 83 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(), periodSize(), and setPeriodSize().

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(), and verbosity().

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

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