gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine > Class Template Reference

Class template representing a CredalNet inference engine using one or more IBayesNet inference engines such as LazyPropagation. More...

#include <multipleInferenceEngine.h>

Inheritance diagram for gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >:

Collaboration diagram for gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >:

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

Public Member Functions
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)
Constructors / Destructors
	MultipleInferenceEngine (const CredalNet< GUM_SCALAR > &credalNet)
	Constructor.
virtual	~MultipleInferenceEngine ()
	Destructor.
Post-inference methods
virtual void	eraseAllEvidence ()
	Erase all inference related data to perform another one.
Pure virtual methods
virtual void	makeInference ()=0
	To be redefined by each credal net algorithm.
Getters and setters
VarMod2BNsMap< GUM_SCALAR > *	getVarMod2BNsMap ()
	Get optimum IBayesNet.
const CredalNet< GUM_SCALAR > &	credalNet () const
	Get this creadal network.
const NodeProperty< std::vector< NodeId > > &	getT0Cluster () const
	Get the t0_ cluster.
const NodeProperty< std::vector< NodeId > > &	getT1Cluster () const
	Get the t1_ cluster.
void	setRepetitiveInd (const bool repetitive)
void	storeVertices (const bool value)
bool	storeVertices () const
	Get the number of iterations without changes used to stop some algorithms.
void	storeBNOpt (const bool value)
bool	storeBNOpt () const
bool	repetitiveInd () const
	Get the current independence status.
Pre-inference initialization methods
void	insertModalsFile (const std::string &path)
	Insert variables modalities from file to compute expectations.
void	insertModals (const std::map< std::string, std::vector< GUM_SCALAR > > &modals)
	Insert variables modalities from map to compute expectations.
virtual void	insertEvidenceFile (const std::string &path)
	Insert evidence from file.
void	insertEvidence (const std::map< std::string, std::vector< GUM_SCALAR > > &eviMap)
	Insert evidence from map.
void	insertEvidence (const NodeProperty< std::vector< GUM_SCALAR > > &evidence)
	Insert evidence from Property.
void	insertQueryFile (const std::string &path)
	Insert query variables states from file.
void	insertQuery (const NodeProperty< std::vector< bool > > &query)
	Insert query variables and states from Property.
Post-inference methods
Tensor< GUM_SCALAR >	marginalMin (const NodeId id) const
	Get the lower marginals of a given node id.
Tensor< GUM_SCALAR >	marginalMin (const std::string &varName) const
	Get the lower marginals of a given variable name.
Tensor< GUM_SCALAR >	marginalMax (const NodeId id) const
	Get the upper marginals of a given node id.
Tensor< GUM_SCALAR >	marginalMax (const std::string &varName) const
	Get the upper marginals of a given variable name.
const GUM_SCALAR &	expectationMin (const NodeId id) const
	Get the lower expectation of a given node id.
const GUM_SCALAR &	expectationMin (const std::string &varName) const
	Get the lower expectation of a given variable name.
const GUM_SCALAR &	expectationMax (const NodeId id) const
	Get the upper expectation of a given node id.
const GUM_SCALAR &	expectationMax (const std::string &varName) const
	Get the upper expectation of a given variable name.
const std::vector< GUM_SCALAR > &	dynamicExpMin (const std::string &varName) const
	Get the lower dynamic expectation of a given variable prefix (without the time step included, i.e.
const std::vector< GUM_SCALAR > &	dynamicExpMax (const std::string &varName) const
	Get the upper dynamic expectation of a given variable prefix (without the time step included, i.e.
const std::vector< std::vector< GUM_SCALAR > > &	vertices (const NodeId id) const
	Get the vertice of a given node id.
void	saveMarginals (const std::string &path) const
	Saves marginals to file.
void	saveExpectations (const std::string &path) const
	Saves expectations to file.
void	saveVertices (const std::string &path) const
	Saves vertices to file.
void	dynamicExpectations ()
	Compute dynamic expectations.
std::string	toString () const
	Print all nodes marginals to standart output.
const std::string	getApproximationSchemeMsg ()
	Get approximation scheme state.
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.
Accessors/Modifiers
virtual void	setNumberOfThreads (Size nb)
	sets the number max of threads to be used by the class containing this ThreadNumberManager
virtual Size	getNumberOfThreads () const
	returns the current max number of threads used by the class containing this ThreadNumberManager
bool	isGumNumberOfThreadsOverriden () const
	indicates whether the class containing this ThreadNumberManager set its own number of threads

Public Attributes
Signaler3< Size, double, double >	onProgress
	Progression, error and time.
Signaler1< const std::string & >	onStop
	Criteria messageApproximationScheme.

Protected Member Functions
Protected initialization methods
Fusion of threads optimal IBayesNet.
void	initThreadsData_ (const Size &num_threads, const bool _storeVertices_, const bool _storeBNOpt_)
	Initialize threads data.
Protected algorithms methods
bool	updateThread_ (Size this_thread, const NodeId &id, const std::vector< GUM_SCALAR > &vertex, const bool &elimRedund=false)
	Update thread information (marginals, expectations, IBayesNet, vertices) for a given node id.
void	updateMarginals_ ()
	Fusion of threads marginals.
const GUM_SCALAR	computeEpsilon_ ()
	Compute epsilon and update old marginals.
void	updateOldMarginals_ ()
	Update old marginals (from current marginals).
Proptected post-inference methods
void	optFusion_ ()
	Fusion of threads optimal IBayesNet.
void	expFusion_ ()
	Fusion of threads expectations.
void	verticesFusion_ ()
Protected initialization methods
void	repetitiveInit_ ()
	Initialize t0_ and t1_ clusters.
void	initExpectations_ ()
	Initialize lower and upper expectations before inference, with the lower expectation being initialized on the highest modality and the upper expectation being initialized on the lowest modality.
void	initMarginals_ ()
	Initialize lower and upper old marginals and marginals before inference, with the lower marginal being 1 and the upper 0.
void	displatchMarginalsToThreads_ ()
	computes Vector threadRanges_, that assigns some part of marginalMin_ and marginalMax_ to the threads
void	initMarginalSets_ ()
	Initialize credal set vertices with empty sets.
Protected algorithms methods
void	updateExpectations_ (const NodeId &id, const std::vector< GUM_SCALAR > &vertex)
	Given a node id and one of it's possible vertex obtained during inference, update this node lower and upper expectations.
void	updateCredalSets_ (const NodeId &id, const std::vector< GUM_SCALAR > &vertex, const bool &elimRedund=false)
	Given a node id and one of it's possible vertex, update it's credal set.
Proptected post-inference methods
void	dynamicExpectations_ ()
	Rearrange lower and upper expectations to suit dynamic networks.

Protected Attributes
_margis_	l_marginalMin_
	Threads lower marginals, one per thread.
_margis_	l_marginalMax_
	Threads upper marginals, one per thread.
_expes_	l_expectationMin_
	Threads lower expectations, one per thread.
_expes_	l_expectationMax_
	Threads upper expectations, one per thread.
_modals_	l_modal_
	Threads modalities.
_credalSets_	l_marginalSets_
	Threads vertices.
_margis_	l_evidence_
	Threads evidence.
_clusters_	l_clusters_
	Threads clusters.
std::vector< _bnet_ * >	workingSet_
	Threads IBayesNet.
std::vector< List< const Tensor< GUM_SCALAR > * > * >	workingSetE_
	Threads evidence.
std::vector< BNInferenceEngine * >	l_inferenceEngine_
	Threads BNInferenceEngine.
std::vector< VarMod2BNsMap< GUM_SCALAR > * >	l_optimalNet_
	Threads optimal IBayesNet.
std::vector< std::mt19937 >	generators_
	the generators used for computing random values
const CredalNet< GUM_SCALAR > *	credalNet_
	A pointer to the Credal Net used.
margi	oldMarginalMin_
	Old lower marginals used to compute epsilon.
margi	oldMarginalMax_
	Old upper marginals used to compute epsilon.
margi	marginalMin_
	Lower marginals.
margi	marginalMax_
	Upper marginals.
credalSet	marginalSets_
	Credal sets vertices, if enabled.
expe	expectationMin_
	Lower expectations, if some variables modalities were inserted.
expe	expectationMax_
	Upper expectations, if some variables modalities were inserted.
dynExpe	dynamicExpMin_
	Lower dynamic expectations.
dynExpe	dynamicExpMax_
	Upper dynamic expectations.
dynExpe	modal_
	Variables modalities used to compute expectations.
margi	evidence_
	Holds observed variables states.
query	query_
	Holds the query nodes states.
cluster	t0_
	Clusters of nodes used with dynamic networks.
cluster	t1_
	Clusters of nodes used with dynamic networks.
bool	storeVertices_
	True if credal sets vertices are stored, False otherwise.
bool	repetitiveInd_
	True if using repetitive independence ( dynamic network only ), False otherwise.
bool	storeBNOpt_
	Iterations limit stopping rule used by some algorithms such as CNMonteCarloSampling.
VarMod2BNsMap< GUM_SCALAR >	dbnOpt_
	Object used to efficiently store optimal bayes net during inference, for some algorithms.
std::vector< std::pair< NodeId, Idx > >	threadRanges_
	the ranges of elements of marginalMin_ and marginalMax_ processed by each thread
int	timeSteps_
	The number of time steps of this network (only usefull for dynamic networks).
Size	threadMinimalNbOps_ {Size(20)}
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 Types
using	_infE_ = InferenceEngine< GUM_SCALAR >
	To easily access InferenceEngine< GUM_SCALAR > methods.
using	_cluster_ = NodeProperty< std::vector< NodeId > >
using	_credalSet_ = NodeProperty< std::vector< std::vector< GUM_SCALAR > > >
using	_margi_ = NodeProperty< std::vector< GUM_SCALAR > >
using	_expe_ = NodeProperty< GUM_SCALAR >
using	_bnet_ = IBayesNet< GUM_SCALAR >
using	_margis_ = std::vector< _margi_ >
using	_expes_ = std::vector< _expe_ >
using	_credalSets_ = std::vector< _credalSet_ >
using	_clusters_ = std::vector< std::vector< _cluster_ > >
using	_modals_ = std::vector< HashTable< std::string, std::vector< GUM_SCALAR > > >
using	credalSet = NodeProperty< std::vector< std::vector< GUM_SCALAR > > >
using	margi = NodeProperty< std::vector< GUM_SCALAR > >
using	expe = NodeProperty< GUM_SCALAR >
using	dynExpe = typename gum::HashTable< std::string, std::vector< GUM_SCALAR > >
using	query = NodeProperty< std::vector< bool > >
using	cluster = NodeProperty< std::vector< NodeId > >

Private Member Functions
void	_updateThreadCredalSets_ (Size this_thread, const NodeId &id, const std::vector< GUM_SCALAR > &vertex, const bool &elimRedund)
	Ask for redundancy elimination of a node credal set of a calling thread.
void	stopScheme_ (ApproximationSchemeSTATE new_state)
	Stop the scheme given a new state.

Private Attributes
Size	_nb_threads_ {0}
	the max number of threads used by the class

Detailed Description

template<typename GUM_SCALAR, class BNInferenceEngine>
class gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >

Class template representing a CredalNet inference engine using one or more IBayesNet inference engines such as LazyPropagation.

Extends InferenceEngine< GUM_SCALAR >. Used for outer multi-threading such as CNMonteCarloSampling.

Template Parameters

GUM_SCALAR	A floating type ( float, double, long double ... ).
BNInferenceEngine	A IBayesNet inference engine such as LazyPropagation.

Author

Matthieu HOURBRACQ and Pierre-Henri WUILLEMIN(_at_LIP6)

Definition at line 74 of file multipleInferenceEngine.h.

Member Typedef Documentation

_bnet_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_bnet_ = IBayesNet< GUM_SCALAR >

private

Definition at line 84 of file multipleInferenceEngine.h.

_cluster_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_cluster_ = NodeProperty< std::vector< NodeId > >

private

Definition at line 79 of file multipleInferenceEngine.h.

_clusters_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_clusters_ = std::vector< std::vector< _cluster_ > >

private

Definition at line 88 of file multipleInferenceEngine.h.

_credalSet_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_credalSet_ = NodeProperty< std::vector< std::vector< GUM_SCALAR > > >

private

Definition at line 80 of file multipleInferenceEngine.h.

_credalSets_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_credalSets_ = std::vector< _credalSet_ >

private

Definition at line 87 of file multipleInferenceEngine.h.

_expe_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expe_ = NodeProperty< GUM_SCALAR >

private

Definition at line 82 of file multipleInferenceEngine.h.

_expes_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expes_ = std::vector< _expe_ >

private

Definition at line 86 of file multipleInferenceEngine.h.

_infE_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_infE_ = InferenceEngine< GUM_SCALAR >

private

To easily access InferenceEngine< GUM_SCALAR > methods.

Definition at line 77 of file multipleInferenceEngine.h.

_margi_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_margi_ = NodeProperty< std::vector< GUM_SCALAR > >

private

Definition at line 81 of file multipleInferenceEngine.h.

_margis_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_margis_ = std::vector< _margi_ >

private

Definition at line 85 of file multipleInferenceEngine.h.

_modals_

template<typename GUM_SCALAR, class BNInferenceEngine>

using gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_modals_ = std::vector< HashTable< std::string, std::vector< GUM_SCALAR > > >

private

Definition at line 90 of file multipleInferenceEngine.h.

cluster

template<typename GUM_SCALAR>

using gum::credal::InferenceEngine< GUM_SCALAR >::cluster = NodeProperty< std::vector< NodeId > >

privateinherited

Definition at line 80 of file inferenceEngine.h.

credalSet

template<typename GUM_SCALAR>

using gum::credal::InferenceEngine< GUM_SCALAR >::credalSet = NodeProperty< std::vector< std::vector< GUM_SCALAR > > >

privateinherited

Definition at line 73 of file inferenceEngine.h.

dynExpe

template<typename GUM_SCALAR>

using gum::credal::InferenceEngine< GUM_SCALAR >::dynExpe = typename gum::HashTable< std::string, std::vector< GUM_SCALAR > >

privateinherited

Definition at line 77 of file inferenceEngine.h.

expe

template<typename GUM_SCALAR>

using gum::credal::InferenceEngine< GUM_SCALAR >::expe = NodeProperty< GUM_SCALAR >

privateinherited

Definition at line 75 of file inferenceEngine.h.

margi

template<typename GUM_SCALAR>

using gum::credal::InferenceEngine< GUM_SCALAR >::margi = NodeProperty< std::vector< GUM_SCALAR > >

privateinherited

Definition at line 74 of file inferenceEngine.h.

query

template<typename GUM_SCALAR>

using gum::credal::InferenceEngine< GUM_SCALAR >::query = NodeProperty< std::vector< bool > >

privateinherited

Definition at line 79 of file inferenceEngine.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
    };

Constructor & Destructor Documentation

MultipleInferenceEngine()

template<typename GUM_SCALAR, class BNInferenceEngine>

gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::MultipleInferenceEngine ( const CredalNet< GUM_SCALAR > & credalNet )

explicit

Constructor.

Parameters

credalNet

The CredalNet to be used.

Definition at line 50 of file multipleInferenceEngine_tpl.h.

                                                  :
        InferenceEngine< GUM_SCALAR >::InferenceEngine(credalNet) {
      GUM_CONSTRUCTOR(MultipleInferenceEngine);
    }

References gum::credal::InferenceEngine< GUM_SCALAR >::InferenceEngine(), MultipleInferenceEngine(), and gum::credal::InferenceEngine< GUM_SCALAR >::credalNet().

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

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~MultipleInferenceEngine()

template<typename GUM_SCALAR, class BNInferenceEngine>

gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::~MultipleInferenceEngine ( )

virtual

Destructor.

Definition at line 57 of file multipleInferenceEngine_tpl.h.

                                                                                       {
      GUM_DESTRUCTOR(MultipleInferenceEngine);
    }

References MultipleInferenceEngine().

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Member Function Documentation

_updateThreadCredalSets_()

template<typename GUM_SCALAR, class BNInferenceEngine>

void gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThreadCredalSets_ ( Size this_thread, const NodeId & id, const std::vector< GUM_SCALAR > & vertex, const bool & elimRedund )

inlineprivate

Ask for redundancy elimination of a node credal set of a calling thread.

Called by updateThread_ if vertices are stored.

Parameters

this_thread	the id of the thread executing this method
id	A constant reference to the node id whose credal set is to be checked for redundancy.
vertex	The vertex to add to the credal set.
elimRedund	true if redundancy elimination is to be performed, false otherwise and by default.

Definition at line 217 of file multipleInferenceEngine_tpl.h.

                                                     {
      auto& nodeCredalSet = l_marginalSets_[tId][id];
      Size  dsize         = Size(vertex.size());
 
      bool eq = true;
 
      for (auto it = nodeCredalSet.cbegin(), itEnd = nodeCredalSet.cend(); it != itEnd; ++it) {
        eq = true;
 
        for (Size i = 0; i < dsize; i++) {
          if (std::fabs(vertex[i] - (*it)[i]) > 1e-6) {
            eq = false;
            break;
          }
        }
 
        if (eq) break;
      }
 
      if (!eq || nodeCredalSet.size() == 0) {
        nodeCredalSet.push_back(vertex);
        return;
      } else return;

      if (nodeCredalSet.size() == 1) return;
 
      // check that the point and all previously added ones are not inside the
      // actual
      // polytope
      auto itEnd = std::remove_if(
          nodeCredalSet.begin(),
          nodeCredalSet.end(),
          [&](const std::vector< GUM_SCALAR >& v) -> bool {
            for (auto jt       = v.cbegin(),
                      jtEnd    = v.cend(),
                      minIt    = l_marginalMin_[tId][id].cbegin(),
                      minItEnd = l_marginalMin_[tId][id].cend(),
                      maxIt    = l_marginalMax_[tId][id].cbegin(),
                      maxItEnd = l_marginalMax_[tId][id].cend();
                 jt != jtEnd && minIt != minItEnd && maxIt != maxItEnd;
                 ++jt, ++minIt, ++maxIt) {
              if ((std::fabs(*jt - *minIt) < 1e-6 || std::fabs(*jt - *maxIt) < 1e-6)
                  && std::fabs(*minIt - *maxIt) > 1e-6)
                return false;
            }
            return true;
          });
 
      nodeCredalSet.erase(itEnd, nodeCredalSet.end());
 
      // we need at least 2 points to make a convex combination
      if (!elimRedund || nodeCredalSet.size() <= 2) return;
 
      // there may be points not inside the polytope but on one of it's facet,
      // meaning it's still a convex combination of vertices of this facet. Here
      // we need lrs.
      Size setSize = Size(nodeCredalSet.size());
 
      LRSWrapper< GUM_SCALAR > lrsWrapper;
      lrsWrapper.setUpV(dsize, setSize);
 
      for (const auto& vtx: nodeCredalSet)
        lrsWrapper.fillV(vtx);
 
      lrsWrapper.elimRedundVrep();
 
      l_marginalSets_[tId][id] = lrsWrapper.getOutput();
    }

addEvidence() [1/7]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< 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 1211 of file inferenceEngine_tpl.h.

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

References addEvidence(), and credalNet_.

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addEvidence() [2/7]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< 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 1223 of file inferenceEngine_tpl.h.

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

References addEvidence(), and credalNet_.

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addEvidence() [3/7]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< 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 1230 of file inferenceEngine_tpl.h.

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

References addEvidence(), and credalNet_.

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addEvidence() [4/7]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< 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 1236 of file inferenceEngine_tpl.h.

                                                                                   {
      const auto id = this->credalNet_->current_bn().idFromName(pot.variable(0).name());
      std::vector< GUM_SCALAR > vals(this->credalNet_->current_bn().variable(id).domainSize(), 0);
      Instantiation             I(pot);
      for (I.setFirst(); !I.end(); I.inc()) {
        vals[I.val(0)] = pot[I];
      }
      addEvidence(id, vals);
    }

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

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addEvidence() [5/7]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< 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 1203 of file inferenceEngine_tpl.h.

                                                                            {
      std::vector< GUM_SCALAR > vals(this->credalNet_->current_bn().variable(id).domainSize(), 0);
      vals[val] = 1;
      addEvidence(id, vals);
    }

References addEvidence(), and credalNet_.

Referenced by addEvidence(), addEvidence(), addEvidence(), addEvidence(), addEvidence(), and addEvidence().

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addEvidence() [6/7]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< 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 1217 of file inferenceEngine_tpl.h.

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

References addEvidence(), and credalNet_.

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addEvidence() [7/7]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< 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 1193 of file inferenceEngine_tpl.h.

                                                                                         {
      evidence_.insert(id, vals);
      // forces the computation of the begin iterator to avoid subsequent data races
      // @TODO make HashTableConstIterator constructors thread safe
      evidence_.begin();
    }

References evidence_.

computeEpsilon_()

template<typename GUM_SCALAR, class BNInferenceEngine>

const GUM_SCALAR gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::computeEpsilon_ ( )

inlineprotectedvirtual

Compute epsilon and update old marginals.

Returns

Epsilon.

Reimplemented from gum::credal::InferenceEngine< GUM_SCALAR >.

Definition at line 369 of file multipleInferenceEngine_tpl.h.

                                                                                  {
      // compute the number of threads (avoid nested threads)
      const Size nb_threads = ThreadExecutor::nbRunningThreadsExecutors() == 0
                                ? this->threadRanges_.size() - 1
                                : 1;   // no nested multithreading
 
      std::vector< GUM_SCALAR > tEps(nb_threads, 0);
 
      // create the function to be executed by the threads
      auto threadedExec = [this, &tEps](const std::size_t                           this_thread,
                                        const std::size_t                           nb_threads,
                                        const std::vector< std::pair< Idx, Idx > >& ranges) {
        auto&      this_tEps = tEps[this_thread];
        GUM_SCALAR delta     = 0;
 
        auto       i                = this->threadRanges_[this_thread].first;
        auto       j                = this->threadRanges_[this_thread].second;
        auto       domain_size      = this->marginalMax_[i].size();
        const auto end_i            = this->threadRanges_[this_thread + 1].first;
        auto       end_j            = this->threadRanges_[this_thread + 1].second;
        const auto marginalMax_size = this->marginalMax_.size();
 
        while ((i < end_i) || (j < end_j)) {
          // on min
          delta = this->marginalMin_[i][j] - this->oldMarginalMin_[i][j];
          delta = (delta < 0) ? (-delta) : delta;
          if (this_tEps < delta) this_tEps = delta;
 
          // on max
          delta = this->marginalMax_[i][j] - this->oldMarginalMax_[i][j];
          delta = (delta < 0) ? (-delta) : delta;
          if (this_tEps < delta) this_tEps = delta;
 
          this->oldMarginalMin_[i][j] = this->marginalMin_[i][j];
          this->oldMarginalMax_[i][j] = this->marginalMax_[i][j];
 
          if (++j == domain_size) {
            j = 0;
            ++i;
            if (i < marginalMax_size) domain_size = this->marginalMax_[i].size();
          }
        }
      };
 
      // launch the threads
      ThreadExecutor::execute(
          nb_threads,
          threadedExec,
          (nb_threads == 1)
              ? std::vector< std::pair< NodeId, Idx > >{{0, 0}, {this->marginalMin_.size(), 0}}
              : this->threadRanges_);
 
      // aggregate all the results
      GUM_SCALAR eps = tEps[0];
      for (const auto nb: tEps)
        if (eps < nb) eps = nb;
 
      return eps;
    }

References computeEpsilon_(), gum::threadsSTL::ThreadExecutor::execute(), gum::credal::InferenceEngine< GUM_SCALAR >::marginalMax_, gum::credal::InferenceEngine< GUM_SCALAR >::marginalMin_, gum::threadsSTL::ThreadExecutor::nbRunningThreadsExecutors(), gum::credal::InferenceEngine< GUM_SCALAR >::oldMarginalMax_, gum::credal::InferenceEngine< GUM_SCALAR >::oldMarginalMin_, and gum::credal::InferenceEngine< GUM_SCALAR >::threadRanges_.

Referenced by computeEpsilon_().

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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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credalNet()

template<typename GUM_SCALAR>

const CredalNet< GUM_SCALAR > & gum::credal::InferenceEngine< GUM_SCALAR >::credalNet ( ) const

inherited

Get this creadal network.

Returns

A constant reference to this CredalNet.

Definition at line 81 of file inferenceEngine_tpl.h.

                                                                                  {
      return *credalNet_;
    }

References credalNet_.

Referenced by gum::credal::CNLoopyPropagation< GUM_SCALAR >::CNLoopyPropagation(), InferenceEngine(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::MultipleInferenceEngine().

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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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displatchMarginalsToThreads_()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::displatchMarginalsToThreads_ ( )

protectedinherited

computes Vector threadRanges_, that assigns some part of marginalMin_ and marginalMax_ to the threads

Definition at line 1133 of file inferenceEngine_tpl.h.

                                                                     {
      // we compute the number of elements in the 2 loops (over i,j in marginalMin_[i][j])
      Size       nb_elements      = 0;
      const auto marginalMin_size = this->marginalMin_.size();
      for (const auto& marg_i: this->marginalMin_)
        nb_elements += marg_i.second.size();
 
      // distribute evenly the elements among the threads
      auto nb_threads = ThreadNumberManager::getNumberOfThreads();
      if (nb_elements < nb_threads) nb_threads = nb_elements;
 
      // the result that we return is a vector of pairs (NodeId, Idx). For thread number i, the
      // pair at index i is the beginning of the range that the thread will have to process: this
      // is the part of the marginal distribution vector of node NodeId starting at index Idx.
      // The pair at index i+1 is the end of this range (not included)
      threadRanges_.clear();
      threadRanges_.reserve(nb_threads + 1);
 
      // try to balance the number of elements among the threads
      Idx nb_elts_par_thread = nb_elements / nb_threads;
      Idx rest_elts          = nb_elements - nb_elts_par_thread * nb_threads;
 
      NodeId current_node         = 0;
      Idx    current_domain_index = 0;
      Size   current_domain_size  = this->marginalMin_[0].size();
      threadRanges_.emplace_back(current_node, current_domain_index);
 
      for (Idx i = Idx(0); i < nb_threads; ++i) {
        // compute the end of the threads, assuming that the current node has a domain
        // sufficiently large
        current_domain_index += nb_elts_par_thread;
        if (rest_elts != Idx(0)) {
          ++current_domain_index;
          --rest_elts;
        }
 
        // if the current node is not sufficient to hold all the elements that
        // the current thread should process. So we should add elements of the
        // next nodes
        while (current_domain_index >= current_domain_size) {
          current_domain_index -= current_domain_size;
          ++current_node;
          current_domain_index = 0;
          if (current_node != marginalMin_size) {
            current_domain_size = this->marginalMin_[current_node].size();
          }
        }
 
        // now we can store the range if elements
        threadRanges_.emplace_back(current_node, current_domain_index);
 
        // compute the next begin_node
        if (current_domain_index == current_domain_size) {
          ++current_node;
          current_domain_index = 0;
        }
      }
    }

References gum::ThreadNumberManager::getNumberOfThreads(), and threadRanges_.

Referenced by initMarginals_().

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dynamicExpectations()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::dynamicExpectations ( )

inherited

Compute dynamic expectations.

See also

dynamicExpectations_ Only call this if an algorithm does not call it by itself.

Definition at line 739 of file inferenceEngine_tpl.h.

                                                            {
      dynamicExpectations_();
    }

References dynamicExpectations_().

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dynamicExpectations_()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::dynamicExpectations_ ( )

protectedinherited

Rearrange lower and upper expectations to suit dynamic networks.

Definition at line 744 of file inferenceEngine_tpl.h.

                                                             {
      // no modals, no expectations computed during inference
      if (expectationMin_.empty() || modal_.empty()) return;
 
      // already called by the algorithm or the user
      if (dynamicExpMax_.size() > 0 && dynamicExpMin_.size() > 0) return;
 
      using innerMap = typename gum::HashTable< int, GUM_SCALAR >;
 
      using outerMap = typename gum::HashTable< std::string, innerMap >;
 
 
      // if non dynamic, directly save expectationMin_ et Max (same but faster)
      outerMap expectationsMin, expectationsMax;
 
      for (const auto& elt: expectationMin_) {
        std::string var_name, time_step;
 
        var_name   = credalNet_->current_bn().variable(elt.first).name();
        auto delim = var_name.find_first_of("_");
        time_step  = var_name.substr(delim + 1, var_name.size());
        var_name   = var_name.substr(0, delim);
 
        // to be sure (don't store not monitored variables' expectations)
        // although it
        // should be taken care of before this point
        if (!modal_.exists(var_name)) continue;
 
        expectationsMin.getWithDefault(var_name, innerMap())
            .getWithDefault(atoi(time_step.c_str()), 0)
            = elt.second;   // we iterate with min iterators
        expectationsMax.getWithDefault(var_name, innerMap())
            .getWithDefault(atoi(time_step.c_str()), 0)
            = expectationMax_[elt.first];
      }
 
      for (const auto& elt: expectationsMin) {
        typename std::vector< GUM_SCALAR > dynExp(elt.second.size());
 
        for (const auto& elt2: elt.second)
          dynExp[elt2.first] = elt2.second;
 
        dynamicExpMin_.insert(elt.first, dynExp);
      }
 
      for (const auto& elt: expectationsMax) {
        typename std::vector< GUM_SCALAR > dynExp(elt.second.size());
 
        for (const auto& elt2: elt.second) {
          dynExp[elt2.first] = elt2.second;
        }
 
        dynamicExpMax_.insert(elt.first, dynExp);
      }
    }

References credalNet_, dynamicExpMax_, dynamicExpMin_, expectationMax_, expectationMin_, and modal_.

Referenced by dynamicExpectations().

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dynamicExpMax()

template<typename GUM_SCALAR>

const std::vector< GUM_SCALAR > & gum::credal::InferenceEngine< GUM_SCALAR >::dynamicExpMax ( const std::string & varName ) const

inherited

Get the upper dynamic expectation of a given variable prefix (without the time step included, i.e.

call with "temp" to get "temp_0", ..., "temp_T").

Parameters

varName

The variable name prefix which upper expectation we want.

Returns

A constant reference to the variable upper expectation over all time steps.

Definition at line 534 of file inferenceEngine_tpl.h.

                                                                                   {
      std::string errTxt = "const std::vector< GUM_SCALAR > & InferenceEngine< "
                           "GUM_SCALAR >::dynamicExpMax ( const std::string & "
                           "varName ) const : ";
 
      if (dynamicExpMax_.empty())
        GUM_ERROR(OperationNotAllowed, errTxt + "_dynamicExpectations() needs to be called before")
 
      if (!dynamicExpMax_.exists(varName) /*dynamicExpMin_.find(varName) == dynamicExpMin_.end()*/)
        GUM_ERROR(NotFound, errTxt + "variable name not found : " << varName)
 
      return dynamicExpMax_[varName];
    }

References InferenceEngine(), dynamicExpMax(), and dynamicExpMax_.

Referenced by dynamicExpMax().

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dynamicExpMin()

template<typename GUM_SCALAR>

const std::vector< GUM_SCALAR > & gum::credal::InferenceEngine< GUM_SCALAR >::dynamicExpMin ( const std::string & varName ) const

inherited

Get the lower dynamic expectation of a given variable prefix (without the time step included, i.e.

call with "temp" to get "temp_0", ..., "temp_T").

Parameters

varName

The variable name prefix which lower expectation we want.

Returns

A constant reference to the variable lower expectation over all time steps.

Definition at line 518 of file inferenceEngine_tpl.h.

                                                                                   {
      std::string errTxt = "const std::vector< GUM_SCALAR > & InferenceEngine< "
                           "GUM_SCALAR >::dynamicExpMin ( const std::string & "
                           "varName ) const : ";
 
      if (dynamicExpMin_.empty())
        GUM_ERROR(OperationNotAllowed, errTxt + "_dynamicExpectations() needs to be called before")
 
      if (!dynamicExpMin_.exists(varName) /*dynamicExpMin_.find(varName) == dynamicExpMin_.end()*/)
        GUM_ERROR(NotFound, errTxt + "variable name not found : " << varName)
 
      return dynamicExpMin_[varName];
    }

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, class BNInferenceEngine>

void gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence ( )

virtual

Erase all inference related data to perform another one.

You need to insert evidence again if needed but modalities are kept. You can insert new ones by using the appropriate method which will delete the old ones.

Reimplemented from gum::credal::InferenceEngine< GUM_SCALAR >.

Definition at line 782 of file multipleInferenceEngine_tpl.h.

                                                                                    {
      _infE_::eraseAllEvidence();
      Size tsize = Size(workingSet_.size());
 
      // delete pointers
      for (Size bn = 0; bn < tsize; bn++) {
        if (_infE_::storeVertices_) l_marginalSets_[bn].clear();
 
        if (workingSet_[bn] != nullptr) delete workingSet_[bn];
 
        if (_infE_::storeBNOpt_)
          if (l_inferenceEngine_[bn] != nullptr) delete l_optimalNet_[bn];
 
        if (this->workingSetE_[bn] != nullptr) {
          for (const auto ev: *workingSetE_[bn])
            delete ev;
 
          delete workingSetE_[bn];
        }
 
        if (l_inferenceEngine_[bn] != nullptr) delete l_inferenceEngine_[bn];
      }
 
      // this is important, those will be resized with the correct number of
      // threads.
 
      workingSet_.clear();
      workingSetE_.clear();
      l_inferenceEngine_.clear();
      l_optimalNet_.clear();
 
      l_marginalMin_.clear();
      l_marginalMax_.clear();
      l_expectationMin_.clear();
      l_expectationMax_.clear();
      l_modal_.clear();
      l_marginalSets_.clear();
      l_evidence_.clear();
      l_clusters_.clear();
    }

References gum::credal::InferenceEngine< GUM_SCALAR >::eraseAllEvidence(), eraseAllEvidence(), l_clusters_, l_evidence_, l_expectationMax_, l_expectationMin_, l_inferenceEngine_, l_marginalMax_, l_marginalMin_, l_marginalSets_, l_modal_, l_optimalNet_, gum::credal::InferenceEngine< GUM_SCALAR >::storeBNOpt_, gum::credal::InferenceEngine< GUM_SCALAR >::storeVertices_, workingSet_, and workingSetE_.

Referenced by eraseAllEvidence().

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expectationMax() [1/2]

template<typename GUM_SCALAR>

const GUM_SCALAR & gum::credal::InferenceEngine< GUM_SCALAR >::expectationMax ( const NodeId id ) const

inherited

Get the upper expectation of a given node id.

Parameters

id	The node id which upper expectation we want.

Returns

A constant reference to this node upper expectation.

Definition at line 510 of file inferenceEngine_tpl.h.

                                                                                         {
      try {
        return expectationMax_[id];
      } catch (NotFound& err) { throw(err); }
    }

References expectationMax_.

expectationMax() [2/2]

template<typename GUM_SCALAR>

const GUM_SCALAR & gum::credal::InferenceEngine< GUM_SCALAR >::expectationMax ( const std::string & varName ) const

inherited

Get the upper expectation of a given variable name.

Parameters

varName

The variable name which upper expectation we want.

Returns

A constant reference to this variable upper expectation.

Definition at line 496 of file inferenceEngine_tpl.h.

                                                                                    {
      try {
        return expectationMax_[credalNet_->current_bn().idFromName(varName)];
      } catch (NotFound& err) { throw(err); }
    }

References credalNet_, and expectationMax_.

expectationMin() [1/2]

template<typename GUM_SCALAR>

const GUM_SCALAR & gum::credal::InferenceEngine< GUM_SCALAR >::expectationMin ( const NodeId id ) const

inherited

Get the lower expectation of a given node id.

Parameters

id	The node id which lower expectation we want.

Returns

A constant reference to this node lower expectation.

Definition at line 503 of file inferenceEngine_tpl.h.

                                                                                         {
      try {
        return expectationMin_[id];
      } catch (NotFound& err) { throw(err); }
    }

References expectationMin_.

expectationMin() [2/2]

template<typename GUM_SCALAR>

const GUM_SCALAR & gum::credal::InferenceEngine< GUM_SCALAR >::expectationMin ( const std::string & varName ) const

inherited

Get the lower expectation of a given variable name.

Parameters

varName

The variable name which lower expectation we want.

Returns

A constant reference to this variable lower expectation.

Definition at line 488 of file inferenceEngine_tpl.h.

                                                                                    {
      try {
        return expectationMin_[credalNet_->current_bn().idFromName(varName)];
      } catch (NotFound& err) { throw(err); }
    }

References credalNet_, and expectationMin_.

expFusion_()

template<typename GUM_SCALAR, class BNInferenceEngine>

void gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::expFusion_ ( )

protected

Fusion of threads expectations.

Definition at line 576 of file multipleInferenceEngine_tpl.h.

                                                                              {
      // don't create threads if there are no modalities to compute expectations
      if (this->modal_.empty()) return;
 
      // compute the max number of threads to use (avoid nested threads)
      const Size nb_threads = ThreadExecutor::nbRunningThreadsExecutors() == 0
                                ? ThreadNumberManager::getNumberOfThreads()
                                : 1;   // no nested multithreading
 
      // we can compute expectations from vertices of the final credal set
      if (_infE_::storeVertices_) {
        // create the function to be executed by the threads
        auto threadedExec = [this](const std::size_t                           this_thread,
                                   const std::size_t                           nb_threads,
                                   Idx                                         work_index,
                                   const std::vector< std::pair< Idx, Idx > >& ranges) {
          for (Idx i = ranges[this_thread].first, end = ranges[this_thread].second; i < end; i++) {
            std::string var_name = workingSet_[work_index]->variable(i).name();
            auto        delim    = var_name.find_first_of("_");
            var_name             = var_name.substr(0, delim);
 
            if (!l_modal_[work_index].exists(var_name)) continue;
 
            for (const auto& vertex: _infE_::marginalSets_[i]) {
              GUM_SCALAR exp   = 0;
              Size       vsize = Size(vertex.size());
 
              for (Size mod = 0; mod < vsize; mod++)
                exp += vertex[mod] * l_modal_[work_index][var_name][mod];
 
              if (exp > _infE_::expectationMax_[i]) _infE_::expectationMax_[i] = exp;
 
              if (exp < _infE_::expectationMin_[i]) _infE_::expectationMin_[i] = exp;
            }
          }
        };
 
        const Size working_size = workingSet_.size();
        for (Idx work_index = 0; work_index < working_size; ++work_index) {
          if (!this->l_modal_[work_index].empty()) {
            // compute the ranges over which the threads will work
            const auto nsize           = workingSet_[work_index]->size();
            const auto real_nb_threads = std::min(nb_threads, nsize);
            const auto ranges
                = gum::dispatchRangeToThreads(0, nsize, (unsigned int)(real_nb_threads));
            ThreadExecutor::execute(real_nb_threads, threadedExec, work_index, ranges);
          }
        }
 
        return;
      }
 
      // create the function to be executed by the threads
      auto threadedExec = [this](const std::size_t                           this_thread,
                                 const std::size_t                           nb_threads,
                                 Idx                                         work_index,
                                 const std::vector< std::pair< Idx, Idx > >& ranges) {
        for (Idx i = ranges[this_thread].first, end = ranges[this_thread].second; i < end; i++) {
          std::string var_name = workingSet_[work_index]->variable(i).name();
          auto        delim    = var_name.find_first_of("_");
          var_name             = var_name.substr(0, delim);
 
          if (!l_modal_[work_index].exists(var_name)) continue;
 
          Size tsize = Size(l_expectationMax_.size());
 
          for (Idx tId = 0; tId < tsize; tId++) {
            if (l_expectationMax_[tId][i] > this->expectationMax_[i])
              this->expectationMax_[i] = l_expectationMax_[tId][i];
 
            if (l_expectationMin_[tId][i] < this->expectationMin_[i])
              this->expectationMin_[i] = l_expectationMin_[tId][i];
          }   // end of : each thread
        }   // end of : each variable
      };
 
      const Size working_size = workingSet_.size();
      for (Idx work_index = 0; work_index < working_size; ++work_index) {
        if (!this->l_modal_[work_index].empty()) {
          const auto nsize           = Size(workingSet_[work_index]->size());
          const auto real_nb_threads = std::min(nb_threads, nsize);
          const auto ranges
              = gum::dispatchRangeToThreads(0, nsize, (unsigned int)(real_nb_threads));
          ThreadExecutor::execute(real_nb_threads, threadedExec, work_index, ranges);
        }
      }
    }

References gum::dispatchRangeToThreads(), gum::threadsSTL::ThreadExecutor::execute(), gum::credal::InferenceEngine< GUM_SCALAR >::expectationMax_, gum::credal::InferenceEngine< GUM_SCALAR >::expectationMin_, expFusion_(), gum::ThreadNumberManager::getNumberOfThreads(), l_expectationMax_, l_expectationMin_, l_modal_, gum::credal::InferenceEngine< GUM_SCALAR >::marginalSets_, gum::credal::InferenceEngine< GUM_SCALAR >::modal_, gum::threadsSTL::ThreadExecutor::nbRunningThreadsExecutors(), gum::credal::InferenceEngine< GUM_SCALAR >::storeVertices_, and workingSet_.

Referenced by expFusion_().

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getApproximationSchemeMsg()

template<typename GUM_SCALAR>

const std::string gum::credal::InferenceEngine< GUM_SCALAR >::getApproximationSchemeMsg ( )

inlineinherited

Get approximation scheme state.

Returns

A constant string about approximation scheme state.

Definition at line 598 of file inferenceEngine.h.

{ return this->messageApproximationScheme(); }

References gum::IApproximationSchemeConfiguration::messageApproximationScheme().

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getNumberOfThreads()

virtual Size gum::ThreadNumberManager::getNumberOfThreads ( ) const

virtualinherited

returns the current max number of threads used by the class containing this ThreadNumberManager

Implements gum::IThreadNumberManager.

Referenced by gum::learning::IBNLearner::createParamEstimator_(), gum::learning::IBNLearner::createScore_(), gum::credal::InferenceEngine< GUM_SCALAR >::displatchMarginalsToThreads_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::expFusion_(), gum::ScheduledInference::scheduler(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::verticesFusion_().

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getT0Cluster()

template<typename GUM_SCALAR>

const NodeProperty< std::vector< NodeId > > & gum::credal::InferenceEngine< GUM_SCALAR >::getT0Cluster ( ) const

inherited

Get the t0_ cluster.

Returns

A constant reference to the t0_ cluster.

Definition at line 1007 of file inferenceEngine_tpl.h.

                                                          {
      return t0_;
    }

References t0_.

getT1Cluster()

template<typename GUM_SCALAR>

const NodeProperty< std::vector< NodeId > > & gum::credal::InferenceEngine< GUM_SCALAR >::getT1Cluster ( ) const

inherited

Get the t1_ cluster.

Returns

A constant reference to the t1_ cluster.

Definition at line 1013 of file inferenceEngine_tpl.h.

                                                          {
      return t1_;
    }

References t1_.

getVarMod2BNsMap()

template<typename GUM_SCALAR>

VarMod2BNsMap< GUM_SCALAR > * gum::credal::InferenceEngine< GUM_SCALAR >::getVarMod2BNsMap ( )

inherited

Get optimum IBayesNet.

Returns

A pointer to the optimal net object.

Definition at line 163 of file inferenceEngine_tpl.h.

                                                                                 {
      return &dbnOpt_;
    }

References dbnOpt_.

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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initExpectations_()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::initExpectations_ ( )

protectedinherited

Initialize lower and upper expectations before inference, with the lower expectation being initialized on the highest modality and the upper expectation being initialized on the lowest modality.

Definition at line 718 of file inferenceEngine_tpl.h.

                                                          {
      expectationMin_.clear();
      expectationMax_.clear();
 
      if (modal_.empty()) return;
 
      for (auto node: credalNet_->current_bn().nodes()) {
        std::string var_name, time_step;
 
        var_name   = credalNet_->current_bn().variable(node).name();
        auto delim = var_name.find_first_of("_");
        var_name   = var_name.substr(0, delim);
 
        if (!modal_.exists(var_name)) continue;
 
        expectationMin_.insert(node, modal_[var_name].back());
        expectationMax_.insert(node, modal_[var_name].front());
      }
    }

References credalNet_, expectationMax_, expectationMin_, and modal_.

Referenced by eraseAllEvidence().

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initMarginals_()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::initMarginals_ ( )

protectedinherited

Initialize lower and upper old marginals and marginals before inference, with the lower marginal being 1 and the upper 0.

Definition at line 682 of file inferenceEngine_tpl.h.

                                                       {
      marginalMin_.clear();
      marginalMax_.clear();
      oldMarginalMin_.clear();
      oldMarginalMax_.clear();
 
      for (auto node: credalNet_->current_bn().nodes()) {
        auto dSize = credalNet_->current_bn().variable(node).domainSize();
        marginalMin_.insert(node, std::vector< GUM_SCALAR >(dSize, 1));
        oldMarginalMin_.insert(node, std::vector< GUM_SCALAR >(dSize, 1));
 
        marginalMax_.insert(node, std::vector< GUM_SCALAR >(dSize, 0));
        oldMarginalMax_.insert(node, std::vector< GUM_SCALAR >(dSize, 0));
      }
 
      // now that we know the sizes of marginalMin_ and marginalMax_, we can
      // dispatch their processes to the threads
      displatchMarginalsToThreads_();
    }

References credalNet_, displatchMarginalsToThreads_(), marginalMax_, marginalMin_, oldMarginalMax_, and oldMarginalMin_.

Referenced by InferenceEngine(), and eraseAllEvidence().

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initMarginalSets_()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::initMarginalSets_ ( )

protectedinherited

Initialize credal set vertices with empty sets.

Definition at line 703 of file inferenceEngine_tpl.h.

                                                          {
      marginalSets_.clear();
 
      if (!storeVertices_) return;
 
      for (auto node: credalNet_->current_bn().nodes())
        marginalSets_.insert(node, std::vector< std::vector< GUM_SCALAR > >());
    }

References credalNet_, marginalSets_, and storeVertices_.

Referenced by eraseAllEvidence(), and storeVertices().

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initThreadsData_()

template<typename GUM_SCALAR, class BNInferenceEngine>

void gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::initThreadsData_ ( const Size & num_threads, const bool _storeVertices_, const bool _storeBNOpt_ )

inlineprotected

Initialize threads data.

Parameters

num_threads	The number of threads.
_storeVertices_	True if vertices should be stored, False otherwise.
_storeBNOpt_	True if optimal IBayesNet should be stored, false otherwise.

Definition at line 62 of file multipleInferenceEngine_tpl.h.

                                  {
      workingSet_.clear();
      workingSet_.resize(num_threads, nullptr);
      workingSetE_.clear();
      workingSetE_.resize(num_threads, nullptr);
 
      l_marginalMin_.clear();
      l_marginalMin_.resize(num_threads);
      l_marginalMax_.clear();
      l_marginalMax_.resize(num_threads);
      l_expectationMin_.clear();
      l_expectationMin_.resize(num_threads);
      l_expectationMax_.clear();
      l_expectationMax_.resize(num_threads);
 
      l_clusters_.clear();
      l_clusters_.resize(num_threads);
 
      if (_storeVertices_) {
        l_marginalSets_.clear();
        l_marginalSets_.resize(num_threads);
      }
 
      if (_storeBNOpt_) {
        for (Size ptr = 0; ptr < this->l_optimalNet_.size(); ptr++)
          if (this->l_optimalNet_[ptr] != nullptr) delete l_optimalNet_[ptr];
 
        l_optimalNet_.clear();
        l_optimalNet_.resize(num_threads);
      }
 
      l_modal_.clear();
      l_modal_.resize(num_threads);
 
      _infE_::oldMarginalMin_.clear();
      this->oldMarginalMin_ = this->marginalMin_;
      this->oldMarginalMax_.clear();
      this->oldMarginalMax_ = this->marginalMax_;
 
      // init the random number generators
      generators_.clear();
      generators_.resize(num_threads);
      auto seed = currentRandomGeneratorValue();
      for (auto& generator: generators_) {
        generator.seed(seed);
        seed = generator();
      }
    }

References generators_, l_clusters_, l_expectationMax_, l_expectationMin_, l_marginalMax_, l_marginalMin_, l_marginalSets_, l_modal_, l_optimalNet_, gum::credal::InferenceEngine< GUM_SCALAR >::marginalMax_, gum::credal::InferenceEngine< GUM_SCALAR >::marginalMin_, gum::credal::InferenceEngine< GUM_SCALAR >::oldMarginalMax_, gum::credal::InferenceEngine< GUM_SCALAR >::oldMarginalMin_, workingSet_, and workingSetE_.

insertEvidence() [1/2]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::insertEvidence ( const NodeProperty< std::vector< GUM_SCALAR > > & evidence )

inherited

Insert evidence from Property.

Parameters

evidence

The on nodes Property containing likelihoods.

Definition at line 277 of file inferenceEngine_tpl.h.

                                                                 {
      if (!evidence_.empty()) evidence_.clear();
 
      // use cbegin() to get const_iterator when available in aGrUM hashtables
      for (const auto& elt: evidence) {
        try {
          credalNet_->current_bn().variable(elt.first);
        } catch (NotFound& err) {
          GUM_SHOWERROR(err);
          continue;
        }
 
        evidence_.insert(elt.first, elt.second);
      }
 
      // forces the computation of the begin iterator to avoid subsequent data races
      // @TODO make HashTableConstIterator constructors thread safe
      evidence_.begin();
    }

References credalNet_, evidence_, and GUM_SHOWERROR.

insertEvidence() [2/2]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::insertEvidence ( const std::map< std::string, std::vector< GUM_SCALAR > > & eviMap )

inherited

Insert evidence from map.

Parameters

eviMap

The map variable name - likelihood.

Definition at line 251 of file inferenceEngine_tpl.h.

                                                                    {
      if (!evidence_.empty()) evidence_.clear();
 
      for (auto it = eviMap.cbegin(), theEnd = eviMap.cend(); it != theEnd; ++it) {
        NodeId id;
 
        try {
          id = credalNet_->current_bn().idFromName(it->first);
        } catch (NotFound& err) {
          GUM_SHOWERROR(err);
          continue;
        }
 
        evidence_.insert(id, it->second);
      }
 
      // forces the computation of the begin iterator to avoid subsequent data races
      // @TODO make HashTableConstIterator constructors thread safe
      evidence_.begin();
    }

References credalNet_, evidence_, and GUM_SHOWERROR.

insertEvidenceFile()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::insertEvidenceFile ( const std::string & path )

virtualinherited

Insert evidence from file.

Parameters

path	The path to the evidence file.

Reimplemented in gum::credal::CNLoopyPropagation< GUM_SCALAR >, and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >.

Definition at line 299 of file inferenceEngine_tpl.h.

                                                                                {
      std::ifstream evi_stream(path.c_str(), std::ios::in);
 
      if (!evi_stream.good()) {
        GUM_ERROR(IOError,
                  "void InferenceEngine< GUM_SCALAR "
                  ">::insertEvidence(const std::string & path) : could not "
                  "open input file : "
                      << path);
      }
 
      if (!evidence_.empty()) evidence_.clear();
 
      std::string line, tmp;
      char *      cstr, *p;
 
      while (evi_stream.good() && std::strcmp(line.c_str(), "[EVIDENCE]") != 0) {
        getline(evi_stream, line);
      }
 
      while (evi_stream.good()) {
        getline(evi_stream, line);
 
        if (std::strcmp(line.c_str(), "[QUERY]") == 0) break;
 
        if (line.size() == 0) continue;
 
        cstr = new char[line.size() + 1];
        strcpy(cstr, line.c_str());
 
        p   = strtok(cstr, " ");
        tmp = p;
 
        // if user input is wrong
        NodeId node = -1;
 
        try {
          node = credalNet_->current_bn().idFromName(tmp);
        } catch (NotFound& err) {
          GUM_SHOWERROR(err);
          continue;
        }
 
        std::vector< GUM_SCALAR > values;
        p = strtok(nullptr, " ");
 
        while (p != nullptr) {
          values.push_back(GUM_SCALAR(atof(p)));
          p = strtok(nullptr, " ");
        }   // end of : line
 
        evidence_.insert(node, values);
 
        delete[] p;
        delete[] cstr;
      }   // end of : file
 
      evi_stream.close();
 
      // forces the computation of the begin iterator to avoid subsequent data races
      // @TODO make HashTableConstIterator constructors thread safe
      evidence_.begin();
    }

Referenced by gum::credal::CNLoopyPropagation< GUM_SCALAR >::insertEvidenceFile(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::insertEvidenceFile().

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insertModals()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::insertModals ( const std::map< std::string, std::vector< GUM_SCALAR > > & modals )

inherited

Insert variables modalities from map to compute expectations.

Parameters

modals

The map variable name - modalities.

Definition at line 215 of file inferenceEngine_tpl.h.

                                                                    {
      if (!modal_.empty()) modal_.clear();
 
      for (auto it = modals.cbegin(), theEnd = modals.cend(); it != theEnd; ++it) {
        NodeId id;
 
        try {
          id = credalNet_->current_bn().idFromName(it->first);
        } catch (NotFound& err) {
          GUM_SHOWERROR(err);
          continue;
        }
 
        // check that modals are net compatible
        auto dSize = credalNet_->current_bn().variable(id).domainSize();
 
        if (dSize != it->second.size()) continue;
 
        // GUM_ERROR(OperationNotAllowed, "void InferenceEngine< GUM_SCALAR
        // >::insertModals( const std::map< std::string, std::vector< GUM_SCALAR
        // > >
        // &modals) : modalities does not respect variable cardinality : " <<
        // credalNet_->current_bn().variable( id ).name() << " : " << dSize << "
        // != "
        // << it->second.size());
 
        modal_.insert(it->first, it->second);   //[ it->first ] = it->second;
      }
 
      //_modal = modals;
 
      initExpectations_();
    }

References credalNet_, GUM_SHOWERROR, and modal_.

insertModalsFile()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::insertModalsFile ( const std::string & path )

inherited

Insert variables modalities from file to compute expectations.

Parameters

path	The path to the modalities file.

Definition at line 168 of file inferenceEngine_tpl.h.

                                                                              {
      std::ifstream mod_stream(path.c_str(), std::ios::in);
 
      if (!mod_stream.good()) {
        GUM_ERROR(OperationNotAllowed,
                  "void InferenceEngine< GUM_SCALAR "
                  ">::insertModals(const std::string & path) : "
                  "could not open input file : "
                      << path);
      }
 
      if (!modal_.empty()) modal_.clear();
 
      std::string line, tmp;
      char *      cstr, *p;
 
      while (mod_stream.good()) {
        getline(mod_stream, line);
 
        if (line.size() == 0) continue;
 
        cstr = new char[line.size() + 1];
        strcpy(cstr, line.c_str());
 
        p   = strtok(cstr, " ");
        tmp = p;
 
        std::vector< GUM_SCALAR > values;
        p = strtok(nullptr, " ");
 
        while (p != nullptr) {
          values.push_back(GUM_SCALAR(atof(p)));
          p = strtok(nullptr, " ");
        }   // end of : line
 
        modal_.insert(tmp, values);   //[tmp] = values;
 
        delete[] p;
        delete[] cstr;
      }   // end of : file
 
      mod_stream.close();
 
      initExpectations_();
    }

References GUM_ERROR, and modal_.

insertQuery()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::insertQuery ( const NodeProperty< std::vector< bool > > & query )

inherited

Insert query variables and states from Property.

Parameters

query

The on nodes Property containing queried variables states.

Definition at line 364 of file inferenceEngine_tpl.h.

                                                        {
      if (!query_.empty()) query_.clear();
 
      for (const auto& elt: query) {
        try {
          credalNet_->current_bn().variable(elt.first);
        } catch (NotFound& err) {
          GUM_SHOWERROR(err);
          continue;
        }
 
        query_.insert(elt.first, elt.second);
      }
    }

References query_.

insertQueryFile()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::insertQueryFile ( const std::string & path )

inherited

Insert query variables states from file.

Parameters

path	The path to the query file.

Definition at line 381 of file inferenceEngine_tpl.h.

                                                                             {
      std::ifstream evi_stream(path.c_str(), std::ios::in);
 
      if (!evi_stream.good()) {
        GUM_ERROR(IOError,
                  "void InferenceEngine< GUM_SCALAR >::insertQuery(const "
                  "std::string & path) : could not open input file : "
                      << path);
      }
 
      if (!query_.empty()) query_.clear();
 
      std::string line, tmp;
      char *      cstr, *p;
 
      while (evi_stream.good() && std::strcmp(line.c_str(), "[QUERY]") != 0) {
        getline(evi_stream, line);
      }
 
      while (evi_stream.good()) {
        getline(evi_stream, line);
 
        if (std::strcmp(line.c_str(), "[EVIDENCE]") == 0) break;
 
        if (line.size() == 0) continue;
 
        cstr = new char[line.size() + 1];
        strcpy(cstr, line.c_str());
 
        p   = strtok(cstr, " ");
        tmp = p;
 
        // if user input is wrong
        NodeId node = -1;
 
        try {
          node = credalNet_->current_bn().idFromName(tmp);
        } catch (NotFound& err) {
          GUM_SHOWERROR(err);
          continue;
        }
 
        auto dSize = credalNet_->current_bn().variable(node).domainSize();
 
        p = strtok(nullptr, " ");
 
        if (p == nullptr) {
          query_.insert(node, std::vector< bool >(dSize, true));
        } else {
          std::vector< bool > values(dSize, false);
 
          while (p != nullptr) {
            if ((Size)atoi(p) >= dSize)
              GUM_ERROR(OutOfBounds,
                        "void InferenceEngine< GUM_SCALAR "
                        ">::insertQuery(const std::string & path) : "
                        "query modality is higher or equal to "
                        "cardinality");
 
            values[atoi(p)] = true;
            p               = strtok(nullptr, " ");
          }   // end of : line
 
          query_.insert(node, values);
        }
 
        delete[] p;
        delete[] cstr;
      }   // end of : file
 
      evi_stream.close();
    }

References GUM_ERROR.

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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isGumNumberOfThreadsOverriden()

bool gum::ThreadNumberManager::isGumNumberOfThreadsOverriden ( ) const

virtualinherited

indicates whether the class containing this ThreadNumberManager set its own number of threads

Implements gum::IThreadNumberManager.

Referenced by gum::learning::IBNLearner::createParamEstimator_(), and gum::learning::IBNLearner::createScore_().

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makeInference()

template<typename GUM_SCALAR, class BNInferenceEngine>

virtual void gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::makeInference ( )

pure virtual

To be redefined by each credal net algorithm.

Starts the inference.

Implements gum::credal::InferenceEngine< GUM_SCALAR >.

Implemented in gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >.

marginalMax() [1/2]

template<typename GUM_SCALAR>

gum::Tensor< GUM_SCALAR > gum::credal::InferenceEngine< GUM_SCALAR >::marginalMax ( const NodeId id ) const

inherited

Get the upper marginals of a given node id.

Parameters

id	The node id which upper marginals we want.

Returns

A constant reference to this node upper marginals.

Definition at line 477 of file inferenceEngine_tpl.h.

                                                                                            {
      try {
        Tensor< GUM_SCALAR > res;
        res.add(credalNet_->current_bn().variable(id));
        res.fillWith(marginalMax_[id]);
        return res;
      } catch (NotFound& err) { throw(err); }
    }

Referenced by marginalMax().

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marginalMax() [2/2]

template<typename GUM_SCALAR>

INLINE Tensor< GUM_SCALAR > gum::credal::InferenceEngine< GUM_SCALAR >::marginalMax ( const std::string & varName ) const

inherited

Get the upper marginals of a given variable name.

Parameters

varName

The variable name which upper marginals we want.

Returns

A constant reference to this variable upper marginals.

Definition at line 462 of file inferenceEngine_tpl.h.

                                                                                    {
      return marginalMax(credalNet_->current_bn().idFromName(varName));
    }

References credalNet_, and marginalMax().

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marginalMin() [1/2]

template<typename GUM_SCALAR>

gum::Tensor< GUM_SCALAR > gum::credal::InferenceEngine< GUM_SCALAR >::marginalMin ( const NodeId id ) const

inherited

Get the lower marginals of a given node id.

Parameters

id	The node id which lower marginals we want.

Returns

A constant reference to this node lower marginals.

Definition at line 467 of file inferenceEngine_tpl.h.

                                                                                            {
      try {
        Tensor< GUM_SCALAR > res;
        res.add(credalNet_->current_bn().variable(id));
        res.fillWith(marginalMin_[id]);
        return res;
      } catch (NotFound& err) { throw(err); }
    }

References credalNet_, and marginalMin_.

Referenced by marginalMin().

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marginalMin() [2/2]

template<typename GUM_SCALAR>

INLINE Tensor< GUM_SCALAR > gum::credal::InferenceEngine< GUM_SCALAR >::marginalMin ( const std::string & varName ) const

inherited

Get the lower marginals of a given variable name.

Parameters

varName

The variable name which lower marginals we want.

Returns

A constant reference to this variable lower marginals.

Definition at line 456 of file inferenceEngine_tpl.h.

                                                                                    {
      return marginalMin(credalNet_->current_bn().idFromName(varName));
    }

References credalNet_, and marginalMin().

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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_.

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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optFusion_()

template<typename GUM_SCALAR, class BNInferenceEngine>

void gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::optFusion_ ( )

protected

Fusion of threads optimal IBayesNet.

Definition at line 735 of file multipleInferenceEngine_tpl.h.

                                                                              {
      using dBN = std::vector< bool >;
 
      Size nsize = Size(workingSet_[0]->size());
 
      // no parallel insert in hash-tables (OptBN)
      for (Idx i = 0; i < nsize; i++) {
        // we don't store anything for observed variables
        if (_infE_::evidence_.exists(i)) continue;
 
        Size dSize = Size(l_marginalMin_[0][i].size());
 
        for (Size j = 0; j < dSize; j++) {
          // go through all threads
          std::vector< Size > keymin(3);
          keymin[0] = i;
          keymin[1] = j;
          keymin[2] = 0;
          std::vector< Size > keymax(keymin);
          keymax[2] = 1;
 
          Size tsize = Size(l_marginalMin_.size());
 
          for (Size tId = 0; tId < tsize; tId++) {
            if (l_marginalMin_[tId][i][j] == this->marginalMin_[i][j]) {
              const std::vector< dBN* >& tOpts = l_optimalNet_[tId]->getBNOptsFromKey(keymin);
              Size                       osize = Size(tOpts.size());
 
              for (Size bn = 0; bn < osize; bn++) {
                _infE_::dbnOpt_.insert(*tOpts[bn], keymin);
              }
            }
 
            if (l_marginalMax_[tId][i][j] == this->marginalMax_[i][j]) {
              const std::vector< dBN* >& tOpts = l_optimalNet_[tId]->getBNOptsFromKey(keymax);
              Size                       osize = Size(tOpts.size());
 
              for (Size bn = 0; bn < osize; bn++) {
                _infE_::dbnOpt_.insert(*tOpts[bn], keymax);
              }
            }
          }   // end of : all threads
        }   // end of : all modalities
      }   // end of : all variables
    }

References gum::credal::InferenceEngine< GUM_SCALAR >::dbnOpt_, gum::credal::InferenceEngine< GUM_SCALAR >::evidence_, l_marginalMax_, l_marginalMin_, l_optimalNet_, gum::credal::InferenceEngine< GUM_SCALAR >::marginalMax_, gum::credal::InferenceEngine< GUM_SCALAR >::marginalMin_, optFusion_(), and workingSet_.

Referenced by optFusion_().

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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_.

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_.

repetitiveInd()

template<typename GUM_SCALAR>

bool gum::credal::InferenceEngine< GUM_SCALAR >::repetitiveInd ( ) const

inherited

Get the current independence status.

Returns

True if repetitive, False otherwise.

Definition at line 142 of file inferenceEngine_tpl.h.

                                                            {
      return repetitiveInd_;
    }

References repetitiveInd_.

repetitiveInit_()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::repetitiveInit_ ( )

protectedinherited

Initialize t0_ and t1_ clusters.

Definition at line 801 of file inferenceEngine_tpl.h.

                                                        {
      timeSteps_ = 0;
      t0_.clear();
      t1_.clear();
 
      // t = 0 vars belongs to t0_ as keys
      for (auto node: credalNet_->current_bn().dag().nodes()) {
        std::string var_name = credalNet_->current_bn().variable(node).name();
        auto        delim    = var_name.find_first_of("_");
 
        if (delim > var_name.size()) {
          GUM_ERROR(InvalidArgument,
                    "void InferenceEngine< GUM_SCALAR "
                    ">::repetitiveInit_() : the network does not "
                    "appear to be dynamic");
        }
 
        std::string time_step = var_name.substr(delim + 1, 1);
 
        if (time_step.compare("0") == 0) t0_.insert(node, std::vector< NodeId >());
      }
 
      // t = 1 vars belongs to either t0_ as member value or t1_ as keys
      for (const auto& node: credalNet_->current_bn().dag().nodes()) {
        std::string var_name  = credalNet_->current_bn().variable(node).name();
        auto        delim     = var_name.find_first_of("_");
        std::string time_step = var_name.substr(delim + 1, var_name.size());
        var_name              = var_name.substr(0, delim);
        delim                 = time_step.find_first_of("_");
        time_step             = time_step.substr(0, delim);
 
        if (time_step.compare("1") == 0) {
          bool found = false;
 
          for (const auto& elt: t0_) {
            std::string var_0_name = credalNet_->current_bn().variable(elt.first).name();
            delim                  = var_0_name.find_first_of("_");
            var_0_name             = var_0_name.substr(0, delim);
 
            if (var_name.compare(var_0_name) == 0) {
              const Tensor< GUM_SCALAR >* tensor(&credalNet_->current_bn().cpt(node));
              const Tensor< GUM_SCALAR >* tensor2(&credalNet_->current_bn().cpt(elt.first));
 
              if (tensor->domainSize() == tensor2->domainSize()) t0_[elt.first].push_back(node);
              else t1_.insert(node, std::vector< NodeId >());
 
              found = true;
              break;
            }
          }
 
          if (!found) { t1_.insert(node, std::vector< NodeId >()); }
        }
      }
 
      // t > 1 vars belongs to either t0_ or t1_ as member value
      // remember timeSteps_
      for (auto node: credalNet_->current_bn().dag().nodes()) {
        std::string var_name  = credalNet_->current_bn().variable(node).name();
        auto        delim     = var_name.find_first_of("_");
        std::string time_step = var_name.substr(delim + 1, var_name.size());
        var_name              = var_name.substr(0, delim);
        delim                 = time_step.find_first_of("_");
        time_step             = time_step.substr(0, delim);
 
        if (time_step.compare("0") != 0 && time_step.compare("1") != 0) {
          // keep max time_step
          if (atoi(time_step.c_str()) > timeSteps_) timeSteps_ = atoi(time_step.c_str());
 
          std::string var_0_name;
          bool        found = false;
 
          for (const auto& elt: t0_) {
            std::string var_0_name = credalNet_->current_bn().variable(elt.first).name();
            delim                  = var_0_name.find_first_of("_");
            var_0_name             = var_0_name.substr(0, delim);
 
            if (var_name.compare(var_0_name) == 0) {
              const Tensor< GUM_SCALAR >* tensor(&credalNet_->current_bn().cpt(node));
              const Tensor< GUM_SCALAR >* tensor2(&credalNet_->current_bn().cpt(elt.first));
 
              if (tensor->domainSize() == tensor2->domainSize()) {
                t0_[elt.first].push_back(node);
                found = true;
                break;
              }
            }
          }
 
          if (!found) {
            for (const auto& elt: t1_) {
              std::string var_0_name = credalNet_->current_bn().variable(elt.first).name();
              auto        delim      = var_0_name.find_first_of("_");
              var_0_name             = var_0_name.substr(0, delim);
 
              if (var_name.compare(var_0_name) == 0) {
                const Tensor< GUM_SCALAR >* tensor(&credalNet_->current_bn().cpt(node));
                const Tensor< GUM_SCALAR >* tensor2(&credalNet_->current_bn().cpt(elt.first));
 
                if (tensor->domainSize() == tensor2->domainSize()) {
                  t1_[elt.first].push_back(node);
                  break;
                }
              }
            }
          }
        }
      }
    }

References credalNet_, GUM_ERROR, t0_, t1_, and timeSteps_.

Referenced by setRepetitiveInd().

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saveExpectations()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::saveExpectations ( const std::string & path ) const

inherited

Saves expectations to file.

Parameters

path	The path to the file to be used.

Definition at line 578 of file inferenceEngine_tpl.h.

                                                                                    {
      if (dynamicExpMin_.empty())   //_modal.empty())
        return;
 
      // else not here, to keep the const (natural with a saving process)
      // else if(dynamicExpMin_.empty() || dynamicExpMax_.empty())
      //_dynamicExpectations(); // works with or without a dynamic network
 
      std::ofstream m_stream(path.c_str(), std::ios::out | std::ios::trunc);
 
      if (!m_stream.good()) {
        GUM_ERROR(IOError,
                  "void InferenceEngine< GUM_SCALAR "
                  ">::saveExpectations(const std::string & path) : could "
                  "not open output file : "
                      << path);
      }
 
      for (const auto& elt: dynamicExpMin_) {
        m_stream << elt.first;   // it->first;
 
        // iterates over a vector
        for (const auto& elt2: elt.second) {
          m_stream << " " << elt2;
        }
 
        m_stream << std::endl;
      }
 
      for (const auto& elt: dynamicExpMax_) {
        m_stream << elt.first;
 
        // iterates over a vector
        for (const auto& elt2: elt.second) {
          m_stream << " " << elt2;
        }
 
        m_stream << std::endl;
      }
 
      m_stream.close();
    }

References dynamicExpMin_.

saveMarginals()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::saveMarginals ( const std::string & path ) const

inherited

Saves marginals to file.

Parameters

path	The path to the file to be used.

Definition at line 555 of file inferenceEngine_tpl.h.

                                                                                 {
      std::ofstream m_stream(path.c_str(), std::ios::out | std::ios::trunc);
 
      if (!m_stream.good()) {
        GUM_ERROR(IOError,
                  "void InferenceEngine< GUM_SCALAR >::saveMarginals(const "
                  "std::string & path) const : could not open output file "
                  ": " << path);
      }
 
      for (const auto& elt: marginalMin_) {
        Size esize = Size(elt.second.size());
 
        for (Size mod = 0; mod < esize; mod++) {
          m_stream << credalNet_->current_bn().variable(elt.first).name() << " " << mod << " "
                   << (elt.second)[mod] << " " << marginalMax_[elt.first][mod] << std::endl;
        }
      }
 
      m_stream.close();
    }

References GUM_ERROR.

saveVertices()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::saveVertices ( const std::string & path ) const

inherited

Saves vertices to file.

Parameters

path	The path to the file to be used.

Definition at line 648 of file inferenceEngine_tpl.h.

                                                                                {
      std::ofstream m_stream(path.c_str(), std::ios::out | std::ios::trunc);
 
      if (!m_stream.good()) {
        GUM_ERROR(IOError,
                  "void InferenceEngine< GUM_SCALAR >::saveVertices(const "
                  "std::string & path) : could not open outpul file : "
                      << path);
      }
 
      for (const auto& elt: marginalSets_) {
        m_stream << credalNet_->current_bn().variable(elt.first).name() << std::endl;
 
        for (const auto& elt2: elt.second) {
          m_stream << "[";
          bool first = true;
 
          for (const auto& elt3: elt2) {
            if (!first) {
              m_stream << ",";
              first = false;
            }
 
            m_stream << elt3;
          }
 
          m_stream << "]\n";
        }
      }
 
      m_stream.close();
    }

References credalNet_, GUM_ERROR, and marginalSets_.

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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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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setNumberOfThreads()

virtual void gum::ThreadNumberManager::setNumberOfThreads ( Size nb )

virtualinherited

sets the number max of threads to be used by the class containing this ThreadNumberManager

Parameters

nb	the number of threads to be used. If this number is set to 0, then it is defaulted to aGrUM's number of threads

Implements gum::IThreadNumberManager.

Reimplemented in gum::learning::IBNLearner, gum::learning::RecordCounter, gum::ScheduledInference, and gum::SchedulerParallel.

Referenced by gum::learning::IBNLearner::setNumberOfThreads(), and gum::ScheduledInference::setNumberOfThreads().

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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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setRepetitiveInd()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::setRepetitiveInd ( const bool repetitive )

inherited

Parameters

repetitive

True if repetitive independence is to be used, false otherwise. Only usefull with dynamic networks.

Definition at line 133 of file inferenceEngine_tpl.h.

                                                                              {
      bool oldValue  = repetitiveInd_;
      repetitiveInd_ = repetitive;
 
      // do not compute clusters more than once
      if (repetitiveInd_ && !oldValue) repetitiveInit_();
    }

References repetitiveInd_, and repetitiveInit_().

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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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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_.

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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storeBNOpt() [1/2]

template<typename GUM_SCALAR>

bool gum::credal::InferenceEngine< GUM_SCALAR >::storeBNOpt ( ) const

inherited

Returns

True if optimal bayes net are stored for each variable and each modality, False otherwise.

Definition at line 158 of file inferenceEngine_tpl.h.

                                                         {
      return storeBNOpt_;
    }

References storeBNOpt_.

storeBNOpt() [2/2]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::storeBNOpt ( const bool value )

inherited

Parameters

value

True if optimal Bayesian networks are to be stored for each variable and each modality.

Definition at line 121 of file inferenceEngine_tpl.h.

                                                                   {
      storeBNOpt_ = value;
    }

References storeBNOpt_.

storeVertices() [1/2]

template<typename GUM_SCALAR>

bool gum::credal::InferenceEngine< GUM_SCALAR >::storeVertices ( ) const

inherited

Get the number of iterations without changes used to stop some algorithms.

Returns

the number of iterations. int iterStop () const;

True if vertice are stored, False otherwise.

Definition at line 153 of file inferenceEngine_tpl.h.

                                                            {
      return storeVertices_;
    }

References storeVertices_.

storeVertices() [2/2]

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::storeVertices ( const bool value )

inherited

Parameters

value

True if vertices are to be stored, false otherwise.

Definition at line 126 of file inferenceEngine_tpl.h.

                                                                      {
      storeVertices_ = value;
 
      if (value) initMarginalSets_();
    }

References initMarginalSets_(), and storeVertices_.

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toString()

template<typename GUM_SCALAR>

std::string gum::credal::InferenceEngine< GUM_SCALAR >::toString ( ) const

inherited

Print all nodes marginals to standart output.

Definition at line 622 of file inferenceEngine_tpl.h.

                                                            {
      std::stringstream output;
      output << std::endl;
 
      // use cbegin() when available
      for (const auto& elt: marginalMin_) {
        Size esize = Size(elt.second.size());
 
        for (Size mod = 0; mod < esize; mod++) {
          output << "P(" << credalNet_->current_bn().variable(elt.first).name() << "=" << mod
                 << "|e) = [ ";
          output << marginalMin_[elt.first][mod] << ", " << marginalMax_[elt.first][mod] << " ]";
 
          if (!query_.empty())
            if (query_.exists(elt.first) && query_[elt.first][mod]) output << " QUERY";
 
          output << std::endl;
        }
 
        output << std::endl;
      }
 
      return output.str();
    }

References credalNet_, marginalMax_, marginalMin_, and query_.

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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updateCredalSets_()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::updateCredalSets_ ( const NodeId & id, const std::vector< GUM_SCALAR > & vertex, const bool & elimRedund = false )

inlineprotectedinherited

Given a node id and one of it's possible vertex, update it's credal set.

To maximise efficiency, don't pass a vertex we know is inside the polytope (i.e. not at an extreme value for any modality)

Parameters

id	The id of the node to be updated
vertex	A (tensor) vertex of the node credal set
elimRedund	remove redundant vertex (inside a facet)

Definition at line 934 of file inferenceEngine_tpl.h.

                                                                                  {
      auto& nodeCredalSet = marginalSets_[id];
      auto  dsize         = vertex.size();
 
      bool eq = true;
 
      for (auto it = nodeCredalSet.cbegin(), itEnd = nodeCredalSet.cend(); it != itEnd; ++it) {
        eq = true;
 
        for (Size i = 0; i < dsize; i++) {
          if (std::fabs(vertex[i] - (*it)[i]) > 1e-6) {
            eq = false;
            break;
          }
        }
 
        if (eq) break;
      }
 
      if (!eq || nodeCredalSet.size() == 0) {
        nodeCredalSet.push_back(vertex);
        return;
      } else return;
 
      // because of next lambda return condition
      if (nodeCredalSet.size() == 1) return;
 
      // check that the point and all previously added ones are not inside the
      // actual
      // polytope
      auto itEnd = std::remove_if(
          nodeCredalSet.begin(),
          nodeCredalSet.end(),
          [&](const std::vector< GUM_SCALAR >& v) -> bool {
            for (auto jt       = v.cbegin(),
                      jtEnd    = v.cend(),
                      minIt    = marginalMin_[id].cbegin(),
                      minItEnd = marginalMin_[id].cend(),
                      maxIt    = marginalMax_[id].cbegin(),
                      maxItEnd = marginalMax_[id].cend();
                 jt != jtEnd && minIt != minItEnd && maxIt != maxItEnd;
                 ++jt, ++minIt, ++maxIt) {
              if ((std::fabs(*jt - *minIt) < 1e-6 || std::fabs(*jt - *maxIt) < 1e-6)
                  && std::fabs(*minIt - *maxIt) > 1e-6)
                return false;
            }
            return true;
          });
 
      nodeCredalSet.erase(itEnd, nodeCredalSet.end());
 
      // we need at least 2 points to make a convex combination
      if (!elimRedund || nodeCredalSet.size() <= 2) return;
 
      // there may be points not inside the polytope but on one of it's facet,
      // meaning it's still a convex combination of vertices of this facet. Here
      // we
      // need lrs.
      LRSWrapper< GUM_SCALAR > lrsWrapper;
      lrsWrapper.setUpV((unsigned int)dsize, (unsigned int)(nodeCredalSet.size()));
 
      for (const auto& vtx: nodeCredalSet)
        lrsWrapper.fillV(vtx);
 
      lrsWrapper.elimRedundVrep();
 
      marginalSets_[id] = lrsWrapper.getOutput();
    }

References marginalSets_.

Referenced by gum::credal::CNLoopyPropagation< GUM_SCALAR >::computeExpectations_(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::verticesFusion_().

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updateExpectations_()

template<typename GUM_SCALAR>

void gum::credal::InferenceEngine< GUM_SCALAR >::updateExpectations_ ( const NodeId & id, const std::vector< GUM_SCALAR > & vertex )

inlineprotectedinherited

Given a node id and one of it's possible vertex obtained during inference, update this node lower and upper expectations.

Parameters

id	The id of the node to be updated
vertex	A (tensor) vertex of the node credal set

Definition at line 912 of file inferenceEngine_tpl.h.

                                               {
      std::string var_name = credalNet_->current_bn().variable(id).name();
      auto        delim    = var_name.find_first_of("_");
 
      var_name = var_name.substr(0, delim);
 
      if (modal_.exists(var_name) /*modal_.find(var_name) != modal_.end()*/) {
        GUM_SCALAR exp   = 0;
        auto       vsize = vertex.size();
 
        for (Size mod = 0; mod < vsize; mod++)
          exp += vertex[mod] * modal_[var_name][mod];
 
        if (exp > expectationMax_[id]) expectationMax_[id] = exp;
 
        if (exp < expectationMin_[id]) expectationMin_[id] = exp;
      }
    }

References credalNet_, expectationMax_, expectationMin_, and modal_.

Referenced by gum::credal::CNLoopyPropagation< GUM_SCALAR >::computeExpectations_().

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updateMarginals_()

template<typename GUM_SCALAR, class BNInferenceEngine>

void gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateMarginals_ ( )

inlineprotected

Fusion of threads marginals.

Definition at line 293 of file multipleInferenceEngine_tpl.h.

                                                                                           {
      // compute the max number of threads to use (avoid nested threads)
      const Size nb_threads = ThreadExecutor::nbRunningThreadsExecutors() == 0
                                ? this->threadRanges_.size() - 1
                                : 1;   // no nested multithreading
 
      // create the function to be executed by the threads
      auto threadedExec = [this](const std::size_t                           this_thread,
                                 const std::size_t                           nb_threads,
                                 const std::vector< std::pair< Idx, Idx > >& ranges) {
        auto       i                = this->threadRanges_[this_thread].first;
        auto       j                = this->threadRanges_[this_thread].second;
        auto       domain_size      = this->marginalMax_[i].size();
        const auto end_i            = this->threadRanges_[this_thread + 1].first;
        auto       end_j            = this->threadRanges_[this_thread + 1].second;
        const auto marginalMax_size = this->marginalMax_.size();
        const auto tsize            = Size(l_marginalMin_.size());
 
        while ((i < end_i) || (j < end_j)) {
          // go through all work indices
          for (Idx tId = 0; tId < tsize; tId++) {
            if (l_marginalMin_[tId][i][j] < this->marginalMin_[i][j])
              this->marginalMin_[i][j] = l_marginalMin_[tId][i][j];
 
            if (l_marginalMax_[tId][i][j] > this->marginalMax_[i][j])
              this->marginalMax_[i][j] = l_marginalMax_[tId][i][j];
          }
 
          if (++j == domain_size) {
            j = 0;
            ++i;
            if (i < marginalMax_size) domain_size = this->marginalMax_[i].size();
          }
        }
      };
 
      // launch the threads
      ThreadExecutor::execute(
          nb_threads,
          threadedExec,
          (nb_threads == 1)
              ? std::vector< std::pair< NodeId, Idx > >{{0, 0}, {this->marginalMin_.size(), 0}}
              : this->threadRanges_);
    }

References gum::threadsSTL::ThreadExecutor::execute(), l_marginalMax_, l_marginalMin_, gum::credal::InferenceEngine< GUM_SCALAR >::marginalMax_, gum::credal::InferenceEngine< GUM_SCALAR >::marginalMin_, gum::threadsSTL::ThreadExecutor::nbRunningThreadsExecutors(), gum::credal::InferenceEngine< GUM_SCALAR >::threadRanges_, and updateMarginals_().

Referenced by updateMarginals_().

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updateOldMarginals_()

template<typename GUM_SCALAR, class BNInferenceEngine>

void gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateOldMarginals_ ( )

protected

Update old marginals (from current marginals).

Call this once to initialize old marginals (after burn-in for example) and then use computeEpsilon_ which does the same job but compute epsilon too.

Definition at line 473 of file multipleInferenceEngine_tpl.h.

                                                                                       {
#pragma omp parallel
      {
        int  threadId = threadsOMP::getThreadNumber();
        long nsize    = long(workingSet_[threadId]->size());
 
#pragma omp for
 
        for (long i = 0; i < nsize; i++) {
          Size dSize = Size(l_marginalMin_[threadId][i].size());
 
          for (Size j = 0; j < dSize; j++) {
            Size tsize = Size(l_marginalMin_.size());
 
            // go through all threads
            for (Size tId = 0; tId < tsize; tId++) {
              if (l_marginalMin_[tId][i][j] < this->oldMarginalMin_[i][j])
                this->oldMarginalMin_[i][j] = l_marginalMin_[tId][i][j];
 
              if (l_marginalMax_[tId][i][j] > this->oldMarginalMax_[i][j])
                this->oldMarginalMax_[i][j] = l_marginalMax_[tId][i][j];
            }   // end of : all threads
          }   // end of : all modalities
        }   // end of : all variables
      }   // end of : parallel region
    }

References gum::threadsOMP::getThreadNumber(), l_marginalMax_, l_marginalMin_, gum::credal::InferenceEngine< GUM_SCALAR >::oldMarginalMax_, gum::credal::InferenceEngine< GUM_SCALAR >::oldMarginalMin_, updateOldMarginals_(), and workingSet_.

Referenced by updateOldMarginals_().

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updateThread_()

template<typename GUM_SCALAR, class BNInferenceEngine>

bool gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateThread_ ( Size this_thread, const NodeId & id, const std::vector< GUM_SCALAR > & vertex, const bool & elimRedund = false )

inlineprotected

Update thread information (marginals, expectations, IBayesNet, vertices) for a given node id.

Parameters

this_thread	the id of the thread executing this method
id	The id of the node to be updated.
vertex	The vertex.
elimRedund	true if redundancy elimination is to be performed, false otherwise and by default.

Returns

True if the IBayesNet is kept (for now), False otherwise.

Definition at line 115 of file multipleInferenceEngine_tpl.h.

                                                     {
      // save E(X) if we don't save vertices
      if (!_infE_::storeVertices_ && !l_modal_[tId].empty()) {
        std::string var_name = workingSet_[tId]->variable(id).name();
        auto        delim    = var_name.find_first_of("_");
        var_name             = var_name.substr(0, delim);
 
        if (l_modal_[tId].exists(var_name)) {
          GUM_SCALAR exp   = 0;
          Size       vsize = Size(vertex.size());
 
          for (Size mod = 0; mod < vsize; mod++)
            exp += vertex[mod] * l_modal_[tId][var_name][mod];
 
          if (exp > l_expectationMax_[tId][id]) l_expectationMax_[tId][id] = exp;
 
          if (exp < l_expectationMin_[tId][id]) l_expectationMin_[tId][id] = exp;
        }
      }   // end of : if modal (map) not empty
 
      bool newOne = false;
      bool added  = false;
      bool result = false;
      // for burn in, we need to keep checking on local marginals and not global
      // ones
      // (faster inference)
      // we also don't want to store dbn for observed variables since there will
      // be a
      // huge number of them (probably all of them).
      Size vsize = Size(vertex.size());
 
      for (Size mod = 0; mod < vsize; mod++) {
        if (vertex[mod] < l_marginalMin_[tId][id][mod]) {
          l_marginalMin_[tId][id][mod] = vertex[mod];
          newOne                       = true;
 
          if (_infE_::storeBNOpt_ && !_infE_::evidence_.exists(id)) {
            std::vector< Size > key(3);
            key[0] = id;
            key[1] = mod;
            key[2] = 0;
 
            if (l_optimalNet_[tId]->insert(key, true)) result = true;
          }
        }
 
        if (vertex[mod] > l_marginalMax_[tId][id][mod]) {
          l_marginalMax_[tId][id][mod] = vertex[mod];
          newOne                       = true;
 
          if (_infE_::storeBNOpt_ && !_infE_::evidence_.exists(id)) {
            std::vector< Size > key(3);
            key[0] = id;
            key[1] = mod;
            key[2] = 1;
 
            if (l_optimalNet_[tId]->insert(key, true)) result = true;
          }
        } else if (vertex[mod] == l_marginalMin_[tId][id][mod]
                   || vertex[mod] == l_marginalMax_[tId][id][mod]) {
          newOne = true;
 
          if (_infE_::storeBNOpt_ && vertex[mod] == l_marginalMin_[tId][id][mod]
              && !_infE_::evidence_.exists(id)) {
            std::vector< Size > key(3);
            key[0] = id;
            key[1] = mod;
            key[2] = 0;
 
            if (l_optimalNet_[tId]->insert(key, false)) result = true;
          }
 
          if (_infE_::storeBNOpt_ && vertex[mod] == l_marginalMax_[tId][id][mod]
              && !_infE_::evidence_.exists(id)) {
            std::vector< Size > key(3);
            key[0] = id;
            key[1] = mod;
            key[2] = 1;
 
            if (l_optimalNet_[tId]->insert(key, false)) result = true;
          }
        }
 
        // store point to compute credal set vertices.
        // check for redundancy at each step or at the end ?
        if (_infE_::storeVertices_ && !added && newOne) {
          _updateThreadCredalSets_(tId, id, vertex, elimRedund);
          added = true;
        }
      }
 
      // if all variables didn't get better marginals, we will delete
      if (_infE_::storeBNOpt_ && result) return true;
 
      return false;
    }

References gum::credal::InferenceEngine< GUM_SCALAR >::evidence_, l_expectationMax_, l_expectationMin_, l_marginalMin_, l_modal_, l_optimalNet_, gum::credal::InferenceEngine< GUM_SCALAR >::storeBNOpt_, gum::credal::InferenceEngine< GUM_SCALAR >::storeVertices_, and workingSet_.

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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vertices()

template<typename GUM_SCALAR>

const std::vector< std::vector< GUM_SCALAR > > & gum::credal::InferenceEngine< GUM_SCALAR >::vertices ( const NodeId id ) const

inherited

Get the vertice of a given node id.

Parameters

id	The node id which vertice we want.

Returns

A constant reference to this node vertice.

Definition at line 550 of file inferenceEngine_tpl.h.

                                                                     {
      return marginalSets_[id];
    }

References marginalSets_.

Referenced by gum::credal::CNLoopyPropagation< GUM_SCALAR >::computeExpectations_().

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verticesFusion_()

template<typename GUM_SCALAR, class BNInferenceEngine>

void gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::verticesFusion_ ( )

protected

Deprecated

Fusion of threads vertices.

Definition at line 501 of file multipleInferenceEngine_tpl.h.

                                                                                   {
      // don't create threads if there are no vertices saved
      if (!_infE_::storeVertices_) return;
 
      // compute the max number of threads to use (avoid nested threads)
      const Size nb_threads = ThreadExecutor::nbRunningThreadsExecutors() == 0
                                ? ThreadNumberManager::getNumberOfThreads()
                                : 1;   // no nested multithreading
 
      // create the function to be executed by the threads
      Size tsize        = Size(l_marginalMin_.size());
      auto threadedExec = [this, tsize](const std::size_t                           this_thread,
                                        const std::size_t                           nb_threads,
                                        Idx                                         work_index,
                                        const std::vector< std::pair< Idx, Idx > >& ranges) {
        for (Idx i = ranges[this_thread].first, end = ranges[this_thread].second; i < end; i++) {
          // go through all threads
          for (Size tId = 0; tId < tsize; ++tId) {
            auto& nodeThreadCredalSet = l_marginalSets_[tId][i];
 
            // for each vertex, if we are at any opt marginal, add it to the set
            for (const auto& vtx: nodeThreadCredalSet) {
              // we run redundancy elimination at each step because there could
              // be 100000 threads and the set will be so huge...
              // BUT not if vertices are of dimension 2 ! opt check and equality
              // should be enough
              _infE_::updateCredalSets_(i, vtx, (vtx.size() > 2) ? true : false);
            }   // end of : nodeThreadCredalSet
          }   // end of : all threads
        }   // end of : all variables
      };
 
      const Size working_size = workingSet_.size();
      for (Idx work_index = 0; work_index < working_size; ++work_index) {
        // compute the ranges over which the threads will work
        const auto nsize           = workingSet_[work_index]->size();
        const auto real_nb_threads = std::min(nb_threads, nsize);
        const auto ranges = gum::dispatchRangeToThreads(0, nsize, (unsigned int)(real_nb_threads));
        ThreadExecutor::execute(real_nb_threads, threadedExec, work_index, ranges);
      }
    }

References gum::dispatchRangeToThreads(), gum::threadsSTL::ThreadExecutor::execute(), gum::ThreadNumberManager::getNumberOfThreads(), l_marginalMin_, l_marginalSets_, gum::threadsSTL::ThreadExecutor::nbRunningThreadsExecutors(), gum::credal::InferenceEngine< GUM_SCALAR >::storeVertices_, gum::credal::InferenceEngine< GUM_SCALAR >::updateCredalSets_(), verticesFusion_(), and workingSet_.

Referenced by verticesFusion_().

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Member Data Documentation

_nb_threads_

Size gum::ThreadNumberManager::_nb_threads_ {0}

privateinherited

the max number of threads used by the class

Definition at line 126 of file threadNumberManager.h.

{0};

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().

credalNet_

template<typename GUM_SCALAR>

const CredalNet< GUM_SCALAR >* gum::credal::InferenceEngine< GUM_SCALAR >::credalNet_

protectedinherited

A pointer to the Credal Net used.

Definition at line 84 of file inferenceEngine.h.

Referenced by InferenceEngine(), addEvidence(), addEvidence(), addEvidence(), addEvidence(), addEvidence(), addEvidence(), credalNet(), dynamicExpectations_(), expectationMax(), expectationMin(), initExpectations_(), initMarginals_(), initMarginalSets_(), insertEvidence(), insertEvidence(), insertModals(), marginalMax(), marginalMin(), marginalMin(), repetitiveInit_(), saveVertices(), toString(), and updateExpectations_().

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().

dbnOpt_

template<typename GUM_SCALAR>

VarMod2BNsMap< GUM_SCALAR > gum::credal::InferenceEngine< GUM_SCALAR >::dbnOpt_

protectedinherited

Object used to efficiently store optimal bayes net during inference, for some algorithms.

Definition at line 158 of file inferenceEngine.h.

Referenced by InferenceEngine(), getVarMod2BNsMap(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::optFusion_().

dynamicExpMax_

template<typename GUM_SCALAR>

dynExpe gum::credal::InferenceEngine< GUM_SCALAR >::dynamicExpMax_

protectedinherited

Upper dynamic expectations.

If the network if not dynamic it's content is the same as expectationMax_.

Definition at line 111 of file inferenceEngine.h.

Referenced by dynamicExpectations_(), dynamicExpMax(), and eraseAllEvidence().

dynamicExpMin_

template<typename GUM_SCALAR>

dynExpe gum::credal::InferenceEngine< GUM_SCALAR >::dynamicExpMin_

protectedinherited

Lower dynamic expectations.

If the network is not dynamic it's content is the same as expectationMin_.

Definition at line 108 of file inferenceEngine.h.

Referenced by dynamicExpectations_(), eraseAllEvidence(), and saveExpectations().

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().

evidence_

template<typename GUM_SCALAR>

margi gum::credal::InferenceEngine< GUM_SCALAR >::evidence_

protectedinherited

Holds observed variables states.

Definition at line 117 of file inferenceEngine.h.

Referenced by addEvidence(), eraseAllEvidence(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::initialize_(), insertEvidence(), insertEvidence(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::msgL_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::msgP_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::optFusion_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::refreshLMsPIs_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::saveInference(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::updateMarginals_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::updateMarginals_(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateThread_().

expectationMax_

template<typename GUM_SCALAR>

expe gum::credal::InferenceEngine< GUM_SCALAR >::expectationMax_

protectedinherited

Upper expectations, if some variables modalities were inserted.

Definition at line 104 of file inferenceEngine.h.

Referenced by dynamicExpectations_(), expectationMax(), expectationMax(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::expFusion_(), initExpectations_(), and updateExpectations_().

expectationMin_

template<typename GUM_SCALAR>

expe gum::credal::InferenceEngine< GUM_SCALAR >::expectationMin_

protectedinherited

Lower expectations, if some variables modalities were inserted.

Definition at line 101 of file inferenceEngine.h.

Referenced by dynamicExpectations_(), expectationMin(), expectationMin(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::expFusion_(), initExpectations_(), and updateExpectations_().

generators_

template<typename GUM_SCALAR, class BNInferenceEngine>

std::vector< std::mt19937 > gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::generators_

protected

the generators used for computing random values

Definition at line 141 of file multipleInferenceEngine.h.

Referenced by initThreadsData_().

history_

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

protectedinherited

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

Definition at line 396 of file approximationScheme.h.

l_clusters_

template<typename GUM_SCALAR, class BNInferenceEngine>

_clusters_ gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_clusters_

protected

Threads clusters.

Definition at line 127 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_mcThreadDataCopy_(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_verticesSampling_(), eraseAllEvidence(), and initThreadsData_().

l_evidence_

template<typename GUM_SCALAR, class BNInferenceEngine>

_margis_ gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_evidence_

protected

Threads evidence.

Definition at line 125 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence().

l_expectationMax_

template<typename GUM_SCALAR, class BNInferenceEngine>

_expes_ gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_expectationMax_

protected

Threads upper expectations, one per thread.

Definition at line 119 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence(), expFusion_(), initThreadsData_(), and updateThread_().

l_expectationMin_

template<typename GUM_SCALAR, class BNInferenceEngine>

_expes_ gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_expectationMin_

protected

Threads lower expectations, one per thread.

Definition at line 117 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence(), expFusion_(), initThreadsData_(), and updateThread_().

l_inferenceEngine_

template<typename GUM_SCALAR, class BNInferenceEngine>

std::vector< BNInferenceEngine* > gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_inferenceEngine_

protected

Threads BNInferenceEngine.

Definition at line 136 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence().

l_marginalMax_

template<typename GUM_SCALAR, class BNInferenceEngine>

_margis_ gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_marginalMax_

protected

Threads upper marginals, one per thread.

Definition at line 115 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence(), initThreadsData_(), optFusion_(), updateMarginals_(), and updateOldMarginals_().

l_marginalMin_

template<typename GUM_SCALAR, class BNInferenceEngine>

_margis_ gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_marginalMin_

protected

Threads lower marginals, one per thread.

Definition at line 113 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence(), initThreadsData_(), optFusion_(), updateMarginals_(), updateOldMarginals_(), updateThread_(), and verticesFusion_().

l_marginalSets_

template<typename GUM_SCALAR, class BNInferenceEngine>

_credalSets_ gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_marginalSets_

protected

Threads vertices.

Definition at line 123 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence(), initThreadsData_(), and verticesFusion_().

l_modal_

template<typename GUM_SCALAR, class BNInferenceEngine>

_modals_ gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_modal_

protected

Threads modalities.

Definition at line 121 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence(), expFusion_(), initThreadsData_(), and updateThread_().

l_optimalNet_

template<typename GUM_SCALAR, class BNInferenceEngine>

std::vector< VarMod2BNsMap< GUM_SCALAR >* > gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::l_optimalNet_

protected

Threads optimal IBayesNet.

Definition at line 138 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence(), initThreadsData_(), optFusion_(), and updateThread_().

last_epsilon_

double gum::ApproximationScheme::last_epsilon_

protectedinherited

Last epsilon value.

Definition at line 381 of file approximationScheme.h.

marginalMax_

template<typename GUM_SCALAR>

margi gum::credal::InferenceEngine< GUM_SCALAR >::marginalMax_

protectedinherited

Upper marginals.

Definition at line 94 of file inferenceEngine.h.

Referenced by computeEpsilon_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::computeEpsilon_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::computeExpectations_(), initMarginals_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::initThreadsData_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::optFusion_(), toString(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::updateMarginals_(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateMarginals_().

marginalMin_

template<typename GUM_SCALAR>

margi gum::credal::InferenceEngine< GUM_SCALAR >::marginalMin_

protectedinherited

Lower marginals.

Definition at line 92 of file inferenceEngine.h.

Referenced by computeEpsilon_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::computeEpsilon_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::computeExpectations_(), initMarginals_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::initThreadsData_(), marginalMin(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::optFusion_(), toString(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::updateMarginals_(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateMarginals_().

marginalSets_

template<typename GUM_SCALAR>

credalSet gum::credal::InferenceEngine< GUM_SCALAR >::marginalSets_

protectedinherited

Credal sets vertices, if enabled.

Definition at line 97 of file inferenceEngine.h.

Referenced by gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::expFusion_(), initMarginalSets_(), saveVertices(), updateCredalSets_(), and vertices().

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().

modal_

template<typename GUM_SCALAR>

dynExpe gum::credal::InferenceEngine< GUM_SCALAR >::modal_

protectedinherited

Variables modalities used to compute expectations.

Definition at line 114 of file inferenceEngine.h.

Referenced by gum::credal::CNLoopyPropagation< GUM_SCALAR >::computeExpectations_(), dynamicExpectations_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::expFusion_(), initExpectations_(), insertModals(), insertModalsFile(), and updateExpectations_().

oldMarginalMax_

template<typename GUM_SCALAR>

margi gum::credal::InferenceEngine< GUM_SCALAR >::oldMarginalMax_

protectedinherited

Old upper marginals used to compute epsilon.

Definition at line 89 of file inferenceEngine.h.

Referenced by computeEpsilon_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::computeEpsilon_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::initialize_(), initMarginals_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::initThreadsData_(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateOldMarginals_().

oldMarginalMin_

template<typename GUM_SCALAR>

margi gum::credal::InferenceEngine< GUM_SCALAR >::oldMarginalMin_

protectedinherited

Old lower marginals used to compute epsilon.

Definition at line 87 of file inferenceEngine.h.

Referenced by computeEpsilon_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::computeEpsilon_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::initialize_(), initMarginals_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::initThreadsData_(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateOldMarginals_().

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().

query_

template<typename GUM_SCALAR>

query gum::credal::InferenceEngine< GUM_SCALAR >::query_

protectedinherited

Holds the query nodes states.

Definition at line 119 of file inferenceEngine.h.

Referenced by eraseAllEvidence(), insertQuery(), and toString().

repetitiveInd_

template<typename GUM_SCALAR>

bool gum::credal::InferenceEngine< GUM_SCALAR >::repetitiveInd_

protectedinherited

True if using repetitive independence ( dynamic network only ), False otherwise.

False by default.

Definition at line 143 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_verticesSampling_(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference(), repetitiveInd(), and setRepetitiveInd().

storeBNOpt_

template<typename GUM_SCALAR>

bool gum::credal::InferenceEngine< GUM_SCALAR >::storeBNOpt_

protectedinherited

Iterations limit stopping rule used by some algorithms such as CNMonteCarloSampling.

The algorithms stops if no changes occured within 1000 iterations by default. int iterStop_;

True is optimal bayes net are stored, for each variable and each modality, False otherwise. Not all algorithms offers this option. False by default.

Definition at line 153 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_mcThreadDataCopy_(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_verticesSampling_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference(), storeBNOpt(), storeBNOpt(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::updateMarginals_(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateThread_().

storeVertices_

template<typename GUM_SCALAR>

bool gum::credal::InferenceEngine< GUM_SCALAR >::storeVertices_

protectedinherited

True if credal sets vertices are stored, False otherwise.

False by default.

Definition at line 139 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_mcThreadDataCopy_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::expFusion_(), initMarginalSets_(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference(), storeVertices(), storeVertices(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateThread_(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::verticesFusion_().

t0_

template<typename GUM_SCALAR>

cluster gum::credal::InferenceEngine< GUM_SCALAR >::t0_

protectedinherited

Clusters of nodes used with dynamic networks.

Any node key in t0_ is present at \( t=0 \) and any node belonging to the node set of this key share the same CPT than the key. Used for sampling with repetitive independence.

Definition at line 127 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_mcThreadDataCopy_(), getT0Cluster(), and repetitiveInit_().

t1_

template<typename GUM_SCALAR>

cluster gum::credal::InferenceEngine< GUM_SCALAR >::t1_

protectedinherited

Clusters of nodes used with dynamic networks.

Any node key in t1_ is present at \( t=1 \) and any node belonging to the node set of this key share the same CPT than the key. Used for sampling with repetitive independence.

Definition at line 134 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_mcThreadDataCopy_(), getT1Cluster(), and repetitiveInit_().

threadMinimalNbOps_

template<typename GUM_SCALAR>

Size gum::credal::InferenceEngine< GUM_SCALAR >::threadMinimalNbOps_ {Size(20)}

protectedinherited

Definition at line 180 of file inferenceEngine.h.

{Size(20)};

Referenced by gum::credal::CNLoopyPropagation< GUM_SCALAR >::enum_combi_(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::enum_combi_().

threadRanges_

template<typename GUM_SCALAR>

std::vector< std::pair< NodeId, Idx > > gum::credal::InferenceEngine< GUM_SCALAR >::threadRanges_

protectedinherited

the ranges of elements of marginalMin_ and marginalMax_ processed by each thread

these ranges are stored into a vector of pairs (NodeId, Idx). For thread number i, the pair at index i is the beginning of the range that the thread will have to process: this is the part of the marginal distribution vector of node NodeId starting at index Idx. The pair at index i+1 is the end of this range (not included).

Warning

the size of threadRanges_ is the number of threads + 1.

Definition at line 170 of file inferenceEngine.h.

Referenced by computeEpsilon_(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::computeEpsilon_(), displatchMarginalsToThreads_(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::updateMarginals_().

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_().

timeSteps_

template<typename GUM_SCALAR>

int gum::credal::InferenceEngine< GUM_SCALAR >::timeSteps_

protectedinherited

The number of time steps of this network (only usefull for dynamic networks).

Deprecated

Definition at line 177 of file inferenceEngine.h.

Referenced by repetitiveInit_().

verbosity_

bool gum::ApproximationScheme::verbosity_

protectedinherited

If true, verbosity is enabled.

Definition at line 429 of file approximationScheme.h.

Referenced by ApproximationScheme(), and verbosity().

workingSet_

template<typename GUM_SCALAR, class BNInferenceEngine>

std::vector< _bnet_* > gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::workingSet_

protected

Threads IBayesNet.

Definition at line 131 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence(), expFusion_(), initThreadsData_(), optFusion_(), updateOldMarginals_(), updateThread_(), and verticesFusion_().

workingSetE_

template<typename GUM_SCALAR, class BNInferenceEngine>

std::vector< List< const Tensor< GUM_SCALAR >* >* > gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::workingSetE_

protected

Threads evidence.

Definition at line 133 of file multipleInferenceEngine.h.

Referenced by eraseAllEvidence(), and initThreadsData_().

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

• agrum/CN/inference/multipleInferenceEngine.h
• agrum/CN/inference/multipleInferenceEngine_tpl.h