<agrum/CN/CNMonteCarloSampling.h> More...

#include <CNMonteCarloSampling.h>

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

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

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

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

Public Member Functions
virtual void	insertEvidenceFile (const std::string &path)
	unsigned int notOptDelete; More...

Constructors / Destructors
	CNMonteCarloSampling (const CredalNet< GUM_SCALAR > &credalNet)
	Constructor. More...

virtual	~CNMonteCarloSampling ()
	Destructor. More...

Public algorithm methods
void	makeInference ()
	Starts the inference. More...

Post-inference methods
virtual void	eraseAllEvidence ()
	Erase all inference related data to perform another one. More...

Getters and setters
VarMod2BNsMap< GUM_SCALAR > *	getVarMod2BNsMap ()
	Get optimum IBayesNet. More...

const CredalNet< GUM_SCALAR > &	credalNet ()
	Get this creadal network. More...

const NodeProperty< std::vector< NodeId > > &	getT0Cluster () const
	Get the _t0 cluster. More...

const NodeProperty< std::vector< NodeId > > &	getT1Cluster () const
	Get the _t1 cluster. More...

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. More...

void	storeBNOpt (const bool value)

bool	storeBNOpt () const

bool	repetitiveInd () const
	Get the current independence status. More...

Pre-inference initialization methods
void	insertModalsFile (const std::string &path)
	Insert variables modalities from file to compute expectations. More...

void	insertModals (const std::map< std::string, std::vector< GUM_SCALAR > > &modals)
	Insert variables modalities from map to compute expectations. More...

void	insertEvidence (const std::map< std::string, std::vector< GUM_SCALAR > > &eviMap)
	Insert evidence from map. More...

void	insertEvidence (const NodeProperty< std::vector< GUM_SCALAR > > &evidence)
	Insert evidence from Property. More...

void	insertQueryFile (const std::string &path)
	Insert query variables states from file. More...

void	insertQuery (const NodeProperty< std::vector< bool > > &query)
	Insert query variables and states from Property. More...

Post-inference methods
const std::vector< GUM_SCALAR > &	marginalMin (const NodeId id) const
	Get the lower marginals of a given node id. More...

const std::vector< GUM_SCALAR > &	marginalMin (const std::string &varName) const
	Get the lower marginals of a given variable name. More...

const std::vector< GUM_SCALAR > &	marginalMax (const NodeId id) const
	Get the upper marginals of a given node id. More...

const std::vector< GUM_SCALAR > &	marginalMax (const std::string &varName) const
	Get the upper marginals of a given variable name. More...

const GUM_SCALAR &	expectationMin (const NodeId id) const
	Get the lower expectation of a given node id. More...

const GUM_SCALAR &	expectationMin (const std::string &varName) const
	Get the lower expectation of a given variable name. More...

const GUM_SCALAR &	expectationMax (const NodeId id) const
	Get the upper expectation of a given node id. More...

const GUM_SCALAR &	expectationMax (const std::string &varName) const
	Get the upper expectation of a given variable name. More...

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. More...

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. More...

const std::vector< std::vector< GUM_SCALAR > > &	vertices (const NodeId id) const
	Get the vertice of a given node id. More...

void	saveMarginals (const std::string &path) const
	Saves marginals to file. More...

void	saveExpectations (const std::string &path) const
	Saves expectations to file. More...

void	saveVertices (const std::string &path) const
	Saves vertices to file. More...

void	dynamicExpectations ()
	Compute dynamic expectations. More...

std::string	toString () const
	Print all nodes marginals to standart output. More...

const std::string	getApproximationSchemeMsg ()
	Get approximation scheme state. More...

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Protected Attributes
bool	_repetitiveInd

__margis	_l_marginalMin
	Threads lower marginals, one per thread. More...

__margis	_l_marginalMax
	Threads upper marginals, one per thread. More...

__expes	_l_expectationMin
	Threads lower expectations, one per thread. More...

__expes	_l_expectationMax
	Threads upper expectations, one per thread. More...

__modals	_l_modal
	Threads modalities. More...

__credalSets	_l_marginalSets
	Threads vertices. More...

__margis	_l_evidence
	Threads evidence. More...

__clusters	_l_clusters
	Threads clusters. More...

std::vector< __bnet *>	_workingSet
	Threads IBayesNet. More...

std::vector< List< const Potential< GUM_SCALAR > > >	_workingSetE
	Threads evidence. More...

std::vector< BNInferenceEngine *>	_l_inferenceEngine
	Threads BNInferenceEngine. More...

std::vector< VarMod2BNsMap< GUM_SCALAR > *>	_l_optimalNet
	Threads optimal IBayesNet. More...

const CredalNet< GUM_SCALAR > *	_credalNet
	A pointer to the Credal Net used. More...

margi	_oldMarginalMin
	Old lower marginals used to compute epsilon. More...

margi	_oldMarginalMax
	Old upper marginals used to compute epsilon. More...

margi	_marginalMin
	Lower marginals. More...

margi	_marginalMax
	Upper marginals. More...

credalSet	_marginalSets
	Credal sets vertices, if enabled. More...

expe	_expectationMin
	Lower expectations, if some variables modalities were inserted. More...

expe	_expectationMax
	Upper expectations, if some variables modalities were inserted. More...

dynExpe	_dynamicExpMin
	Lower dynamic expectations. More...

dynExpe	_dynamicExpMax
	Upper dynamic expectations. More...

dynExpe	_modal
	Variables modalities used to compute expectations. More...

margi	_evidence
	Holds observed variables states. More...

query	_query
	Holds the query nodes states. More...

cluster	_t0
	Clusters of nodes used with dynamic networks. More...

cluster	_t1
	Clusters of nodes used with dynamic networks. More...

bool	_storeVertices
	`True` if credal sets vertices are stored, `False` otherwise. More...

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

VarMod2BNsMap< GUM_SCALAR >	_dbnOpt
	Object used to efficiently store optimal bayes net during inference, for some algorithms. More...

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

double	_current_epsilon
	Current epsilon. More...

double	_last_epsilon
	Last epsilon value. More...

double	_current_rate
	Current rate. More...

Size	_current_step
	The current step. More...

Timer	_timer
	The timer. More...

ApproximationSchemeSTATE	_current_state
	The current state. More...

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

double	_eps
	Threshold for convergence. More...

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

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

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

double	_max_time
	The timeout. More...

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

Size	_max_iter
	The maximum iterations. More...

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

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

Size	_period_size
	Checking criteria frequency. More...

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

Protected Member Functions
Protected initialization methods
Fusion of threads optimal IBayesNet.
void	_initThreadsData (const Size &num_threads, const bool __storeVertices, const bool __storeBNOpt)
	Initialize threads data. More...

Protected algorithms methods
bool	_updateThread (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. More...

void	_updateMarginals ()
	Fusion of threads marginals. More...

const GUM_SCALAR	_computeEpsilon ()
	Compute epsilon and update old marginals. More...

void	_updateOldMarginals ()
	Update old marginals (from current marginals). More...

Proptected post-inference methods
void	_optFusion ()
	Fusion of threads optimal IBayesNet. More...

void	_expFusion ()
	Fusion of threads expectations. More...

void	_verticesFusion ()

Protected initialization methods
void	_repetitiveInit ()
	Initialize _t0 and _t1 clusters. More...

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. More...

void	_initMarginals ()
	Initialize lower and upper old marginals and marginals before inference, with the lower marginal being 1 and the upper 0. More...

void	_initMarginalSets ()
	Initialize credal set vertices with empty sets. More...

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. More...

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. More...

Proptected post-inference methods
void	_dynamicExpectations ()
	Rearrange lower and upper expectations to suit dynamic networks. More...

Detailed Description

template<typename GUM_SCALAR, class BNInferenceEngine = LazyPropagation< GUM_SCALAR >>
class gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >

Inference by basic sampling algorithm (pure random) of bnet in credal networks.

Template Parameters

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

Author: Matthieu HOURBRACQ and Pierre-Henri WUILLEMIN

Warning: p(e) must be available ( by a call to my_BNInferenceEngine.evidenceMarginal() ) !! the vertices are correct if p(e) > 0 for a sample the test is made once

Definition at line 62 of file CNMonteCarloSampling.h.

Member Typedef Documentation

◆ __infEs

template<typename GUM_SCALAR , class BNInferenceEngine = LazyPropagation< GUM_SCALAR >>

using gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__infEs = MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >

private

To easily acces MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine

methods.

Definition at line 68 of file CNMonteCarloSampling.h.

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

enum gum::IApproximationSchemeConfiguration::ApproximationSchemeSTATE : char

stronginherited

The different state of an approximation scheme.

Enumerator
Undefined
Continue
Epsilon
Rate
Limit
TimeLimit
Stopped

Definition at line 65 of file IApproximationSchemeConfiguration.h.

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

Constructor & Destructor Documentation

◆ CNMonteCarloSampling()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

explicit

Constructor.

Parameters

credalNet The CredalNet to be used by the algorithm.

Definition at line 30 of file CNMonteCarloSampling_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_repetitiveInd, gum::credal::InferenceEngine< GUM_SCALAR >::_storeBNOpt, gum::credal::InferenceEngine< GUM_SCALAR >::_storeVertices, gum::ApproximationScheme::enableMaxTime(), gum::ApproximationScheme::setMaxTime(), and gum::ApproximationScheme::setPeriodSize().

                                                  :
         MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::
            MultipleInferenceEngine(credalNet) {
       __infEs::_repetitiveInd = false;
       //__infEs::_iterStop = 1000;
       __infEs::_storeVertices = false;
       __infEs::_storeBNOpt = false;
 
       this->setMaxTime(60);
       this->enableMaxTime();
 
       this->setPeriodSize(1000);
 
       GUM_CONSTRUCTOR(CNMonteCarloSampling);
     }

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◆ ~CNMonteCarloSampling()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

virtual

Destructor.

Definition at line 50 of file CNMonteCarloSampling_tpl.h.

                                                                      {
       GUM_DESTRUCTOR(CNMonteCarloSampling);
     }

Member Function Documentation

◆ __binaryRep()

template<typename GUM_SCALAR , class BNInferenceEngine >

void gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__binaryRep	(	std::vector< bool > &	toFill,
		const Idx	value
	)		const

inlineprivate

Get the binary representation of a given value.

Parameters

toFill	A reference to the bits to fill. Size must be correct before passing argument (i.e. big enough to represent `value`)
value	The constant integer we want to binarize.

Definition at line 285 of file CNMonteCarloSampling_tpl.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__verticesSampling().

                                                          {
       Idx  n = value;
       auto tfsize = toFill.size();
 
       // get bits of choosen_vertex
       for (decltype(tfsize) i = 0; i < tfsize; i++) {
         toFill[i] = n & 1;
         n /= 2;
       }
     }

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◆ __insertEvidence()

template<typename GUM_SCALAR , class BNInferenceEngine >

void gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__insertEvidence ( )

inlineprivate

Insert CredalNet evidence into a thread BNInferenceEngine.

Definition at line 426 of file CNMonteCarloSampling_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_evidence, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_inferenceEngine, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_workingSet, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_workingSetE, gum::Potential< GUM_SCALAR >::fillWith(), gum::getThreadNumber(), GUM_SHOWERROR, gum::List< Val, Alloc >::insert(), gum::List< Val, Alloc >::size(), gum::HashTable< Key, Val, Alloc >::size(), and gum::IBayesNet< GUM_SCALAR >::variable().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__threadInference().

                                                                                {
       if (this->_evidence.size() == 0) { return; }
 
       int this_thread = getThreadNumber();
 
       BNInferenceEngine* inference_engine = this->_l_inferenceEngine[this_thread];
 
       IBayesNet< GUM_SCALAR >* working_bn = this->_workingSet[this_thread];
 
       List< const Potential< GUM_SCALAR >* >* evi_list =
          this->_workingSetE[this_thread];
 
       if (evi_list->size() > 0) {
         for (const auto pot : *evi_list)
           inference_engine->addEvidence(*pot);
         return;
       }
 
       for (const auto& elt : this->_evidence) {
         Potential< GUM_SCALAR >* p = new Potential< GUM_SCALAR >;
         (*p) << working_bn->variable(elt.first);
 
         try {
           p->fillWith(elt.second);
         } catch (Exception& err) {
           GUM_SHOWERROR(err);
           throw(err);
         }
 
         evi_list->insert(p);
       }
 
       if (evi_list->size() > 0) {
         for (const auto pot : *evi_list)
           inference_engine->addEvidence(*pot);
       }
     }

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◆ __mcInitApproximationScheme()

template<typename GUM_SCALAR , class BNInferenceEngine >

void gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme ( )

private

Initialize approximation Scheme.

Definition at line 196 of file CNMonteCarloSampling_tpl.h.

References gum::ApproximationScheme::disableMaxIter(), gum::ApproximationScheme::disableMinEpsilonRate(), gum::ApproximationScheme::enableEpsilon(), gum::ApproximationScheme::initApproximationScheme(), and gum::ApproximationScheme::setEpsilon().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                                 {
       // this->setEpsilon ( std::numeric_limits< GUM_SCALAR >::min() );
       this->setEpsilon(0.);
       this->enableEpsilon();   // to be sure
 
       this->disableMinEpsilonRate();
       this->disableMaxIter();
 
       this->initApproximationScheme();
     }

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◆ __mcThreadDataCopy()

template<typename GUM_SCALAR , class BNInferenceEngine >

void gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy ( )

private

Initialize threads data.

Definition at line 212 of file CNMonteCarloSampling_tpl.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                        {
       int num_threads;
 #pragma omp parallel
       {
         int this_thread = getThreadNumber();
 
 // implicit wait clause (don't put nowait)
 #pragma omp single
         {
           // should we ask for max threads instead ( no differences here in
           // practice
           // )
           num_threads = getNumberOfRunningThreads();
 
           this->_initThreadsData(
              num_threads, __infEs::_storeVertices, __infEs::_storeBNOpt);
           this->_l_inferenceEngine.resize(num_threads, nullptr);
 
           // if ( __infEs::_storeBNOpt )
           // this->_l_sampledNet.resize ( num_threads );
         }   // end of : single region
 
         // we could put those below in a function in InferenceEngine, but let's
         // keep
         // this parallel region instead of breaking it and making another one to
         // do
         // the same stuff in 2 places since :
         // !!! BNInferenceEngine still needs to be initialized here anyway !!!
 
         BayesNet< GUM_SCALAR >* thread_bn = new BayesNet< GUM_SCALAR >();
 #pragma omp critical(Init)
         {
           // IBayesNet< GUM_SCALAR > * thread_bn = new IBayesNet< GUM_SCALAR
           // >();//(this->_credalNet->current_bn());
           *thread_bn = this->_credalNet->current_bn();
         }
         this->_workingSet[this_thread] = thread_bn;
 
         this->_l_marginalMin[this_thread] = this->_marginalMin;
         this->_l_marginalMax[this_thread] = this->_marginalMax;
         this->_l_expectationMin[this_thread] = this->_expectationMin;
         this->_l_expectationMax[this_thread] = this->_expectationMax;
         this->_l_modal[this_thread] = this->_modal;
 
         __infEs::_l_clusters[this_thread].resize(2);
         __infEs::_l_clusters[this_thread][0] = __infEs::_t0;
         __infEs::_l_clusters[this_thread][1] = __infEs::_t1;
 
         if (__infEs::_storeVertices) {
           this->_l_marginalSets[this_thread] = this->_marginalSets;
         }
 
         List< const Potential< GUM_SCALAR >* >* evi_list =
            new List< const Potential< GUM_SCALAR >* >();
         this->_workingSetE[this_thread] = evi_list;
 
         // #TODO: the next instruction works only for lazy propagation.
         //        => find a way to remove the second argument
         BNInferenceEngine* inference_engine =
            new BNInferenceEngine((this->_workingSet[this_thread]),
                                  RelevantPotentialsFinderType::FIND_ALL);
 
         this->_l_inferenceEngine[this_thread] = inference_engine;
 
         if (__infEs::_storeBNOpt) {
           VarMod2BNsMap< GUM_SCALAR >* threadOpt =
              new VarMod2BNsMap< GUM_SCALAR >(*this->_credalNet);
           this->_l_optimalNet[this_thread] = threadOpt;
         }
       }
     }

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◆ __threadInference()

template<typename GUM_SCALAR , class BNInferenceEngine >

void gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__threadInference ( )

inlineprivate

Thread performs an inference using BNInferenceEngine.

Calls __verticesSampling and __insertEvidence.

Definition at line 185 of file CNMonteCarloSampling_tpl.h.

References gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__insertEvidence(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__verticesSampling(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_inferenceEngine, and gum::getThreadNumber().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                                 {
       int tId = getThreadNumber();
       __verticesSampling();
 
       this->_l_inferenceEngine[tId]->eraseAllEvidence();
       __insertEvidence();
       this->_l_inferenceEngine[tId]->makeInference();
     }

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◆ __threadUpdate()

template<typename GUM_SCALAR , class BNInferenceEngine >

void gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__threadUpdate ( )

inlineprivate

Update thread data after a IBayesNet inference.

Definition at line 156 of file CNMonteCarloSampling_tpl.h.

References gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_inferenceEngine, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThread(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_workingSet, gum::Instantiation::end(), gum::getThreadNumber(), gum::NodeGraphPart::nodes(), and gum::Instantiation::setFirst().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                              {
       int tId = getThreadNumber();
       // bool keepSample = false;
 
       if (this->_l_inferenceEngine[tId]->evidenceProbability() > 0) {
         const DAG& tDag = this->_workingSet[tId]->dag();
 
         for (auto node : tDag.nodes()) {
           const Potential< GUM_SCALAR >& potential(
              this->_l_inferenceEngine[tId]->posterior(node));
           Instantiation             ins(potential);
           std::vector< GUM_SCALAR > vertex;
 
           for (ins.setFirst(); !ins.end(); ++ins) {
             vertex.push_back(potential[ins]);
           }
 
           // true for redundancy elimination of node it credal set
           // but since global marginals are only updated at the end of each
           // period of
           // approximationScheme, it is "useless" ( and expensive ) to check now
           this->_updateThread(node, vertex, false);
 
         }   // end of : for all nodes
       }     // end of : if ( p(e) > 0 )
     }

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◆ __verticesSampling()

template<typename GUM_SCALAR , class BNInferenceEngine >

void gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__verticesSampling ( )

inlineprivate

Thread samples a IBayesNet from the CredalNet.

Definition at line 299 of file CNMonteCarloSampling_tpl.h.

References gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__binaryRep(), gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_clusters, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_optimalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_repetitiveInd, gum::credal::InferenceEngine< GUM_SCALAR >::_storeBNOpt, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_workingSet, gum::IBayesNet< GUM_SCALAR >::cpt(), gum::MultiDimDecorator< GUM_SCALAR >::domainSize(), gum::Potential< GUM_SCALAR >::fillWith(), gum::getThreadNumber(), gum::DAGmodel::nodes(), and gum::IBayesNet< GUM_SCALAR >::variable().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__threadInference().

                                                                               {
       int                      this_thread = getThreadNumber();
       IBayesNet< GUM_SCALAR >* working_bn = this->_workingSet[this_thread];
 
       const auto cpt = &this->_credalNet->credalNet_currentCpt();
 
       using dBN = std::vector< std::vector< std::vector< bool > > >;
 
       dBN sample;
 
       if (__infEs::_storeBNOpt) {
         sample = dBN(this->_l_optimalNet[this_thread]->getSampleDef());
       }
 
       if (__infEs::_repetitiveInd) {
         const auto& t0 = __infEs::_l_clusters[this_thread][0];
         const auto& t1 = __infEs::_l_clusters[this_thread][1];
 
         for (const auto& elt : t0) {
           auto dSize = working_bn->variable(elt.first).domainSize();
           Potential< GUM_SCALAR >* potential(
              const_cast< Potential< GUM_SCALAR >* >(&working_bn->cpt(elt.first)));
           std::vector< GUM_SCALAR > var_cpt(potential->domainSize());
 
           Size pconfs = Size((*cpt)[elt.first].size());
 
           for (Size pconf = 0; pconf < pconfs; pconf++) {
             Size choosen_vertex = rand() % (*cpt)[elt.first][pconf].size();
 
             if (__infEs::_storeBNOpt) {
               __binaryRep(sample[elt.first][pconf], choosen_vertex);
             }
 
             for (Size mod = 0; mod < dSize; mod++) {
               var_cpt[pconf * dSize + mod] =
                  (*cpt)[elt.first][pconf][choosen_vertex][mod];
             }
           }   // end of : pconf
 
           potential->fillWith(var_cpt);
 
           Size t0esize = Size(elt.second.size());
 
           for (Size pos = 0; pos < t0esize; pos++) {
             if (__infEs::_storeBNOpt) {
               sample[elt.second[pos]] = sample[elt.first];
             }
 
             Potential< GUM_SCALAR >* potential2(
                const_cast< Potential< GUM_SCALAR >* >(
                   &working_bn->cpt(elt.second[pos])));
             potential2->fillWith(var_cpt);
           }
         }
 
         for (const auto& elt : t1) {
           auto dSize = working_bn->variable(elt.first).domainSize();
           Potential< GUM_SCALAR >* potential(
              const_cast< Potential< GUM_SCALAR >* >(&working_bn->cpt(elt.first)));
           std::vector< GUM_SCALAR > var_cpt(potential->domainSize());
 
           for (Size pconf = 0; pconf < (*cpt)[elt.first].size(); pconf++) {
             Idx choosen_vertex = Idx(rand() % (*cpt)[elt.first][pconf].size());
 
             if (__infEs::_storeBNOpt) {
               __binaryRep(sample[elt.first][pconf], choosen_vertex);
             }
 
             for (decltype(dSize) mod = 0; mod < dSize; mod++) {
               var_cpt[pconf * dSize + mod] =
                  (*cpt)[elt.first][pconf][choosen_vertex][mod];
             }
           }   // end of : pconf
 
           potential->fillWith(var_cpt);
 
           auto t1esize = elt.second.size();
 
           for (decltype(t1esize) pos = 0; pos < t1esize; pos++) {
             if (__infEs::_storeBNOpt) {
               sample[elt.second[pos]] = sample[elt.first];
             }
 
             Potential< GUM_SCALAR >* potential2(
                const_cast< Potential< GUM_SCALAR >* >(
                   &working_bn->cpt(elt.second[pos])));
             potential2->fillWith(var_cpt);
           }
         }
 
         if (__infEs::_storeBNOpt) {
           this->_l_optimalNet[this_thread]->setCurrentSample(sample);
         }
       } else {
         for (auto node : working_bn->nodes()) {
           auto                     dSize = working_bn->variable(node).domainSize();
           Potential< GUM_SCALAR >* potential(
              const_cast< Potential< GUM_SCALAR >* >(&working_bn->cpt(node)));
           std::vector< GUM_SCALAR > var_cpt(potential->domainSize());
 
           auto pConfs = (*cpt)[node].size();
 
           for (decltype(pConfs) pconf = 0; pconf < pConfs; pconf++) {
             Size nVertices = Size((*cpt)[node][pconf].size());
             Idx  choosen_vertex = Idx(rand() % nVertices);
 
             if (__infEs::_storeBNOpt) {
               __binaryRep(sample[node][pconf], choosen_vertex);
             }
 
             for (decltype(dSize) mod = 0; mod < dSize; mod++) {
               var_cpt[pconf * dSize + mod] =
                  (*cpt)[node][pconf][choosen_vertex][mod];
             }
           }   // end of : pconf
 
           potential->fillWith(var_cpt);
         }
 
         if (__infEs::_storeBNOpt) {
           this->_l_optimalNet[this_thread]->setCurrentSample(sample);
         }
       }
     }

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◆ _computeEpsilon()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

inlineprotectedinherited

Compute epsilon and update old marginals.

Returns: Epsilon.

Definition at line 304 of file multipleInferenceEngine_tpl.h.

References gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalMin, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMax, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMin, gum::credal::InferenceEngine< GUM_SCALAR >::_oldMarginalMax, gum::credal::InferenceEngine< GUM_SCALAR >::_oldMarginalMin, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_workingSet, and gum::getThreadNumber().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                      {
       GUM_SCALAR eps = 0;
 #pragma omp parallel
       {
         GUM_SCALAR tEps = 0;
         GUM_SCALAR delta;
 
         int  tId = getThreadNumber();
         long nsize = long(_workingSet[tId]->size());
 
 #pragma omp for
 
         for (long i = 0; i < nsize; i++) {
           Size dSize = Size(_l_marginalMin[tId][i].size());
 
           for (Size j = 0; j < dSize; j++) {
             // on min
             delta = this->_marginalMin[i][j] - this->_oldMarginalMin[i][j];
             delta = (delta < 0) ? (-delta) : delta;
             tEps = (tEps < delta) ? delta : tEps;
 
             // on max
             delta = this->_marginalMax[i][j] - this->_oldMarginalMax[i][j];
             delta = (delta < 0) ? (-delta) : delta;
             tEps = (tEps < delta) ? delta : tEps;
 
             this->_oldMarginalMin[i][j] = this->_marginalMin[i][j];
             this->_oldMarginalMax[i][j] = this->_marginalMax[i][j];
           }
         }   // end of : all variables
 
 #pragma omp critical(epsilon_max)
         {
 #pragma omp flush(eps)
           eps = (eps < tEps) ? tEps : eps;
         }
 
       }   // end of : parallel region
       return eps;
     }

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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 721 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpMax, gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpMin, gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMax, gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMin, gum::credal::InferenceEngine< GUM_SCALAR >::_modal, and gum::HashTable< Key, Val, Alloc >::empty().

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::dynamicExpectations(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                              {
       // 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;
 
       // typedef typename std::map< int, GUM_SCALAR > innerMap;
       using innerMap = typename gum::HashTable< int, GUM_SCALAR >;
 
       // typedef typename std::map< std::string, innerMap > outerMap;
       using outerMap = typename gum::HashTable< std::string, innerMap >;
 
       // typedef typename std::map< std::string, std::vector< GUM_SCALAR > >
       // mod;
 
       // si non dynamique, sauver directement _expectationMin et Max (revient au
       // meme
       // mais plus rapide)
       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);
       }
     }

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◆ _expFusion()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

protectedinherited

Fusion of threads expectations.

Definition at line 410 of file multipleInferenceEngine_tpl.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                               {
       // don't create threads if there are no modalities to compute expectations
       if (this->_modal.empty()) return;
 
       // we can compute expectations from vertices of the final credal set
       if (__infE::_storeVertices) {
 #pragma omp parallel
         {
           int threadId = getThreadNumber();
 
           if (!this->_l_modal[threadId].empty()) {
             Size nsize = Size(_workingSet[threadId]->size());
 
 #pragma omp for
 
             for (long i = 0; i < long(nsize);
                  i++) {   // i needs to be signed (due to omp with visual c++
                           // 15)
               std::string var_name = _workingSet[threadId]->variable(i).name();
               auto        delim = var_name.find_first_of("_");
               var_name = var_name.substr(0, delim);
 
               if (!_l_modal[threadId].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[threadId][var_name][mod];
 
                 if (exp > __infE::_expectationMax[i])
                   __infE::_expectationMax[i] = exp;
 
                 if (exp < __infE::_expectationMin[i])
                   __infE::_expectationMin[i] = exp;
               }
             }   // end of : each variable parallel for
           }     // end of : if this thread has modals
         }       // end of parallel region
         return;
       }
 
 #pragma omp parallel
       {
         int threadId = getThreadNumber();
 
         if (!this->_l_modal[threadId].empty()) {
           Size nsize = Size(_workingSet[threadId]->size());
 #pragma omp for
           for (long i = 0; i < long(nsize);
                i++) {   // long instead of Idx due to omp for visual C++15
             std::string var_name = _workingSet[threadId]->variable(i).name();
             auto        delim = var_name.find_first_of("_");
             var_name = var_name.substr(0, delim);
 
             if (!_l_modal[threadId].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
         }       // end of : if modals not empty
       }         // end of : parallel region
     }

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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 695 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMax, gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMin, gum::credal::InferenceEngine< GUM_SCALAR >::_modal, gum::HashTable< Key, Val, Alloc >::clear(), and gum::HashTable< Key, Val, Alloc >::insert().

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::eraseAllEvidence(), gum::credal::InferenceEngine< GUM_SCALAR >::insertModals(), and gum::credal::InferenceEngine< GUM_SCALAR >::insertModalsFile().

                                                           {
       _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());
       }
     }

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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 663 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMax, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMin, gum::credal::InferenceEngine< GUM_SCALAR >::_oldMarginalMax, gum::credal::InferenceEngine< GUM_SCALAR >::_oldMarginalMin, gum::HashTable< Key, Val, Alloc >::clear(), and gum::HashTable< Key, Val, Alloc >::insert().

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::eraseAllEvidence(), and gum::credal::InferenceEngine< GUM_SCALAR >::InferenceEngine().

                                                        {
       _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));
       }
     }

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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 680 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalSets, gum::credal::InferenceEngine< GUM_SCALAR >::_storeVertices, gum::HashTable< Key, Val, Alloc >::clear(), and gum::HashTable< Key, Val, Alloc >::insert().

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::eraseAllEvidence(), and gum::credal::InferenceEngine< GUM_SCALAR >::storeVertices().

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

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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
	)

inlineprotectedinherited

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 44 of file multipleInferenceEngine_tpl.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy().

                                     {
       _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;
     }

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◆ _optFusion()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

protectedinherited

Fusion of threads optimal IBayesNet.

Definition at line 483 of file multipleInferenceEngine_tpl.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                               {
       typedef std::vector< bool > dBN;
 
       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
     }

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◆ _repetitiveInit()

template<typename GUM_SCALAR >

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

protectedinherited

Initialize _t0 and _t1 clusters.

Definition at line 784 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_t0, gum::credal::InferenceEngine< GUM_SCALAR >::_t1, gum::credal::InferenceEngine< GUM_SCALAR >::_timeSteps, gum::HashTable< Key, Val, Alloc >::clear(), GUM_ERROR, and gum::HashTable< Key, Val, Alloc >::insert().

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

                                                         {
       _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 Potential< GUM_SCALAR >* potential(
                  &_credalNet->current_bn().cpt(node));
               const Potential< GUM_SCALAR >* potential2(
                  &_credalNet->current_bn().cpt(elt.first));
 
               if (potential->domainSize() == potential2->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 Potential< GUM_SCALAR >* potential(
                  &_credalNet->current_bn().cpt(node));
               const Potential< GUM_SCALAR >* potential2(
                  &_credalNet->current_bn().cpt(elt.first));
 
               if (potential->domainSize() == potential2->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 Potential< GUM_SCALAR >* potential(
                    &_credalNet->current_bn().cpt(node));
                 const Potential< GUM_SCALAR >* potential2(
                    &_credalNet->current_bn().cpt(elt.first));
 
                 if (potential->domainSize() == potential2->domainSize()) {
                   _t1[elt.first].push_back(node);
                   break;
                 }
               }
             }
           }
         }
       }
     }

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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 (potential) vertex of the node credal set
elimRedund	remove redundant vertex (inside a facet)

Definition at line 928 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMax, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMin, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalSets, gum::HashTable< Key, Val, Alloc >::cbegin(), gum::HashTable< Key, Val, Alloc >::cend(), gum::credal::LRSWrapper< GUM_SCALAR >::elimRedundVrep(), gum::credal::LRSWrapper< GUM_SCALAR >::fillV(), gum::credal::LRSWrapper< GUM_SCALAR >::getOutput(), and gum::credal::LRSWrapper< GUM_SCALAR >::setUpV().

Referenced by gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_verticesFusion().

                                                     {
       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();
     }

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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 (potential) vertex of the node credal set

Definition at line 907 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMax, gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMin, and gum::credal::InferenceEngine< GUM_SCALAR >::_modal.

                                                                 {
       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;
       }
     }

◆ _updateMarginals()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

inlineprotectedinherited

Fusion of threads marginals.

Definition at line 274 of file multipleInferenceEngine_tpl.h.

References gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalMax, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalMin, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMax, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMin, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_workingSet, and gum::getThreadNumber().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                                {
 #pragma omp parallel
       {
         int  threadId = 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->_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];
             }   // end of : all threads
           }     // end of : all modalities
         }       // end of : all variables
       }         // end of : parallel region
     }

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◆ _updateOldMarginals()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

protectedinherited

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 347 of file multipleInferenceEngine_tpl.h.

References gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalMax, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalMin, gum::credal::InferenceEngine< GUM_SCALAR >::_oldMarginalMax, gum::credal::InferenceEngine< GUM_SCALAR >::_oldMarginalMin, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_workingSet, and gum::getThreadNumber().

                                                                            {
 #pragma omp parallel
       {
         int  threadId = 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
     }

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◆ _updateThread()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

inlineprotectedinherited

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

Parameters

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 89 of file multipleInferenceEngine_tpl.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__threadUpdate().

                                                        {
       int tId = getThreadNumber();
 
       // 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(id, vertex, elimRedund);
           added = true;
         }
       }
 
       // if all variables didn't get better marginals, we will delete
       if (__infE::_storeBNOpt && result) return true;
 
       return false;
     }

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◆ _verticesFusion()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

protectedinherited

Deprecated:: Fusion of threads vertices.

Definition at line 376 of file multipleInferenceEngine_tpl.h.

References gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalMin, gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalSets, gum::credal::InferenceEngine< GUM_SCALAR >::_storeVertices, gum::credal::InferenceEngine< GUM_SCALAR >::_updateCredalSets(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_workingSet, and gum::getThreadNumber().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

                                                                        {
       // don't create threads if there are no vertices saved
       if (!__infE::_storeVertices) return;
 
 #pragma omp parallel
       {
         int  threadId = getThreadNumber();
         Size nsize = Size(_workingSet[threadId]->size());
 
 #pragma omp for
 
         for (long i = 0; i < long(nsize); i++) {
           Size tsize = Size(_l_marginalMin.size());
 
           // go through all threads
           for (long tId = 0; tId < long(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
       }         // end of : parallel region
     }

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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 227 of file approximationScheme_inl.h.

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

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

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◆ credalNet()

template<typename GUM_SCALAR >

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

inherited

Get this creadal network.

Returns: A constant reference to this CredalNet.

Definition at line 59 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet.

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::InferenceEngine().

                                                                             {
       return *_credalNet;
     }

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◆ currentTime()

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

virtualinherited

Returns the current running time in second.

Returns: Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 128 of file approximationScheme_inl.h.

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

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

128 { return _timer.step(); }

gum::Timer::step

double step() const

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

Definition: timer_inl.h:42

gum::ApproximationScheme::_timer

Timer _timer

The timer.

Definition: approximationScheme.h:381

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◆ disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

virtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 54 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

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

54 { _enabled_eps = false; }

gum::ApproximationScheme::_enabled_eps

bool _enabled_eps

If true, the threshold convergence is enabled.

Definition: approximationScheme.h:393

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◆ disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

virtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 105 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

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

105 { _enabled_max_iter = false; }

gum::ApproximationScheme::_enabled_max_iter

bool _enabled_max_iter

If true, the maximum iterations stopping criterion is enabled.

Definition: approximationScheme.h:411

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◆ disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

virtualinherited

Disable stopping criterion on timeout.

Returns: Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 131 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

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

131 { _enabled_max_time = false; }

gum::ApproximationScheme::_enabled_max_time

bool _enabled_max_time

If true, the timeout is enabled.

Definition: approximationScheme.h:405

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◆ disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

virtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 79 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

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

                                                          {
     _enabled_min_rate_eps = false;
   }

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◆ 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 716 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpectations().

                                                             {
       _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 504 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpMax, and GUM_ERROR.

                                        {
       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];
     }

◆ 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 486 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpMin, and GUM_ERROR.

                                        {
       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 ( )

virtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 57 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

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

57 { _enabled_eps = true; }

gum::ApproximationScheme::_enabled_eps

bool _enabled_eps

If true, the threshold convergence is enabled.

Definition: approximationScheme.h:393

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◆ enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

virtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 108 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

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

108 { _enabled_max_iter = true; }

gum::ApproximationScheme::_enabled_max_iter

bool _enabled_max_iter

If true, the maximum iterations stopping criterion is enabled.

Definition: approximationScheme.h:411

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◆ enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

virtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 134 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

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

134 { _enabled_max_time = true; }

gum::ApproximationScheme::_enabled_max_time

bool _enabled_max_time

If true, the timeout is enabled.

Definition: approximationScheme.h:405

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◆ enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

virtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 84 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

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

                                                         {
     _enabled_min_rate_eps = true;
   }

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◆ epsilon()

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

virtualinherited

Returns the value of epsilon.

Returns: Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 51 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_eps.

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

51 { return _eps; }

gum::ApproximationScheme::_eps

double _eps

Threshold for convergence.

Definition: approximationScheme.h:390

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◆ eraseAllEvidence()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

virtualinherited

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 533 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();
     }

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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 479 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMax.

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

◆ 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 462 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, and gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMax.

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

◆ 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 471 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMin.

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

◆ 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 454 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, and gum::credal::InferenceEngine< GUM_SCALAR >::_expectationMin.

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

◆ 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 515 of file inferenceEngine.h.

References gum::IApproximationSchemeConfiguration::messageApproximationScheme().

                                                   {
         return this->messageApproximationScheme();
       }

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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 1005 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_t0.

                                                          {
       return _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 1011 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_t1.

                                                          {
       return _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 141 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_dbnOpt.

                                                        {
       return &_dbnOpt;
     }

◆ history()

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

virtualinherited

Returns the scheme history.

Returns: Returns the scheme history.

Exceptions

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 173 of file approximationScheme_inl.h.

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

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

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

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◆ initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 187 of file approximationScheme_inl.h.

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

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

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

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◆ insertEvidence() [1/2]

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 229 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_evidence, gum::HashTable< Key, Val, Alloc >::clear(), gum::HashTable< Key, Val, Alloc >::empty(), GUM_SHOWERROR, and gum::HashTable< Key, Val, Alloc >::insert().

                                                                    {
       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);
       }
     }

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◆ insertEvidence() [2/2]

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 251 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_evidence, gum::HashTable< Key, Val, Alloc >::clear(), gum::HashTable< Key, Val, Alloc >::empty(), GUM_SHOWERROR, and gum::HashTable< Key, Val, Alloc >::insert().

                                                                 {
       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);
       }
     }

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◆ insertEvidenceFile()

template<typename GUM_SCALAR , class BNInferenceEngine = LazyPropagation< GUM_SCALAR >>

virtual void gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::insertEvidenceFile ( const std::string & path )

inlinevirtual

unsigned int notOptDelete;

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

Definition at line 127 of file CNMonteCarloSampling.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::insertEvidenceFile().

                                                              {
         InferenceEngine< GUM_SCALAR >::insertEvidenceFile(path);
       };

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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 193 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_initExpectations(), gum::credal::InferenceEngine< GUM_SCALAR >::_modal, and GUM_SHOWERROR.

                                                                    {
       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();
     }

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◆ 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 146 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_initExpectations(), gum::credal::InferenceEngine< GUM_SCALAR >::_modal, and GUM_ERROR.

                                                                               {
       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();
     }

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◆ 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 331 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_query, gum::HashTable< Key, Val, Alloc >::clear(), gum::HashTable< Key, Val, Alloc >::empty(), GUM_SHOWERROR, and gum::HashTable< Key, Val, Alloc >::insert().

                                                        {
       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);
       }
     }

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◆ 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 348 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_query, gum::HashTable< Key, Val, Alloc >::clear(), gum::HashTable< Key, Val, Alloc >::empty(), GUM_ERROR, GUM_SHOWERROR, and gum::HashTable< Key, Val, Alloc >::insert().

                                                                              {
       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();
     }

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◆ isEnabledEpsilon()

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

virtualinherited

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

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 61 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

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

                                                           {
     return _enabled_eps;
   }

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◆ isEnabledMaxIter()

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

virtualinherited

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

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 112 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

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

                                                           {
     return _enabled_max_iter;
   }

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◆ isEnabledMaxTime()

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

virtualinherited

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

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 138 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

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

                                                           {
     return _enabled_max_time;
   }

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◆ isEnabledMinEpsilonRate()

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

virtualinherited

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

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 90 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

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

                                                                  {
     return _enabled_min_rate_eps;
   }

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◆ makeInference()

template<typename GUM_SCALAR , class BNInferenceEngine >

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

virtual

Starts the inference.

Implements gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >.

Definition at line 55 of file CNMonteCarloSampling_tpl.h.

                                                                               {
       if (__infEs::_repetitiveInd) {
         try {
           this->_repetitiveInit();
         } catch (InvalidArgument& err) {
           GUM_SHOWERROR(err);
           __infEs::_repetitiveInd = false;
         }
       }
 
       // debug
 
       __mcInitApproximationScheme();
 
       __mcThreadDataCopy();
 
       // don't put it after burnIn, it could stop with timeout : we want at
       // least one
       // burnIn and one periodSize
       GUM_SCALAR eps = 1.;   // to validate testSuite ?
 
       auto psize = this->periodSize();
       /*
 
             auto remaining = this->remainingBurnIn();
 
       be
             if ( remaining != 0 ) {
       and
       )
               do {
                 eps = 0;
 
                 int iters = int( ( remaining < psize ) ? remaining : psize );
 
       #pragma omp parallel for
 
                 for ( int iter = 0; iter < iters; iter++ ) {
                   __threadInference();
                   __threadUpdate();
                 }  // end of : parallel periodSize
 
                 this->updateApproximationScheme( iters );
 
 
 
                 remaining = this->remainingBurnIn();
 
               } while ( ( remaining > 0 ) && this->continueApproximationScheme( eps
       ) );
             }
       */
 
       if (this->continueApproximationScheme(eps)) {
         do {
           eps = 0;
 
 // less overheads with high periodSize
 #pragma omp parallel for
 
           for (int iter = 0; iter < int(psize); iter++) {
             __threadInference();
             __threadUpdate();
           }   // end of : parallel periodSize
 
           this->updateApproximationScheme(int(psize));
 
           this->_updateMarginals();   // fusion threads + update margi
 
           eps = this->_computeEpsilon();   // also updates oldMargi
 
         } while (this->continueApproximationScheme(eps));
       }
 
       if (!this->_modal.empty()) { this->_expFusion(); }
 
       if (__infEs::_storeBNOpt) { this->_optFusion(); }
 
       if (__infEs::_storeVertices) { this->_verticesFusion(); }
 
       if (!this->_modal.empty()) {
         this->_dynamicExpectations();   // work with any network
       }
 
     }

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◆ marginalMax() [1/2]

template<typename GUM_SCALAR >

const std::vector< 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 447 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMax.

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

◆ marginalMax() [2/2]

template<typename GUM_SCALAR >

const std::vector< 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 430 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, and gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMax.

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

◆ marginalMin() [1/2]

template<typename GUM_SCALAR >

const std::vector< 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 439 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMin.

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

◆ marginalMin() [2/2]

template<typename GUM_SCALAR >

const std::vector< 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 422 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, and gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMin.

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

◆ maxIter()

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

virtualinherited

Returns the criterion on number of iterations.

Returns: Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 102 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_iter.

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

102 { return _max_iter; }

gum::ApproximationScheme::_max_iter

Size _max_iter

The maximum iterations.

Definition: approximationScheme.h:408

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◆ maxTime()

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

virtualinherited

Returns the timeout (in seconds).

Returns: Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 125 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_time.

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

125 { return _max_time; }

gum::ApproximationScheme::_max_time

double _max_time

The timeout.

Definition: approximationScheme.h:402

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◆ messageApproximationScheme()

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

inherited

Returns the approximation scheme message.

Returns: Returns the approximation scheme message.

Definition at line 40 of file IApproximationSchemeConfiguration_inl.h.

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

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

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◆ minEpsilonRate()

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

virtualinherited

Returns the value of the minimal epsilon rate.

Returns: Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 74 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_min_rate_eps.

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

                                                           {
     return _min_rate_eps;
   }

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◆ nbrIterations()

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

virtualinherited

Returns the number of iterations.

Returns: Returns the number of iterations.

Exceptions

OperationNotAllowed Raised if the scheme did not perform.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 163 of file approximationScheme_inl.h.

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

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

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

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◆ periodSize()

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

virtualinherited

Returns the period size.

Returns: Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 149 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_period_size.

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

149 { return _period_size; }

gum::ApproximationScheme::_period_size

Size _period_size

Checking criteria frequency.

Definition: approximationScheme.h:417

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◆ remainingBurnIn()

INLINE Size gum::ApproximationScheme::remainingBurnIn ( )

inherited

Returns the remaining burn in.

Returns: Returns the remaining burn in.

Definition at line 210 of file approximationScheme_inl.h.

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

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

◆ 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 120 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_repetitiveInd.

                                                             {
       return _repetitiveInd;
     }

◆ 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 554 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpMax, gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpMin, and GUM_ERROR.

                                     {
       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();
     }

◆ 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 528 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMax, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMin, and GUM_ERROR.

                                     {
       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();
     }

◆ 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 628 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalSets, and GUM_ERROR.

                                                                               {
       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();
     }

◆ setEpsilon()

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

virtualinherited

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

If the criterion was disabled it will be enabled.

Parameters

eps	The new epsilon value.

Exceptions

OutOfLowerBound Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 43 of file approximationScheme_inl.h.

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

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

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

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◆ setMaxIter()

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

virtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfLowerBound Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 95 of file approximationScheme_inl.h.

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

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

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

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◆ setMaxTime()

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

virtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout The timeout value in seconds.

Exceptions

OutOfLowerBound Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 118 of file approximationScheme_inl.h.

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

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

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

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◆ setMinEpsilonRate()

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

virtualinherited

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

If the criterion was disabled it will be enabled

Parameters

rate	The minimal epsilon rate.

Exceptions

OutOfLowerBound if rate<0

Implements gum::IApproximationSchemeConfiguration.

Definition at line 66 of file approximationScheme_inl.h.

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

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

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

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◆ setPeriodSize()

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

virtualinherited

How many samples between two stopping is enable.

Parameters

p	The new period value.

Exceptions

OutOfLowerBound Raised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 143 of file approximationScheme_inl.h.

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

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

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

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◆ 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 111 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_repetitiveInd, and gum::credal::InferenceEngine< GUM_SCALAR >::_repetitiveInit().

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

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◆ setVerbosity()

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

virtualinherited

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

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 152 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

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

152 { _verbosity = v; }

gum::ApproximationScheme::_verbosity

bool _verbosity

If true, verbosity is enabled.

Definition: approximationScheme.h:420

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◆ startOfPeriod()

INLINE bool gum::ApproximationScheme::startOfPeriod ( )

inherited

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

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

Definition at line 197 of file approximationScheme_inl.h.

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

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

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

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◆ stateApproximationScheme()

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

virtualinherited

Returns the approximation scheme state.

Returns: Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 158 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_state.

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

                                                              {
     return _current_state;
   }

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◆ stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 219 of file approximationScheme_inl.h.

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

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

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

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◆ storeBNOpt() [1/2]

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 99 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_storeBNOpt.

                                                                    {
       _storeBNOpt = value;
     }

◆ storeBNOpt() [2/2]

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 135 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_storeBNOpt.

                                                          {
       return _storeBNOpt;
     }

◆ storeVertices() [1/2]

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 104 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_initMarginalSets(), and gum::credal::InferenceEngine< GUM_SCALAR >::_storeVertices.

                                                                       {
       _storeVertices = value;
 
       if (value) _initMarginalSets();
     }

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◆ storeVertices() [2/2]

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 130 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_storeVertices.

                                                             {
       return _storeVertices;
     }

◆ 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 599 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMax, gum::credal::InferenceEngine< GUM_SCALAR >::_marginalMin, gum::credal::InferenceEngine< GUM_SCALAR >::_query, gum::HashTable< Key, Val, Alloc >::empty(), and gum::HashTable< Key, Val, Alloc >::exists().

                                                             {
       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();
     }

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◆ updateApproximationScheme()

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

inherited

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

Parameters

incr	The new increment steps.

Definition at line 206 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_step.

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

                                                                               {
     _current_step += incr;
   }

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◆ verbosity()

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

virtualinherited

Returns true if verbosity is enabled.

Returns: Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 154 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

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

154 { return _verbosity; }

gum::ApproximationScheme::_verbosity

bool _verbosity

If true, verbosity is enabled.

Definition: approximationScheme.h:420

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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 523 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_marginalSets.

                                                                     {
       return _marginalSets[id];
     }

Member Data Documentation

◆ _burn_in

Size gum::ApproximationScheme::_burn_in

protectedinherited

Number of iterations before checking stopping criteria.

Definition at line 414 of file approximationScheme.h.

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

◆ _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 74 of file inferenceEngine.h.

◆ _current_epsilon

double gum::ApproximationScheme::_current_epsilon

protectedinherited

Current epsilon.

Definition at line 369 of file approximationScheme.h.

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

◆ _current_rate

double gum::ApproximationScheme::_current_rate

protectedinherited

Current rate.

Definition at line 375 of file approximationScheme.h.

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

◆ _current_state

ApproximationSchemeSTATE gum::ApproximationScheme::_current_state

protectedinherited

The current state.

Definition at line 384 of file approximationScheme.h.

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

◆ _current_step

Size gum::ApproximationScheme::_current_step

protectedinherited

The current step.

Definition at line 378 of file approximationScheme.h.

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

◆ _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 147 of file inferenceEngine.h.

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

◆ _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 101 of file inferenceEngine.h.

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpectations(), gum::credal::InferenceEngine< GUM_SCALAR >::dynamicExpMax(), gum::credal::InferenceEngine< GUM_SCALAR >::eraseAllEvidence(), and gum::credal::InferenceEngine< GUM_SCALAR >::saveExpectations().

◆ _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 98 of file inferenceEngine.h.

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpectations(), gum::credal::InferenceEngine< GUM_SCALAR >::dynamicExpMin(), gum::credal::InferenceEngine< GUM_SCALAR >::eraseAllEvidence(), and gum::credal::InferenceEngine< GUM_SCALAR >::saveExpectations().

◆ _enabled_eps

bool gum::ApproximationScheme::_enabled_eps

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 393 of file approximationScheme.h.

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

◆ _enabled_max_iter

bool gum::ApproximationScheme::_enabled_max_iter

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 411 of file approximationScheme.h.

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

◆ _enabled_max_time

bool gum::ApproximationScheme::_enabled_max_time

protectedinherited

If true, the timeout is enabled.

Definition at line 405 of file approximationScheme.h.

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

◆ _enabled_min_rate_eps

bool gum::ApproximationScheme::_enabled_min_rate_eps

protectedinherited

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

Definition at line 399 of file approximationScheme.h.

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

◆ _eps

double gum::ApproximationScheme::_eps

protectedinherited

Threshold for convergence.

Definition at line 390 of file approximationScheme.h.

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

◆ _evidence

template<typename GUM_SCALAR >

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

protectedinherited

Holds observed variables states.

Definition at line 107 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__insertEvidence(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_optFusion(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThread(), gum::credal::InferenceEngine< GUM_SCALAR >::eraseAllEvidence(), gum::credal::InferenceEngine< GUM_SCALAR >::insertEvidence(), and gum::credal::InferenceEngine< GUM_SCALAR >::insertEvidenceFile().

◆ _expectationMax

template<typename GUM_SCALAR >

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

protectedinherited

Upper expectations, if some variables modalities were inserted.

Definition at line 94 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpectations(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expFusion(), gum::credal::InferenceEngine< GUM_SCALAR >::_initExpectations(), gum::credal::InferenceEngine< GUM_SCALAR >::_updateExpectations(), and gum::credal::InferenceEngine< GUM_SCALAR >::expectationMax().

◆ _expectationMin

template<typename GUM_SCALAR >

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

protectedinherited

Lower expectations, if some variables modalities were inserted.

Definition at line 91 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpectations(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expFusion(), gum::credal::InferenceEngine< GUM_SCALAR >::_initExpectations(), gum::credal::InferenceEngine< GUM_SCALAR >::_updateExpectations(), and gum::credal::InferenceEngine< GUM_SCALAR >::expectationMin().

◆ _history

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

protectedinherited

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

Definition at line 387 of file approximationScheme.h.

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

◆ _l_clusters

template<typename GUM_SCALAR , class BNInferenceEngine >

__clusters gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_clusters

protectedinherited

Threads clusters.

Definition at line 108 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__verticesSampling(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_initThreadsData(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ _l_evidence

template<typename GUM_SCALAR , class BNInferenceEngine >

__margis gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_evidence

protectedinherited

Threads evidence.

Definition at line 106 of file multipleInferenceEngine.h.

Referenced by gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ _l_expectationMax

template<typename GUM_SCALAR , class BNInferenceEngine >

__expes gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_expectationMax

protectedinherited

Threads upper expectations, one per thread.

Definition at line 100 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expFusion(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_initThreadsData(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThread(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ _l_expectationMin

template<typename GUM_SCALAR , class BNInferenceEngine >

__expes gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_expectationMin

protectedinherited

Threads lower expectations, one per thread.

Definition at line 98 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expFusion(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_initThreadsData(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThread(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ _l_inferenceEngine

template<typename GUM_SCALAR , class BNInferenceEngine >

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

protectedinherited

Threads BNInferenceEngine.

Definition at line 116 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__insertEvidence(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__threadInference(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__threadUpdate(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ _l_marginalMax

template<typename GUM_SCALAR , class BNInferenceEngine >

__margis gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalMax

protectedinherited

Threads upper marginals, one per thread.

Definition at line 96 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::__updateThreadCredalSets(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_initThreadsData(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_optFusion(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateMarginals(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateOldMarginals(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThread(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ _l_marginalMin

template<typename GUM_SCALAR , class BNInferenceEngine >

__margis gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalMin

protectedinherited

Threads lower marginals, one per thread.

Definition at line 94 of file multipleInferenceEngine.h.

◆ _l_marginalSets

template<typename GUM_SCALAR , class BNInferenceEngine >

__credalSets gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_marginalSets

protectedinherited

Threads vertices.

Definition at line 104 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::__updateThreadCredalSets(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_initThreadsData(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_verticesFusion(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ _l_modal

template<typename GUM_SCALAR , class BNInferenceEngine >

__modals gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_l_modal

protectedinherited

Threads modalities.

Definition at line 102 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expFusion(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_initThreadsData(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThread(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ _l_optimalNet

template<typename GUM_SCALAR , class BNInferenceEngine >

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

protectedinherited

Threads optimal IBayesNet.

Definition at line 118 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__verticesSampling(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_initThreadsData(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_optFusion(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThread(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ _last_epsilon

double gum::ApproximationScheme::_last_epsilon

protectedinherited

Last epsilon value.

Definition at line 372 of file approximationScheme.h.

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

◆ _marginalMax

template<typename GUM_SCALAR >

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

protectedinherited

Upper marginals.

Definition at line 84 of file inferenceEngine.h.

◆ _marginalMin

template<typename GUM_SCALAR >

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

protectedinherited

Lower marginals.

Definition at line 82 of file inferenceEngine.h.

◆ _marginalSets

template<typename GUM_SCALAR >

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

protectedinherited

Credal sets vertices, if enabled.

Definition at line 87 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expFusion(), gum::credal::InferenceEngine< GUM_SCALAR >::_initMarginalSets(), gum::credal::InferenceEngine< GUM_SCALAR >::_updateCredalSets(), gum::credal::InferenceEngine< GUM_SCALAR >::saveVertices(), and gum::credal::InferenceEngine< GUM_SCALAR >::vertices().

◆ _max_iter

Size gum::ApproximationScheme::_max_iter

protectedinherited

The maximum iterations.

Definition at line 408 of file approximationScheme.h.

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

◆ _max_time

double gum::ApproximationScheme::_max_time

protectedinherited

The timeout.

Definition at line 402 of file approximationScheme.h.

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

◆ _min_rate_eps

double gum::ApproximationScheme::_min_rate_eps

protectedinherited

Threshold for the epsilon rate.

Definition at line 396 of file approximationScheme.h.

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

◆ _modal

template<typename GUM_SCALAR >

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

protectedinherited

Variables modalities used to compute expectations.

Definition at line 104 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpectations(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expFusion(), gum::credal::InferenceEngine< GUM_SCALAR >::_initExpectations(), gum::credal::InferenceEngine< GUM_SCALAR >::_updateExpectations(), gum::credal::InferenceEngine< GUM_SCALAR >::insertModals(), gum::credal::InferenceEngine< GUM_SCALAR >::insertModalsFile(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

◆ _oldMarginalMax

template<typename GUM_SCALAR >

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

protectedinherited

Old upper marginals used to compute epsilon.

Definition at line 79 of file inferenceEngine.h.

Referenced by gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_computeEpsilon(), gum::credal::InferenceEngine< GUM_SCALAR >::_computeEpsilon(), gum::credal::InferenceEngine< GUM_SCALAR >::_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 77 of file inferenceEngine.h.

Referenced by gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_computeEpsilon(), gum::credal::InferenceEngine< GUM_SCALAR >::_computeEpsilon(), gum::credal::InferenceEngine< GUM_SCALAR >::_initMarginals(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_initThreadsData(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateOldMarginals().

◆ _period_size

Size gum::ApproximationScheme::_period_size

protectedinherited

Checking criteria frequency.

Definition at line 417 of file approximationScheme.h.

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

◆ _query

template<typename GUM_SCALAR >

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

protectedinherited

Holds the query nodes states.

Definition at line 109 of file inferenceEngine.h.

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::eraseAllEvidence(), gum::credal::InferenceEngine< GUM_SCALAR >::insertQuery(), gum::credal::InferenceEngine< GUM_SCALAR >::insertQueryFile(), and gum::credal::InferenceEngine< GUM_SCALAR >::toString().

◆ _repetitiveInd

template<typename GUM_SCALAR , class BNInferenceEngine = LazyPropagation< GUM_SCALAR >>

bool gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_repetitiveInd

protected

Definition at line 129 of file CNMonteCarloSampling.h.

◆ _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 143 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__verticesSampling(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThread(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference(), and gum::credal::InferenceEngine< GUM_SCALAR >::storeBNOpt().

◆ _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 129 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_expFusion(), gum::credal::InferenceEngine< GUM_SCALAR >::_initMarginalSets(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_updateThread(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_verticesFusion(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference(), and gum::credal::InferenceEngine< GUM_SCALAR >::storeVertices().

◆ _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 117 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::InferenceEngine< GUM_SCALAR >::_repetitiveInit(), and gum::credal::InferenceEngine< GUM_SCALAR >::getT0Cluster().

◆ _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 124 of file inferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::InferenceEngine< GUM_SCALAR >::_repetitiveInit(), and gum::credal::InferenceEngine< GUM_SCALAR >::getT1Cluster().

◆ _timer

Timer gum::ApproximationScheme::_timer

protectedinherited

The timer.

Definition at line 381 of file approximationScheme.h.

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

◆ _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 154 of file inferenceEngine.h.

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::_repetitiveInit().

◆ _verbosity

bool gum::ApproximationScheme::_verbosity

protectedinherited

If true, verbosity is enabled.

Definition at line 420 of file approximationScheme.h.

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

◆ _workingSet

template<typename GUM_SCALAR , class BNInferenceEngine >

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

protectedinherited

Threads IBayesNet.

Definition at line 111 of file multipleInferenceEngine.h.

◆ _workingSetE

template<typename GUM_SCALAR , class BNInferenceEngine >

std::vector< List< const Potential< GUM_SCALAR >* >* > gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_workingSetE

protectedinherited

Threads evidence.

Definition at line 113 of file multipleInferenceEngine.h.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__insertEvidence(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcThreadDataCopy(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::_initThreadsData(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, BNInferenceEngine >::eraseAllEvidence().

◆ onProgress

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

inherited

Progression, error and time.

Definition at line 59 of file IApproximationSchemeConfiguration.h.

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

◆ onStop

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

inherited

Criteria messageApproximationScheme.

Definition at line 62 of file IApproximationSchemeConfiguration.h.

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

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

agrum/CN/CNMonteCarloSampling.h
agrum/CN/CNMonteCarloSampling_tpl.h

Public Attributes

Public Member Functions

Public Types

Protected Attributes

Protected Member Functions

Detailed Description

template<typename GUM_SCALAR, class BNInferenceEngine = LazyPropagation< GUM_SCALAR >> class gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >

Member Typedef Documentation

◆ __infEs

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

Constructor & Destructor Documentation

◆ CNMonteCarloSampling()

◆ ~CNMonteCarloSampling()

Member Function Documentation

◆ __binaryRep()

◆ __insertEvidence()

◆ __mcInitApproximationScheme()

◆ __mcThreadDataCopy()

◆ __threadInference()

◆ __threadUpdate()

◆ __verticesSampling()

◆ _computeEpsilon()

◆ _dynamicExpectations()

◆ _expFusion()

◆ _initExpectations()

◆ _initMarginals()

◆ _initMarginalSets()

◆ _initThreadsData()

◆ _optFusion()

◆ _repetitiveInit()

◆ _updateCredalSets()

◆ _updateExpectations()

◆ _updateMarginals()

◆ _updateOldMarginals()

◆ _updateThread()

◆ _verticesFusion()

◆ continueApproximationScheme()

◆ credalNet()

◆ currentTime()

◆ disableEpsilon()

◆ disableMaxIter()

◆ disableMaxTime()

◆ disableMinEpsilonRate()

◆ dynamicExpectations()

◆ dynamicExpMax()

◆ dynamicExpMin()

◆ enableEpsilon()

◆ enableMaxIter()

◆ enableMaxTime()

◆ enableMinEpsilonRate()

◆ epsilon()

◆ eraseAllEvidence()

◆ expectationMax() [1/2]

◆ expectationMax() [2/2]

◆ expectationMin() [1/2]

◆ expectationMin() [2/2]

◆ getApproximationSchemeMsg()

◆ getT0Cluster()

◆ getT1Cluster()

◆ getVarMod2BNsMap()

◆ history()

◆ initApproximationScheme()

◆ insertEvidence() [1/2]

◆ insertEvidence() [2/2]

◆ insertEvidenceFile()

◆ insertModals()

◆ insertModalsFile()

◆ insertQuery()

◆ insertQueryFile()

◆ isEnabledEpsilon()

◆ isEnabledMaxIter()

◆ isEnabledMaxTime()

◆ isEnabledMinEpsilonRate()

◆ makeInference()

◆ marginalMax() [1/2]

◆ marginalMax() [2/2]

◆ marginalMin() [1/2]

◆ marginalMin() [2/2]

◆ maxIter()

template<typename GUM_SCALAR, class BNInferenceEngine = LazyPropagation< GUM_SCALAR >>
class gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >