Abstract class template representing a CredalNet inference engine. More...

#include <agrum/CN/inferenceEngine.h>

Inheritance diagram for gum::credal::InferenceEngine< GUM_SCALAR >:

Collaboration diagram for gum::credal::InferenceEngine< GUM_SCALAR >:

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

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

Public Member Functions
Constructors / Destructors
	InferenceEngine (const CredalNet< GUM_SCALAR > &credalNet)
	Construtor. More...

virtual	~InferenceEngine ()
	Destructor. More...

Pure virtual methods
virtual void	makeInference ()=0
	To be redefined by each credal net algorithm. 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)

void	storeBNOpt (const bool value)

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

bool	storeVertices () const
	Get the number of iterations without changes used to stop some algorithms. More...

bool	storeBNOpt () const

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...

virtual void	insertEvidenceFile (const std::string &path)
	Insert evidence from file. 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
virtual void	eraseAllEvidence ()
	Erase all inference related data to perform another one. More...

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 > &	marginalMax (const NodeId id) const
	Get the upper 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 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 &	expectationMax (const NodeId id) const
	Get the upper 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 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
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	_repetitiveInd
	`True` if using repetitive independence ( dynamic network only ), `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
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
const GUM_SCALAR	_computeEpsilon ()
	Compute approximation scheme epsilon using the old marginals and the new ones. More...

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 gum::credal::InferenceEngine< GUM_SCALAR >

Abstract class template representing a CredalNet inference engine.

Used by credal network inference algorithms such as CNLoopyPropagation (inner multi-threading) or CNMonteCarloSampling (outer multi-threading).

Template Parameters

GUM_SCALAR A floating type ( float, double, long double ... ).

Author: Matthieu HOURBRACQ and Pierre-Henri WUILLEMIN

Definition at line 59 of file inferenceEngine.h.

Member Typedef Documentation

◆ cluster

template<typename GUM_SCALAR >

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

private

Definition at line 70 of file inferenceEngine.h.

◆ credalSet

template<typename GUM_SCALAR >

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

private

Definition at line 62 of file inferenceEngine.h.

◆ dynExpe

template<typename GUM_SCALAR >

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

private

Definition at line 67 of file inferenceEngine.h.

◆ expe

template<typename GUM_SCALAR >

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

private

Definition at line 64 of file inferenceEngine.h.

◆ margi

template<typename GUM_SCALAR >

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

private

Definition at line 63 of file inferenceEngine.h.

◆ query

template<typename GUM_SCALAR >

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

private

Definition at line 69 of file inferenceEngine.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

◆ InferenceEngine()

template<typename GUM_SCALAR >

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

explicit

Construtor.

Parameters

credalNet The credal net to be used with this inference engine.

Definition at line 41 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_credalNet, gum::credal::InferenceEngine< GUM_SCALAR >::_dbnOpt, gum::credal::InferenceEngine< GUM_SCALAR >::_initMarginals(), and gum::credal::InferenceEngine< GUM_SCALAR >::credalNet().

                                                  :
         ApproximationScheme() {
       _credalNet = &credalNet;
 
       _dbnOpt.setCNet(credalNet);
 
       _initMarginals();
 
       GUM_CONSTRUCTOR(InferenceEngine);
     }

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◆ ~InferenceEngine()

template<typename GUM_SCALAR >

gum::credal::InferenceEngine< GUM_SCALAR >::~InferenceEngine ( )

virtual

Destructor.

Definition at line 54 of file inferenceEngine_tpl.h.

                                                     {
       GUM_DESTRUCTOR(InferenceEngine);
     }

Member Function Documentation

◆ _computeEpsilon()

template<typename GUM_SCALAR >

const GUM_SCALAR gum::credal::InferenceEngine< GUM_SCALAR >::_computeEpsilon ( )

inlineprotected

Compute approximation scheme epsilon using the old marginals and the new ones.

Highest delta on either lower or upper marginal is epsilon.

Also updates oldMarginals to current marginals.

Returns: Epsilon.

Definition at line 1016 of file inferenceEngine_tpl.h.

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

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

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◆ _dynamicExpectations()

template<typename GUM_SCALAR >

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

protected

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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◆ _initExpectations()

template<typename GUM_SCALAR >

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

protected

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 ( )

protected

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 ( )

protected

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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◆ _repetitiveInit()

template<typename GUM_SCALAR >

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

protected

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`
	)

inlineprotected

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
	)

inlineprotected

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;
       }
     }

◆ 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 ( )

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 ( )

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

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

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 >

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

virtual

Erase all inference related data to perform another one.

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

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

Definition at line 64 of file inferenceEngine_tpl.h.

References gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpMax, gum::credal::InferenceEngine< GUM_SCALAR >::_dynamicExpMin, gum::credal::InferenceEngine< GUM_SCALAR >::_evidence, gum::credal::InferenceEngine< GUM_SCALAR >::_initExpectations(), gum::credal::InferenceEngine< GUM_SCALAR >::_initMarginals(), gum::credal::InferenceEngine< GUM_SCALAR >::_initMarginalSets(), gum::credal::InferenceEngine< GUM_SCALAR >::_query, and gum::HashTable< Key, Val, Alloc >::clear().

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

                                                          {
       _evidence.clear();
       _query.clear();
       /*
           _marginalMin.clear();
           _marginalMax.clear();
           _oldMarginalMin.clear();
           _oldMarginalMax.clear();
       */
       _initMarginals();
       /*
           _expectationMin.clear();
           _expectationMax.clear();
       */
       _initExpectations();
 
       //  _marginalSets.clear();
       _initMarginalSets();
 
       _dynamicExpMin.clear();
       _dynamicExpMax.clear();
 
       //_modal.clear();
 
       //_t0.clear();
       //_t1.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

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

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

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

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 ( )

inline

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

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

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 ( )

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 )

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 )

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 >

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

virtual

Insert evidence from file.

Parameters

path	The path to the evidence file.

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

Definition at line 270 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_ERROR, GUM_SHOWERROR, and gum::HashTable< Key, Val, Alloc >::insert().

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

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

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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 )

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 )

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 )

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 )

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 >

virtual void gum::credal::InferenceEngine< GUM_SCALAR >::makeInference ( )

pure virtual

To be redefined by each credal net algorithm.

Starts the inference.

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

◆ marginalMax() [1/2]

template<typename GUM_SCALAR >

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

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

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

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

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

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

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

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

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 )

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 )

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

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 )

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

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

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

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

protected

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

protected

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

protected

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

protected

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

protected

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

protected

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

protected

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().

◆ _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

protected

Upper marginals.

Definition at line 84 of file inferenceEngine.h.

◆ _marginalMin

template<typename GUM_SCALAR >

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

protected

Lower marginals.

Definition at line 82 of file inferenceEngine.h.

◆ _marginalSets

template<typename GUM_SCALAR >

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

protected

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

protected

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

protected

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

protected

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

protected

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 >

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

protected

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

False by default.

Definition at line 133 of file inferenceEngine.h.

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

◆ _storeBNOpt

template<typename GUM_SCALAR >

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

protected

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

protected

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

protected

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

protected

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

protected

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().

◆ 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/inferenceEngine.h
agrum/CN/inferenceEngine_tpl.h

Public Attributes

Public Member Functions

Public Types

Protected Attributes

Protected Member Functions

Detailed Description

template<typename GUM_SCALAR> class gum::credal::InferenceEngine< GUM_SCALAR >

Member Typedef Documentation

◆ cluster

◆ credalSet

◆ dynExpe

◆ expe

◆ margi

◆ query

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

Constructor & Destructor Documentation

◆ InferenceEngine()

◆ ~InferenceEngine()

Member Function Documentation

◆ _computeEpsilon()

◆ _dynamicExpectations()

◆ _initExpectations()

◆ _initMarginals()

◆ _initMarginalSets()

◆ _repetitiveInit()

◆ _updateCredalSets()

◆ _updateExpectations()

◆ 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()

◆ maxTime()

◆ messageApproximationScheme()

◆ minEpsilonRate()

◆ nbrIterations()

◆ periodSize()

◆ remainingBurnIn()

◆ repetitiveInd()

◆ saveExpectations()

◆ saveMarginals()

template<typename GUM_SCALAR>
class gum::credal::InferenceEngine< GUM_SCALAR >