gum::learning::BNLearner< GUM_SCALAR > Class Template Reference

A pack of learning algorithms that can easily be used. More...

#include <BNLearner.h>

Inheritance diagram for gum::learning::BNLearner< GUM_SCALAR >:

Collaboration diagram for gum::learning::BNLearner< GUM_SCALAR >:

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

Public Member Functions
BayesNet< GUM_SCALAR >	learnBN ()
	learn a Bayes Net from a file (must have read the db before) More...
BayesNet< GUM_SCALAR >	learnParameters (const DAG &dag, bool takeIntoAccountScore=true)
	learns a BN (its parameters) when its structure is known More...
BayesNet< GUM_SCALAR >	learnParameters (bool take_into_account_score=true)
std::string	toString () const
std::vector< std::tuple< std::string, std::string, std::string > >	state () const
void	_setAprioriWeight_ (double weight)
	sets the apriori weight More...
void	setMandatoryArcs (const ArcSet &set)
	assign a set of forbidden arcs More...
Constructors / Destructors
	BNLearner (const std::string &filename, const std::vector< std::string > &missing_symbols={"?"})
	default constructor More...
	BNLearner (const DatabaseTable<> &db)
	default constructor More...
	BNLearner (const std::string &filename, const gum::BayesNet< GUM_SCALAR > &src, const std::vector< std::string > &missing_symbols={"?"})
	Read the database file for the score / parameter estimation and var names. More...
	BNLearner (const BNLearner &)
	copy constructor More...
	BNLearner (BNLearner &&)
	move constructor More...
virtual	~BNLearner ()
	destructor More...
Operators
BNLearner &	operator= (const BNLearner &)
	copy operator More...
BNLearner &	operator= (BNLearner &&)
	move operator More...
Accessors / Modifiers
DAG	learnDAG ()
	learn a structure from a file (must have read the db before) More...
MixedGraph	learnMixedStructure ()
	learn a partial structure from a file (must have read the db before and must have selected miic or 3off2) More...
void	setInitialDAG (const DAG &)
	sets an initial DAG structure More...
DAG	initialDAG ()
	returns the initial DAG structure More...
const std::vector< std::string > &	names () const
	returns the names of the variables in the database More...
const std::vector< std::size_t > &	domainSizes () const
	returns the domain sizes of the variables in the database More...
Size	domainSize (NodeId var) const
	learn a structure from a file (must have read the db before) More...
Size	domainSize (const std::string &var) const
	learn a structure from a file (must have read the db before) More...
NodeId	idFromName (const std::string &var_name) const
	returns the node id corresponding to a variable name More...
const DatabaseTable &	database () const
	returns the database used by the BNLearner More...
void	setDatabaseWeight (const double new_weight)
	assign a weight to all the rows of the learning database so that the sum of their weights is equal to new_weight More...
void	setRecordWeight (const std::size_t i, const double weight)
	sets the weight of the ith record of the database More...
double	recordWeight (const std::size_t i) const
	returns the weight of the ith record More...
double	databaseWeight () const
	returns the weight of the whole database More...
const std::string &	nameFromId (NodeId id) const
	returns the variable name corresponding to a given node id More...
template<template< typename > class XALLOC>
void	useDatabaseRanges (const std::vector< std::pair< std::size_t, std::size_t >, XALLOC< std::pair< std::size_t, std::size_t > > > &new_ranges)
	use a new set of database rows' ranges to perform learning More...
void	clearDatabaseRanges ()
	reset the ranges to the one range corresponding to the whole database More...
const std::vector< std::pair< std::size_t, std::size_t > > &	databaseRanges () const
	returns the current database rows' ranges used for learning More...
std::pair< std::size_t, std::size_t >	useCrossValidationFold (const std::size_t learning_fold, const std::size_t k_fold)
	sets the ranges of rows to be used for cross-validation learning More...
std::pair< double, double >	chi2 (const NodeId id1, const NodeId id2, const std::vector< NodeId > &knowing={})
	Return the <statistic,pvalue> pair for chi2 test in the database. More...
std::pair< double, double >	chi2 (const std::string &name1, const std::string &name2, const std::vector< std::string > &knowing={})
	Return the <statistic,pvalue> pair for the BNLearner. More...
std::pair< double, double >	G2 (const NodeId id1, const NodeId id2, const std::vector< NodeId > &knowing={})
	Return the <statistic,pvalue> pair for for G2 test in the database. More...
std::pair< double, double >	G2 (const std::string &name1, const std::string &name2, const std::vector< std::string > &knowing={})
	Return the <statistic,pvalue> pair for for G2 test in the database. More...
double	logLikelihood (const std::vector< NodeId > &vars, const std::vector< NodeId > &knowing={})
	Return the loglikelihood of vars in the base, conditioned by knowing for the BNLearner. More...
double	logLikelihood (const std::vector< std::string > &vars, const std::vector< std::string > &knowing={})
	Return the loglikelihood of vars in the base, conditioned by knowing for the BNLearner. More...
std::vector< double >	rawPseudoCount (const std::vector< NodeId > &vars)
	Return the pseudoconts ofNodeIds vars in the base in a raw array. More...
std::vector< double >	rawPseudoCount (const std::vector< std::string > &vars)
	Return the pseudoconts of vars in the base in a raw array. More...
Size	nbCols () const
Size	nbRows () const
void	useEM (const double epsilon)
	use The EM algorithm to learn paramters More...
bool	hasMissingValues () const
	returns true if the learner's database has missing values More...
Score selection
void	useScoreAIC ()
	indicate that we wish to use an AIC score More...
void	useScoreBD ()
	indicate that we wish to use a BD score More...
void	useScoreBDeu ()
	indicate that we wish to use a BDeu score More...
void	useScoreBIC ()
	indicate that we wish to use a BIC score More...
void	useScoreK2 ()
	indicate that we wish to use a K2 score More...
void	useScoreLog2Likelihood ()
	indicate that we wish to use a Log2Likelihood score More...
A priori selection / parameterization
void	useNoApriori ()
	use no apriori More...
void	useAprioriBDeu (double weight=1)
	use the BDeu apriori More...
void	useAprioriSmoothing (double weight=1)
	use the apriori smoothing More...
void	useAprioriDirichlet (const std::string &filename, double weight=1)
	use the Dirichlet apriori More...
std::string	checkScoreAprioriCompatibility () const
	checks whether the current score and apriori are compatible More...
Learning algorithm selection
void	useGreedyHillClimbing ()
	indicate that we wish to use a greedy hill climbing algorithm More...
void	useLocalSearchWithTabuList (Size tabu_size=100, Size nb_decrease=2)
	indicate that we wish to use a local search with tabu list More...
void	useK2 (const Sequence< NodeId > &order)
	indicate that we wish to use K2 More...
void	useK2 (const std::vector< NodeId > &order)
	indicate that we wish to use K2 More...
void	use3off2 ()
	indicate that we wish to use 3off2 More...
void	useMIIC ()
	indicate that we wish to use MIIC More...
3off2/MIIC parameterization and specific results
void	useNMLCorrection ()
	indicate that we wish to use the NML correction for 3off2 and MIIC More...
void	useMDLCorrection ()
	indicate that we wish to use the MDL correction for 3off2 and MIIC More...
void	useNoCorrection ()
	indicate that we wish to use the NoCorr correction for 3off2 and MIIC More...
const std::vector< Arc >	latentVariables () const
	get the list of arcs hiding latent variables More...
Accessors / Modifiers for adding constraints on learning
void	setMaxIndegree (Size max_indegree)
	sets the max indegree More...
void	setSliceOrder (const NodeProperty< NodeId > &slice_order)
	sets a partial order on the nodes More...
void	setSliceOrder (const std::vector< std::vector< std::string > > &slices)
	sets a partial order on the nodes More...
void	setForbiddenArcs (const ArcSet &set)
	assign a set of forbidden arcs More...
assign a new forbidden arc
void	addForbiddenArc (const Arc &arc)
void	addForbiddenArc (const NodeId tail, const NodeId head)
void	addForbiddenArc (const std::string &tail, const std::string &head)
void	addMandatoryArc (const Arc &arc)
void	addMandatoryArc (const NodeId tail, const NodeId head)
void	addMandatoryArc (const std::string &tail, const std::string &head)
remove a forbidden arc
void	eraseForbiddenArc (const Arc &arc)
void	eraseForbiddenArc (const NodeId tail, const NodeId head)
void	eraseForbiddenArc (const std::string &tail, const std::string &head)
void	eraseMandatoryArc (const Arc &arc)
void	eraseMandatoryArc (const NodeId tail, const NodeId head)
void	eraseMandatoryArc (const std::string &tail, const std::string &head)
void	setPossibleEdges (const EdgeSet &set)
	assign a set of forbidden edges More...
void	setPossibleSkeleton (const UndiGraph &skeleton)
	assign a set of forbidden edges More...
assign a new possible edge
Warning Once at least one possible edge is defined, all other edges are not possible anymore
void	addPossibleEdge (const Edge &edge)
void	addPossibleEdge (const NodeId tail, const NodeId head)
void	addPossibleEdge (const std::string &tail, const std::string &head)
remove a possible edge
void	erasePossibleEdge (const Edge &edge)
void	erasePossibleEdge (const NodeId tail, const NodeId head)
void	erasePossibleEdge (const std::string &tail, const std::string &head)
redistribute signals AND implementation of interface
IApproximationSchemeConfiguration
INLINE void	setCurrentApproximationScheme (const ApproximationScheme *approximationScheme)
	{@ /// distribute signals More...
INLINE void	distributeProgress (const ApproximationScheme *approximationScheme, Size pourcent, double error, double time)
	{@ /// distribute signals More...
INLINE void	distributeStop (const ApproximationScheme *approximationScheme, std::string message)
	distribute signals More...
void	setEpsilon (double eps)
	Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)| If the criterion was disabled it will be enabled. More...
double	epsilon () const
	Get the value of epsilon. More...
void	disableEpsilon ()
	Disable stopping criterion on epsilon. More...
void	enableEpsilon ()
	Enable stopping criterion on epsilon. More...
bool	isEnabledEpsilon () const
void	setMinEpsilonRate (double rate)
	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. More...
double	minEpsilonRate () const
	Get 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
void	setMaxIter (Size max)
	stopping criterion on number of iterationsIf the criterion was disabled it will be enabled More...
Size	maxIter () const
void	disableMaxIter ()
	Disable stopping criterion on max iterations. More...
void	enableMaxIter ()
	Enable stopping criterion on max iterations. More...
bool	isEnabledMaxIter () const
void	setMaxTime (double timeout)
	stopping criterion on timeout If the criterion was disabled it will be enabled More...
double	maxTime () const
	returns the timeout (in seconds) More...
double	currentTime () const
	get the current running time in second (double) More...
void	disableMaxTime ()
	Disable stopping criterion on timeout. More...
void	enableMaxTime ()
	stopping criterion on timeout If the criterion was disabled it will be enabled More...
bool	isEnabledMaxTime () const
void	setPeriodSize (Size p)
	how many samples between 2 stopping isEnableds More...
Size	periodSize () const
	how many samples between 2 stopping isEnableds More...
void	setVerbosity (bool v)
	verbosity More...
bool	verbosity () const
	verbosity More...
ApproximationSchemeSTATE	stateApproximationScheme () const
	history More...
Size	nbrIterations () const
const std::vector< double > &	history () const
Getters and setters
std::string	messageApproximationScheme () const
	Returns the approximation scheme message. More...

Public Types
enum	ScoreType {   ScoreType::AIC, ScoreType::BD, ScoreType::BDeu, ScoreType::BIC,   ScoreType::K2, ScoreType::LOG2LIKELIHOOD }
	an enumeration enabling to select easily the score we wish to use More...
enum	ParamEstimatorType { ParamEstimatorType::ML }
	an enumeration to select the type of parameter estimation we shall apply More...
enum	AprioriType { AprioriType::NO_APRIORI, AprioriType::SMOOTHING, AprioriType::DIRICHLET_FROM_DATABASE, AprioriType::BDEU }
	an enumeration to select the apriori More...
enum	AlgoType {   AlgoType::K2, AlgoType::GREEDY_HILL_CLIMBING, AlgoType::LOCAL_SEARCH_WITH_TABU_LIST, AlgoType::MIIC,   AlgoType::THREE_OFF_TWO }
	an enumeration to select easily the learning algorithm to use More...
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
ScoreType	scoreType_ {ScoreType::BDeu}
	the score selected for learning More...
Score *	score_ {nullptr}
	the score used More...
ParamEstimatorType	paramEstimatorType_ {ParamEstimatorType::ML}
	the type of the parameter estimator More...
double	epsilonEM_ {0.0}
	epsilon for EM. if espilon=0.0 : no EM More...
CorrectedMutualInformation *	mutualInfo_ {nullptr}
	the selected correction for 3off2 and miic More...
AprioriType	aprioriType_ {AprioriType::NO_APRIORI}
	the a priori selected for the score and parameters More...
Apriori *	apriori_ {nullptr}
	the apriori used More...
AprioriNoApriori *	noApriori_ {nullptr}
double	aprioriWeight_ {1.0f}
	the weight of the apriori More...
StructuralConstraintSliceOrder	constraintSliceOrder_
	the constraint for 2TBNs More...
StructuralConstraintIndegree	constraintIndegree_
	the constraint for indegrees More...
StructuralConstraintTabuList	constraintTabuList_
	the constraint for tabu lists More...
StructuralConstraintForbiddenArcs	constraintForbiddenArcs_
	the constraint on forbidden arcs More...
StructuralConstraintPossibleEdges	constraintPossibleEdges_
	the constraint on possible Edges More...
StructuralConstraintMandatoryArcs	constraintMandatoryArcs_
	the constraint on mandatory arcs More...
AlgoType	selectedAlgo_ {AlgoType::GREEDY_HILL_CLIMBING}
	the selected learning algorithm More...
K2	algoK2_
	the K2 algorithm More...
Miic	algoMiic3off2_
	the MIIC or 3off2 algorithm More...
CorrectedMutualInformation ::KModeTypes	kmode3Off2_
	the penalty used in 3off2 More...
DAG2BNLearner	Dag2BN_
	the parametric EM More...
GreedyHillClimbing	greedyHillClimbing_
	the greedy hill climbing algorithm More...
LocalSearchWithTabuList	localSearchWithTabuList_
	the local search with tabu list algorithm More...
Database	scoreDatabase_
	the database to be used by the scores and parameter estimators More...
std::vector< std::pair< std::size_t, std::size_t > >	ranges_
	the set of rows' ranges within the database in which learning is done More...
Database *	aprioriDatabase_ {nullptr}
	the database used by the Dirichlet a priori More...
std::string	aprioriDbname_
	the filename for the Dirichlet a priori, if any More...
DAG	initialDag_
	an initial DAG given to learners More...
std::string	filename_
	the filename database More...
Size	nbDecreasingChanges_ {2}
const ApproximationScheme *	currentAlgorithm_ {nullptr}

Protected Member Functions
void	createApriori_ ()
	create the apriori used for learning More...
void	createScore_ ()
	create the score used for learning More...
ParamEstimator *	createParamEstimator_ (DBRowGeneratorParser<> &parser, bool take_into_account_score=true)
	create the parameter estimator used for learning More...
DAG	learnDag_ ()
	returns the DAG learnt More...
MixedGraph	prepareMiic3Off2_ ()
	prepares the initial graph for 3off2 or miic More...
const std::string &	getAprioriType_ () const
	returns the type (as a string) of a given apriori More...
void	createCorrectedMutualInformation_ ()
	create the Corrected Mutual Information instance for Miic/3off2 More...

Static Protected Member Functions
static DatabaseTable	readFile_ (const std::string &filename, const std::vector< std::string > &missing_symbols)
	reads a file and returns a databaseVectInRam More...
static void	isCSVFileName_ (const std::string &filename)
	checks whether the extension of a CSV filename is correct More...

Detailed Description

template<typename GUM_SCALAR>
class gum::learning::BNLearner< GUM_SCALAR >

A pack of learning algorithms that can easily be used.

The pack currently contains K2, GreedyHillClimbing and LocalSearchWithTabuList

Definition at line 59 of file BNLearner.h.

Member Enumeration Documentation

AlgoType

enum gum::learning::genericBNLearner::AlgoType

stronginherited

an enumeration to select easily the learning algorithm to use

Enumerator
K2
GREEDY_HILL_CLIMBING
LOCAL_SEARCH_WITH_TABU_LIST
MIIC
THREE_OFF_TWO

Definition at line 137 of file genericBNLearner.h.

      {
        K2,
        GREEDY_HILL_CLIMBING,
        LOCAL_SEARCH_WITH_TABU_LIST,
        MIIC,
        THREE_OFF_TWO
      };

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 64 of file IApproximationSchemeConfiguration.h.

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

AprioriType

enum gum::learning::genericBNLearner::AprioriType

stronginherited

an enumeration to select the apriori

Enumerator
NO_APRIORI
SMOOTHING
DIRICHLET_FROM_DATABASE
BDEU

Definition at line 128 of file genericBNLearner.h.

      {
        NO_APRIORI,
        SMOOTHING,
        DIRICHLET_FROM_DATABASE,
        BDEU
      };

ParamEstimatorType

enum gum::learning::genericBNLearner::ParamEstimatorType

stronginherited

an enumeration to select the type of parameter estimation we shall apply

Enumerator
ML

Definition at line 122 of file genericBNLearner.h.

      {
        ML
      };

ScoreType

enum gum::learning::genericBNLearner::ScoreType

stronginherited

an enumeration enabling to select easily the score we wish to use

Enumerator
AIC
BD
BDeu
BIC
K2
LOG2LIKELIHOOD

Definition at line 110 of file genericBNLearner.h.

      {
        AIC,
        BD,
        BDeu,
        BIC,
        K2,
        LOG2LIKELIHOOD
      };

Constructor & Destructor Documentation

BNLearner() [1/5]

template<typename GUM_SCALAR >

gum::learning::BNLearner< GUM_SCALAR >::BNLearner	(	const std::string &	filename,
		const std::vector< std::string > &	missing_symbols = {"?"}
	)

default constructor

read the database file for the score / parameter estimation and var names

BNLearner() [2/5]

template<typename GUM_SCALAR >

gum::learning::BNLearner< GUM_SCALAR >::BNLearner

(

const DatabaseTable<> &

db

)

default constructor

read the database file for the score / parameter estimation and var names

BNLearner() [3/5]

template<typename GUM_SCALAR >

gum::learning::BNLearner< GUM_SCALAR >::BNLearner	(	const std::string &	filename,
		const gum::BayesNet< GUM_SCALAR > &	src,
		const std::vector< std::string > &	missing_symbols = {"?"}
	)

Read the database file for the score / parameter estimation and var names.

If modalities = { 1 -> {True, False, Big} }, then the node of id 1 in the BN will have 3 modalities, the first one being True, the second one being False, and the third bein Big.

A parsing of the database will allow to determine which ones are really necessary and will keep them in the order specified by the user (NodeProperty modalities). If parse_database is set to false (the default), then the modalities specified by the user will be considered as being exactly those of the variables of the BN (as a consequence, if we find other values in the database, an exception will be raised during learning).

Parameters

filename	The file to learn from.
modalities	indicate for some nodes (not necessarily all the nodes of the BN) which modalities they should have and in which order these modalities should be stored into the nodes.
parse_database	if true, the modalities specified by the user will be considered as a superset of the modalities of the variables. Wrapper for BNLearner (filename,modalities,parse_database) using a bn to find those modalities and nodeids.

BNLearner() [4/5]

template<typename GUM_SCALAR >

gum::learning::BNLearner< GUM_SCALAR >::BNLearner

(

const BNLearner< GUM_SCALAR > &

)

copy constructor

BNLearner() [5/5]

template<typename GUM_SCALAR >

gum::learning::BNLearner< GUM_SCALAR >::BNLearner

(

BNLearner< GUM_SCALAR > &&

)

move constructor

~BNLearner()

template<typename GUM_SCALAR >

virtual gum::learning::BNLearner< GUM_SCALAR >::~BNLearner ( )

virtual

destructor

Member Function Documentation

_labelsFromBN_()

template<typename GUM_SCALAR >

NodeProperty< Sequence< std::string > > gum::learning::BNLearner< GUM_SCALAR >::_labelsFromBN_ ( const std::string &  filename, const BayesNet< GUM_SCALAR > &  src  )

private

read the first line of a file to find column names

_setAprioriWeight_()

INLINE void gum::learning::genericBNLearner::_setAprioriWeight_ ( double  weight )

inherited

sets the apriori weight

Definition at line 400 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                  {
      if (weight < 0) { GUM_ERROR(OutOfBounds, "the weight of the apriori must be positive") }

      aprioriWeight_ = weight;
      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

addForbiddenArc() [1/3]

INLINE void gum::learning::genericBNLearner::addForbiddenArc ( const Arc &  arc )

inherited

Definition at line 313 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                {
      constraintForbiddenArcs_.addArc(arc);
    }

Here is the call graph for this function:

addForbiddenArc() [2/3]

INLINE void gum::learning::genericBNLearner::addForbiddenArc ( const NodeId  tail, const NodeId  head  )

inherited

Definition at line 323 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                      {
      addForbiddenArc(Arc(tail, head));
    }

Here is the call graph for this function:

addForbiddenArc() [3/3]

INLINE void gum::learning::genericBNLearner::addForbiddenArc ( const std::string &  tail, const std::string &  head  )

inherited

Definition at line 333 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                         {
      addForbiddenArc(Arc(idFromName(tail), idFromName(head)));
    }

Here is the call graph for this function:

addMandatoryArc() [1/3]

INLINE void gum::learning::genericBNLearner::addMandatoryArc ( const Arc &  arc )

inherited

Definition at line 350 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                {
      constraintMandatoryArcs_.addArc(arc);
    }

Here is the call graph for this function:

addMandatoryArc() [2/3]

INLINE void gum::learning::genericBNLearner::addMandatoryArc ( const NodeId  tail, const NodeId  head  )

inherited

Definition at line 372 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                      {
      addMandatoryArc(Arc(tail, head));
    }

Here is the call graph for this function:

addMandatoryArc() [3/3]

INLINE void gum::learning::genericBNLearner::addMandatoryArc ( const std::string &  tail, const std::string &  head  )

inherited

Definition at line 360 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                         {
      addMandatoryArc(Arc(idFromName(tail), idFromName(head)));
    }

Here is the call graph for this function:

addPossibleEdge() [1/3]

INLINE void gum::learning::genericBNLearner::addPossibleEdge ( const Edge &  edge )

inherited

Definition at line 276 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                  {
      constraintPossibleEdges_.addEdge(edge);
    }

Here is the call graph for this function:

addPossibleEdge() [2/3]

INLINE void gum::learning::genericBNLearner::addPossibleEdge ( const NodeId  tail, const NodeId  head  )

inherited

Definition at line 286 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                      {
      addPossibleEdge(Edge(tail, head));
    }

Here is the call graph for this function:

addPossibleEdge() [3/3]

INLINE void gum::learning::genericBNLearner::addPossibleEdge ( const std::string &  tail, const std::string &  head  )

inherited

Definition at line 296 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                         {
      addPossibleEdge(Edge(idFromName(tail), idFromName(head)));
    }

Here is the call graph for this function:

checkScoreAprioriCompatibility()

std::string gum::learning::genericBNLearner::checkScoreAprioriCompatibility ( ) const

inherited

checks whether the current score and apriori are compatible

Returns

a non empty string if the apriori is somehow compatible with the score.

Definition at line 828 of file genericBNLearner.cpp.

                                                                     {
      const std::string& apriori = getAprioriType_();

      switch (scoreType_) {
        case ScoreType::AIC:
          return ScoreAIC<>::isAprioriCompatible(apriori, aprioriWeight_);

        case ScoreType::BD:
          return ScoreBD<>::isAprioriCompatible(apriori, aprioriWeight_);

        case ScoreType::BDeu:
          return ScoreBDeu<>::isAprioriCompatible(apriori, aprioriWeight_);

        case ScoreType::BIC:
          return ScoreBIC<>::isAprioriCompatible(apriori, aprioriWeight_);

        case ScoreType::K2:
          return ScoreK2<>::isAprioriCompatible(apriori, aprioriWeight_);

        case ScoreType::LOG2LIKELIHOOD:
          return ScoreLog2Likelihood<>::isAprioriCompatible(apriori, aprioriWeight_);

        default:
          return "genericBNLearner does not support yet this score";
      }
    }

chi2() [1/2]

std::pair< double, double > gum::learning::genericBNLearner::chi2 ( const NodeId  id1, const NodeId  id2, const std::vector< NodeId > &  knowing = {}  )

inherited

Return the <statistic,pvalue> pair for chi2 test in the database.

Parameters

id1	first variable
id2	second variable
knowing	list of observed variables

Returns

a std::pair<double,double>

Definition at line 897 of file genericBNLearner.cpp.

                                                                                           {
      createApriori_();
      gum::learning::IndepTestChi2<> chi2score(scoreDatabase_.parser(),
                                               *apriori_,
                                               databaseRanges());

      return chi2score.statistics(id1, id2, knowing);
    }

chi2() [2/2]

std::pair< double, double > gum::learning::genericBNLearner::chi2 ( const std::string &  name1, const std::string &  name2, const std::vector< std::string > &  knowing = {}  )

inherited

Return the <statistic,pvalue> pair for the BNLearner.

Parameters

id1	first variable
id2	second variable
knowing	list of observed variables

Returns

a std::pair<double,double>

Definition at line 908 of file genericBNLearner.cpp.

                                                                                              {
      std::vector< NodeId > knowingIds;
      std::transform(knowing.begin(),
                     knowing.end(),
                     std::back_inserter(knowingIds),
                     [this](const std::string& c) -> NodeId { return this->idFromName(c); });
      return chi2(idFromName(name1), idFromName(name2), knowingIds);
    }

clearDatabaseRanges()

INLINE void gum::learning::genericBNLearner::clearDatabaseRanges ( )

inherited

reset the ranges to the one range corresponding to the whole database

Definition at line 494 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

{ ranges_.clear(); }

Here is the call graph for this function:

createApriori_()

void gum::learning::genericBNLearner::createApriori_ ( )

protectedinherited

create the apriori used for learning

Definition at line 439 of file genericBNLearner.cpp.

                                          {
      // first, save the old apriori, to be delete if everything is ok
      Apriori<>* old_apriori = apriori_;

      // create the new apriori
      switch (aprioriType_) {
        case AprioriType::NO_APRIORI:
          apriori_ = new AprioriNoApriori<>(scoreDatabase_.databaseTable(),
                                            scoreDatabase_.nodeId2Columns());
          break;

        case AprioriType::SMOOTHING:
          apriori_ = new AprioriSmoothing<>(scoreDatabase_.databaseTable(),
                                            scoreDatabase_.nodeId2Columns());
          break;

        case AprioriType::DIRICHLET_FROM_DATABASE:
          if (aprioriDatabase_ != nullptr) {
            delete aprioriDatabase_;
            aprioriDatabase_ = nullptr;
          }

          aprioriDatabase_
             = new Database(aprioriDbname_, scoreDatabase_, scoreDatabase_.missingSymbols());

          apriori_ = new AprioriDirichletFromDatabase<>(scoreDatabase_.databaseTable(),
                                                        aprioriDatabase_->parser(),
                                                        aprioriDatabase_->nodeId2Columns());
          break;

        case AprioriType::BDEU:
          apriori_
             = new AprioriBDeu<>(scoreDatabase_.databaseTable(), scoreDatabase_.nodeId2Columns());
          break;

        default:
          GUM_ERROR(OperationNotAllowed, "The BNLearner does not support yet this apriori")
      }

      // do not forget to assign a weight to the apriori
      apriori_->setWeight(aprioriWeight_);

      // remove the old apriori, if any
      if (old_apriori != nullptr) delete old_apriori;
    }

createCorrectedMutualInformation_()

void gum::learning::genericBNLearner::createCorrectedMutualInformation_ ( )

protectedinherited

create the Corrected Mutual Information instance for Miic/3off2

Definition at line 633 of file genericBNLearner.cpp.

                                                             {
      if (mutualInfo_ != nullptr) delete mutualInfo_;

      mutualInfo_ = new CorrectedMutualInformation<>(scoreDatabase_.parser(),
                                                     *noApriori_,
                                                     ranges_,
                                                     scoreDatabase_.nodeId2Columns());
      switch (kmode3Off2_) {
        case CorrectedMutualInformation<>::KModeTypes::MDL:
          mutualInfo_->useMDL();
          break;

        case CorrectedMutualInformation<>::KModeTypes::NML:
          mutualInfo_->useNML();
          break;

        case CorrectedMutualInformation<>::KModeTypes::NoCorr:
          mutualInfo_->useNoCorr();
          break;

        default:
          GUM_ERROR(NotImplementedYet,
                    "The BNLearner's corrected mutual information class does "
                       << "not implement yet this correction : " << int(kmode3Off2_));
      }
    }

createParamEstimator_()

ParamEstimator * gum::learning::genericBNLearner::createParamEstimator_ ( DBRowGeneratorParser<> &  parser, bool  take_into_account_score = true  )

protectedinherited

create the parameter estimator used for learning

Definition at line 541 of file genericBNLearner.cpp.

                                                                                            {
      ParamEstimator<>* param_estimator = nullptr;

      // create the new estimator
      switch (paramEstimatorType_) {
        case ParamEstimatorType::ML:
          if (take_into_account_score && (score_ != nullptr)) {
            param_estimator = new ParamEstimatorML<>(parser,
                                                     *apriori_,
                                                     score_->internalApriori(),
                                                     ranges_,
                                                     scoreDatabase_.nodeId2Columns());
          } else {
            param_estimator = new ParamEstimatorML<>(parser,
                                                     *apriori_,
                                                     *noApriori_,
                                                     ranges_,
                                                     scoreDatabase_.nodeId2Columns());
          }

          break;

        default:
          GUM_ERROR(OperationNotAllowed,
                    "genericBNLearner does not support "
                       << "yet this parameter estimator");
      }

      // assign the set of ranges
      param_estimator->setRanges(ranges_);

      return param_estimator;
    }

createScore_()

void gum::learning::genericBNLearner::createScore_ ( )

protectedinherited

create the score used for learning

Definition at line 485 of file genericBNLearner.cpp.

                                        {
      // first, save the old score, to be delete if everything is ok
      Score<>* old_score = score_;

      // create the new scoring function
      switch (scoreType_) {
        case ScoreType::AIC:
          score_ = new ScoreAIC<>(scoreDatabase_.parser(),
                                  *apriori_,
                                  ranges_,
                                  scoreDatabase_.nodeId2Columns());
          break;

        case ScoreType::BD:
          score_ = new ScoreBD<>(scoreDatabase_.parser(),
                                 *apriori_,
                                 ranges_,
                                 scoreDatabase_.nodeId2Columns());
          break;

        case ScoreType::BDeu:
          score_ = new ScoreBDeu<>(scoreDatabase_.parser(),
                                   *apriori_,
                                   ranges_,
                                   scoreDatabase_.nodeId2Columns());
          break;

        case ScoreType::BIC:
          score_ = new ScoreBIC<>(scoreDatabase_.parser(),
                                  *apriori_,
                                  ranges_,
                                  scoreDatabase_.nodeId2Columns());
          break;

        case ScoreType::K2:
          score_ = new ScoreK2<>(scoreDatabase_.parser(),
                                 *apriori_,
                                 ranges_,
                                 scoreDatabase_.nodeId2Columns());
          break;

        case ScoreType::LOG2LIKELIHOOD:
          score_ = new ScoreLog2Likelihood<>(scoreDatabase_.parser(),
                                             *apriori_,
                                             ranges_,
                                             scoreDatabase_.nodeId2Columns());
          break;

        default:
          GUM_ERROR(OperationNotAllowed, "genericBNLearner does not support yet this score")
      }

      // remove the old score, if any
      if (old_score != nullptr) delete old_score;
    }

currentTime()

double gum::learning::genericBNLearner::currentTime ( ) const

inlinevirtualinherited

get the current running time in second (double)

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1061 of file genericBNLearner.h.

                                 {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->currentTime();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

database()

INLINE const DatabaseTable & gum::learning::genericBNLearner::database ( ) const

inherited

returns the database used by the BNLearner

Definition at line 497 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                   {
      return scoreDatabase_.databaseTable();
    }

Here is the call graph for this function:

databaseRanges()

INLINE const std::vector< std::pair< std::size_t, std::size_t > > & gum::learning::genericBNLearner::databaseRanges ( ) const

inherited

returns the current database rows' ranges used for learning

Returns

The method returns a vector of pairs [Xi,Yi) of indices of rows in the database. The learning is performed on these set of rows.

Warning

an empty set of ranges means the whole database.

Definition at line 489 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                        {
      return ranges_;
    }

Here is the call graph for this function:

databaseWeight()

INLINE double gum::learning::genericBNLearner::databaseWeight ( ) const

inherited

returns the weight of the whole database

Definition at line 153 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

{ return scoreDatabase_.weight(); }

Here is the call graph for this function:

disableEpsilon()

void gum::learning::genericBNLearner::disableEpsilon ( )

inlinevirtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 926 of file genericBNLearner.h.

                            {
        algoK2_.approximationScheme().disableEpsilon();
        greedyHillClimbing_.disableEpsilon();
        localSearchWithTabuList_.disableEpsilon();
        Dag2BN_.disableEpsilon();
      };

disableMaxIter()

void gum::learning::genericBNLearner::disableMaxIter ( )

inlinevirtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1016 of file genericBNLearner.h.

                            {
        algoK2_.approximationScheme().disableMaxIter();
        greedyHillClimbing_.disableMaxIter();
        localSearchWithTabuList_.disableMaxIter();
        Dag2BN_.disableMaxIter();
      };

disableMaxTime()

void gum::learning::genericBNLearner::disableMaxTime ( )

inlinevirtualinherited

Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1069 of file genericBNLearner.h.

                            {
        algoK2_.approximationScheme().disableMaxTime();
        greedyHillClimbing_.disableMaxTime();
        localSearchWithTabuList_.disableMaxTime();
        Dag2BN_.disableMaxTime();
      };

disableMinEpsilonRate()

void gum::learning::genericBNLearner::disableMinEpsilonRate ( )

inlinevirtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 972 of file genericBNLearner.h.

                                   {
        algoK2_.approximationScheme().disableMinEpsilonRate();
        greedyHillClimbing_.disableMinEpsilonRate();
        localSearchWithTabuList_.disableMinEpsilonRate();
        Dag2BN_.disableMinEpsilonRate();
      };

distributeProgress()

INLINE void gum::learning::genericBNLearner::distributeProgress ( const ApproximationScheme *  approximationScheme, Size  pourcent, double  error, double  time  )

inlineinherited

{@ /// distribute signals

Definition at line 888 of file genericBNLearner.h.

                                                                      {
        setCurrentApproximationScheme(approximationScheme);

        if (onProgress.hasListener()) GUM_EMIT3(onProgress, pourcent, error, time);
      };

distributeStop()

INLINE void gum::learning::genericBNLearner::distributeStop ( const ApproximationScheme *  approximationScheme, std::string  message  )

inlineinherited

distribute signals

Definition at line 898 of file genericBNLearner.h.

                                                                   {
        setCurrentApproximationScheme(approximationScheme);

        if (onStop.hasListener()) GUM_EMIT1(onStop, message);
      };

domainSize() [1/2]

INLINE Size gum::learning::genericBNLearner::domainSize ( NodeId  var ) const

inherited

learn a structure from a file (must have read the db before)

Definition at line 479 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                             {
      return scoreDatabase_.domainSizes()[var];
    }

Here is the call graph for this function:

domainSize() [2/2]

INLINE Size gum::learning::genericBNLearner::domainSize ( const std::string &  var ) const

inherited

learn a structure from a file (must have read the db before)

Definition at line 483 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                       {
      return scoreDatabase_.domainSizes()[idFromName(var)];
    }

Here is the call graph for this function:

domainSizes()

INLINE const std::vector< std::size_t > & gum::learning::genericBNLearner::domainSizes ( ) const

inherited

returns the domain sizes of the variables in the database

Definition at line 474 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                             {
      return scoreDatabase_.domainSizes();
    }

Here is the call graph for this function:

enableEpsilon()

void gum::learning::genericBNLearner::enableEpsilon ( )

inlinevirtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 934 of file genericBNLearner.h.

                           {
        algoK2_.approximationScheme().enableEpsilon();
        greedyHillClimbing_.enableEpsilon();
        localSearchWithTabuList_.enableEpsilon();
        Dag2BN_.enableEpsilon();
      };

enableMaxIter()

void gum::learning::genericBNLearner::enableMaxIter ( )

inlinevirtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1023 of file genericBNLearner.h.

                           {
        algoK2_.approximationScheme().enableMaxIter();
        greedyHillClimbing_.enableMaxIter();
        localSearchWithTabuList_.enableMaxIter();
        Dag2BN_.enableMaxIter();
      };

enableMaxTime()

void gum::learning::genericBNLearner::enableMaxTime ( )

inlinevirtualinherited

stopping criterion on timeout If the criterion was disabled it will be enabled

Exceptions

OutOfBounds

if timeout<=0.0 timeout is time in second (double).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1075 of file genericBNLearner.h.

                           {
        algoK2_.approximationScheme().enableMaxTime();
        greedyHillClimbing_.enableMaxTime();
        localSearchWithTabuList_.enableMaxTime();
        Dag2BN_.enableMaxTime();
      };

enableMinEpsilonRate()

void gum::learning::genericBNLearner::enableMinEpsilonRate ( )

inlinevirtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 979 of file genericBNLearner.h.

                                  {
        algoK2_.approximationScheme().enableMinEpsilonRate();
        greedyHillClimbing_.enableMinEpsilonRate();
        localSearchWithTabuList_.enableMinEpsilonRate();
        Dag2BN_.enableMinEpsilonRate();
      };

epsilon()

double gum::learning::genericBNLearner::epsilon ( ) const

inlinevirtualinherited

Get the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 918 of file genericBNLearner.h.

                             {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->epsilon();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

eraseForbiddenArc() [1/3]

INLINE void gum::learning::genericBNLearner::eraseForbiddenArc ( const Arc &  arc )

inherited

Definition at line 318 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                  {
      constraintForbiddenArcs_.eraseArc(arc);
    }

Here is the call graph for this function:

eraseForbiddenArc() [2/3]

INLINE void gum::learning::genericBNLearner::eraseForbiddenArc ( const NodeId  tail, const NodeId  head  )

inherited

Definition at line 328 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                        {
      eraseForbiddenArc(Arc(tail, head));
    }

Here is the call graph for this function:

eraseForbiddenArc() [3/3]

INLINE void gum::learning::genericBNLearner::eraseForbiddenArc ( const std::string &  tail, const std::string &  head  )

inherited

Definition at line 339 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                           {
      eraseForbiddenArc(Arc(idFromName(tail), idFromName(head)));
    }

Here is the call graph for this function:

eraseMandatoryArc() [1/3]

INLINE void gum::learning::genericBNLearner::eraseMandatoryArc ( const Arc &  arc )

inherited

Definition at line 355 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                  {
      constraintMandatoryArcs_.eraseArc(arc);
    }

Here is the call graph for this function:

eraseMandatoryArc() [2/3]

INLINE void gum::learning::genericBNLearner::eraseMandatoryArc ( const NodeId  tail, const NodeId  head  )

inherited

Definition at line 377 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                        {
      eraseMandatoryArc(Arc(tail, head));
    }

Here is the call graph for this function:

eraseMandatoryArc() [3/3]

INLINE void gum::learning::genericBNLearner::eraseMandatoryArc ( const std::string &  tail, const std::string &  head  )

inherited

Definition at line 366 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                           {
      eraseMandatoryArc(Arc(idFromName(tail), idFromName(head)));
    }

Here is the call graph for this function:

erasePossibleEdge() [1/3]

INLINE void gum::learning::genericBNLearner::erasePossibleEdge ( const Edge &  edge )

inherited

Definition at line 281 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                    {
      constraintPossibleEdges_.eraseEdge(edge);
    }

Here is the call graph for this function:

erasePossibleEdge() [2/3]

INLINE void gum::learning::genericBNLearner::erasePossibleEdge ( const NodeId  tail, const NodeId  head  )

inherited

Definition at line 291 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                        {
      erasePossibleEdge(Edge(tail, head));
    }

Here is the call graph for this function:

erasePossibleEdge() [3/3]

INLINE void gum::learning::genericBNLearner::erasePossibleEdge ( const std::string &  tail, const std::string &  head  )

inherited

Definition at line 302 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                           {
      erasePossibleEdge(Edge(idFromName(tail), idFromName(head)));
    }

Here is the call graph for this function:

G2() [1/2]

std::pair< double, double > gum::learning::genericBNLearner::G2 ( const NodeId  id1, const NodeId  id2, const std::vector< NodeId > &  knowing = {}  )

inherited

Return the <statistic,pvalue> pair for for G2 test in the database.

Parameters

id1	first variable
id2	second variable
knowing	list of observed variables

Returns

a std::pair<double,double>

Definition at line 919 of file genericBNLearner.cpp.

                                                                                         {
      createApriori_();
      gum::learning::IndepTestG2<> g2score(scoreDatabase_.parser(), *apriori_, databaseRanges());
      return g2score.statistics(id1, id2, knowing);
    }

G2() [2/2]

std::pair< double, double > gum::learning::genericBNLearner::G2 ( const std::string &  name1, const std::string &  name2, const std::vector< std::string > &  knowing = {}  )

inherited

Return the <statistic,pvalue> pair for for G2 test in the database.

Parameters

id1	first variable
id2	second variable
knowing	list of observed variables

Returns

a std::pair<double,double>

Definition at line 927 of file genericBNLearner.cpp.

                                                                                            {
      std::vector< NodeId > knowingIds;
      std::transform(knowing.begin(),
                     knowing.end(),
                     std::back_inserter(knowingIds),
                     [this](const std::string& c) -> NodeId { return this->idFromName(c); });
      return G2(idFromName(name1), idFromName(name2), knowingIds);
    }

getAprioriType_()

INLINE const std::string & gum::learning::genericBNLearner::getAprioriType_ ( ) const

protectedinherited

returns the type (as a string) of a given apriori

Definition at line 447 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                    {
      switch (aprioriType_) {
        case AprioriType::NO_APRIORI:
          return AprioriNoApriori<>::type::type;

        case AprioriType::SMOOTHING:
          return AprioriSmoothing<>::type::type;

        case AprioriType::DIRICHLET_FROM_DATABASE:
          return AprioriDirichletFromDatabase<>::type::type;

        case AprioriType::BDEU:
          return AprioriBDeu<>::type::type;

        default:
          GUM_ERROR(OperationNotAllowed,
                    "genericBNLearner getAprioriType does "
                    "not support yet this apriori");
      }
    }

Here is the call graph for this function:

hasMissingValues()

INLINE bool gum::learning::genericBNLearner::hasMissingValues ( ) const

inherited

returns true if the learner's database has missing values

Definition at line 262 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                         {
      return scoreDatabase_.databaseTable().hasMissingValues();
    }

Here is the call graph for this function:

history()

const std::vector< double >& gum::learning::genericBNLearner::history ( ) const

inlinevirtualinherited

Exceptions

OperationNotAllowed

if scheme not performed or verbosity=false

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1145 of file genericBNLearner.h.

                                                 {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->history();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

idFromName()

INLINE NodeId gum::learning::genericBNLearner::idFromName ( const std::string &  var_name ) const

inherited

returns the node id corresponding to a variable name

Exceptions

MissingVariableInDatabase

if a variable of the BN is not found in the database.

Definition at line 128 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                              {
      return scoreDatabase_.idFromName(var_name);
    }

Here is the call graph for this function:

initialDAG()

INLINE DAG gum::learning::genericBNLearner::initialDAG ( )

inherited

returns the initial DAG structure

Definition at line 158 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

{ return initialDag_; }

Here is the call graph for this function:

isCSVFileName_()

void gum::learning::genericBNLearner::isCSVFileName_ ( const std::string &  filename )

staticprotectedinherited

checks whether the extension of a CSV filename is correct

Definition at line 393 of file genericBNLearner.cpp.

                                                                   {
      // get the extension of the file
      Size filename_size = Size(filename.size());

      if (filename_size < 4) {
        GUM_ERROR(FormatNotFound,
                  "genericBNLearner could not determine the "
                  "file type of the database");
      }

      std::string extension = filename.substr(filename.size() - 4);
      std::transform(extension.begin(), extension.end(), extension.begin(), ::tolower);

      if (extension != ".csv") {
        GUM_ERROR(OperationNotAllowed,
                  "genericBNLearner does not support yet this type of database file");
      }
    }

isEnabledEpsilon()

bool gum::learning::genericBNLearner::isEnabledEpsilon ( ) const

inlinevirtualinherited

Returns

true if stopping criterion on epsilon is enabled, false otherwise

Implements gum::IApproximationSchemeConfiguration.

Definition at line 943 of file genericBNLearner.h.

                                    {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->isEnabledEpsilon();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

isEnabledMaxIter()

bool gum::learning::genericBNLearner::isEnabledMaxIter ( ) const

inlinevirtualinherited

Returns

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1031 of file genericBNLearner.h.

                                    {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->isEnabledMaxIter();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

isEnabledMaxTime()

bool gum::learning::genericBNLearner::isEnabledMaxTime ( ) const

inlinevirtualinherited

Returns

true if stopping criterion on timeout is enabled, false otherwise

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1083 of file genericBNLearner.h.

                                    {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->isEnabledMaxTime();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

isEnabledMinEpsilonRate()

bool gum::learning::genericBNLearner::isEnabledMinEpsilonRate ( ) const

inlinevirtualinherited

Returns

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

Implements gum::IApproximationSchemeConfiguration.

Definition at line 987 of file genericBNLearner.h.

                                           {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->isEnabledMinEpsilonRate();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

latentVariables()

INLINE const std::vector< Arc > gum::learning::genericBNLearner::latentVariables ( ) const

inherited

get the list of arcs hiding latent variables

Exceptions

OperationNotAllowed

when 3off2 or MIIC is not the selected algorithm

Definition at line 229 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                          {
      return algoMiic3off2_.latentVariables();
    }

Here is the call graph for this function:

learnBN()

template<typename GUM_SCALAR >

BayesNet< GUM_SCALAR > gum::learning::BNLearner< GUM_SCALAR >::learnBN

(

)

learn a Bayes Net from a file (must have read the db before)

learnDAG()

DAG gum::learning::genericBNLearner::learnDAG ( )

inherited

learn a structure from a file (must have read the db before)

Definition at line 625 of file genericBNLearner.cpp.

                                   {
      // create the score and the apriori
      createApriori_();
      createScore_();

      return learnDag_();
    }

learnDag_()

DAG gum::learning::genericBNLearner::learnDag_ ( )

protectedinherited

returns the DAG learnt

Definition at line 660 of file genericBNLearner.cpp.

                                    {
      // check that the database does not contain any missing value
      if (scoreDatabase_.databaseTable().hasMissingValues()
          || ((aprioriDatabase_ != nullptr)
              && (aprioriType_ == AprioriType::DIRICHLET_FROM_DATABASE)
              && aprioriDatabase_->databaseTable().hasMissingValues())) {
        GUM_ERROR(MissingValueInDatabase,
                  "For the moment, the BNLearner is unable to cope "
                  "with missing values in databases");
      }
      // add the mandatory arcs to the initial dag and remove the forbidden ones
      // from the initial graph
      DAG init_graph = initialDag_;

      const ArcSet& mandatory_arcs = constraintMandatoryArcs_.arcs();

      for (const auto& arc: mandatory_arcs) {
        if (!init_graph.exists(arc.tail())) init_graph.addNodeWithId(arc.tail());

        if (!init_graph.exists(arc.head())) init_graph.addNodeWithId(arc.head());

        init_graph.addArc(arc.tail(), arc.head());
      }

      const ArcSet& forbidden_arcs = constraintForbiddenArcs_.arcs();

      for (const auto& arc: forbidden_arcs) {
        init_graph.eraseArc(arc);
      }

      switch (selectedAlgo_) {
        // ========================================================================
        case AlgoType::MIIC:
        case AlgoType::THREE_OFF_TWO: {
          BNLearnerListener listener(this, algoMiic3off2_);
          // create the mixedGraph and the corrected mutual information
          MixedGraph mgraph = this->prepareMiic3Off2_();

          return algoMiic3off2_.learnStructure(*mutualInfo_, mgraph);
        }

        // ========================================================================
        case AlgoType::GREEDY_HILL_CLIMBING: {
          BNLearnerListener listener(this, greedyHillClimbing_);
          StructuralConstraintSetStatic< StructuralConstraintMandatoryArcs,
                                         StructuralConstraintForbiddenArcs,
                                         StructuralConstraintPossibleEdges,
                                         StructuralConstraintSliceOrder >
             gen_constraint;
          static_cast< StructuralConstraintMandatoryArcs& >(gen_constraint)
             = constraintMandatoryArcs_;
          static_cast< StructuralConstraintForbiddenArcs& >(gen_constraint)
             = constraintForbiddenArcs_;
          static_cast< StructuralConstraintPossibleEdges& >(gen_constraint)
             = constraintPossibleEdges_;
          static_cast< StructuralConstraintSliceOrder& >(gen_constraint) = constraintSliceOrder_;

          GraphChangesGenerator4DiGraph< decltype(gen_constraint) > op_set(gen_constraint);

          StructuralConstraintSetStatic< StructuralConstraintIndegree, StructuralConstraintDAG >
             sel_constraint;
          static_cast< StructuralConstraintIndegree& >(sel_constraint) = constraintIndegree_;

          GraphChangesSelector4DiGraph< decltype(sel_constraint), decltype(op_set) > selector(
             *score_,
             sel_constraint,
             op_set);

          return greedyHillClimbing_.learnStructure(selector, init_graph);
        }

        // ========================================================================
        case AlgoType::LOCAL_SEARCH_WITH_TABU_LIST: {
          BNLearnerListener listener(this, localSearchWithTabuList_);
          StructuralConstraintSetStatic< StructuralConstraintMandatoryArcs,
                                         StructuralConstraintForbiddenArcs,
                                         StructuralConstraintPossibleEdges,
                                         StructuralConstraintSliceOrder >
             gen_constraint;
          static_cast< StructuralConstraintMandatoryArcs& >(gen_constraint)
             = constraintMandatoryArcs_;
          static_cast< StructuralConstraintForbiddenArcs& >(gen_constraint)
             = constraintForbiddenArcs_;
          static_cast< StructuralConstraintPossibleEdges& >(gen_constraint)
             = constraintPossibleEdges_;
          static_cast< StructuralConstraintSliceOrder& >(gen_constraint) = constraintSliceOrder_;

          GraphChangesGenerator4DiGraph< decltype(gen_constraint) > op_set(gen_constraint);

          StructuralConstraintSetStatic< StructuralConstraintTabuList,
                                         StructuralConstraintIndegree,
                                         StructuralConstraintDAG >
             sel_constraint;
          static_cast< StructuralConstraintTabuList& >(sel_constraint) = constraintTabuList_;
          static_cast< StructuralConstraintIndegree& >(sel_constraint) = constraintIndegree_;

          GraphChangesSelector4DiGraph< decltype(sel_constraint), decltype(op_set) > selector(
             *score_,
             sel_constraint,
             op_set);

          return localSearchWithTabuList_.learnStructure(selector, init_graph);
        }

        // ========================================================================
        case AlgoType::K2: {
          BNLearnerListener listener(this, algoK2_.approximationScheme());
          StructuralConstraintSetStatic< StructuralConstraintMandatoryArcs,
                                         StructuralConstraintForbiddenArcs,
                                         StructuralConstraintPossibleEdges >
             gen_constraint;
          static_cast< StructuralConstraintMandatoryArcs& >(gen_constraint)
             = constraintMandatoryArcs_;
          static_cast< StructuralConstraintForbiddenArcs& >(gen_constraint)
             = constraintForbiddenArcs_;
          static_cast< StructuralConstraintPossibleEdges& >(gen_constraint)
             = constraintPossibleEdges_;

          GraphChangesGenerator4K2< decltype(gen_constraint) > op_set(gen_constraint);

          // if some mandatory arcs are incompatible with the order, use a DAG
          // constraint instead of a DiGraph constraint to avoid cycles
          const ArcSet& mandatory_arcs
             = static_cast< StructuralConstraintMandatoryArcs& >(gen_constraint).arcs();
          const Sequence< NodeId >& order            = algoK2_.order();
          bool                      order_compatible = true;

          for (const auto& arc: mandatory_arcs) {
            if (order.pos(arc.tail()) >= order.pos(arc.head())) {
              order_compatible = false;
              break;
            }
          }

          if (order_compatible) {
            StructuralConstraintSetStatic< StructuralConstraintIndegree,
                                           StructuralConstraintDiGraph >
               sel_constraint;
            static_cast< StructuralConstraintIndegree& >(sel_constraint) = constraintIndegree_;

            GraphChangesSelector4DiGraph< decltype(sel_constraint), decltype(op_set) > selector(
               *score_,
               sel_constraint,
               op_set);

            return algoK2_.learnStructure(selector, init_graph);
          } else {
            StructuralConstraintSetStatic< StructuralConstraintIndegree, StructuralConstraintDAG >
               sel_constraint;
            static_cast< StructuralConstraintIndegree& >(sel_constraint) = constraintIndegree_;

            GraphChangesSelector4DiGraph< decltype(sel_constraint), decltype(op_set) > selector(
               *score_,
               sel_constraint,
               op_set);

            return algoK2_.learnStructure(selector, init_graph);
          }
        }

        // ========================================================================
        default:
          GUM_ERROR(OperationNotAllowed,
                    "the learnDAG method has not been implemented for this "
                    "learning algorithm");
      }
    }

learnMixedStructure()

MixedGraph gum::learning::genericBNLearner::learnMixedStructure ( )

inherited

learn a partial structure from a file (must have read the db before and must have selected miic or 3off2)

Definition at line 607 of file genericBNLearner.cpp.

                                                     {
      if (selectedAlgo_ != AlgoType::MIIC && selectedAlgo_ != AlgoType::THREE_OFF_TWO) {
        GUM_ERROR(OperationNotAllowed, "Must be using the miic/3off2 algorithm")
      }
      // check that the database does not contain any missing value
      if (scoreDatabase_.databaseTable().hasMissingValues()) {
        GUM_ERROR(MissingValueInDatabase,
                  "For the moment, the BNLearner is unable to learn "
                     << "structures with missing values in databases");
      }
      BNLearnerListener listener(this, algoMiic3off2_);

      // create the mixedGraph_constraint_MandatoryArcs.arcs();
      MixedGraph mgraph = this->prepareMiic3Off2_();

      return algoMiic3off2_.learnMixedStructure(*mutualInfo_, mgraph);
    }

learnParameters() [1/2]

template<typename GUM_SCALAR >

BayesNet< GUM_SCALAR > gum::learning::BNLearner< GUM_SCALAR >::learnParameters	(	const DAG &	dag,
		bool	takeIntoAccountScore = true
	)

learns a BN (its parameters) when its structure is known

Parameters

dag

the structure of the Bayesian network

takeIntoAccountScore

The dag passed in argument may have been learnt from a structure learning. In this case, if the score used to learn the structure has an implicit apriori (like K2 which has a 1-smoothing apriori), it is important to also take into account this implicit apriori for parameter learning. By default, if a score exists, we will learn parameters by taking into account the apriori specified by methods useAprioriXXX () + the implicit apriori of the score, else we just take into account the apriori specified by useAprioriXXX ()

learnParameters() [2/2]

template<typename GUM_SCALAR >

BayesNet< GUM_SCALAR > gum::learning::BNLearner< GUM_SCALAR >::learnParameters

(

bool

take_into_account_score = true

)

Parameters

take_into_account_score

The dag of the BN which was passed in argument to the BNLearner may have been learnt from a structure learning. In this case, if the score used to learn the structure has an implicit apriori (like K2 which has a 1-smoothing apriori), it is important to also take into account this implicit apriori for parameter learning. By default, if a score exists, we will learn parameters by taking into account the apriori specified by methods useAprioriXXX () + the implicit apriori of the score, else we just take into account the apriori specified by useAprioriXXX ()

Exceptions

MissingVariableInDatabase	if a variable of the BN is not found in the database.
UnknownLabelInDatabase	if a label is found in the databast that do not correpond to the variable.

logLikelihood() [1/2]

double gum::learning::genericBNLearner::logLikelihood ( const std::vector< NodeId > &  vars, const std::vector< NodeId > &  knowing = {}  )

inherited

Return the loglikelihood of vars in the base, conditioned by knowing for the BNLearner.

Parameters

vars	a vector of NodeIds
knowing	an optional vector of conditioning NodeIds

Returns

a std::pair<double,double>

Definition at line 938 of file genericBNLearner.cpp.

                                                                               {
      createApriori_();
      gum::learning::ScoreLog2Likelihood<> ll2score(scoreDatabase_.parser(),
                                                    *apriori_,
                                                    databaseRanges());

      std::vector< NodeId > total(vars);
      total.insert(total.end(), knowing.begin(), knowing.end());
      double LLtotal = ll2score.score(IdCondSet<>(total, false, true));
      if (knowing.size() == (Size)0) {
        return LLtotal;
      } else {
        double LLknw = ll2score.score(IdCondSet<>(knowing, false, true));
        return LLtotal - LLknw;
      }
    }

logLikelihood() [2/2]

double gum::learning::genericBNLearner::logLikelihood ( const std::vector< std::string > &  vars, const std::vector< std::string > &  knowing = {}  )

inherited

Return the loglikelihood of vars in the base, conditioned by knowing for the BNLearner.

Parameters

vars	a vector of name of rows
knowing	an optional vector of conditioning rows

Returns

a std::pair<double,double>

Definition at line 956 of file genericBNLearner.cpp.

                                                                                  {
      std::vector< NodeId > ids;
      std::vector< NodeId > knowingIds;

      auto mapper = [this](const std::string& c) -> NodeId {
        return this->idFromName(c);
      };

      std::transform(vars.begin(), vars.end(), std::back_inserter(ids), mapper);
      std::transform(knowing.begin(), knowing.end(), std::back_inserter(knowingIds), mapper);

      return logLikelihood(ids, knowingIds);
    }

maxIter()

Size gum::learning::genericBNLearner::maxIter ( ) const

inlinevirtualinherited

Returns

the criterion on number of iterations

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1008 of file genericBNLearner.h.

                           {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->maxIter();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

maxTime()

double gum::learning::genericBNLearner::maxTime ( ) const

inlinevirtualinherited

returns the timeout (in seconds)

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1053 of file genericBNLearner.h.

                             {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->maxTime();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

messageApproximationScheme()

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

inherited

Returns the approximation scheme message.

Returns

Returns the approximation scheme message.

Definition at line 38 of file IApproximationSchemeConfiguration_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                {
    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();
  }

Here is the call graph for this function:

minEpsilonRate()

double gum::learning::genericBNLearner::minEpsilonRate ( ) const

inlinevirtualinherited

Get the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 964 of file genericBNLearner.h.

                                    {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->minEpsilonRate();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

nameFromId()

INLINE const std::string & gum::learning::genericBNLearner::nameFromId ( NodeId  id ) const

inherited

returns the variable name corresponding to a given node id

Definition at line 133 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                        {
      return scoreDatabase_.nameFromId(id);
    }

Here is the call graph for this function:

names()

INLINE const std::vector< std::string > & gum::learning::genericBNLearner::names ( ) const

inherited

returns the names of the variables in the database

Definition at line 469 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                       {
      return scoreDatabase_.names();
    }

Here is the call graph for this function:

nbCols()

INLINE Size gum::learning::genericBNLearner::nbCols ( ) const

inherited

Returns

the number of cols in the database

Definition at line 501 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

{ return scoreDatabase_.domainSizes().size(); }

Here is the call graph for this function:

nbrIterations()

Size gum::learning::genericBNLearner::nbrIterations ( ) const

inlinevirtualinherited

Exceptions

OperationNotAllowed

if scheme not performed

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1137 of file genericBNLearner.h.

                                 {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->nbrIterations();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

nbRows()

INLINE Size gum::learning::genericBNLearner::nbRows ( ) const

inherited

Returns

the number of rows in the database

Definition at line 503 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

{ return scoreDatabase_.databaseTable().size(); }

Here is the call graph for this function:

operator=() [1/2]

template<typename GUM_SCALAR >

BNLearner& gum::learning::BNLearner< GUM_SCALAR >::operator=

(

const BNLearner< GUM_SCALAR > &

)

copy operator

operator=() [2/2]

template<typename GUM_SCALAR >

BNLearner& gum::learning::BNLearner< GUM_SCALAR >::operator=

(

BNLearner< GUM_SCALAR > &&

)

move operator

periodSize()

Size gum::learning::genericBNLearner::periodSize ( ) const

inlinevirtualinherited

how many samples between 2 stopping isEnableds

Exceptions

OutOfBounds

if p<1

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1101 of file genericBNLearner.h.

                              {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->periodSize();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

prepareMiic3Off2_()

MixedGraph gum::learning::genericBNLearner::prepareMiic3Off2_ ( )

protectedinherited

prepares the initial graph for 3off2 or miic

Definition at line 577 of file genericBNLearner.cpp.

                                                   {
      // Initialize the mixed graph to the fully connected graph
      MixedGraph mgraph;
      for (Size i = 0; i < scoreDatabase_.databaseTable().nbVariables(); ++i) {
        mgraph.addNodeWithId(i);
        for (Size j = 0; j < i; ++j) {
          mgraph.addEdge(j, i);
        }
      }

      // translating the constraints for 3off2 or miic
      HashTable< std::pair< NodeId, NodeId >, char > initial_marks;
      const ArcSet& mandatory_arcs = constraintMandatoryArcs_.arcs();
      for (const auto& arc: mandatory_arcs) {
        initial_marks.insert({arc.tail(), arc.head()}, '>');
      }

      const ArcSet& forbidden_arcs = constraintForbiddenArcs_.arcs();
      for (const auto& arc: forbidden_arcs) {
        initial_marks.insert({arc.tail(), arc.head()}, '-');
      }
      algoMiic3off2_.addConstraints(initial_marks);

      // create the mutual entropy object
      // if ( _mutual_info_ == nullptr) { this->useNMLCorrection(); }
      createCorrectedMutualInformation_();

      return mgraph;
    }

rawPseudoCount() [1/2]

std::vector< double > gum::learning::genericBNLearner::rawPseudoCount ( const std::vector< NodeId > &  vars )

inherited

Return the pseudoconts ofNodeIds vars in the base in a raw array.

Parameters

vars	a vector of

Returns

a a std::vector<double> containing the contingency table

Definition at line 971 of file genericBNLearner.cpp.

                                                                                        {
      Potential< double > res;

      createApriori_();
      gum::learning::PseudoCount<> count(scoreDatabase_.parser(), *apriori_, databaseRanges());
      return count.get(vars);
    }

rawPseudoCount() [2/2]

std::vector< double > gum::learning::genericBNLearner::rawPseudoCount ( const std::vector< std::string > &  vars )

inherited

Return the pseudoconts of vars in the base in a raw array.

Parameters

vars	a vector of name

Returns

a std::vector<double> containing the contingency table

Definition at line 980 of file genericBNLearner.cpp.

                                                                                           {
      std::vector< NodeId > ids;

      auto mapper = [this](const std::string& c) -> NodeId {
        return this->idFromName(c);
      };

      std::transform(vars.begin(), vars.end(), std::back_inserter(ids), mapper);

      return rawPseudoCount(ids);
    }

readFile_()

DatabaseTable gum::learning::genericBNLearner::readFile_ ( const std::string &  filename, const std::vector< std::string > &  missing_symbols  )

staticprotectedinherited

reads a file and returns a databaseVectInRam

Definition at line 413 of file genericBNLearner.cpp.

                                                                                               {
      // get the extension of the file
      isCSVFileName_(filename);

      DBInitializerFromCSV<> initializer(filename);

      const auto&       var_names = initializer.variableNames();
      const std::size_t nb_vars   = var_names.size();

      DBTranslatorSet<>                translator_set;
      DBTranslator4LabelizedVariable<> translator(missing_symbols);
      for (std::size_t i = 0; i < nb_vars; ++i) {
        translator_set.insertTranslator(translator, i);
      }

      DatabaseTable<> database(missing_symbols, translator_set);
      database.setVariableNames(initializer.variableNames());
      initializer.fillDatabase(database);

      database.reorder();

      return database;
    }

recordWeight()

INLINE double gum::learning::genericBNLearner::recordWeight ( const std::size_t  i ) const

inherited

returns the weight of the ith record

Exceptions

OutOfBounds

if i is outside the set of indices of the records

Definition at line 148 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                        {
      return scoreDatabase_.weight(i);
    }

Here is the call graph for this function:

setCurrentApproximationScheme()

INLINE void gum::learning::genericBNLearner::setCurrentApproximationScheme ( const ApproximationScheme *  approximationScheme )

inlineinherited

{@ /// distribute signals

Definition at line 884 of file genericBNLearner.h.

                                                                                                {
        currentAlgorithm_ = approximationScheme;
      }

setDatabaseWeight()

INLINE void gum::learning::genericBNLearner::setDatabaseWeight ( const double  new_weight )

inherited

assign a weight to all the rows of the learning database so that the sum of their weights is equal to new_weight

assign new weight to the rows of the learning database

Definition at line 138 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                           {
      scoreDatabase_.setDatabaseWeight(new_weight);
    }

Here is the call graph for this function:

setEpsilon()

void gum::learning::genericBNLearner::setEpsilon ( double  eps )

inlinevirtualinherited

Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)| If the criterion was disabled it will be enabled.

Exceptions

OutOfBounds

if eps<0

Implements gum::IApproximationSchemeConfiguration.

Definition at line 910 of file genericBNLearner.h.

                                  {
        algoK2_.approximationScheme().setEpsilon(eps);
        greedyHillClimbing_.setEpsilon(eps);
        localSearchWithTabuList_.setEpsilon(eps);
        Dag2BN_.setEpsilon(eps);
      };

setForbiddenArcs()

INLINE void gum::learning::genericBNLearner::setForbiddenArcs ( const ArcSet &  set )

inherited

assign a set of forbidden arcs

Definition at line 308 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                    {
      constraintForbiddenArcs_.setArcs(set);
    }

Here is the call graph for this function:

setInitialDAG()

INLINE void gum::learning::genericBNLearner::setInitialDAG ( const DAG &  dag )

inherited

sets an initial DAG structure

Definition at line 156 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

{ initialDag_ = dag; }

Here is the call graph for this function:

setMandatoryArcs()

INLINE void gum::learning::genericBNLearner::setMandatoryArcs ( const ArcSet &  set )

inherited

assign a set of forbidden arcs

Definition at line 345 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                    {
      constraintMandatoryArcs_.setArcs(set);
    }

Here is the call graph for this function:

setMaxIndegree()

INLINE void gum::learning::genericBNLearner::setMaxIndegree ( Size  max_indegree )

inherited

sets the max indegree

Definition at line 197 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                  {
      constraintIndegree_.setMaxIndegree(max_indegree);
    }

Here is the call graph for this function:

setMaxIter()

void gum::learning::genericBNLearner::setMaxIter ( Size  max )

inlinevirtualinherited

stopping criterion on number of iterationsIf the criterion was disabled it will be enabled

Parameters

max	The maximum number of iterations

Exceptions

OutOfBounds

if max<=1

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1000 of file genericBNLearner.h.

                                {
        algoK2_.approximationScheme().setMaxIter(max);
        greedyHillClimbing_.setMaxIter(max);
        localSearchWithTabuList_.setMaxIter(max);
        Dag2BN_.setMaxIter(max);
      };

setMaxTime()

void gum::learning::genericBNLearner::setMaxTime ( double  timeout )

inlinevirtualinherited

stopping criterion on timeout If the criterion was disabled it will be enabled

Exceptions

OutOfBounds

if timeout<=0.0 timeout is time in second (double).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1045 of file genericBNLearner.h.

                                      {
        algoK2_.approximationScheme().setMaxTime(timeout);
        greedyHillClimbing_.setMaxTime(timeout);
        localSearchWithTabuList_.setMaxTime(timeout);
        Dag2BN_.setMaxTime(timeout);
      }

setMinEpsilonRate()

void gum::learning::genericBNLearner::setMinEpsilonRate ( double  rate )

inlinevirtualinherited

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.

Exceptions

OutOfBounds

if rate<0

Implements gum::IApproximationSchemeConfiguration.

Definition at line 956 of file genericBNLearner.h.

                                          {
        algoK2_.approximationScheme().setMinEpsilonRate(rate);
        greedyHillClimbing_.setMinEpsilonRate(rate);
        localSearchWithTabuList_.setMinEpsilonRate(rate);
        Dag2BN_.setMinEpsilonRate(rate);
      };

setPeriodSize()

void gum::learning::genericBNLearner::setPeriodSize ( Size  p )

inlinevirtualinherited

how many samples between 2 stopping isEnableds

Exceptions

OutOfBounds

if p<1

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1094 of file genericBNLearner.h.

                                 {
        algoK2_.approximationScheme().setPeriodSize(p);
        greedyHillClimbing_.setPeriodSize(p);
        localSearchWithTabuList_.setPeriodSize(p);
        Dag2BN_.setPeriodSize(p);
      };

setPossibleEdges()

INLINE void gum::learning::genericBNLearner::setPossibleEdges ( const EdgeSet &  set )

inherited

assign a set of forbidden edges

Warning

Once at least one possible edge is defined, all other edges are not possible anymore

Definition at line 267 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                     {
      constraintPossibleEdges_.setEdges(set);
    }

Here is the call graph for this function:

setPossibleSkeleton()

INLINE void gum::learning::genericBNLearner::setPossibleSkeleton ( const UndiGraph &  skeleton )

inherited

assign a set of forbidden edges

Warning

Once at least one possible edge is defined, all other edges are not possible anymore

Definition at line 271 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                           {
      setPossibleEdges(g.edges());
    }

Here is the call graph for this function:

setRecordWeight()

INLINE void gum::learning::genericBNLearner::setRecordWeight ( const std::size_t  i, const double  weight  )

inherited

sets the weight of the ith record of the database

assign new weight to the ith row of the learning database

Exceptions

OutOfBounds

if i is outside the set of indices of the records or if the weight is negative

Definition at line 143 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                            {
      scoreDatabase_.setWeight(i, new_weight);
    }

Here is the call graph for this function:

setSliceOrder() [1/2]

INLINE void gum::learning::genericBNLearner::setSliceOrder ( const NodeProperty< NodeId > &  slice_order )

inherited

sets a partial order on the nodes

Parameters

slice_order

a NodeProperty given the rank (priority) of nodes in the partial order

Definition at line 382 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                         {
      constraintSliceOrder_ = StructuralConstraintSliceOrder(slice_order);
    }

Here is the call graph for this function:

setSliceOrder() [2/2]

INLINE void gum::learning::genericBNLearner::setSliceOrder ( const std::vector< std::vector< std::string > > &  slices )

inherited

sets a partial order on the nodes

Parameters

slices

the list of list of variable names

Definition at line 387 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                        {
      NodeProperty< NodeId > slice_order;
      NodeId                 rank = 0;
      for (const auto& slice: slices) {
        for (const auto& name: slice) {
          slice_order.insert(idFromName(name), rank);
        }
        rank++;
      }
      setSliceOrder(slice_order);
    }

Here is the call graph for this function:

setVerbosity()

void gum::learning::genericBNLearner::setVerbosity ( bool  v )

inlinevirtualinherited

verbosity

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1111 of file genericBNLearner.h.

                                {
        algoK2_.approximationScheme().setVerbosity(v);
        greedyHillClimbing_.setVerbosity(v);
        localSearchWithTabuList_.setVerbosity(v);
        Dag2BN_.setVerbosity(v);
      };

state()

template<typename GUM_SCALAR >

std::vector< std::tuple< std::string, std::string, std::string > > gum::learning::BNLearner< GUM_SCALAR >::state

(

)

const

Returns

a representation of the state of the learner in the form vector<key,value,comment>

stateApproximationScheme()

ApproximationSchemeSTATE gum::learning::genericBNLearner::stateApproximationScheme ( ) const

inlinevirtualinherited

history

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1129 of file genericBNLearner.h.

                                                                {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->stateApproximationScheme();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

toString()

template<typename GUM_SCALAR >

std::string gum::learning::BNLearner< GUM_SCALAR >::toString

(

)

const

Returns

Returns a string representation of this BNLearner's current features.

use3off2()

INLINE void gum::learning::genericBNLearner::use3off2 ( )

inherited

indicate that we wish to use 3off2

Definition at line 202 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                           {
      selectedAlgo_ = AlgoType::THREE_OFF_TWO;
      algoMiic3off2_.set3of2Behaviour();
    }

Here is the call graph for this function:

useAprioriBDeu()

INLINE void gum::learning::genericBNLearner::useAprioriBDeu ( double  weight = 1 )

inherited

use the BDeu apriori

The BDeu apriori adds weight to all the cells of the countings tables. In other words, it adds weight rows in the database with equally probable values.

Definition at line 436 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                              {
      if (weight < 0) { GUM_ERROR(OutOfBounds, "the weight of the apriori must be positive") }

      aprioriType_ = AprioriType::BDEU;
      _setAprioriWeight_(weight);

      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

useAprioriDirichlet()

INLINE void gum::learning::genericBNLearner::useAprioriDirichlet ( const std::string &  filename, double  weight = 1  )

inherited

use the Dirichlet apriori

Definition at line 424 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                              {
      if (weight < 0) { GUM_ERROR(OutOfBounds, "the weight of the apriori must be positive") }

      aprioriDbname_ = filename;
      aprioriType_   = AprioriType::DIRICHLET_FROM_DATABASE;
      _setAprioriWeight_(weight);

      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

useAprioriSmoothing()

INLINE void gum::learning::genericBNLearner::useAprioriSmoothing ( double  weight = 1 )

inherited

use the apriori smoothing

Parameters

weight

pass in argument a weight if you wish to assign a weight to the smoothing, else the current weight of the genericBNLearner will be used.

Definition at line 414 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                   {
      if (weight < 0) { GUM_ERROR(OutOfBounds, "the weight of the apriori must be positive") }

      aprioriType_ = AprioriType::SMOOTHING;
      _setAprioriWeight_(weight);

      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

useCrossValidationFold()

std::pair< std::size_t, std::size_t > gum::learning::genericBNLearner::useCrossValidationFold ( const std::size_t  learning_fold, const std::size_t  k_fold  )

inherited

sets the ranges of rows to be used for cross-validation learning

When applied on (x,k), the method indicates to the subsequent learnings that they should be performed on the xth fold in a k-fold cross-validation context. For instance, if a database has 1000 rows, and if we perform a 10-fold cross-validation, then, the first learning fold (learning_fold=0) corresponds to rows interval [100,1000) and the test dataset corresponds to [0,100). The second learning fold (learning_fold=1) is [0,100) U [200,1000) and the corresponding test dataset is [100,200).

Parameters

learning_fold	a number indicating the set of rows used for learning. If N denotes the size of the database, and k_fold represents the number of folds in the cross validation, then the set of rows used for testing is [learning_fold * N / k_fold, (learning_fold+1) * N / k_fold) and the learning database is the complement in the database
k_fold	the value of "k" in k-fold cross validation

Returns

a pair [x,y) of rows' indices that corresponds to the indices of rows in the original database that constitute the test dataset

Exceptions

OutOfBounds

is raised if k_fold is equal to 0 or learning_fold is greater than or eqal to k_fold, or if k_fold is greater than or equal to the size of the database.

Definition at line 858 of file genericBNLearner.cpp.

                                                                        {
      if (k_fold == 0) { GUM_ERROR(OutOfBounds, "K-fold cross validation with k=0 is forbidden") }

      if (learning_fold >= k_fold) {
        GUM_ERROR(OutOfBounds,
                  "In " << k_fold << "-fold cross validation, the learning "
                        << "fold should be strictly lower than " << k_fold
                        << " but, here, it is equal to " << learning_fold);
      }

      const std::size_t db_size = scoreDatabase_.databaseTable().nbRows();
      if (k_fold >= db_size) {
        GUM_ERROR(OutOfBounds,
                  "In " << k_fold << "-fold cross validation, the database's "
                        << "size should be strictly greater than " << k_fold
                        << " but, here, the database has only " << db_size << "rows");
      }

      // create the ranges of rows of the test database
      const std::size_t foldSize   = db_size / k_fold;
      const std::size_t unfold_deb = learning_fold * foldSize;
      const std::size_t unfold_end = unfold_deb + foldSize;

      ranges_.clear();
      if (learning_fold == std::size_t(0)) {
        ranges_.push_back(std::pair< std::size_t, std::size_t >(unfold_end, db_size));
      } else {
        ranges_.push_back(std::pair< std::size_t, std::size_t >(std::size_t(0), unfold_deb));

        if (learning_fold != k_fold - 1) {
          ranges_.push_back(std::pair< std::size_t, std::size_t >(unfold_end, db_size));
        }
      }

      return std::pair< std::size_t, std::size_t >(unfold_deb, unfold_end);
    }

useDatabaseRanges()

template<template< typename > class XALLOC>

void gum::learning::genericBNLearner::useDatabaseRanges ( const std::vector< std::pair< std::size_t, std::size_t >, XALLOC< std::pair< std::size_t, std::size_t > > > &  new_ranges )

inherited

use a new set of database rows' ranges to perform learning

Parameters

ranges

a set of pairs {(X1,Y1),...,(Xn,Yn)} of database's rows indices. The subsequent learnings are then performed only on the union of the rows [Xi,Yi), i in {1,...,n}. This is useful, e.g, when performing cross validation tasks, in which part of the database should be ignored. An empty set of ranges is equivalent to an interval [X,Y) ranging over the whole database.

Definition at line 98 of file genericBNLearner_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                   {
      // use a score to detect whether the ranges are ok
      ScoreLog2Likelihood<> score(scoreDatabase_.parser(), *noApriori_);
      score.setRanges(new_ranges);
      ranges_ = score.ranges();
    }

Here is the call graph for this function:

useEM()

INLINE void gum::learning::genericBNLearner::useEM ( const double  epsilon )

inherited

use The EM algorithm to learn paramters

if epsilon=0, EM is not used

Definition at line 259 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

{ epsilonEM_ = epsilon; }

Here is the call graph for this function:

useGreedyHillClimbing()

INLINE void gum::learning::genericBNLearner::useGreedyHillClimbing ( )

inherited

indicate that we wish to use a greedy hill climbing algorithm

Definition at line 246 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                        {
      selectedAlgo_ = AlgoType::GREEDY_HILL_CLIMBING;
    }

Here is the call graph for this function:

useK2() [1/2]

INLINE void gum::learning::genericBNLearner::useK2 ( const Sequence< NodeId > &  order )

inherited

indicate that we wish to use K2

Definition at line 234 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                       {
      selectedAlgo_ = AlgoType::K2;
      algoK2_.setOrder(order);
    }

Here is the call graph for this function:

useK2() [2/2]

INLINE void gum::learning::genericBNLearner::useK2 ( const std::vector< NodeId > &  order )

inherited

indicate that we wish to use K2

Definition at line 240 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                        {
      selectedAlgo_ = AlgoType::K2;
      algoK2_.setOrder(order);
    }

Here is the call graph for this function:

useLocalSearchWithTabuList()

INLINE void gum::learning::genericBNLearner::useLocalSearchWithTabuList ( Size  tabu_size = 100, Size  nb_decrease = 2  )

inherited

indicate that we wish to use a local search with tabu list

Parameters

tabu_size	indicate the size of the tabu list
nb_decrease	indicate the max number of changes decreasing the score consecutively that we allow to apply

Definition at line 251 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                                             {
      selectedAlgo_        = AlgoType::LOCAL_SEARCH_WITH_TABU_LIST;
      nbDecreasingChanges_ = nb_decrease;
      constraintTabuList_.setTabuListSize(tabu_size);
      localSearchWithTabuList_.setMaxNbDecreasingChanges(nb_decrease);
    }

Here is the call graph for this function:

useMDLCorrection()

INLINE void gum::learning::genericBNLearner::useMDLCorrection ( )

inherited

indicate that we wish to use the MDL correction for 3off2 and MIIC

indicate that we wish to use the MDL correction for 3off2

Exceptions

OperationNotAllowed

when 3off2 is not the selected algorithm

Definition at line 219 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                   {
      kmode3Off2_ = CorrectedMutualInformation<>::KModeTypes::MDL;
    }

Here is the call graph for this function:

useMIIC()

INLINE void gum::learning::genericBNLearner::useMIIC ( )

inherited

indicate that we wish to use MIIC

Definition at line 208 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                          {
      selectedAlgo_ = AlgoType::MIIC;
      algoMiic3off2_.setMiicBehaviour();
    }

Here is the call graph for this function:

useNMLCorrection()

INLINE void gum::learning::genericBNLearner::useNMLCorrection ( )

inherited

indicate that we wish to use the NML correction for 3off2 and MIIC

indicate that we wish to use the NML correction for 3off2

Exceptions

OperationNotAllowed

when 3off2 is not the selected algorithm

Definition at line 214 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                   {
      kmode3Off2_ = CorrectedMutualInformation<>::KModeTypes::NML;
    }

Here is the call graph for this function:

useNoApriori()

INLINE void gum::learning::genericBNLearner::useNoApriori ( )

inherited

use no apriori

Definition at line 408 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                               {
      aprioriType_ = AprioriType::NO_APRIORI;
      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

useNoCorrection()

INLINE void gum::learning::genericBNLearner::useNoCorrection ( )

inherited

indicate that we wish to use the NoCorr correction for 3off2 and MIIC

indicate that we wish to use the NoCorr correction for 3off2

Exceptions

OperationNotAllowed

when 3off2 is not the selected algorithm

Definition at line 224 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                  {
      kmode3Off2_ = CorrectedMutualInformation<>::KModeTypes::NoCorr;
    }

Here is the call graph for this function:

useScoreAIC()

INLINE void gum::learning::genericBNLearner::useScoreAIC ( )

inherited

indicate that we wish to use an AIC score

Definition at line 161 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                              {
      scoreType_ = ScoreType::AIC;
      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

useScoreBD()

INLINE void gum::learning::genericBNLearner::useScoreBD ( )

inherited

indicate that we wish to use a BD score

Definition at line 167 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                             {
      scoreType_ = ScoreType::BD;
      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

useScoreBDeu()

INLINE void gum::learning::genericBNLearner::useScoreBDeu ( )

inherited

indicate that we wish to use a BDeu score

Definition at line 173 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                               {
      scoreType_ = ScoreType::BDeu;
      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

useScoreBIC()

INLINE void gum::learning::genericBNLearner::useScoreBIC ( )

inherited

indicate that we wish to use a BIC score

Definition at line 179 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                              {
      scoreType_ = ScoreType::BIC;
      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

useScoreK2()

INLINE void gum::learning::genericBNLearner::useScoreK2 ( )

inherited

indicate that we wish to use a K2 score

Definition at line 185 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                             {
      scoreType_ = ScoreType::K2;
      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

useScoreLog2Likelihood()

INLINE void gum::learning::genericBNLearner::useScoreLog2Likelihood ( )

inherited

indicate that we wish to use a Log2Likelihood score

Definition at line 191 of file genericBNLearner_inl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                         {
      scoreType_ = ScoreType::LOG2LIKELIHOOD;
      checkScoreAprioriCompatibility();
    }

Here is the call graph for this function:

verbosity()

bool gum::learning::genericBNLearner::verbosity ( ) const

inlinevirtualinherited

verbosity

Implements gum::IApproximationSchemeConfiguration.

Definition at line 1118 of file genericBNLearner.h.

                             {
        if (currentAlgorithm_ != nullptr)
          return currentAlgorithm_->verbosity();
        else
          GUM_ERROR(FatalError, "No chosen algorithm for learning")
      }

Member Data Documentation

algoK2_

K2 gum::learning::genericBNLearner::algoK2_

protectedinherited

the K2 algorithm

Definition at line 801 of file genericBNLearner.h.

algoMiic3off2_

Miic gum::learning::genericBNLearner::algoMiic3off2_

protectedinherited

the MIIC or 3off2 algorithm

Definition at line 804 of file genericBNLearner.h.

apriori_

Apriori* gum::learning::genericBNLearner::apriori_ {nullptr}

protectedinherited

the apriori used

Definition at line 772 of file genericBNLearner.h.

aprioriDatabase_

Database* gum::learning::genericBNLearner::aprioriDatabase_ {nullptr}

protectedinherited

the database used by the Dirichlet a priori

Definition at line 826 of file genericBNLearner.h.

aprioriDbname_

std::string gum::learning::genericBNLearner::aprioriDbname_

protectedinherited

the filename for the Dirichlet a priori, if any

Definition at line 829 of file genericBNLearner.h.

aprioriType_

AprioriType gum::learning::genericBNLearner::aprioriType_ {AprioriType::NO_APRIORI}

protectedinherited

the a priori selected for the score and parameters

Definition at line 769 of file genericBNLearner.h.

aprioriWeight_

double gum::learning::genericBNLearner::aprioriWeight_ {1.0f}

protectedinherited

the weight of the apriori

Definition at line 777 of file genericBNLearner.h.

constraintForbiddenArcs_

StructuralConstraintForbiddenArcs gum::learning::genericBNLearner::constraintForbiddenArcs_

protectedinherited

the constraint on forbidden arcs

Definition at line 789 of file genericBNLearner.h.

constraintIndegree_

StructuralConstraintIndegree gum::learning::genericBNLearner::constraintIndegree_

protectedinherited

the constraint for indegrees

Definition at line 783 of file genericBNLearner.h.

constraintMandatoryArcs_

StructuralConstraintMandatoryArcs gum::learning::genericBNLearner::constraintMandatoryArcs_

protectedinherited

the constraint on mandatory arcs

Definition at line 795 of file genericBNLearner.h.

constraintPossibleEdges_

StructuralConstraintPossibleEdges gum::learning::genericBNLearner::constraintPossibleEdges_

protectedinherited

the constraint on possible Edges

Definition at line 792 of file genericBNLearner.h.

constraintSliceOrder_

StructuralConstraintSliceOrder gum::learning::genericBNLearner::constraintSliceOrder_

protectedinherited

the constraint for 2TBNs

Definition at line 780 of file genericBNLearner.h.

constraintTabuList_

StructuralConstraintTabuList gum::learning::genericBNLearner::constraintTabuList_

protectedinherited

the constraint for tabu lists

Definition at line 786 of file genericBNLearner.h.

currentAlgorithm_

const ApproximationScheme* gum::learning::genericBNLearner::currentAlgorithm_ {nullptr}

protectedinherited

Definition at line 842 of file genericBNLearner.h.

Dag2BN_

DAG2BNLearner gum::learning::genericBNLearner::Dag2BN_

protectedinherited

the parametric EM

Definition at line 811 of file genericBNLearner.h.

epsilonEM_

double gum::learning::genericBNLearner::epsilonEM_ {0.0}

protectedinherited

epsilon for EM. if espilon=0.0 : no EM

Definition at line 763 of file genericBNLearner.h.

filename_

std::string gum::learning::genericBNLearner::filename_

protectedinherited

the filename database

Definition at line 836 of file genericBNLearner.h.

greedyHillClimbing_

GreedyHillClimbing gum::learning::genericBNLearner::greedyHillClimbing_

protectedinherited

the greedy hill climbing algorithm

Definition at line 814 of file genericBNLearner.h.

initialDag_

DAG gum::learning::genericBNLearner::initialDag_

protectedinherited

an initial DAG given to learners

Definition at line 833 of file genericBNLearner.h.

kmode3Off2_

CorrectedMutualInformation ::KModeTypes gum::learning::genericBNLearner::kmode3Off2_

protectedinherited

Initial value:

{
         CorrectedMutualInformation<>::KModeTypes::MDL}

the penalty used in 3off2

Definition at line 807 of file genericBNLearner.h.

localSearchWithTabuList_

LocalSearchWithTabuList gum::learning::genericBNLearner::localSearchWithTabuList_

protectedinherited

the local search with tabu list algorithm

Definition at line 817 of file genericBNLearner.h.

mutualInfo_

CorrectedMutualInformation* gum::learning::genericBNLearner::mutualInfo_ {nullptr}

protectedinherited

the selected correction for 3off2 and miic

Definition at line 766 of file genericBNLearner.h.

nbDecreasingChanges_

Size gum::learning::genericBNLearner::nbDecreasingChanges_ {2}

protectedinherited

Definition at line 839 of file genericBNLearner.h.

noApriori_

AprioriNoApriori* gum::learning::genericBNLearner::noApriori_ {nullptr}

protectedinherited

Definition at line 774 of file genericBNLearner.h.

onProgress

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

inherited

Progression, error and time.

Definition at line 58 of file IApproximationSchemeConfiguration.h.

onStop

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

inherited

Criteria messageApproximationScheme.

Definition at line 61 of file IApproximationSchemeConfiguration.h.

paramEstimatorType_

ParamEstimatorType gum::learning::genericBNLearner::paramEstimatorType_ {ParamEstimatorType::ML}

protectedinherited

the type of the parameter estimator

Definition at line 760 of file genericBNLearner.h.

ranges_

std::vector< std::pair< std::size_t, std::size_t > > gum::learning::genericBNLearner::ranges_

protectedinherited

the set of rows' ranges within the database in which learning is done

Definition at line 823 of file genericBNLearner.h.

score_

Score* gum::learning::genericBNLearner::score_ {nullptr}

protectedinherited

the score used

Definition at line 757 of file genericBNLearner.h.

scoreDatabase_

Database gum::learning::genericBNLearner::scoreDatabase_

protectedinherited

the database to be used by the scores and parameter estimators

Definition at line 820 of file genericBNLearner.h.

scoreType_

ScoreType gum::learning::genericBNLearner::scoreType_ {ScoreType::BDeu}

protectedinherited

the score selected for learning

Definition at line 754 of file genericBNLearner.h.

selectedAlgo_

AlgoType gum::learning::genericBNLearner::selectedAlgo_ {AlgoType::GREEDY_HILL_CLIMBING}

protectedinherited

the selected learning algorithm

Definition at line 798 of file genericBNLearner.h.

---

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

• agrum/BN/learning/BNLearner.h