Class representing a Bayesian network. More...

#include <agrum/BN/BayesNet.h>

Inheritance diagram for gum::BayesNet< GUM_SCALAR >:

Collaboration diagram for gum::BayesNet< GUM_SCALAR >:

Public Member Functions
NodeId	addNoisyAND (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyAND implementation. More...

NodeId	addNoisyAND (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, its associate node and a noisyAND implementation. More...

NodeId	addLogit (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a Logit implementation. More...

NodeId	addLogit (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, its associate node and a Logit implementation. More...

NodeId	addOR (const DiscreteVariable &var)
	Add a variable, it's associate node and an OR implementation. More...

NodeId	addAND (const DiscreteVariable &var)
	Add a variable, it's associate node and an AND implementation. More...

void	addWeightedArc (NodeId tail, NodeId head, GUM_SCALAR causalWeight)
	Add an arc in the BN, and update arc.head's CPT. More...

void	addWeightedArc (const std::string &tail, const std::string &head, GUM_SCALAR causalWeight)
	Add an arc in the BN, and update arc.head's CPT. More...

void	generateCPTs () const
	randomly generates CPTs for a given structure More...

void	generateCPT (NodeId node) const
	randomly generate CPT for a given node in a given structure More...

void	generateCPT (const std::string &name) const

void	changePotential (NodeId id, Potential< GUM_SCALAR > *newPot)
	change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId. More...

void	changePotential (const std::string &name, Potential< GUM_SCALAR > *newPot)

bool	operator== (const IBayesNet< GUM_SCALAR > &from) const
	This operator compares 2 BNs ! More...

bool	operator!= (const IBayesNet< GUM_SCALAR > &from) const

Size	dim () const
	Returns the dimension (the number of free parameters) in this bayes net. More...

Size	maxVarDomainSize () const

GUM_SCALAR	minParam () const

GUM_SCALAR	maxParam () const

GUM_SCALAR	minNonZeroParam () const

GUM_SCALAR	maxNonOneParam () const

virtual std::string	toDot () const

std::string	toString () const

NodeSet	minimalCondSet (NodeId target, const NodeSet &soids) const

NodeSet	minimalCondSet (const NodeSet &targets, const NodeSet &soids) const

bool	hasSameStructure (const DAGmodel &other)

double	log10DomainSize () const

Constructors and Destructor
	BayesNet ()
	Default constructor. More...

	BayesNet (std::string name)
	Default constructor. More...

virtual	~BayesNet () final
	Destructor. More...

	BayesNet (const BayesNet< GUM_SCALAR > &source)
	Copy constructor. More...

Operators
BayesNet< GUM_SCALAR > &	operator= (const BayesNet< GUM_SCALAR > &source)
	Copy operator. More...

Variable manipulation methods
const Potential< GUM_SCALAR > &	cpt (NodeId varId) const final
	Returns the CPT of a variable. More...

const Potential< GUM_SCALAR > &	cpt (const std::string &name) const
	Returns the CPT of a variable. More...

const VariableNodeMap &	variableNodeMap () const final
	Returns a map between variables and nodes of this gum::BayesNet. More...

NodeId	add (const DiscreteVariable &var)
	Add a variable to the gum::BayesNet. More...

NodeId	add (const std::string &name, unsigned int nbrmod)
	Shortcut for add(gum::LabelizedVariable(name,name,nbrmod)) More...

NodeId	add (const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent)
	Add a variable to the gum::BayesNet. More...

NodeId	add (const DiscreteVariable &var, NodeId id)
	Add a variable to the gum::BayesNet. More...

NodeId	add (const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent, NodeId id)
	Add a variable to the gum::BayesNet. More...

void	clear ()
	clear the whole Bayes net * More...

void	erase (NodeId varId)
	Remove a variable from the gum::BayesNet. More...

void	erase (const std::string &name)
	Removes a variable from the gum::BayesNet. More...

void	erase (const DiscreteVariable &var)
	Remove a variable from the gum::BayesNet. More...

const DiscreteVariable &	variable (NodeId id) const final
	Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet. More...

const DiscreteVariable &	variable (const std::string &name) const
	Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet. More...

void	changeVariableName (NodeId id, const std::string &new_name)
	Changes a variable's name in the gum::BayesNet. More...

void	changeVariableName (const std::string &name, const std::string &new_name)
	Changes a variable's name. More...

void	changeVariableLabel (NodeId id, const std::string &old_label, const std::string &new_label)
	Changes a variable's label in the gum::BayesNet. More...

void	changeVariableLabel (const std::string &name, const std::string &old_label, const std::string &new_label)
	Changes a variable's name. More...

NodeId	nodeId (const DiscreteVariable &var) const final
	Returns a variable's id in the gum::BayesNet. More...

NodeId	idFromName (const std::string &name) const final
	Returns a variable's id given its name in the gum::BayesNet. More...

const DiscreteVariable &	variableFromName (const std::string &name) const final
	Returns a variable given its name in the gum::BayesNet. More...

Arc manipulation methods.
void	addArc (NodeId tail, NodeId head)
	Add an arc in the BN, and update arc.head's CPT. More...

void	addArc (const std::string &tail, const std::string &head)
	Add an arc in the BN, and update arc.head's CPT. More...

void	eraseArc (const Arc &arc)
	Removes an arc in the BN, and update head's CTP. More...

void	eraseArc (NodeId tail, NodeId head)
	Removes an arc in the BN, and update head's CTP. More...

void	eraseArc (const std::string &tail, const std::string &head)
	Removes an arc in the BN, and update head's CTP. More...

void	beginTopologyTransformation ()
	When inserting/removing arcs, node CPTs change their dimension with a cost in time. More...

void	endTopologyTransformation ()
	terminates a sequence of insertions/deletions of arcs by adjusting all CPTs dimensions. More...


void	reverseArc (NodeId tail, NodeId head)
	Reverses an arc while preserving the same joint distribution. More...

void	reverseArc (const std::string &tail, const std::string &head)
	Reverses an arc while preserving the same joint distribution. More...

void	reverseArc (const Arc &arc)
	Reverses an arc while preserving the same joint distribution. More...

Accessors for nodes with CI or logical implementation
NodeId	addNoisyOR (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, it's associate node and a gum::noisyOR implementation. More...

NodeId	addNoisyORNet (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, it's associate node and a gum::noisyOR implementation. More...

NodeId	addNoisyORCompound (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, it's associate node and a gum::noisyOR implementation. More...


NodeId	addNoisyOR (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyOR implementation. More...

NodeId	addNoisyORNet (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyOR implementation. More...

NodeId	addNoisyORCompound (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyOR implementation. More...


NodeId	addAMPLITUDE (const DiscreteVariable &var)
	Others aggregators. More...

NodeId	addCOUNT (const DiscreteVariable &var, Idx value=1)
	Others aggregators. More...

NodeId	addEXISTS (const DiscreteVariable &var, Idx value=1)
	Others aggregators. More...

NodeId	addFORALL (const DiscreteVariable &var, Idx value=1)
	Others aggregators. More...

NodeId	addMAX (const DiscreteVariable &var)
	Others aggregators. More...

NodeId	addMEDIAN (const DiscreteVariable &var)
	Others aggregators. More...

NodeId	addMIN (const DiscreteVariable &var)
	Others aggregators. More...

NodeId	addSUM (const DiscreteVariable &var)
	Others aggregators. More...

Joint Probability manipulation methods
GUM_SCALAR	jointProbability (const Instantiation &i) const
	Compute a parameter of the joint probability for the BN (given an instantiation of the vars) More...

GUM_SCALAR	log2JointProbability (const Instantiation &i) const
	Compute a parameter of the log joint probability for the BN (given an instantiation of the vars) More...

Variable manipulation methods.
const DAG &	dag () const
	Returns a constant reference to the dag of this Bayes Net. More...

virtual Size	size () const final
	Returns the number of variables in this Directed Graphical Model. More...

Size	sizeArcs () const
	Returns the number of arcs in this Directed Graphical Model. More...

const NodeGraphPart &	nodes () const final
	Returns a constant reference to the dag of this Bayes Net. More...

bool	exists (NodeId node) const final
	Return true if this node exists in this graphical model. More...

Variable manipulation methods.
bool	exists (const std::string &name) const
	Return true if this graphical model is empty. More...

virtual bool	empty () const
	Return true if this graphical model is empty. More...

std::vector< std::string >	names (const std::vector< NodeId > &ids) const
	transform a vector of NodeId in a vector of names More...

std::vector< std::string >	names (const NodeSet &ids) const
	transform a NodeSet in a vector of names More...

std::vector< NodeId >	ids (const std::vector< std::string > &names) const
	transform a vector of names into a vector of nodeId More...

NodeSet	nodeset (const std::vector< std::string > &names) const
	transform a vector of names into a NodeSet More...

Instantiation	completeInstantiation () const
	Get an instantiation over all the variables of the model. More...

Arc manipulation methods.
const ArcSet &	arcs () const
	return true if the arc tail->head exists in the DAGmodel More...

bool	existsArc (const NodeId tail, const NodeId head) const
	return true if the arc tail->head exists in the DAGmodel More...

bool	existsArc (const std::string &nametail, const std::string &namehead) const
	return true if the arc tail->head exists in the DAGmodel More...

const NodeSet &	parents (const NodeId id) const
	returns the set of nodes with arc ingoing to a given node More...

const NodeSet &	parents (const std::string &name) const
	return true if the arc tail->head exists in the DAGmodel More...

NodeSet	parents (const NodeSet &ids) const
	returns the parents of a set of nodes More...

NodeSet	parents (const std::vector< std::string > &names) const
	return true if the arc tail->head exists in the DAGmodel More...

NodeSet	family (const NodeId id) const
	returns the parents of a node and the node More...

NodeSet	family (const std::string &name) const
	return true if the arc tail->head exists in the DAGmodel More...

const NodeSet &	children (const NodeId id) const
	returns the set of nodes with arc outgoing from a given node More...

const NodeSet &	children (const std::string &name) const
	return true if the arc tail->head exists in the DAGmodel More...

NodeSet	children (const NodeSet &ids) const
	returns the children of a set of nodes More...

NodeSet	children (const std::vector< std::string > &names) const
	return true if the arc tail->head exists in the DAGmodel More...

NodeSet	descendants (const NodeId id) const
	returns the set of nodes with directed path outgoing from a given node More...

NodeSet	descendants (const std::string &name) const
	return true if the arc tail->head exists in the DAGmodel More...

NodeSet	ancestors (const NodeId id) const
	returns the set of nodes with directed path ingoing to a given node More...

NodeSet	ancestors (const std::string &name) const
	return true if the arc tail->head exists in the DAGmodel More...

Graphical methods
UndiGraph	moralizedAncestralGraph (const NodeSet &nodes) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes More...

UndiGraph	moralizedAncestralGraph (const std::vector< std::string > &nodenames) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes More...

bool	isIndependent (NodeId X, NodeId Y, const NodeSet &Z) const final
	check if node X and node Y are independent given nodes Z More...

bool	isIndependent (const NodeSet &X, const NodeSet &Y, const NodeSet &Z) const final
	check if nodes X and nodes Y are independent given nodes Z More...

bool	isIndependent (const std::string &Xname, const std::string &Yname, const std::vector< std::string > &Znames) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes More...

bool	isIndependent (const std::vector< std::string > &Xnames, const std::vector< std::string > &Ynames, const std::vector< std::string > &Znames) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes More...

const UndiGraph &	moralGraph (bool clear=true) const
	The node's id are coherent with the variables and nodes of the topology. More...

const Sequence< NodeId > &	topologicalOrder (bool clear=true) const
	The topological order stays the same as long as no variable or arcs are added or erased src the topology. More...

Getter and setters
const std::string &	property (const std::string &name) const
	Return the value of the property name of this GraphicalModel. More...

const std::string &	propertyWithDefault (const std::string &name, const std::string &byDefault) const
	Return the value of the property name of this GraphicalModel. More...

void	setProperty (const std::string &name, const std::string &value)
	Add or change a property of this GraphicalModel. More...

Static Public Member Functions
static BayesNet< GUM_SCALAR >	fastPrototype (const std::string &dotlike, Size domainSize=2)
	Create a Bayesian network with a dot-like syntax which specifies: More...

Protected Attributes
DAG	dag_
	The DAG of this Directed Graphical Model. More...

Friends
class	BayesNetFactory< GUM_SCALAR >

Detailed Description

template<typename GUM_SCALAR>
class gum::BayesNet< GUM_SCALAR >

Class representing a Bayesian network.

Bayesian networks are a probabilistic graphical model in which nodes are random variables and the probability distribution is defined by the product:

\(P(X_1, \ldots, X_n) = \prod_{i=1}^{n} P(X_i | \pi(X_i))\),

where \(\pi(X_i)\) is the parent of \(X_i\).

The probability distribution can be represented as a directed acyclic graph (DAG) where:

Nodes are discrete random variables.
An arc A -> B represent a dependency between variables A and B, i.e. B conditional probability distribution is defined as \(P(B| \pi(B)\).

After a variable is added to the BN, it's domain cannot change. But it arcs are added, the data in its CPT are lost.

You should look a the gum::BayesNetFactory class which can help build Bayesian networks.

You can print a BayesNet using gum::operator<<(std::ostream&, const BayesNet<GUM_SCALAR>&).

Definition at line 77 of file BayesNet.h.

Constructor & Destructor Documentation

◆ BayesNet() [1/3]

template<typename GUM_SCALAR >

INLINE gum::BayesNet< GUM_SCALAR >::BayesNet ( )

Default constructor.

Definition at line 178 of file BayesNet_tpl.h.

                                           : IBayesNet< GUM_SCALAR >() {
     GUM_CONSTRUCTOR(BayesNet);
   }

◆ BayesNet() [2/3]

template<typename GUM_SCALAR >

INLINE gum::BayesNet< GUM_SCALAR >::BayesNet ( std::string name )

explicit

Default constructor.

Parameters

name	The BayesNet's name.

Definition at line 183 of file BayesNet_tpl.h.

                                                         : IBayesNet< GUM_SCALAR >(name) {
     GUM_CONSTRUCTOR(BayesNet);
   }

◆ ~BayesNet()

template<typename GUM_SCALAR >

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

finalvirtual

Destructor.

Definition at line 209 of file BayesNet_tpl.h.

                                     {
     GUM_DESTRUCTOR(BayesNet);
     for (const auto p: _probaMap_) {
       delete p.second;
     }
   }

◆ BayesNet() [3/3]

template<typename GUM_SCALAR>

gum::BayesNet< GUM_SCALAR >::BayesNet ( const BayesNet< GUM_SCALAR > & source )

Copy constructor.

Definition at line 188 of file BayesNet_tpl.h.

                                                                        :
       IBayesNet< GUM_SCALAR >(source), _varMap_(source._varMap_) {
     GUM_CONS_CPY(BayesNet);
 
     _copyPotentials_(source);
   }

Member Function Documentation

◆ _clearPotentials_()

template<typename GUM_SCALAR >

void gum::BayesNet< GUM_SCALAR >::_clearPotentials_ ( )

private

clear all potentials

Definition at line 624 of file BayesNet_tpl.h.

                                                  {
     // Removing previous potentials
     for (const auto& elt: _probaMap_) {
       delete elt.second;
     }
 
     _probaMap_.clear();
   }

◆ _copyPotentials_()

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::_copyPotentials_ ( const BayesNet< GUM_SCALAR > & source )

private

copy of potentials from a BN to another, using names of vars as ref.

Definition at line 635 of file BayesNet_tpl.h.

                                                                                     {
     // Copying potentials
 
     for (const auto src: source._probaMap_) {
       // First we build the node's CPT
       Potential< GUM_SCALAR >* copy_array = new Potential< GUM_SCALAR >();
       copy_array->beginMultipleChanges();
       for (gum::Idx i = 0; i < src.second->nbrDim(); i++) {
         (*copy_array) << variableFromName(src.second->variable(i).name());
       }
       copy_array->endMultipleChanges();
       copy_array->copyFrom(*(src.second));
 
       // We add the CPT to the CPT hashmap
       _probaMap_.insert(src.first, copy_array);
     }
   }

◆ _unsafeChangePotential_()

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::_unsafeChangePotential_	(	NodeId	id,
		Potential< GUM_SCALAR > *	newPot
	)

private

change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.

Warning: no verification of dimensions are performer

See also: changePotential

Definition at line 687 of file BayesNet_tpl.h.

                                                                                                  {
     delete _probaMap_[id];
     _probaMap_[id] = newPot;
   }

◆ add() [1/5]

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::add ( const DiscreteVariable & var )

Add a variable to the gum::BayesNet.

Add a gum::DiscreteVariable, it's associated gum::NodeId and it's gum::Potential.

The variable is added by copy to the gum::BayesNet. The variable's gum::Potential implementation will be a gum::MultiDimArray.

Parameters

var	The variable added by copy.

Returns: Returns the variable's id in the gum::BayesNet.

Exceptions

DuplicateLabel Raised if variable.name() is already used in this gum::BayesNet.

Definition at line 246 of file BayesNet_tpl.h.

                                                                        {
     auto ptr = new MultiDimArray< GUM_SCALAR >();
     try {
       return add(var, ptr);
     } catch (Exception&) {
       delete ptr;
       throw;
     }
   }

◆ add() [2/5]

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::add	(	const std::string &	name,
		unsigned int	nbrmod
	)

Shortcut for add(gum::LabelizedVariable(name,name,nbrmod))

Add a gum::LabelizedVariable to the gum::BayesNet

This method is just a shortcut for a often used pattern

Exceptions

DuplicateLabel	Raised if variable.name() is already used in this gum::BayesNet.
NotAllowed	if nbrmod<2

Definition at line 257 of file BayesNet_tpl.h.

                                                                                       {
     if (nbrmod < 2) {
       GUM_ERROR(OperationNotAllowed,
                 "Variable " << name << "needs more than " << nbrmod << " modalities")
     }
 
     RangeVariable v(name, name, 0, nbrmod - 1);
     return add(v);
   }

◆ add() [3/5]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::add	(	const DiscreteVariable &	var,
		MultiDimImplementation< GUM_SCALAR > *	aContent
	)

Add a variable to the gum::BayesNet.

Add a gum::DiscreteVariable, it's associated gum::NodeId and it's gum::Potential.

The variable is added by copy to the gum::BayesNet.

Parameters

var	The variable added by copy.
aContent	The gum::MultiDimImplementation to use for this variable's gum::Potential implementation.

Returns: Returns the variable's id in the gum::BayesNet.

Exceptions

DuplicateLabel Raised if variable.name() is already used in this gum::BayesNet.

Definition at line 268 of file BayesNet_tpl.h.

                                                                                             {
     NodeId proposedId = dag().nextNodeId();
 
     return add(var, aContent, proposedId);
   }

◆ add() [4/5]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::add	(	const DiscreteVariable &	var,
		NodeId	id
	)

Add a variable to the gum::BayesNet.

Add a gum::DiscreteVariable, it's associated gum::NodeId and it's gum::Potential.

The variable is added by copy to the gum::BayesNet. The variable's gum::Potential implementation will be a gum::MultiDimArray.

Parameters

var	The variable added by copy.
id	The variable's forced gum::NodeId in the gum::BayesNet.

Returns: Returns the variable's id in the gum::BayesNet.

Exceptions

DuplicateElement	Raised id is already used.
DuplicateLabel	Raised if variable.name() is already used in this gum::BayesNet.

Definition at line 276 of file BayesNet_tpl.h.

                                                                                   {
     auto ptr = new MultiDimArray< GUM_SCALAR >();
 
     try {
       return add(var, ptr, id);
 
     } catch (Exception&) {
       delete ptr;
       throw;
     }
   }

◆ add() [5/5]

template<typename GUM_SCALAR>

NodeId gum::BayesNet< GUM_SCALAR >::add	(	const DiscreteVariable &	var,
		MultiDimImplementation< GUM_SCALAR > *	aContent,
		NodeId	id
	)

Add a variable to the gum::BayesNet.

Add a gum::DiscreteVariable, it's associated gum::NodeId and it's gum::Potential.

Parameters

var	The variable added by copy.
aContent	The gum::MultiDimImplementation to use for this variable's gum::Potential implementation.
id	The variable's forced gum::NodeId in the gum::BayesNet.

Returns: Returns the variable's id in the gum::BayesNet.

Exceptions

DuplicateElement	Raised id is already used.
DuplicateLabel	Raised if variable.name() is already used in this gum::BayesNet.

Definition at line 289 of file BayesNet_tpl.h.

                                                                                {
     _varMap_.insert(id, var);
     this->dag_.addNodeWithId(id);
 
     auto cpt = new Potential< GUM_SCALAR >(aContent);
     (*cpt) << variable(id);
     _probaMap_.insert(id, cpt);
     return id;
   }

◆ addAMPLITUDE()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addAMPLITUDE ( const DiscreteVariable & var )

Others aggregators.

Definition at line 462 of file BayesNet_tpl.h.

                                                                                 {
     return add(var, new aggregator::Amplitude< GUM_SCALAR >());
   }

◆ addAND()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addAND ( const DiscreteVariable & var )

Add a variable, it's associate node and an AND implementation.

The id of the new variable is automatically generated.

Warning: AND is implemented as a gum::aggregator::And which means that if parents are not boolean, all value>1 is True

Parameters

var	The variable added by copy.

Returns: the id of the added variable.

Exceptions

SizeError if variable.domainSize()>2

Definition at line 467 of file BayesNet_tpl.h.

                                                                           {
     if (var.domainSize() > 2) GUM_ERROR(SizeError, "an AND has to be boolean")
 
     return add(var, new aggregator::And< GUM_SCALAR >());
   }

◆ addArc() [1/2]

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::addArc	(	NodeId	tail,
		NodeId	head
	)

Add an arc in the BN, and update arc.head's CPT.

Parameters

head	and
tail	as NodeId

Exceptions

InvalidEdge	If arc.tail and/or arc.head are not in the BN.
DuplicateElement	if the arc already exists

Definition at line 356 of file BayesNet_tpl.h.

                                                                      {
     if (this->dag_.existsArc(tail, head)) {
       GUM_ERROR(DuplicateElement, "The arc (" << tail << "," << head << ") already exists.")
     }
 
     this->dag_.addArc(tail, head);
     // Add parent in the child's CPT
     (*(_probaMap_[head])) << variable(tail);
   }

◆ addArc() [2/2]

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::addArc	(	const std::string &	tail,
		const std::string &	head
	)

Add an arc in the BN, and update arc.head's CPT.

Exceptions

gum::DuplicateElement if the arc already exists

Definition at line 367 of file BayesNet_tpl.h.

                                                                                          {
     try {
       addArc(this->idFromName(tail), this->idFromName(head));
     } catch (DuplicateElement) {
       GUM_ERROR(DuplicateElement, "The arc " << tail << "->" << head << " already exists.")
     }
   }

◆ addCOUNT()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addCOUNT	(	const DiscreteVariable &	var,
		Idx	value = `1`
	)

Others aggregators.

Definition at line 474 of file BayesNet_tpl.h.

                                                                                        {
     return add(var, new aggregator::Count< GUM_SCALAR >(value));
   }

◆ addEXISTS()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addEXISTS	(	const DiscreteVariable &	var,
		Idx	value = `1`
	)

Others aggregators.

Definition at line 479 of file BayesNet_tpl.h.

                                                                                         {
     if (var.domainSize() > 2) GUM_ERROR(SizeError, "an EXISTS has to be boolean")
 
     return add(var, new aggregator::Exists< GUM_SCALAR >(value));
   }

◆ addFORALL()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addFORALL	(	const DiscreteVariable &	var,
		Idx	value = `1`
	)

Others aggregators.

Definition at line 486 of file BayesNet_tpl.h.

                                                                                         {
     if (var.domainSize() > 2) GUM_ERROR(SizeError, "an EXISTS has to be boolean")
 
     return add(var, new aggregator::Forall< GUM_SCALAR >(value));
   }

◆ addLogit() [1/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addLogit	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight,
		NodeId	id
	)

Add a variable, its associate node and a Logit implementation.

Parameters

var	The variable added by copy
external_weight	see gum::MultiDimLogit
id	proposed gum::nodeId for the variable

Warning: give an id should be reserved for rare and specific situations !!!

Returns: the id of the added variable.

Definition at line 567 of file BayesNet_tpl.h.

                                                                              {
     return add(var, new MultiDimLogit< GUM_SCALAR >(external_weight), id);
   }

◆ addLogit() [2/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addLogit	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight
	)

Add a variable, its associate node and a Logit implementation.

The id of the new variable is automatically generated.

Parameters

var	The variable added by copy.
external_weight	see gum::MultiDimLogit

Returns: the id of the added variable.

Definition at line 547 of file BayesNet_tpl.h.

                                                                                           {
     return add(var, new MultiDimLogit< GUM_SCALAR >(external_weight));
   }

◆ addMAX()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addMAX ( const DiscreteVariable & var )

Others aggregators.

Definition at line 493 of file BayesNet_tpl.h.

                                                                           {
     return add(var, new aggregator::Max< GUM_SCALAR >());
   }

◆ addMEDIAN()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addMEDIAN ( const DiscreteVariable & var )

Others aggregators.

Definition at line 498 of file BayesNet_tpl.h.

                                                                              {
     return add(var, new aggregator::Median< GUM_SCALAR >());
   }

◆ addMIN()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addMIN ( const DiscreteVariable & var )

Others aggregators.

Definition at line 503 of file BayesNet_tpl.h.

                                                                           {
     return add(var, new aggregator::Min< GUM_SCALAR >());
   }

◆ addNoisyAND() [1/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyAND	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight,
		NodeId	id
	)

Add a variable, its associate node and a noisyAND implementation.

Parameters

var	The variable added by copy
external_weight	see gum::MultiDimNoisyAND
id	proposed gum::nodeId for the variable

Warning: give an id should be reserved for rare and specific situations !!!

Returns: the id of the added variable.

Definition at line 560 of file BayesNet_tpl.h.

                                                                                 {
     return add(var, new MultiDimNoisyAND< GUM_SCALAR >(external_weight), id);
   }

◆ addNoisyAND() [2/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyAND	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight
	)

Add a variable, its associate node and a noisyAND implementation.

The id of the new variable is automatically generated.

Parameters

var	The variable added by copy.
external_weight	see gum::MultiDimNoisyAND

Returns: the id of the added variable.

Definition at line 541 of file BayesNet_tpl.h.

                                                                                              {
     return add(var, new MultiDimNoisyAND< GUM_SCALAR >(external_weight));
   }

◆ addNoisyOR() [1/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyOR	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight
	)

Add a variable, it's associate node and a gum::noisyOR implementation.

The id of the new variable is automatically generated. Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the gum::BayesNet::addNoisyOR as an alias for gum::BayesNet::addNoisyORCompound

Parameters

var	The variable added by copy.
external_weight	see ref gum::MultiDimNoisyORNet,gum::MultiDimNoisyORCompound

Returns: the id of the added variable.

Definition at line 523 of file BayesNet_tpl.h.

                                                                                             {
     return addNoisyORCompound(var, external_weight);
   }

◆ addNoisyOR() [2/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyOR	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight,
		NodeId	id
	)

Add a variable, its associate node and a noisyOR implementation.

Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the addNoisyOR as an alias for addNoisyORCompound.

Parameters

var	The variable added by copy.
external_weight	see gum::MultiDimNoisyORNet, gum::MultiDimNoisyORCompound
id	The chosen id

Warning: give an id should be reserved for rare and specific situations !!!

Returns: the id of the added variable.

Exceptions

DuplicateElement if id is already used

Definition at line 553 of file BayesNet_tpl.h.

                                                                                {
     return addNoisyORCompound(var, external_weight, id);
   }

◆ addNoisyORCompound() [1/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyORCompound	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight
	)

Add a variable, it's associate node and a gum::noisyOR implementation.

The id of the new variable is automatically generated. Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the gum::BayesNet::addNoisyOR as an alias for gum::BayesNet::addNoisyORCompound

Parameters

var	The variable added by copy.
external_weight	see ref gum::MultiDimNoisyORNet,gum::MultiDimNoisyORCompound

Returns: the id of the added variable.

Definition at line 529 of file BayesNet_tpl.h.

                                                                                        {
     return add(var, new MultiDimNoisyORCompound< GUM_SCALAR >(external_weight));
   }

◆ addNoisyORCompound() [2/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyORCompound	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight,
		NodeId	id
	)

Add a variable, its associate node and a noisyOR implementation.

Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the addNoisyOR as an alias for addNoisyORCompound.

Parameters

var	The variable added by copy.
external_weight	see gum::MultiDimNoisyORNet, gum::MultiDimNoisyORCompound
id	The chosen id

Warning: give an id should be reserved for rare and specific situations !!!

Returns: the id of the added variable.

Exceptions

DuplicateElement if id is already used

Definition at line 574 of file BayesNet_tpl.h.

                                                                                        {
     return add(var, new MultiDimNoisyORCompound< GUM_SCALAR >(external_weight), id);
   }

◆ addNoisyORNet() [1/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyORNet	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight
	)

Add a variable, it's associate node and a gum::noisyOR implementation.

The id of the new variable is automatically generated. Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the gum::BayesNet::addNoisyOR as an alias for gum::BayesNet::addNoisyORCompound

Parameters

var	The variable added by copy.
external_weight	see ref gum::MultiDimNoisyORNet,gum::MultiDimNoisyORCompound

Returns: the id of the added variable.

Definition at line 535 of file BayesNet_tpl.h.

                                                                                                {
     return add(var, new MultiDimNoisyORNet< GUM_SCALAR >(external_weight));
   }

◆ addNoisyORNet() [2/2]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyORNet	(	const DiscreteVariable &	var,
		GUM_SCALAR	external_weight,
		NodeId	id
	)

Add a variable, its associate node and a noisyOR implementation.

Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the addNoisyOR as an alias for addNoisyORCompound.

Parameters

var	The variable added by copy.
external_weight	see gum::MultiDimNoisyORNet, gum::MultiDimNoisyORCompound
id	The chosen id

Warning: give an id should be reserved for rare and specific situations !!!

Returns: the id of the added variable.

Exceptions

DuplicateElement if id is already used

Definition at line 581 of file BayesNet_tpl.h.

                                                                                   {
     return add(var, new MultiDimNoisyORNet< GUM_SCALAR >(external_weight), id);
   }

◆ addOR()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addOR ( const DiscreteVariable & var )

Add a variable, it's associate node and an OR implementation.

The id of the new variable is automatically generated.

Warning: OR is implemented as a gum::aggregator::Or which means that if parents are not boolean, all value>1 is True

Parameters

var	The variable added by copy.

Returns: the id of the added variable.

Exceptions

SizeError if variable.domainSize()>2

Definition at line 508 of file BayesNet_tpl.h.

                                                                          {
     if (var.domainSize() > 2) GUM_ERROR(SizeError, "an OR has to be boolean")
 
     return add(var, new aggregator::Or< GUM_SCALAR >());
   }

◆ addSUM()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addSUM ( const DiscreteVariable & var )

Others aggregators.

Definition at line 515 of file BayesNet_tpl.h.

                                                                           {
     return add(var, new aggregator::Sum< GUM_SCALAR >());
   }

◆ addWeightedArc() [1/2]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::addWeightedArc	(	NodeId	tail,
		NodeId	head,
		GUM_SCALAR	causalWeight
	)

Add an arc in the BN, and update arc.head's CPT.

Parameters

head	and
tail	as NodeId
causalWeight	see gum::MultiDimICIModel

Exceptions

InvalidArc	If arc.tail and/or arc.head are not in the BN.
InvalidArc	If variable in arc.head is not a NoisyOR variable.

Definition at line 588 of file BayesNet_tpl.h.

                                                                                                {
     auto* CImodel = dynamic_cast< const MultiDimICIModel< GUM_SCALAR >* >(cpt(head).content());
 
     if (CImodel != 0) {
       // or is OK
       addArc(tail, head);
 
       CImodel->causalWeight(variable(tail), causalWeight);
     } else {
       GUM_ERROR(InvalidArc,
                 "Head variable (" << variable(head).name() << ") is not a CIModel variable !")
     }
   }

◆ addWeightedArc() [2/2]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::addWeightedArc	(	const std::string &	tail,
		const std::string &	head,
		GUM_SCALAR	causalWeight
	)

inline

Add an arc in the BN, and update arc.head's CPT.

Parameters

head	and
tail	as std::string
causalWeight	see gum::MultiDimICIModel
NotFound	if no node with sun names is found

Exceptions

InvalidArc	If arc.tail and/or arc.head are not in the BN.
InvalidArc	If variable in arc.head is not a NoisyOR variable.

Definition at line 620 of file BayesNet.h.

                                                                                                {
       addWeightedArc(idFromName(tail), idFromName(head), causalWeight);
     };

◆ ancestors() [1/2]

INLINE NodeSet gum::DAGmodel::ancestors ( const NodeId id ) const

inherited

returns the set of nodes with directed path ingoing to a given node

Note that the set of nodes returned may be empty if no path within the ArcGraphPart is ingoing to the given node.

Parameters

id	the node which is the head of a directed path with the returned nodes
name	the name of the node which is the head of a directed path with the returned nodes

Definition at line 96 of file DAGmodel_inl.h.

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

96 { return dag().ancestors(id); }

gum::ArcGraphPart::ancestors

NodeSet ancestors(NodeId id) const

returns the set of nodes with directed path ingoing to a given node

Definition: arcGraphPart.cpp:172

gum::DAGmodel::dag

const DAG & dag() const

Returns a constant reference to the dag of this Bayes Net.

Definition: DAGmodel_inl.h:35

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◆ ancestors() [2/2]

INLINE NodeSet gum::DAGmodel::ancestors ( const std::string & name ) const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 98 of file DAGmodel_inl.h.

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

                                                                 {
     return ancestors(idFromName(name));
   }

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◆ arcs()

INLINE const ArcSet & gum::DAGmodel::arcs ( ) const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 43 of file DAGmodel_inl.h.

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

43 { return dag_.arcs(); }

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

gum::ArcGraphPart::arcs

const ArcSet & arcs() const

returns the set of arcs stored within the ArcGraphPart

Definition: arcGraphPart_inl.h:38

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◆ beginTopologyTransformation()

template<typename GUM_SCALAR >

void gum::BayesNet< GUM_SCALAR >::beginTopologyTransformation ( )

When inserting/removing arcs, node CPTs change their dimension with a cost in time.

begin Multiple Change for all CPTs

These functions delay the CPTs change to be done just once at the end of a sequence of topology modification. begins a sequence of insertions/deletions of arcs without changing the dimensions of the CPTs.

Definition at line 610 of file BayesNet_tpl.h.

                                                            {
     for (const auto node: nodes())
       _probaMap_[node]->beginMultipleChanges();
   }

◆ changePotential() [1/2]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changePotential	(	NodeId	id,
		Potential< GUM_SCALAR > *	newPot
	)

change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.

Exceptions

NotAllowed if newPot has not the same signature as probaMap[NodeId]

Definition at line 667 of file BayesNet_tpl.h.

                                                                                          {
     if (cpt(id).nbrDim() != newPot->nbrDim()) {
       GUM_ERROR(OperationNotAllowed,
                 "cannot exchange potentials with different "
                 "dimensions for variable with id "
                    << id)
     }
 
     for (Idx i = 0; i < cpt(id).nbrDim(); i++) {
       if (&cpt(id).variable(i) != &(newPot->variable(i))) {
         GUM_ERROR(OperationNotAllowed,
                   "cannot exchange potentials because, for variable with id "
                      << id << ", dimension " << i << " differs. ")
       }
     }
 
     _unsafeChangePotential_(id, newPot);
   }

◆ changePotential() [2/2]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changePotential	(	const std::string &	name,
		Potential< GUM_SCALAR > *	newPot
	)

Definition at line 693 of file BayesNet_tpl.h.

                                                                                 {
     changePotential(idFromName(name), newPot);
   }

◆ changeVariableLabel() [1/2]

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::changeVariableLabel	(	NodeId	id,
		const std::string &	old_label,
		const std::string &	new_label
	)

Changes a variable's label in the gum::BayesNet.

This will change the gum::LabelizedVariable names in the gum::BayesNet.

Exceptions

DuplicateLabel	Raised if new_label is already used in this gum::LabelizedVariable.
NotFound	Raised if no variable matches id or if the variable is not a LabelizedVariable

Definition at line 227 of file BayesNet_tpl.h.

                                                                                       {
     if (variable(id).varType() != VarType::Labelized)
       GUM_ERROR(NotFound, "Variable " << id << " is not a LabelizedVariable.")
 
     LabelizedVariable* var
        = dynamic_cast< LabelizedVariable* >(const_cast< DiscreteVariable* >(&variable(id)));
 
     var->changeLabel(var->posLabel(old_label), new_label);
   }

◆ changeVariableLabel() [2/2]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeVariableLabel	(	const std::string &	name,
		const std::string &	old_label,
		const std::string &	new_label
	)

inline

Changes a variable's name.

Definition at line 346 of file BayesNet.h.

                                                          {
       changeVariableLabel(idFromName(name), old_label, new_label);
     }

◆ changeVariableName() [1/2]

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::changeVariableName	(	NodeId	id,
		const std::string &	new_name
	)

Changes a variable's name in the gum::BayesNet.

This will change the gum::DiscreteVariable names in the gum::BayesNet.

Exceptions

DuplicateLabel	Raised if newName is already used in this gum::BayesNet.
NotFound	Raised if no variable matches id.

Definition at line 222 of file BayesNet_tpl.h.

                                                                                              {
     _varMap_.changeName(id, new_name);
   }

◆ changeVariableName() [2/2]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeVariableName	(	const std::string &	name,
		const std::string &	new_name
	)

inline

Changes a variable's name.

Definition at line 327 of file BayesNet.h.

                                                                               {
       changeVariableName(idFromName(name), new_name);
     }

◆ children() [1/4]

INLINE const NodeSet & gum::DAGmodel::children ( const NodeId id ) const

inherited

returns the set of nodes with arc outgoing from a given node

Note that the set of nodes returned may be empty if no node is outgoing from the given node.

Parameters

id	the node which is the tail of an arc with the returned nodes
name	the name of the node which is the tail of an arc with the returned nodes

Definition at line 65 of file DAGmodel_inl.h.

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

65 { return dag_.children(id); }

gum::ArcGraphPart::children

NodeSet children(const NodeSet &ids) const

returns the set of children of a set of nodes

Definition: arcGraphPart_inl.h:66

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

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◆ children() [2/4]

INLINE const NodeSet & gum::DAGmodel::children ( const std::string & name ) const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 66 of file DAGmodel_inl.h.

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

                                                                       {
     return dag_.children(idFromName(name));
   }

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◆ children() [3/4]

INLINE NodeSet gum::DAGmodel::children ( const NodeSet & ids ) const

inherited

returns the children of a set of nodes

Definition at line 70 of file DAGmodel_inl.h.

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

70 { return dag_.children(ids); }

gum::GraphicalModel::ids

std::vector< NodeId > ids(const std::vector< std::string > &names) const

transform a vector of names into a vector of nodeId

Definition: graphicalModel_inl.h:117

gum::ArcGraphPart::children

NodeSet children(const NodeSet &ids) const

returns the set of children of a set of nodes

Definition: arcGraphPart_inl.h:66

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

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◆ children() [4/4]

INLINE NodeSet gum::DAGmodel::children ( const std::vector< std::string > & names ) const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 72 of file DAGmodel_inl.h.

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

                                                                              {
     return children(nodeset(names));
   }

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◆ clear()

template<typename GUM_SCALAR >

void gum::BayesNet< GUM_SCALAR >::clear ( )

clear the whole Bayes net *

Definition at line 346 of file BayesNet_tpl.h.

                                      {
     if (!this->empty()) {
       auto l = this->nodes();
       for (const auto no: l) {
         this->erase(no);
       }
     }
   }

◆ completeInstantiation()

INLINE Instantiation gum::GraphicalModel::completeInstantiation ( ) const

inherited

Get an instantiation over all the variables of the model.

Definition at line 84 of file graphicalModel_inl.h.

                                                             {
     Instantiation I;
 
     for (const auto node: nodes())
       I << variable(node);
 
     return I;
   }

◆ cpt() [1/2]

template<typename GUM_SCALAR >

INLINE const Potential< GUM_SCALAR > & gum::BayesNet< GUM_SCALAR >::cpt ( NodeId varId ) const

finalvirtual

Returns the CPT of a variable.

Parameters

varId A variable's id in the gum::BayesNet.

Returns: The variable's CPT.

Exceptions

NotFound If no variable's id matches varId.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 313 of file BayesNet_tpl.h.

                                                                                       {
     return *(_probaMap_[varId]);
   }

◆ cpt() [2/2]

template<typename GUM_SCALAR>

const Potential< GUM_SCALAR >& gum::BayesNet< GUM_SCALAR >::cpt ( const std::string & name ) const

inline

Returns the CPT of a variable.

Definition at line 164 of file BayesNet.h.

                                                                     {
       return cpt(idFromName(name));
     };

◆ dag()

INLINE const DAG & gum::DAGmodel::dag ( ) const

inherited

Returns a constant reference to the dag of this Bayes Net.

Definition at line 35 of file DAGmodel_inl.h.

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

35 { return dag_; }

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

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◆ descendants() [1/2]

INLINE NodeSet gum::DAGmodel::descendants ( const NodeId id ) const

inherited

returns the set of nodes with directed path outgoing from a given node

Note that the set of nodes returned may be empty if no path within the ArcGraphPart is outgoing from the given node.

Parameters

id	the node which is the tail of a directed path with the returned nodes
name	the name of the node which is the tail of a directed path with the returned nodes

Definition at line 90 of file DAGmodel_inl.h.

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

90 { return dag().descendants(id); }

gum::ArcGraphPart::descendants

NodeSet descendants(NodeId id) const

returns the set of nodes with directed path outgoing from a given node

Definition: arcGraphPart.cpp:154

gum::DAGmodel::dag

const DAG & dag() const

Returns a constant reference to the dag of this Bayes Net.

Definition: DAGmodel_inl.h:35

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◆ descendants() [2/2]

INLINE NodeSet gum::DAGmodel::descendants ( const std::string & name ) const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 92 of file DAGmodel_inl.h.

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

                                                                   {
     return descendants(idFromName(name));
   }

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◆ dim()

template<typename GUM_SCALAR >

Size gum::IBayesNet< GUM_SCALAR >::dim ( ) const

inherited

Returns the dimension (the number of free parameters) in this bayes net.

\( dim(G)=\sum_{i \in nodes} ((r_i-1)\cdot q_i) \) where \( r_i \) is the number of instantiations of node \( i \) and \( q_i \) is the number of instantiations of its parents.

Definition at line 78 of file IBayesNet_tpl.h.

                                           {
     Size dim = 0;
 
     for (auto node: nodes()) {
       Size q = 1;
 
       for (auto parent: parents(node))
         q *= variable(parent).domainSize();
 
       dim += (variable(node).domainSize() - 1) * q;
     }
 
     return dim;
   }

◆ empty()

INLINE bool gum::GraphicalModel::empty ( ) const

virtualinherited

Return true if this graphical model is empty.

Definition at line 94 of file graphicalModel_inl.h.

94 { return size() == 0; }

gum::GraphicalModel::size

virtual Size size() const =0

Returns the number of variables in this Directed Graphical Model.

◆ endTopologyTransformation()

template<typename GUM_SCALAR >

void gum::BayesNet< GUM_SCALAR >::endTopologyTransformation ( )

terminates a sequence of insertions/deletions of arcs by adjusting all CPTs dimensions.

end Multiple Change for all CPTs

Definition at line 617 of file BayesNet_tpl.h.

                                                          {
     for (const auto node: nodes())
       _probaMap_[node]->endMultipleChanges();
   }

◆ erase() [1/3]

template<typename GUM_SCALAR >

void gum::BayesNet< GUM_SCALAR >::erase ( NodeId varId )

Remove a variable from the gum::BayesNet.

Removes the corresponding variable from the gum::BayesNet and from all of it's children gum::Potential.

If no variable matches the given id, then nothing is done.

Parameters

varId The variable's id to remove.

Definition at line 328 of file BayesNet_tpl.h.

                                                  {
     if (_varMap_.exists(varId)) {
       // Reduce the variable child's CPT
       const NodeSet& children = this->children(varId);
 
       for (const auto c: children) {
         _probaMap_[c]->erase(variable(varId));
       }
 
       delete _probaMap_[varId];
 
       _probaMap_.erase(varId);
       _varMap_.erase(varId);
       this->dag_.eraseNode(varId);
     }
   }

◆ erase() [2/3]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::erase ( const std::string & name )

inline

Removes a variable from the gum::BayesNet.

Definition at line 279 of file BayesNet.h.

279 { erase(idFromName(name)); };

gum::BayesNet::idFromName

NodeId idFromName(const std::string &name) const final

Returns a variable's id given its name in the gum::BayesNet.

Definition: BayesNet_tpl.h:302

gum::BayesNet::erase

void erase(NodeId varId)

Remove a variable from the gum::BayesNet.

Definition: BayesNet_tpl.h:328

◆ erase() [3/3]

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::erase ( const DiscreteVariable & var )

Remove a variable from the gum::BayesNet.

Removes the corresponding variable from the gum::BayesNet and from all of it's children gum::Potential.

If no variable matches the given variable, then nothing is done.

Parameters

var	A reference on the variable to remove.

Definition at line 323 of file BayesNet_tpl.h.

                                                                        {
     erase(_varMap_.get(var));
   }

◆ eraseArc() [1/3]

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::eraseArc ( const Arc & arc )

Removes an arc in the BN, and update head's CTP.

If (tail, head) doesn't exist, the nothing happens.

Parameters

arc	The arc removed.

Definition at line 376 of file BayesNet_tpl.h.

                                                              {
     if (_varMap_.exists(arc.tail()) && _varMap_.exists(arc.head())) {
       NodeId head = arc.head(), tail = arc.tail();
       this->dag_.eraseArc(arc);
       // Remove parent from child's CPT
       (*(_probaMap_[head])) >> variable(tail);
     }
   }

◆ eraseArc() [2/3]

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::eraseArc	(	NodeId	tail,
		NodeId	head
	)

Removes an arc in the BN, and update head's CTP.

If (tail, head) doesn't exist, the nothing happens.

Parameters

head	and
tail	as NodeId

Definition at line 386 of file BayesNet_tpl.h.

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

◆ eraseArc() [3/3]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::eraseArc	(	const std::string &	tail,
		const std::string &	head
	)

inline

Removes an arc in the BN, and update head's CTP.

Definition at line 426 of file BayesNet.h.

                                                                 {
       eraseArc(idFromName(tail), idFromName(head));
     }

◆ exists() [1/2]

INLINE bool gum::DAGmodel::exists ( NodeId node ) const

finalvirtualinherited

Return true if this node exists in this graphical model.

Implements gum::GraphicalModel.

Definition at line 82 of file DAGmodel_inl.h.

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

82 { return dag_.exists(node); }

gum::NodeGraphPart::exists

bool exists(const NodeId id) const

alias for existsNode

Definition: nodeGraphPart_inl.h:277

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

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◆ exists() [2/2]

bool gum::GraphicalModel::exists ( const std::string & name ) const

inlineinherited

Return true if this graphical model is empty.

Definition at line 112 of file graphicalModel.h.

112 { return exists(idFromName(name)); };

gum::GraphicalModel::exists

virtual bool exists(NodeId node) const =0

Return true if this node exists in this graphical model.

gum::GraphicalModel::idFromName

virtual NodeId idFromName(const std::string &name) const =0

Getter by name.

◆ existsArc() [1/2]

INLINE bool gum::DAGmodel::existsArc	(	const NodeId	tail,
		const NodeId	head
	)		const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 45 of file DAGmodel_inl.h.

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

                                                                             {
     return dag_.existsArc(tail, head);
   }

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◆ existsArc() [2/2]

INLINE bool gum::DAGmodel::existsArc	(	const std::string &	nametail,
		const std::string &	namehead
	)		const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 49 of file DAGmodel_inl.h.

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

                                                                                             {
     return existsArc(idFromName(nametail), idFromName(namehead));
   }

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◆ family() [1/2]

INLINE NodeSet gum::DAGmodel::family ( const NodeId id ) const

inherited

returns the parents of a node and the node

Note that the set of nodes returned may be empty if no arc within the ArcGraphPart is ingoing into the given node.

Parameters

id	the node which is the head of an arc with the returned nodes
name	the name of the node the node which is the head of an arc with the returned nodes

Definition at line 59 of file DAGmodel_inl.h.

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

59 { return dag_.family(id); }

gum::ArcGraphPart::family

NodeSet family(NodeId id) const

returns the set of nodes which consists in the node and its parents

Definition: arcGraphPart_inl.h:59

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

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◆ family() [2/2]

INLINE NodeSet gum::DAGmodel::family ( const std::string & name ) const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 61 of file DAGmodel_inl.h.

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

                                                              {
     return dag_.family(idFromName(name));
   }

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◆ fastPrototype()

template<typename GUM_SCALAR >

BayesNet< GUM_SCALAR > gum::BayesNet< GUM_SCALAR >::fastPrototype	(	const std::string &	dotlike,
		Size	domainSize = `2`
	)

static

Create a Bayesian network with a dot-like syntax which specifies:

the structure "a->b->c;b->d<-e;".
the type of the variables with different syntax:
- by default, a variable is a gum::RangeVariable using the default domainSize (second argument)
- with "a[10]", the variable is a gum::RangeVariable using 10 as domainSize (from 0 to 9)
- with "a[3,7]", the variable is a gum::RangeVariable using a domainSize from 3 to 7
- with "a[1,3.14,5,6.2]", the variable is a gum::DiscretizedVariable using the given ticks (at least 3 values)
- with "a{top|middle|bottom}", the variable is a gum::LabelizedVariable using the given labels.

Note that if the dot-like string contains such a specification more than once for a variable, the first specification will be used.

Parameters

dotlike	the string containing the specification
domainSize	the default domain size for variables

Returns: the resulting Bayesian network

Definition at line 145 of file BayesNet_tpl.h.

                                                                                               {
     gum::BayesNet< GUM_SCALAR > bn;
 
 
     for (const auto& chaine: split(dotlike, ";")) {
       NodeId lastId   = 0;
       bool   notfirst = false;
       for (const auto& souschaine: split(chaine, "->")) {
         bool forward = true;
         for (const auto& node: split(souschaine, "<-")) {
           auto idVar = build_node(bn, node, domainSize);
           if (notfirst) {
             if (forward) {
               bn.addArc(lastId, idVar);
               forward = false;
             } else {
               bn.addArc(idVar, lastId);
             }
           } else {
             notfirst = true;
             forward  = false;
           }
           lastId = idVar;
         }
       }
     }
     bn.generateCPTs();
     bn.setProperty("name", "fastPrototype");
     return bn;
   }

◆ generateCPT() [1/2]

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::generateCPT ( NodeId node ) const

randomly generate CPT for a given node in a given structure

Definition at line 660 of file BayesNet_tpl.h.

                                                                    {
     SimpleCPTGenerator< GUM_SCALAR > generator;
 
     generator.generateCPT(cpt(node).pos(variable(node)), cpt(node));
   }

◆ generateCPT() [2/2]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::generateCPT ( const std::string & name ) const

inline

Definition at line 630 of file BayesNet.h.

630 { generateCPT(idFromName(name)); };

gum::BayesNet::generateCPT

void generateCPT(NodeId node) const

randomly generate CPT for a given node in a given structure

Definition: BayesNet_tpl.h:660

gum::BayesNet::idFromName

NodeId idFromName(const std::string &name) const final

Returns a variable's id given its name in the gum::BayesNet.

Definition: BayesNet_tpl.h:302

◆ generateCPTs()

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::generateCPTs ( ) const

randomly generates CPTs for a given structure

Definition at line 654 of file BayesNet_tpl.h.

                                                          {
     for (const auto node: nodes())
       generateCPT(node);
   }

◆ hasSameStructure()

bool gum::DAGmodel::hasSameStructure ( const DAGmodel & other )

inherited

Returns: true if all the named node are the same and all the named arcs are the same

Definition at line 69 of file DAGmodel.cpp.

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

                                                        {
     if (this == &other) return true;
 
     if (size() != other.size()) return false;
 
     if (sizeArcs() != other.sizeArcs()) return false;
 
     for (const auto& nid: nodes()) {
       try {
         other.idFromName(variable(nid).name());
       } catch (NotFound) { return false; }
     }
 
     for (const auto& arc: arcs()) {
       if (!other.arcs().exists(Arc(other.idFromName(variable(arc.tail()).name()),
                                    other.idFromName(variable(arc.head()).name()))))
         return false;
     }
 
     return true;
   }

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◆ idFromName()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::idFromName ( const std::string & name ) const

finalvirtual

Returns a variable's id given its name in the gum::BayesNet.

Parameters

name	The variable's name from which the gum::NodeId is returned.

Returns: Returns the variable gum::NodeId in the gum::BayesNet.

Exceptions

NotFound Raised if name does not match a variable in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 302 of file BayesNet_tpl.h.

                                                                               {
     return _varMap_.idFromName(name);
   }

◆ ids()

INLINE std::vector< NodeId > gum::GraphicalModel::ids ( const std::vector< std::string > & names ) const

inherited

transform a vector of names into a vector of nodeId

Returns: the vector of names

Definition at line 117 of file graphicalModel_inl.h.

                                                                                    {
     std::vector< NodeId >  res;
     const VariableNodeMap& v = variableNodeMap();
     std::transform(names.cbegin(),
                    names.cend(),
                    std::back_inserter(res),
                    [v](const std::string& n) { return v.idFromName(n); });
     return res;
   }

◆ isIndependent() [1/4]

INLINE bool gum::DAGmodel::isIndependent	(	NodeId	X,
		NodeId	Y,
		const NodeSet &	Z
	)		const

finalvirtualinherited

check if node X and node Y are independent given nodes Z

Implements gum::GraphicalModel.

Definition at line 113 of file DAGmodel_inl.h.

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

                                                                                 {
     return dag().dSeparation(X, Y, Z);
   }

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◆ isIndependent() [2/4]

INLINE bool gum::DAGmodel::isIndependent	(	const NodeSet &	X,
		const NodeSet &	Y,
		const NodeSet &	Z
	)		const

finalvirtualinherited

check if nodes X and nodes Y are independent given nodes Z

Implements gum::GraphicalModel.

Definition at line 117 of file DAGmodel_inl.h.

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

                                                                                                 {
     return dag().dSeparation(X, Y, Z);
   }

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◆ isIndependent() [3/4]

bool gum::DAGmodel::isIndependent	(	const std::string &	Xname,
		const std::string &	Yname,
		const std::vector< std::string > &	Znames
	)		const

inlineinherited

build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes

Parameters

nodes	the set of nodeId
nodenames	the vector of names of nodes

Returns: the moralized ancestral graph

Definition at line 185 of file DAGmodel.h.

                                                                    {
       return isIndependent(idFromName(Xname), idFromName(Yname), nodeset(Znames));
     };

◆ isIndependent() [4/4]

bool gum::DAGmodel::isIndependent	(	const std::vector< std::string > &	Xnames,
		const std::vector< std::string > &	Ynames,
		const std::vector< std::string > &	Znames
	)		const

inlineinherited

build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes

Parameters

nodes	the set of nodeId
nodenames	the vector of names of nodes

Returns: the moralized ancestral graph

Definition at line 191 of file DAGmodel.h.

                                                                    {
       return isIndependent(nodeset(Xnames), nodeset(Ynames), nodeset(Znames));
     };

◆ jointProbability()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::jointProbability ( const Instantiation & i ) const

inherited

Compute a parameter of the joint probability for the BN (given an instantiation of the vars)

Warning: a variable not present in the instantiation is assumed to be instantiated to 0.

Definition at line 207 of file IBayesNet_tpl.h.

                                                                                    {
     auto value = (GUM_SCALAR)1.0;
 
     GUM_SCALAR tmp;
 
     for (auto node: nodes()) {
       if ((tmp = cpt(node)[i]) == (GUM_SCALAR)0) { return (GUM_SCALAR)0; }
 
       value *= tmp;
     }
 
     return value;
   }

◆ log10DomainSize()

INLINE double gum::GraphicalModel::log10DomainSize ( ) const

inherited

Definition at line 73 of file graphicalModel_inl.h.

                                                {
     double dSize = 0.0;
 
     for (const auto node: nodes()) {
       dSize += std::log10(variable(node).domainSize());
     }
 
     return dSize;
   }

◆ log2JointProbability()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::log2JointProbability ( const Instantiation & i ) const

inherited

Compute a parameter of the log joint probability for the BN (given an instantiation of the vars)

Compute a parameter of the joint probability for the BN (given an instantiation of the vars)

Warning: a variable not present in the instantiation is assumed to be instantiated to 0.

Definition at line 225 of file IBayesNet_tpl.h.

                                                                                        {
     auto value = (GUM_SCALAR)0.0;
 
     GUM_SCALAR tmp;
 
     for (auto node: nodes()) {
       if ((tmp = cpt(node)[i]) == (GUM_SCALAR)0) {
         return (GUM_SCALAR)(-std::numeric_limits< double >::infinity());
       }
 
       value += std::log2(cpt(node)[i]);
     }
 
     return value;
   }

◆ maxNonOneParam()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::maxNonOneParam ( ) const

inherited

Returns: the biggest value (not equal to 1) in the CPTs of *this

Warning: can return one if no other value in the CPTs than one....

Definition at line 134 of file IBayesNet_tpl.h.

                                                            {
     GUM_SCALAR res = 0.0;
     for (auto node: nodes()) {
       auto v = cpt(node).maxNonOne();
       if (v > res) { res = v; }
     }
     return res;
   }

◆ maxParam()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::maxParam ( ) const

inherited

Returns: the biggest value in the CPTs of *this

Definition at line 114 of file IBayesNet_tpl.h.

                                                      {
     GUM_SCALAR res = 1.0;
     for (auto node: nodes()) {
       auto v = cpt(node).max();
       if (v > res) { res = v; }
     }
     return res;
   }

◆ maxVarDomainSize()

template<typename GUM_SCALAR >

Size gum::IBayesNet< GUM_SCALAR >::maxVarDomainSize ( ) const

inherited

Returns: the biggest domainSize among the variables of *this

Definition at line 94 of file IBayesNet_tpl.h.

                                                        {
     Size res = 0;
     for (auto node: nodes()) {
       auto v = variable(node).domainSize();
       if (v > res) { res = v; }
     }
     return res;
   }

◆ minimalCondSet() [1/2]

template<typename GUM_SCALAR >

NodeSet gum::IBayesNet< GUM_SCALAR >::minimalCondSet	(	NodeId	target,
		const NodeSet &	soids
	)		const

inherited

Definition at line 332 of file IBayesNet_tpl.h.

                                                                                            {
     if (soids.contains(target)) return NodeSet({target});
 
     NodeSet res;
     NodeSet alreadyVisitedUp;
     NodeSet alreadyVisitedDn;
     alreadyVisitedDn << target;
     alreadyVisitedUp << target;
 
     for (auto fath: dag_.parents(target))
       _minimalCondSetVisitUp_(fath, soids, res, alreadyVisitedUp, alreadyVisitedDn);
     for (auto chil: dag_.children(target))
       _minimalCondSetVisitDn_(chil, soids, res, alreadyVisitedUp, alreadyVisitedDn);
     return res;
   }

◆ minimalCondSet() [2/2]

template<typename GUM_SCALAR >

NodeSet gum::IBayesNet< GUM_SCALAR >::minimalCondSet	(	const NodeSet &	targets,
		const NodeSet &	soids
	)		const

inherited

Definition at line 349 of file IBayesNet_tpl.h.

                                                                               {
     NodeSet res;
     for (auto node: targets) {
       res += minimalCondSet(node, soids);
     }
     return res;
   }

◆ minNonZeroParam()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::minNonZeroParam ( ) const

inherited

Returns: the smallest value (not equal to 0) in the CPTs of *this

Warning: can return 0 if no other value in the CPTs than 0...

Definition at line 124 of file IBayesNet_tpl.h.

                                                             {
     GUM_SCALAR res = 1.0;
     for (auto node: nodes()) {
       auto v = cpt(node).minNonZero();
       if (v < res) { res = v; }
     }
     return res;
   }

◆ minParam()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::minParam ( ) const

inherited

Returns: the smallest value in the CPTs of *this

Definition at line 104 of file IBayesNet_tpl.h.

                                                      {
     GUM_SCALAR res = 1.0;
     for (auto node: nodes()) {
       auto v = cpt(node).min();
       if (v < res) { res = v; }
     }
     return res;
   }

◆ moralGraph()

const UndiGraph & gum::DAGmodel::moralGraph ( bool clear = true ) const

inherited

The node's id are coherent with the variables and nodes of the topology.

Parameters

clear If false returns the previously created moral graph.

Definition at line 55 of file DAGmodel.cpp.

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

                                                         {
     if (clear || (_mutableMoralGraph_ == nullptr)) {   // we have to call dag().moralGraph()
       if (_mutableMoralGraph_ == nullptr) {
         _mutableMoralGraph_ = new UndiGraph();
       } else {
         // clear is True , __mutableMoralGraph exists
         _mutableMoralGraph_->clear();
       }
       *_mutableMoralGraph_ = dag().moralGraph();
     }
 
     return *_mutableMoralGraph_;
   }

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◆ moralizedAncestralGraph() [1/2]

INLINE UndiGraph gum::DAGmodel::moralizedAncestralGraph ( const NodeSet & nodes ) const

inherited

build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes

Parameters

nodes	the set of nodeId
nodenames	the vector of names of nodes

Returns: the moralized ancestral graph

Definition at line 109 of file DAGmodel_inl.h.

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

                                                                                {
     return dag().moralizedAncestralGraph(nodes);
   }

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◆ moralizedAncestralGraph() [2/2]

INLINE UndiGraph gum::DAGmodel::moralizedAncestralGraph ( const std::vector< std::string > & nodenames ) const

inherited

build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes

Parameters

nodes	the set of nodeId
nodenames	the vector of names of nodes

Returns: the moralized ancestral graph

Definition at line 104 of file DAGmodel_inl.h.

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

                                                                                     {
     return moralizedAncestralGraph(nodeset(nodenames));
   }

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◆ names() [1/2]

INLINE std::vector< std::string > gum::GraphicalModel::names ( const std::vector< NodeId > & ids ) const

inherited

transform a vector of NodeId in a vector of names

Returns: the vector of names

Definition at line 97 of file graphicalModel_inl.h.

                                                                                    {
     std::vector< std::string > res;
     const VariableNodeMap&     v = variableNodeMap();
     std::transform(ids.cbegin(), ids.cend(), std::back_inserter(res), [v](NodeId n) {
       return v[n].name();
     });
     return res;
   }

◆ names() [2/2]

INLINE std::vector< std::string > gum::GraphicalModel::names ( const NodeSet & ids ) const

inherited

transform a NodeSet in a vector of names

Returns: the vector of names

Definition at line 107 of file graphicalModel_inl.h.

                                                                        {
     const VariableNodeMap&     v = variableNodeMap();
     std::vector< std::string > res;
     for (auto n: ids) {
       res.push_back(v.name(n));
     }
     return res;
   }

◆ nodeId()

template<typename GUM_SCALAR >

INLINE NodeId gum::BayesNet< GUM_SCALAR >::nodeId ( const DiscreteVariable & var ) const

finalvirtual

Returns a variable's id in the gum::BayesNet.

Parameters

var	The variable from which the gum::NodeId is returned.

Returns: Returns the gum::DiscreteVariable gum::NodeId in the gum::BayesNet.

Exceptions

NotFound If var is not in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 241 of file BayesNet_tpl.h.

                                                                                 {
     return _varMap_.get(var);
   }

◆ nodes()

INLINE const NodeGraphPart & gum::DAGmodel::nodes ( ) const

finalvirtualinherited

Returns a constant reference to the dag of this Bayes Net.

Implements gum::GraphicalModel.

Definition at line 84 of file DAGmodel_inl.h.

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

84 { return (NodeGraphPart&)dag_; }

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

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◆ nodeset()

NodeSet gum::GraphicalModel::nodeset ( const std::vector< std::string > & names ) const

inherited

transform a vector of names into a NodeSet

Returns: NodeSet

Definition at line 58 of file graphicalModel.cpp.

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

                                                                            {
     NodeSet res;
     for (const auto& name: names) {
       res.insert(idFromName(name));
     }
     return res;
   }

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◆ operator!=()

template<typename GUM_SCALAR>

bool gum::IBayesNet< GUM_SCALAR >::operator!= ( const IBayesNet< GUM_SCALAR > & from ) const

inherited

Returns: Returns false if the src and this are equal.

Definition at line 286 of file IBayesNet_tpl.h.

                                                                       {
     return !this->operator==(from);
   }

◆ operator=()

template<typename GUM_SCALAR>

BayesNet< GUM_SCALAR > & gum::BayesNet< GUM_SCALAR >::operator= ( const BayesNet< GUM_SCALAR > & source )

Copy operator.

Parameters

source The copied BayesNet.

Returns: The copy of source.

Definition at line 196 of file BayesNet_tpl.h.

                                                                                                 {
     if (this != &source) {
       IBayesNet< GUM_SCALAR >::operator=(source);
       _varMap_                         = source._varMap_;
 
       _clearPotentials_();
       _copyPotentials_(source);
     }
 
     return *this;
   }

◆ operator==()

template<typename GUM_SCALAR>

bool gum::IBayesNet< GUM_SCALAR >::operator== ( const IBayesNet< GUM_SCALAR > & from ) const

inherited

This operator compares 2 BNs !

Warning: To identify nodes between BNs, it is assumed that they share the same name.

Returns: true if the src and this are equal.

Definition at line 242 of file IBayesNet_tpl.h.

                                                                       {
     if (size() != from.size()) { return false; }
 
     if (sizeArcs() != from.sizeArcs()) { return false; }
 
     // alignment of variables between the 2 BNs
     Bijection< const DiscreteVariable*, const DiscreteVariable* > alignment;
 
     for (auto node: nodes()) {
       try {
         alignment.insert(&variable(node), &from.variableFromName(variable(node).name()));
       } catch (NotFound&) {
         // a name is not found in from
         return false;
       }
     }
 
     for (auto node: nodes()) {
       NodeId fromnode = from.idFromName(variable(node).name());
 
       if (cpt(node).nbrDim() != from.cpt(fromnode).nbrDim()) { return false; }
 
       if (cpt(node).domainSize() != from.cpt(fromnode).domainSize()) { return false; }
 
       Instantiation i(cpt(node));
       Instantiation j(from.cpt(fromnode));
 
       for (i.setFirst(); !i.end(); i.inc()) {
         for (Idx indice = 0; indice < cpt(node).nbrDim(); ++indice) {
           const DiscreteVariable* p = &(i.variable(indice));
           j.chgVal(*(alignment.second(p)), i.val(*p));
         }
 
         if (std::pow(cpt(node).get(i) - from.cpt(fromnode).get(j), (GUM_SCALAR)2)
             > (GUM_SCALAR)1e-6) {
           return false;
         }
       }
     }
 
     return true;
   }

◆ parents() [1/4]

INLINE const NodeSet & gum::DAGmodel::parents ( const NodeId id ) const

inherited

returns the set of nodes with arc ingoing to a given node

Note that the set of nodes returned may be empty if no arc within the ArcGraphPart is ingoing into the given node.

Parameters

id	the node which is the head of an arc with the returned nodes
name	the name of the node the node which is the head of an arc with the returned nodes

Definition at line 53 of file DAGmodel_inl.h.

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

53 { return dag_.parents(id); }

gum::ArcGraphPart::parents

const NodeSet & parents(NodeId id) const

returns the set of nodes with arc ingoing to a given node

Definition: arcGraphPart_inl.h:54

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

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◆ parents() [2/4]

INLINE const NodeSet & gum::DAGmodel::parents ( const std::string & name ) const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 55 of file DAGmodel_inl.h.

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

                                                                      {
     return parents(idFromName(name));
   }

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◆ parents() [3/4]

INLINE NodeSet gum::DAGmodel::parents ( const NodeSet & ids ) const

inherited

returns the parents of a set of nodes

Definition at line 76 of file DAGmodel_inl.h.

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

76 { return dag_.children(ids); }

gum::GraphicalModel::ids

std::vector< NodeId > ids(const std::vector< std::string > &names) const

transform a vector of names into a vector of nodeId

Definition: graphicalModel_inl.h:117

gum::ArcGraphPart::children

NodeSet children(const NodeSet &ids) const

returns the set of children of a set of nodes

Definition: arcGraphPart_inl.h:66

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

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◆ parents() [4/4]

INLINE NodeSet gum::DAGmodel::parents ( const std::vector< std::string > & names ) const

inherited

return true if the arc tail->head exists in the DAGmodel

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns: true if the arc exists

Definition at line 78 of file DAGmodel_inl.h.

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

                                                                             {
     return parents(nodeset(names));
   }

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◆ property()

INLINE const std::string & gum::GraphicalModel::property ( const std::string & name ) const

inherited

Return the value of the property name of this GraphicalModel.

Exceptions

NotFound Raised if no name property is found.

Definition at line 38 of file graphicalModel_inl.h.

                                                                        {
     try {
       return _properties_()[name];
     } catch (NotFound&) {
       std::string msg = "The following property does not exists: ";
       GUM_ERROR(NotFound, msg + name)
     }
   }

◆ propertyWithDefault()

INLINE const std::string & gum::GraphicalModel::propertyWithDefault	(	const std::string &	name,
		const std::string &	byDefault
	)		const

inherited

Return the value of the property name of this GraphicalModel.

return byDefault if the property name is not found

Definition at line 57 of file graphicalModel_inl.h.

                                                                                            {
     try {
       return _properties_()[name];
     } catch (NotFound&) { return byDefault; }
   }

◆ reverseArc() [1/3]

template<typename GUM_SCALAR >

INLINE void gum::BayesNet< GUM_SCALAR >::reverseArc	(	NodeId	tail,
		NodeId	head
	)

Reverses an arc while preserving the same joint distribution.

This method uses Shachter's 1986 algorithm for reversing an arc in the Bayes net while preserving the same joint distribution. By performing this reversal, we also add new arcs (required to not alter the joint distribution)

Exceptions

InvalidArc exception if the arc does not exist or if its reversal would induce a directed cycle.

Definition at line 453 of file BayesNet_tpl.h.

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

◆ reverseArc() [2/3]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::reverseArc	(	const std::string &	tail,
		const std::string &	head
	)

inline

Reverses an arc while preserving the same joint distribution.

This method uses Shachter's 1986 algorithm for reversing an arc in the Bayes net while preserving the same joint distribution. By performing this reversal, we also add new arcs (required to not alter the joint distribution)

Exceptions

InvalidArc exception if the arc does not exist or if its reversal would induce a directed cycle.

Definition at line 459 of file BayesNet.h.

                                                                   {
       reverseArc(idFromName(tail), idFromName(head));
     }

◆ reverseArc() [3/3]

template<typename GUM_SCALAR >

void gum::BayesNet< GUM_SCALAR >::reverseArc ( const Arc & arc )

Reverses an arc while preserving the same joint distribution.

This method uses Shachter's 1986 algorithm for reversing an arc in the Bayes net while preserving the same joint distribution. By performing this reversal, we also add new arcs (required to not alter the joint distribution)

Exceptions

InvalidArc exception if the arc does not exist or if its reversal would induce a directed cycle.

Definition at line 391 of file BayesNet_tpl.h.

                                                         {
     // check that the arc exists
     if (!_varMap_.exists(arc.tail()) || !_varMap_.exists(arc.head()) || !dag().existsArc(arc)) {
       GUM_ERROR(InvalidArc, "a non-existing arc cannot be reversed")
     }
 
     NodeId tail = arc.tail(), head = arc.head();
 
     // check that the reversal does not induce a cycle
     try {
       DAG d = dag();
       d.eraseArc(arc);
       d.addArc(head, tail);
     } catch (Exception&) {
       GUM_ERROR(InvalidArc, "this arc reversal would induce a directed cycle")
     }
 
     // with the same notations as Shachter (1986), "evaluating influence
     // diagrams", p.878, we shall first compute the product of probabilities:
     // pi_j^old (x_j | x_c^old(j) ) * pi_i^old (x_i | x_c^old(i) )
     Potential< GUM_SCALAR > prod{cpt(tail) * cpt(head)};
 
     // modify the topology of the graph: add to tail all the parents of head
     // and add to head all the parents of tail
     beginTopologyTransformation();
     NodeSet new_parents;
     for (const auto node: this->parents(tail))
       new_parents.insert(node);
     for (const auto node: this->parents(head))
       new_parents.insert(node);
     // remove arc (head, tail)
     eraseArc(arc);
 
     // add the necessary arcs to the tail
     for (const auto p: new_parents) {
       if ((p != tail) && !dag().existsArc(p, tail)) { addArc(p, tail); }
     }
 
     addArc(head, tail);
     // add the necessary arcs to the head
     new_parents.erase(tail);
 
     for (const auto p: new_parents) {
       if ((p != head) && !dag().existsArc(p, head)) { addArc(p, head); }
     }
 
     endTopologyTransformation();
 
     // update the conditional distributions of head and tail
     Set< const DiscreteVariable* > del_vars;
     del_vars << &(variable(tail));
     Potential< GUM_SCALAR > new_cpt_head = prod.margSumOut(del_vars).putFirst(&variable(head));
 
     auto& cpt_head = const_cast< Potential< GUM_SCALAR >& >(cpt(head));
     cpt_head       = std::move(new_cpt_head);
 
     Potential< GUM_SCALAR > new_cpt_tail{(prod / cpt_head).putFirst(&variable(tail))};
     auto&                   cpt_tail = const_cast< Potential< GUM_SCALAR >& >(cpt(tail));
     cpt_tail                         = std::move(new_cpt_tail);
   }

◆ setProperty()

INLINE void gum::GraphicalModel::setProperty	(	const std::string &	name,
		const std::string &	value
	)

inherited

Add or change a property of this GraphicalModel.

Definition at line 65 of file graphicalModel_inl.h.

                                                                                 {
     try {
       _properties_()[name] = value;
     } catch (NotFound&) { _properties_().insert(name, value); }
   }

◆ size()

INLINE Size gum::DAGmodel::size ( ) const

finalvirtualinherited

Returns the number of variables in this Directed Graphical Model.

Implements gum::GraphicalModel.

Definition at line 38 of file DAGmodel_inl.h.

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

38 { return dag().size(); }

gum::NodeGraphPart::size

Size size() const

alias for sizeNodes

Definition: nodeGraphPart_inl.h:269

gum::DAGmodel::dag

const DAG & dag() const

Returns a constant reference to the dag of this Bayes Net.

Definition: DAGmodel_inl.h:35

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◆ sizeArcs()

INLINE Size gum::DAGmodel::sizeArcs ( ) const

inherited

Returns the number of arcs in this Directed Graphical Model.

Definition at line 41 of file DAGmodel_inl.h.

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

41 { return dag_.sizeArcs(); }

gum::ArcGraphPart::sizeArcs

Size sizeArcs() const

indicates the number of arcs stored within the ArcGraphPart

Definition: arcGraphPart_inl.h:36

gum::DAGmodel::dag_

DAG dag_

The DAG of this Directed Graphical Model.

Definition: DAGmodel.h:222

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◆ toDot()

template<typename GUM_SCALAR >

std::string gum::IBayesNet< GUM_SCALAR >::toDot ( ) const

virtualinherited

Returns: Returns a dot representation of this IBayesNet.

Reimplemented in gum::BayesNetFragment< GUM_SCALAR >, gum::prm::ClassBayesNet< GUM_SCALAR >, and gum::prm::InstanceBayesNet< GUM_SCALAR >.

Definition at line 165 of file IBayesNet_tpl.h.

                                                  {
     std::stringstream output;
     output << "digraph \"";
 
     std::string bn_name;
 
     try {
       bn_name = this->property("name");
     } catch (NotFound&) { bn_name = "no_name"; }
 
     output << bn_name << "\" {" << std::endl;
     output << "  graph [bgcolor=transparent,label=\"" << bn_name << "\"];" << std::endl;
     output << "  node [style=filled fillcolor=\"#ffffaa\"];" << std::endl << std::endl;
 
     for (auto node: nodes())
       output << "\"" << variable(node).name() << "\" [comment=\"" << node << ":"
              << variable(node).toStringWithDescription() << "\"];" << std::endl;
 
     output << std::endl;
 
     std::string tab = "  ";
 
     for (auto node: nodes()) {
       if (children(node).size() > 0) {
         for (auto child: children(node)) {
           output << tab << "\"" << variable(node).name() << "\" -> "
                  << "\"" << variable(child).name() << "\";" << std::endl;
         }
       } else if (parents(node).size() == 0) {
         output << tab << "\"" << variable(node).name() << "\";" << std::endl;
       }
     }
 
     output << "}" << std::endl;
 
     return output.str();
   }

◆ topologicalOrder()

INLINE const Sequence< NodeId > & gum::DAGmodel::topologicalOrder ( bool clear = true ) const

inherited

The topological order stays the same as long as no variable or arcs are added or erased src the topology.

Parameters

clear If false returns the previously created topology.

Definition at line 86 of file DAGmodel_inl.h.

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

                                                                               {
     return dag().topologicalOrder(clear);
   }

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◆ toString()

template<typename GUM_SCALAR >

INLINE std::string gum::IBayesNet< GUM_SCALAR >::toString ( ) const

inherited

Returns: Returns a string representation of this IBayesNet.

Definition at line 144 of file IBayesNet_tpl.h.

                                                            {
     Size   param = 0;
     double dSize = log10DomainSize();
 
     for (auto node: nodes())
       param += cpt(node).content()->realSize();
 
     std::stringstream s;
     s << "BN{nodes: " << size() << ", arcs: " << dag().sizeArcs() << ", ";
 
     if (dSize > 6)
       s << "domainSize: 10^" << dSize;
     else
       s << "domainSize: " << std::round(std::pow(10.0, dSize));
 
     s << ", dim: " << param << "}";
 
     return s.str();
   }

◆ variable() [1/2]

template<typename GUM_SCALAR >

INLINE const DiscreteVariable & gum::BayesNet< GUM_SCALAR >::variable ( NodeId id ) const

finalvirtual

Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.

Parameters

id	The variable's id to return.

Returns: Returns a constant reference of the gum::DiscreteVariable corresponding to id in the gum::BayesNet.

Exceptions

NotFound Raised if id does not match a a variable in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 217 of file BayesNet_tpl.h.

                                                                                  {
     return _varMap_.get(id);
   }

◆ variable() [2/2]

template<typename GUM_SCALAR>

const DiscreteVariable& gum::BayesNet< GUM_SCALAR >::variable ( const std::string & name ) const

inline

Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.

Definition at line 309 of file BayesNet.h.

                                                                   {
       return variable(idFromName(name));
     };

◆ variableFromName()

template<typename GUM_SCALAR >

INLINE const DiscreteVariable & gum::BayesNet< GUM_SCALAR >::variableFromName ( const std::string & name ) const

finalvirtual

Returns a variable given its name in the gum::BayesNet.

Parameters

name	The variable's name in the gum::BayesNet.

Returns: Returns the gum::DiscreteVariable named name in the gum::BayesNet.

Exceptions

NotFound Raised if name does not match a variable in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 308 of file BayesNet_tpl.h.

                                                                                    {
     return _varMap_.variableFromName(name);
   }

◆ variableNodeMap()

template<typename GUM_SCALAR >

INLINE const VariableNodeMap & gum::BayesNet< GUM_SCALAR >::variableNodeMap ( ) const

finalvirtual

Returns a map between variables and nodes of this gum::BayesNet.

Returns: Returns a constant reference to the gum::VariableNodeMap.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 318 of file BayesNet_tpl.h.

                                                                               {
     return _varMap_;
   }

Friends And Related Function Documentation

◆ BayesNetFactory< GUM_SCALAR >

template<typename GUM_SCALAR>

friend class BayesNetFactory< GUM_SCALAR >

friend

Definition at line 78 of file BayesNet.h.

Member Data Documentation

◆ _probaMap_

template<typename GUM_SCALAR>

NodeProperty< Potential< GUM_SCALAR >* > gum::BayesNet< GUM_SCALAR >::_probaMap_

private

Mapping between the variable's id and their CPT.

Definition at line 650 of file BayesNet.h.

◆ _varMap_

template<typename GUM_SCALAR>

VariableNodeMap gum::BayesNet< GUM_SCALAR >::_varMap_

private

the map between variable and id

Definition at line 647 of file BayesNet.h.

◆ dag_

DAG gum::DAGmodel::dag_

protectedinherited

The DAG of this Directed Graphical Model.

Definition at line 222 of file DAGmodel.h.

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

agrum/BN/BayesNet.h
agrum/BN/BayesNet_tpl.h

Public Member Functions

Static Public Member Functions

Protected Attributes

Friends

Detailed Description

template<typename GUM_SCALAR> class gum::BayesNet< GUM_SCALAR >

Constructor & Destructor Documentation

◆ BayesNet() [1/3]

◆ BayesNet() [2/3]

◆ ~BayesNet()

◆ BayesNet() [3/3]

Member Function Documentation

◆ _clearPotentials_()

◆ _copyPotentials_()

◆ _unsafeChangePotential_()

◆ add() [1/5]

◆ add() [2/5]

◆ add() [3/5]

◆ add() [4/5]

◆ add() [5/5]

◆ addAMPLITUDE()

◆ addAND()

◆ addArc() [1/2]

◆ addArc() [2/2]

◆ addCOUNT()

◆ addEXISTS()

◆ addFORALL()

◆ addLogit() [1/2]

◆ addLogit() [2/2]

◆ addMAX()

◆ addMEDIAN()

◆ addMIN()

◆ addNoisyAND() [1/2]

◆ addNoisyAND() [2/2]

◆ addNoisyOR() [1/2]

◆ addNoisyOR() [2/2]

◆ addNoisyORCompound() [1/2]

◆ addNoisyORCompound() [2/2]

◆ addNoisyORNet() [1/2]

◆ addNoisyORNet() [2/2]

◆ addOR()

◆ addSUM()

◆ addWeightedArc() [1/2]

◆ addWeightedArc() [2/2]

◆ ancestors() [1/2]

◆ ancestors() [2/2]

◆ arcs()

◆ beginTopologyTransformation()

◆ changePotential() [1/2]

◆ changePotential() [2/2]

◆ changeVariableLabel() [1/2]

◆ changeVariableLabel() [2/2]

◆ changeVariableName() [1/2]

◆ changeVariableName() [2/2]

◆ children() [1/4]

◆ children() [2/4]

◆ children() [3/4]

◆ children() [4/4]

◆ clear()

◆ completeInstantiation()

◆ cpt() [1/2]

◆ cpt() [2/2]

◆ dag()

◆ descendants() [1/2]

◆ descendants() [2/2]

◆ dim()

◆ empty()

◆ endTopologyTransformation()

◆ erase() [1/3]

◆ erase() [2/3]

◆ erase() [3/3]

◆ eraseArc() [1/3]

◆ eraseArc() [2/3]

◆ eraseArc() [3/3]

◆ exists() [1/2]

◆ exists() [2/2]

◆ existsArc() [1/2]

◆ existsArc() [2/2]

◆ family() [1/2]

◆ family() [2/2]

template<typename GUM_SCALAR>
class gum::BayesNet< GUM_SCALAR >