gum::BayesNet< GUM_SCALAR > Class Template Reference

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.
NodeId	addNoisyAND (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, its associate node and a noisyAND implementation.
NodeId	addLogit (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a Logit implementation.
NodeId	addLogit (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, its associate node and a Logit implementation.
NodeId	addOR (const DiscreteVariable &var)
	Add a variable, it's associate node and an OR implementation.
NodeId	addAND (const DiscreteVariable &var)
	Add a variable, it's associate node and an AND implementation.
void	addWeightedArc (NodeId tail, NodeId head, GUM_SCALAR causalWeight)
	Add an arc in the BN, and update arc.head's CPT.
void	addWeightedArc (std::string_view tail, std::string_view head, GUM_SCALAR causalWeight)
	Add an arc in the BN, and update arc.head's CPT.
void	generateCPTs () const
	randomly generates CPTs for a given structure
void	generateCPT (NodeId node) const
	randomly generate CPT for a given node in a given structure
void	generateCPT (std::string_view name) const
void	changeTensor (NodeId id, Tensor< GUM_SCALAR > *newPot)
	change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.
void	changeTensor (std::string_view name, Tensor< GUM_SCALAR > *newPot)
BayesNet< GUM_SCALAR >	contextualize (const gum::Instantiation &observations, const gum::Instantiation &interventions) const
	create a contextual BN from this and a set of hard observations and hard interventions.
NodeId	idFromName (std::string_view name) const override
	Returns the NodeId of a variable given its name.
const VariableNodeMap &	variableNodeMap () const override
	Returns a constant reference to the VariableNodeMap of this model.
const DiscreteVariable &	variable (NodeId id) const override
	Returns a constant reference over a variable given its node id.
NodeId	nodeId (const DiscreteVariable &var) const override
	Returns the NodeId of a variable.
const DiscreteVariable &	variableFromName (std::string_view name) const override
	Returns a constant reference over a variable given its name.
std::vector< std::string >	check () const
	Check if the BayesNet is consistent (variables, CPT).
bool	operator== (const IBayesNet< GUM_SCALAR > &from) const
	This operator compares 2 BNs !
Size	dim () const
	Returns the dimension (the number of free parameters) in this bayes net.
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
Tensor< GUM_SCALAR >	evEq (std::string_view name, double value) const
Tensor< GUM_SCALAR >	evIn (std::string_view name, double val1, double val2) const
Tensor< GUM_SCALAR >	evLt (std::string_view name, double value) const
Tensor< GUM_SCALAR >	evGt (std::string_view name, double value) const
Size	memoryFootprint () const
	compute the (approximated) footprint in memory of the model (the footprints of CPTs)
bool	hasSameStructure (const DAGmodel &other) const
NodeSet	minimalCondSet (NodeId target, const NodeSet &soids) const
NodeSet	minimalCondSet (const NodeSet &targets, const NodeSet &soids) const
NodeSet	minimalCondSet (std::string_view target, const std::vector< std::string > &soids) const
NodeSet	minimalCondSet (const std::vector< std::string > &targets, const std::vector< std::string > &soids) const
const DAG &	internalDag () const
	Returns a const reference to the internal (unnamed) DAG. O(1), no copy. Use for stable references or pointers (e.g. graph listeners). For named node access, use dag() instead.
double	log10DomainSize () const
Constructors and Destructor
	BayesNet ()
	Default constructor.
	BayesNet (std::string_view name)
	Default constructor.
	~BayesNet () override
	Destructor.
	BayesNet (const BayesNet< GUM_SCALAR > &source)
	Copy constructor.
	BayesNet (BayesNet< GUM_SCALAR > &&source)
	Move constructor.
Operators
BayesNet< GUM_SCALAR > &	operator= (const BayesNet< GUM_SCALAR > &source)
	Copy operator.
BayesNet< GUM_SCALAR > &	operator= (BayesNet< GUM_SCALAR > &&source)
	Move operator.
Variable manipulation methods
const Tensor< GUM_SCALAR > &	cpt (NodeId varId) const final
	Returns the CPT of a variable.
const Tensor< GUM_SCALAR > &	cpt (std::string_view name) const
	Returns the CPT of a variable.
NodeId	add (const DiscreteVariable &var)
	Add a variable to the gum::BayesNet.
NodeId	add (std::string_view fast_description, unsigned int default_nbrmod=2)
	Use "fast" syntax to add a variable in the BayesNet.
NodeId	add (const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent)
	Add a variable to the gum::BayesNet.
NodeId	add (const DiscreteVariable &var, NodeId id)
	Add a variable to the gum::BayesNet.
NodeId	add (const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent, NodeId id)
	Add a variable to the gum::BayesNet.
void	clear ()
	clear the whole Bayes net *
void	erase (NodeId varId)
	Remove a variable from the gum::BayesNet.
void	erase (std::string_view name)
	Removes a variable from the gum::BayesNet.
void	erase (const DiscreteVariable &var)
	Remove a variable from the gum::BayesNet.
const DiscreteVariable &	variable (std::string_view name) const
	Returns a gum::DiscreteVariable given its name in the gum::BayesNet.
void	changeVariableName (NodeId id, std::string_view new_name)
	Changes a variable's name in the gum::BayesNet.
void	changeVariableName (std::string_view name, std::string_view new_name)
	Changes a variable's name.
void	changeVariableLabel (NodeId id, std::string_view old_label, std::string_view new_label)
	Changes a variable's label in the gum::BayesNet.
void	changeVariableLabel (std::string_view name, std::string_view old_label, std::string_view new_label)
	Changes a variable's name.
Arc manipulation methods.
void	addArc (NodeId tail, NodeId head)
	Add an arc in the BN, and update arc.head's CPT.
void	addArc (std::string_view tail, std::string_view head)
	Add an arc in the BN, and update arc.head's CPT.
void	eraseArc (const Arc &arc)
	Removes an arc in the BN, and update head's CTP.
void	eraseArc (NodeId tail, NodeId head)
	Removes an arc in the BN, and update head's CTP.
void	eraseArc (std::string_view tail, std::string_view head)
	Removes an arc in the BN, and update head's CTP.
void	beginTopologyTransformation ()
	When inserting/removing arcs, node CPTs change their dimension with a cost in time.
void	endTopologyTransformation ()
	terminates a sequence of insertions/deletions of arcs by adjusting all CPTs dimensions.
void	reverseArc (NodeId tail, NodeId head)
	Reverses an arc while preserving the same joint distribution.
void	reverseArc (std::string_view tail, std::string_view head)
	Reverses an arc while preserving the same joint distribution.
void	reverseArc (const Arc &arc)
	Reverses an arc while preserving the same joint distribution.
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.
NodeId	addNoisyORNet (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, it's associate node and a gum::noisyOR implementation.
NodeId	addNoisyORCompound (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, it's associate node and a gum::noisyOR implementation.
NodeId	addNoisyOR (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyOR implementation.
NodeId	addNoisyORNet (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyOR implementation.
NodeId	addNoisyORCompound (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyOR implementation.
NodeId	addAMPLITUDE (const DiscreteVariable &var)
	Others aggregators.
NodeId	addCOUNT (const DiscreteVariable &var, Idx value=1)
	Others aggregators.
NodeId	addEXISTS (const DiscreteVariable &var, Idx value=1)
	Others aggregators.
NodeId	addFORALL (const DiscreteVariable &var, Idx value=1)
	Others aggregators.
NodeId	addMAX (const DiscreteVariable &var)
	Others aggregators.
NodeId	addMEDIAN (const DiscreteVariable &var)
	Others aggregators.
NodeId	addMIN (const DiscreteVariable &var)
	Others aggregators.
NodeId	addSUM (const DiscreteVariable &var)
	Others aggregators.
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).
GUM_SCALAR	log2JointProbability (const Instantiation &i) const
	Compute a parameter of the log joint probability for the BN (given an instantiation of the vars).
Variable manipulation methods.
DAG	dag () const
	Returns a named copy of the internal DAG: each node id is assigned the name of the corresponding variable.
Size	size () const final
	Returns the number of variables in this Directed Graphical Model.
Size	sizeArcs () const
	Returns the number of arcs in this Directed Graphical Model.
const NodeGraphPart &	nodes () const final
	Returns a named copy of the internal DAG: each node id is assigned the name of the corresponding variable.
bool	exists (NodeId node) const final
	Return true if this node exists in this graphical model.
bool	exists (std::string_view name) const final
	Returns a named copy of the internal DAG: each node id is assigned the name of the corresponding variable.
Arc manipulation methods.
const ArcSet &	arcs () const
	return true if the arc tail->head exists in the DAGmodel
bool	existsArc (const NodeId tail, const NodeId head) const
	return true if the arc tail->head exists in the DAGmodel
bool	existsArc (std::string_view nametail, std::string_view namehead) const
	return true if the arc tail->head exists in the DAGmodel
const NodeSet &	parents (const NodeId id) const
	returns the set of nodes with arc ingoing to a given node
const NodeSet &	parents (std::string_view name) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	parents (const NodeSet &ids) const
	returns the parents of a set of nodes
NodeSet	parents (const std::vector< std::string > &names) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	family (const NodeId id) const final
	returns the parents of a node and the node
NodeSet	family (std::string_view name) const final
	return true if the arc tail->head exists in the DAGmodel
const NodeSet &	children (const NodeId id) const
	returns the set of nodes with arc outgoing from a given node
const NodeSet &	children (std::string_view name) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	children (const NodeSet &ids) const
	returns the children of a set of nodes
NodeSet	children (const std::vector< std::string > &names) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	descendants (const NodeId id) const
	returns the set of nodes with directed path outgoing from a given node
NodeSet	descendants (std::string_view name) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	ancestors (const NodeId id) const
	returns the set of nodes with directed path ingoing to a given node
NodeSet	ancestors (std::string_view name) const
	return true if the arc tail->head exists in the DAGmodel
Graphical methods
UndiGraph	moralizedAncestralGraph (const NodeSet &nodes) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
UndiGraph	moralizedAncestralGraph (const std::vector< std::string > &nodenames) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
bool	isIndependent (NodeId X, NodeId Y, const NodeSet &Z) const final
	check if node X and node Y are independent given nodes Z
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
bool	isIndependent (std::string_view Xname, std::string_view Yname, const std::vector< std::string > &Znames) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
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
UndiGraph	moralGraph () const
	The node's id are coherent with the variables and nodes of the topology.
Sequence< NodeId >	topologicalOrder () const
	The topological order stays the same as long as no variable or arcs are added or erased src the topology.
NodeProperty< NodeId >	connectedComponents () const
	Returns the weakly connected components of the underlying DAG. Each node maps to the id of its component root.
Getter and setters
const std::string &	property (std::string_view name) const
	Return the value of the property name of this GraphicalModel.
const std::string &	propertyWithDefault (std::string_view name, const std::string &byDefault) const
	Return the value of the property name of this GraphicalModel.
void	setProperty (std::string_view name, std::string_view value)
	Add or change a property of this GraphicalModel.
std::vector< std::string >	properties () const
	List of all the names of property in the Graphical model.
bool	existsProperty (std::string_view name) const
	check wether a property exists in this GraphicalModel
void	updateMetaData ()
	update the meta data of this Graphical Model (version, creation date, last modification date) This method is called by the writers ONLY before writing the model to a file.
Variable manipulation methods.
virtual bool	empty () const
	Return true if this graphical model is empty.
std::vector< std::string >	names (const std::vector< NodeId > &ids) const
	transform a vector of NodeId in a vector of names
std::vector< std::string >	names (const NodeSet &ids) const
	transform a NodeSet in a vector of names
std::vector< NodeId >	ids (const std::vector< std::string > &names) const
	transform a vector of names into a vector of nodeId
NodeSet	nodeset (const std::vector< std::string > &names) const
	transform a vector of names into a NodeSet
gum::VariableSet	variables (const std::vector< std::string > &l) const
	transform a vector of names into a VariableeSet
gum::VariableSet	variables (const NodeSet &ids) const
	transform a vector of NodeId into a VariableeSet
Instantiation	completeInstantiation () const
	Get an instantiation over all the variables of the model.

Static Public Member Functions
static BayesNet< GUM_SCALAR >	fastPrototype (std::string_view dotlike, Size domainSize)
	Create a Bayesian network with a dot-like syntax which specifies:
static BayesNet< GUM_SCALAR >	fastPrototype (std::string_view dotlike, std::string_view domainSize="[2]")
static std::string	spaceCplxToString (double dSize, int dim, Size usedMem)
	send to the stream the space complexity with 3 parametrs

Protected Member Functions
void	_nameNodes_ (NodeGraphPart &g) const
	Names every node of g using variable(id).name() for each node id in g.

Protected Attributes
DAG	dag_
	The DAG of this Directed Graphical Model.
VariableNodeMap	varMap_
	Mapping between NodeIds and discrete variables.

Private Member Functions
void	_clearTensors_ ()
	clear all tensors
void	_copyTensors_ (const BayesNet< GUM_SCALAR > &source)
	copy of tensors from a BN to another, using names of vars as ref.
void	_unsafeChangeTensor_ (NodeId id, Tensor< GUM_SCALAR > *newPot)
	change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.
const HashTable< std::string, std::string > &	_properties_ () const
	Return the properties of this Directed Graphical Model.

Private Attributes
NodeProperty< Tensor< GUM_SCALAR > * >	_probaMap_
	Mapping between the variable's id and their CPT.
HashTable< std::string, std::string >	_propertiesMap_
	The properties of this Directed Graphical Model.

Friends
class	BayesNetFactory< GUM_SCALAR >

Detailed Description

template<GUM_Numeric 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 93 of file BayesNet.h.

Constructor & Destructor Documentation

BayesNet() [1/4]

template<GUM_Numeric GUM_SCALAR>

gum::BayesNet< GUM_SCALAR >::BayesNet

(

)

Default constructor.

Definition at line 128 of file BayesNet_tpl.h.

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

References BayesNet(), and gum::IBayesNet< GUM_SCALAR >::IBayesNet().

Referenced by BayesNet(), BayesNet(), BayesNet(), BayesNet(), ~BayesNet(), addMAX(), and operator=().

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BayesNet() [2/4]

template<GUM_Numeric GUM_SCALAR>

gum::BayesNet< GUM_SCALAR >::BayesNet ( std::string_view name )

explicit

Default constructor.

Parameters

name	The BayesNet's name.

Definition at line 133 of file BayesNet_tpl.h.

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

References BayesNet(), and gum::IBayesNet< GUM_SCALAR >::IBayesNet().

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~BayesNet()

template<GUM_Numeric GUM_SCALAR>

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

override

Destructor.

Definition at line 175 of file BayesNet_tpl.h.

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

References BayesNet(), and _probaMap_.

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BayesNet() [3/4]

template<GUM_Numeric GUM_SCALAR>

gum::BayesNet< GUM_SCALAR >::BayesNet

(

const BayesNet< GUM_SCALAR > &

source

)

Copy constructor.

Definition at line 138 of file BayesNet_tpl.h.

                                                                       :
      IBayesNet< GUM_SCALAR >(source) {
    GUM_CONS_CPY(BayesNet)
 
    _copyTensors_(source);
  }

References BayesNet(), gum::IBayesNet< GUM_SCALAR >::IBayesNet(), and _copyTensors_().

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BayesNet() [4/4]

template<GUM_Numeric GUM_SCALAR>

gum::BayesNet< GUM_SCALAR >::BayesNet

(

BayesNet< GUM_SCALAR > &&

source

)

Move constructor.

Definition at line 146 of file BayesNet_tpl.h.

                                                                  :
      IBayesNet< GUM_SCALAR >(std::move(source)), _probaMap_(std::move(source._probaMap_)) {
    GUM_CONS_MOV(BayesNet)
  }

References BayesNet(), gum::IBayesNet< GUM_SCALAR >::IBayesNet(), and _probaMap_.

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Member Function Documentation

_clearTensors_()

template<GUM_Numeric GUM_SCALAR>

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

private

clear all tensors

Definition at line 557 of file BayesNet_tpl.h.

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

References _probaMap_.

Referenced by operator=().

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_copyTensors_()

template<GUM_Numeric GUM_SCALAR>

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

private

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

Definition at line 568 of file BayesNet_tpl.h.

                                                                                 {
    // Copying tensors
 
    for (const auto& src: source._probaMap_) {
      // First we build the node's CPT
      auto copy_array = new Tensor< 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);
    }
  }

Referenced by BayesNet(), and operator=().

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_nameNodes_()

INLINE void gum::GraphicalModel::_nameNodes_ ( NodeGraphPart & g ) const

protectedinherited

Names every node of g using variable(id).name() for each node id in g.

Call this before returning any newly constructed graph from a model method.

Definition at line 175 of file graphicalModel_inl.h.

                                                         {
    for (auto id: g)
      g.setName(id, variable(id).name());
  }

References gum::NodeGraphPart::setName(), and variable().

Referenced by gum::DAGmodel::dag(), gum::UGmodel::graph(), gum::DAGmodel::moralGraph(), and gum::DAGmodel::moralizedAncestralGraph().

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_properties_()

INLINE const HashTable< std::string, std::string > & gum::GraphicalModel::_properties_ ( ) const

privateinherited

Return the properties of this Directed Graphical Model.

Definition at line 67 of file graphicalModel_inl.h.

                                                                              {
    return _propertiesMap_;
  }

References _propertiesMap_.

Referenced by property().

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_unsafeChangeTensor_()

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::_unsafeChangeTensor_ ( NodeId id, Tensor< 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

changeTensor

Definition at line 620 of file BayesNet_tpl.h.

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

References _probaMap_.

add() [1/5]

template<GUM_Numeric GUM_SCALAR>

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::Tensor.

The variable is added by copy to the gum::BayesNet. The variable's gum::Tensor 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 201 of file BayesNet_tpl.h.

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

References add().

Referenced by add(), add(), add(), add(), addAMPLITUDE(), addAND(), addCOUNT(), addEXISTS(), addLogit(), addMAX(), addMEDIAN(), addMIN(), addNoisyAND(), addNoisyAND(), addNoisyORCompound(), addNoisyORNet(), addNoisyORNet(), addOR(), addSUM(), gum::build_node(), and gum::BayesNetFragment< GUM_SCALAR >::toBN().

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add() [2/5]

template<GUM_Numeric GUM_SCALAR>

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::Tensor.

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::Tensor 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 220 of file BayesNet_tpl.h.

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

References add(), and gum::DAGmodel::dag().

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add() [3/5]

template<GUM_Numeric 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::Tensor.

Parameters

var	The variable added by copy.
aContent	The gum::MultiDimImplementation to use for this variable's gum::Tensor 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 240 of file BayesNet_tpl.h.

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

References _probaMap_, cpt(), gum::DAGmodel::dag_, variable(), and gum::DiscreteGraphicalModel::varMap_.

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add() [4/5]

template<GUM_Numeric GUM_SCALAR>

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::Tensor.

The variable is added by copy to the gum::BayesNet. The variable's gum::Tensor 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 228 of file BayesNet_tpl.h.

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

References add().

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add() [5/5]

template<GUM_Numeric GUM_SCALAR>

NodeId gum::BayesNet< GUM_SCALAR >::add	(	std::string_view	fast_description,
		unsigned int	default_nbrmod = 2 )

Use "fast" syntax to add a variable in the BayesNet.

• a : range variable from 0 to default_nbrmod-1
• a[5] : range variable from 0 to 5
• a[-3,5] : range variable from -3 to 5
• a[1,3.14,5,3] : discretized variable
• a{x|y|z} : labelized variable
• a{-3|0|3|100} : integer variable

Parameters

fast_description(	str) following "fast" syntax description
default_nbrmod(	int) nbr of modality if fast_description do not indicate it. default_nbrmod=1 is the way to create a variable with only one value (for instance for reward in influence diagram).

Exceptions

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

Definition at line 212 of file BayesNet_tpl.h.

                                                                      {
    auto v = fastVariable< GUM_SCALAR >(std::string(fast_description), default_nbrmod);
    if (v->domainSize() < 2) GUM_ERROR(OperationNotAllowed, v->name() << " has a domain size <2")
    return add(*v);
  }

References add(), gum::fastVariable(), and GUM_ERROR.

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addAMPLITUDE()

template<GUM_Numeric GUM_SCALAR>

NodeId gum::BayesNet< GUM_SCALAR >::addAMPLITUDE

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 397 of file BayesNet_tpl.h.

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

References add().

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addAND()

template<GUM_Numeric GUM_SCALAR>

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 402 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 >());
  }

References add(), gum::DiscreteVariable::domainSize(), and GUM_ERROR.

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addArc() [1/2]

template<GUM_Numeric GUM_SCALAR>

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 289 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);
  }

References gum::DAGmodel::dag_.

Referenced by addArc(), fastPrototype(), and gum::BayesNetFragment< GUM_SCALAR >::toBN().

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addArc() [2/2]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::addArc	(	std::string_view	tail,
		std::string_view	head )

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

Exceptions

gum::DuplicateElement

if the arc already exists

Definition at line 300 of file BayesNet_tpl.h.

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

References addArc(), and idFromName().

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addCOUNT()

template<GUM_Numeric GUM_SCALAR>

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

Others aggregators.

Definition at line 409 of file BayesNet_tpl.h.

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

References add().

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addEXISTS()

template<GUM_Numeric GUM_SCALAR>

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

Others aggregators.

Definition at line 414 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));
  }

References add(), gum::DiscreteVariable::domainSize(), and GUM_ERROR.

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addFORALL()

template<GUM_Numeric GUM_SCALAR>

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

Others aggregators.

Definition at line 421 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));
  }

References gum::DiscreteVariable::domainSize(), and GUM_ERROR.

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addLogit() [1/2]

template<GUM_Numeric GUM_SCALAR>

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 482 of file BayesNet_tpl.h.

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

References add().

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addLogit() [2/2]

template<GUM_Numeric GUM_SCALAR>

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 501 of file BayesNet_tpl.h.

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

addMAX()

template<GUM_Numeric GUM_SCALAR>

NodeId gum::BayesNet< GUM_SCALAR >::addMAX

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 428 of file BayesNet_tpl.h.

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

References BayesNet(), add(), and addMAX().

Referenced by addMAX().

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addMEDIAN()

template<GUM_Numeric GUM_SCALAR>

NodeId gum::BayesNet< GUM_SCALAR >::addMEDIAN

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 433 of file BayesNet_tpl.h.

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

References add().

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addMIN()

template<GUM_Numeric GUM_SCALAR>

NodeId gum::BayesNet< GUM_SCALAR >::addMIN

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 438 of file BayesNet_tpl.h.

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

References add().

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addNoisyAND() [1/2]

template<GUM_Numeric GUM_SCALAR>

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 476 of file BayesNet_tpl.h.

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

References add().

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addNoisyAND() [2/2]

template<GUM_Numeric GUM_SCALAR>

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 494 of file BayesNet_tpl.h.

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

References add().

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addNoisyOR() [1/2]

template<GUM_Numeric GUM_SCALAR>

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 458 of file BayesNet_tpl.h.

                                                                                     {
    return addNoisyORCompound(var, external_weight);
  }

References addNoisyORCompound().

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addNoisyOR() [2/2]

template<GUM_Numeric GUM_SCALAR>

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 487 of file BayesNet_tpl.h.

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

References addNoisyORCompound().

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addNoisyORCompound() [1/2]

template<GUM_Numeric GUM_SCALAR>

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 464 of file BayesNet_tpl.h.

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

Referenced by addNoisyOR(), and addNoisyOR().

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addNoisyORCompound() [2/2]

template<GUM_Numeric GUM_SCALAR>

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 508 of file BayesNet_tpl.h.

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

References add().

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addNoisyORNet() [1/2]

template<GUM_Numeric GUM_SCALAR>

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 470 of file BayesNet_tpl.h.

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

References add().

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addNoisyORNet() [2/2]

template<GUM_Numeric GUM_SCALAR>

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 515 of file BayesNet_tpl.h.

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

References add().

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addOR()

template<GUM_Numeric GUM_SCALAR>

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 443 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 >());
  }

References add(), gum::DiscreteVariable::domainSize(), and GUM_ERROR.

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addSUM()

template<GUM_Numeric GUM_SCALAR>

NodeId gum::BayesNet< GUM_SCALAR >::addSUM

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 450 of file BayesNet_tpl.h.

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

References add().

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addWeightedArc() [1/2]

template<GUM_Numeric 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 522 of file BayesNet_tpl.h.

                                                                                               {
    if (auto* CImodel
        = dynamic_cast< const MultiDimICIModel< GUM_SCALAR >* >(cpt(head).content())) {
      // 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 !")
    }
  }

Referenced by addWeightedArc().

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addWeightedArc() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

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

Parameters

head	and
tail	as std::string
causalWeight	see gum::MultiDimICIModel

Exceptions

NotFound	if no node with sun names is found
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 731 of file BayesNet_tpl.h.

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

References addWeightedArc(), and idFromName().

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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 135 of file DAGmodel_inl.h.

{ return dag_.ancestors(id); }

References dag_.

Referenced by ancestors().

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ancestors() [2/2]

INLINE NodeSet gum::DAGmodel::ancestors ( std::string_view 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 137 of file DAGmodel_inl.h.

                                                              {
    return ancestors(idFromName(name));
  }

References ancestors(), and gum::DiscreteGraphicalModel::idFromName().

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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 73 of file DAGmodel_inl.h.

{ return dag_.arcs(); }

References dag_.

Referenced by hasSameStructure(), gum::MarkovBlanket::hasSameStructure(), and gum::BayesNetFragment< GUM_SCALAR >::toBN().

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beginTopologyTransformation()

template<GUM_Numeric 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 543 of file BayesNet_tpl.h.

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

References _probaMap_, and gum::DAGmodel::nodes().

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changeTensor() [1/2]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeTensor	(	NodeId	id,
		Tensor< 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 600 of file BayesNet_tpl.h.

                                                                                   {
    if (cpt(id).nbrDim() != newPot->nbrDim()) {
      GUM_ERROR(OperationNotAllowed,
                "cannot exchange tensors 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 tensors because, for variable with id " << id << ", dimension "
                                                                           << i << " differs. ")
      }
    }
 
    _unsafeChangeTensor_(id, newPot);
  }

References cpt(), and GUM_ERROR.

Referenced by changeTensor().

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changeTensor() [2/2]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeTensor	(	std::string_view	name,
		Tensor< GUM_SCALAR > *	newPot )

Definition at line 626 of file BayesNet_tpl.h.

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

References changeTensor(), and idFromName().

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changeVariableLabel() [1/2]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeVariableLabel	(	NodeId	id,
		std::string_view	old_label,
		std::string_view	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 188 of file BayesNet_tpl.h.

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

Referenced by changeVariableLabel().

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changeVariableLabel() [2/2]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeVariableLabel	(	std::string_view	name,
		std::string_view	old_label,
		std::string_view	new_label )

Changes a variable's name.

Definition at line 714 of file BayesNet_tpl.h.

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

References changeVariableLabel(), and idFromName().

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changeVariableName() [1/2]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeVariableName	(	NodeId	id,
		std::string_view	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 183 of file BayesNet_tpl.h.

                                                                                    {
    this->varMap_.changeName(id, new_name);
  }

References gum::DiscreteGraphicalModel::varMap_.

Referenced by changeVariableName().

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changeVariableName() [2/2]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeVariableName	(	std::string_view	name,
		std::string_view	new_name )

Changes a variable's name.

Definition at line 708 of file BayesNet_tpl.h.

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

References changeVariableName(), and idFromName().

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check()

template<GUM_Numeric GUM_SCALAR>

std::vector< std::string > gum::IBayesNet< GUM_SCALAR >::check ( ) const

inherited

Check if the BayesNet is consistent (variables, CPT).

Returns

a list of comments on the consistency of the Bayes Net

Definition at line 322 of file IBayesNet_tpl.h.

                                                              {
    std::vector< std::string > comments;
 
    const double epsilon       = 1e-8;
    const double error_epsilon = 1e-1;
 
    // CHECKING domain
    for (const auto i: nodes())
      if (variable(i).domainSize() < 2) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << ": not consistent (domainSize=1).";
        comments.push_back(s.str());
      }
 
    // CHECKING parameters are probabilities
    // >0
    for (const auto i: nodes()) {
      const auto [amin, minval] = cpt(i).argmin();
      if (minval < (GUM_SCALAR)0.0) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << " : P(" << *(amin.begin()) << ") < 0.0";
        comments.push_back(s.str());
      }
    }
    // <1
    for (const auto i: nodes()) {
      const auto [amax, maxval] = cpt(i).argmax();
      if (maxval > (GUM_SCALAR)1.0) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << " : P(" << *(amax.begin()) << ") > 1.0";
        comments.push_back(s.str());
      }
    }
 
    // CHECKING distributions sum to 1
    for (const auto i: nodes()) {
      const auto p              = cpt(i).sumOut({&variable(i)});
      const auto [amin, minval] = p.argmin();
      if (minval < (GUM_SCALAR)(1.0 - epsilon)) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << " : ";
        if (!parents(i).empty()) s << "with (at least) parents " << *(amin.begin()) << ", ";
        s << "the CPT sum to less than 1";
        if (minval > (GUM_SCALAR)(1.0 - error_epsilon)) s << " (normalization problem ?)";
        s << ".";
        comments.push_back(s.str());
        continue;
      }
      const auto [amax, maxval] = p.argmax();
      if (maxval > (GUM_SCALAR)(1.0 + epsilon)) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << " : ";
        if (!parents(i).empty()) s << "with (at least) parents " << *(amax.begin()) << ", ";
        s << "the CPT sum to more than 1";
        if (maxval < (GUM_SCALAR)(1.0 + error_epsilon)) s << " (normalization problem ?)";
        s << ".";
        comments.push_back(s.str());
      }
    }
 
    return comments;
  }

References cpt(), gum::GraphicalModel::empty(), gum::DAGmodel::nodes(), gum::DAGmodel::parents(), and gum::DiscreteGraphicalModel::variable().

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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 95 of file DAGmodel_inl.h.

{ return dag_.children(id); }

References dag_.

Referenced by children(), gum::BayesNet< GUM_SCALAR >::erase(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), and gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot().

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children() [2/4]

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

inherited

returns the children of a set of nodes

Definition at line 101 of file DAGmodel_inl.h.

{ return dag_.children(ids); }

References dag_, and gum::GraphicalModel::ids().

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children() [3/4]

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 103 of file DAGmodel_inl.h.

                                                                             {
    return children(nodeset(names));
  }

References children(), gum::GraphicalModel::names(), and gum::GraphicalModel::nodeset().

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children() [4/4]

INLINE const NodeSet & gum::DAGmodel::children ( std::string_view 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 97 of file DAGmodel_inl.h.

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

clear()

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::clear

(

)

clear the whole Bayes net *

Definition at line 279 of file BayesNet_tpl.h.

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

References gum::GraphicalModel::empty(), erase(), and gum::DAGmodel::nodes().

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completeInstantiation()

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

inherited

Get an instantiation over all the variables of the model.

Definition at line 104 of file graphicalModel_inl.h.

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

References nodes(), and variable().

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connectedComponents()

INLINE NodeProperty< NodeId > gum::DAGmodel::connectedComponents ( ) const

inherited

Returns the weakly connected components of the underlying DAG. Each node maps to the id of its component root.

Definition at line 125 of file DAGmodel_inl.h.

                                                                    {
    return dag_.connectedComponents();
  }

References dag_.

contextualize()

template<GUM_Numeric GUM_SCALAR>

BayesNet< GUM_SCALAR > gum::BayesNet< GUM_SCALAR >::contextualize	(	const gum::Instantiation &	observations,
		const gum::Instantiation &	interventions ) const

create a contextual BN from this and a set of hard observations and hard interventions.

Parameters

observations	the hard observations
interventions	the hard interventions

Returns

a new BN with the same structure as this, but with the CPTs modified to reflect the observations and interventions

Warning

The original BN is not modified. The returned BN is not a copy of the original BN, but a new BN with copied variables and modified structure and CPTs.

Exceptions

ArgumentError

if the observations and interventions are not mutually exclusive

Definition at line 632 of file BayesNet_tpl.h.

                                                                                         {
    Instantiation all;
    for (gum::Idx i = 0; i < observations.nbrDim(); i++) {
      if (interventions.contains(observations.variable(i))) {
        GUM_ERROR(ArgumentError,
                  "Cannot have both an observation and an intervention on the same variable")
      }
      all.add(observations.variable(i));
    }
    for (gum::Idx i = 0; i < interventions.nbrDim(); i++) {
      all.add(interventions.variable(i));
    }
    all.setVals(observations);
    all.setVals(interventions);
 
    NodeSet cpt_changed;
 
    gum::BayesNet< GUM_SCALAR > bn(*this);
 
    bn.beginTopologyTransformation();
    for (gum::Idx i = 0; i < observations.nbrDim(); i++) {
      const std::string& nam = observations.variable(i).name();
      const gum::NodeId  nod = this->idFromName(nam);
      for (gum::NodeId child: this->children(nod)) {
        bn.eraseArc(bn.idFromName(nam), bn.idFromName(this->variable(child).name()));
      }
    }
    for (gum::Idx i = 0; i < interventions.nbrDim(); i++) {
      const std::string& nam = interventions.variable(i).name();
      const gum::NodeId  nod = this->idFromName(nam);
      for (gum::NodeId child: this->children(nod)) {
        bn.eraseArc(bn.idFromName(nam), bn.idFromName(this->variable(child).name()));
      }
      for (gum::NodeId par: this->parents(nod)) {
        const auto v1 = bn.idFromName(this->variable(par).name());
        const auto v2 = bn.idFromName(nam);
        if (bn.existsArc(v1, v2)) bn.eraseArc(v1, v2);
      }
      cpt_changed.insert(bn.idFromName(nam));
      bn.cpt(bn.idFromName(nam))
          .fillWith(Tensor< GUM_SCALAR >::deterministicTensor(bn.variable(bn.idFromName(nam)),
                                                              interventions.val(i)));
    }
    bn.endTopologyTransformation();
 
    for (gum::Idx i = 0; i < all.nbrDim(); i++) {
      const gum::NodeId nod = this->idFromName(all.variable(i).name());
      for (gum::NodeId child: this->children(nod)) {
        if (!cpt_changed.contains(child)) {
          cpt_changed.insert(child);
          bn.cpt(bn.idFromName(this->variable(child).name()))
              .fillWith(this->cpt(child).extract(all));
        }
      }
    }
 
    return bn;
  }

cpt() [1/2]

template<GUM_Numeric GUM_SCALAR>

const Tensor< 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 253 of file BayesNet_tpl.h.

                                                                            {
    return *(_probaMap_[varId]);
  }

References _probaMap_.

Referenced by add(), changeTensor(), cpt(), generateCPT(), and gum::BayesNetFragment< GUM_SCALAR >::toBN().

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cpt() [2/2]

template<GUM_Numeric GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::BayesNet< GUM_SCALAR >::cpt

(

std::string_view

name

)

const

Returns the CPT of a variable.

Definition at line 693 of file BayesNet_tpl.h.

                                                                                   {
    return cpt(idFromName(name));
  }

References cpt(), and idFromName().

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dag()

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

nodiscardinherited

Returns a named copy of the internal DAG: each node id is assigned the name of the corresponding variable.

O(n) — allocates a new DAG. For a stable reference (listeners, long-lived pointers), use internalDag().

Definition at line 61 of file DAGmodel_inl.h.

                          {
    DAG g = dag_;
    _nameNodes_(g);
    return g;
  }

References gum::GraphicalModel::_nameNodes_(), and dag_.

Referenced by gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment(), gum::MarginalTargetedInference< GUM_SCALAR >::MarginalTargetedInference(), gum::BayesNet< GUM_SCALAR >::add(), gum::BayesNet< GUM_SCALAR >::reverseArc(), and gum::InfluenceDiagram< GUM_SCALAR >::toString().

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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 129 of file DAGmodel_inl.h.

{ return dag_.descendants(id); }

References dag_.

descendants() [2/2]

INLINE NodeSet gum::DAGmodel::descendants ( std::string_view 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 131 of file DAGmodel_inl.h.

                                                                {
    return descendants(idFromName(name));
  }

dim()

template<GUM_Numeric 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 114 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;
  }

References dim(), gum::DAGmodel::nodes(), gum::DAGmodel::parents(), and gum::DiscreteGraphicalModel::variable().

Referenced by dim().

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empty()

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

virtualinherited

Return true if this graphical model is empty.

Definition at line 114 of file graphicalModel_inl.h.

{ return size() == 0; }

References size().

Referenced by gum::IBayesNet< GUM_SCALAR >::check(), and gum::BayesNet< GUM_SCALAR >::clear().

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endTopologyTransformation()

template<GUM_Numeric 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 550 of file BayesNet_tpl.h.

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

References _probaMap_, and gum::DAGmodel::nodes().

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erase() [1/3]

template<GUM_Numeric GUM_SCALAR>

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::Tensor.

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

Parameters

var	A reference on the variable to remove.

Definition at line 258 of file BayesNet_tpl.h.

                                                                {
    erase(this->varMap_.get(var));
  }

References erase(), and gum::DiscreteGraphicalModel::varMap_.

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erase() [2/3]

template<GUM_Numeric 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::Tensor.

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

Parameters

varId

The variable's id to remove.

Definition at line 263 of file BayesNet_tpl.h.

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

References _probaMap_, gum::DAGmodel::children(), gum::DAGmodel::dag_, variable(), and gum::DiscreteGraphicalModel::varMap_.

Referenced by clear(), erase(), and erase().

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erase() [3/3]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::erase

(

std::string_view

name

)

Removes a variable from the gum::BayesNet.

Definition at line 698 of file BayesNet_tpl.h.

                                                        {
    erase(idFromName(name));
  }

References erase(), and idFromName().

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eraseArc() [1/3]

template<GUM_Numeric GUM_SCALAR>

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 309 of file BayesNet_tpl.h.

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

References _probaMap_, gum::DAGmodel::dag_, gum::Arc::head(), gum::Arc::tail(), variable(), and gum::DiscreteGraphicalModel::varMap_.

Referenced by eraseArc(), and eraseArc().

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eraseArc() [2/3]

template<GUM_Numeric GUM_SCALAR>

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 320 of file BayesNet_tpl.h.

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

References eraseArc().

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eraseArc() [3/3]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::eraseArc	(	std::string_view	tail,
		std::string_view	head )

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

Definition at line 721 of file BayesNet_tpl.h.

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

References eraseArc(), and idFromName().

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evEq()

template<GUM_Numeric GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evEq ( std::string_view name, double value ) const

inherited

Returns

a Tensor for a (numerical) discrete variable representing an evidence with a float as observed value

Definition at line 386 of file IBayesNet_tpl.h.

                                                                                            {
    return Tensor< GUM_SCALAR >::evEq(variableFromName(name), value);
  }

References gum::Tensor< GUM_SCALAR >::evEq(), and gum::DiscreteGraphicalModel::variableFromName().

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evGt()

template<GUM_Numeric GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evGt ( std::string_view name, double value ) const

inherited

Returns

a Tensor for a (numerical) discrete variable representing an evidence with an observed value greater than the parameter

Definition at line 397 of file IBayesNet_tpl.h.

                                                                                          {
    return Tensor< GUM_SCALAR >::evGt(variableFromName(name), val);
  }

References gum::Tensor< GUM_SCALAR >::evGt(), and gum::DiscreteGraphicalModel::variableFromName().

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evIn()

template<GUM_Numeric GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evIn ( std::string_view name, double val1, double val2 ) const

inherited

Returns

a Tensor for a (numerical) discrete variable representing an evidence with a interval of float as observed value

Definition at line 392 of file IBayesNet_tpl.h.

                                                                                       {
    return Tensor< GUM_SCALAR >::evIn(variableFromName(name), val1, val2);
  }

References gum::Tensor< GUM_SCALAR >::evIn(), and gum::DiscreteGraphicalModel::variableFromName().

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evLt()

template<GUM_Numeric GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evLt ( std::string_view name, double value ) const

inherited

Returns

a Tensor for a (numerical) discrete variable representing an evidence with an observed value less than the parameter

Definition at line 402 of file IBayesNet_tpl.h.

                                                                                          {
    return Tensor< GUM_SCALAR >::evLt(variableFromName(name), val);
  }

References gum::Tensor< GUM_SCALAR >::evLt(), and gum::DiscreteGraphicalModel::variableFromName().

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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 113 of file DAGmodel_inl.h.

{ return dag_.exists(node); }

References dag_.

Referenced by gum::build_node(), gum::build_node_for_ID(), hasSameStructure(), gum::MarkovBlanket::hasSameStructure(), gum::IBayesNet< GUM_SCALAR >::operator==(), and gum::InfluenceDiagram< GUM_SCALAR >::operator==().

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exists() [2/2]

INLINE bool gum::DAGmodel::exists ( std::string_view name ) const

finalvirtualinherited

Returns a named copy of the internal DAG: each node id is assigned the name of the corresponding variable.

O(n) — allocates a new DAG. For a stable reference (listeners, long-lived pointers), use internalDag().

Implements gum::GraphicalModel.

Definition at line 115 of file DAGmodel_inl.h.

                                                        {
    return variableNodeMap().exists(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 75 of file DAGmodel_inl.h.

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

References dag_.

Referenced by existsArc(), gum::BayesNet< GUM_SCALAR >::reverseArc(), gum::BayesNetFragment< GUM_SCALAR >::toDot(), and gum::BayesNetFragment< GUM_SCALAR >::whenArcDeleted().

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existsArc() [2/2]

INLINE bool gum::DAGmodel::existsArc ( std::string_view nametail, std::string_view 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 79 of file DAGmodel_inl.h.

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

References existsArc(), and gum::DiscreteGraphicalModel::idFromName().

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existsProperty()

INLINE bool gum::GraphicalModel::existsProperty ( std::string_view name ) const

inherited

check wether a property exists in this GraphicalModel

Definition at line 170 of file graphicalModel_inl.h.

                                                               {
    return _propertiesMap_.exists(name);
  }

References _propertiesMap_.

family() [1/2]

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

finalvirtualinherited

returns the parents of a node and the 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

Implements gum::GraphicalModel.

Definition at line 89 of file DAGmodel_inl.h.

{ return dag_.family(id); }

References dag_.

family() [2/2]

INLINE NodeSet gum::DAGmodel::family ( std::string_view name ) const

finalvirtualinherited

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

Implements gum::GraphicalModel.

Definition at line 91 of file DAGmodel_inl.h.

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

References dag_, and gum::DiscreteGraphicalModel::idFromName().

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fastPrototype() [1/2]

template<GUM_Numeric GUM_SCALAR>

BayesNet< GUM_SCALAR > gum::BayesNet< GUM_SCALAR >::fastPrototype ( std::string_view dotlike, Size domainSize )

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 90 of file BayesNet_tpl.h.

                                                                                            {
    return fastPrototype(dotlike, "[" + std::to_string(domainSize) + "]");
  }

References fastPrototype().

Referenced by fastPrototype().

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fastPrototype() [2/2]

template<GUM_Numeric GUM_SCALAR>

BayesNet< GUM_SCALAR > gum::BayesNet< GUM_SCALAR >::fastPrototype ( std::string_view dotlike, std::string_view domainSize = "[2]" )

static

Definition at line 96 of file BayesNet_tpl.h.

                                                                                      {
    gum::BayesNet< GUM_SCALAR > bn;
 
    for (const auto& chaine: split(remove_newline(dotlike), ";")) {
      NodeId lastId   = 0;
      bool   notfirst = false;
      for (const auto& souschaine: split(chaine, "->")) {
        bool forward = true;
        for (auto& node: split(souschaine, "<-")) {
          auto idVar = build_node(bn, node, domain);
          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", "anonymousBN");
    return bn;
  }

References addArc(), gum::build_node(), gum::remove_newline(), and gum::split().

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generateCPT() [1/2]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::generateCPT

(

NodeId

node

)

const

randomly generate CPT for a given node in a given structure

Definition at line 593 of file BayesNet_tpl.h.

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

References cpt(), and variable().

Referenced by generateCPT(), and generateCPTs().

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generateCPT() [2/2]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::generateCPT

(

std::string_view

name

)

const

Definition at line 738 of file BayesNet_tpl.h.

                                                                    {
    generateCPT(idFromName(name));
  }

References generateCPT(), and idFromName().

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generateCPTs()

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::generateCPTs

(

)

const

randomly generates CPTs for a given structure

Definition at line 587 of file BayesNet_tpl.h.

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

References generateCPT(), and gum::DAGmodel::nodes().

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hasSameStructure()

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

inherited

Returns

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

Definition at line 87 of file DAGmodel.cpp.

                                                             {
    if (this == &other) return true;
 
    if (size() != other.size()) return false;
 
    if (sizeArcs() != other.sizeArcs()) return false;
 
    for (const auto& nid: nodes()) {
      if (!other.exists(variable(nid).name())) 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;
  }

References DAGmodel(), arcs(), exists(), gum::Set< Key >::exists(), gum::DiscreteGraphicalModel::idFromName(), nodes(), size(), sizeArcs(), and gum::DiscreteGraphicalModel::variable().

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idFromName()

template<GUM_Numeric GUM_SCALAR>

INLINE NodeId gum::DiscreteGraphicalModel::idFromName ( std::string_view name ) const

overridevirtual

Returns the NodeId of a variable given its name.

Exceptions

NotFound

if no such name exists in the model.

Implements gum::GraphicalModel.

Definition at line 104 of file discreteGraphicalModel_inl.h.

                                                                     {
    return varMap_.idFromName(name);
  }

Referenced by addArc(), addWeightedArc(), gum::build_node(), changeTensor(), changeVariableLabel(), changeVariableName(), cpt(), erase(), eraseArc(), generateCPT(), reverseArc(), and variable().

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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 139 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;
  }

References names(), and variableNodeMap().

Referenced by gum::DAGmodel::children(), exists(), names(), names(), and gum::DAGmodel::parents().

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internalDag()

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

inherited

Returns a const reference to the internal (unnamed) DAG. O(1), no copy. Use for stable references or pointers (e.g. graph listeners). For named node access, use dag() instead.

Definition at line 58 of file DAGmodel_inl.h.

{ return dag_; }

References dag_.

Referenced by gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment(), gum::MarkovBlanket::MarkovBlanket(), gum::BayesNetFragment< GUM_SCALAR >::installCPT(), gum::BayesNetFragment< GUM_SCALAR >::isInstalledNode(), gum::BayesBall::relevantTensors(), gum::dSeparationAlgorithm::relevantTensors(), gum::BayesNetFragment< GUM_SCALAR >::toBN(), gum::BayesNetFragment< GUM_SCALAR >::toDot(), and gum::BayesNetFragment< GUM_SCALAR >::whenArcDeleted().

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isIndependent() [1/4]

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 156 of file DAGmodel_inl.h.

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

References dag_.

isIndependent() [2/4]

INLINE bool gum::DAGmodel::isIndependent ( const std::vector< std::string > & Xnames, const std::vector< std::string > & Ynames, const std::vector< std::string > & Znames ) 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 184 of file DAGmodel_inl.h.

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

isIndependent() [3/4]

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 152 of file DAGmodel_inl.h.

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

Referenced by gum::BayesNet< double >::ancestors(), and isIndependent().

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isIndependent() [4/4]

INLINE bool gum::DAGmodel::isIndependent ( std::string_view Xname, std::string_view Yname, const std::vector< std::string > & Znames ) 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 178 of file DAGmodel_inl.h.

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

References gum::DiscreteGraphicalModel::idFromName(), isIndependent(), and gum::GraphicalModel::nodeset().

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jointProbability()

template<GUM_Numeric 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 238 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 93 of file graphicalModel_inl.h.

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

References nodes().

Referenced by gum::IMarkovRandomField< GUM_SCALAR >::toString(), and gum::InfluenceDiagram< GUM_SCALAR >::toString().

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log2JointProbability()

template<GUM_Numeric 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 256 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;
  }

References cpt(), and gum::DAGmodel::nodes().

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maxNonOneParam()

template<GUM_Numeric 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 170 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<GUM_Numeric GUM_SCALAR>

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

inherited

Returns

the biggest value in the CPTs of *this

Definition at line 150 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;
  }

References gum::DAGmodel::nodes().

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maxVarDomainSize()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Returns

the biggest domainSize among the variables of *this

Definition at line 130 of file IBayesNet_tpl.h.

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

References gum::DAGmodel::nodes(), and gum::DiscreteGraphicalModel::variable().

Referenced by gum::ImportanceSampling< GUM_SCALAR >::onContextualize_().

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memoryFootprint()

template<GUM_Numeric GUM_SCALAR>

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

inherited

compute the (approximated) footprint in memory of the model (the footprints of CPTs)

Returns

the size in bytes

Definition at line 180 of file IBayesNet_tpl.h.

                                                      {
    Size usedMem = 0;
 
    for (auto node: nodes())
      usedMem += cpt(node).memoryFootprint();
    return usedMem;
  }

minimalCondSet() [1/4]

INLINE NodeSet gum::DAGmodel::minimalCondSet ( const NodeSet & targets, const NodeSet & soids ) const

inherited

Definition at line 164 of file DAGmodel_inl.h.

                                                                                            {
    return dag_.minimalCondSet(targets, soids);
  }

minimalCondSet() [2/4]

INLINE NodeSet gum::DAGmodel::minimalCondSet ( const std::vector< std::string > & targets, const std::vector< std::string > & soids ) const

inherited

Definition at line 173 of file DAGmodel_inl.h.

                                                                                     {
    return dag_.minimalCondSet(nodeset(targets), nodeset(soids));
  }

minimalCondSet() [3/4]

INLINE NodeSet gum::DAGmodel::minimalCondSet ( NodeId target, const NodeSet & soids ) const

inherited

Definition at line 160 of file DAGmodel_inl.h.

                                                                                   {
    return dag_.minimalCondSet(target, soids);
  }

Referenced by gum::ASTposteriorProba< GUM_SCALAR >::_compute_knw_from_bn().

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minimalCondSet() [4/4]

INLINE NodeSet gum::DAGmodel::minimalCondSet ( std::string_view target, const std::vector< std::string > & soids ) const

inherited

Definition at line 168 of file DAGmodel_inl.h.

                                                                                     {
    return dag_.minimalCondSet(idFromName(target), nodeset(soids));
  }

References dag_, gum::DiscreteGraphicalModel::idFromName(), and gum::GraphicalModel::nodeset().

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minNonZeroParam()

template<GUM_Numeric 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 160 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;
  }

Referenced by gum::ImportanceSampling< GUM_SCALAR >::onContextualize_().

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minParam()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Returns

the smallest value in the CPTs of *this

Definition at line 140 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;
  }

References gum::DAGmodel::nodes().

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moralGraph()

UndiGraph gum::DAGmodel::moralGraph ( ) const

inherited

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

Definition at line 81 of file DAGmodel.cpp.

                                       {
    auto g = dag_.moralGraph();
    _nameNodes_(g);
    return g;
  }

References gum::GraphicalModel::_nameNodes_(), and dag_.

Referenced by gum::prm::SVE< GUM_SCALAR >::_eliminateNodes_(), gum::prm::SVED< GUM_SCALAR >::_eliminateNodes_(), gum::prm::SVE< GUM_SCALAR >::_eliminateNodesWithEvidence_(), gum::prm::SVED< GUM_SCALAR >::_eliminateNodesWithEvidence_(), gum::prm::SVE< GUM_SCALAR >::_initLiftedNodes_(), and gum::prm::SVED< GUM_SCALAR >::_initLiftedNodes_().

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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 146 of file DAGmodel_inl.h.

                                                                               {
    auto g = dag_.moralizedAncestralGraph(nodes);
    _nameNodes_(g);
    return g;
  }

References gum::GraphicalModel::_nameNodes_(), dag_, and nodes().

Referenced by moralizedAncestralGraph().

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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 142 of file DAGmodel_inl.h.

                                                                                     {
    return moralizedAncestralGraph(nodeset(nodenames));
  }

References moralizedAncestralGraph(), and gum::GraphicalModel::nodeset().

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names() [1/2]

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 129 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;
  }

References ids(), gum::VariableNodeMap::name(), and variableNodeMap().

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names() [2/2]

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 117 of file graphicalModel_inl.h.

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

References ids().

Referenced by gum::DAGmodel::children(), exists(), ids(), nodeset(), and gum::DAGmodel::parents().

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nodeId()

template<GUM_Numeric GUM_SCALAR>

INLINE NodeId gum::DiscreteGraphicalModel::nodeId ( const DiscreteVariable & var ) const

overridevirtual

Returns the NodeId of a variable.

Exceptions

NotFound

if no variable matches var.

Implements gum::GraphicalModel.

Definition at line 98 of file discreteGraphicalModel_inl.h.

                                                                         {
    return varMap_.get(var);
  }

nodes()

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

finalvirtualinherited

Returns a named copy of the internal DAG: each node id is assigned the name of the corresponding variable.

O(n) — allocates a new DAG. For a stable reference (listeners, long-lived pointers), use internalDag().

Implements gum::GraphicalModel.

Definition at line 119 of file DAGmodel_inl.h.

                                                    {
    return static_cast< const NodeGraphPart& >(dag_);
  }

References dag_.

Referenced by gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment(), gum::Estimator< GUM_SCALAR >::Estimator(), gum::BayesNetFragment< GUM_SCALAR >::~BayesNetFragment(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_verticesSampling_(), gum::BayesNet< GUM_SCALAR >::beginTopologyTransformation(), gum::InfluenceDiagram< GUM_SCALAR >::beginTopologyTransformation(), gum::IBayesNet< GUM_SCALAR >::check(), gum::BayesNetFragment< GUM_SCALAR >::checkConsistency(), gum::BayesNet< GUM_SCALAR >::clear(), gum::InfluenceDiagram< GUM_SCALAR >::copyStructureAndTables_(), gum::IBayesNet< GUM_SCALAR >::dim(), gum::BayesNet< GUM_SCALAR >::endTopologyTransformation(), gum::InfluenceDiagram< GUM_SCALAR >::endTopologyTransformation(), gum::InfluenceDiagram< GUM_SCALAR >::fastPrototype(), gum::BayesNet< GUM_SCALAR >::generateCPTs(), gum::getMaxModality(), hasSameStructure(), gum::IBayesNet< GUM_SCALAR >::log2JointProbability(), gum::IBayesNet< GUM_SCALAR >::maxParam(), gum::IBayesNet< GUM_SCALAR >::maxVarDomainSize(), gum::IBayesNet< GUM_SCALAR >::minParam(), gum::prm::ClassBayesNet< GUM_SCALAR >::modalities(), gum::prm::InstanceBayesNet< GUM_SCALAR >::modalities(), moralizedAncestralGraph(), gum::IBayesNet< GUM_SCALAR >::operator==(), gum::InfluenceDiagram< GUM_SCALAR >::operator==(), gum::Estimator< GUM_SCALAR >::setFromBN(), gum::BayesNetFragment< GUM_SCALAR >::toBN(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), and gum::ImportanceSampling< GUM_SCALAR >::unsharpenBN_().

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 102 of file graphicalModel.cpp.

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

References idFromName(), gum::Set< Key >::insert(), and names().

Referenced by gum::BayesNet< double >::ancestors(), gum::DAGmodel::children(), gum::DAGmodel::isIndependent(), gum::UGmodel::isIndependent(), gum::UGmodel::isIndependent(), gum::DAGmodel::minimalCondSet(), and gum::DAGmodel::moralizedAncestralGraph().

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operator=() [1/2]

template<GUM_Numeric GUM_SCALAR>

BayesNet< GUM_SCALAR > & gum::BayesNet< GUM_SCALAR >::operator=

(

BayesNet< GUM_SCALAR > &&

source

)

Move operator.

Parameters

source

The moved BayesNet.

Returns

The moved-to BayesNet.

Definition at line 164 of file BayesNet_tpl.h.

                                                                                         {
    if (this != &source) {
      IBayesNet< GUM_SCALAR >::operator=(std::move(source));
      _clearTensors_();
      _probaMap_ = std::move(source._probaMap_);
      GUM_OP_MOV(BayesNet);
    }
    return *this;
  }

References gum::IBayesNet< GUM_SCALAR >::operator=().

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operator=() [2/2]

template<GUM_Numeric 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 152 of file BayesNet_tpl.h.

                                                                                              {
    if (this != &source) {
      IBayesNet< GUM_SCALAR >::operator=(source);
      _clearTensors_();
      _copyTensors_(source);
      GUM_OP_CPY(BayesNet);
    }
 
    return *this;
  }

References BayesNet(), _clearTensors_(), _copyTensors_(), and gum::IBayesNet< GUM_SCALAR >::operator=().

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operator==()

template<GUM_Numeric 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 273 of file IBayesNet_tpl.h.

                                                                    {
    if (size() != from.size()) { return false; }
 
    if (sizeArcs() != from.sizeArcs()) { return false; }
 
    for (auto node: nodes()) {
      const auto& v1 = variable(node);
      if (!from.exists(v1.name())) { return false; }
      const auto& v2 = from.variableFromName(v1.name());
      if (v1 != v2) { 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; }
 
      for (Idx i = 0; i < cpt(node).nbrDim(); ++i) {
        if (!from.cpt(fromnode).contains(from.variableFromName(cpt(node).variable(i).name()))) {
          return false;
        }
      }
 
      Instantiation i(cpt(node));
      Instantiation j(from.cpt(fromnode));
 
      AlmostDifferent< GUM_SCALAR > cmp;
      for (i.setFirst(); !i.end(); i.inc()) {
        for (Idx indice = 0; indice < cpt(node).nbrDim(); ++indice) {
          const DiscreteVariable* p = &(i.variable(indice));
          j.chgVal(j.pos(from.variableFromName(p->name())), i.val(*p));
        }
 
        if (cmp(cpt(node).get(i), from.cpt(fromnode).get(j))) { return false; }
      }
    }
 
    return true;
  }

References IBayesNet(), gum::Instantiation::chgVal(), cpt(), gum::Instantiation::end(), gum::DAGmodel::exists(), gum::DiscreteGraphicalModel::idFromName(), gum::Instantiation::inc(), gum::Variable::name(), gum::DAGmodel::nodes(), gum::Instantiation::pos(), gum::Instantiation::setFirst(), gum::DAGmodel::size(), gum::DAGmodel::sizeArcs(), gum::Instantiation::val(), gum::DiscreteGraphicalModel::variable(), gum::Instantiation::variable(), and gum::DiscreteGraphicalModel::variableFromName().

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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 83 of file DAGmodel_inl.h.

{ return dag_.parents(id); }

References dag_.

Referenced by gum::IBayesNet< GUM_SCALAR >::check(), gum::BayesNetFragment< GUM_SCALAR >::checkConsistency(), gum::InfluenceDiagram< GUM_SCALAR >::copyStructureAndTables_(), gum::IBayesNet< GUM_SCALAR >::dim(), gum::ASTposteriorProba< GUM_SCALAR >::eval(), gum::BayesNetFragment< GUM_SCALAR >::installCPT(), gum::BayesNetFragment< GUM_SCALAR >::installCPT_(), parents(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), and gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot().

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parents() [2/4]

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

inherited

returns the parents of a set of nodes

Definition at line 107 of file DAGmodel_inl.h.

{ return dag_.parents(ids); }

References dag_, and gum::GraphicalModel::ids().

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parents() [3/4]

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 109 of file DAGmodel_inl.h.

                                                                            {
    return parents(nodeset(names));
  }

References gum::GraphicalModel::names().

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parents() [4/4]

INLINE const NodeSet & gum::DAGmodel::parents ( std::string_view 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 85 of file DAGmodel_inl.h.

                                                                   {
    return parents(idFromName(name));
  }

References gum::DiscreteGraphicalModel::idFromName(), and parents().

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properties()

INLINE std::vector< std::string > gum::GraphicalModel::properties ( ) const

inherited

List of all the names of property in the Graphical model.

Definition at line 79 of file graphicalModel_inl.h.

                                                          {
    std::vector< std::string > prop;
    for (const auto& [p, v]: _propertiesMap_)
      prop.push_back(p);
    return prop;
  }

References _propertiesMap_.

property()

INLINE const std::string & gum::GraphicalModel::property ( std::string_view 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 60 of file graphicalModel_inl.h.

                                                                     {
    auto p = _properties_().tryGet(name);
    if (!p) { GUM_ERROR(NotFound, "The following property does not exists: " << name) }
    return *p;
  }

References _properties_(), GUM_ERROR, and gum::HashTable< Key, Val >::tryGet().

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propertyWithDefault()

INLINE const std::string & gum::GraphicalModel::propertyWithDefault ( std::string_view 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 72 of file graphicalModel_inl.h.

                                                                                           {
    auto p = _propertiesMap_.tryGet(name);
    return p ? *p : byDefault;
  }

References _propertiesMap_.

Referenced by gum::IBayesNet< GUM_SCALAR >::toDot(), gum::IMarkovRandomField< GUM_SCALAR >::toDot(), gum::InfluenceDiagram< GUM_SCALAR >::toDot(), and gum::IMarkovRandomField< GUM_SCALAR >::toDotAsFactorGraph().

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reverseArc() [1/3]

template<GUM_Numeric 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 325 of file BayesNet_tpl.h.

                                                        {
    // check that the arc exists
    if (!this->varMap_.exists(arc.tail()) || !this->varMap_.exists(arc.head())
        || !dag().existsArc(arc)) {
      GUM_ERROR(InvalidArc, "a non-existing arc cannot be reversed")
    }
 
    NodeId tail = arc.tail();
    NodeId 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 const&) {
      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) )
    Tensor< 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
    gum::VariableSet del_vars;
    del_vars << &(variable(tail));
    Tensor< GUM_SCALAR > new_cpt_head = prod.sumOut(del_vars).putFirst(&variable(head));
 
    auto& cpt_head = const_cast< Tensor< GUM_SCALAR >& >(cpt(head));
    cpt_head       = std::move(new_cpt_head);
 
    Tensor< GUM_SCALAR > new_cpt_tail{(prod / cpt_head).putFirst(&variable(tail))};
    auto&                cpt_tail = const_cast< Tensor< GUM_SCALAR >& >(cpt(tail));
    cpt_tail                      = std::move(new_cpt_tail);
  }

References gum::DAGmodel::dag(), gum::DAGmodel::existsArc(), GUM_ERROR, gum::Arc::head(), gum::Arc::tail(), and gum::DiscreteGraphicalModel::varMap_.

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reverseArc() [2/3]

template<GUM_Numeric GUM_SCALAR>

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 389 of file BayesNet_tpl.h.

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

References reverseArc().

Referenced by reverseArc(), and reverseArc().

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reverseArc() [3/3]

template<GUM_Numeric GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::reverseArc	(	std::string_view	tail,
		std::string_view	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 726 of file BayesNet_tpl.h.

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

References idFromName(), and reverseArc().

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setProperty()

INLINE void gum::GraphicalModel::setProperty ( std::string_view name, std::string_view value )

inherited

Add or change a property of this GraphicalModel.

Definition at line 87 of file graphicalModel_inl.h.

                                                                            {
    if (auto p = _propertiesMap_.tryGet(name)) *p = value;
    else _propertiesMap_.insert(std::string(name), std::string(value));
  }

References _propertiesMap_.

Referenced by gum::IBayesNet< GUM_SCALAR >::IBayesNet(), gum::IMarkovRandomField< GUM_SCALAR >::IMarkovRandomField(), and gum::InfluenceDiagram< GUM_SCALAR >::fastPrototype().

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size()

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

finalvirtualinherited

Returns the number of variables in this Directed Graphical Model.

Implements gum::GraphicalModel.

Definition at line 68 of file DAGmodel_inl.h.

{ return dag_.size(); }

References dag_.

Referenced by gum::InfluenceDiagram< GUM_SCALAR >::copyStructureAndTables_(), gum::InfluenceDiagram< GUM_SCALAR >::decisionNodeSize(), hasSameStructure(), gum::MarkovBlanket::hasSameStructure(), gum::IBayesNet< GUM_SCALAR >::operator==(), gum::InfluenceDiagram< GUM_SCALAR >::operator==(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), and gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot().

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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 71 of file DAGmodel_inl.h.

{ return dag_.sizeArcs(); }

References dag_.

Referenced by hasSameStructure(), gum::MarkovBlanket::hasSameStructure(), gum::IBayesNet< GUM_SCALAR >::operator==(), gum::InfluenceDiagram< GUM_SCALAR >::operator==(), and gum::InfluenceDiagram< GUM_SCALAR >::toString().

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spaceCplxToString()

std::string gum::GraphicalModel::spaceCplxToString ( double dSize, int dim, Size usedMem )

staticinherited

send to the stream the space complexity with 3 parametrs

Parameters

s	the stream
dSize	the log10domainSize
dim	the dimension
usedMem	the memory needed for the params

Definition at line 110 of file graphicalModel.cpp.

                                                                                 {
    std::string result;
    if (dSize > 6) result = std::format("domainSize: 10^{:.6g}", dSize);
    else result = std::format("domainSize: {}", (long long)std::round(std::pow(10.0, dSize)));
 
    result += std::format(", dim: {}, mem: ", dim);
 
    if (const Size go = usedMem / (1024 * 1024 * 1024); go > 0) result += std::format("{}Go ", go);
    if (const Size mo = (usedMem / (1024 * 1024)) % 1024; mo > 0)
      result += std::format("{}Mo ", mo);
    if (const Size ko = (usedMem / 1024) % 1024; ko > 0) result += std::format("{}Ko ", ko);
    result += std::format("{}o", usedMem % 1024);
    return result;
  }

toDot()

template<GUM_Numeric 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 198 of file IBayesNet_tpl.h.

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

References gum::GraphicalModel::propertyWithDefault().

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topologicalOrder()

INLINE Sequence< NodeId > gum::DAGmodel::topologicalOrder ( ) 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 123 of file DAGmodel_inl.h.

{ return dag_.topologicalOrder(); }

References dag_.

Referenced by gum::InfluenceDiagramGenerator< GUM_SCALAR >::_checkTemporalOrder_(), gum::InfluenceDiagram< GUM_SCALAR >::decisionOrder(), and gum::InfluenceDiagram< GUM_SCALAR >::decisionOrderExists().

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toString()

template<GUM_Numeric GUM_SCALAR>

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

inherited

Returns

Returns a string representation of this IBayesNet.

Definition at line 189 of file IBayesNet_tpl.h.

                                                    {
    std::stringstream s;
    s << std::format("BN{{nodes: {}, arcs: {}, ", size(), dag().sizeArcs());
    s << spaceCplxToString(log10DomainSize(), (int)dim(), memoryFootprint());
    s << "}";
    return s.str();
  }

Referenced by gum::operator<<().

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updateMetaData()

void gum::GraphicalModel::updateMetaData ( )

inherited

update the meta data of this Graphical Model (version, creation date, last modification date) This method is called by the writers ONLY before writing the model to a file.

Definition at line 81 of file graphicalModel.cpp.

                                      {
    auto const time = std::chrono::time_point_cast< std::chrono::milliseconds >(
        std::chrono::system_clock::now());
    auto const currentdate = std::format("{:%Y-%m-%d %T}", time);
 
    const std::string currentSoftware = "aGrUM " GUM_VERSION;
    if (auto software = _propertiesMap_.tryGet("software")) {
      *software = currentSoftware;
    } else {
      _propertiesMap_.insert("software", currentSoftware);
    }
 
    if (!_propertiesMap_.tryGet("creation")) { _propertiesMap_.insert("creation", currentdate); }
 
    if (auto lastModification = _propertiesMap_.tryGet("lastModification")) {
      *lastModification = currentdate;
    } else {
      _propertiesMap_.insert("lastModification", currentdate);
    }
  }

References _propertiesMap_.

variable() [1/2]

template<GUM_Numeric GUM_SCALAR>

INLINE const DiscreteVariable & gum::DiscreteGraphicalModel::variable ( NodeId id ) const

overridevirtual

Returns a constant reference over a variable given its node id.

Exceptions

NotFound

if no variable's id matches id.

Implements gum::GraphicalModel.

Definition at line 92 of file discreteGraphicalModel_inl.h.

                                                                          {
    return varMap_.get(id);
  }

variable() [2/2]

template<GUM_Numeric GUM_SCALAR>

const DiscreteVariable & gum::BayesNet< GUM_SCALAR >::variable

(

std::string_view

name

)

const

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

Definition at line 703 of file BayesNet_tpl.h.

                                                                                    {
    return variable(idFromName(name));
  }

References idFromName(), and variable().

Referenced by add(), erase(), eraseArc(), generateCPT(), gum::getMaxModality(), and variable().

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variableFromName()

template<GUM_Numeric GUM_SCALAR>

INLINE const DiscreteVariable & gum::DiscreteGraphicalModel::variableFromName ( std::string_view name ) const

overridevirtual

Returns a constant reference over a variable given its name.

Exceptions

NotFound

if no such name exists in the model.

Implements gum::GraphicalModel.

Definition at line 110 of file discreteGraphicalModel_inl.h.

                                                                                            {
    return varMap_.variableFromName(name);
  }

variableNodeMap()

template<GUM_Numeric GUM_SCALAR>

INLINE const VariableNodeMap & gum::DiscreteGraphicalModel::variableNodeMap ( ) const

overridevirtual

Returns a constant reference to the VariableNodeMap of this model.

Implements gum::GraphicalModel.

Definition at line 86 of file discreteGraphicalModel_inl.h.

{ return varMap_; }

variables() [1/2]

INLINE VariableSet gum::GraphicalModel::variables ( const NodeSet & ids ) const

inherited

transform a vector of NodeId into a VariableeSet

Returns

NodeSet

Definition at line 160 of file graphicalModel_inl.h.

                                                              {
    VariableSet            s;
    const VariableNodeMap& v = variableNodeMap();
    for (const auto& node: l) {
      s.insert(&v.get(node));
    }
    return s;
  }

variables() [2/2]

INLINE VariableSet gum::GraphicalModel::variables ( const std::vector< std::string > & l ) const

inherited

transform a vector of names into a VariableeSet

Returns

NodeSet

Definition at line 150 of file graphicalModel_inl.h.

                                                                             {
    VariableSet            s;
    const VariableNodeMap& v = variableNodeMap();
    for (const auto& name: l) {
      s.insert(&v.variableFromName(name));
    }
    return s;
  }

References gum::Set< Key >::insert(), gum::VariableNodeMap::variableFromName(), and variableNodeMap().

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Friends And Related Symbol Documentation

BayesNetFactory< GUM_SCALAR >

template<GUM_Numeric GUM_SCALAR>

friend class BayesNetFactory< GUM_SCALAR >

friend

Definition at line 665 of file BayesNet.h.

Member Data Documentation

_probaMap_

template<GUM_Numeric GUM_SCALAR>

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

private

Mapping between the variable's id and their CPT.

Definition at line 639 of file BayesNet.h.

Referenced by BayesNet(), ~BayesNet(), _clearTensors_(), _unsafeChangeTensor_(), add(), beginTopologyTransformation(), cpt(), endTopologyTransformation(), erase(), and eraseArc().

_propertiesMap_

HashTable< std::string, std::string > gum::GraphicalModel::_propertiesMap_

privateinherited

The properties of this Directed Graphical Model.

Definition at line 262 of file graphicalModel.h.

Referenced by GraphicalModel(), GraphicalModel(), _properties_(), existsProperty(), operator=(), operator=(), properties(), propertyWithDefault(), setProperty(), and updateMetaData().

dag_

DAG gum::DAGmodel::dag_

protectedinherited

The DAG of this Directed Graphical Model.

Definition at line 284 of file DAGmodel.h.

Referenced by DAGmodel(), DAGmodel(), gum::prm::ClassBayesNet< GUM_SCALAR >::_get_(), gum::prm::ClassBayesNet< GUM_SCALAR >::_init_(), gum::prm::InstanceBayesNet< GUM_SCALAR >::_init_(), gum::BayesNet< GUM_SCALAR >::add(), gum::BayesNet< GUM_SCALAR >::addArc(), gum::InfluenceDiagram< GUM_SCALAR >::addArc(), gum::InfluenceDiagram< GUM_SCALAR >::addNode_(), ancestors(), arcs(), children(), children(), connectedComponents(), dag(), descendants(), gum::BayesNet< GUM_SCALAR >::erase(), gum::InfluenceDiagram< GUM_SCALAR >::erase(), gum::BayesNet< GUM_SCALAR >::eraseArc(), gum::InfluenceDiagram< GUM_SCALAR >::eraseArc(), exists(), existsArc(), gum::InfluenceDiagram< GUM_SCALAR >::existsPathBetween(), family(), family(), gum::InfluenceDiagram< GUM_SCALAR >::getChildrenDecision_(), gum::InfluenceDiagram< GUM_SCALAR >::getDecisionGraph(), gum::InfluenceDiagram< GUM_SCALAR >::getPartialTemporalOrder(), gum::BayesNetFragment< GUM_SCALAR >::installArc_(), gum::BayesNetFragment< GUM_SCALAR >::installNode(), internalDag(), isIndependent(), minimalCondSet(), moralGraph(), gum::InfluenceDiagram< GUM_SCALAR >::moralGraph_(), moralizedAncestralGraph(), nodes(), operator=(), operator=(), parents(), parents(), size(), sizeArcs(), gum::InfluenceDiagram< GUM_SCALAR >::toDot(), topologicalOrder(), gum::BayesNetFragment< GUM_SCALAR >::uninstallArc_(), and gum::BayesNetFragment< GUM_SCALAR >::uninstallNode().

varMap_

VariableNodeMap gum::DiscreteGraphicalModel::varMap_

protectedinherited

Mapping between NodeIds and discrete variables.

Definition at line 119 of file discreteGraphicalModel.h.

Referenced by DiscreteGraphicalModel(), DiscreteGraphicalModel(), gum::BayesNet< GUM_SCALAR >::add(), gum::InfluenceDiagram< GUM_SCALAR >::addNode_(), gum::BayesNet< GUM_SCALAR >::changeVariableName(), gum::InfluenceDiagram< GUM_SCALAR >::changeVariableName(), gum::BayesNet< GUM_SCALAR >::erase(), gum::BayesNet< GUM_SCALAR >::erase(), gum::InfluenceDiagram< GUM_SCALAR >::erase(), gum::InfluenceDiagram< GUM_SCALAR >::erase(), gum::BayesNet< GUM_SCALAR >::eraseArc(), idFromName(), nodeId(), operator=(), operator=(), gum::BayesNet< GUM_SCALAR >::reverseArc(), variable(), variableFromName(), and variableNodeMap().

---

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

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