gum::IBayesNet< GUM_SCALAR > Class Template Reference

Class representing the minimal interface for Bayesian network with no numerical data. More...

#include <agrum/BN/IBayesNet.h>

Inheritance diagram for gum::IBayesNet< GUM_SCALAR >:

Collaboration diagram for gum::IBayesNet< GUM_SCALAR >:

Public Member Functions
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 / Destructors
	IBayesNet ()
	Default constructor.
	IBayesNet (std::string_view name)
	Default constructor.
	~IBayesNet () override
	Destructor.
	IBayesNet (const IBayesNet< GUM_SCALAR > &source)
	Copy constructor.
	IBayesNet (IBayesNet< GUM_SCALAR > &&source)
	Move constructor.
IBayesNet< GUM_SCALAR > &	operator= (const IBayesNet< GUM_SCALAR > &source)
	Copy operator.
IBayesNet< GUM_SCALAR > &	operator= (IBayesNet< GUM_SCALAR > &&source)
	Move operator.
Pure Virtual methods
virtual const Tensor< GUM_SCALAR > &	cpt (NodeId varId) const =0
	Returns the CPT of a variable.
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.
Variable accessor methods
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.
NodeId	idFromName (std::string_view name) const override
	Returns the NodeId of a variable given its name.
const DiscreteVariable &	variableFromName (std::string_view name) const override
	Returns a constant reference over a variable given its name.
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 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
const HashTable< std::string, std::string > &	_properties_ () const
	Return the properties of this Directed Graphical Model.

Private Attributes
HashTable< std::string, std::string >	_propertiesMap_
	The properties of this Directed Graphical Model.

Detailed Description

template<GUM_Numeric GUM_SCALAR>
class gum::IBayesNet< GUM_SCALAR >

Class representing the minimal interface for Bayesian network with no numerical data.

This class is used as a base class for different versions of Bayesian Networks. No data (except the dag herited from DAGmodel are included in this class.

Many algorithms inference for instance) may use this class when a generic BN is needed.

Definition at line 75 of file IBayesNet.h.

Constructor & Destructor Documentation

IBayesNet() [1/4]

template<GUM_Numeric GUM_SCALAR>

gum::IBayesNet< GUM_SCALAR >::IBayesNet

(

)

Default constructor.

Definition at line 67 of file IBayesNet_tpl.h.

                                     : DAGmodel() {
    GUM_CONSTRUCTOR(IBayesNet)
  }

References gum::DAGmodel::DAGmodel(), and IBayesNet().

Referenced by gum::BayesNet< GUM_SCALAR >::BayesNet(), gum::BayesNet< GUM_SCALAR >::BayesNet(), gum::BayesNet< GUM_SCALAR >::BayesNet(), gum::BayesNet< GUM_SCALAR >::BayesNet(), gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment(), gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment(), gum::prm::ClassBayesNet< GUM_SCALAR >::ClassBayesNet(), gum::prm::ClassBayesNet< GUM_SCALAR >::ClassBayesNet(), IBayesNet(), IBayesNet(), IBayesNet(), IBayesNet(), gum::prm::InstanceBayesNet< GUM_SCALAR >::InstanceBayesNet(), gum::prm::InstanceBayesNet< GUM_SCALAR >::InstanceBayesNet(), ~IBayesNet(), cpt(), operator=(), operator=(), and operator==().

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IBayesNet() [2/4]

template<GUM_Numeric GUM_SCALAR>

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

explicit

Default constructor.

Definition at line 72 of file IBayesNet_tpl.h.

                                                        : DAGmodel() {
    GUM_CONSTRUCTOR(IBayesNet)
    this->setProperty("name", name);
  }

References gum::DAGmodel::DAGmodel(), IBayesNet(), and gum::GraphicalModel::setProperty().

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~IBayesNet()

template<GUM_Numeric GUM_SCALAR>

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

override

Destructor.

Definition at line 109 of file IBayesNet_tpl.h.

                                      {
    GUM_DESTRUCTOR(IBayesNet)
  }

References IBayesNet().

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IBayesNet() [3/4]

template<GUM_Numeric GUM_SCALAR>

gum::IBayesNet< GUM_SCALAR >::IBayesNet

(

const IBayesNet< GUM_SCALAR > &

source

)

Copy constructor.

Definition at line 78 of file IBayesNet_tpl.h.

                                                                          : DAGmodel(source) {
    GUM_CONS_CPY(IBayesNet)
  }

References gum::DAGmodel::DAGmodel(), and IBayesNet().

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IBayesNet() [4/4]

template<GUM_Numeric GUM_SCALAR>

gum::IBayesNet< GUM_SCALAR >::IBayesNet

(

IBayesNet< GUM_SCALAR > &&

source

)

Move constructor.

Definition at line 83 of file IBayesNet_tpl.h.

                                                                     :
      DAGmodel(std::move(source)) {
    GUM_CONS_MOV(IBayesNet)
  }

References gum::DAGmodel::DAGmodel(), and IBayesNet().

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Member Function Documentation

_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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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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check()

template<GUM_Numeric GUM_SCALAR>

std::vector< std::string > gum::IBayesNet< GUM_SCALAR >::check

(

)

const

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));
  }

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_.

cpt()

template<GUM_Numeric GUM_SCALAR>

virtual const Tensor< GUM_SCALAR > & gum::IBayesNet< GUM_SCALAR >::cpt ( NodeId varId ) const

pure virtual

Returns the CPT of a variable.

Exceptions

NotFound

If no variable's id matches varId.

Implemented in gum::BayesNet< GUM_SCALAR >, gum::BayesNet< double >, gum::BayesNetFragment< GUM_SCALAR >, gum::prm::ClassBayesNet< GUM_SCALAR >, and gum::prm::InstanceBayesNet< GUM_SCALAR >.

References IBayesNet().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_verticesSampling_(), gum::BarrenNodesFinder::barrenTensors(), check(), gum::ASTposteriorProba< GUM_SCALAR >::eval(), log2JointProbability(), and operator==().

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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

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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evEq()

template<GUM_Numeric GUM_SCALAR>

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

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

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

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

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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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()

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

overridevirtualinherited

Returns the NodeId of a variable given its name.

Exceptions

NotFound

if no such name exists in the model.

Implements gum::GraphicalModel.

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

Definition at line 61 of file discreteGraphicalModel_inl.h.

                                                                     {
    return varMap_.idFromName(name);
  }

References varMap_.

Referenced by gum::ASTposteriorProba< GUM_SCALAR >::_compute_knw_from_bn(), gum::DAGmodel::ancestors(), gum::InfluenceDiagram< GUM_SCALAR >::clear(), gum::ASTjointProba< GUM_SCALAR >::eval(), gum::ASTposteriorProba< GUM_SCALAR >::eval(), gum::ASTsum< GUM_SCALAR >::eval(), gum::UGmodel::exists(), gum::DAGmodel::existsArc(), gum::UGmodel::existsEdge(), gum::DAGmodel::family(), gum::UGmodel::family(), gum::DAGmodel::hasSameStructure(), gum::MarkovBlanket::hasSameStructure(), gum::UGmodel::hasSameStructure(), gum::DAGmodel::isIndependent(), gum::UGmodel::isIndependent(), gum::DAGmodel::minimalCondSet(), gum::UGmodel::neighbours(), gum::IBayesNet< GUM_SCALAR >::operator==(), gum::IMarkovRandomField< GUM_SCALAR >::operator==(), gum::DAGmodel::parents(), and gum::IMarkovRandomField< GUM_SCALAR >::smallestFactorFromNode().

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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

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

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

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

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

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

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

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

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()

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

overridevirtualinherited

Returns the NodeId of a variable.

Exceptions

NotFound

if no variable matches var.

Implements gum::GraphicalModel.

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

Definition at line 56 of file discreteGraphicalModel_inl.h.

                                                                         {
    return varMap_.get(var);
  }

References varMap_.

Referenced by gum::InfluenceDiagram< GUM_SCALAR >::clear(), gum::BayesBall::relevantTensors(), and gum::dSeparationAlgorithm::relevantTensors().

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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>

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

(

const IBayesNet< GUM_SCALAR > &

source

)

Copy operator.

Definition at line 90 of file IBayesNet_tpl.h.

                                                                            {
    if (this != &source) {
      DAGmodel::operator=(source);
      GUM_OP_CPY(IBayesNet);
    }
 
    return *this;
  }

References IBayesNet(), and gum::DAGmodel::operator=().

Referenced by gum::BayesNet< GUM_SCALAR >::operator=(), gum::BayesNet< GUM_SCALAR >::operator=(), gum::prm::ClassBayesNet< GUM_SCALAR >::operator=(), and gum::prm::InstanceBayesNet< GUM_SCALAR >::operator=().

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operator=() [2/2]

template<GUM_Numeric GUM_SCALAR>

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

(

IBayesNet< GUM_SCALAR > &&

source

)

Move operator.

Definition at line 100 of file IBayesNet_tpl.h.

                                                                                            {
    if (this != &source) {
      DAGmodel::operator=(std::move(source));
      GUM_OP_MOV(IBayesNet);
    }
    return *this;
  }

References IBayesNet(), and gum::DAGmodel::operator=().

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operator==()

template<GUM_Numeric GUM_SCALAR>

bool gum::IBayesNet< GUM_SCALAR >::operator==

(

const IBayesNet< GUM_SCALAR > &

from

)

const

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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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

virtual

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

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()

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

overridevirtualinherited

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

Exceptions

NotFound

if no variable's id matches id.

Implements gum::GraphicalModel.

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

Definition at line 51 of file discreteGraphicalModel_inl.h.

                                                                          {
    return varMap_.get(id);
  }

References varMap_.

Referenced by gum::Estimator< GUM_SCALAR >::Estimator(), gum::ASTposteriorProba< GUM_SCALAR >::_compute_knw_from_bn(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_insertEvidence_(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_verticesSampling_(), gum::IBayesNet< GUM_SCALAR >::check(), gum::InfluenceDiagram< GUM_SCALAR >::clear(), gum::IBayesNet< GUM_SCALAR >::dim(), gum::ASTsum< GUM_SCALAR >::eval(), gum::DAGmodel::hasSameStructure(), gum::UGmodel::hasSameStructure(), gum::IBayesNet< GUM_SCALAR >::maxVarDomainSize(), gum::IMarkovRandomField< GUM_SCALAR >::maxVarDomainSize(), gum::IBayesNet< GUM_SCALAR >::operator==(), gum::IMarkovRandomField< GUM_SCALAR >::operator==(), gum::Estimator< GUM_SCALAR >::setFromBN(), gum::IMarkovRandomField< GUM_SCALAR >::toDot(), and gum::IMarkovRandomField< GUM_SCALAR >::toDotAsFactorGraph().

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variableFromName()

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

overridevirtualinherited

Returns a constant reference over a variable given its name.

Exceptions

NotFound

if no such name exists in the model.

Implements gum::GraphicalModel.

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

Definition at line 66 of file discreteGraphicalModel_inl.h.

                                                                                            {
    return varMap_.variableFromName(name);
  }

References varMap_.

Referenced by gum::InfluenceDiagram< GUM_SCALAR >::clear(), gum::IBayesNet< GUM_SCALAR >::evEq(), gum::IBayesNet< GUM_SCALAR >::evGt(), gum::IBayesNet< GUM_SCALAR >::evIn(), gum::IBayesNet< GUM_SCALAR >::evLt(), gum::IBayesNet< GUM_SCALAR >::operator==(), and gum::IMarkovRandomField< GUM_SCALAR >::operator==().

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variableNodeMap()

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

overridevirtualinherited

Returns a constant reference to the VariableNodeMap of this model.

Implements gum::GraphicalModel.

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

Definition at line 48 of file discreteGraphicalModel_inl.h.

{ return varMap_; }

References varMap_.

Referenced by gum::InfluenceDiagram< GUM_SCALAR >::clear().

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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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Member Data Documentation

_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/IBayesNet.h
• agrum/BN/IBayesNet_tpl.h