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 !
bool	operator!= (const IBayesNet< GUM_SCALAR > &from) const
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 (const std::string &name, double value) const
Tensor< GUM_SCALAR >	evIn (const std::string &name, double val1, double val2) const
Tensor< GUM_SCALAR >	evLt (const std::string &name, double value) const
Tensor< GUM_SCALAR >	evGt (const std::string &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)
NodeSet	minimalCondSet (NodeId target, const NodeSet &soids) const
NodeSet	minimalCondSet (const NodeSet &targets, const NodeSet &soids) const
NodeSet	minimalCondSet (const std::string &target, const std::vector< std::string > &soids) const
NodeSet	minimalCondSet (const std::vector< std::string > &targets, const std::vector< std::string > &soids) const
double	log10DomainSize () const
Constructors / Destructors
	IBayesNet ()
	Default constructor.
	IBayesNet (std::string name)
	Default constructor.
virtual	~IBayesNet ()
	Destructor.
	IBayesNet (const IBayesNet< GUM_SCALAR > &source)
	Copy constructor.
IBayesNet< GUM_SCALAR > &	operator= (const IBayesNet< GUM_SCALAR > &source)
	Copy operator.
Pure Virtual methods
virtual const Tensor< GUM_SCALAR > &	cpt (NodeId varId) const =0
	Returns the CPT of a variable.
virtual const VariableNodeMap &	variableNodeMap () const =0
	Returns a constant reference to the VariableNodeMap of thisBN.
virtual const DiscreteVariable &	variable (NodeId id) const =0
	Returns a constant reference over a variable given it's node id.
virtual NodeId	nodeId (const DiscreteVariable &var) const =0
	Return id node from discrete var pointer.
virtual NodeId	idFromName (const std::string &name) const =0
	Getter by name.
virtual const DiscreteVariable &	variableFromName (const std::string &name) const =0
	Getter by name.
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.
const DAG &	dag () const
	Returns a constant reference to the dag of this Bayes Net.
virtual 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 constant reference to the dag of this Bayes Net.
bool	exists (NodeId node) const final
	Return true if this node exists in this graphical model.
bool	exists (const std::string &name) const final
	Returns a constant reference to the dag of this Bayes Net.
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 (const std::string &nametail, const std::string &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 (const std::string &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 (const std::string &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 (const std::string &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 (const std::string &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 (const std::string &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 (const std::string &Xname, const std::string &Yname, const std::vector< std::string > &Znames) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
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.
Getter and setters
const std::string &	property (const std::string &name) const
	Return the value of the property name of this GraphicalModel.
const std::string &	propertyWithDefault (const std::string &name, const std::string &byDefault) const
	Return the value of the property name of this GraphicalModel.
void	setProperty (const std::string &name, const std::string &value)
	Add or change a property of this GraphicalModel.
std::vector< std::string >	properties () const
	List of all the properties.
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 void	spaceCplxToStream (std::stringstream &s, double dSize, int dim, Size usedMem)
	send to the stream the space complexity with 3 parametrs

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

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<typename 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() [1/3]

template<typename GUM_SCALAR>

INLINE gum::IBayesNet< GUM_SCALAR >::IBayesNet

(

)

Default constructor.

Definition at line 66 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::BayesNetFragment< GUM_SCALAR >::BayesNetFragment(), gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment(), gum::prm::ClassBayesNet< GUM_SCALAR >::ClassBayesNet(), gum::prm::ClassBayesNet< GUM_SCALAR >::ClassBayesNet(), IBayesNet(), IBayesNet(), IBayesNet(), gum::prm::InstanceBayesNet< GUM_SCALAR >::InstanceBayesNet(), gum::prm::InstanceBayesNet< GUM_SCALAR >::InstanceBayesNet(), operator!=(), operator=(), operator==(), and variableFromName().

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IBayesNet() [2/3]

template<typename GUM_SCALAR>

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

explicit

Default constructor.

Definition at line 71 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<typename GUM_SCALAR>

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

virtual

Destructor.

Definition at line 90 of file IBayesNet_tpl.h.

                                      {
    GUM_DESTRUCTOR(IBayesNet)
  }

IBayesNet() [3/3]

template<typename GUM_SCALAR>

gum::IBayesNet< GUM_SCALAR >::IBayesNet

(

const IBayesNet< GUM_SCALAR > &

source

)

Copy constructor.

Definition at line 77 of file IBayesNet_tpl.h.

                                                                          : DAGmodel(source) {
    GUM_CONS_CPY(IBayesNet)
  }

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

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Member Function Documentation

_properties_()

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

privateinherited

Return the properties of this Directed Graphical Model.

Definition at line 70 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 123 of file DAGmodel_inl.h.

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

References gum::ArcGraphPart::ancestors(), and dag().

Referenced by ancestors().

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ancestors() [2/2]

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

inherited

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

Parameters

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

Returns

true if the arc exists

Definition at line 125 of file DAGmodel_inl.h.

                                                                {
    return ancestors(idFromName(name));
  }

References ancestors(), and gum::GraphicalModel::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 65 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<typename 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 314 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 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 87 of file DAGmodel_inl.h.

{ return dag_.children(id); }

References dag_.

Referenced by 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 93 of file DAGmodel_inl.h.

{ return dag_.children(ids); }

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

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children() [3/4]

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

inherited

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

Parameters

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

Returns

true if the arc exists

Definition at line 89 of file DAGmodel_inl.h.

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

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

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children() [4/4]

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

inherited

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

Parameters

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

Returns

true if the arc exists

Definition at line 95 of file DAGmodel_inl.h.

                                                                             {
    return children(nodeset(names));
  }

References gum::GraphicalModel::names().

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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 106 of file graphicalModel_inl.h.

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

References nodes().

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cpt()

template<typename 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 >.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_verticesSampling_(), gum::BarrenNodesFinder::barrenTensors(), check(), jointProbability(), maxNonOneParam(), maxParam(), minNonZeroParam(), and minParam().

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dag()

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

inherited

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

Definition at line 57 of file DAGmodel_inl.h.

{ return dag_; }

References dag_.

Referenced by gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment(), gum::MarginalTargetedInference< GUM_SCALAR >::MarginalTargetedInference(), gum::BayesNet< GUM_SCALAR >::add(), ancestors(), gum::IBayesNet< double >::arcs(), descendants(), gum::BayesNetFragment< GUM_SCALAR >::installCPT(), isIndependent(), isIndependent(), gum::BayesNetFragment< GUM_SCALAR >::isInstalledNode(), moralGraph(), moralizedAncestralGraph(), gum::BayesBall::relevantTensors(), gum::dSeparationAlgorithm::relevantTensors(), gum::BayesNet< GUM_SCALAR >::reverseArc(), size(), gum::BayesNetFragment< GUM_SCALAR >::toBN(), gum::BayesNetFragment< GUM_SCALAR >::toDot(), gum::IBayesNet< GUM_SCALAR >::toString(), gum::InfluenceDiagram< GUM_SCALAR >::toString(), and gum::BayesNetFragment< GUM_SCALAR >::whenArcDeleted().

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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 117 of file DAGmodel_inl.h.

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

References dag(), and gum::ArcGraphPart::descendants().

Referenced by descendants().

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descendants() [2/2]

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

inherited

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

Parameters

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

Returns

true if the arc exists

Definition at line 119 of file DAGmodel_inl.h.

                                                                  {
    return descendants(idFromName(name));
  }

References descendants(), and gum::GraphicalModel::idFromName().

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dim()

template<typename 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 95 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(), and gum::DAGmodel::nodes().

Referenced by dim(), and toString().

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empty()

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

virtualinherited

Return true if this graphical model is empty.

Definition at line 116 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<typename GUM_SCALAR>

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

Returns

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

Definition at line 378 of file IBayesNet_tpl.h.

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

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

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evGt()

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evGt	(	const std::string &	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 389 of file IBayesNet_tpl.h.

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

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

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evIn()

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evIn	(	const std::string &	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 384 of file IBayesNet_tpl.h.

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

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

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evLt()

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evLt	(	const std::string &	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 394 of file IBayesNet_tpl.h.

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

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

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exists() [1/2]

INLINE bool gum::DAGmodel::exists ( const std::string & name ) const

finalvirtualinherited

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

Implements gum::GraphicalModel.

Definition at line 107 of file DAGmodel_inl.h.

                                                          {
    try {
      return exists(idFromName(name));
    } catch ([[maybe_unused]] gum::NotFound& e) { return false; }
  }

exists() [2/2]

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 105 of file DAGmodel_inl.h.

{ return dag_.exists(node); }

References dag_.

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 67 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 ( const std::string & nametail, const std::string & namehead ) const

inherited

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

Parameters

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

Returns

true if the arc exists

Definition at line 71 of file DAGmodel_inl.h.

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

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

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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 81 of file DAGmodel_inl.h.

{ return dag_.family(id); }

References dag_.

family() [2/2]

INLINE NodeSet gum::DAGmodel::family ( const std::string & 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 83 of file DAGmodel_inl.h.

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

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

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hasSameStructure()

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

inherited

Returns

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

Definition at line 66 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()) {
      try {
        other.idFromName(variable(nid).name());
      } catch (NotFound const&) { 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(), gum::Set< Key >::exists(), gum::GraphicalModel::idFromName(), nodes(), size(), sizeArcs(), and gum::GraphicalModel::variable().

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idFromName()

template<typename GUM_SCALAR>

virtual NodeId gum::IBayesNet< GUM_SCALAR >::idFromName ( const std::string & name ) const

pure virtual

Getter by name.

Exceptions

NotFound

if no such name exists in the graph.

Implements gum::GraphicalModel.

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

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().

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

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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 142 of file DAGmodel_inl.h.

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

References dag(), and gum::DAG::dSeparation().

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isIndependent() [2/4]

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

inlineinherited

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

Parameters

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

Returns

the moralized ancestral graph

Definition at line 202 of file DAGmodel.h.

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

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

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isIndependent() [3/4]

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

inlineinherited

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

Parameters

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

Returns

the moralized ancestral graph

Definition at line 208 of file DAGmodel.h.

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

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

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isIndependent() [4/4]

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 138 of file DAGmodel_inl.h.

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

References dag(), and gum::DAG::dSeparation().

Referenced by isIndependent(), and isIndependent().

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jointProbability()

template<typename 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 221 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;
  }

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

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log10DomainSize()

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

inherited

Definition at line 95 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::IBayesNet< GUM_SCALAR >::toString(), gum::IMarkovRandomField< GUM_SCALAR >::toString(), and gum::InfluenceDiagram< GUM_SCALAR >::toString().

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log2JointProbability()

template<typename 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 239 of file IBayesNet_tpl.h.

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

maxNonOneParam()

template<typename GUM_SCALAR>

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::maxNonOneParam

(

)

const

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 151 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;
  }

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

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maxParam()

template<typename GUM_SCALAR>

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::maxParam

(

)

const

Returns

the biggest value in the CPTs of *this

Definition at line 131 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 cpt(), and gum::DAGmodel::nodes().

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maxVarDomainSize()

template<typename GUM_SCALAR>

Size gum::IBayesNet< GUM_SCALAR >::maxVarDomainSize

(

)

const

Returns

the biggest domainSize among the variables of *this

Definition at line 111 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 variable().

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

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memoryFootprint()

template<typename GUM_SCALAR>

INLINE 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 161 of file IBayesNet_tpl.h.

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

References gum::DAGmodel::nodes().

Referenced by toString().

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minimalCondSet() [1/4]

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

inherited

Definition at line 150 of file DAGmodel_inl.h.

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

References dag_.

minimalCondSet() [2/4]

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

inherited

Definition at line 154 of file DAGmodel_inl.h.

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

minimalCondSet() [3/4]

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

inherited

Definition at line 159 of file DAGmodel_inl.h.

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

minimalCondSet() [4/4]

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

inherited

Definition at line 146 of file DAGmodel_inl.h.

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

minNonZeroParam()

template<typename 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 141 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;
  }

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

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

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minParam()

template<typename GUM_SCALAR>

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::minParam

(

)

const

Returns

the smallest value in the CPTs of *this

Definition at line 121 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 cpt(), and 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 64 of file DAGmodel.cpp.

{ return dag().moralGraph(); }

References dag(), and gum::DAG::moralGraph().

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 134 of file DAGmodel_inl.h.

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

References dag(), gum::DAG::moralizedAncestralGraph(), 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 130 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;
  }

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 119 of file graphicalModel_inl.h.

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

References ids(), and variableNodeMap().

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

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nodeId()

template<typename GUM_SCALAR>

virtual NodeId gum::IBayesNet< GUM_SCALAR >::nodeId ( const DiscreteVariable & var ) const

pure virtual

Return id node from discrete var pointer.

Exceptions

NotFound

If no variable matches var.

Implements gum::GraphicalModel.

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

Referenced by gum::BayesBall::relevantTensors(), and gum::dSeparationAlgorithm::relevantTensors().

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nodes()

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

finalvirtualinherited

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

Implements gum::GraphicalModel.

Definition at line 113 of file DAGmodel_inl.h.

{ return (NodeGraphPart&)dag_; }

References dag_.

Referenced by 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 >::jointProbability(), gum::IBayesNet< GUM_SCALAR >::maxNonOneParam(), gum::IBayesNet< GUM_SCALAR >::maxParam(), gum::IBayesNet< GUM_SCALAR >::maxVarDomainSize(), gum::IBayesNet< GUM_SCALAR >::memoryFootprint(), gum::IBayesNet< GUM_SCALAR >::minNonZeroParam(), gum::IBayesNet< GUM_SCALAR >::minParam(), gum::prm::ClassBayesNet< GUM_SCALAR >::modalities(), gum::prm::InstanceBayesNet< GUM_SCALAR >::modalities(), moralizedAncestralGraph(), 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 60 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::IBayesNet< double >::children(), gum::IBayesNet< double >::children(), gum::DAGmodel::isIndependent(), gum::DAGmodel::isIndependent(), gum::UGmodel::isIndependent(), gum::UGmodel::isIndependent(), gum::DAGmodel::moralizedAncestralGraph(), and gum::DAGmodel::parents().

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operator!=()

template<typename GUM_SCALAR>

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

(

const IBayesNet< GUM_SCALAR > &

from

)

const

Returns

Returns false if the src and this are equal.

Definition at line 303 of file IBayesNet_tpl.h.

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

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

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operator=()

template<typename GUM_SCALAR>

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

(

const IBayesNet< GUM_SCALAR > &

source

)

Copy operator.

Definition at line 83 of file IBayesNet_tpl.h.

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

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

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

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operator==()

template<typename 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 256 of file IBayesNet_tpl.h.

                                                                    {
    if (size() != from.size()) { return false; }
 
    if (sizeArcs() != from.sizeArcs()) { return false; }
 
    for (auto node: nodes()) {
      try {
        const auto& v1 = variable(node);
        const auto& v2 = from.variableFromName(variable(node).name());
        if (v1 != v2) { return false; }
      } catch (NotFound const&) {
        // a name is not found in from
        return false;
      }
    }
 
    for (auto node: nodes()) {
      NodeId fromnode = from.idFromName(variable(node).name());
 
      if (cpt(node).nbrDim() != from.cpt(fromnode).nbrDim()) { return false; }
 
      if (cpt(node).domainSize() != from.cpt(fromnode).domainSize()) { return false; }
 
      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(), and gum::DAGmodel::size().

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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 75 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::BayesNetFragment< GUM_SCALAR >::installCPT(), gum::BayesNetFragment< GUM_SCALAR >::installCPT_(), parents(), 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 99 of file DAGmodel_inl.h.

{ return dag_.children(ids); }

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

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parents() [3/4]

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

inherited

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

Parameters

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

Returns

true if the arc exists

Definition at line 77 of file DAGmodel_inl.h.

                                                                     {
    return parents(idFromName(name));
  }

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

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parents() [4/4]

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

inherited

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

Parameters

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

Returns

true if the arc exists

Definition at line 101 of file DAGmodel_inl.h.

                                                                            {
    return parents(nodeset(names));
  }

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

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properties()

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

inherited

List of all the properties.

Definition at line 81 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 ( const std::string & name ) const

inherited

Return the value of the property name of this GraphicalModel.

Exceptions

NotFound

Raised if no name property is found.

Definition at line 60 of file graphicalModel_inl.h.

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

References _properties_(), and GUM_ERROR.

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

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propertyWithDefault()

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

inherited

Return the value of the property name of this GraphicalModel.

return byDefault if the property name is not found

Definition at line 75 of file graphicalModel_inl.h.

                                                                                           {
    return (_propertiesMap_.exists(name)) ? _propertiesMap_[name] : byDefault;
  }

References _propertiesMap_.

setProperty()

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

inherited

Add or change a property of this GraphicalModel.

Definition at line 89 of file graphicalModel_inl.h.

                                                                                {
    if (_propertiesMap_.exists(name)) _propertiesMap_[name] = value;
    else _propertiesMap_.insert(name, 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 60 of file DAGmodel_inl.h.

{ return dag().size(); }

References dag(), and gum::NodeGraphPart::size().

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(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), and gum::IBayesNet< GUM_SCALAR >::toString().

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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 63 of file DAGmodel_inl.h.

{ return dag_.sizeArcs(); }

References dag_.

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

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spaceCplxToStream()

void gum::GraphicalModel::spaceCplxToStream ( std::stringstream & s, 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 69 of file graphicalModel.cpp.

                                                                                               {
    if (dSize > 6) s << "domainSize: 10^" << dSize;
    else s << "domainSize: " << std::round(std::pow(10.0, dSize));
 
    s << ", dim: " << dim << ", mem: ";
 
    if (const Size go = usedMem / (1024 * 1024 * 1024); go > 0) s << go << "Go ";
    if (const Size mo = (usedMem / (1024 * 1024)) % 1024; mo > 0) s << mo << "Mo ";
    if (const Size ko = (usedMem / 1024) % 1024; ko > 0) s << ko << "Ko ";
    s << usedMem % 1024 << "o";
  }

Referenced by gum::IBayesNet< GUM_SCALAR >::toString().

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toDot()

template<typename 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 179 of file IBayesNet_tpl.h.

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

topologicalOrder()

INLINE 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 115 of file DAGmodel_inl.h.

{ return dag().topologicalOrder(); }

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

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toString()

template<typename GUM_SCALAR>

INLINE std::string gum::IBayesNet< GUM_SCALAR >::toString

(

)

const

Returns

Returns a string representation of this IBayesNet.

Definition at line 170 of file IBayesNet_tpl.h.

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

References gum::DAGmodel::dag(), dim(), gum::GraphicalModel::log10DomainSize(), memoryFootprint(), gum::DAGmodel::size(), and gum::GraphicalModel::spaceCplxToStream().

Referenced by gum::operator<<().

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variable()

template<typename GUM_SCALAR>

virtual const DiscreteVariable & gum::IBayesNet< GUM_SCALAR >::variable ( NodeId id ) const

pure virtual

Returns a constant reference over a variable given it's node id.

Exceptions

NotFound

If no variable's id matches varId.

Implements gum::GraphicalModel.

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

Referenced by gum::Estimator< GUM_SCALAR >::Estimator(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_insertEvidence_(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_verticesSampling_(), check(), maxVarDomainSize(), and gum::Estimator< GUM_SCALAR >::setFromBN().

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variableFromName()

template<typename GUM_SCALAR>

virtual const DiscreteVariable & gum::IBayesNet< GUM_SCALAR >::variableFromName ( const std::string & name ) const

pure virtual

Getter by name.

Exceptions

NotFound

if no such name exists in the graph.

Implements gum::GraphicalModel.

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 evEq(), evGt(), evIn(), and evLt().

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variableNodeMap()

template<typename GUM_SCALAR>

virtual const VariableNodeMap & gum::IBayesNet< GUM_SCALAR >::variableNodeMap ( ) const

pure virtual

Returns a constant reference to the VariableNodeMap of thisBN.

Implements gum::GraphicalModel.

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

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;
  }

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

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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 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 236 of file graphicalModel.h.

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

dag_

DAG gum::DAGmodel::dag_

protectedinherited

The DAG of this Directed Graphical Model.

Definition at line 272 of file DAGmodel.h.

Referenced by 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_(), arcs(), children(), children(), children(), dag(), 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(), minimalCondSet(), gum::InfluenceDiagram< GUM_SCALAR >::moralGraph_(), nodes(), operator=(), parents(), parents(), gum::InfluenceDiagram< GUM_SCALAR >::removeTables_(), sizeArcs(), gum::InfluenceDiagram< GUM_SCALAR >::toDot(), gum::BayesNetFragment< GUM_SCALAR >::uninstallArc_(), and gum::BayesNetFragment< GUM_SCALAR >::uninstallNode().

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

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