gum::BayesNetFragment< GUM_SCALAR > Class Template Reference

Portion of a BN identified by the list of nodes and a BayesNet. More...

#include <agrum/BN/BayesNetFragment.h>

Inheritance diagram for gum::BayesNetFragment< GUM_SCALAR >:

Collaboration diagram for gum::BayesNetFragment< GUM_SCALAR >:

Public Member Functions
gum::BayesNet< GUM_SCALAR >	toBN () const
	create a brand new BayesNet from a fragment.
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
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
void	_attachSignal_ (__sig__::ISignaler *sender)
void	_detachSignal_ (__sig__::ISignaler *sender)
Constructors / Destructors
	BayesNetFragment ()=delete
	BayesNetFragment (const BayesNetFragment< GUM_SCALAR > &fragment)=delete
	BayesNetFragment (BayesNetFragment< GUM_SCALAR > &&fragment)=delete
	BayesNetFragment (const IBayesNet< GUM_SCALAR > &bn)
virtual	~BayesNetFragment ()
signals
virtual void	whenNodeAdded (const void *src, NodeId id) final
	the action to take when a new node is inserted into the graph
virtual void	whenNodeDeleted (const void *src, NodeId id) final
	the action to take when a node has just been removed from the graph
virtual void	whenArcAdded (const void *src, NodeId from, NodeId to) final
	the action to take when a new arc is inserted into the graph
virtual void	whenArcDeleted (const void *src, NodeId from, NodeId to) final
	the action to take when an arc has just been removed from the graph
IBayesNet interface
const Tensor< GUM_SCALAR > &	cpt (NodeId varId) const final
	Returns the CPT of a variable.
const Tensor< GUM_SCALAR > &	cpt (const std::string &name) const
	Returns the CPT of a variable.
const VariableNodeMap &	variableNodeMap () const final
	Returns a constant reference to the VariableNodeMap of this BN.
virtual const DiscreteVariable &	variable (NodeId id) const final
	Returns a constant reference over a variabe given it's node id.
virtual const DiscreteVariable &	variable (const std::string &name) const final
	Returns the CPT of a variable.
virtual NodeId	nodeId (const DiscreteVariable &var) const final
	Return id node from discrete var pointer.
virtual NodeId	idFromName (const std::string &name) const final
	Getter by name.
virtual const DiscreteVariable &	variableFromName (const std::string &name) const final
	Getter by name.
virtual std::string	toDot () const final
	creates a dot representing the whole referred BN hilighting the fragment.
API for Fragment
bool	isInstalledNode (NodeId id) const
	check if a certain NodeId exists in the fragment
bool	isInstalledNode (const std::string &name) const
	check if a certain NodeId exists in the fragment
void	installNode (NodeId id)
	install a node referenced by its nodeId
void	installNode (const std::string &name)
	check if a certain NodeId exists in the fragment
void	installAscendants (NodeId id)
	install a node and all its ascendants
void	installAscendants (const std::string &name)
	check if a certain NodeId exists in the fragment
void	uninstallNode (NodeId id)
	uninstall a node referenced by its nodeId
void	uninstallNode (const std::string &name)
	check if a certain NodeId exists in the fragment
void	installMarginal (NodeId id, const Tensor< GUM_SCALAR > &pot)
	install a local marginal BY COPY for a node into the fragment.
void	installMarginal (const std::string &name, const Tensor< GUM_SCALAR > &pot)
	check if a certain NodeId exists in the fragment
void	installCPT (NodeId id, const Tensor< GUM_SCALAR > &pot)
	install a local cpt BY COPYfor a node into the fragment.
void	installCPT (const std::string &name, const Tensor< GUM_SCALAR > &pot)
	check if a certain NodeId exists in the fragment
void	uninstallCPT (NodeId id)
	uninstall a local CPT.
void	uninstallCPT (const std::string &name)
	check if a certain NodeId exists in the fragment
bool	checkConsistency (NodeId id) const
	returns true if the nodeId's (local or not) cpt is consistent with its parents in the fragment
bool	checkConsistency (const std::string &name) const
	check if a certain NodeId exists in the fragment
bool	checkConsistency () const
	returns true if all nodes in the fragment are consistent
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 Member Functions
void	uninstallArc_ (NodeId from, NodeId to)
void	installArc_ (NodeId from, NodeId to)
void	installCPT_ (NodeId id, const Tensor< GUM_SCALAR > &pot)
void	uninstallCPT_ (NodeId id)
	uninstall a local CPT.

Protected Attributes
DAG	dag_
	The DAG of this Directed Graphical Model.
DiGraph *	graph_
	the graph to listen to

Private Types
using	Senders_list = std::vector< __sig__::ISignaler* >
	Alias for the list of signal senders.

Private Member Functions
const HashTable< std::string, std::string > &	_properties_ () const
	Return the properties of this Directed Graphical Model.

Private Attributes
const IBayesNet< GUM_SCALAR > &	_bn_
	The referred BayesNet.
NodeProperty< const Tensor< GUM_SCALAR > * >	_localCPTs_
	Mapping between the variable's id and their CPT specific to this Fragment.
HashTable< std::string, std::string >	_propertiesMap_
	The properties of this Directed Graphical Model.
Senders_list	_senders_

Detailed Description

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

Portion of a BN identified by the list of nodes and a BayesNet.

Author

Pierre-Henri WUILLEMIN(_at_LIP6)

This class is a decorator of a BayesNet implementing the IBayesNet interface. CPTs can be shared with the BN or can be specific to the Fragment if different.

BayesNetFragment is a DiGraphListener in order to be synchronized (especially when removing nodes or arcs).

In a BayesNetFragment, one can install or remove nodes. An arc can be in the fragment if and only if its head and tail are installed in the fragment. *When installing a node, all the arcs that can be added in the fragment are *effectively installed (resp. *when uninstalling a node, etc.).

A BayesNetFragment can redefine tensor for node. The main reason is to be able to install a node without installing all its parents (and its ascendants). So local CPT to the node can be installed. However, it is not done automatically.

If a cpt is not locally defined, the fragment uses the cpt defined in the referred BN. The checkConsistency() method verifies that, for all installed nodes, either all the parents are installed or a local CPT is defined.

Definition at line 90 of file BayesNetFragment.h.

Member Typedef Documentation

Senders_list

using gum::Listener::Senders_list = std::vector< __sig__::ISignaler* >

privateinherited

Alias for the list of signal senders.

Definition at line 96 of file listener.h.

Constructor & Destructor Documentation

BayesNetFragment() [1/4]

template<typename GUM_SCALAR>

gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment ( )

delete

Referenced by BayesNetFragment(), BayesNetFragment(), BayesNetFragment(), and ~BayesNetFragment().

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BayesNetFragment() [2/4]

template<typename GUM_SCALAR>

gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment ( const BayesNetFragment< GUM_SCALAR > & fragment )

delete

References BayesNetFragment().

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BayesNetFragment() [3/4]

template<typename GUM_SCALAR>

gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment ( BayesNetFragment< GUM_SCALAR > && fragment )

delete

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

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BayesNetFragment() [4/4]

template<typename GUM_SCALAR>

gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment ( const IBayesNet< GUM_SCALAR > & bn )

explicit

Definition at line 55 of file BayesNetFragment_tpl.h.

                                                                                    :
      DiGraphListener(&bn.dag()), _bn_(bn) {
    GUM_CONSTRUCTOR(BayesNetFragment)
  }

References BayesNetFragment(), gum::DiGraphListener::DiGraphListener(), gum::IBayesNet< GUM_SCALAR >::IBayesNet(), _bn_, and gum::DAGmodel::dag().

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~BayesNetFragment()

template<typename GUM_SCALAR>

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

virtual

Definition at line 61 of file BayesNetFragment_tpl.h.

                                                    {
    GUM_DESTRUCTOR(BayesNetFragment)
 
    for (auto node: nodes())
      if (_localCPTs_.exists(node)) uninstallCPT_(node);
  }

References BayesNetFragment(), _localCPTs_, gum::DAGmodel::nodes(), and uninstallCPT_().

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Member Function Documentation

_attachSignal_()

INLINE void gum::Listener::_attachSignal_ ( __sig__::ISignaler * sender )

inherited

Definition at line 56 of file listener_inl.h.

{ _senders_.push_back(sender); }

References _senders_.

_detachSignal_()

INLINE void gum::Listener::_detachSignal_ ( __sig__::ISignaler * sender )

inherited

Definition at line 58 of file listener_inl.h.

                                                               {
    auto del = std::remove(_senders_.begin(), _senders_.end(), sender);
 
    if (del != _senders_.end()) _senders_.erase(del, _senders_.end());
  }

References _senders_.

_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

inherited

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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checkConsistency() [1/3]

template<typename GUM_SCALAR>

INLINE bool gum::BayesNetFragment< GUM_SCALAR >::checkConsistency

(

)

const

returns true if all nodes in the fragment are consistent

Exceptions

gum::OperatioNotAllowed

if the fragment is not consistent.

Definition at line 295 of file BayesNetFragment_tpl.h.

                                                                     {
    for (auto node: nodes())
      if (!checkConsistency(node)) return false;
 
    return true;
  }

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

Referenced by checkConsistency().

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checkConsistency() [2/3]

template<typename GUM_SCALAR>

bool gum::BayesNetFragment< GUM_SCALAR >::checkConsistency ( const std::string & name ) const

inline

check if a certain NodeId exists in the fragment

Definition at line 289 of file BayesNetFragment.h.

                                                       {
      return checkConsistency(idFromName(name));
    }

References checkConsistency(), and idFromName().

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checkConsistency() [3/3]

template<typename GUM_SCALAR>

bool gum::BayesNetFragment< GUM_SCALAR >::checkConsistency

(

NodeId

id

)

const

returns true if the nodeId's (local or not) cpt is consistent with its parents in the fragment

Exceptions

NotFound

if the id is not in the fragment

Definition at line 280 of file BayesNetFragment_tpl.h.

                                                                       {
    if (!isInstalledNode(id))
      GUM_ERROR(NotFound, "The node " << id << " is not part of this fragment")
 
    const auto& cpt = this->cpt(id);
    NodeSet     cpt_parents;
 
    for (Idx i = 1; i < cpt.nbrDim(); i++) {
      cpt_parents.insert(_bn_.idFromName(cpt.variable(i).name()));
    }
 
    return (this->parents(id) == cpt_parents);
  }

References _bn_, cpt(), GUM_ERROR, gum::Set< Key >::insert(), isInstalledNode(), and gum::DAGmodel::parents().

Referenced by checkConsistency(), toBN(), and toDot().

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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() [1/2]

template<typename GUM_SCALAR>

const Tensor< GUM_SCALAR > & gum::BayesNetFragment< GUM_SCALAR >::cpt ( const std::string & name ) const

inline

Returns the CPT of a variable.

Exceptions

NotFound

If no variable's id matches varId.

Definition at line 146 of file BayesNetFragment.h.

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

References cpt(), and idFromName().

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cpt() [2/2]

template<typename GUM_SCALAR>

INLINE const Tensor< GUM_SCALAR > & gum::BayesNetFragment< GUM_SCALAR >::cpt ( NodeId varId ) const

finalvirtual

Returns the CPT of a variable.

Exceptions

NotFound

If no variable's id matches varId.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 96 of file BayesNetFragment_tpl.h.

                                                                                        {
    if (!isInstalledNode(id)) GUM_ERROR(NotFound, "NodeId " << id << " is not installed")
 
    if (_localCPTs_.exists(id)) return *_localCPTs_[id];
    else return _bn_.cpt(id);
  }

References _bn_, _localCPTs_, GUM_ERROR, and isInstalledNode().

Referenced by checkConsistency(), cpt(), toBN(), uninstallCPT(), and gum::ImportanceSampling< GUM_SCALAR >::unsharpenBN_().

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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

inherited

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

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

Definition at line 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

inherited

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

inherited

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

inherited

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

inherited

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>

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

finalvirtual

Getter by name.

Exceptions

NotFound

if no such name exists in the graph.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 125 of file BayesNetFragment_tpl.h.

                                                                                      {
    NodeId id = _bn_.idFromName(name);
 
    if (!isInstalledNode(id)) GUM_ERROR(NotFound, "variable " << name << " is not installed")
 
    return id;
  }

References _bn_, GUM_ERROR, and isInstalledNode().

Referenced by checkConsistency(), cpt(), isInstalledNode(), uninstallCPT(), uninstallNode(), variable(), and variableFromName().

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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().

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

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installArc_()

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::installArc_ ( NodeId from, NodeId to )

protected

Definition at line 192 of file BayesNetFragment_tpl.h.

                                                                                {
    this->dag_.addArc(from, to);
  }

References gum::DAGmodel::dag_.

Referenced by installCPT_(), and uninstallCPT().

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installAscendants() [1/2]

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::installAscendants ( const std::string & name )

inline

check if a certain NodeId exists in the fragment

Definition at line 225 of file BayesNetFragment.h.

{ installAscendants(_bn_.idFromName(name)); }

References _bn_, and installAscendants().

Referenced by installAscendants().

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installAscendants() [2/2]

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::installAscendants

(

NodeId

id

)

install a node and all its ascendants

Exceptions

NotFound

if the node does not exist in the referred BN

Warning

nothing happens if the node is already installed

Definition at line 170 of file BayesNetFragment_tpl.h.

                                                                         {
    installNode(id);
 
    // bn is a dag => this will have an end ...
    for (auto pa: this->_bn_.parents(id))
      installAscendants(pa);
  }

References _bn_, installAscendants(), and installNode().

Referenced by installAscendants().

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installCPT() [1/2]

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::installCPT ( const std::string & name, const Tensor< GUM_SCALAR > & pot )

inline

check if a certain NodeId exists in the fragment

Definition at line 266 of file BayesNetFragment.h.

                                                                            {
      installCPT(_bn_.idFromName(name), pot);
    };

References _bn_, and installCPT().

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installCPT() [2/2]

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::installCPT	(	NodeId	id,
		const Tensor< GUM_SCALAR > &	pot )

install a local cpt BY COPYfor a node into the fragment.

This function will change the arcs from the parents to the node in order to be consistent with the new local tensor.

Parameters

id	the nodeId
pot	the tensor to be copied

Exceptions

NotFound	if the id is not in the fragment
OperationNotAllowed	if the tensor is not compliant with the variable or if a variable in the CPT is not a parent in the referred bn.

Definition at line 217 of file BayesNetFragment_tpl.h.

                                                                                            {
    if (!dag().existsNode(id))
      GUM_ERROR(NotFound, "Node " << id << " is not installed in the fragment")
 
    if (&(pot.variable(0)) != &(variable(id))) {
      GUM_ERROR(OperationNotAllowed,
                "The tensor is not a marginal for  _bn_.variable <" << variable(id).name() << ">")
    }
 
    const NodeSet& parents = _bn_.parents(id);
 
    for (Idx i = 1; i < pot.nbrDim(); i++) {
      if (!parents.contains(_bn_.idFromName(pot.variable(i).name())))
        GUM_ERROR(OperationNotAllowed,
                  "Variable <" << pot.variable(i).name() << "> is not in the parents of node "
                               << id)
    }
 
    installCPT_(id, pot);
  }

References _bn_, gum::DAGmodel::dag(), GUM_ERROR, installCPT_(), gum::DAGmodel::parents(), and variable().

Referenced by installCPT(), gum::ImportanceSampling< GUM_SCALAR >::onContextualize_(), and gum::ImportanceSampling< GUM_SCALAR >::unsharpenBN_().

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installCPT_()

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::installCPT_ ( NodeId id, const Tensor< GUM_SCALAR > & pot )

protected

Definition at line 197 of file BayesNetFragment_tpl.h.

                                                                                             {
    // topology
    const auto& parents = this->parents(id);
    for (auto node_it = parents.beginSafe(); node_it != parents.endSafe();
         ++node_it)   // safe iterator needed here
      uninstallArc_(*node_it, id);
 
    for (Idx i = 1; i < pot.nbrDim(); i++) {
      NodeId parent = _bn_.idFromName(pot.variable(i).name());
 
      if (isInstalledNode(parent)) installArc_(parent, id);
    }
 
    // local cpt
    if (_localCPTs_.exists(id)) uninstallCPT_(id);
 
    _localCPTs_.insert(id, new gum::Tensor< GUM_SCALAR >(pot));
  }

References _bn_, _localCPTs_, installArc_(), isInstalledNode(), gum::DAGmodel::parents(), uninstallArc_(), and uninstallCPT_().

Referenced by installCPT(), and installMarginal().

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installMarginal() [1/2]

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::installMarginal ( const std::string & name, const Tensor< GUM_SCALAR > & pot )

inline

check if a certain NodeId exists in the fragment

Definition at line 248 of file BayesNetFragment.h.

                                                                                 {
      installMarginal(_bn_.idFromName(name), pot);
    }

References _bn_, and installMarginal().

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installMarginal() [2/2]

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::installMarginal	(	NodeId	id,
		const Tensor< GUM_SCALAR > &	pot )

install a local marginal BY COPY for a node into the fragment.

This function will remove all the arcs from the parents to the node.

Parameters

id	the nodeId
pot	the tensor

Exceptions

NotFound	if the id is not in the fragment
OperationNotAllowed	if the tensor is not compliant with the variable (or is not a marginal)

Definition at line 261 of file BayesNetFragment_tpl.h.

                                                                                                 {
    if (!isInstalledNode(id)) {
      GUM_ERROR(NotFound, "The node " << id << " is not part of this fragment")
    }
 
    if (pot.nbrDim() > 1) {
      GUM_ERROR(OperationNotAllowed, "The tensor is not a marginal :" << pot)
    }
 
    if (&(pot.variable(0)) != &(_bn_.variable(id))) {
      GUM_ERROR(OperationNotAllowed,
                "The tensor is not a marginal for  _bn_.variable <" << _bn_.variable(id).name()
                                                                    << ">")
    }
 
    installCPT_(id, pot);
  }

References _bn_, GUM_ERROR, installCPT_(), and isInstalledNode().

Referenced by installMarginal().

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installNode() [1/2]

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::installNode ( const std::string & name )

inline

check if a certain NodeId exists in the fragment

Definition at line 215 of file BayesNetFragment.h.

{ installNode(_bn_.idFromName(name)); }

References _bn_, and installNode().

Referenced by installNode().

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installNode() [2/2]

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::installNode

(

NodeId

id

)

install a node referenced by its nodeId

Exceptions

NotFound

if the node does not exist in the referred BN

Warning

nothing happens if the node is already installed

Definition at line 151 of file BayesNetFragment_tpl.h.

                                                            {
    if (!_bn_.dag().existsNode(id))
      GUM_ERROR(NotFound, "Node " << id << " does not exist in referred BayesNet")
 
    if (!isInstalledNode(id)) {
      this->dag_.addNodeWithId(id);
 
      // adding arcs with id as a tail
      for (auto pa: this->_bn_.parents(id)) {
        if (isInstalledNode(pa)) this->dag_.addArc(pa, id);
      }
 
      // adding arcs with id as a head
      for (auto son: this->_bn_.children(id))
        if (isInstalledNode(son)) this->dag_.addArc(id, son);
    }
  }

References _bn_, gum::DAGmodel::dag_, GUM_ERROR, and isInstalledNode().

Referenced by installAscendants().

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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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isInstalledNode() [1/2]

template<typename GUM_SCALAR>

bool gum::BayesNetFragment< GUM_SCALAR >::isInstalledNode ( const std::string & name ) const

inline

check if a certain NodeId exists in the fragment

Definition at line 203 of file BayesNetFragment.h.

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

References idFromName(), and isInstalledNode().

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isInstalledNode() [2/2]

template<typename GUM_SCALAR>

INLINE bool gum::BayesNetFragment< GUM_SCALAR >::isInstalledNode

(

NodeId

id

)

const

check if a certain NodeId exists in the fragment

Definition at line 146 of file BayesNetFragment_tpl.h.

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

References gum::DAGmodel::dag().

Referenced by checkConsistency(), cpt(), idFromName(), installCPT_(), installMarginal(), installNode(), isInstalledNode(), nodeId(), toDot(), uninstallCPT(), uninstallNode(), variable(), and variableFromName().

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jointProbability()

template<typename GUM_SCALAR>

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

inherited

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

Warning

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

Definition at line 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

inherited

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

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

Warning

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

Definition at line 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

inherited

Returns

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

Warning

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

Definition at line 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

inherited

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

inherited

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

inherited

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

inherited

Returns

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

Warning

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

Definition at line 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

inherited

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>

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

finalvirtual

Return id node from discrete var pointer.

Exceptions

NotFound

If no variable match var.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 116 of file BayesNetFragment_tpl.h.

                                                                                        {
    NodeId id = _bn_.nodeId(var);
 
    if (!isInstalledNode(id)) GUM_ERROR(NotFound, "variable " << var.name() << " is not installed")
 
    return id;
  }

References _bn_, GUM_ERROR, isInstalledNode(), and gum::Variable::name().

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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

inherited

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>

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

inherited

This operator compares 2 BNs !

Warning

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

Returns

true if the src and this are equal.

Definition at line 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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toBN()

template<typename GUM_SCALAR>

gum::BayesNet< GUM_SCALAR > gum::BayesNetFragment< GUM_SCALAR >::toBN

(

)

const

create a brand new BayesNet from a fragment.

Returns

the new BayesNet<GUM_SCALAR>

Definition at line 370 of file BayesNetFragment_tpl.h.

                                                                       {
    if (!checkConsistency()) {
      GUM_ERROR(OperationNotAllowed, "The fragment contains un-consistent node(s)")
    }
    gum::BayesNet< GUM_SCALAR > res;
    for (const auto nod: nodes()) {
      res.add(variable(nod), nod);
    }
    for (const auto& arc: dag().arcs()) {
      res.addArc(arc.tail(), arc.head());
    }
    for (const auto nod: nodes()) {
      res.cpt(nod).fillWith(cpt(nod));
    }
 
    return res;
  }

References gum::BayesNet< GUM_SCALAR >::add(), gum::BayesNet< GUM_SCALAR >::addArc(), gum::DAGmodel::arcs(), checkConsistency(), gum::BayesNet< GUM_SCALAR >::cpt(), cpt(), gum::DAGmodel::dag(), GUM_ERROR, gum::DAGmodel::nodes(), and variable().

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toDot()

template<typename GUM_SCALAR>

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

finalvirtual

creates a dot representing the whole referred BN hilighting the fragment.

Returns

Returns a dot representation of this fragment

Reimplemented from gum::IBayesNet< GUM_SCALAR >.

Definition at line 303 of file BayesNetFragment_tpl.h.

                                                        {
    std::stringstream output;
    output << "digraph \"";
 
    std::string bn_name;
 
    static std::string inFragmentStyle    = "fillcolor=\"#ffffaa\","
                                            "color=\"#000000\","
                                            "fontcolor=\"#000000\"";
    static std::string styleWithLocalCPT  = "fillcolor=\"#ffddaa\","
                                            "color=\"#000000\","
                                            "fontcolor=\"#000000\"";
    static std::string notConsistantStyle = "fillcolor=\"#ff0000\","
                                            "color=\"#000000\","
                                            "fontcolor=\"#ffff00\"";
    static std::string outFragmentStyle   = "fillcolor=\"#f0f0f0\","
                                            "color=\"#f0f0f0\","
                                            "fontcolor=\"#000000\"";
 
    try {
      bn_name = _bn_.property("name");
    } catch (NotFound const&) { bn_name = "no_name"; }
 
    bn_name = "Fragment of " + bn_name;
 
    output << bn_name << "\" {" << std::endl;
    output << "  graph [bgcolor=transparent,label=\"" << bn_name << "\"];" << std::endl;
    output << "  node [style=filled];" << std::endl << std::endl;
 
    for (auto node: _bn_.nodes()) {
      output << "\"" << _bn_.variable(node).name() << "\" [comment=\"" << node << ":"
             << _bn_.variable(node) << ", \"";
 
      if (isInstalledNode(node)) {
        if (!checkConsistency(node)) {
          output << notConsistantStyle;
        } else if (_localCPTs_.exists(node)) output << styleWithLocalCPT;
        else output << inFragmentStyle;
      } else output << outFragmentStyle;
 
      output << "];" << std::endl;
    }
 
    output << std::endl;
 
    std::string tab = "  ";
 
    for (auto node: _bn_.nodes()) {
      if (_bn_.children(node).size() > 0) {
        for (auto child: _bn_.children(node)) {
          output << tab << "\"" << _bn_.variable(node).name() << "\" -> "
                 << "\"" << _bn_.variable(child).name() << "\" [";
 
          if (dag().existsArc(Arc(node, child))) output << inFragmentStyle;
          else output << outFragmentStyle;
 
          output << "];" << std::endl;
        }
      }
    }
 
    output << "}" << std::endl;
 
    return output.str();
  }

References _bn_, _localCPTs_, checkConsistency(), gum::DAGmodel::dag(), gum::DAGmodel::existsArc(), and isInstalledNode().

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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 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

inherited

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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uninstallArc_()

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::uninstallArc_ ( NodeId from, NodeId to )

protected

Definition at line 187 of file BayesNetFragment_tpl.h.

                                                                                  {
    this->dag_.eraseArc(Arc(from, to));
  }

References gum::DAGmodel::dag_.

Referenced by installCPT_(), and whenArcDeleted().

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uninstallCPT() [1/2]

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::uninstallCPT ( const std::string & name )

inline

check if a certain NodeId exists in the fragment

Definition at line 279 of file BayesNetFragment.h.

{ uninstallCPT(idFromName(name)); }

References idFromName(), and uninstallCPT().

Referenced by uninstallCPT().

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uninstallCPT() [2/2]

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::uninstallCPT

(

NodeId

id

)

uninstall a local CPT.

Warning

Nothing happens if no local CPT for this nodeId or if the node is not installed.

Definition at line 245 of file BayesNetFragment_tpl.h.

                                                                    {
    if (_localCPTs_.exists(id)) {
      uninstallCPT_(id);
 
      // re-create arcs from referred tensor
      const Tensor< GUM_SCALAR >& pot = cpt(id);
 
      for (Idx i = 1; i < pot.nbrDim(); i++) {
        NodeId parent = _bn_.idFromName(pot.variable(i).name());
 
        if (isInstalledNode(parent)) installArc_(parent, id);
      }
    }
  }

References _bn_, _localCPTs_, cpt(), installArc_(), isInstalledNode(), and uninstallCPT_().

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

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uninstallCPT_()

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::uninstallCPT_ ( NodeId id )

protected

uninstall a local CPT.

Does nothing if no local CPT for this nodeId No check. No change in the topology. Checks are made in public methods.

Definition at line 239 of file BayesNetFragment_tpl.h.

                                                                     {
    delete _localCPTs_[id];
    _localCPTs_.erase(id);
  }

References _localCPTs_.

Referenced by ~BayesNetFragment(), installCPT_(), and uninstallCPT().

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uninstallNode() [1/2]

template<typename GUM_SCALAR>

void gum::BayesNetFragment< GUM_SCALAR >::uninstallNode ( const std::string & name )

inline

check if a certain NodeId exists in the fragment

Definition at line 234 of file BayesNetFragment.h.

{ uninstallNode(idFromName(name)); }

References idFromName(), and uninstallNode().

Referenced by uninstallNode().

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uninstallNode() [2/2]

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::uninstallNode

(

NodeId

id

)

uninstall a node referenced by its nodeId

Warning

nothing happens if the node is not installed

Definition at line 179 of file BayesNetFragment_tpl.h.

                                                                     {
    if (isInstalledNode(id)) {
      uninstallCPT(id);
      this->dag_.eraseNode(id);
    }
  }

References gum::DAGmodel::dag_, isInstalledNode(), and uninstallCPT().

Referenced by whenNodeDeleted().

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variable() [1/2]

template<typename GUM_SCALAR>

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

inlinefinalvirtual

Returns the CPT of a variable.

Exceptions

NotFound

If no variable's id matches varId.

Definition at line 162 of file BayesNetFragment.h.

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

References idFromName(), and variable().

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variable() [2/2]

template<typename GUM_SCALAR>

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

finalvirtual

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

Exceptions

NotFound

If no variable's id matches varId.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 109 of file BayesNetFragment_tpl.h.

                                                                                         {
    if (!isInstalledNode(id)) GUM_ERROR(NotFound, "NodeId " << id << " is not installed")
 
    return _bn_.variable(id);
  }

References _bn_, GUM_ERROR, and isInstalledNode().

Referenced by installCPT(), toBN(), and variable().

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variableFromName()

template<typename GUM_SCALAR>

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

finalvirtual

Getter by name.

Exceptions

NotFound

if no such name exists in the graph.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 135 of file BayesNetFragment_tpl.h.

                                                                                  {
    NodeId id = idFromName(name);
 
    if (!isInstalledNode(id)) GUM_ERROR(NotFound, "variable " << name << " is not installed")
 
    return _bn_.variable(id);
  }

References _bn_, GUM_ERROR, idFromName(), and isInstalledNode().

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variableNodeMap()

template<typename GUM_SCALAR>

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

finalvirtual

Returns a constant reference to the VariableNodeMap of this BN.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 104 of file BayesNetFragment_tpl.h.

                                                                                      {
    return this->_bn_.variableNodeMap();
  }

References _bn_.

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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whenArcAdded()

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::whenArcAdded ( const void * src, NodeId from, NodeId to )

finalvirtual

the action to take when a new arc is inserted into the graph

Parameters

src	the object that sent the signal
from	the id of tail of the new arc inserted into the graph
to	the id of head of the new arc inserted into the graph

Implements gum::DiGraphListener.

Definition at line 82 of file BayesNetFragment_tpl.h.

                                                                                          {
    // nothing to do
  }

whenArcDeleted()

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::whenArcDeleted ( const void * src, NodeId from, NodeId to )

finalvirtual

the action to take when an arc has just been removed from the graph

Parameters

src	the object that sent the signal
from	the id of tail of the arc removed from the graph
to	the id of head of the arc removed from the graph

Implements gum::DiGraphListener.

Definition at line 88 of file BayesNetFragment_tpl.h.

                                                                                            {
    if (dag().existsArc(from, to)) uninstallArc_(from, to);
  }

References gum::DAGmodel::dag(), gum::DAGmodel::existsArc(), and uninstallArc_().

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whenNodeAdded()

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::whenNodeAdded ( const void * src, NodeId id )

finalvirtual

the action to take when a new node is inserted into the graph

Parameters

src	the object that sent the signal
id	the id of the new node inserted into the graph

Implements gum::DiGraphListener.

Definition at line 71 of file BayesNetFragment_tpl.h.

                                                                                      {
    // nothing to do
  }

whenNodeDeleted()

template<typename GUM_SCALAR>

INLINE void gum::BayesNetFragment< GUM_SCALAR >::whenNodeDeleted ( const void * src, NodeId id )

finalvirtual

the action to take when a node has just been removed from the graph

Parameters

src	the object that sent the signal
id	the id of the node has just been removed from the graph

Implements gum::DiGraphListener.

Definition at line 76 of file BayesNetFragment_tpl.h.

                                                                                        {
    uninstallNode(id);
  }

References uninstallNode().

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Member Data Documentation

_bn_

template<typename GUM_SCALAR>

const IBayesNet< GUM_SCALAR >& gum::BayesNetFragment< GUM_SCALAR >::_bn_

private

The referred BayesNet.

Definition at line 92 of file BayesNetFragment.h.

Referenced by BayesNetFragment(), checkConsistency(), cpt(), idFromName(), installAscendants(), installAscendants(), installCPT(), installCPT(), installCPT_(), installMarginal(), installMarginal(), installNode(), installNode(), nodeId(), toDot(), uninstallCPT(), variable(), variableFromName(), and variableNodeMap().

_localCPTs_

template<typename GUM_SCALAR>

NodeProperty< const Tensor< GUM_SCALAR >* > gum::BayesNetFragment< GUM_SCALAR >::_localCPTs_

private

Mapping between the variable's id and their CPT specific to this Fragment.

Definition at line 96 of file BayesNetFragment.h.

Referenced by ~BayesNetFragment(), cpt(), installCPT_(), toDot(), uninstallCPT(), and uninstallCPT_().

_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().

_senders_

Senders_list gum::Listener::_senders_

privateinherited

Definition at line 113 of file listener.h.

Referenced by Listener(), ~Listener(), _attachSignal_(), and _detachSignal_().

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().

graph_

DiGraph* gum::DiGraphListener::graph_

protectedinherited

the graph to listen to

Definition at line 117 of file diGraphListener.h.

Referenced by DiGraphListener().

---

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

• agrum/BN/BayesNetFragment.h
• agrum/BN/BayesNetFragment_tpl.h