gum::prm::ClassBayesNet< GUM_SCALAR > Class Template Reference

This class decorates a gum::prm::Class<GUM_SCALAR> has an IBaseBayesNet. More...

#include <agrum/PRM/classBayesNet.h>

Inheritance diagram for gum::prm::ClassBayesNet< GUM_SCALAR >:

Collaboration diagram for gum::prm::ClassBayesNet< GUM_SCALAR >:

Public Member Functions
bool	operator== (const IBayesNet< GUM_SCALAR > &from) const
	This operator compares 2 BNs ! More...
bool	operator!= (const IBayesNet< GUM_SCALAR > &from) const
Size	dim () const
	Returns the dimension (the number of free parameters) in this bayes net. More...
Size	maxVarDomainSize () const
GUM_SCALAR	minParam () const
GUM_SCALAR	maxParam () const
GUM_SCALAR	minNonZeroParam () const
GUM_SCALAR	maxNonOneParam () const
std::string	toString () const
NodeSet	minimalCondSet (NodeId target, const NodeSet &soids) const
NodeSet	minimalCondSet (const NodeSet &targets, const NodeSet &soids) const
double	log10DomainSize () const
bool	hasSameStructure (const DAGmodel &other)
Constructors & destructor.
	ClassBayesNet (const PRMClass< GUM_SCALAR > &c)
	Default constructor. More...
	ClassBayesNet (const ClassBayesNet< GUM_SCALAR > &from)
	Copy constructor. More...
ClassBayesNet< GUM_SCALAR > &	operator= (const ClassBayesNet< GUM_SCALAR > &from)
	Copy operator. More...
virtual	~ClassBayesNet ()
	Destructor. More...
Variable manipulation methods.
virtual const Potential< GUM_SCALAR > &	cpt (NodeId varId) const
	Returns the CPT of a node in this ClassBayesNet<GUM_SCALAR>. More...
virtual const VariableNodeMap &	variableNodeMap () const
	See gum::IBaseBayesNet::variableNodeMap(). More...
virtual const DiscreteVariable &	variable (NodeId id) const
	See gum::IBaseBayesNet::variable(). More...
virtual NodeId	nodeId (const DiscreteVariable &var) const
	See gum::IBaseBayesNet::nodeId(). More...
virtual NodeId	idFromName (const std::string &name) const
	See gum::IBaseBayesNet::idFromName(). More...
virtual const DiscreteVariable &	variableFromName (const std::string &name) const
	See gum::IBaseBayesNet::variableFromName(). More...
const NodeProperty< Size > &	modalities () const
	See gum::IBaseBayesNet::modalities(). More...
Graphical methods
virtual std::string	toDot () const
Joint Probability manipulation methods
GUM_SCALAR	jointProbability (const Instantiation &i) const
	Compute a parameter of the joint probability for the BN (given an instantiation of the vars) More...
GUM_SCALAR	log2JointProbability (const Instantiation &i) const
	Compute a parameter of the log joint probability for the BN (given an instantiation of the vars) More...
Getter and setters
const std::string &	property (const std::string &name) const
	Return the value of the property name of this DAGModel. More...
const std::string &	propertyWithDefault (const std::string &name, const std::string &byDefault) const
	Return the value of the property name of this DAGModel. More...
void	setProperty (const std::string &name, const std::string &value)
	Add or change a property of this DAGModel. More...
Variable manipulation methods.
const DAG &	dag () const
	Returns a constant reference to the dag of this Bayes Net. More...
Size	size () const
	Returns the number of variables in this Directed Graphical Model. More...
Size	sizeArcs () const
	Returns the number of arcs in this Directed Graphical Model. More...
bool	empty () const
	Retursn true if this Directed Graphical Model is empty. More...
const NodeGraphPart &	nodes () const
	Returns a constant reference to the dag of this Bayes Net. More...
virtual Instantiation	completeInstantiation () const final
	Get an instantiation over all the variables of the model. More...
Arc manipulation methods.
const ArcSet &	arcs () const
	returns the set of nodes with arc ingoing to a given node More...
const NodeSet &	parents (const NodeId id) const
	returns the set of nodes with arc ingoing to a given node More...
const NodeSet &	parents (const std::string &name) const
	returns the set of nodes with arc ingoing to a given node More...
const NodeSet &	children (const NodeId id) const
	returns the set of nodes with arc outgoing from a given node More...
const NodeSet &	children (const std::string &name) const
	returns the set of nodes with arc ingoing to a given node More...
Graphical methods
const UndiGraph &	moralGraph (bool clear=true) const
	The node's id are coherent with the variables and nodes of the topology. More...
const Sequence< NodeId > &	topologicalOrder (bool clear=true) const
	The topological order stays the same as long as no variable or arcs are added or erased src the topology. More...

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

Detailed Description

template<typename GUM_SCALAR>
class gum::prm::ClassBayesNet< GUM_SCALAR >

This class decorates a gum::prm::Class<GUM_SCALAR> has an IBaseBayesNet.

Template Parameters

GUM_SCALAR

The type of scalar to use.

This class filters PRMAttribute and PRMAggregate in a way it can be interpreted as a IBayesNet.

SlotChains and PRMReferenceSlot are not represented.

Remember that a ClassBayesNet<GUM_SCALAR> does not contain input nodes parents and output nodes children. Thus you should be careful when using one of the BayesNetInference over a ClassBayesNet<GUM_SCALAR> since some variables are missing in the DAG but not in the nodes CPT.

Definition at line 60 of file classBayesNet.h.

Constructor & Destructor Documentation

ClassBayesNet() [1/2]

template<typename GUM_SCALAR >

INLINE gum::prm::ClassBayesNet< GUM_SCALAR >::ClassBayesNet

(

const PRMClass< GUM_SCALAR > &

c

)

Default constructor.

Parameters

c	The Class<GUM_SCALAR> decorated by this ClassBayesNet<GUM_SCALAR>.

Definition at line 61 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__init().

                                        :
        IBayesNet< GUM_SCALAR >(),
        __class(&c) {
      GUM_CONSTRUCTOR(ClassBayesNet);
      __init(c);
    }

Here is the call graph for this function:

ClassBayesNet() [2/2]

template<typename GUM_SCALAR >

INLINE gum::prm::ClassBayesNet< GUM_SCALAR >::ClassBayesNet

(

const ClassBayesNet< GUM_SCALAR > &

from

)

Copy constructor.

Definition at line 70 of file classBayesNet_tpl.h.

                                                :
        IBayesNet< GUM_SCALAR >(from),
        __class(from.__class) {
      GUM_CONS_CPY(ClassBayesNet);
    }

~ClassBayesNet()

template<typename GUM_SCALAR >

INLINE gum::prm::ClassBayesNet< GUM_SCALAR >::~ClassBayesNet ( )

virtual

Destructor.

Definition at line 78 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::operator=().

                                                       {
      GUM_DESTRUCTOR(ClassBayesNet);
    }

Here is the call graph for this function:

Member Function Documentation

__get() [1/2]

template<typename GUM_SCALAR >

INLINE const PRMClassElement< GUM_SCALAR > & gum::prm::ClassBayesNet< GUM_SCALAR >::__get ( NodeId  id ) const

private

Private getter with type checking in case the id is not a formal PRMAttribute.

Exceptions

NotFound

Raised if id is not a formal attribute.

Definition at line 132 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__class, gum::DAGmodel::_dag, gum::NodeGraphPart::exists(), and GUM_ERROR.

Referenced by gum::prm::ClassBayesNet< GUM_SCALAR >::cpt(), gum::prm::ClassBayesNet< GUM_SCALAR >::idFromName(), gum::prm::ClassBayesNet< GUM_SCALAR >::variable(), and gum::prm::ClassBayesNet< GUM_SCALAR >::variableFromName().

                                                                   {
      if (this->_dag.exists(id)) {
        return __class->get(id);
      } else {
        GUM_ERROR(NotFound, "no element found with that id.");
      }
    }

Here is the call graph for this function:

Here is the caller graph for this function:

__get() [2/2]

template<typename GUM_SCALAR >

INLINE const PRMClassElement< GUM_SCALAR > & gum::prm::ClassBayesNet< GUM_SCALAR >::__get ( const std::string &  name ) const

private

Private getter with type checking in case the id is not a formal PRMAttribute.

Exceptions

NotFound

Raised if id is not a formal attribute.

Definition at line 142 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__class, and GUM_ERROR.

                                                                               {
      try {
        return __class->get(name);
      } catch (NotFound&) {
        GUM_ERROR(NotFound, "no element found with that id.");
      }
    }

__init()

template<typename GUM_SCALAR >

void gum::prm::ClassBayesNet< GUM_SCALAR >::__init ( const PRMClass< GUM_SCALAR > &  c )

private

Definition at line 36 of file classBayesNet_tpl.h.

References gum::prm::PRMClassElementContainer< GUM_SCALAR >::containerDag(), gum::prm::PRMClass< GUM_SCALAR >::get(), gum::prm::PRMClassElement< GUM_SCALAR >::id(), gum::prm::PRMClassElement< GUM_SCALAR >::type(), and gum::prm::PRMType::variable().

Referenced by gum::prm::ClassBayesNet< GUM_SCALAR >::ClassBayesNet().

                                                                            {
      for (const auto node : c.containerDag().nodes()) {
        try {
          // Adding the attribute
          if (PRMClassElement< GUM_SCALAR >::isAttribute(c.get(node))
              || PRMClassElement< GUM_SCALAR >::isAggregate(c.get(node))) {
            const PRMClassElement< GUM_SCALAR >& elt = c.get(node);
            this->_dag.addNodeWithId(elt.id());
            this->__varNodeMap.insert(&(elt.type().variable()), &elt);
          }
        } catch (NotFound&) {
          // Not an attribute
        }
      }

      for (const auto& arc : c.containerDag().arcs()) {
        try {
          this->_dag.addArc(arc.tail(), arc.head());
        } catch (InvalidNode&) {
          // Not added means not an attribute
        }
      }
    }

Here is the call graph for this function:

Here is the caller graph for this function:

arcs()

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

inherited

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

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

Parameters

id	the node toward which the arcs returned are pointing

Definition at line 104 of file DAGmodel_inl.h.

References gum::DAGmodel::_dag, and gum::ArcGraphPart::arcs().

Referenced by gum::EssentialGraph::__buildEssentialGraph(), gum::DAGmodel::__moralGraph(), gum::MarkovBlanket::hasSameStructure(), and gum::DAGmodel::hasSameStructure().

{ return _dag.arcs(); }

Here is the call graph for this function:

Here is the caller graph for this function:

children() [1/2]

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 arcs returned may be empty if no arc within the ArcGraphPart is outgoing from the given node.

Parameters

id	the node which is the tail of the arcs returned

Definition at line 111 of file DAGmodel_inl.h.

References gum::DAGmodel::_dag, and gum::ArcGraphPart::children().

Referenced by gum::MarkovBlanket::__buildMarkovBlanket(), gum::DAGmodel::parents(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), and gum::prm::ClassBayesNet< GUM_SCALAR >::toDot().

                                                                {
    return _dag.children(id);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

children() [2/2]

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

inlineinherited

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

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

Parameters

id	the node toward which the arcs returned are pointing

Definition at line 165 of file DAGmodel.h.

References gum::DAGmodel::hasSameStructure(), gum::DAGmodel::idFromName(), gum::DAGmodel::log10DomainSize(), gum::DAGmodel::moralGraph(), gum::DAGmodel::operator=(), gum::DAGmodel::parents(), and gum::DAGmodel::topologicalOrder().

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

Here is the call graph for this function:

completeInstantiation()

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

finalvirtualinherited

Get an instantiation over all the variables of the model.

Definition at line 86 of file DAGmodel_inl.h.

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

                                                      {
    Instantiation I;

    for (const auto node : dag())
      I << variable(node);

    return I;
  }

Here is the call graph for this function:

cpt()

template<typename GUM_SCALAR >

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

virtual

Returns the CPT of a node in this ClassBayesNet<GUM_SCALAR>.

If the node is an PRMAggregate, this will raise an OperationNotAllowed since PRMAggregate's CPT are define at instantiation only.

Parameters

varId

a variable id.

Returns

the Potential of varId.

Exceptions

NotFound	raised if varId does not match any variable in this IBayesNet.
OperationNotAllowed	raised if varId is an PRMAggregate.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 96 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__get().

                                                                    {
      return __get(varId).cpf();
    }

Here is the call graph for this function:

dag()

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

inherited

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

Definition at line 63 of file DAGmodel_inl.h.

References gum::DAGmodel::_dag.

Referenced by gum::DAGmodel::__moralGraph(), gum::DAGmodel::completeInstantiation(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::learning::genericBNLearner::Database::Database(), gum::BayesNetFragment< GUM_SCALAR >::installCPT(), gum::BayesNetFragment< GUM_SCALAR >::isInstalledNode(), gum::MarginalTargetedInference< GUM_SCALAR >::MarginalTargetedInference(), gum::BayesBall::relevantPotentials(), gum::dSeparation::relevantPotentials(), gum::DAGmodel::size(), gum::BayesNetFragment< GUM_SCALAR >::toDot(), gum::DAGmodel::topologicalOrder(), gum::InfluenceDiagram< GUM_SCALAR >::toString(), and gum::BayesNetFragment< GUM_SCALAR >::whenArcDeleted().

{ return _dag; }

Here is the caller graph for this function:

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 79 of file IBayesNet_tpl.h.

                                          {
    Size dim = 0;

    for (auto node : nodes()) {
      Size q = 1;

      for (auto parent : parents(node))
        q *= variable(parent).domainSize();

      dim += (variable(node).domainSize() - 1) * q;
    }

    return dim;
  }

empty()

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

inherited

Retursn true if this Directed Graphical Model is empty.

Definition at line 99 of file DAGmodel_inl.h.

References gum::DAGmodel::size().

{ return size() == 0; }

Here is the call graph for this function:

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 121 of file DAGmodel.cpp.

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

Referenced by gum::DAGmodel::children().

                                                       {
    if (this == &other) return true;

    if (size() != other.size()) return false;

    if (sizeArcs() != other.sizeArcs()) return false;

    for (const auto& nid : nodes()) {
      try {
        other.idFromName(variable(nid).name());
      } catch (NotFound) { return false; }
    }

    for (const auto& arc : arcs()) {
      if (!other.arcs().exists(Arc(other.idFromName(variable(arc.tail()).name()),
                                   other.idFromName(variable(arc.head()).name()))))
        return false;
    }

    return true;
  }

Here is the call graph for this function:

Here is the caller graph for this function:

idFromName()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::idFromName().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 120 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__get().

                                                                              {
      return __get(name).id();
    }

Here is the call graph for this function:

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 220 of file IBayesNet_tpl.h.

                                                                           {
    auto value = (GUM_SCALAR)1.0;

    GUM_SCALAR tmp;

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

      value *= tmp;
    }

    return value;
  }

log10DomainSize()

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

inherited

Returns

Returns the log10 domain size of the joint probabilty for the Directed Graphical Model

Definition at line 75 of file DAGmodel_inl.h.

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

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

                                         {
    double dSize = 0.0;

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

    return dSize;
  }

Here is the call graph for this function:

Here is the caller graph for this function:

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 += 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

Definition at line 135 of file IBayesNet_tpl.h.

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

maxParam()

template<typename GUM_SCALAR >

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

inherited

Returns

the biggest value in the CPTs of *this

Definition at line 115 of file IBayesNet_tpl.h.

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

maxVarDomainSize()

template<typename GUM_SCALAR >

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

inherited

Returns

the biggest domainSize among the variables of *this

Definition at line 95 of file IBayesNet_tpl.h.

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

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

Here is the caller graph for this function:

minimalCondSet() [1/2]

template<typename GUM_SCALAR >

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

inherited

Definition at line 355 of file IBayesNet_tpl.h.

                                                                              {
    if (soids.contains(target)) return NodeSet({target});

    NodeSet res;
    NodeSet alreadyVisitedUp;
    NodeSet alreadyVisitedDn;
    alreadyVisitedDn << target;
    alreadyVisitedUp << target;

    for (auto fath : _dag.parents(target))
      __minimalCondSetVisitUp(
         fath, soids, res, alreadyVisitedUp, alreadyVisitedDn);
    for (auto chil : _dag.children(target))
      __minimalCondSetVisitDn(
         chil, soids, res, alreadyVisitedUp, alreadyVisitedDn);
    return res;
  }

minimalCondSet() [2/2]

template<typename GUM_SCALAR >

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

inherited

Definition at line 375 of file IBayesNet_tpl.h.

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

minNonZeroParam()

template<typename GUM_SCALAR >

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

inherited

Returns

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

Definition at line 125 of file IBayesNet_tpl.h.

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

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

Here is the caller graph for this function:

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 105 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;
  }

modalities()

template<typename GUM_SCALAR >

INLINE const NodeProperty< Size > & gum::prm::ClassBayesNet< GUM_SCALAR >::modalities

(

)

const

See gum::IBaseBayesNet::modalities().

Definition at line 152 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__modalities, gum::DAGmodel::nodes(), and gum::prm::ClassBayesNet< GUM_SCALAR >::variable().

Referenced by gum::prm::SVED< GUM_SCALAR >::__initLiftedNodes(), and gum::prm::SVE< GUM_SCALAR >::__initLiftedNodes().

                                                               {
      if (__modalities.empty()) {
        for (const auto node : this->nodes()) {
          __modalities.insert(node, (Size)variable(node).domainSize());
        }
      }

      return __modalities;
    }

Here is the call graph for this function:

Here is the caller graph for this function:

moralGraph()

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

inherited

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

Parameters

clear

If false returns the previously created moral graph.

Definition at line 101 of file DAGmodel.cpp.

References gum::DAGmodel::__moralGraph(), gum::DAGmodel::__mutableMoralGraph, and gum::UndiGraph::clear().

Referenced by gum::prm::SVED< GUM_SCALAR >::__eliminateNodes(), gum::prm::SVE< GUM_SCALAR >::__eliminateNodes(), gum::prm::SVED< GUM_SCALAR >::__eliminateNodesWithEvidence(), gum::prm::SVE< GUM_SCALAR >::__eliminateNodesWithEvidence(), gum::prm::SVED< GUM_SCALAR >::__initLiftedNodes(), gum::prm::SVE< GUM_SCALAR >::__initLiftedNodes(), and gum::DAGmodel::children().

                                                        {
    if (clear
        || (__mutableMoralGraph == nullptr)) {   // we have to call _moralGraph
      if (__mutableMoralGraph == nullptr) {
        __mutableMoralGraph = new UndiGraph();
      } else {
        // clear is True ,__mutableMoralGraph exists
        __mutableMoralGraph->clear();
      }

      __moralGraph();
    }

    return *__mutableMoralGraph;
  }

Here is the call graph for this function:

Here is the caller graph for this function:

nodeId()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::nodeId().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 114 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__varNodeMap.

                                                                                {
      return __varNodeMap[&var]->id();
    }

nodes()

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

inherited

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

Definition at line 115 of file DAGmodel_inl.h.

References gum::DAGmodel::_dag.

Referenced by gum::credal::CredalNet< GUM_SCALAR >::__bnCopy(), gum::EssentialGraph::__buildEssentialGraph(), gum::MarkovBlanket::__buildMarkovBlanket(), gum::DAGmodel::__moralGraph(), gum::credal::CredalNet< GUM_SCALAR >::__sort_varType(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__verticesSampling(), gum::ImportanceSampling< GUM_SCALAR >::_unsharpenBN(), gum::BayesNetFactory< GUM_SCALAR >::BayesNetFactory(), gum::BayesNetFragment< GUM_SCALAR >::checkConsistency(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), gum::Estimator< GUM_SCALAR >::Estimator(), gum::getMaxModality(), gum::DAGmodel::hasSameStructure(), gum::DAGmodel::log10DomainSize(), gum::prm::InstanceBayesNet< GUM_SCALAR >::modalities(), gum::prm::ClassBayesNet< GUM_SCALAR >::modalities(), gum::Estimator< GUM_SCALAR >::setFromBN(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), gum::credal::CredalNet< GUM_SCALAR >::toString(), and gum::BayesNetFragment< GUM_SCALAR >::~BayesNetFragment().

                                                    {
    return (NodeGraphPart&)_dag;
  }

Here is the caller graph for this function:

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);
  }

operator=()

template<typename GUM_SCALAR >

INLINE ClassBayesNet< GUM_SCALAR > & gum::prm::ClassBayesNet< GUM_SCALAR >::operator=

(

const ClassBayesNet< GUM_SCALAR > &

from

)

Copy operator.

Definition at line 84 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__class, and gum::IBayesNet< GUM_SCALAR >::operator=().

Referenced by gum::prm::ClassBayesNet< GUM_SCALAR >::~ClassBayesNet().

                                                                                           {
      if (this != &from) {
        IBayesNet< GUM_SCALAR >::operator=(from);

        __class = from.__class;
      }

      return *this;
    }

Here is the call graph for this function:

Here is the caller graph for this function:

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; }

    // alignment of variables between the 2 BNs
    Bijection< const DiscreteVariable*, const DiscreteVariable* > alignment;

    for (auto node : nodes()) {
      try {
        alignment.insert(&variable(node),
                         &from.variableFromName(variable(node).name()));
      } catch (NotFound&) {
        // a name is not found in from
        return false;
      }
    }

    for (auto node : nodes()) {
      NodeId fromnode = from.idFromName(variable(node).name());

      if (cpt(node).nbrDim() != from.cpt(fromnode).nbrDim()) { return false; }

      if (cpt(node).domainSize() != from.cpt(fromnode).domainSize()) {
        return false;
      }

      Instantiation i(cpt(node));
      Instantiation j(from.cpt(fromnode));

      for (i.setFirst(); !i.end(); i.inc()) {
        for (Idx indice = 0; indice < cpt(node).nbrDim(); ++indice) {
          const DiscreteVariable* p = &(i.variable(indice));
          j.chgVal(*(alignment.second(p)), i.val(*p));
        }

        if (std::pow(cpt(node).get(i) - from.cpt(fromnode).get(j), (GUM_SCALAR)2)
            > (GUM_SCALAR)1e-6) {
          return false;
        }
      }
    }

    return true;
  }

parents() [1/2]

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 arcs returned may be empty if no arc within the ArcGraphPart is ingoing into the given node.

Parameters

id	the node toward which the arcs returned are pointing

Definition at line 106 of file DAGmodel_inl.h.

References gum::DAGmodel::_dag, and gum::ArcGraphPart::parents().

Referenced by gum::MarkovBlanket::__buildMarkovBlanket(), gum::DAGmodel::__moralGraph(), gum::BayesNetFragment< GUM_SCALAR >::_installCPT(), gum::BayesNetFragment< GUM_SCALAR >::checkConsistency(), gum::DAGmodel::children(), gum::BayesNetFragment< GUM_SCALAR >::installCPT(), gum::DAGmodel::parents(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), and gum::prm::ClassBayesNet< GUM_SCALAR >::toDot().

                                                               {
    return _dag.parents(id);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

parents() [2/2]

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

inlineinherited

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

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

Parameters

id	the node toward which the arcs returned are pointing

Definition at line 156 of file DAGmodel.h.

References gum::DAGmodel::children(), gum::DAGmodel::idFromName(), and gum::DAGmodel::parents().

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

Here is the call graph for this function:

property()

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

inherited

Return the value of the property name of this DAGModel.

Exceptions

NotFound

Raised if no name property is found.

Definition at line 37 of file DAGmodel_inl.h.

References gum::DAGmodel::__properties(), and GUM_ERROR.

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

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

Here is the call graph for this function:

Here is the caller graph for this function:

propertyWithDefault()

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

inherited

Return the value of the property name of this DAGModel.

return byDefault if the property name is not found

Definition at line 48 of file DAGmodel_inl.h.

References gum::DAGmodel::__properties().

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

Here is the call graph for this function:

setProperty()

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

inherited

Add or change a property of this DAGModel.

Definition at line 56 of file DAGmodel_inl.h.

References gum::DAGmodel::__properties(), and gum::HashTable< Key, Val, Alloc >::insert().

Referenced by gum::BayesNet< double >::fastPrototype().

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

Here is the call graph for this function:

Here is the caller graph for this function:

size()

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

inherited

Returns the number of variables in this Directed Graphical Model.

Definition at line 96 of file DAGmodel_inl.h.

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

Referenced by gum::credal::CredalNet< GUM_SCALAR >::__initCNNets(), gum::InfluenceDiagram< GUM_SCALAR >::decisionNodeSize(), gum::DAGmodel::empty(), gum::MarkovBlanket::hasSameStructure(), gum::DAGmodel::hasSameStructure(), gum::IBayesNet< double >::operator==(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), and gum::prm::ClassBayesNet< GUM_SCALAR >::toDot().

{ return dag().size(); }

Here is the call graph for this function:

Here is the caller graph for this function:

sizeArcs()

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

inherited

Returns the number of arcs in this Directed Graphical Model.

Definition at line 102 of file DAGmodel_inl.h.

References gum::DAGmodel::_dag, and gum::ArcGraphPart::sizeArcs().

Referenced by gum::MarkovBlanket::hasSameStructure(), gum::DAGmodel::hasSameStructure(), and gum::IBayesNet< double >::operator==().

{ return _dag.sizeArcs(); }

Here is the call graph for this function:

Here is the caller graph for this function:

toDot()

template<typename GUM_SCALAR >

INLINE std::string gum::prm::ClassBayesNet< GUM_SCALAR >::toDot ( ) const

virtual

Returns

Returns a dot representation of this IBayesNet.

Reimplemented from gum::IBayesNet< GUM_SCALAR >.

Definition at line 163 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__class, gum::DAGmodel::children(), gum::Variable::name(), gum::DAGmodel::nodes(), gum::DAGmodel::parents(), gum::DAGmodel::size(), and gum::prm::ClassBayesNet< GUM_SCALAR >::variable().

                                                              {
      std::string       tab = "  ";
      std::stringstream output;
      output << "digraph \"";
      output << __class->name() << "\" {" << std::endl;

      for (const auto node : this->nodes()) {
        if (this->children(node).size() > 0)
          for (const auto chi : this->children(node)) {
            output << tab << "\"" << variable(node).name() << "\" -> ";
            output << "\"" << variable(chi).name() << "\";" << std::endl;
          }
        else if (this->parents(node).size() == 0) {
          output << tab << "\"" << variable(node).name() << "\";" << std::endl;
        }
      }

      output << "}" << std::endl;
      return output.str();
    }

Here is the call graph for this function:

topologicalOrder()

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

inherited

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

Parameters

clear

If false returns the previously created topology.

Definition at line 117 of file DAGmodel.cpp.

References gum::DAGmodel::dag(), and gum::DiGraph::topologicalOrder().

Referenced by gum::EssentialGraph::__buildEssentialGraph(), gum::InfluenceDiagramGenerator< GUM_SCALAR >::__checkTemporalOrder(), gum::DAGmodel::children(), gum::InfluenceDiagram< GUM_SCALAR >::decisionOrderExists(), and gum::InfluenceDiagram< GUM_SCALAR >::getDecisionOrder().

                                                                       {
    return this->dag().topologicalOrder(clear);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

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 145 of file IBayesNet_tpl.h.

Referenced by gum::operator<<().

                                                           {
    Size   param = 0;
    double dSize = log10DomainSize();

    for (auto node : nodes())
      param += cpt(node).content()->realSize();

    double compressionRatio = log10(1.0 * param) - dSize;

    std::stringstream s;
    s << "BN{nodes: " << size() << ", arcs: " << dag().sizeArcs() << ", ";

    if (dSize > 6)
      s << "domainSize: 10^" << dSize;
    else
      s << "domainSize: " << std::round(std::pow(10.0, dSize));

    s << ", parameters: " << param << ", compression ratio: ";

    if (compressionRatio > -3)
      s << trunc(100.0 - std::pow(10.0, compressionRatio + 2.0));
    else
      s << "100-10^" << compressionRatio + 2.0;

    s << "% }";

    return s.str();
  }

Here is the caller graph for this function:

variable()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::variable().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 108 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__get().

Referenced by gum::prm::ClassBayesNet< GUM_SCALAR >::modalities(), and gum::prm::ClassBayesNet< GUM_SCALAR >::toDot().

                                                                      {
      return __get(id).type().variable();
    }

Here is the call graph for this function:

Here is the caller graph for this function:

variableFromName()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::variableFromName().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 125 of file classBayesNet_tpl.h.

References gum::prm::ClassBayesNet< GUM_SCALAR >::__get().

                                    {
      return __get(name).type().variable();
    }

Here is the call graph for this function:

variableNodeMap()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::variableNodeMap().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 102 of file classBayesNet_tpl.h.

References GUM_ERROR.

                                                                    {
      GUM_ERROR(FatalError, "Sorry no VarMap in a ClassBayesNet.");
    }

Member Data Documentation

__class

template<typename GUM_SCALAR>

const PRMClass< GUM_SCALAR >* gum::prm::ClassBayesNet< GUM_SCALAR >::__class

private

The PRMClassElementContainer decorated by this.

Definition at line 147 of file classBayesNet.h.

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

__modalities

template<typename GUM_SCALAR>

NodeProperty< Size > gum::prm::ClassBayesNet< GUM_SCALAR >::__modalities

mutableprivate

Definition at line 149 of file classBayesNet.h.

Referenced by gum::prm::ClassBayesNet< GUM_SCALAR >::modalities().

__varNodeMap

template<typename GUM_SCALAR>

HashTable< const DiscreteVariable*, const PRMClassElement< GUM_SCALAR >* > gum::prm::ClassBayesNet< GUM_SCALAR >::__varNodeMap

private

Mapping between DiscreteVariable and their NodeId.

Definition at line 134 of file classBayesNet.h.

Referenced by gum::prm::ClassBayesNet< GUM_SCALAR >::nodeId().

_dag

DAG gum::DAGmodel::_dag

protectedinherited

The DAG of this Directed Graphical Model.

Definition at line 203 of file DAGmodel.h.

Referenced by gum::prm::ClassBayesNet< GUM_SCALAR >::__get(), gum::InfluenceDiagram< GUM_SCALAR >::_addNode(), gum::InfluenceDiagram< GUM_SCALAR >::_copyTables(), gum::InfluenceDiagram< GUM_SCALAR >::_getChildrenDecision(), gum::BayesNetFragment< GUM_SCALAR >::_installArc(), gum::InfluenceDiagram< GUM_SCALAR >::_moralGraph(), gum::InfluenceDiagram< GUM_SCALAR >::_removeTables(), gum::BayesNetFragment< GUM_SCALAR >::_uninstallArc(), gum::InfluenceDiagram< GUM_SCALAR >::addArc(), gum::DAGmodel::arcs(), gum::DAGmodel::children(), gum::DAGmodel::dag(), gum::InfluenceDiagram< GUM_SCALAR >::erase(), gum::InfluenceDiagram< GUM_SCALAR >::eraseArc(), gum::InfluenceDiagram< GUM_SCALAR >::existsPathBetween(), gum::InfluenceDiagram< GUM_SCALAR >::getDecisionGraph(), gum::InfluenceDiagram< GUM_SCALAR >::getPartialTemporalOrder(), gum::BayesNetFragment< GUM_SCALAR >::installNode(), gum::DAGmodel::nodes(), gum::DAGmodel::operator=(), gum::DAGmodel::parents(), gum::DAGmodel::sizeArcs(), gum::InfluenceDiagram< GUM_SCALAR >::toDot(), and gum::BayesNetFragment< GUM_SCALAR >::uninstallNode().

---

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

• agrum/PRM/classBayesNet.h
• agrum/PRM/classBayesNet_tpl.h