gum::IBayesNet< GUM_SCALAR > Class Template Reference

Class representing the minimal interface for Bayesian Network. More...

#include <agrum/BN/IBayesNet.h>

Inheritance diagram for gum::IBayesNet< GUM_SCALAR >:

Collaboration diagram for gum::IBayesNet< 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
virtual std::string	toDot () 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 / Destructors
	IBayesNet ()
	Default constructor. More...
	IBayesNet (std::string name)
	Default constructor. More...
virtual	~IBayesNet ()
	Destructor. More...
	IBayesNet (const IBayesNet< GUM_SCALAR > &source)
	Copy constructor. More...
IBayesNet< GUM_SCALAR > &	operator= (const IBayesNet< GUM_SCALAR > &source)
	Copy operator. More...
Pure Virtual methods
virtual const Potential< GUM_SCALAR > &	cpt (NodeId varId) const =0
	Returns the CPT of a variable. More...
virtual const VariableNodeMap &	variableNodeMap () const =0
	Returns a constant reference to the VariableNodeMap of thisBN. More...
virtual const DiscreteVariable &	variable (NodeId id) const =0
	Returns a constant reference over a variable given it's node id. More...
virtual NodeId	nodeId (const DiscreteVariable &var) const =0
	Return id node from discrete var pointer. More...
virtual NodeId	idFromName (const std::string &name) const =0
	Getter by name. More...
virtual const DiscreteVariable &	variableFromName (const std::string &name) const =0
	Getter by name. More...
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::IBayesNet< GUM_SCALAR >

Class representing the minimal interface for Bayesian Network.

This class is used as a base class for different versions of Bayesian Networks. No data (except the dag herited from DAGmodel are included in this class. Many algorithms (inference for instance) may use this class when a simple BN is needed.

Definition at line 62 of file IBayesNet.h.

Constructor & Destructor Documentation

IBayesNet() [1/3]

template<typename GUM_SCALAR >

INLINE gum::IBayesNet< GUM_SCALAR >::IBayesNet

(

)

Default constructor.

Definition at line 49 of file IBayesNet_tpl.h.

                                            : DAGmodel() {
    GUM_CONSTRUCTOR(IBayesNet);
  }

IBayesNet() [2/3]

template<typename GUM_SCALAR >

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

explicit

Default constructor.

Definition at line 54 of file IBayesNet_tpl.h.

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

~IBayesNet()

template<typename GUM_SCALAR >

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

virtual

Destructor.

Definition at line 74 of file IBayesNet_tpl.h.

                                      {
    GUM_DESTRUCTOR(IBayesNet);
  }

IBayesNet() [3/3]

template<typename GUM_SCALAR>

gum::IBayesNet< GUM_SCALAR >::IBayesNet

(

const IBayesNet< GUM_SCALAR > &

source

)

Copy constructor.

Definition at line 60 of file IBayesNet_tpl.h.

                                                                          :
      DAGmodel(source) {
    GUM_CONS_CPY(IBayesNet);
  }

Member Function Documentation

__minimalCondSetVisitDn()

template<typename GUM_SCALAR >

void gum::IBayesNet< GUM_SCALAR >::__minimalCondSetVisitDn ( NodeId  node, const NodeSet &  soids, NodeSet &  minimal, NodeSet &  alreadyVisitedUp, NodeSet &  alreadyVisitedDn  ) const

private

Definition at line 332 of file IBayesNet_tpl.h.

                                            {
    if (alreadyVisitedDn.contains(node)) return;
    alreadyVisitedDn << node;

    if (soids.contains(node)) {
      minimal << node;
      for (auto fath : _dag.parents(node))
        __minimalCondSetVisitUp(
           fath, soids, minimal, alreadyVisitedUp, alreadyVisitedDn);
    } else {
      for (auto chil : _dag.children(node))
        __minimalCondSetVisitDn(
           chil, soids, minimal, alreadyVisitedUp, alreadyVisitedDn);
    }
  }

__minimalCondSetVisitUp()

template<typename GUM_SCALAR >

void gum::IBayesNet< GUM_SCALAR >::__minimalCondSetVisitUp ( NodeId  node, const NodeSet &  soids, NodeSet &  minimal, NodeSet &  alreadyVisitedUp, NodeSet &  alreadyVisitedDn  ) const

private

Definition at line 309 of file IBayesNet_tpl.h.

                                            {
    if (alreadyVisitedUp.contains(node)) return;
    alreadyVisitedUp << node;

    if (soids.contains(node)) {
      minimal << node;
    } else {
      for (auto fath : _dag.parents(node))
        __minimalCondSetVisitUp(
           fath, soids, minimal, alreadyVisitedUp, alreadyVisitedDn);
      for (auto chil : _dag.children(node))
        __minimalCondSetVisitDn(
           chil, soids, minimal, alreadyVisitedUp, alreadyVisitedDn);
    }
  }

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(); }

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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);
  }

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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));
    };

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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;
  }

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cpt()

template<typename GUM_SCALAR>

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

pure virtual

Returns the CPT of a variable.

Exceptions

NotFound

If no variable's id matches varId.

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

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__verticesSampling(), gum::BarrenNodesFinder::barrenPotentials(), and gum::IBayesNet< double >::operator==().

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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 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; }

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dim()

template<typename GUM_SCALAR >

Size gum::IBayesNet< GUM_SCALAR >::dim

(

)

const

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

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

Definition at line 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; }

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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 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;
  }

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idFromName()

template<typename GUM_SCALAR>

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

pure virtual

Getter by name.

Exceptions

NotFound

if no such name exists in the graph.

Implements gum::DAGmodel.

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

Referenced by gum::IBayesNet< double >::operator==().

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jointProbability()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::jointProbability

(

const Instantiation &

i

)

const

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

Warning

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

Definition at line 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;
  }

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log2JointProbability()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::log2JointProbability

(

const Instantiation &

i

)

const

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

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

Warning

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

Definition at line 239 of file IBayesNet_tpl.h.

                                                                               {
    auto value = (GUM_SCALAR)0.0;

    GUM_SCALAR tmp;

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

      value += log2(cpt(node)[i]);
    }

    return value;
  }

maxNonOneParam()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::maxNonOneParam

(

)

const

Returns

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

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

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

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;
  }

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minimalCondSet() [1/2]

template<typename GUM_SCALAR >

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

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

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

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;
  }

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minParam()

template<typename GUM_SCALAR >

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::minParam

(

)

const

Returns

the smallest value in the CPTs of *this

Definition at line 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;
  }

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;
  }

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nodeId()

template<typename GUM_SCALAR>

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

pure virtual

Return id node from discrete var pointer.

Exceptions

NotFound

If no variable matches var.

Implements gum::DAGmodel.

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

Referenced by gum::BayesBall::relevantPotentials(), and gum::dSeparation::relevantPotentials().

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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;
  }

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operator!=()

template<typename GUM_SCALAR>

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

(

const IBayesNet< GUM_SCALAR > &

from

)

const

Returns

Returns false if the src and this are equal.

Definition at line 303 of file IBayesNet_tpl.h.

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

operator=()

template<typename GUM_SCALAR>

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

(

const IBayesNet< GUM_SCALAR > &

source

)

Copy operator.

Definition at line 67 of file IBayesNet_tpl.h.

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

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

    return *this;
  }

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operator==()

template<typename GUM_SCALAR>

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

(

const IBayesNet< GUM_SCALAR > &

from

)

const

This operator compares 2 BNs !

Warning

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

Returns

true if the src and this are equal.

Definition at line 256 of file IBayesNet_tpl.h.

                                                                      {
    if (size() != from.size()) { return false; }

    if (sizeArcs() != from.sizeArcs()) { return false; }

    // 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);
  }

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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));
    };

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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);
    }
  }

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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; }
  }

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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); }
  }

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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(); }

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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 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(); }

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toDot()

template<typename GUM_SCALAR >

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

virtual

Returns

Returns a dot representation of this IBayesNet.

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

Definition at line 175 of file IBayesNet_tpl.h.

                                                 {
    std::stringstream output;
    output << "digraph \"";

    std::string bn_name;

    try {
      bn_name = this->property("name");
    } catch (NotFound&) { bn_name = "no_name"; }

    output << bn_name << "\" {" << std::endl;
    output << "  graph [bgcolor=transparent,label=\"" << bn_name << "\"];"
           << std::endl;
    output << "  node [style=filled fillcolor=\"#ffffaa\"];" << std::endl
           << std::endl;

    for (auto node : nodes())
      output << "\"" << variable(node).name() << "\" [comment=\"" << node << ":"
             << variable(node).toStringWithDescription() << "\"];" << std::endl;

    output << std::endl;

    std::string tab = "  ";

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

    output << "}" << std::endl;

    return output.str();
  }

topologicalOrder()

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);
  }

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toString()

template<typename GUM_SCALAR >

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

(

)

const

Returns

Returns a string representation of this IBayesNet.

Definition at line 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();
  }

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variable()

template<typename GUM_SCALAR>

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

pure virtual

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

Exceptions

NotFound

If no variable's id matches varId.

Implements gum::DAGmodel.

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

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__insertEvidence(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__verticesSampling(), gum::Estimator< GUM_SCALAR >::Estimator(), and gum::Estimator< GUM_SCALAR >::setFromBN().

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variableFromName()

template<typename GUM_SCALAR>

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

pure virtual

Getter by name.

Exceptions

NotFound

if no such name exists in the graph.

Implements gum::DAGmodel.

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

Referenced by gum::IBayesNet< double >::operator==().

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variableNodeMap()

template<typename GUM_SCALAR>

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

pure virtual

Returns a constant reference to the VariableNodeMap of thisBN.

Implements gum::DAGmodel.

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

Member Data Documentation

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