aGrUM  0.18.1
a C++ library for (probabilistic) graphical models
gum::IBayesNet< GUM_SCALAR > Class Template Referenceabstract

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
 
bool hasSameStructure (const DAGmodel &other)
 
double log10DomainSize () const
 
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 VariableNodeMapvariableNodeMap () const =0
 Returns a constant reference to the VariableNodeMap of thisBN. More...
 
virtual const DiscreteVariablevariable (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 DiscreteVariablevariableFromName (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...
 
Variable manipulation methods.
const DAGdag () const
 Returns a constant reference to the dag of this Bayes Net. More...
 
virtual Size size () const final
 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...
 
const NodeGraphPartnodes () const
 Returns a constant reference to the dag of this Bayes Net. More...
 
Arc manipulation methods.
const ArcSetarcs () const
 return true if the arc tail->head exists in the DAGmodel More...
 
bool existsArc (const NodeId tail, const NodeId head) const
 return true if the arc tail->head exists in the DAGmodel More...
 
bool existsArc (const std::string &nametail, const std::string &namehead) const
 return true if the arc tail->head exists in the DAGmodel More...
 
const NodeSetparents (const NodeId id) const
 returns the set of nodes with arc ingoing to a given node More...
 
const NodeSetparents (const std::string &name) const
 return true if the arc tail->head exists in the DAGmodel More...
 
const NodeSetchildren (const NodeId id) const
 returns the set of nodes with arc outgoing from a given node More...
 
const NodeSetchildren (const std::string &name) const
 return true if the arc tail->head exists in the DAGmodel More...
 
NodeSet descendants (const NodeId id) const
 returns the set of nodes with directed path outgoing from a given node More...
 
NodeSet descendants (const std::string &name) const
 return true if the arc tail->head exists in the DAGmodel More...
 
NodeSet ancestors (const NodeId id) const
 returns the set of nodes with directed path ingoing to a given node More...
 
NodeSet ancestors (const std::string &name) const
 return true if the arc tail->head exists in the DAGmodel More...
 
Graphical methods
UndiGraph moralizedAncestralGraph (const NodeSet &nodes) const
 build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes More...
 
UndiGraph moralizedAncestralGraph (const std::vector< std::string > &nodenames) const
 build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes More...
 
bool isIndependent (NodeId X, NodeId Y, const NodeSet &Z) const
 check if X and Y are independent given Z More...
 
bool isIndependent (const std::string &Xname, const std::string &Yname, const std::vector< std::string > &Zanmes) const
 build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes More...
 
const UndiGraphmoralGraph (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...
 
Getter and setters
const std::string & property (const std::string &name) const
 Return the value of the property name of this GraphicalModel. More...
 
const std::string & propertyWithDefault (const std::string &name, const std::string &byDefault) const
 Return the value of the property name of this GraphicalModel. More...
 
void setProperty (const std::string &name, const std::string &value)
 Add or change a property of this GraphicalModel. More...
 
Variable manipulation methods.
virtual bool empty () const
 Retursn true if this Directed Graphical Model is empty. More...
 
Instantiation completeInstantiation () const
 Get an instantiation over all the variables of the model. 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.

49  : DAGmodel() {
50  GUM_CONSTRUCTOR(IBayesNet);
51  }
DAGmodel()
Default constructor.
Definition: DAGmodel.cpp:30
IBayesNet()
Default constructor.
Definition: IBayesNet_tpl.h:49

◆ 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.

54  : DAGmodel() {
55  GUM_CONSTRUCTOR(IBayesNet);
56  this->setProperty("name", name);
57  }
void setProperty(const std::string &name, const std::string &value)
Add or change a property of this GraphicalModel.
DAGmodel()
Default constructor.
Definition: DAGmodel.cpp:30
IBayesNet()
Default constructor.
Definition: IBayesNet_tpl.h:49

◆ ~IBayesNet()

template<typename GUM_SCALAR >
gum::IBayesNet< GUM_SCALAR >::~IBayesNet ( )
virtual

Destructor.

Definition at line 74 of file IBayesNet_tpl.h.

74  {
75  GUM_DESTRUCTOR(IBayesNet);
76  }
IBayesNet()
Default constructor.
Definition: IBayesNet_tpl.h:49

◆ 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.

60  :
61  DAGmodel(source) {
62  GUM_CONS_CPY(IBayesNet);
63  }
DAGmodel()
Default constructor.
Definition: DAGmodel.cpp:30
IBayesNet()
Default constructor.
Definition: IBayesNet_tpl.h:49

Member Function Documentation

◆ ancestors() [1/2]

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
idthe node which is the head of a directed path with the returned nodes
namethe name of the node which is the head of a directed path with the returned nodes

Definition at line 145 of file DAGmodel.cpp.

References gum::Set< Key, Alloc >::begin(), gum::Set< Key, Alloc >::contains(), gum::Set< Key, Alloc >::empty(), gum::Set< Key, Alloc >::erase(), gum::Set< Key, Alloc >::insert(), and gum::DAGmodel::parents().

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

145  {
146  NodeSet res;
147  NodeSet tmp;
148  for (auto next: parents(id))
149  tmp.insert(next);
150 
151  while (!tmp.empty()) {
152  auto current = *(tmp.begin());
153  tmp.erase(current);
154  res.insert(current);
155  for (auto next: parents(current)) {
156  if (!tmp.contains(next) && !res.contains(next)) { tmp.insert(next); }
157  }
158  }
159  return res;
160  }
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:55
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
void insert(const Key &k)
Inserts a new element into the set.
Definition: set_tpl.h:613
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◆ ancestors() [2/2]

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

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

Parameters
tailthe nodeId (or the name) of the tail in tail->head
headthe nodeId (or the name) of the head in tail->head
Returns
true if the arc exists

Definition at line 162 of file DAGmodel.cpp.

References gum::DAGmodel::ancestors(), and gum::DAGmodel::idFromName().

162  {
163  return ancestors(idFromName(name));
164  }
virtual NodeId idFromName(const std::string &name) const =0
Getter by name.
NodeSet ancestors(const NodeId id) const
returns the set of nodes with directed path ingoing to a given node
Definition: DAGmodel.cpp:145
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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
tailthe nodeId (or the name) of the tail in tail->head
headthe nodeId (or the name) of the head in tail->head
Returns
true if the arc exists

Definition at line 44 of file DAGmodel_inl.h.

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

Referenced by gum::EssentialGraph::buildEssentialGraph__(), gum::MarkovBlanket::hasSameStructure(), gum::DAGmodel::hasSameStructure(), gum::DAGmodel::moralGraph__(), and gum::BayesNetFragment< GUM_SCALAR >::toBN().

44 { return dag_.arcs(); }
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
const ArcSet & arcs() const
returns the set of arcs stored within the ArcGraphPart
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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 nodes returned may be empty if no node is outgoing from the given node.

Parameters
idthe node which is the tail of an arc with the returned nodes
namethe name of the node which is the tail of an arc with the returned nodes

Definition at line 63 of file DAGmodel_inl.h.

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

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

63  {
64  return dag_.children(id);
65  }
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
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◆ children() [2/2]

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

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

Parameters
tailthe nodeId (or the name) of the tail in tail->head
headthe nodeId (or the name) of the head in tail->head
Returns
true if the arc exists

Definition at line 66 of file DAGmodel_inl.h.

References gum::ArcGraphPart::children(), gum::DAGmodel::dag_, and gum::DAGmodel::idFromName().

66  {
67  return dag_.children(idFromName(name));
68  }
virtual NodeId idFromName(const std::string &name) const =0
Getter by name.
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
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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 85 of file graphicalModel_inl.h.

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

85  {
86  Instantiation I;
87 
88  for (const auto node: nodes())
89  I << variable(node);
90 
91  return I;
92  }
virtual const NodeGraphPart & nodes() const =0
Returns a constant reference to the VariableNodeMap of this Graphical Model.
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variable given it&#39;s node id.
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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
NotFoundIf 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::BarrenNodesFinder::barrenPotentials(), gum::IBayesNet< double >::operator==(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::verticesSampling__().

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◆ dag()

◆ descendants() [1/2]

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
idthe node which is the tail of a directed path with the returned nodes
namethe name of the node which is the tail of a directed path with the returned nodes

Definition at line 124 of file DAGmodel.cpp.

References gum::Set< Key, Alloc >::begin(), gum::DAGmodel::children(), gum::Set< Key, Alloc >::contains(), gum::Set< Key, Alloc >::empty(), gum::Set< Key, Alloc >::erase(), and gum::Set< Key, Alloc >::insert().

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

124  {
125  NodeSet res;
126  NodeSet tmp;
127  for (auto next: children(id))
128  tmp.insert(next);
129 
130  while (!tmp.empty()) {
131  auto current = *(tmp.begin());
132  tmp.erase(current);
133  res.insert(current);
134  for (auto next: children(current)) {
135  if (!tmp.contains(next) && !res.contains(next)) { tmp.insert(next); }
136  }
137  }
138  return res;
139  }
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
Definition: DAGmodel_inl.h:63
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
void insert(const Key &k)
Inserts a new element into the set.
Definition: set_tpl.h:613
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◆ descendants() [2/2]

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

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

Parameters
tailthe nodeId (or the name) of the tail in tail->head
headthe nodeId (or the name) of the head in tail->head
Returns
true if the arc exists

Definition at line 141 of file DAGmodel.cpp.

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

141  {
142  return descendants(idFromName(name));
143  }
virtual NodeId idFromName(const std::string &name) const =0
Getter by name.
NodeSet descendants(const NodeId id) const
returns the set of nodes with directed path outgoing from a given node
Definition: DAGmodel.cpp:124
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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.

79  {
80  Size dim = 0;
81 
82  for (auto node: nodes()) {
83  Size q = 1;
84 
85  for (auto parent: parents(node))
86  q *= variable(parent).domainSize();
87 
88  dim += (variable(node).domainSize() - 1) * q;
89  }
90 
91  return dim;
92  }
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:55
virtual Size domainSize() const =0
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variable given it&#39;s node id.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70
Size dim() const
Returns the dimension (the number of free parameters) in this bayes net.
Definition: IBayesNet_tpl.h:79
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition: types.h:48

◆ empty()

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

Retursn true if this Directed Graphical Model is empty.

Definition at line 95 of file graphicalModel_inl.h.

References gum::GraphicalModel::size().

95 { return size() == 0; }
virtual Size size() const =0
Returns the number of variables in this Directed Graphical Model.
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◆ 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
tailthe nodeId (or the name) of the tail in tail->head
headthe nodeId (or the name) of the head in tail->head
Returns
true if the arc exists

Definition at line 46 of file DAGmodel_inl.h.

References gum::DAGmodel::dag_, and gum::ArcGraphPart::existsArc().

Referenced by gum::DAGmodel::existsArc(), gum::BayesNetFragment< GUM_SCALAR >::toDot(), and gum::BayesNetFragment< GUM_SCALAR >::whenArcDeleted().

46  {
47  return dag_.existsArc(tail, head);
48  }
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
bool existsArc(const Arc &arc) const
indicates whether a given arc exists
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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
tailthe nodeId (or the name) of the tail in tail->head
headthe nodeId (or the name) of the head in tail->head
Returns
true if the arc exists

Definition at line 50 of file DAGmodel_inl.h.

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

51  {
52  return existsArc(idFromName(nametail), idFromName(namehead));
53  }
bool existsArc(const NodeId tail, const NodeId head) const
return true if the arc tail->head exists in the DAGmodel
Definition: DAGmodel_inl.h:46
virtual NodeId idFromName(const std::string &name) const =0
Getter by name.
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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 102 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().

102  {
103  if (this == &other) return true;
104 
105  if (size() != other.size()) return false;
106 
107  if (sizeArcs() != other.sizeArcs()) return false;
108 
109  for (const auto& nid: nodes()) {
110  try {
111  other.idFromName(variable(nid).name());
112  } catch (NotFound) { return false; }
113  }
114 
115  for (const auto& arc: arcs()) {
116  if (!other.arcs().exists(Arc(other.idFromName(variable(arc.tail()).name()),
117  other.idFromName(variable(arc.head()).name()))))
118  return false;
119  }
120 
121  return true;
122  }
const ArcSet & arcs() const
return true if the arc tail->head exists in the DAGmodel
Definition: DAGmodel_inl.h:44
Size sizeArcs() const
Returns the number of arcs in this Directed Graphical Model.
Definition: DAGmodel_inl.h:42
virtual Size size() const final
Returns the number of variables in this Directed Graphical Model.
Definition: DAGmodel_inl.h:39
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variable given it&#39;s node id.
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◆ idFromName()

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

◆ isIndependent() [1/2]

bool gum::DAGmodel::isIndependent ( NodeId  X,
NodeId  Y,
const NodeSet Z 
) const
inherited

check if X and Y are independent given Z

Definition at line 200 of file DAGmodel.cpp.

References gum::DAGmodel::moralizedAncestralGraph().

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

200  {
201  NodeSet cumul{Z};
202  cumul << X << Y;
203  auto g = moralizedAncestralGraph(cumul);
204  for (auto node: Z)
205  g.eraseNode(node);
206  return !g.hasUndirectedPath(X, Y);
207  }
UndiGraph moralizedAncestralGraph(const NodeSet &nodes) const
build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
Definition: DAGmodel.cpp:166
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
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◆ isIndependent() [2/2]

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

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

Parameters
nodesthe set of nodeId
nodenamesthe vector of names of nodes
Returns
the moralized ancestral graph

Definition at line 209 of file DAGmodel.cpp.

References gum::DAGmodel::idFromName(), gum::Set< Key, Alloc >::insert(), and gum::DAGmodel::isIndependent().

211  {
212  NodeSet Z;
213  for (const auto& name: Znames)
214  Z.insert(idFromName(name));
215  return isIndependent(idFromName(Xname), idFromName(Yname), Z);
216  }
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
virtual NodeId idFromName(const std::string &name) const =0
Getter by name.
bool isIndependent(NodeId X, NodeId Y, const NodeSet &Z) const
check if X and Y are independent given Z
Definition: DAGmodel.cpp:200
void insert(const Key &k)
Inserts a new element into the set.
Definition: set_tpl.h:613
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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 211 of file IBayesNet_tpl.h.

211  {
212  auto value = (GUM_SCALAR)1.0;
213 
214  GUM_SCALAR tmp;
215 
216  for (auto node: nodes()) {
217  if ((tmp = cpt(node)[i]) == (GUM_SCALAR)0) { return (GUM_SCALAR)0; }
218 
219  value *= tmp;
220  }
221 
222  return value;
223  }
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70

◆ log10DomainSize()

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

Definition at line 74 of file graphicalModel_inl.h.

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

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

74  {
75  double dSize = 0.0;
76 
77  for (const auto node: nodes()) {
78  dSize += std::log10(variable(node).domainSize());
79  }
80 
81  return dSize;
82  }
virtual const NodeGraphPart & nodes() const =0
Returns a constant reference to the VariableNodeMap of this Graphical Model.
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variable given it&#39;s node id.
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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 230 of file IBayesNet_tpl.h.

230  {
231  auto value = (GUM_SCALAR)0.0;
232 
233  GUM_SCALAR tmp;
234 
235  for (auto node: nodes()) {
236  if ((tmp = cpt(node)[i]) == (GUM_SCALAR)0) {
237  return (GUM_SCALAR)(-std::numeric_limits< double >::infinity());
238  }
239 
240  value += log2(cpt(node)[i]);
241  }
242 
243  return value;
244  }
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70

◆ maxNonOneParam()

template<typename GUM_SCALAR >
GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::maxNonOneParam ( ) const
Returns
the biggest value (not equal to 1) in the CPTs of *this
Warning
can return one if no other value in the CPTs than one....

Definition at line 135 of file IBayesNet_tpl.h.

135  {
136  GUM_SCALAR res = 0.0;
137  for (auto node: nodes()) {
138  auto v = cpt(node).maxNonOne();
139  if (v > res) { res = v; }
140  }
141  return res;
142  }
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70

◆ 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.

115  {
116  GUM_SCALAR res = 1.0;
117  for (auto node: nodes()) {
118  auto v = cpt(node).max();
119  if (v > res) { res = v; }
120  }
121  return res;
122  }
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70

◆ 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_().

95  {
96  Size res = 0;
97  for (auto node: nodes()) {
98  auto v = variable(node).domainSize();
99  if (v > res) { res = v; }
100  }
101  return res;
102  }
virtual Size domainSize() const =0
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variable given it&#39;s node id.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition: types.h:48
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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 346 of file IBayesNet_tpl.h.

347  {
348  if (soids.contains(target)) return NodeSet({target});
349 
350  NodeSet res;
351  NodeSet alreadyVisitedUp;
352  NodeSet alreadyVisitedDn;
353  alreadyVisitedDn << target;
354  alreadyVisitedUp << target;
355 
356  for (auto fath: dag_.parents(target))
358  fath, soids, res, alreadyVisitedUp, alreadyVisitedDn);
359  for (auto chil: dag_.children(target))
361  chil, soids, res, alreadyVisitedUp, alreadyVisitedDn);
362  return res;
363  }
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
void minimalCondSetVisitDn__(NodeId node, const NodeSet &soids, NodeSet &minimal, NodeSet &alreadyVisitedUp, NodeSet &alreadyVisitedDn) const
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
void minimalCondSetVisitUp__(NodeId node, const NodeSet &soids, NodeSet &minimal, NodeSet &alreadyVisitedUp, NodeSet &alreadyVisitedDn) const

◆ minimalCondSet() [2/2]

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

Definition at line 366 of file IBayesNet_tpl.h.

367  {
368  NodeSet res;
369  for (auto node: targets) {
370  res += minimalCondSet(node, soids);
371  }
372  return res;
373  }
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
NodeSet minimalCondSet(NodeId target, const NodeSet &soids) const

◆ 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 323 of file IBayesNet_tpl.h.

328  {
329  if (alreadyVisitedDn.contains(node)) return;
330  alreadyVisitedDn << node;
331 
332  if (soids.contains(node)) {
333  minimal << node;
334  for (auto fath: dag_.parents(node))
336  fath, soids, minimal, alreadyVisitedUp, alreadyVisitedDn);
337  } else {
338  for (auto chil: dag_.children(node))
340  chil, soids, minimal, alreadyVisitedUp, alreadyVisitedDn);
341  }
342  }
void minimalCondSetVisitDn__(NodeId node, const NodeSet &soids, NodeSet &minimal, NodeSet &alreadyVisitedUp, NodeSet &alreadyVisitedDn) const
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
void minimalCondSetVisitUp__(NodeId node, const NodeSet &soids, NodeSet &minimal, NodeSet &alreadyVisitedUp, NodeSet &alreadyVisitedDn) const

◆ 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 300 of file IBayesNet_tpl.h.

305  {
306  if (alreadyVisitedUp.contains(node)) return;
307  alreadyVisitedUp << node;
308 
309  if (soids.contains(node)) {
310  minimal << node;
311  } else {
312  for (auto fath: dag_.parents(node))
314  fath, soids, minimal, alreadyVisitedUp, alreadyVisitedDn);
315  for (auto chil: dag_.children(node))
317  chil, soids, minimal, alreadyVisitedUp, alreadyVisitedDn);
318  }
319  }
void minimalCondSetVisitDn__(NodeId node, const NodeSet &soids, NodeSet &minimal, NodeSet &alreadyVisitedUp, NodeSet &alreadyVisitedDn) const
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
void minimalCondSetVisitUp__(NodeId node, const NodeSet &soids, NodeSet &minimal, NodeSet &alreadyVisitedUp, NodeSet &alreadyVisitedDn) const

◆ minNonZeroParam()

template<typename GUM_SCALAR >
GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::minNonZeroParam ( ) const
Returns
the smallest value (not equal to 0) in the CPTs of *this
Warning
can return 0 if no other value in the CPTs than 0...

Definition at line 125 of file IBayesNet_tpl.h.

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

125  {
126  GUM_SCALAR res = 1.0;
127  for (auto node: nodes()) {
128  auto v = cpt(node).minNonZero();
129  if (v < res) { res = v; }
130  }
131  return res;
132  }
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70
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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.

105  {
106  GUM_SCALAR res = 1.0;
107  for (auto node: nodes()) {
108  auto v = cpt(node).min();
109  if (v < res) { res = v; }
110  }
111  return res;
112  }
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70

◆ 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
clearIf false returns the previously created moral graph.

Definition at line 82 of file DAGmodel.cpp.

References gum::UndiGraph::clear(), gum::DAGmodel::moralGraph__(), and gum::DAGmodel::mutableMoralGraph__.

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__(), and gum::prm::SVE< GUM_SCALAR >::initLiftedNodes__().

82  {
83  if (clear
84  || (mutableMoralGraph__ == nullptr)) { // we have to call moralGraph_
85  if (mutableMoralGraph__ == nullptr) {
86  mutableMoralGraph__ = new UndiGraph();
87  } else {
88  // clear is True ,__mutableMoralGraph exists
90  }
91 
92  moralGraph__();
93  }
94 
95  return *mutableMoralGraph__;
96  }
virtual void clear()
removes all the nodes and edges from the graph
Definition: undiGraph_inl.h:47
UndiGraph * mutableMoralGraph__
The moral graph of this Directed Graphical Model.
Definition: DAGmodel.h:221
void moralGraph__() const
Returns the moral graph of this DAGModel.
Definition: DAGmodel.cpp:44
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◆ moralizedAncestralGraph() [1/2]

UndiGraph gum::DAGmodel::moralizedAncestralGraph ( const NodeSet nodes) const
inherited

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

Parameters
nodesthe set of nodeId
nodenamesthe vector of names of nodes
Returns
the moralized ancestral graph

Definition at line 166 of file DAGmodel.cpp.

References gum::NodeGraphPart::addNodeWithId(), gum::Set< Key, Alloc >::erase(), gum::NodeGraphPart::exists(), and gum::DAGmodel::parents().

Referenced by gum::DAGmodel::isIndependent(), and gum::DAGmodel::moralizedAncestralGraph().

166  {
167  UndiGraph res;
168  NodeSet tmp{nodes};
169 
170  // findings all nodes
171  while (!tmp.empty()) {
172  auto current = *(tmp.begin());
173  tmp.erase(current);
174 
175  res.addNodeWithId(current);
176  for (auto next: parents(current))
177  if (!tmp.contains(next) && !res.exists(next)) tmp.insert(next);
178  }
179 
180  // finding all edges and moralizing
181  for (auto current: res)
182  for (auto father: parents(current)) {
183  res.addEdge(current,
184  father); // addEdge does not complain if edge already exists
185  for (auto other_father: parents(current))
186  if (other_father != father) res.addEdge(father, other_father);
187  }
188 
189  return res;
190  }
virtual void addNodeWithId(const NodeId id)
try to insert a node with the given id
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:55
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70
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◆ moralizedAncestralGraph() [2/2]

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
nodesthe set of nodeId
nodenamesthe vector of names of nodes
Returns
the moralized ancestral graph

Definition at line 192 of file DAGmodel.cpp.

References gum::DAGmodel::idFromName(), gum::Set< Key, Alloc >::insert(), gum::DAGmodel::moralizedAncestralGraph(), and gum::DAGmodel::nodes().

193  {
194  NodeSet nodes;
195  for (const auto& name: nodenames)
196  nodes.insert(idFromName(name));
197  return moralizedAncestralGraph(nodes);
198  }
UndiGraph moralizedAncestralGraph(const NodeSet &nodes) const
build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
Definition: DAGmodel.cpp:166
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
virtual NodeId idFromName(const std::string &name) const =0
Getter by name.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70
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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
NotFoundIf 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
virtualinherited

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

Implements gum::GraphicalModel.

Definition at line 70 of file DAGmodel_inl.h.

References gum::DAGmodel::dag_.

Referenced by gum::BayesNetFactory< GUM_SCALAR >::BayesNetFactory(), gum::credal::CredalNet< GUM_SCALAR >::bnCopy__(), gum::build_node(), gum::EssentialGraph::buildEssentialGraph__(), gum::MarkovBlanket::buildMarkovBlanket__(), gum::BayesNetFragment< GUM_SCALAR >::checkConsistency(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), gum::Estimator< GUM_SCALAR >::Estimator(), gum::AggregatorDecomposition< GUM_SCALAR >::getDecomposedAggregator(), gum::getMaxModality(), gum::DAGmodel::hasSameStructure(), gum::prm::InstanceBayesNet< GUM_SCALAR >::modalities(), gum::prm::ClassBayesNet< GUM_SCALAR >::modalities(), gum::DAGmodel::moralGraph__(), gum::DAGmodel::moralizedAncestralGraph(), gum::Estimator< GUM_SCALAR >::setFromBN(), gum::credal::CredalNet< GUM_SCALAR >::sort_varType__(), gum::BayesNetFragment< GUM_SCALAR >::toBN(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), gum::credal::CredalNet< GUM_SCALAR >::toString(), gum::ImportanceSampling< GUM_SCALAR >::unsharpenBN_(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::verticesSampling__(), and gum::BayesNetFragment< GUM_SCALAR >::~BayesNetFragment().

70  {
71  return (NodeGraphPart&)dag_;
72  }
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
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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 294 of file IBayesNet_tpl.h.

294  {
295  return !this->operator==(from);
296  }
bool operator==(const IBayesNet< GUM_SCALAR > &from) const
This operator compares 2 BNs !

◆ 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=().

67  {
68  if (this != &source) { DAGmodel::operator=(source); }
69 
70  return *this;
71  }
DAGmodel & operator=(const DAGmodel &source)
Private copy operator.
Definition: DAGmodel.cpp:68
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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 247 of file IBayesNet_tpl.h.

247  {
248  if (size() != from.size()) { return false; }
249 
250  if (sizeArcs() != from.sizeArcs()) { return false; }
251 
252  // alignment of variables between the 2 BNs
253  Bijection< const DiscreteVariable*, const DiscreteVariable* > alignment;
254 
255  for (auto node: nodes()) {
256  try {
257  alignment.insert(&variable(node),
258  &from.variableFromName(variable(node).name()));
259  } catch (NotFound&) {
260  // a name is not found in from
261  return false;
262  }
263  }
264 
265  for (auto node: nodes()) {
266  NodeId fromnode = from.idFromName(variable(node).name());
267 
268  if (cpt(node).nbrDim() != from.cpt(fromnode).nbrDim()) { return false; }
269 
270  if (cpt(node).domainSize() != from.cpt(fromnode).domainSize()) {
271  return false;
272  }
273 
274  Instantiation i(cpt(node));
275  Instantiation j(from.cpt(fromnode));
276 
277  for (i.setFirst(); !i.end(); i.inc()) {
278  for (Idx indice = 0; indice < cpt(node).nbrDim(); ++indice) {
279  const DiscreteVariable* p = &(i.variable(indice));
280  j.chgVal(*(alignment.second(p)), i.val(*p));
281  }
282 
283  if (std::pow(cpt(node).get(i) - from.cpt(fromnode).get(j), (GUM_SCALAR)2)
284  > (GUM_SCALAR)1e-6) {
285  return false;
286  }
287  }
288  }
289 
290  return true;
291  }
Size sizeArcs() const
Returns the number of arcs in this Directed Graphical Model.
Definition: DAGmodel_inl.h:42
virtual Size size() const final
Returns the number of variables in this Directed Graphical Model.
Definition: DAGmodel_inl.h:39
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variable given it&#39;s node id.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70
Size NodeId
Type for node ids.
Definition: graphElements.h:98

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

Parameters
idthe node which is the head of an arc with the returned nodes
namethe name of the node the node which is the head of an arc with the returned nodes

Definition at line 55 of file DAGmodel_inl.h.

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

Referenced by gum::DAGmodel::ancestors(), gum::MarkovBlanket::buildMarkovBlanket__(), gum::BayesNetFragment< GUM_SCALAR >::checkConsistency(), gum::AggregatorDecomposition< GUM_SCALAR >::decomposeAggregator_(), gum::BayesNetFragment< GUM_SCALAR >::installCPT(), gum::BayesNetFragment< GUM_SCALAR >::installCPT_(), gum::DAGmodel::moralGraph__(), gum::DAGmodel::moralizedAncestralGraph(), gum::DAGmodel::parents(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), and gum::prm::ClassBayesNet< GUM_SCALAR >::toDot().

55  {
56  return dag_.parents(id);
57  }
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
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◆ parents() [2/2]

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

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

Parameters
tailthe nodeId (or the name) of the tail in tail->head
headthe nodeId (or the name) of the head in tail->head
Returns
true if the arc exists

Definition at line 59 of file DAGmodel_inl.h.

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

59  {
60  return parents(idFromName(name));
61  }
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:55
virtual NodeId idFromName(const std::string &name) const =0
Getter by name.
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◆ 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
NotFoundRaised if no name property is found.

Definition at line 37 of file graphicalModel_inl.h.

References GUM_ERROR, and gum::GraphicalModel::properties__().

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

37  {
38  try {
39  return properties__()[name];
40  } catch (NotFound&) {
41  std::string msg = "The following property does not exists: ";
42  GUM_ERROR(NotFound, msg + name);
43  }
44  }
#define GUM_ERROR(type, msg)
Definition: exceptions.h:55
HashTable< std::string, std::string > & properties__() const
Return the properties of this Directed Graphical Model and initialize the hash table is necessary...
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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 57 of file graphicalModel_inl.h.

References gum::GraphicalModel::properties__().

Referenced by gum::build_node().

58  {
59  try {
60  return properties__()[name];
61  } catch (NotFound&) { return byDefault; }
62  }
HashTable< std::string, std::string > & properties__() const
Return the properties of this Directed Graphical Model and initialize the hash table is necessary...
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◆ 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 65 of file graphicalModel_inl.h.

References gum::HashTable< Key, Val, Alloc >::insert(), and gum::GraphicalModel::properties__().

Referenced by gum::BayesNet< double >::fastPrototype(), and gum::IMarkovNet< GUM_SCALAR >::IMarkovNet().

66  {
67  try {
68  properties__()[name] = value;
69  } catch (NotFound&) { properties__().insert(name, value); }
70  }
value_type & insert(const Key &key, const Val &val)
Adds a new element (actually a copy of this element) into the hash table.
HashTable< std::string, std::string > & properties__() const
Return the properties of this Directed Graphical Model and initialize the hash table is necessary...
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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 39 of file DAGmodel_inl.h.

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

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

39 { return dag().size(); }
Size size() const
alias for sizeNodes
const DAG & dag() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:36
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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 42 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==().

42 { return dag_.sizeArcs(); }
Size sizeArcs() const
indicates the number of arcs stored within the ArcGraphPart
DAG dag_
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:213
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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 166 of file IBayesNet_tpl.h.

166  {
167  std::stringstream output;
168  output << "digraph \"";
169 
170  std::string bn_name;
171 
172  try {
173  bn_name = this->property("name");
174  } catch (NotFound&) { bn_name = "no_name"; }
175 
176  output << bn_name << "\" {" << std::endl;
177  output << " graph [bgcolor=transparent,label=\"" << bn_name << "\"];"
178  << std::endl;
179  output << " node [style=filled fillcolor=\"#ffffaa\"];" << std::endl
180  << std::endl;
181 
182  for (auto node: nodes())
183  output << "\"" << variable(node).name() << "\" [comment=\"" << node << ":"
184  << variable(node).toStringWithDescription() << "\"];" << std::endl;
185 
186  output << std::endl;
187 
188  std::string tab = " ";
189 
190  for (auto node: nodes()) {
191  if (children(node).size() > 0) {
192  for (auto child: children(node)) {
193  output << tab << "\"" << variable(node).name() << "\" -> "
194  << "\"" << variable(child).name() << "\";" << std::endl;
195  }
196  } else if (parents(node).size() == 0) {
197  output << tab << "\"" << variable(node).name() << "\";" << std::endl;
198  }
199  }
200 
201  output << "}" << std::endl;
202 
203  return output.str();
204  }
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
Definition: DAGmodel_inl.h:63
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:55
virtual Size size() const final
Returns the number of variables in this Directed Graphical Model.
Definition: DAGmodel_inl.h:39
const std::string toStringWithDescription() const
string version of *this using description attribute instead of name.
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variable given it&#39;s node id.
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70
const std::string & name() const
returns the name of the variable
const std::string & property(const std::string &name) const
Return the value of the property name of this GraphicalModel.

◆ 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
clearIf false returns the previously created topology.

Definition at line 98 of file DAGmodel.cpp.

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

Referenced by gum::EssentialGraph::buildEssentialGraph__(), gum::InfluenceDiagramGenerator< GUM_SCALAR >::checkTemporalOrder__(), gum::InfluenceDiagram< GUM_SCALAR >::decisionOrderExists(), and gum::InfluenceDiagram< GUM_SCALAR >::getDecisionOrder().

98  {
99  return this->dag().topologicalOrder(clear);
100  }
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 topol...
Definition: diGraph.cpp:91
const DAG & dag() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:36
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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<<().

145  {
146  Size param = 0;
147  double dSize = log10DomainSize();
148 
149  for (auto node: nodes())
150  param += cpt(node).content()->realSize();
151 
152  std::stringstream s;
153  s << "BN{nodes: " << size() << ", arcs: " << dag().sizeArcs() << ", ";
154 
155  if (dSize > 6)
156  s << "domainSize: 10^" << dSize;
157  else
158  s << "domainSize: " << std::round(std::pow(10.0, dSize));
159 
160  s << ", dim: " << param << "}";
161 
162  return s.str();
163  }
virtual Size size() const final
Returns the number of variables in this Directed Graphical Model.
Definition: DAGmodel_inl.h:39
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
double log10DomainSize() const
Size sizeArcs() const
indicates the number of arcs stored within the ArcGraphPart
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:70
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition: types.h:48
const DAG & dag() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:36
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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
NotFoundIf 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::Estimator< GUM_SCALAR >::Estimator(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::insertEvidence__(), gum::Estimator< GUM_SCALAR >::setFromBN(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::verticesSampling__().

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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
NotFoundif 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

Member Data Documentation

◆ dag_


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