aGrUM  0.14.1
gum::BayesNet< GUM_SCALAR > Class Template Reference

Class representing a Bayesian Network. More...

#include <agrum/BN/BayesNet.h>

+ Inheritance diagram for gum::BayesNet< GUM_SCALAR >:
+ Collaboration diagram for gum::BayesNet< GUM_SCALAR >:

Public Member Functions

NodeId addNoisyAND (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
 Add a variable, its associate node and a noisyAND implementation. More...
 
NodeId addNoisyAND (const DiscreteVariable &var, GUM_SCALAR external_weight)
 Add a variable, its associate node and a noisyAND implementation. More...
 
NodeId addLogit (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
 Add a variable, its associate node and a Logit implementation. More...
 
NodeId addLogit (const DiscreteVariable &var, GUM_SCALAR external_weight)
 Add a variable, its associate node and a Logit implementation. More...
 
NodeId addOR (const DiscreteVariable &var)
 Add a variable, it's associate node and an OR implementation. More...
 
NodeId addAND (const DiscreteVariable &var)
 Add a variable, it's associate node and an AND implementation. More...
 
void addWeightedArc (NodeId tail, NodeId head, GUM_SCALAR causalWeight)
 Add an arc in the BN, and update arc.head's CPT. More...
 
void addWeightedArc (const std::string &tail, const std::string &head, GUM_SCALAR causalWeight)
 Add an arc in the BN, and update arc.head's CPT. More...
 
void generateCPTs () const
 randomly generates CPTs for a given structure More...
 
void generateCPT (NodeId node) const
 randomly generate CPT for a given node in a given structure More...
 
void generateCPT (const std::string &name) const
 
void changePotential (NodeId id, Potential< GUM_SCALAR > *newPot)
 change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId. More...
 
void changePotential (const std::string &name, Potential< GUM_SCALAR > *newPot)
 
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 and Destructor
 BayesNet ()
 Default constructor. More...
 
 BayesNet (std::string name)
 Default constructor. More...
 
 ~BayesNet () final
 Destructor. More...
 
 BayesNet (const BayesNet< GUM_SCALAR > &source)
 Copy constructor. More...
 
Operators
BayesNet< GUM_SCALAR > & operator= (const BayesNet< GUM_SCALAR > &source)
 Copy operator. More...
 
Variable manipulation methods
const Potential< GUM_SCALAR > & cpt (NodeId varId) const final
 Returns the CPT of a variable. More...
 
const Potential< GUM_SCALAR > & cpt (const std::string &name) const
 Returns the CPT of a variable. More...
 
const VariableNodeMapvariableNodeMap () const final
 Returns a map between variables and nodes of this gum::BayesNet. More...
 
NodeId add (const DiscreteVariable &var)
 Add a variable to the gum::BayesNet. More...
 
NodeId add (const std::string &name, unsigned int nbrmod)
 Shortcut for add(gum::LabelizedVariable(name,name,nbrmod)) More...
 
NodeId add (const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent)
 Add a variable to the gum::BayesNet. More...
 
NodeId add (const DiscreteVariable &var, NodeId id)
 Add a variable to the gum::BayesNet. More...
 
NodeId add (const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent, NodeId id)
 Add a variable to the gum::BayesNet. More...
 
void erase (NodeId varId)
 Remove a variable from the gum::BayesNet. More...
 
void erase (const std::string &name)
 Removes a variable from the gum::BayesNet. More...
 
void erase (const DiscreteVariable &var)
 Remove a variable from the gum::BayesNet. More...
 
const DiscreteVariablevariable (NodeId id) const final
 Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet. More...
 
const DiscreteVariablevariable (const std::string &name) const
 Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet. More...
 
void changeVariableName (NodeId id, const std::string &new_name)
 Changes a variable's name in the gum::BayesNet. More...
 
void changeVariableName (const std::string &name, const std::string &new_name)
 Changes a variable's name. More...
 
void changeVariableLabel (NodeId id, const std::string &old_label, const std::string &new_label)
 Changes a variable's label in the gum::BayesNet. More...
 
void changeVariableLabel (const std::string &name, const std::string &old_label, const std::string &new_label)
 Changes a variable's name. More...
 
NodeId nodeId (const DiscreteVariable &var) const final
 Returns a variable's id in the gum::BayesNet. More...
 
NodeId idFromName (const std::string &name) const final
 Returns a variable's id given its name in the gum::BayesNet. More...
 
const DiscreteVariablevariableFromName (const std::string &name) const final
 Returns a variable given its name in the gum::BayesNet. More...
 
Arc manipulation methods.
void addArc (NodeId tail, NodeId head)
 Add an arc in the BN, and update arc.head's CPT. More...
 
void addArc (const std::string &tail, const std::string &head)
 Add an arc in the BN, and update arc.head's CPT. More...
 
void eraseArc (const Arc &arc)
 Removes an arc in the BN, and update head's CTP. More...
 
void eraseArc (NodeId tail, NodeId head)
 Removes an arc in the BN, and update head's CTP. More...
 
void eraseArc (const std::string &tail, const std::string &head)
 Removes an arc in the BN, and update head's CTP. More...
 
void beginTopologyTransformation ()
 When inserting/removing arcs, node CPTs change their dimension with a cost in time. More...
 
void endTopologyTransformation ()
 terminates a sequence of insertions/deletions of arcs by adjusting all CPTs dimensions. More...
 
void reverseArc (NodeId tail, NodeId head)
 Reverses an arc while preserving the same joint distribution. More...
 
void reverseArc (const std::string &tail, const std::string &head)
 Reverses an arc while preserving the same joint distribution. More...
 
void reverseArc (const Arc &arc)
 Reverses an arc while preserving the same joint distribution. More...
 
Accessors for nodes with CI or logical implementation
NodeId addNoisyOR (const DiscreteVariable &var, GUM_SCALAR external_weight)
 Add a variable, it's associate node and a gum::noisyOR implementation. More...
 
NodeId addNoisyORNet (const DiscreteVariable &var, GUM_SCALAR external_weight)
 Add a variable, it's associate node and a gum::noisyOR implementation. More...
 
NodeId addNoisyORCompound (const DiscreteVariable &var, GUM_SCALAR external_weight)
 Add a variable, it's associate node and a gum::noisyOR implementation. More...
 
NodeId addNoisyOR (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
 Add a variable, its associate node and a noisyOR implementation. More...
 
NodeId addNoisyORNet (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
 Add a variable, its associate node and a noisyOR implementation. More...
 
NodeId addNoisyORCompound (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
 Add a variable, its associate node and a noisyOR implementation. More...
 
NodeId addAMPLITUDE (const DiscreteVariable &var)
 Others aggregators. More...
 
NodeId addCOUNT (const DiscreteVariable &var, Idx value=1)
 Others aggregators. More...
 
NodeId addEXISTS (const DiscreteVariable &var, Idx value=1)
 Others aggregators. More...
 
NodeId addFORALL (const DiscreteVariable &var, Idx value=1)
 Others aggregators. More...
 
NodeId addMAX (const DiscreteVariable &var)
 Others aggregators. More...
 
NodeId addMEDIAN (const DiscreteVariable &var)
 Others aggregators. More...
 
NodeId addMIN (const DiscreteVariable &var)
 Others aggregators. 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 DAGdag () 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 NodeGraphPartnodes () 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 ArcSetarcs () const
 returns the set of nodes with arc ingoing to a given node 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
 returns the set of nodes with arc ingoing to a given node 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
 returns the set of nodes with arc ingoing to a given node More...
 
Graphical methods
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...
 

Static Public Member Functions

static BayesNet< GUM_SCALAR > fastPrototype (const std::string &dotlike, Size domainSize=2)
 Create a bn with a dotlike syntax : 'a->b->c;b->d;'. More...
 

Protected Attributes

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

Friends

class BayesNetFactory< GUM_SCALAR >
 

Detailed Description

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

Class representing a Bayesian Network.

Bayesian Networks are a probabilistic graphical model in which nodes are random variables and the probability distribution is defined by the product:

\(P(X_1, \ldots, X_2) = \prod_{i=1}^{n} P(X_i | \pi(X_i))\),

where \(\pi(X_i)\) is the parent of \(X_i\).

The probability distribution can be represented as a directed acyclic graph (DAG) where:

  • Nodes are discrete random variables.
  • An arc A -> B represent a dependency between variables A and B, i.e. B conditional probability distribution is defined as \(P(B| \pi(B)\).

After a variable is added to the BN, if it's domain size changes, then the data in it's CPT is lost.

You should look a the gum::BayesNetFactory class which can help build Bayesian Networks.

You can print a BayesNet using gum::operator<<(std::ostream&, const BayesNet<GUM_SCALAR>&).

Definition at line 76 of file BayesNet.h.

Constructor & Destructor Documentation

◆ BayesNet() [1/3]

template<typename GUM_SCALAR >
INLINE gum::BayesNet< GUM_SCALAR >::BayesNet ( )

Default constructor.

Definition at line 148 of file BayesNet_tpl.h.

148  : IBayesNet< GUM_SCALAR >() {
149  GUM_CONSTRUCTOR(BayesNet);
150  }
BayesNet()
Default constructor.
Definition: BayesNet_tpl.h:148

◆ BayesNet() [2/3]

template<typename GUM_SCALAR >
INLINE gum::BayesNet< GUM_SCALAR >::BayesNet ( std::string  name)
explicit

Default constructor.

Parameters
nameThe BayesNet's name.

Definition at line 153 of file BayesNet_tpl.h.

153  :
154  IBayesNet< GUM_SCALAR >(name) {
155  GUM_CONSTRUCTOR(BayesNet);
156  }
BayesNet()
Default constructor.
Definition: BayesNet_tpl.h:148

◆ ~BayesNet()

template<typename GUM_SCALAR >
gum::BayesNet< GUM_SCALAR >::~BayesNet ( )
final

Destructor.

Definition at line 181 of file BayesNet_tpl.h.

181  {
182  GUM_DESTRUCTOR(BayesNet);
183  for (const auto p : __probaMap) {
184  delete p.second;
185  }
186  }
BayesNet()
Default constructor.
Definition: BayesNet_tpl.h:148
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651

◆ BayesNet() [3/3]

template<typename GUM_SCALAR>
gum::BayesNet< GUM_SCALAR >::BayesNet ( const BayesNet< GUM_SCALAR > &  source)

Copy constructor.

Definition at line 159 of file BayesNet_tpl.h.

159  :
160  IBayesNet< GUM_SCALAR >(source), __varMap(source.__varMap) {
161  GUM_CONS_CPY(BayesNet);
162 
163  __copyPotentials(source);
164  }
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
BayesNet()
Default constructor.
Definition: BayesNet_tpl.h:148
void __copyPotentials(const BayesNet< GUM_SCALAR > &source)
copy of potentials from a BN to another, using names of vars as ref.
Definition: BayesNet_tpl.h:607

Member Function Documentation

◆ __clearPotentials()

template<typename GUM_SCALAR >
void gum::BayesNet< GUM_SCALAR >::__clearPotentials ( )
private

clear all potentials

Definition at line 596 of file BayesNet_tpl.h.

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

596  {
597  // Removing previous potentials
598  for (const auto& elt : __probaMap) {
599  delete elt.second;
600  }
601 
602  __probaMap.clear();
603  }
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651
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◆ __copyPotentials()

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::__copyPotentials ( const BayesNet< GUM_SCALAR > &  source)
private

copy of potentials from a BN to another, using names of vars as ref.

Definition at line 607 of file BayesNet_tpl.h.

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

608  {
609  // Copying potentials
610 
611  for (const auto src : source.__probaMap) {
612  // First we build the node's CPT
613  Potential< GUM_SCALAR >* copy_array = new Potential< GUM_SCALAR >();
614  copy_array->beginMultipleChanges();
615  for (gum::Idx i = 0; i < src.second->nbrDim(); i++) {
616  (*copy_array) << variableFromName(src.second->variable(i).name());
617  }
618  copy_array->endMultipleChanges();
619  copy_array->copyFrom(*(src.second));
620 
621  // We add the CPT to the CPT's hashmap
622  __probaMap.insert(src.first, copy_array);
623  }
624  }
const DiscreteVariable & variableFromName(const std::string &name) const final
Returns a variable given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:297
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651
Size Idx
Type for indexes.
Definition: types.h:50
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◆ _unsafeChangePotential()

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::_unsafeChangePotential ( NodeId  id,
Potential< GUM_SCALAR > *  newPot 
)
private

change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.

Warning
no verification of dimensions are performer
See also
changePotential

Definition at line 660 of file BayesNet_tpl.h.

661  {
662  delete __probaMap[id];
663  __probaMap[id] = newPot;
664  }
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651

◆ add() [1/5]

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::add ( const DiscreteVariable var)

Add a variable to the gum::BayesNet.

Add a gum::DiscreteVariable, it's associated gum::NodeId and it's gum::Potential.

The variable is added by copy to the gum::BayesNet. The variable's gum::Potential implementation will be a gum::MultiDimArray.

Parameters
varThe variable added by copy.
Returns
Returns the variable's id in the gum::BayesNet.
Exceptions
DuplicateLabelRaised if variable.name() is already used in this gum::BayesNet.

Definition at line 220 of file BayesNet_tpl.h.

Referenced by gum::credal::CredalNet< GUM_SCALAR >::__bnCopy(), gum::learning::genericBNLearner::Database::__BNVars(), gum::credal::CredalNet< GUM_SCALAR >::approximatedBinarization(), gum::build_node(), gum::BayesNet< double >::cpt(), and gum::learning::DAG2BNLearner< ALLOC >::createBN().

220  {
221  auto ptr = new MultiDimArray< GUM_SCALAR >();
222  NodeId res = 0;
223 
224  try {
225  res = add(var, ptr);
226 
227  } catch (Exception&) {
228  delete ptr;
229  throw;
230  }
231 
232  return res;
233  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
Size NodeId
Type for node ids.
Definition: graphElements.h:97
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◆ add() [2/5]

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::add ( const std::string &  name,
unsigned int  nbrmod 
)

Shortcut for add(gum::LabelizedVariable(name,name,nbrmod))

Add a gum::LabelizedVariable to the gum::BayesNet

This method is just a shortcut for a often used pattern

Exceptions
DuplicateLabelRaised if variable.name() is already used in this gum::BayesNet.
NotAllowedif nbrmod<2

Definition at line 236 of file BayesNet_tpl.h.

237  {
238  if (nbrmod < 2) {
239  GUM_ERROR(OperationNotAllowed,
240  "Variable " << name << "needs more than " << nbrmod
241  << " modalities");
242  }
243 
244  LabelizedVariable v(name, name, nbrmod);
245  return add(v);
246  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52

◆ add() [3/5]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::add ( const DiscreteVariable var,
MultiDimImplementation< GUM_SCALAR > *  aContent 
)

Add a variable to the gum::BayesNet.

Add a gum::DiscreteVariable, it's associated gum::NodeId and it's gum::Potential.

The variable is added by copy to the gum::BayesNet.

Parameters
varThe variable added by copy.
aContentThe gum::MultiDimImplementation to use for this variable's gum::Potential implementation.
Returns
Returns the variable's id in the gum::BayesNet.
Exceptions
DuplicateLabelRaised if variable.name() is already used in this gum::BayesNet.

Definition at line 249 of file BayesNet_tpl.h.

250  {
251  NodeId proposedId = dag().nextNodeId();
252  NodeId res = 0;
253 
254  res = add(var, aContent, proposedId);
255 
256  return res;
257  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
NodeId nextNodeId() const
returns a new node id, not yet used by any node
const DAG & dag() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:60
Size NodeId
Type for node ids.
Definition: graphElements.h:97

◆ add() [4/5]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::add ( const DiscreteVariable var,
NodeId  id 
)

Add a variable to the gum::BayesNet.

Add a gum::DiscreteVariable, it's associated gum::NodeId and it's gum::Potential.

The variable is added by copy to the gum::BayesNet. The variable's gum::Potential implementation will be a gum::MultiDimArray.

Parameters
varThe variable added by copy.
idThe variable's forced gum::NodeId in the gum::BayesNet.
Returns
Returns the variable's id in the gum::BayesNet.
Exceptions
DuplicateElementRaised id is already used.
DuplicateLabelRaised if variable.name() is already used in this gum::BayesNet.

Definition at line 260 of file BayesNet_tpl.h.

261  {
262  auto ptr = new MultiDimArray< GUM_SCALAR >();
263  NodeId res = 0;
264 
265  try {
266  res = add(var, ptr, id);
267 
268  } catch (Exception&) {
269  delete ptr;
270  throw;
271  }
272 
273  return res;
274  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
Size NodeId
Type for node ids.
Definition: graphElements.h:97

◆ add() [5/5]

template<typename GUM_SCALAR>
NodeId gum::BayesNet< GUM_SCALAR >::add ( const DiscreteVariable var,
MultiDimImplementation< GUM_SCALAR > *  aContent,
NodeId  id 
)

Add a variable to the gum::BayesNet.

Add a gum::DiscreteVariable, it's associated gum::NodeId and it's gum::Potential.

Parameters
varThe variable added by copy.
aContentThe gum::MultiDimImplementation to use for this variable's gum::Potential implementation.
idThe variable's forced gum::NodeId in the gum::BayesNet.
Returns
Returns the variable's id in the gum::BayesNet.
Exceptions
DuplicateElementRaised id is already used.
DuplicateLabelRaised if variable.name() is already used in this gum::BayesNet.

Definition at line 278 of file BayesNet_tpl.h.

280  {
281  __varMap.insert(id, var);
282  this->_dag.addNodeWithId(id);
283 
284  auto cpt = new Potential< GUM_SCALAR >(aContent);
285  (*cpt) << variable(id);
286  __probaMap.insert(id, cpt);
287  return id;
288  }
virtual void addNodeWithId(const NodeId id)
try to insert a node with the given id
const DiscreteVariable & variable(NodeId id) const final
Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
Definition: BayesNet_tpl.h:190
NodeId insert(NodeId id, const DiscreteVariable &var)
Maps id with var.
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651
const Potential< GUM_SCALAR > & cpt(NodeId varId) const final
Returns the CPT of a variable.
Definition: BayesNet_tpl.h:303

◆ addAMPLITUDE()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addAMPLITUDE ( const DiscreteVariable var)

Others aggregators.

Definition at line 430 of file BayesNet_tpl.h.

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

430  {
431  return add(var, new aggregator::Amplitude< GUM_SCALAR >());
432  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
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◆ addAND()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addAND ( const DiscreteVariable var)

Add a variable, it's associate node and an AND implementation.

The id of the new variable is automatically generated.

Warning
AND is implemented as a gum::aggregator::And which means that if parents are not boolean, all value>1 is True
Parameters
varThe variable added by copy.
Returns
the id of the added variable.
Exceptions
SizeErrorif variable.domainSize()>2

Definition at line 435 of file BayesNet_tpl.h.

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

435  {
436  if (var.domainSize() > 2) GUM_ERROR(SizeError, "an AND has to be boolean");
437 
438  return add(var, new aggregator::And< GUM_SCALAR >());
439  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52
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◆ addArc() [1/2]

template<typename GUM_SCALAR >
INLINE void gum::BayesNet< GUM_SCALAR >::addArc ( NodeId  tail,
NodeId  head 
)

Add an arc in the BN, and update arc.head's CPT.

Parameters
headand
tailas NodeId
Exceptions
InvalidEdgeIf arc.tail and/or arc.head are not in the BN.

Definition at line 336 of file BayesNet_tpl.h.

Referenced by gum::credal::CredalNet< GUM_SCALAR >::__bnCopy(), gum::BayesNet< double >::addArc(), gum::credal::CredalNet< GUM_SCALAR >::approximatedBinarization(), gum::BayesNet< double >::changeVariableLabel(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), and gum::BayesNet< double >::fastPrototype().

336  {
337  this->_dag.addArc(tail, head);
338  // Add parent in the child's CPT
339  (*(__probaMap[head])) << variable(tail);
340  }
const DiscreteVariable & variable(NodeId id) const final
Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
Definition: BayesNet_tpl.h:190
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
virtual void addArc(const NodeId tail, const NodeId head)
insert a new arc into the directed graph
Definition: DAG_inl.h:40
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651
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◆ addArc() [2/2]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::addArc ( const std::string &  tail,
const std::string &  head 
)
inline

Add an arc in the BN, and update arc.head's CPT.

Definition at line 389 of file BayesNet.h.

389  {
390  addArc(idFromName(tail), idFromName(head));
391  }
void addArc(NodeId tail, NodeId head)
Add an arc in the BN, and update arc.head&#39;s CPT.
Definition: BayesNet_tpl.h:336
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291

◆ addCOUNT()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addCOUNT ( const DiscreteVariable var,
Idx  value = 1 
)

Others aggregators.

Definition at line 442 of file BayesNet_tpl.h.

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

443  {
444  return add(var, new aggregator::Count< GUM_SCALAR >(value));
445  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
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◆ addEXISTS()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addEXISTS ( const DiscreteVariable var,
Idx  value = 1 
)

Others aggregators.

Definition at line 448 of file BayesNet_tpl.h.

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

449  {
450  if (var.domainSize() > 2) GUM_ERROR(SizeError, "an EXISTS has to be boolean");
451 
452  return add(var, new aggregator::Exists< GUM_SCALAR >(value));
453  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52
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◆ addFORALL()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addFORALL ( const DiscreteVariable var,
Idx  value = 1 
)

Others aggregators.

Definition at line 456 of file BayesNet_tpl.h.

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

457  {
458  if (var.domainSize() > 2) GUM_ERROR(SizeError, "an EXISTS has to be boolean");
459 
460  return add(var, new aggregator::Forall< GUM_SCALAR >(value));
461  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52
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◆ addLogit() [1/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addLogit ( const DiscreteVariable var,
GUM_SCALAR  external_weight,
NodeId  id 
)

Add a variable, its associate node and a Logit implementation.

Parameters
varThe variable added by copy
external_weightsee gum::MultiDimLogit
idproposed gum::nodeId for the variable
Warning
give an id should be reserved for rare and specific situations !!!
Returns
the id of the added variable.

Definition at line 534 of file BayesNet_tpl.h.

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

536  {
537  return add(var, new MultiDimLogit< GUM_SCALAR >(external_weight), id);
538  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
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◆ addLogit() [2/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addLogit ( const DiscreteVariable var,
GUM_SCALAR  external_weight 
)

Add a variable, its associate node and a Logit implementation.

The id of the new variable is automatically generated.

Parameters
varThe variable added by copy.
external_weightsee gum::MultiDimLogit
Returns
the id of the added variable.

Definition at line 514 of file BayesNet_tpl.h.

515  {
516  return add(var, new MultiDimLogit< GUM_SCALAR >(external_weight));
517  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220

◆ addMAX()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addMAX ( const DiscreteVariable var)

Others aggregators.

Definition at line 464 of file BayesNet_tpl.h.

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

464  {
465  return add(var, new aggregator::Max< GUM_SCALAR >());
466  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
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◆ addMEDIAN()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addMEDIAN ( const DiscreteVariable var)

Others aggregators.

Definition at line 469 of file BayesNet_tpl.h.

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

469  {
470  return add(var, new aggregator::Median< GUM_SCALAR >());
471  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
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◆ addMIN()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addMIN ( const DiscreteVariable var)

Others aggregators.

Definition at line 474 of file BayesNet_tpl.h.

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

474  {
475  return add(var, new aggregator::Min< GUM_SCALAR >());
476  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
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◆ addNoisyAND() [1/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyAND ( const DiscreteVariable var,
GUM_SCALAR  external_weight,
NodeId  id 
)

Add a variable, its associate node and a noisyAND implementation.

Parameters
varThe variable added by copy
external_weightsee gum::MultiDimNoisyAND
idproposed gum::nodeId for the variable
Warning
give an id should be reserved for rare and specific situations !!!
Returns
the id of the added variable.

Definition at line 527 of file BayesNet_tpl.h.

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

529  {
530  return add(var, new MultiDimNoisyAND< GUM_SCALAR >(external_weight), id);
531  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
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◆ addNoisyAND() [2/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyAND ( const DiscreteVariable var,
GUM_SCALAR  external_weight 
)

Add a variable, its associate node and a noisyAND implementation.

The id of the new variable is automatically generated.

Parameters
varThe variable added by copy.
external_weightsee gum::MultiDimNoisyAND
Returns
the id of the added variable.

Definition at line 508 of file BayesNet_tpl.h.

509  {
510  return add(var, new MultiDimNoisyAND< GUM_SCALAR >(external_weight));
511  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220

◆ addNoisyOR() [1/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyOR ( const DiscreteVariable var,
GUM_SCALAR  external_weight 
)

Add a variable, it's associate node and a gum::noisyOR implementation.

The id of the new variable is automatically generated. Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the gum::BayesNet::addNoisyOR as an alias for gum::BayesNet::addNoisyORCompound

Parameters
varThe variable added by copy.
external_weightsee ref gum::MultiDimNoisyORNet,gum::MultiDimNoisyORCompound
Returns
the id of the added variable.

Definition at line 490 of file BayesNet_tpl.h.

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

491  {
492  return addNoisyORCompound(var, external_weight);
493  }
NodeId addNoisyORCompound(const DiscreteVariable &var, GUM_SCALAR external_weight)
Add a variable, it&#39;s associate node and a gum::noisyOR implementation.
Definition: BayesNet_tpl.h:496
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◆ addNoisyOR() [2/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyOR ( const DiscreteVariable var,
GUM_SCALAR  external_weight,
NodeId  id 
)

Add a variable, its associate node and a noisyOR implementation.

Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the addNoisyOR as an alias for addNoisyORCompound.

Parameters
varThe variable added by copy.
external_weightsee gum::MultiDimNoisyORNet, gum::MultiDimNoisyORCompound
idThe chosen id
Warning
give an id should be reserved for rare and specific situations !!!
Returns
the id of the added variable.
Exceptions
DuplicateElementif id is already used

Definition at line 520 of file BayesNet_tpl.h.

522  {
523  return addNoisyORCompound(var, external_weight, id);
524  }
NodeId addNoisyORCompound(const DiscreteVariable &var, GUM_SCALAR external_weight)
Add a variable, it&#39;s associate node and a gum::noisyOR implementation.
Definition: BayesNet_tpl.h:496

◆ addNoisyORCompound() [1/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyORCompound ( const DiscreteVariable var,
GUM_SCALAR  external_weight 
)

Add a variable, it's associate node and a gum::noisyOR implementation.

The id of the new variable is automatically generated. Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the gum::BayesNet::addNoisyOR as an alias for gum::BayesNet::addNoisyORCompound

Parameters
varThe variable added by copy.
external_weightsee ref gum::MultiDimNoisyORNet,gum::MultiDimNoisyORCompound
Returns
the id of the added variable.

Definition at line 496 of file BayesNet_tpl.h.

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

497  {
498  return add(var, new MultiDimNoisyORCompound< GUM_SCALAR >(external_weight));
499  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
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◆ addNoisyORCompound() [2/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyORCompound ( const DiscreteVariable var,
GUM_SCALAR  external_weight,
NodeId  id 
)

Add a variable, its associate node and a noisyOR implementation.

Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the addNoisyOR as an alias for addNoisyORCompound.

Parameters
varThe variable added by copy.
external_weightsee gum::MultiDimNoisyORNet, gum::MultiDimNoisyORCompound
idThe chosen id
Warning
give an id should be reserved for rare and specific situations !!!
Returns
the id of the added variable.
Exceptions
DuplicateElementif id is already used

Definition at line 541 of file BayesNet_tpl.h.

542  {
543  return add(
544  var, new MultiDimNoisyORCompound< GUM_SCALAR >(external_weight), id);
545  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220

◆ addNoisyORNet() [1/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyORNet ( const DiscreteVariable var,
GUM_SCALAR  external_weight 
)

Add a variable, it's associate node and a gum::noisyOR implementation.

The id of the new variable is automatically generated. Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the gum::BayesNet::addNoisyOR as an alias for gum::BayesNet::addNoisyORCompound

Parameters
varThe variable added by copy.
external_weightsee ref gum::MultiDimNoisyORNet,gum::MultiDimNoisyORCompound
Returns
the id of the added variable.

Definition at line 502 of file BayesNet_tpl.h.

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

503  {
504  return add(var, new MultiDimNoisyORNet< GUM_SCALAR >(external_weight));
505  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
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◆ addNoisyORNet() [2/2]

template<typename GUM_SCALAR>
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addNoisyORNet ( const DiscreteVariable var,
GUM_SCALAR  external_weight,
NodeId  id 
)

Add a variable, its associate node and a noisyOR implementation.

Since it seems that the 'classical' noisyOR is the Compound noisyOR, we keep the addNoisyOR as an alias for addNoisyORCompound.

Parameters
varThe variable added by copy.
external_weightsee gum::MultiDimNoisyORNet, gum::MultiDimNoisyORCompound
idThe chosen id
Warning
give an id should be reserved for rare and specific situations !!!
Returns
the id of the added variable.
Exceptions
DuplicateElementif id is already used

Definition at line 548 of file BayesNet_tpl.h.

550  {
551  return add(var, new MultiDimNoisyORNet< GUM_SCALAR >(external_weight), id);
552  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220

◆ addOR()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::addOR ( const DiscreteVariable var)

Add a variable, it's associate node and an OR implementation.

The id of the new variable is automatically generated.

Warning
OR is implemented as a gum::aggregator::Or which means that if parents are not boolean, all value>1 is True
Parameters
varThe variable added by copy.
Returns
the id of the added variable.
Exceptions
SizeErrorif variable.domainSize()>2

Definition at line 479 of file BayesNet_tpl.h.

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

479  {
480  if (var.domainSize() > 2) GUM_ERROR(SizeError, "an OR has to be boolean");
481 
482  return add(var, new aggregator::Or< GUM_SCALAR >());
483  }
NodeId add(const DiscreteVariable &var)
Add a variable to the gum::BayesNet.
Definition: BayesNet_tpl.h:220
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52
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◆ addWeightedArc() [1/2]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::addWeightedArc ( NodeId  tail,
NodeId  head,
GUM_SCALAR  causalWeight 
)

Add an arc in the BN, and update arc.head's CPT.

Parameters
headand
tailas NodeId
causalWeightsee gum::MultiDimICIModel
Exceptions
InvalidArcIf arc.tail and/or arc.head are not in the BN.
InvalidArcIf variable in arc.head is not a NoisyOR variable.

Definition at line 555 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::addWeightedArc(), and gum::BayesNet< double >::reverseArc().

557  {
558  auto* CImodel =
559  dynamic_cast< const MultiDimICIModel< GUM_SCALAR >* >(cpt(head).content());
560 
561  if (CImodel != 0) {
562  // or is OK
563  addArc(tail, head);
564 
565  CImodel->causalWeight(variable(tail), causalWeight);
566  } else {
567  GUM_ERROR(InvalidArc,
568  "Head variable (" << variable(head).name()
569  << ") is not a CIModel variable !");
570  }
571  }
void addArc(NodeId tail, NodeId head)
Add an arc in the BN, and update arc.head&#39;s CPT.
Definition: BayesNet_tpl.h:336
const DiscreteVariable & variable(NodeId id) const final
Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
Definition: BayesNet_tpl.h:190
const Potential< GUM_SCALAR > & cpt(NodeId varId) const final
Returns the CPT of a variable.
Definition: BayesNet_tpl.h:303
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52
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◆ addWeightedArc() [2/2]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::addWeightedArc ( const std::string &  tail,
const std::string &  head,
GUM_SCALAR  causalWeight 
)
inline

Add an arc in the BN, and update arc.head's CPT.

Parameters
headand
tailas std::string
causalWeightsee gum::MultiDimICIModel
NotFoundif no node with sun names is found
Exceptions
InvalidArcIf arc.tail and/or arc.head are not in the BN.
InvalidArcIf variable in arc.head is not a NoisyOR variable.

Definition at line 617 of file BayesNet.h.

619  {
620  addWeightedArc(idFromName(tail), idFromName(head), causalWeight);
621  };
void addWeightedArc(NodeId tail, NodeId head, GUM_SCALAR causalWeight)
Add an arc in the BN, and update arc.head&#39;s CPT.
Definition: BayesNet_tpl.h:555
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291

◆ 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
idthe node toward which the arcs returned are pointing

Definition at line 101 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().

101 { return _dag.arcs(); }
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
const ArcSet & arcs() const
returns the set of arcs stored within the ArcGraphPart
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◆ beginTopologyTransformation()

template<typename GUM_SCALAR >
void gum::BayesNet< GUM_SCALAR >::beginTopologyTransformation ( )

When inserting/removing arcs, node CPTs change their dimension with a cost in time.

begin Multiple Change for all CPTs

These functions delay the CPTs change to be done just once at the end of a sequence of topology modification. begins a sequence of insertions/deletions of arcs without changing the dimensions of the CPTs.

Definition at line 582 of file BayesNet_tpl.h.

Referenced by gum::credal::CredalNet< GUM_SCALAR >::__bnCopy(), gum::credal::CredalNet< GUM_SCALAR >::approximatedBinarization(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), and gum::BayesNet< double >::eraseArc().

582  {
583  for (const auto node : nodes())
584  __probaMap[node]->beginMultipleChanges();
585  }
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:112
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651
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◆ changePotential() [1/2]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::changePotential ( NodeId  id,
Potential< GUM_SCALAR > *  newPot 
)

change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.

Exceptions
NotAllowedif newPot has not the same signature as __probaMap[NodeId]

Definition at line 639 of file BayesNet_tpl.h.

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

640  {
641  if (cpt(id).nbrDim() != newPot->nbrDim()) {
642  GUM_ERROR(OperationNotAllowed,
643  "cannot exchange potentials with different "
644  "dimensions for variable with id "
645  << id);
646  }
647 
648  for (Idx i = 0; i < cpt(id).nbrDim(); i++) {
649  if (&cpt(id).variable(i) != &(newPot->variable(i))) {
650  GUM_ERROR(OperationNotAllowed,
651  "cannot exchange potentials because, for variable with id "
652  << id << ", dimension " << i << " differs. ");
653  }
654  }
655 
656  _unsafeChangePotential(id, newPot);
657  }
void _unsafeChangePotential(NodeId id, Potential< GUM_SCALAR > *newPot)
change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.
Definition: BayesNet_tpl.h:660
const Potential< GUM_SCALAR > & cpt(NodeId varId) const final
Returns the CPT of a variable.
Definition: BayesNet_tpl.h:303
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52
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◆ changePotential() [2/2]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::changePotential ( const std::string &  name,
Potential< GUM_SCALAR > *  newPot 
)

Definition at line 667 of file BayesNet_tpl.h.

668  {
669  changePotential(idFromName(name), newPot);
670  }
void changePotential(NodeId id, Potential< GUM_SCALAR > *newPot)
change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.
Definition: BayesNet_tpl.h:639
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291

◆ changeVariableLabel() [1/2]

template<typename GUM_SCALAR >
INLINE void gum::BayesNet< GUM_SCALAR >::changeVariableLabel ( NodeId  id,
const std::string &  old_label,
const std::string &  new_label 
)

Changes a variable's label in the gum::BayesNet.

This will change the gum::LabelizedVariable names in the gum::BayesNet.

Exceptions
DuplicateLabelRaised if new_label is already used in this gum::LabelizedVariable.
NotFoundRaised if no variable matches id or if the variable is not a LabelizedVariable

Definition at line 202 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::changeVariableLabel(), and gum::BayesNet< double >::changeVariableName().

203  {
204  if (variable(id).varType() != VarType::Labelized) {
205  GUM_ERROR(NotFound, "Variable " << id << " is not a LabelizedVariable.");
206  }
207  LabelizedVariable* var = dynamic_cast< LabelizedVariable* >(
208  const_cast< DiscreteVariable* >(&variable(id)));
209 
210  var->changeLabel(var->posLabel(old_label), new_label);
211  }
const DiscreteVariable & variable(NodeId id) const final
Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
Definition: BayesNet_tpl.h:190
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52
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◆ changeVariableLabel() [2/2]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::changeVariableLabel ( const std::string &  name,
const std::string &  old_label,
const std::string &  new_label 
)
inline

Changes a variable's name.

Definition at line 334 of file BayesNet.h.

336  {
337  changeVariableLabel(idFromName(name), old_label, new_label);
338  }
void changeVariableLabel(NodeId id, const std::string &old_label, const std::string &new_label)
Changes a variable&#39;s label in the gum::BayesNet.
Definition: BayesNet_tpl.h:202
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291

◆ changeVariableName() [1/2]

template<typename GUM_SCALAR >
INLINE void gum::BayesNet< GUM_SCALAR >::changeVariableName ( NodeId  id,
const std::string &  new_name 
)

Changes a variable's name in the gum::BayesNet.

This will change the gum::DiscreteVariable names in the gum::BayesNet.

Exceptions
DuplicateLabelRaised if newName is already used in this gum::BayesNet.
NotFoundRaised if no variable matches id.

Definition at line 196 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::changeVariableName(), and gum::BayesNet< double >::variable().

197  {
198  __varMap.changeName(id, new_name);
199  }
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
void changeName(NodeId id, const std::string &new_name)
we allow the user to change the name of a variable
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◆ changeVariableName() [2/2]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::changeVariableName ( const std::string &  name,
const std::string &  new_name 
)
inline

Changes a variable's name.

Definition at line 313 of file BayesNet.h.

313  {
314  changeVariableName(idFromName(name), new_name);
315  }
void changeVariableName(NodeId id, const std::string &new_name)
Changes a variable&#39;s name in the gum::BayesNet.
Definition: BayesNet_tpl.h:196
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291

◆ 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
idthe node which is the tail of the arcs returned

Definition at line 108 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().

108  {
109  return _dag.children(id);
110  }
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
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]

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
idthe node toward which the arcs returned are pointing

Definition at line 162 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().

162  {
163  return parents(idFromName(name));
164  };
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:103
virtual NodeId idFromName(const std::string &name) const =0
Getter by 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 83 of file DAGmodel_inl.h.

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

83  {
84  Instantiation I;
85 
86  for (const auto node : dag())
87  I << variable(node);
88 
89  return I;
90  }
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variabe given it&#39;s node id.
const DAG & dag() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:60
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◆ cpt() [1/2]

template<typename GUM_SCALAR >
INLINE const Potential< GUM_SCALAR > & gum::BayesNet< GUM_SCALAR >::cpt ( NodeId  varId) const
finalvirtual

Returns the CPT of a variable.

Parameters
varIdA variable's id in the gum::BayesNet.
Returns
The variable's CPT.
Exceptions
NotFoundIf no variable's id matches varId.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 303 of file BayesNet_tpl.h.

Referenced by gum::credal::CredalNet< GUM_SCALAR >::__bnCopy(), gum::credal::CredalNet< GUM_SCALAR >::approximatedBinarization(), gum::BayesNet< double >::cpt(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), gum::SimpleCPTDisturber< GUM_SCALAR >::disturbAugmCPT(), gum::SimpleCPTDisturber< GUM_SCALAR >::disturbReducCPT(), and gum::credal::CredalNet< GUM_SCALAR >::toString().

303  {
304  return *(__probaMap[varId]);
305  }
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651
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◆ cpt() [2/2]

template<typename GUM_SCALAR>
const Potential< GUM_SCALAR >& gum::BayesNet< GUM_SCALAR >::cpt ( const std::string &  name) const
inline

Returns the CPT of a variable.

Definition at line 153 of file BayesNet.h.

153  {
154  return cpt(idFromName(name));
155  };
const Potential< GUM_SCALAR > & cpt(NodeId varId) const final
Returns the CPT of a variable.
Definition: BayesNet_tpl.h:303
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291

◆ dag()

◆ dim()

template<typename GUM_SCALAR >
Size gum::IBayesNet< GUM_SCALAR >::dim ( ) const
inherited

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

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

Definition at line 76 of file IBayesNet_tpl.h.

76  {
77  Size dim = 0;
78 
79  for (auto node : nodes()) {
80  Size q = 1;
81 
82  for (auto parent : parents(node))
83  q *= variable(parent).domainSize();
84 
85  dim += (variable(node).domainSize() - 1) * q;
86  }
87 
88  return dim;
89  }
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:103
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:112
Size dim() const
Returns the dimension (the number of free parameters) in this bayes net.
Definition: IBayesNet_tpl.h:76
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition: types.h:45

◆ empty()

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

Retursn true if this Directed Graphical Model is empty.

Definition at line 96 of file DAGmodel_inl.h.

References gum::DAGmodel::size().

96 { return size() == 0; }
Size size() const
Returns the number of variables in this Directed Graphical Model.
Definition: DAGmodel_inl.h:93
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◆ endTopologyTransformation()

template<typename GUM_SCALAR >
void gum::BayesNet< GUM_SCALAR >::endTopologyTransformation ( )

terminates a sequence of insertions/deletions of arcs by adjusting all CPTs dimensions.

end Multiple Change for all CPTs

Definition at line 589 of file BayesNet_tpl.h.

Referenced by gum::credal::CredalNet< GUM_SCALAR >::__bnCopy(), gum::credal::CredalNet< GUM_SCALAR >::approximatedBinarization(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), and gum::BayesNet< double >::eraseArc().

589  {
590  for (const auto node : nodes())
591  __probaMap[node]->endMultipleChanges();
592  }
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:112
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651
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◆ erase() [1/3]

template<typename GUM_SCALAR >
void gum::BayesNet< GUM_SCALAR >::erase ( NodeId  varId)

Remove a variable from the gum::BayesNet.

Removes the corresponding variable from the gum::BayesNet and from all of it's children gum::Potential.

If no variable matches the given id, then nothing is done.

Parameters
varIdThe variable's id to remove.

Definition at line 318 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::cpt(), and gum::BayesNet< double >::erase().

318  {
319  if (__varMap.exists(varId)) {
320  // Reduce the variable child's CPT
321  const NodeSet& children = this->children(varId);
322 
323  for (const auto c : children) {
324  __probaMap[c]->erase(variable(varId));
325  }
326 
327  delete __probaMap[varId];
328 
329  __probaMap.erase(varId);
330  __varMap.erase(varId);
331  this->_dag.eraseNode(varId);
332  }
333  }
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
Definition: DAGmodel_inl.h:108
const DiscreteVariable & variable(NodeId id) const final
Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
Definition: BayesNet_tpl.h:190
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
bool exists(NodeId id) const
Return true if id matches a node.
void erase(NodeId id)
Removes a var and it&#39;s id of this mapping. The pointer is deleted.
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651
virtual void eraseNode(const NodeId id)
remove a node and its adjacent arcs from the graph
Definition: diGraph_inl.h:66
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◆ erase() [2/3]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::erase ( const std::string &  name)
inline

Removes a variable from the gum::BayesNet.

Definition at line 265 of file BayesNet.h.

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

265 { erase(idFromName(name)); };
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291
void erase(NodeId varId)
Remove a variable from the gum::BayesNet.
Definition: BayesNet_tpl.h:318
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◆ erase() [3/3]

template<typename GUM_SCALAR >
INLINE void gum::BayesNet< GUM_SCALAR >::erase ( const DiscreteVariable var)

Remove a variable from the gum::BayesNet.

Removes the corresponding variable from the gum::BayesNet and from all of it's children gum::Potential.

If no variable matches the given variable, then nothing is done.

Parameters
varA reference on the variable to remove.

Definition at line 313 of file BayesNet_tpl.h.

313  {
314  erase(__varMap.get(var));
315  }
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
void erase(NodeId varId)
Remove a variable from the gum::BayesNet.
Definition: BayesNet_tpl.h:318
const DiscreteVariable & get(NodeId id) const
Returns a discrete variable given it&#39;s node id.

◆ eraseArc() [1/3]

template<typename GUM_SCALAR >
INLINE void gum::BayesNet< GUM_SCALAR >::eraseArc ( const Arc arc)

Removes an arc in the BN, and update head's CTP.

If (tail, head) doesn't exist, the nothing happens.

Parameters
arcThe arc removed.

Definition at line 343 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::addArc(), and gum::BayesNet< double >::eraseArc().

343  {
344  if (__varMap.exists(arc.tail()) && __varMap.exists(arc.head())) {
345  NodeId head = arc.head(), tail = arc.tail();
346  this->_dag.eraseArc(arc);
347  // Remove parent froms child's CPT
348  (*(__probaMap[head])) >> variable(tail);
349  }
350  }
const DiscreteVariable & variable(NodeId id) const final
Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
Definition: BayesNet_tpl.h:190
virtual void eraseArc(const Arc &arc)
removes an arc from the ArcGraphPart
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
bool exists(NodeId id) const
Return true if id matches a node.
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
NodeProperty< Potential< GUM_SCALAR > *> __probaMap
Mapping between the variable&#39;s id and their CPT.
Definition: BayesNet.h:651
Size NodeId
Type for node ids.
Definition: graphElements.h:97
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◆ eraseArc() [2/3]

template<typename GUM_SCALAR >
INLINE void gum::BayesNet< GUM_SCALAR >::eraseArc ( NodeId  tail,
NodeId  head 
)

Removes an arc in the BN, and update head's CTP.

If (tail, head) doesn't exist, the nothing happens.

Parameters
headand
tailas NodeId

Definition at line 353 of file BayesNet_tpl.h.

353  {
354  eraseArc(Arc(tail, head));
355  }
void eraseArc(const Arc &arc)
Removes an arc in the BN, and update head&#39;s CTP.
Definition: BayesNet_tpl.h:343

◆ eraseArc() [3/3]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::eraseArc ( const std::string &  tail,
const std::string &  head 
)
inline

Removes an arc in the BN, and update head's CTP.

Definition at line 413 of file BayesNet.h.

413  {
414  eraseArc(idFromName(tail), idFromName(head));
415  }
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291
void eraseArc(const Arc &arc)
Removes an arc in the BN, and update head&#39;s CTP.
Definition: BayesNet_tpl.h:343

◆ fastPrototype()

template<typename GUM_SCALAR >
BayesNet< GUM_SCALAR > gum::BayesNet< GUM_SCALAR >::fastPrototype ( const std::string &  dotlike,
Size  domainSize = 2 
)
static

Create a bn with a dotlike syntax : 'a->b->c;b->d;'.

The domain size maybe specified using 'a[10]' or using 'a{yes|maybe|no}'. Note that if the dotlike string contains such a specification for an already defined variable, the first specification will be used.

Parameters
dotlikethe string containing the specification
domainSizethe default domain size for variables
Returns
the resulting bayesian network

Definition at line 115 of file BayesNet_tpl.h.

116  {
118 
119 
120  for (const auto& chaine : split(dotlike, ";")) {
121  NodeId lastId = 0;
122  bool notfirst = false;
123  for (const auto& souschaine : split(chaine, "->")) {
124  bool forward = true;
125  for (const auto& node : split(souschaine, "<-")) {
126  auto idVar = build_node(bn, node, domainSize);
127  if (notfirst) {
128  if (forward) {
129  bn.addArc(lastId, idVar);
130  forward = false;
131  } else {
132  bn.addArc(idVar, lastId);
133  }
134  } else {
135  notfirst = true;
136  forward = false;
137  }
138  lastId = idVar;
139  }
140  }
141  }
142  bn.generateCPTs();
143  bn.setProperty("name", dotlike);
144  return bn;
145  }
void addArc(NodeId tail, NodeId head)
Add an arc in the BN, and update arc.head&#39;s CPT.
Definition: BayesNet_tpl.h:336
Class representing a Bayesian Network.
Definition: BayesNet.h:76
NodeId build_node(gum::BayesNet< GUM_SCALAR > &bn, std::string node, gum::Size domainSize)
Definition: BayesNet_tpl.h:57
std::vector< std::string > split(const std::string &str, const std::string &delim)
Split str using the delimiter.
void setProperty(const std::string &name, const std::string &value)
Add or change a property of this DAGModel.
Definition: DAGmodel_inl.h:53
void generateCPTs() const
randomly generates CPTs for a given structure
Definition: BayesNet_tpl.h:627
Size NodeId
Type for node ids.
Definition: graphElements.h:97

◆ generateCPT() [1/2]

template<typename GUM_SCALAR >
INLINE void gum::BayesNet< GUM_SCALAR >::generateCPT ( NodeId  node) const

randomly generate CPT for a given node in a given structure

Definition at line 632 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::addWeightedArc(), gum::learning::DAG2BNLearner< ALLOC >::createBN(), and gum::BayesNet< double >::generateCPT().

632  {
633  SimpleCPTGenerator< GUM_SCALAR > generator;
634 
635  generator.generateCPT(cpt(node).pos(variable(node)), cpt(node));
636  }
const DiscreteVariable & variable(NodeId id) const final
Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
Definition: BayesNet_tpl.h:190
const Potential< GUM_SCALAR > & cpt(NodeId varId) const final
Returns the CPT of a variable.
Definition: BayesNet_tpl.h:303
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◆ generateCPT() [2/2]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::generateCPT ( const std::string &  name) const
inline

Definition at line 629 of file BayesNet.h.

629  {
630  generateCPT(idFromName(name));
631  };
void generateCPT(NodeId node) const
randomly generate CPT for a given node in a given structure
Definition: BayesNet_tpl.h:632
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291

◆ generateCPTs()

template<typename GUM_SCALAR >
INLINE void gum::BayesNet< GUM_SCALAR >::generateCPTs ( ) const

randomly generates CPTs for a given structure

Definition at line 627 of file BayesNet_tpl.h.

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

627  {
628  for (const auto node : nodes())
629  generateCPT(node);
630  }
void generateCPT(NodeId node) const
randomly generate CPT for a given node in a given structure
Definition: BayesNet_tpl.h:632
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:112
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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 119 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().

119  {
120  if (this == &other) return true;
121 
122  if (size() != other.size()) return false;
123 
124  if (sizeArcs() != other.sizeArcs()) return false;
125 
126  for (const auto& nid : nodes()) {
127  try {
128  other.idFromName(variable(nid).name());
129  } catch (NotFound) { return false; }
130  }
131 
132  for (const auto& arc : arcs()) {
133  if (!other.arcs().exists(Arc(other.idFromName(variable(arc.tail()).name()),
134  other.idFromName(variable(arc.head()).name()))))
135  return false;
136  }
137 
138  return true;
139  }
const ArcSet & arcs() const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:101
Size sizeArcs() const
Returns the number of arcs in this Directed Graphical Model.
Definition: DAGmodel_inl.h:99
Size size() const
Returns the number of variables in this Directed Graphical Model.
Definition: DAGmodel_inl.h:93
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:112
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variabe given it&#39;s node id.
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◆ idFromName()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::idFromName ( const std::string &  name) const
finalvirtual

Returns a variable's id given its name in the gum::BayesNet.

Parameters
nameThe variable's name from which the gum::NodeId is returned.
Returns
Returns the variable gum::NodeId in the gum::BayesNet.
Exceptions
NotFoundRaised if name does not match a variable in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 291 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::addArc(), gum::BayesNet< double >::addWeightedArc(), gum::build_node(), gum::BayesNet< double >::changeVariableLabel(), gum::BayesNet< double >::changeVariableName(), gum::BayesNet< double >::cpt(), gum::BayesNet< double >::erase(), gum::BayesNet< double >::eraseArc(), gum::BayesNet< double >::generateCPT(), gum::BayesNet< double >::idFromName(), gum::BayesNet< double >::reverseArc(), and gum::BayesNet< double >::variable().

291  {
292  return __varMap.idFromName(name);
293  }
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
NodeId idFromName(const std::string &name) const
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◆ jointProbability()

template<typename GUM_SCALAR >
GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::jointProbability ( const Instantiation i) const
inherited

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

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

Definition at line 217 of file IBayesNet_tpl.h.

217  {
218  auto value = (GUM_SCALAR)1.0;
219 
220  GUM_SCALAR tmp;
221 
222  for (auto node : nodes()) {
223  if ((tmp = cpt(node)[i]) == (GUM_SCALAR)0) { return (GUM_SCALAR)0; }
224 
225  value *= tmp;
226  }
227 
228  return value;
229  }
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:112

◆ 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 72 of file DAGmodel_inl.h.

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

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

72  {
73  double dSize = 0.0;
74 
75  for (const auto node : nodes()) {
76  dSize += std::log10(variable(node).domainSize());
77  }
78 
79  return dSize;
80  }
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:112
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variabe 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
inherited

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

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

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

Definition at line 236 of file IBayesNet_tpl.h.

236  {
237  auto value = (GUM_SCALAR)0.0;
238 
239  GUM_SCALAR tmp;
240 
241  for (auto node : nodes()) {
242  if ((tmp = cpt(node)[i]) == (GUM_SCALAR)0) {
243  return (GUM_SCALAR)(-std::numeric_limits< double >::infinity());
244  }
245 
246  value += log2(cpt(node)[i]);
247  }
248 
249  return value;
250  }
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:112

◆ maxNonOneParam()

template<typename GUM_SCALAR >
GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::maxNonOneParam ( ) const
inherited
Returns
the biggest value (not equal to 1) in the CPTs of *this

Definition at line 132 of file IBayesNet_tpl.h.

132  {
133  GUM_SCALAR res = 0.0;
134  for (auto node : nodes()) {
135  auto v = cpt(node).maxNonOne();
136  if (v > res) { res = v; }
137  }
138  return res;
139  }
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:112

◆ maxParam()

template<typename GUM_SCALAR >
GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::maxParam ( ) const
inherited
Returns
the biggest value in the CPTs of *this

Definition at line 112 of file IBayesNet_tpl.h.

112  {
113  GUM_SCALAR res = 1.0;
114  for (auto node : nodes()) {
115  auto v = cpt(node).max();
116  if (v > res) { res = v; }
117  }
118  return res;
119  }
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:112

◆ maxVarDomainSize()

template<typename GUM_SCALAR >
Size gum::IBayesNet< GUM_SCALAR >::maxVarDomainSize ( ) const
inherited
Returns
the biggest domainSize among the variables of *this

Definition at line 92 of file IBayesNet_tpl.h.

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

92  {
93  Size res = 0;
94  for (auto node : nodes()) {
95  auto v = variable(node).domainSize();
96  if (v > res) { res = v; }
97  }
98  return res;
99  }
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:112
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition: types.h:45
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◆ minimalCondSet() [1/2]

template<typename GUM_SCALAR >
NodeSet gum::IBayesNet< GUM_SCALAR >::minimalCondSet ( NodeId  target,
const NodeSet soids 
) const
inherited

Definition at line 352 of file IBayesNet_tpl.h.

353  {
354  if (soids.contains(target)) return NodeSet({target});
355 
356  NodeSet res;
357  NodeSet alreadyVisitedUp;
358  NodeSet alreadyVisitedDn;
359  alreadyVisitedDn << target;
360  alreadyVisitedUp << target;
361 
362  for (auto fath : _dag.parents(target))
364  fath, soids, res, alreadyVisitedUp, alreadyVisitedDn);
365  for (auto chil : _dag.children(target))
367  chil, soids, res, alreadyVisitedUp, alreadyVisitedDn);
368  return res;
369  }
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
void __minimalCondSetVisitUp(NodeId node, const NodeSet &soids, NodeSet &minimal, NodeSet &alreadyVisitedUp, NodeSet &alreadyVisitedDn) const
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
void __minimalCondSetVisitDn(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
inherited

Definition at line 372 of file IBayesNet_tpl.h.

373  {
374  NodeSet res;
375  for (auto node : targets) {
376  res += minimalCondSet(node, soids);
377  }
378  return res;
379  }
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
NodeSet minimalCondSet(NodeId target, const NodeSet &soids) const

◆ minNonZeroParam()

template<typename GUM_SCALAR >
GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::minNonZeroParam ( ) const
inherited
Returns
the smallest value (not equal to 0) in the CPTs of *this

Definition at line 122 of file IBayesNet_tpl.h.

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

122  {
123  GUM_SCALAR res = 1.0;
124  for (auto node : nodes()) {
125  auto v = cpt(node).minNonZero();
126  if (v < res) { res = v; }
127  }
128  return res;
129  }
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:112
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◆ minParam()

template<typename GUM_SCALAR >
GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::minParam ( ) const
inherited
Returns
the smallest value in the CPTs of *this

Definition at line 102 of file IBayesNet_tpl.h.

102  {
103  GUM_SCALAR res = 1.0;
104  for (auto node : nodes()) {
105  auto v = cpt(node).min();
106  if (v < res) { res = v; }
107  }
108  return res;
109  }
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:112

◆ 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 99 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().

99  {
100  if (clear
101  || (__mutableMoralGraph == nullptr)) { // we have to call _moralGraph
102  if (__mutableMoralGraph == nullptr) {
103  __mutableMoralGraph = new UndiGraph();
104  } else {
105  // clear is True ,__mutableMoralGraph exists
107  }
108 
109  __moralGraph();
110  }
111 
112  return *__mutableMoralGraph;
113  }
virtual void clear()
removes all the nodes and edges from the graph
Definition: undiGraph_inl.h:40
UndiGraph * __mutableMoralGraph
The moral graph of this Directed Graphical Model.
Definition: DAGmodel.h:208
void __moralGraph() const
Returns the moral graph of this DAGModel.
Definition: DAGmodel.cpp:51
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◆ nodeId()

template<typename GUM_SCALAR >
INLINE NodeId gum::BayesNet< GUM_SCALAR >::nodeId ( const DiscreteVariable var) const
finalvirtual

Returns a variable's id in the gum::BayesNet.

Parameters
varThe variable from which the gum::NodeId is returned.
Returns
Returns the gum::DiscreteVariable gum::NodeId in the gum::BayesNet.
Exceptions
NotFoundIf var is not in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 215 of file BayesNet_tpl.h.

Referenced by gum::credal::CredalNet< GUM_SCALAR >::__bnCopy(), and gum::BayesNet< double >::changeVariableLabel().

215  {
216  return __varMap.get(var);
217  }
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
const DiscreteVariable & get(NodeId id) const
Returns a discrete variable given it&#39;s node id.
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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 112 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::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().

112  {
113  return (NodeGraphPart&)_dag;
114  }
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
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◆ operator!=()

template<typename GUM_SCALAR>
bool gum::IBayesNet< GUM_SCALAR >::operator!= ( const IBayesNet< GUM_SCALAR > &  from) const
inherited
Returns
Returns false if the src and this are equal.

Definition at line 300 of file IBayesNet_tpl.h.

300  {
301  return !this->operator==(from);
302  }
bool operator==(const IBayesNet< GUM_SCALAR > &from) const
This operator compares 2 BNs !

◆ operator=()

template<typename GUM_SCALAR>
BayesNet< GUM_SCALAR > & gum::BayesNet< GUM_SCALAR >::operator= ( const BayesNet< GUM_SCALAR > &  source)

Copy operator.

Parameters
sourceThe copied BayesNet.
Returns
The copy of source.

Definition at line 168 of file BayesNet_tpl.h.

168  {
169  if (this != &source) {
171  __varMap = source.__varMap;
172 
174  __copyPotentials(source);
175  }
176 
177  return *this;
178  }
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
IBayesNet< GUM_SCALAR > & operator=(const IBayesNet< GUM_SCALAR > &source)
Copy operator.
Definition: IBayesNet_tpl.h:64
void __copyPotentials(const BayesNet< GUM_SCALAR > &source)
copy of potentials from a BN to another, using names of vars as ref.
Definition: BayesNet_tpl.h:607
void __clearPotentials()
clear all potentials
Definition: BayesNet_tpl.h:596

◆ operator==()

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

This operator compares 2 BNs !

Warning
To identify nodes between BNs, it is assumed that they share the same name.
Returns
true if the src and this are equal.

Definition at line 253 of file IBayesNet_tpl.h.

253  {
254  if (size() != from.size()) { return false; }
255 
256  if (sizeArcs() != from.sizeArcs()) { return false; }
257 
258  // alignment of variables between the 2 BNs
259  Bijection< const DiscreteVariable*, const DiscreteVariable* > alignment;
260 
261  for (auto node : nodes()) {
262  try {
263  alignment.insert(&variable(node),
264  &from.variableFromName(variable(node).name()));
265  } catch (NotFound&) {
266  // a name is not found in from
267  return false;
268  }
269  }
270 
271  for (auto node : nodes()) {
272  NodeId fromnode = from.idFromName(variable(node).name());
273 
274  if (cpt(node).nbrDim() != from.cpt(fromnode).nbrDim()) { return false; }
275 
276  if (cpt(node).domainSize() != from.cpt(fromnode).domainSize()) {
277  return false;
278  }
279 
280  Instantiation i(cpt(node));
281  Instantiation j(from.cpt(fromnode));
282 
283  for (i.setFirst(); !i.end(); i.inc()) {
284  for (Idx indice = 0; indice < cpt(node).nbrDim(); ++indice) {
285  const DiscreteVariable* p = &(i.variable(indice));
286  j.chgVal(*(alignment.second(p)), i.val(*p));
287  }
288 
289  if (std::pow(cpt(node).get(i) - from.cpt(fromnode).get(j), (GUM_SCALAR)2)
290  > (GUM_SCALAR)1e-6) {
291  return false;
292  }
293  }
294  }
295 
296  return true;
297  }
Size sizeArcs() const
Returns the number of arcs in this Directed Graphical Model.
Definition: DAGmodel_inl.h:99
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
Size size() const
Returns the number of variables in this Directed Graphical Model.
Definition: DAGmodel_inl.h:93
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:112
Size NodeId
Type for node ids.
Definition: graphElements.h:97

◆ 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
idthe node toward which the arcs returned are pointing

Definition at line 103 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().

103  {
104  return _dag.parents(id);
105  }
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
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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
idthe node toward which the arcs returned are pointing

Definition at line 153 of file DAGmodel.h.

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

153  {
154  return parents(idFromName(name));
155  };
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:103
virtual NodeId idFromName(const std::string &name) const =0
Getter by 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
NotFoundRaised if no name property is found.

Definition at line 34 of file DAGmodel_inl.h.

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

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

34  {
35  try {
36  return __properties()[name];
37  } catch (NotFound&) {
38  std::string msg = "The following property does not exists: ";
39  GUM_ERROR(NotFound, msg + name);
40  }
41  }
HashTable< std::string, std::string > & __properties() const
Return the properties of this Directed Graphical Model and initialize the hash table is necessary...
Definition: DAGmodel_inl.h:63
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52
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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 45 of file DAGmodel_inl.h.

References gum::DAGmodel::__properties().

46  {
47  try {
48  return __properties()[name];
49  } catch (NotFound&) { return byDefault; }
50  }
HashTable< std::string, std::string > & __properties() const
Return the properties of this Directed Graphical Model and initialize the hash table is necessary...
Definition: DAGmodel_inl.h:63
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◆ reverseArc() [1/3]

template<typename GUM_SCALAR >
INLINE void gum::BayesNet< GUM_SCALAR >::reverseArc ( NodeId  tail,
NodeId  head 
)

Reverses an arc while preserving the same joint distribution.

This method uses Shachter's 1986 algorithm for reversing an arc in the Bayes net while preserving the same joint distribution. By performing this reversal, we also add new arcs (required to not alter the joint distribution)

Exceptions
InvalidArcexception if the arc does not exist or if its reversal would induce a directed cycle.

Definition at line 421 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::eraseArc(), and gum::BayesNet< double >::reverseArc().

421  {
422  reverseArc(Arc(tail, head));
423  }
void reverseArc(NodeId tail, NodeId head)
Reverses an arc while preserving the same joint distribution.
Definition: BayesNet_tpl.h:421
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◆ reverseArc() [2/3]

template<typename GUM_SCALAR>
void gum::BayesNet< GUM_SCALAR >::reverseArc ( const std::string &  tail,
const std::string &  head 
)
inline

Reverses an arc while preserving the same joint distribution.

This method uses Shachter's 1986 algorithm for reversing an arc in the Bayes net while preserving the same joint distribution. By performing this reversal, we also add new arcs (required to not alter the joint distribution)

Exceptions
InvalidArcexception if the arc does not exist or if its reversal would induce a directed cycle.

Definition at line 446 of file BayesNet.h.

446  {
447  reverseArc(idFromName(tail), idFromName(head));
448  }
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291
void reverseArc(NodeId tail, NodeId head)
Reverses an arc while preserving the same joint distribution.
Definition: BayesNet_tpl.h:421

◆ reverseArc() [3/3]

template<typename GUM_SCALAR >
void gum::BayesNet< GUM_SCALAR >::reverseArc ( const Arc arc)

Reverses an arc while preserving the same joint distribution.

This method uses Shachter's 1986 algorithm for reversing an arc in the Bayes net while preserving the same joint distribution. By performing this reversal, we also add new arcs (required to not alter the joint distribution)

Exceptions
InvalidArcexception if the arc does not exist or if its reversal would induce a directed cycle.

Definition at line 358 of file BayesNet_tpl.h.

358  {
359  // check that the arc exsists
360  if (!__varMap.exists(arc.tail()) || !__varMap.exists(arc.head())
361  || !dag().existsArc(arc)) {
362  GUM_ERROR(InvalidArc, "a nonexisting arc cannot be reversed");
363  }
364 
365  NodeId tail = arc.tail(), head = arc.head();
366 
367  // check that the reversal does not induce a cycle
368  try {
369  DAG d = dag();
370  d.eraseArc(arc);
371  d.addArc(head, tail);
372  } catch (Exception&) {
373  GUM_ERROR(InvalidArc, "this arc reversal would induce a directed cycle");
374  }
375 
376  // with the same notations as Shachter (1986), "evaluating influence
377  // diagrams",
378  // p.878, we shall first compute the product of probabilities:
379  // pi_j^old (x_j | x_c^old(j) ) * pi_i^old (x_i | x_c^old(i) )
380  Potential< GUM_SCALAR > prod{cpt(tail) * cpt(head)};
381 
382  // modify the topology of the graph: add to tail all the parents of head
383  // and add to head all the parents of tail
385  NodeSet new_parents;
386  for (const auto node : this->parents(tail))
387  new_parents.insert(node);
388  for (const auto node : this->parents(head))
389  new_parents.insert(node);
390  // remove arc (head, tail)
391  eraseArc(arc);
392 
393  // add the necessary arcs to the tail
394  for (const auto p : new_parents) {
395  if ((p != tail) && !dag().existsArc(p, tail)) { addArc(p, tail); }
396  }
397 
398  addArc(head, tail);
399  // add the necessary arcs to the head
400  new_parents.erase(tail);
401 
402  for (const auto p : new_parents) {
403  if ((p != head) && !dag().existsArc(p, head)) { addArc(p, head); }
404  }
405 
407 
408  // update the conditional distributions of head and tail
409  Set< const DiscreteVariable* > del_vars;
410  del_vars << &(variable(tail));
411  Potential< GUM_SCALAR > new_cpt_head = prod.margSumOut(del_vars);
412  auto& cpt_head = const_cast< Potential< GUM_SCALAR >& >(cpt(head));
413  cpt_head = std::move(new_cpt_head);
414 
415  Potential< GUM_SCALAR > new_cpt_tail{prod / cpt_head};
416  auto& cpt_tail = const_cast< Potential< GUM_SCALAR >& >(cpt(tail));
417  cpt_tail = std::move(new_cpt_tail);
418  }
void addArc(NodeId tail, NodeId head)
Add an arc in the BN, and update arc.head&#39;s CPT.
Definition: BayesNet_tpl.h:336
const DiscreteVariable & variable(NodeId id) const final
Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
Definition: BayesNet_tpl.h:190
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:103
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
bool exists(NodeId id) const
Return true if id matches a node.
void beginTopologyTransformation()
When inserting/removing arcs, node CPTs change their dimension with a cost in time.
Definition: BayesNet_tpl.h:582
const Potential< GUM_SCALAR > & cpt(NodeId varId) const final
Returns the CPT of a variable.
Definition: BayesNet_tpl.h:303
void endTopologyTransformation()
terminates a sequence of insertions/deletions of arcs by adjusting all CPTs dimensions.
Definition: BayesNet_tpl.h:589
bool existsArc(const Arc &arc) const
indicates whether a given arc exists
const DAG & dag() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:60
Size NodeId
Type for node ids.
Definition: graphElements.h:97
void insert(const Key &k)
Inserts a new element into the set.
Definition: set_tpl.h:610
#define GUM_ERROR(type, msg)
Definition: exceptions.h:52
void eraseArc(const Arc &arc)
Removes an arc in the BN, and update head&#39;s CTP.
Definition: BayesNet_tpl.h:343

◆ 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 53 of file DAGmodel_inl.h.

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

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

53  {
54  try {
55  __properties()[name] = value;
56  } catch (NotFound&) { __properties().insert(name, value); }
57  }
HashTable< std::string, std::string > & __properties() const
Return the properties of this Directed Graphical Model and initialize the hash table is necessary...
Definition: DAGmodel_inl.h:63
value_type & insert(const Key &key, const Val &val)
Adds a new element (actually a copy of this element) into the hash table.
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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 93 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().

93 { 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:60
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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 99 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==().

99 { return _dag.sizeArcs(); }
DAG _dag
The DAG of this Directed Graphical Model.
Definition: DAGmodel.h:200
Size sizeArcs() const
indicates the number of arcs stored within the ArcGraphPart
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◆ toDot()

template<typename GUM_SCALAR >
std::string gum::IBayesNet< GUM_SCALAR >::toDot ( ) const
virtualinherited
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 172 of file IBayesNet_tpl.h.

172  {
173  std::stringstream output;
174  output << "digraph \"";
175 
176  std::string bn_name;
177 
178  try {
179  bn_name = this->property("name");
180  } catch (NotFound&) { bn_name = "no_name"; }
181 
182  output << bn_name << "\" {" << std::endl;
183  output << " graph [bgcolor=transparent,label=\"" << bn_name << "\"];"
184  << std::endl;
185  output << " node [style=filled fillcolor=\"#ffffaa\"];" << std::endl
186  << std::endl;
187 
188  for (auto node : nodes())
189  output << "\"" << variable(node).name() << "\" [comment=\"" << node << ":"
190  << variable(node).toStringWithDescription() << "\"];" << std::endl;
191 
192  output << std::endl;
193 
194  std::string tab = " ";
195 
196  for (auto node : nodes()) {
197  if (children(node).size() > 0) {
198  for (auto child : children(node)) {
199  output << tab << "\"" << variable(node).name() << "\" -> "
200  << "\"" << variable(child).name() << "\";" << std::endl;
201  }
202  } else if (parents(node).size() == 0) {
203  output << tab << "\"" << variable(node).name() << "\";" << std::endl;
204  }
205  }
206 
207  output << "}" << std::endl;
208 
209  return output.str();
210  }
const NodeSet & children(const NodeId id) const
returns the set of nodes with arc outgoing from a given node
Definition: DAGmodel_inl.h:108
const NodeSet & parents(const NodeId id) const
returns the set of nodes with arc ingoing to a given node
Definition: DAGmodel_inl.h:103
const std::string toStringWithDescription() const
string version of *this using description attribute instead of name.
Size size() const
Returns the number of variables in this Directed Graphical Model.
Definition: DAGmodel_inl.h:93
virtual const DiscreteVariable & variable(NodeId id) const =0
Returns a constant reference over a variable given it&#39;s node id.
const std::string & property(const std::string &name) const
Return the value of the property name of this DAGModel.
Definition: DAGmodel_inl.h:34
const NodeGraphPart & nodes() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:112
const std::string & name() const
returns the name of the variable

◆ 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 115 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().

115  {
116  return this->dag().topologicalOrder(clear);
117  }
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:88
const DAG & dag() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:60
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◆ toString()

template<typename GUM_SCALAR >
INLINE std::string gum::IBayesNet< GUM_SCALAR >::toString ( ) const
inherited
Returns
Returns a string representation of this IBayesNet.

Definition at line 142 of file IBayesNet_tpl.h.

Referenced by gum::operator<<().

142  {
143  Size param = 0;
144  double dSize = log10DomainSize();
145 
146  for (auto node : nodes())
147  param += cpt(node).content()->realSize();
148 
149  double compressionRatio = log10(1.0 * param) - dSize;
150 
151  std::stringstream s;
152  s << "BN{nodes: " << size() << ", arcs: " << dag().sizeArcs() << ", ";
153 
154  if (dSize > 6)
155  s << "domainSize: 10^" << dSize;
156  else
157  s << "domainSize: " << std::round(std::pow(10.0, dSize));
158 
159  s << ", parameters: " << param << ", compression ratio: ";
160 
161  if (compressionRatio > -3)
162  s << trunc(100.0 - std::pow(10.0, compressionRatio + 2.0));
163  else
164  s << "100-10^" << compressionRatio + 2.0;
165 
166  s << "% }";
167 
168  return s.str();
169  }
virtual const Potential< GUM_SCALAR > & cpt(NodeId varId) const =0
Returns the CPT of a variable.
Size size() const
Returns the number of variables in this Directed Graphical Model.
Definition: DAGmodel_inl.h:93
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:112
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition: types.h:45
double log10DomainSize() const
Definition: DAGmodel_inl.h:72
const DAG & dag() const
Returns a constant reference to the dag of this Bayes Net.
Definition: DAGmodel_inl.h:60
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◆ variable() [1/2]

template<typename GUM_SCALAR >
INLINE const DiscreteVariable & gum::BayesNet< GUM_SCALAR >::variable ( NodeId  id) const
finalvirtual

Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.

Parameters
idThe variable's id to return.
Returns
Returns a constant reference of the gum::DiscreteVariable corresponding to id in the gum::BayesNet.
Exceptions
NotFoundRaised if id does not match a a variable in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 190 of file BayesNet_tpl.h.

Referenced by gum::credal::CredalNet< GUM_SCALAR >::__bnCopy(), gum::credal::CredalNet< GUM_SCALAR >::approximatedBinarization(), gum::BayesNetFactory< GUM_SCALAR >::BayesNetFactory(), gum::learning::genericBNLearner::Database::Database(), gum::BayesNet< double >::erase(), gum::getMaxModality(), gum::credal::CredalNet< GUM_SCALAR >::toString(), and gum::BayesNet< double >::variable().

190  {
191  return __varMap.get(id);
192  }
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
const DiscreteVariable & get(NodeId id) const
Returns a discrete variable given it&#39;s node id.
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◆ variable() [2/2]

template<typename GUM_SCALAR>
const DiscreteVariable& gum::BayesNet< GUM_SCALAR >::variable ( const std::string &  name) const
inline

Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.

Definition at line 295 of file BayesNet.h.

295  {
296  return variable(idFromName(name));
297  };
const DiscreteVariable & variable(NodeId id) const final
Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
Definition: BayesNet_tpl.h:190
NodeId idFromName(const std::string &name) const final
Returns a variable&#39;s id given its name in the gum::BayesNet.
Definition: BayesNet_tpl.h:291

◆ variableFromName()

template<typename GUM_SCALAR >
INLINE const DiscreteVariable & gum::BayesNet< GUM_SCALAR >::variableFromName ( const std::string &  name) const
finalvirtual

Returns a variable given its name in the gum::BayesNet.

Parameters
nameThe variable's name in the gum::BayesNet.
Returns
Returns the gum::DiscreteVariable named name in the gum::BayesNet.
Exceptions
NotFoundRaised if name does not match a variable in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 297 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::changeVariableLabel(), and gum::BayesNet< double >::variableFromName().

297  {
298  return __varMap.variableFromName(name);
299  }
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
const DiscreteVariable & variableFromName(const std::string &name) const
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◆ variableNodeMap()

template<typename GUM_SCALAR >
INLINE const VariableNodeMap & gum::BayesNet< GUM_SCALAR >::variableNodeMap ( ) const
finalvirtual

Returns a map between variables and nodes of this gum::BayesNet.

Returns
Returns a constant reference to the gum::VariableNodeMap.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 308 of file BayesNet_tpl.h.

Referenced by gum::BayesNet< double >::cpt(), and gum::learning::DAG2BNLearner< ALLOC >::createBN().

308  {
309  return __varMap;
310  }
VariableNodeMap __varMap
the map between variable and id
Definition: BayesNet.h:648
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Friends And Related Function Documentation

◆ BayesNetFactory< GUM_SCALAR >

template<typename GUM_SCALAR>
friend class BayesNetFactory< GUM_SCALAR >
friend

Definition at line 77 of file BayesNet.h.

Member Data Documentation

◆ __probaMap

template<typename GUM_SCALAR>
NodeProperty< Potential< GUM_SCALAR >* > gum::BayesNet< GUM_SCALAR >::__probaMap
private

Mapping between the variable's id and their CPT.

Definition at line 651 of file BayesNet.h.

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

◆ __varMap

template<typename GUM_SCALAR>
VariableNodeMap gum::BayesNet< GUM_SCALAR >::__varMap
private

the map between variable and id

Definition at line 648 of file BayesNet.h.

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

◆ _dag


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