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.
NodeId	addNoisyAND (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, its associate node and a noisyAND implementation.
NodeId	addLogit (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a Logit implementation.
NodeId	addLogit (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, its associate node and a Logit implementation.
NodeId	addOR (const DiscreteVariable &var)
	Add a variable, it's associate node and an OR implementation.
NodeId	addAND (const DiscreteVariable &var)
	Add a variable, it's associate node and an AND implementation.
void	addWeightedArc (NodeId tail, NodeId head, GUM_SCALAR causalWeight)
	Add an arc in the BN, and update arc.head's CPT.
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.
void	generateCPTs () const
	randomly generates CPTs for a given structure
void	generateCPT (NodeId node) const
	randomly generate CPT for a given node in a given structure
void	generateCPT (const std::string &name) const
void	changeTensor (NodeId id, Tensor< GUM_SCALAR > *newPot)
	change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.
void	changeTensor (const std::string &name, Tensor< GUM_SCALAR > *newPot)
BayesNet< GUM_SCALAR >	contextualize (const gum::Instantiation &observations, const gum::Instantiation &interventions) const
	create a contextual BN from this and a set of hard observations and hard interventions.
std::vector< std::string >	check () const
	Check if the BayesNet is consistent (variables, CPT).
bool	operator== (const IBayesNet< GUM_SCALAR > &from) const
	This operator compares 2 BNs !
bool	operator!= (const IBayesNet< GUM_SCALAR > &from) const
Size	dim () const
	Returns the dimension (the number of free parameters) in this bayes net.
Size	maxVarDomainSize () const
GUM_SCALAR	minParam () const
GUM_SCALAR	maxParam () const
GUM_SCALAR	minNonZeroParam () const
GUM_SCALAR	maxNonOneParam () const
virtual std::string	toDot () const
std::string	toString () const
Tensor< GUM_SCALAR >	evEq (const std::string &name, double value) const
Tensor< GUM_SCALAR >	evIn (const std::string &name, double val1, double val2) const
Tensor< GUM_SCALAR >	evLt (const std::string &name, double value) const
Tensor< GUM_SCALAR >	evGt (const std::string &name, double value) const
Size	memoryFootprint () const
	compute the (approximated) footprint in memory of the model (the footprints of CPTs)
bool	hasSameStructure (const DAGmodel &other)
NodeSet	minimalCondSet (NodeId target, const NodeSet &soids) const
NodeSet	minimalCondSet (const NodeSet &targets, const NodeSet &soids) const
NodeSet	minimalCondSet (const std::string &target, const std::vector< std::string > &soids) const
NodeSet	minimalCondSet (const std::vector< std::string > &targets, const std::vector< std::string > &soids) const
double	log10DomainSize () const
Constructors and Destructor
	BayesNet ()
	Default constructor.
	BayesNet (std::string name)
	Default constructor.
virtual	~BayesNet ()
	Destructor.
	BayesNet (const BayesNet< GUM_SCALAR > &source)
	Copy constructor.
Operators
BayesNet< GUM_SCALAR > &	operator= (const BayesNet< GUM_SCALAR > &source)
	Copy operator.
Variable manipulation methods
const Tensor< GUM_SCALAR > &	cpt (NodeId varId) const final
	Returns the CPT of a variable.
const Tensor< GUM_SCALAR > &	cpt (const std::string &name) const
	Returns the CPT of a variable.
const VariableNodeMap &	variableNodeMap () const final
	Returns a map between variables and nodes of this gum::BayesNet.
NodeId	add (const DiscreteVariable &var)
	Add a variable to the gum::BayesNet.
NodeId	add (const std::string &fast_description, unsigned int default_nbrmod=2)
	Use "fast" syntax to add a variable in the BayesNet.
NodeId	add (const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent)
	Add a variable to the gum::BayesNet.
NodeId	add (const DiscreteVariable &var, NodeId id)
	Add a variable to the gum::BayesNet.
NodeId	add (const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent, NodeId id)
	Add a variable to the gum::BayesNet.
void	clear ()
	clear the whole Bayes net *
void	erase (NodeId varId)
	Remove a variable from the gum::BayesNet.
void	erase (const std::string &name)
	Removes a variable from the gum::BayesNet.
void	erase (const DiscreteVariable &var)
	Remove a variable from the gum::BayesNet.
const DiscreteVariable &	variable (NodeId id) const final
	Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
const DiscreteVariable &	variable (const std::string &name) const
	Returns a gum::DiscreteVariable given its gum::NodeId in the gum::BayesNet.
void	changeVariableName (NodeId id, const std::string &new_name)
	Changes a variable's name in the gum::BayesNet.
void	changeVariableName (const std::string &name, const std::string &new_name)
	Changes a variable's name.
void	changeVariableLabel (NodeId id, const std::string &old_label, const std::string &new_label)
	Changes a variable's label in the gum::BayesNet.
void	changeVariableLabel (const std::string &name, const std::string &old_label, const std::string &new_label)
	Changes a variable's name.
NodeId	nodeId (const DiscreteVariable &var) const final
	Returns a variable's id in the gum::BayesNet.
NodeId	idFromName (const std::string &name) const final
	Returns a variable's id given its name in the gum::BayesNet.
const DiscreteVariable &	variableFromName (const std::string &name) const final
	Returns a variable given its name in the gum::BayesNet.
Arc manipulation methods.
void	addArc (NodeId tail, NodeId head)
	Add an arc in the BN, and update arc.head's CPT.
void	addArc (const std::string &tail, const std::string &head)
	Add an arc in the BN, and update arc.head's CPT.
void	eraseArc (const Arc &arc)
	Removes an arc in the BN, and update head's CTP.
void	eraseArc (NodeId tail, NodeId head)
	Removes an arc in the BN, and update head's CTP.
void	eraseArc (const std::string &tail, const std::string &head)
	Removes an arc in the BN, and update head's CTP.
void	beginTopologyTransformation ()
	When inserting/removing arcs, node CPTs change their dimension with a cost in time.
void	endTopologyTransformation ()
	terminates a sequence of insertions/deletions of arcs by adjusting all CPTs dimensions.
void	reverseArc (NodeId tail, NodeId head)
	Reverses an arc while preserving the same joint distribution.
void	reverseArc (const std::string &tail, const std::string &head)
	Reverses an arc while preserving the same joint distribution.
void	reverseArc (const Arc &arc)
	Reverses an arc while preserving the same joint distribution.
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.
NodeId	addNoisyORNet (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, it's associate node and a gum::noisyOR implementation.
NodeId	addNoisyORCompound (const DiscreteVariable &var, GUM_SCALAR external_weight)
	Add a variable, it's associate node and a gum::noisyOR implementation.
NodeId	addNoisyOR (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyOR implementation.
NodeId	addNoisyORNet (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyOR implementation.
NodeId	addNoisyORCompound (const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id)
	Add a variable, its associate node and a noisyOR implementation.
NodeId	addAMPLITUDE (const DiscreteVariable &var)
	Others aggregators.
NodeId	addCOUNT (const DiscreteVariable &var, Idx value=1)
	Others aggregators.
NodeId	addEXISTS (const DiscreteVariable &var, Idx value=1)
	Others aggregators.
NodeId	addFORALL (const DiscreteVariable &var, Idx value=1)
	Others aggregators.
NodeId	addMAX (const DiscreteVariable &var)
	Others aggregators.
NodeId	addMEDIAN (const DiscreteVariable &var)
	Others aggregators.
NodeId	addMIN (const DiscreteVariable &var)
	Others aggregators.
NodeId	addSUM (const DiscreteVariable &var)
	Others aggregators.
Joint Probability manipulation methods
GUM_SCALAR	jointProbability (const Instantiation &i) const
	Compute a parameter of the joint probability for the BN (given an instantiation of the vars).
GUM_SCALAR	log2JointProbability (const Instantiation &i) const
	Compute a parameter of the log joint probability for the BN (given an instantiation of the vars).
Variable manipulation methods.
const DAG &	dag () const
	Returns a constant reference to the dag of this Bayes Net.
virtual Size	size () const final
	Returns the number of variables in this Directed Graphical Model.
Size	sizeArcs () const
	Returns the number of arcs in this Directed Graphical Model.
const NodeGraphPart &	nodes () const final
	Returns a constant reference to the dag of this Bayes Net.
bool	exists (NodeId node) const final
	Return true if this node exists in this graphical model.
bool	exists (const std::string &name) const final
	Returns a constant reference to the dag of this Bayes Net.
Arc manipulation methods.
const ArcSet &	arcs () const
	return true if the arc tail->head exists in the DAGmodel
bool	existsArc (const NodeId tail, const NodeId head) const
	return true if the arc tail->head exists in the DAGmodel
bool	existsArc (const std::string &nametail, const std::string &namehead) const
	return true if the arc tail->head exists in the DAGmodel
const NodeSet &	parents (const NodeId id) const
	returns the set of nodes with arc ingoing to a given node
const NodeSet &	parents (const std::string &name) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	parents (const NodeSet &ids) const
	returns the parents of a set of nodes
NodeSet	parents (const std::vector< std::string > &names) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	family (const NodeId id) const final
	returns the parents of a node and the node
NodeSet	family (const std::string &name) const final
	return true if the arc tail->head exists in the DAGmodel
const NodeSet &	children (const NodeId id) const
	returns the set of nodes with arc outgoing from a given node
const NodeSet &	children (const std::string &name) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	children (const NodeSet &ids) const
	returns the children of a set of nodes
NodeSet	children (const std::vector< std::string > &names) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	descendants (const NodeId id) const
	returns the set of nodes with directed path outgoing from a given node
NodeSet	descendants (const std::string &name) const
	return true if the arc tail->head exists in the DAGmodel
NodeSet	ancestors (const NodeId id) const
	returns the set of nodes with directed path ingoing to a given node
NodeSet	ancestors (const std::string &name) const
	return true if the arc tail->head exists in the DAGmodel
Graphical methods
UndiGraph	moralizedAncestralGraph (const NodeSet &nodes) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
UndiGraph	moralizedAncestralGraph (const std::vector< std::string > &nodenames) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
bool	isIndependent (NodeId X, NodeId Y, const NodeSet &Z) const final
	check if node X and node Y are independent given nodes Z
bool	isIndependent (const NodeSet &X, const NodeSet &Y, const NodeSet &Z) const final
	check if nodes X and nodes Y are independent given nodes Z
bool	isIndependent (const std::string &Xname, const std::string &Yname, const std::vector< std::string > &Znames) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
bool	isIndependent (const std::vector< std::string > &Xnames, const std::vector< std::string > &Ynames, const std::vector< std::string > &Znames) const
	build a UndiGraph by moralizing the Ancestral Graph of a set of Nodes
UndiGraph	moralGraph () const
	The node's id are coherent with the variables and nodes of the topology.
Sequence< NodeId >	topologicalOrder () const
	The topological order stays the same as long as no variable or arcs are added or erased src the topology.
Getter and setters
const std::string &	property (const std::string &name) const
	Return the value of the property name of this GraphicalModel.
const std::string &	propertyWithDefault (const std::string &name, const std::string &byDefault) const
	Return the value of the property name of this GraphicalModel.
void	setProperty (const std::string &name, const std::string &value)
	Add or change a property of this GraphicalModel.
std::vector< std::string >	properties () const
	List of all the properties.
Variable manipulation methods.
virtual bool	empty () const
	Return true if this graphical model is empty.
std::vector< std::string >	names (const std::vector< NodeId > &ids) const
	transform a vector of NodeId in a vector of names
std::vector< std::string >	names (const NodeSet &ids) const
	transform a NodeSet in a vector of names
std::vector< NodeId >	ids (const std::vector< std::string > &names) const
	transform a vector of names into a vector of nodeId
NodeSet	nodeset (const std::vector< std::string > &names) const
	transform a vector of names into a NodeSet
gum::VariableSet	variables (const std::vector< std::string > &l) const
	transform a vector of names into a VariableeSet
gum::VariableSet	variables (const NodeSet &ids) const
	transform a vector of NodeId into a VariableeSet
Instantiation	completeInstantiation () const
	Get an instantiation over all the variables of the model.

Static Public Member Functions
static BayesNet< GUM_SCALAR >	fastPrototype (const std::string &dotlike, Size domainSize)
	Create a Bayesian network with a dot-like syntax which specifies:
static BayesNet< GUM_SCALAR >	fastPrototype (const std::string &dotlike, const std::string &domainSize="[2]")
static void	spaceCplxToStream (std::stringstream &s, double dSize, int dim, Size usedMem)
	send to the stream the space complexity with 3 parametrs

Protected Attributes
DAG	dag_
	The DAG of this Directed Graphical Model.

Private Member Functions
void	_clearTensors_ ()
	clear all tensors
void	_copyTensors_ (const BayesNet< GUM_SCALAR > &source)
	copy of tensors from a BN to another, using names of vars as ref.
void	_unsafeChangeTensor_ (NodeId id, Tensor< GUM_SCALAR > *newPot)
	change the CPT associated to nodeId to newPot delete the old CPT associated to nodeId.
const HashTable< std::string, std::string > &	_properties_ () const
	Return the properties of this Directed Graphical Model.

Private Attributes
VariableNodeMap	_varMap_
	the map between variable and id
NodeProperty< Tensor< GUM_SCALAR > * >	_probaMap_
	Mapping between the variable's id and their CPT.
HashTable< std::string, std::string >	_propertiesMap_
	The properties of this Directed Graphical Model.

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:

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

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, it's domain cannot change. But it arcs are added, the data in its CPT are 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 93 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 128 of file BayesNet_tpl.h.

                                          : IBayesNet< GUM_SCALAR >() {
    GUM_CONSTRUCTOR(BayesNet)
  }

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

Referenced by BayesNet(), BayesNet(), BayesNet(), ~BayesNet(), and add().

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BayesNet() [2/3]

template<typename GUM_SCALAR>

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

explicit

Default constructor.

Parameters

name	The BayesNet's name.

Definition at line 133 of file BayesNet_tpl.h.

                                                        : IBayesNet< GUM_SCALAR >(name) {
    GUM_CONSTRUCTOR(BayesNet)
  }

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

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~BayesNet()

template<typename GUM_SCALAR>

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

virtual

Destructor.

Definition at line 159 of file BayesNet_tpl.h.

                                    {
    GUM_DESTRUCTOR(BayesNet)
    for (const auto& p: _probaMap_) {
      delete p.second;
    }
  }

References BayesNet(), and _probaMap_.

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BayesNet() [3/3]

template<typename GUM_SCALAR>

gum::BayesNet< GUM_SCALAR >::BayesNet

(

const BayesNet< GUM_SCALAR > &

source

)

Copy constructor.

Definition at line 138 of file BayesNet_tpl.h.

                                                                       :
      IBayesNet< GUM_SCALAR >(source), _varMap_(source._varMap_) {
    GUM_CONS_CPY(BayesNet)
 
    _copyTensors_(source);
  }

References BayesNet(), gum::IBayesNet< GUM_SCALAR >::IBayesNet(), _copyTensors_(), and _varMap_.

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Member Function Documentation

_clearTensors_()

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::_clearTensors_ ( )

private

clear all tensors

Definition at line 568 of file BayesNet_tpl.h.

                                              {
    // Removing previous tensors
    for (const auto& elt: _probaMap_) {
      delete elt.second;
    }
 
    _probaMap_.clear();
  }

References _probaMap_.

Referenced by operator=().

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_copyTensors_()

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::_copyTensors_ ( const BayesNet< GUM_SCALAR > & source )

private

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

Definition at line 579 of file BayesNet_tpl.h.

                                                                                 {
    // Copying tensors
 
    for (const auto& src: source._probaMap_) {
      // First we build the node's CPT
      auto copy_array = new Tensor< GUM_SCALAR >();
      copy_array->beginMultipleChanges();
      for (gum::Idx i = 0; i < src.second->nbrDim(); i++) {
        (*copy_array) << variableFromName(src.second->variable(i).name());
      }
      copy_array->endMultipleChanges();
      copy_array->copyFrom(*(src.second));
 
      // We add the CPT to the CPT hashmap
      _probaMap_.insert(src.first, copy_array);
    }
  }

References _probaMap_, and variableFromName().

Referenced by BayesNet(), and operator=().

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_properties_()

INLINE const HashTable< std::string, std::string > & gum::GraphicalModel::_properties_ ( ) const

privateinherited

Return the properties of this Directed Graphical Model.

Definition at line 70 of file graphicalModel_inl.h.

                                                                              {
    return _propertiesMap_;
  }

References _propertiesMap_.

Referenced by property().

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_unsafeChangeTensor_()

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::_unsafeChangeTensor_ ( NodeId id, Tensor< 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

changeTensor

Definition at line 631 of file BayesNet_tpl.h.

                                                                                           {
    delete _probaMap_[id];
    _probaMap_[id] = newPot;
  }

References _probaMap_.

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::Tensor.

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

Parameters

var	The variable added by copy.

Returns

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

Exceptions

DuplicateLabel

Raised if variable.name() is already used in this gum::BayesNet.

Definition at line 195 of file BayesNet_tpl.h.

                                                                       {
    auto ptr = new MultiDimArray< GUM_SCALAR >();
    try {
      return add(var, ptr);
    } catch (Exception const&) {
      delete ptr;
      throw;
    }
  }

References add().

Referenced by add(), add(), add(), addAMPLITUDE(), addAND(), addCOUNT(), addEXISTS(), addFORALL(), addLogit(), addLogit(), addMAX(), addMEDIAN(), addMIN(), addNoisyAND(), addNoisyAND(), addNoisyORCompound(), addNoisyORCompound(), addNoisyORNet(), addNoisyORNet(), addOR(), addSUM(), gum::build_node(), and gum::BayesNetFragment< GUM_SCALAR >::toBN().

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add() [2/5]

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::Tensor.

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

Parameters

var	The variable added by copy.
aContent	The gum::MultiDimImplementation to use for this variable's gum::Tensor implementation.

Returns

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

Exceptions

DuplicateLabel

Raised if variable.name() is already used in this gum::BayesNet.

Definition at line 214 of file BayesNet_tpl.h.

                                                                                            {
    NodeId proposedId = dag().nextNodeId();
 
    return add(var, aContent, proposedId);
  }

References BayesNet(), add(), and gum::DAGmodel::dag().

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add() [3/5]

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::Tensor.

Parameters

var	The variable added by copy.
aContent	The gum::MultiDimImplementation to use for this variable's gum::Tensor implementation.
id	The variable's forced gum::NodeId in the gum::BayesNet.

Returns

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

Exceptions

DuplicateElement	Raised id is already used.
DuplicateLabel	Raised if variable.name() is already used in this gum::BayesNet.

Definition at line 234 of file BayesNet_tpl.h.

                                                                               {
    _varMap_.insert(id, var);
    this->dag_.addNodeWithId(id);
 
    auto cpt = new Tensor< GUM_SCALAR >(aContent);
    (*cpt) << variable(id);
    _probaMap_.insert(id, cpt);
    return id;
  }

References _probaMap_, _varMap_, cpt(), gum::DAGmodel::dag_, and variable().

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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::Tensor.

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

Parameters

var	The variable added by copy.
id	The variable's forced gum::NodeId in the gum::BayesNet.

Returns

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

Exceptions

DuplicateElement	Raised id is already used.
DuplicateLabel	Raised if variable.name() is already used in this gum::BayesNet.

Definition at line 222 of file BayesNet_tpl.h.

                                                                                  {
    auto ptr = new MultiDimArray< GUM_SCALAR >();
 
    try {
      return add(var, ptr, id);
    } catch (Exception const&) {
      delete ptr;
      throw;
    }
  }

References add().

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add() [5/5]

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::add	(	const std::string &	fast_description,
		unsigned int	default_nbrmod = 2 )

Use "fast" syntax to add a variable in the BayesNet.

• a : range variable from 0 to default_nbrmod-1
• a[5] : range variable from 0 to 5
• a[-3,5] : range variable from -3 to 5
• a[1,3.14,5,3] : discretized variable
• a{x|y|z} : labelized variable
• a{-3|0|3|100} : integer variable

Parameters

fast_description(	str) following "fast" syntax description
default_nbrmod(	int) nbr of modality if fast_description do not indicate it. default_nbrmod=1 is the way to create a variable with only one value (for instance for reward in influence diagram).

Exceptions

DuplicateLabel	Raised if variable.name() is already used in this gum::BayesNet.
NotAllowed	if nbrmod<2

Definition at line 206 of file BayesNet_tpl.h.

                                                                               {
    auto v = fastVariable< GUM_SCALAR >(fast_description, default_nbrmod);
    if (v->domainSize() < 2) GUM_ERROR(OperationNotAllowed, v->name() << " has a domain size <2")
    return add(*v);
  }

addAMPLITUDE()

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addAMPLITUDE

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 406 of file BayesNet_tpl.h.

                                                                                {
    return add(var, new aggregator::Amplitude< GUM_SCALAR >());
  }

References add().

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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

var	The variable added by copy.

Returns

the id of the added variable.

Exceptions

SizeError

if variable.domainSize()>2

Definition at line 411 of file BayesNet_tpl.h.

                                                                          {
    if (var.domainSize() > 2) GUM_ERROR(SizeError, "an AND has to be boolean")
 
    return add(var, new aggregator::And< GUM_SCALAR >());
  }

References add(), gum::DiscreteVariable::domainSize(), and GUM_ERROR.

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addArc() [1/2]

template<typename GUM_SCALAR>

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

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

Exceptions

gum::DuplicateElement

if the arc already exists

Definition at line 310 of file BayesNet_tpl.h.

                                                                                         {
    try {
      addArc(this->idFromName(tail), this->idFromName(head));
    } catch (DuplicateElement const&) {
      GUM_ERROR(DuplicateElement, "The arc " << tail << "->" << head << " already exists.")
    }
  }

References addArc(), GUM_ERROR, and idFromName().

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addArc() [2/2]

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

head	and
tail	as NodeId

Exceptions

InvalidEdge	If arc.tail and/or arc.head are not in the BN.
DuplicateElement	if the arc already exists

Definition at line 299 of file BayesNet_tpl.h.

                                                                     {
    if (this->dag_.existsArc(tail, head)) {
      GUM_ERROR(DuplicateElement, "The arc (" << tail << "," << head << ") already exists.")
    }
 
    this->dag_.addArc(tail, head);
    // Add parent in the child's CPT
    (*(_probaMap_[head])) << variable(tail);
  }

References _probaMap_, gum::DAGmodel::dag_, GUM_ERROR, and variable().

Referenced by addArc(), addWeightedArc(), fastPrototype(), and gum::BayesNetFragment< GUM_SCALAR >::toBN().

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addCOUNT()

template<typename GUM_SCALAR>

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

Others aggregators.

Definition at line 418 of file BayesNet_tpl.h.

                                                                                       {
    return add(var, new aggregator::Count< GUM_SCALAR >(value));
  }

References add().

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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 423 of file BayesNet_tpl.h.

                                                                                        {
    if (var.domainSize() > 2) GUM_ERROR(SizeError, "an EXISTS has to be boolean")
 
    return add(var, new aggregator::Exists< GUM_SCALAR >(value));
  }

References add(), gum::DiscreteVariable::domainSize(), and GUM_ERROR.

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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 430 of file BayesNet_tpl.h.

                                                                                        {
    if (var.domainSize() > 2) GUM_ERROR(SizeError, "an EXISTS has to be boolean")
 
    return add(var, new aggregator::Forall< GUM_SCALAR >(value));
  }

References add(), gum::DiscreteVariable::domainSize(), and GUM_ERROR.

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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 )

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

The id of the new variable is automatically generated.

Parameters

var	The variable added by copy.
external_weight	see gum::MultiDimLogit

Returns

the id of the added variable.

Definition at line 491 of file BayesNet_tpl.h.

                                                                                          {
    return add(var, new MultiDimLogit< GUM_SCALAR >(external_weight));
  }

References add().

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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,
		NodeId	id )

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

Parameters

var	The variable added by copy
external_weight	see gum::MultiDimLogit
id	proposed 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 511 of file BayesNet_tpl.h.

                                                                             {
    return add(var, new MultiDimLogit< GUM_SCALAR >(external_weight), id);
  }

References add().

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addMAX()

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addMAX

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 437 of file BayesNet_tpl.h.

                                                                          {
    return add(var, new aggregator::Max< GUM_SCALAR >());
  }

References add().

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addMEDIAN()

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addMEDIAN

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 442 of file BayesNet_tpl.h.

                                                                             {
    return add(var, new aggregator::Median< GUM_SCALAR >());
  }

References add().

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addMIN()

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addMIN

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 447 of file BayesNet_tpl.h.

                                                                          {
    return add(var, new aggregator::Min< GUM_SCALAR >());
  }

References add().

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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 )

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

The id of the new variable is automatically generated.

Parameters

var	The variable added by copy.
external_weight	see gum::MultiDimNoisyAND

Returns

the id of the added variable.

Definition at line 485 of file BayesNet_tpl.h.

                                                                                             {
    return add(var, new MultiDimNoisyAND< GUM_SCALAR >(external_weight));
  }

References add().

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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,
		NodeId	id )

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

Parameters

var	The variable added by copy
external_weight	see gum::MultiDimNoisyAND
id	proposed 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 504 of file BayesNet_tpl.h.

                                                                                {
    return add(var, new MultiDimNoisyAND< GUM_SCALAR >(external_weight), id);
  }

References add().

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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

var	The variable added by copy.
external_weight	see ref gum::MultiDimNoisyORNet,gum::MultiDimNoisyORCompound

Returns

the id of the added variable.

Definition at line 467 of file BayesNet_tpl.h.

                                                                                            {
    return addNoisyORCompound(var, external_weight);
  }

References addNoisyORCompound().

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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

var	The variable added by copy.
external_weight	see gum::MultiDimNoisyORNet, gum::MultiDimNoisyORCompound
id	The chosen id

Warning

give an id should be reserved for rare and specific situations !!!

Returns

the id of the added variable.

Exceptions

DuplicateElement

if id is already used

Definition at line 497 of file BayesNet_tpl.h.

                                                                               {
    return addNoisyORCompound(var, external_weight, id);
  }

References addNoisyORCompound().

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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

var	The variable added by copy.
external_weight	see ref gum::MultiDimNoisyORNet,gum::MultiDimNoisyORCompound

Returns

the id of the added variable.

Definition at line 473 of file BayesNet_tpl.h.

                                                                                       {
    return add(var, new MultiDimNoisyORCompound< GUM_SCALAR >(external_weight));
  }

References add().

Referenced by addNoisyOR(), and addNoisyOR().

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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

var	The variable added by copy.
external_weight	see gum::MultiDimNoisyORNet, gum::MultiDimNoisyORCompound
id	The chosen id

Warning

give an id should be reserved for rare and specific situations !!!

Returns

the id of the added variable.

Exceptions

DuplicateElement

if id is already used

Definition at line 518 of file BayesNet_tpl.h.

                                                                                       {
    return add(var, new MultiDimNoisyORCompound< GUM_SCALAR >(external_weight), id);
  }

References add().

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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

var	The variable added by copy.
external_weight	see ref gum::MultiDimNoisyORNet,gum::MultiDimNoisyORCompound

Returns

the id of the added variable.

Definition at line 479 of file BayesNet_tpl.h.

                                                                                               {
    return add(var, new MultiDimNoisyORNet< GUM_SCALAR >(external_weight));
  }

References add().

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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

var	The variable added by copy.
external_weight	see gum::MultiDimNoisyORNet, gum::MultiDimNoisyORCompound
id	The chosen id

Warning

give an id should be reserved for rare and specific situations !!!

Returns

the id of the added variable.

Exceptions

DuplicateElement

if id is already used

Definition at line 525 of file BayesNet_tpl.h.

                                                                                  {
    return add(var, new MultiDimNoisyORNet< GUM_SCALAR >(external_weight), id);
  }

References add().

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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

var	The variable added by copy.

Returns

the id of the added variable.

Exceptions

SizeError

if variable.domainSize()>2

Definition at line 452 of file BayesNet_tpl.h.

                                                                         {
    if (var.domainSize() > 2) GUM_ERROR(SizeError, "an OR has to be boolean")
 
    return add(var, new aggregator::Or< GUM_SCALAR >());
  }

References add(), gum::DiscreteVariable::domainSize(), and GUM_ERROR.

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addSUM()

template<typename GUM_SCALAR>

INLINE NodeId gum::BayesNet< GUM_SCALAR >::addSUM

(

const DiscreteVariable &

var

)

Others aggregators.

Definition at line 459 of file BayesNet_tpl.h.

                                                                          {
    return add(var, new aggregator::Sum< GUM_SCALAR >());
  }

References add().

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addWeightedArc() [1/2]

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

head	and
tail	as std::string
causalWeight	see gum::MultiDimICIModel

Exceptions

NotFound	if no node with sun names is found
InvalidArc	If arc.tail and/or arc.head are not in the BN.
InvalidArc	If variable in arc.head is not a NoisyOR variable.

Definition at line 647 of file BayesNet.h.

                                                                                               {
      addWeightedArc(idFromName(tail), idFromName(head), causalWeight);
    };

References addWeightedArc(), and idFromName().

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addWeightedArc() [2/2]

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

head	and
tail	as NodeId
causalWeight	see gum::MultiDimICIModel

Exceptions

InvalidArc	If arc.tail and/or arc.head are not in the BN.
InvalidArc	If variable in arc.head is not a NoisyOR variable.

Definition at line 532 of file BayesNet_tpl.h.

                                                                                               {
    auto* CImodel = dynamic_cast< const MultiDimICIModel< GUM_SCALAR >* >(cpt(head).content());
 
    if (CImodel != 0) {
      // or is OK
      addArc(tail, head);
 
      CImodel->causalWeight(variable(tail), causalWeight);
    } else {
      GUM_ERROR(InvalidArc,
                "Head variable (" << variable(head).name() << ") is not a CIModel variable !")
    }
  }

References addArc(), cpt(), GUM_ERROR, and variable().

Referenced by addWeightedArc().

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ancestors() [1/2]

INLINE NodeSet gum::DAGmodel::ancestors ( const NodeId id ) const

inherited

returns the set of nodes with directed path ingoing to a given node

Note that the set of nodes returned may be empty if no path within the ArcGraphPart is ingoing to the given node.

Parameters

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

Definition at line 123 of file DAGmodel_inl.h.

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

References gum::ArcGraphPart::ancestors(), and dag().

Referenced by ancestors().

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ancestors() [2/2]

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

inherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Definition at line 125 of file DAGmodel_inl.h.

                                                                {
    return ancestors(idFromName(name));
  }

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

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arcs()

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

inherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Definition at line 65 of file DAGmodel_inl.h.

{ return dag_.arcs(); }

References dag_.

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

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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 554 of file BayesNet_tpl.h.

                                                           {
    for (const auto node: nodes())
      _probaMap_[node]->beginMultipleChanges();
  }

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

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changeTensor() [1/2]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeTensor	(	const std::string &	name,
		Tensor< GUM_SCALAR > *	newPot )

Definition at line 637 of file BayesNet_tpl.h.

                                                                                               {
    changeTensor(idFromName(name), newPot);
  }

References changeTensor(), and idFromName().

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changeTensor() [2/2]

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::changeTensor	(	NodeId	id,
		Tensor< GUM_SCALAR > *	newPot )

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

Exceptions

NotAllowed

if newPot has not the same signature as probaMap[NodeId]

Definition at line 611 of file BayesNet_tpl.h.

                                                                                   {
    if (cpt(id).nbrDim() != newPot->nbrDim()) {
      GUM_ERROR(OperationNotAllowed,
                "cannot exchange tensors with different "
                "dimensions for variable with id "
                    << id)
    }
 
    for (Idx i = 0; i < cpt(id).nbrDim(); i++) {
      if (&cpt(id).variable(i) != &(newPot->variable(i))) {
        GUM_ERROR(OperationNotAllowed,
                  "cannot exchange tensors because, for variable with id " << id << ", dimension "
                                                                           << i << " differs. ")
      }
    }
 
    _unsafeChangeTensor_(id, newPot);
  }

References cpt(), and GUM_ERROR.

Referenced by changeTensor().

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changeVariableLabel() [1/2]

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 371 of file BayesNet.h.

                                                         {
      changeVariableLabel(idFromName(name), old_label, new_label);
    }

References changeVariableLabel(), and idFromName().

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changeVariableLabel() [2/2]

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

DuplicateLabel	Raised if new_label is already used in this gum::LabelizedVariable.
NotFound	Raised if no variable matches id or if the variable is not a LabelizedVariable

Definition at line 177 of file BayesNet_tpl.h.

                                                                                      {
    if (variable(id).varType() != VarType::LABELIZED)
      GUM_ERROR(NotFound, "Variable " << id << " is not a LabelizedVariable.")
 
    LabelizedVariable const* var
        = dynamic_cast< LabelizedVariable* >(const_cast< DiscreteVariable* >(&variable(id)));
 
    var->changeLabel(var->posLabel(old_label), new_label);
  }

References gum::LabelizedVariable::changeLabel(), GUM_ERROR, gum::LABELIZED, gum::LabelizedVariable::posLabel(), and variable().

Referenced by changeVariableLabel().

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changeVariableName() [1/2]

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 352 of file BayesNet.h.

                                                                              {
      changeVariableName(idFromName(name), new_name);
    }

References changeVariableName(), and idFromName().

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changeVariableName() [2/2]

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

DuplicateLabel	Raised if newName is already used in this gum::BayesNet.
NotFound	Raised if no variable matches id.

Definition at line 172 of file BayesNet_tpl.h.

                                                                                             {
    _varMap_.changeName(id, new_name);
  }

References _varMap_.

Referenced by changeVariableName().

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check()

template<typename GUM_SCALAR>

std::vector< std::string > gum::IBayesNet< GUM_SCALAR >::check ( ) const

inherited

Check if the BayesNet is consistent (variables, CPT).

Returns

a list of comments on the consistency of the Bayes Net

Definition at line 314 of file IBayesNet_tpl.h.

                                                              {
    std::vector< std::string > comments;
 
    const double epsilon       = 1e-8;
    const double error_epsilon = 1e-1;
 
    // CHECKING domain
    for (const auto i: nodes())
      if (variable(i).domainSize() < 2) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << ": not consistent (domainSize=1).";
        comments.push_back(s.str());
      }
 
    // CHECKING parameters are probabilities
    // >0
    for (const auto i: nodes()) {
      const auto [amin, minval] = cpt(i).argmin();
      if (minval < (GUM_SCALAR)0.0) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << " : P(" << *(amin.begin()) << ") < 0.0";
        comments.push_back(s.str());
      }
    }
    // <1
    for (const auto i: nodes()) {
      const auto [amax, maxval] = cpt(i).argmax();
      if (maxval > (GUM_SCALAR)1.0) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << " : P(" << *(amax.begin()) << ") > 1.0";
        comments.push_back(s.str());
      }
    }
 
    // CHECKING distributions sum to 1
    for (const auto i: nodes()) {
      const auto p              = cpt(i).sumOut({&variable(i)});
      const auto [amin, minval] = p.argmin();
      if (minval < (GUM_SCALAR)(1.0 - epsilon)) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << " : ";
        if (!parents(i).empty()) s << "with (at least) parents " << *(amin.begin()) << ", ";
        s << "the CPT sum to less than 1";
        if (minval > (GUM_SCALAR)(1.0 - error_epsilon)) s << " (normalization problem ?)";
        s << ".";
        comments.push_back(s.str());
        continue;
      }
      const auto [amax, maxval] = p.argmax();
      if (maxval > (GUM_SCALAR)(1.0 + epsilon)) {
        std::stringstream s;
        s << "Variable " << variable(i).name() << " : ";
        if (!parents(i).empty()) s << "with (at least) parents " << *(amax.begin()) << ", ";
        s << "the CPT sum to more than 1";
        if (maxval < (GUM_SCALAR)(1.0 + error_epsilon)) s << " (normalization problem ?)";
        s << ".";
        comments.push_back(s.str());
      }
    }
 
    return comments;
  }

References cpt(), gum::GraphicalModel::empty(), gum::DAGmodel::nodes(), gum::DAGmodel::parents(), and variable().

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children() [1/4]

INLINE const NodeSet & gum::DAGmodel::children ( const NodeId id ) const

inherited

returns the set of nodes with arc outgoing from a given node

Note that the set of nodes returned may be empty if no node is outgoing from the given node.

Parameters

id	the node which is the tail of an arc with the returned nodes
name	the name of the node which is the tail of an arc with the returned nodes

Definition at line 87 of file DAGmodel_inl.h.

{ return dag_.children(id); }

References dag_.

Referenced by gum::BayesNet< GUM_SCALAR >::erase(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), and gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot().

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children() [2/4]

INLINE NodeSet gum::DAGmodel::children ( const NodeSet & ids ) const

inherited

returns the children of a set of nodes

Definition at line 93 of file DAGmodel_inl.h.

{ return dag_.children(ids); }

References dag_, and gum::GraphicalModel::ids().

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children() [3/4]

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

inherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Definition at line 89 of file DAGmodel_inl.h.

                                                                      {
    return dag_.children(idFromName(name));
  }

References dag_, and gum::GraphicalModel::idFromName().

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children() [4/4]

INLINE NodeSet gum::DAGmodel::children ( const std::vector< std::string > & names ) const

inherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Definition at line 95 of file DAGmodel_inl.h.

                                                                             {
    return children(nodeset(names));
  }

References gum::GraphicalModel::names().

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clear()

template<typename GUM_SCALAR>

void gum::BayesNet< GUM_SCALAR >::clear

(

)

clear the whole Bayes net *

Definition at line 289 of file BayesNet_tpl.h.

                                     {
    if (!this->empty()) {
      auto l = this->nodes();
      for (const auto no: l) {
        this->erase(no);
      }
    }
  }

References gum::GraphicalModel::empty(), erase(), and gum::DAGmodel::nodes().

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completeInstantiation()

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

inherited

Get an instantiation over all the variables of the model.

Definition at line 106 of file graphicalModel_inl.h.

                                                            {
    Instantiation I;
 
    for (const auto node: nodes())
      I << variable(node);
 
    return I;
  }

References nodes().

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contextualize()

template<typename GUM_SCALAR>

BayesNet< GUM_SCALAR > gum::BayesNet< GUM_SCALAR >::contextualize	(	const gum::Instantiation &	observations,
		const gum::Instantiation &	interventions ) const

create a contextual BN from this and a set of hard observations and hard interventions.

Parameters

observations	the hard observations
interventions	the hard interventions

Returns

a new BN with the same structure as this, but with the CPTs modified to reflect the observations and interventions

Warning

The original BN is not modified. The returned BN is not a copy of the original BN, but a new BN with copied variables and modified structure and CPTs.

Exceptions

ArgumentError

if the observations and interventions are not mutually exclusive

Definition at line 643 of file BayesNet_tpl.h.

                                                                                         {
    Instantiation all;
    for (gum::Idx i = 0; i < observations.nbrDim(); i++) {
      if (interventions.contains(observations.variable(i))) {
        GUM_ERROR(ArgumentError,
                  "Cannot have both an observation and an intervention on the same variable")
      }
      all.add(observations.variable(i));
    }
    for (gum::Idx i = 0; i < observations.nbrDim(); i++) {
      all.add(interventions.variable(i));
    }
    all.setVals(observations);
    all.setVals(interventions);
 
    NodeSet cpt_changed;
 
    gum::BayesNet< GUM_SCALAR > bn(*this);
 
    bn.beginTopologyTransformation();
    for (gum::Idx i = 0; i < observations.nbrDim(); i++) {
      const std::string& nam = observations.variable(i).name();
      const gum::NodeId  nod = this->idFromName(nam);
      for (gum::NodeId child: this->children(nod)) {
        bn.eraseArc(bn.idFromName(nam), bn.idFromName(this->variable(child).name()));
      }
    }
    for (gum::Idx i = 0; i < interventions.nbrDim(); i++) {
      const std::string& nam = interventions.variable(i).name();
      const gum::NodeId  nod = this->idFromName(nam);
      for (gum::NodeId child: this->children(nod)) {
        bn.eraseArc(bn.idFromName(nam), bn.idFromName(this->variable(child).name()));
      }
      for (gum::NodeId par: this->parents(nod)) {
        const auto v1 = bn.idFromName(this->variable(par).name());
        const auto v2 = bn.idFromName(nam);
        if (bn.existsArc(v1, v2)) bn.eraseArc(v1, v2);
      }
      cpt_changed.insert(bn.idFromName(nam));
      bn.cpt(bn.idFromName(nam))
          .fillWith(Tensor< GUM_SCALAR >::deterministicTensor(bn.variable(bn.idFromName(nam)),
                                                              interventions.val(i)));
    }
    bn.endTopologyTransformation();
 
    for (gum::Idx i = 0; i < all.nbrDim(); i++) {
      const gum::NodeId nod = this->idFromName(all.variable(i).name());
      for (gum::NodeId child: this->children(nod)) {
        if (!cpt_changed.contains(child)) {
          cpt_changed.insert(child);
          bn.cpt(bn.idFromName(this->variable(child).name()))
              .fillWith(this->cpt(child).extract(all));
        }
      }
    }
 
    return bn;
  }

References gum::Instantiation::add(), gum::Instantiation::contains(), GUM_ERROR, gum::Instantiation::nbrDim(), and gum::Instantiation::variable().

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cpt() [1/2]

template<typename GUM_SCALAR>

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

inline

Returns the CPT of a variable.

Definition at line 182 of file BayesNet.h.

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

References cpt(), and idFromName().

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cpt() [2/2]

template<typename GUM_SCALAR>

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

finalvirtual

Returns the CPT of a variable.

Parameters

varId

A variable's id in the gum::BayesNet.

Returns

The variable's CPT.

Exceptions

NotFound

If no variable's id matches varId.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 258 of file BayesNet_tpl.h.

                                                                                   {
    return *(_probaMap_[varId]);
  }

References _probaMap_.

Referenced by gum::BayesNet< double >::_copyTensors_(), add(), addWeightedArc(), changeTensor(), cpt(), generateCPT(), and gum::BayesNetFragment< GUM_SCALAR >::toBN().

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dag()

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

inherited

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

Definition at line 57 of file DAGmodel_inl.h.

{ return dag_; }

References dag_.

Referenced by gum::BayesNetFragment< GUM_SCALAR >::BayesNetFragment(), gum::MarginalTargetedInference< GUM_SCALAR >::MarginalTargetedInference(), gum::BayesNet< GUM_SCALAR >::add(), ancestors(), gum::IBayesNet< double >::arcs(), descendants(), gum::BayesNetFragment< GUM_SCALAR >::installCPT(), isIndependent(), isIndependent(), gum::BayesNetFragment< GUM_SCALAR >::isInstalledNode(), moralGraph(), moralizedAncestralGraph(), gum::BayesBall::relevantTensors(), gum::dSeparationAlgorithm::relevantTensors(), gum::BayesNet< GUM_SCALAR >::reverseArc(), size(), gum::BayesNetFragment< GUM_SCALAR >::toBN(), gum::BayesNetFragment< GUM_SCALAR >::toDot(), gum::IBayesNet< GUM_SCALAR >::toString(), gum::InfluenceDiagram< GUM_SCALAR >::toString(), and gum::BayesNetFragment< GUM_SCALAR >::whenArcDeleted().

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descendants() [1/2]

INLINE NodeSet gum::DAGmodel::descendants ( const NodeId id ) const

inherited

returns the set of nodes with directed path outgoing from a given node

Note that the set of nodes returned may be empty if no path within the ArcGraphPart is outgoing from the given node.

Parameters

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

Definition at line 117 of file DAGmodel_inl.h.

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

References dag(), and gum::ArcGraphPart::descendants().

Referenced by descendants().

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descendants() [2/2]

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

inherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Definition at line 119 of file DAGmodel_inl.h.

                                                                  {
    return descendants(idFromName(name));
  }

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

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dim()

template<typename GUM_SCALAR>

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

inherited

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

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

Definition at line 95 of file IBayesNet_tpl.h.

                                          {
    Size dim = 0;
 
    for (auto node: nodes()) {
      Size q = 1;
 
      for (auto parent: parents(node))
        q *= variable(parent).domainSize();
 
      dim += (variable(node).domainSize() - 1) * q;
    }
 
    return dim;
  }

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

Referenced by dim(), and toString().

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empty()

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

virtualinherited

Return true if this graphical model is empty.

Definition at line 116 of file graphicalModel_inl.h.

{ return size() == 0; }

References size().

Referenced by gum::IBayesNet< GUM_SCALAR >::check(), and gum::BayesNet< GUM_SCALAR >::clear().

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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 561 of file BayesNet_tpl.h.

                                                         {
    for (const auto node: nodes())
      _probaMap_[node]->endMultipleChanges();
  }

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

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erase() [1/3]

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::Tensor.

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

Parameters

var	A reference on the variable to remove.

Definition at line 268 of file BayesNet_tpl.h.

                                                                       {
    erase(_varMap_.get(var));
  }

References _varMap_, and erase().

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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 304 of file BayesNet.h.

{ erase(idFromName(name)); };

References erase(), erase(), and idFromName().

Referenced by erase().

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erase() [3/3]

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::Tensor.

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

Parameters

varId

The variable's id to remove.

Definition at line 273 of file BayesNet_tpl.h.

                                                 {
    if (_varMap_.exists(varId)) {
      // Reduce the variable child's CPT
      for (const NodeSet& children = this->children(varId); const auto c: children) {
        _probaMap_[c]->erase(variable(varId));
      }
 
      delete _probaMap_[varId];
 
      _probaMap_.erase(varId);
      _varMap_.erase(varId);
      this->dag_.eraseNode(varId);
    }
  }

References _probaMap_, _varMap_, gum::DAGmodel::children(), gum::DAGmodel::dag_, and variable().

Referenced by clear(), erase(), and erase().

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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

arc	The arc removed.

Definition at line 319 of file BayesNet_tpl.h.

                                                             {
    if (_varMap_.exists(arc.tail()) && _varMap_.exists(arc.head())) {
      NodeId head = arc.head();
      NodeId tail = arc.tail();
      this->dag_.eraseArc(arc);
      // Remove parent from child's CPT
      (*(_probaMap_[head])) >> variable(tail);
    }
  }

References _probaMap_, _varMap_, gum::DAGmodel::dag_, gum::Arc::head(), gum::Arc::tail(), and variable().

Referenced by eraseArc(), and eraseArc().

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eraseArc() [2/3]

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 451 of file BayesNet.h.

                                                                {
      eraseArc(idFromName(tail), idFromName(head));
    }

References eraseArc(), and idFromName().

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eraseArc() [3/3]

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

head	and
tail	as NodeId

Definition at line 330 of file BayesNet_tpl.h.

                                                                       {
    eraseArc(Arc(tail, head));
  }

References eraseArc().

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evEq()

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evEq ( const std::string & name, double value ) const

inherited

Returns

a Tensor for a (numerical) discrete variable representing an evidence with a float as observed value

Definition at line 378 of file IBayesNet_tpl.h.

                                                                                              {
    return Tensor< GUM_SCALAR >::evEq(variableFromName(name), value);
  }

References gum::Tensor< GUM_SCALAR >::evEq(), and variableFromName().

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evGt()

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evGt ( const std::string & name, double value ) const

inherited

Returns

a Tensor for a (numerical) discrete variable representing an evidence with an observed value greater than the parameter

Definition at line 389 of file IBayesNet_tpl.h.

                                                                                            {
    return Tensor< GUM_SCALAR >::evGt(variableFromName(name), val);
  }

References gum::Tensor< GUM_SCALAR >::evGt(), and variableFromName().

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evIn()

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evIn ( const std::string & name, double val1, double val2 ) const

inherited

Returns

a Tensor for a (numerical) discrete variable representing an evidence with a interval of float as observed value

Definition at line 384 of file IBayesNet_tpl.h.

                                                                                         {
    return Tensor< GUM_SCALAR >::evIn(variableFromName(name), val1, val2);
  }

References gum::Tensor< GUM_SCALAR >::evIn(), and variableFromName().

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evLt()

template<typename GUM_SCALAR>

Tensor< GUM_SCALAR > gum::IBayesNet< GUM_SCALAR >::evLt ( const std::string & name, double value ) const

inherited

Returns

a Tensor for a (numerical) discrete variable representing an evidence with an observed value less than the parameter

Definition at line 394 of file IBayesNet_tpl.h.

                                                                                            {
    return Tensor< GUM_SCALAR >::evLt(variableFromName(name), val);
  }

References gum::Tensor< GUM_SCALAR >::evLt(), and variableFromName().

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exists() [1/2]

INLINE bool gum::DAGmodel::exists ( const std::string & name ) const

finalvirtualinherited

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

Implements gum::GraphicalModel.

Definition at line 107 of file DAGmodel_inl.h.

                                                          {
    try {
      return exists(idFromName(name));
    } catch ([[maybe_unused]] gum::NotFound& e) { return false; }
  }

exists() [2/2]

INLINE bool gum::DAGmodel::exists ( NodeId node ) const

finalvirtualinherited

Return true if this node exists in this graphical model.

Implements gum::GraphicalModel.

Definition at line 105 of file DAGmodel_inl.h.

{ return dag_.exists(node); }

References dag_.

existsArc() [1/2]

INLINE bool gum::DAGmodel::existsArc ( const NodeId tail, const NodeId head ) const

inherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Definition at line 67 of file DAGmodel_inl.h.

                                                                            {
    return dag_.existsArc(tail, head);
  }

References dag_.

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

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existsArc() [2/2]

INLINE bool gum::DAGmodel::existsArc ( const std::string & nametail, const std::string & namehead ) const

inherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Definition at line 71 of file DAGmodel_inl.h.

                                                                                            {
    return existsArc(idFromName(nametail), idFromName(namehead));
  }

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

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family() [1/2]

INLINE NodeSet gum::DAGmodel::family ( const NodeId id ) const

finalvirtualinherited

returns the parents of a node and the node

Parameters

id	the node which is the head of an arc with the returned nodes
name	the name of the node the node which is the head of an arc with the returned nodes

Implements gum::GraphicalModel.

Definition at line 81 of file DAGmodel_inl.h.

{ return dag_.family(id); }

References dag_.

family() [2/2]

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

finalvirtualinherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Implements gum::GraphicalModel.

Definition at line 83 of file DAGmodel_inl.h.

                                                             {
    return dag_.family(idFromName(name));
  }

References dag_, and gum::GraphicalModel::idFromName().

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fastPrototype() [1/2]

template<typename GUM_SCALAR>

BayesNet< GUM_SCALAR > gum::BayesNet< GUM_SCALAR >::fastPrototype ( const std::string & dotlike, const std::string & domainSize = "[2]" )

static

Definition at line 96 of file BayesNet_tpl.h.

                                                                                        {
    gum::BayesNet< GUM_SCALAR > bn;
 
    for (const auto& chaine: split(remove_newline(dotlike), ";")) {
      NodeId lastId   = 0;
      bool   notfirst = false;
      for (const auto& souschaine: split(chaine, "->")) {
        bool forward = true;
        for (auto& node: split(souschaine, "<-")) {
          auto idVar = build_node(bn, node, domain);
          if (notfirst) {
            if (forward) {
              bn.addArc(lastId, idVar);
              forward = false;
            } else {
              bn.addArc(idVar, lastId);
            }
          } else {
            notfirst = true;
            forward  = false;
          }
          lastId = idVar;
        }
      }
    }
    bn.generateCPTs();
    bn.setProperty("name", "fastPrototype");
    return bn;
  }

References addArc(), gum::build_node(), gum::remove_newline(), and gum::split().

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fastPrototype() [2/2]

template<typename GUM_SCALAR>

BayesNet< GUM_SCALAR > gum::BayesNet< GUM_SCALAR >::fastPrototype ( const std::string & dotlike, Size domainSize )

static

Create a Bayesian network with a dot-like syntax which specifies:

• the structure "a->b->c;b->d<-e;".
• the type of the variables with different syntax:
  • by default, a variable is a gum::RangeVariable using the default domainSize (second argument)
  • with "a[10]", the variable is a gum::RangeVariable using 10 as domainSize (from 0 to 9)
  • with "a[3,7]", the variable is a gum::RangeVariable using a domainSize from 3 to 7
  • with "a[1,3.14,5,6.2]", the variable is a gum::DiscretizedVariable using the given ticks (at least 3 values)
  • with "a{top|middle|bottom}", the variable is a gum::LabelizedVariable using the given labels.

Note that if the dot-like string contains such a specification more than once for a variable, the first specification will be used.

Parameters

dotlike	the string containing the specification
domainSize	the default domain size for variables

Returns

the resulting Bayesian network

Definition at line 90 of file BayesNet_tpl.h.

                                                                                              {
    return fastPrototype(dotlike, "[" + std::to_string(domainSize) + "]");
  }

References fastPrototype().

Referenced by fastPrototype().

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generateCPT() [1/2]

template<typename GUM_SCALAR>

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

inline

Definition at line 659 of file BayesNet.h.

{ generateCPT(idFromName(name)); };

References generateCPT(), and idFromName().

Referenced by generateCPT().

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generateCPT() [2/2]

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 604 of file BayesNet_tpl.h.

                                                                   {
    SimpleCPTGenerator< GUM_SCALAR > generator;
 
    generator.generateCPT(cpt(node).pos(variable(node)), cpt(node));
  }

References cpt(), and variable().

Referenced by generateCPTs().

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generateCPTs()

template<typename GUM_SCALAR>

INLINE void gum::BayesNet< GUM_SCALAR >::generateCPTs

(

)

const

randomly generates CPTs for a given structure

Definition at line 598 of file BayesNet_tpl.h.

                                                         {
    for (const auto node: nodes())
      generateCPT(node);
  }

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

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hasSameStructure()

bool gum::DAGmodel::hasSameStructure ( const DAGmodel & other )

inherited

Returns

true if all the named node are the same and all the named arcs are the same

Definition at line 66 of file DAGmodel.cpp.

                                                       {
    if (this == &other) return true;
 
    if (size() != other.size()) return false;
 
    if (sizeArcs() != other.sizeArcs()) return false;
 
    for (const auto& nid: nodes()) {
      try {
        other.idFromName(variable(nid).name());
      } catch (NotFound const&) { return false; }
    }
 
    for (const auto& arc: arcs()) {
      if (!other.arcs().exists(Arc(other.idFromName(variable(arc.tail()).name()),
                                   other.idFromName(variable(arc.head()).name()))))
        return false;
    }
 
    return true;
  }

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

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idFromName()

template<typename GUM_SCALAR>

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

finalvirtual

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

Parameters

name	The variable's name from which the gum::NodeId is returned.

Returns

Returns the variable gum::NodeId in the gum::BayesNet.

Exceptions

NotFound

Raised if name does not match a variable in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 247 of file BayesNet_tpl.h.

                                                                              {
    return _varMap_.idFromName(name);
  }

References _varMap_.

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

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ids()

INLINE std::vector< NodeId > gum::GraphicalModel::ids ( const std::vector< std::string > & names ) const

inherited

transform a vector of names into a vector of nodeId

Returns

the vector of names

Definition at line 139 of file graphicalModel_inl.h.

                                                                                   {
    std::vector< NodeId >  res;
    const VariableNodeMap& v = variableNodeMap();
    std::transform(names.cbegin(),
                   names.cend(),
                   std::back_inserter(res),
                   [v](const std::string& n) { return v.idFromName(n); });
    return res;
  }

References names().

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

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isIndependent() [1/4]

INLINE bool gum::DAGmodel::isIndependent ( const NodeSet & X, const NodeSet & Y, const NodeSet & Z ) const

finalvirtualinherited

check if nodes X and nodes Y are independent given nodes Z

Implements gum::GraphicalModel.

Definition at line 142 of file DAGmodel_inl.h.

                                                                                                {
    return dag().dSeparation(X, Y, Z);
  }

References dag(), and gum::DAG::dSeparation().

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isIndependent() [2/4]

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

inlineinherited

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

Parameters

nodes	the set of nodeId
nodenames	the vector of names of nodes

Returns

the moralized ancestral graph

Definition at line 202 of file DAGmodel.h.

                                                                   {
      return isIndependent(idFromName(Xname), idFromName(Yname), nodeset(Znames));
    };

References gum::GraphicalModel::idFromName(), isIndependent(), and gum::GraphicalModel::nodeset().

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isIndependent() [3/4]

bool gum::DAGmodel::isIndependent ( const std::vector< std::string > & Xnames, const std::vector< std::string > & Ynames, const std::vector< std::string > & Znames ) const

inlineinherited

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

Parameters

nodes	the set of nodeId
nodenames	the vector of names of nodes

Returns

the moralized ancestral graph

Definition at line 208 of file DAGmodel.h.

                                                                   {
      return isIndependent(nodeset(Xnames), nodeset(Ynames), nodeset(Znames));
    };

References isIndependent(), and gum::GraphicalModel::nodeset().

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isIndependent() [4/4]

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

finalvirtualinherited

check if node X and node Y are independent given nodes Z

Implements gum::GraphicalModel.

Definition at line 138 of file DAGmodel_inl.h.

                                                                                {
    return dag().dSeparation(X, Y, Z);
  }

References dag(), and gum::DAG::dSeparation().

Referenced by isIndependent(), and isIndependent().

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jointProbability()

template<typename GUM_SCALAR>

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

inherited

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

Warning

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

Definition at line 221 of file IBayesNet_tpl.h.

                                                                                   {
    auto value = (GUM_SCALAR)1.0;
 
    GUM_SCALAR tmp;
 
    for (auto node: nodes()) {
      if ((tmp = cpt(node)[i]) == (GUM_SCALAR)0) { return (GUM_SCALAR)0; }
 
      value *= tmp;
    }
 
    return value;
  }

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

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log10DomainSize()

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

inherited

Definition at line 95 of file graphicalModel_inl.h.

                                               {
    double dSize = 0.0;
 
    for (const auto node: nodes()) {
      dSize += std::log10(variable(node).domainSize());
    }
 
    return dSize;
  }

References nodes().

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

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log2JointProbability()

template<typename GUM_SCALAR>

GUM_SCALAR gum::IBayesNet< GUM_SCALAR >::log2JointProbability ( const Instantiation & i ) const

inherited

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

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

Warning

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

Definition at line 239 of file IBayesNet_tpl.h.

                                                                                       {
    auto value = (GUM_SCALAR)0.0;
 
    GUM_SCALAR tmp;
 
    for (auto node: nodes()) {
      if ((tmp = cpt(node)[i]) == (GUM_SCALAR)0) {
        return (GUM_SCALAR)(-std::numeric_limits< double >::infinity());
      }
 
      value += std::log2(cpt(node)[i]);
    }
 
    return value;
  }

maxNonOneParam()

template<typename GUM_SCALAR>

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

inherited

Returns

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

Warning

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

Definition at line 151 of file IBayesNet_tpl.h.

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

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

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maxParam()

template<typename GUM_SCALAR>

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

inherited

Returns

the biggest value in the CPTs of *this

Definition at line 131 of file IBayesNet_tpl.h.

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

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

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maxVarDomainSize()

template<typename GUM_SCALAR>

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

inherited

Returns

the biggest domainSize among the variables of *this

Definition at line 111 of file IBayesNet_tpl.h.

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

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

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

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memoryFootprint()

template<typename GUM_SCALAR>

INLINE Size gum::IBayesNet< GUM_SCALAR >::memoryFootprint ( ) const

inherited

compute the (approximated) footprint in memory of the model (the footprints of CPTs)

Returns

the size in bytes

Definition at line 161 of file IBayesNet_tpl.h.

                                                             {
    Size usedMem = 0;
 
    for (auto node: nodes())
      usedMem += cpt(node).memoryFootprint();
    return usedMem;
  }

References gum::DAGmodel::nodes().

Referenced by toString().

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minimalCondSet() [1/4]

INLINE NodeSet gum::DAGmodel::minimalCondSet ( const NodeSet & targets, const NodeSet & soids ) const

inherited

Definition at line 150 of file DAGmodel_inl.h.

                                                                                            {
    return dag_.minimalCondSet(targets, soids);
  }

References dag_.

minimalCondSet() [2/4]

INLINE NodeSet gum::DAGmodel::minimalCondSet ( const std::string & target, const std::vector< std::string > & soids ) const

inherited

Definition at line 154 of file DAGmodel_inl.h.

                                                                                     {
    return dag_.minimalCondSet(idFromName(target), nodeset(soids));
  }

minimalCondSet() [3/4]

INLINE NodeSet gum::DAGmodel::minimalCondSet ( const std::vector< std::string > & targets, const std::vector< std::string > & soids ) const

inherited

Definition at line 159 of file DAGmodel_inl.h.

                                                                                     {
    return dag_.minimalCondSet(nodeset(targets), nodeset(soids));
  }

minimalCondSet() [4/4]

INLINE NodeSet gum::DAGmodel::minimalCondSet ( NodeId target, const NodeSet & soids ) const

inherited

Definition at line 146 of file DAGmodel_inl.h.

                                                                                   {
    return dag_.minimalCondSet(target, soids);
  }

minNonZeroParam()

template<typename GUM_SCALAR>

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

inherited

Returns

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

Warning

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

Definition at line 141 of file IBayesNet_tpl.h.

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

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

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

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minParam()

template<typename GUM_SCALAR>

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

inherited

Returns

the smallest value in the CPTs of *this

Definition at line 121 of file IBayesNet_tpl.h.

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

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

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moralGraph()

UndiGraph gum::DAGmodel::moralGraph ( ) const

inherited

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

Definition at line 64 of file DAGmodel.cpp.

{ return dag().moralGraph(); }

References dag(), and gum::DAG::moralGraph().

Referenced by gum::prm::SVE< GUM_SCALAR >::_eliminateNodes_(), gum::prm::SVED< GUM_SCALAR >::_eliminateNodes_(), gum::prm::SVE< GUM_SCALAR >::_eliminateNodesWithEvidence_(), gum::prm::SVED< GUM_SCALAR >::_eliminateNodesWithEvidence_(), gum::prm::SVE< GUM_SCALAR >::_initLiftedNodes_(), and gum::prm::SVED< GUM_SCALAR >::_initLiftedNodes_().

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moralizedAncestralGraph() [1/2]

INLINE UndiGraph gum::DAGmodel::moralizedAncestralGraph ( const NodeSet & nodes ) const

inherited

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

Parameters

nodes	the set of nodeId
nodenames	the vector of names of nodes

Returns

the moralized ancestral graph

Definition at line 134 of file DAGmodel_inl.h.

                                                                               {
    return dag().moralizedAncestralGraph(nodes);
  }

References dag(), gum::DAG::moralizedAncestralGraph(), and nodes().

Referenced by moralizedAncestralGraph().

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moralizedAncestralGraph() [2/2]

INLINE UndiGraph gum::DAGmodel::moralizedAncestralGraph ( const std::vector< std::string > & nodenames ) const

inherited

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

Parameters

nodes	the set of nodeId
nodenames	the vector of names of nodes

Returns

the moralized ancestral graph

Definition at line 130 of file DAGmodel_inl.h.

                                                                                     {
    return moralizedAncestralGraph(nodeset(nodenames));
  }

References moralizedAncestralGraph(), and gum::GraphicalModel::nodeset().

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names() [1/2]

INLINE std::vector< std::string > gum::GraphicalModel::names ( const NodeSet & ids ) const

inherited

transform a NodeSet in a vector of names

Returns

the vector of names

Definition at line 129 of file graphicalModel_inl.h.

                                                                       {
    const VariableNodeMap&     v = variableNodeMap();
    std::vector< std::string > res;
    for (auto n: ids) {
      res.push_back(v.name(n));
    }
    return res;
  }

names() [2/2]

INLINE std::vector< std::string > gum::GraphicalModel::names ( const std::vector< NodeId > & ids ) const

inherited

transform a vector of NodeId in a vector of names

Returns

the vector of names

Definition at line 119 of file graphicalModel_inl.h.

                                                                                   {
    std::vector< std::string > res;
    const VariableNodeMap&     v = variableNodeMap();
    std::transform(ids.cbegin(), ids.cend(), std::back_inserter(res), [v](NodeId n) {
      return v[n].name();
    });
    return res;
  }

References ids(), and variableNodeMap().

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

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nodeId()

template<typename GUM_SCALAR>

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

finalvirtual

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

Parameters

var	The variable from which the gum::NodeId is returned.

Returns

Returns the gum::DiscreteVariable gum::NodeId in the gum::BayesNet.

Exceptions

NotFound

If var is not in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 190 of file BayesNet_tpl.h.

                                                                                {
    return _varMap_.get(var);
  }

nodes()

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

finalvirtualinherited

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

Implements gum::GraphicalModel.

Definition at line 113 of file DAGmodel_inl.h.

{ return (NodeGraphPart&)dag_; }

References dag_.

Referenced by gum::Estimator< GUM_SCALAR >::Estimator(), gum::BayesNetFragment< GUM_SCALAR >::~BayesNetFragment(), gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::_verticesSampling_(), gum::BayesNet< GUM_SCALAR >::beginTopologyTransformation(), gum::InfluenceDiagram< GUM_SCALAR >::beginTopologyTransformation(), gum::IBayesNet< GUM_SCALAR >::check(), gum::BayesNetFragment< GUM_SCALAR >::checkConsistency(), gum::BayesNet< GUM_SCALAR >::clear(), gum::InfluenceDiagram< GUM_SCALAR >::copyStructureAndTables_(), gum::IBayesNet< GUM_SCALAR >::dim(), gum::BayesNet< GUM_SCALAR >::endTopologyTransformation(), gum::InfluenceDiagram< GUM_SCALAR >::endTopologyTransformation(), gum::InfluenceDiagram< GUM_SCALAR >::fastPrototype(), gum::BayesNet< GUM_SCALAR >::generateCPTs(), gum::getMaxModality(), hasSameStructure(), gum::IBayesNet< GUM_SCALAR >::jointProbability(), gum::IBayesNet< GUM_SCALAR >::maxNonOneParam(), gum::IBayesNet< GUM_SCALAR >::maxParam(), gum::IBayesNet< GUM_SCALAR >::maxVarDomainSize(), gum::IBayesNet< GUM_SCALAR >::memoryFootprint(), gum::IBayesNet< GUM_SCALAR >::minNonZeroParam(), gum::IBayesNet< GUM_SCALAR >::minParam(), gum::prm::ClassBayesNet< GUM_SCALAR >::modalities(), gum::prm::InstanceBayesNet< GUM_SCALAR >::modalities(), moralizedAncestralGraph(), gum::InfluenceDiagram< GUM_SCALAR >::operator==(), gum::Estimator< GUM_SCALAR >::setFromBN(), gum::BayesNetFragment< GUM_SCALAR >::toBN(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), and gum::ImportanceSampling< GUM_SCALAR >::unsharpenBN_().

nodeset()

NodeSet gum::GraphicalModel::nodeset ( const std::vector< std::string > & names ) const

inherited

transform a vector of names into a NodeSet

Returns

NodeSet

Definition at line 60 of file graphicalModel.cpp.

                                                                           {
    NodeSet res;
    for (const auto& name: names) {
      res.insert(idFromName(name));
    }
    return res;
  }

References idFromName(), gum::Set< Key >::insert(), and names().

Referenced by gum::IBayesNet< double >::children(), gum::IBayesNet< double >::children(), gum::DAGmodel::isIndependent(), gum::DAGmodel::isIndependent(), gum::UGmodel::isIndependent(), gum::UGmodel::isIndependent(), gum::DAGmodel::moralizedAncestralGraph(), and gum::DAGmodel::parents().

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operator!=()

template<typename GUM_SCALAR>

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

inherited

Returns

Returns false if the src and this are equal.

Definition at line 303 of file IBayesNet_tpl.h.

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

References IBayesNet(), and gum::operator==().

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operator=()

template<typename GUM_SCALAR>

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

(

const BayesNet< GUM_SCALAR > &

source

)

Copy operator.

Parameters

source

The copied BayesNet.

Returns

The copy of source.

Definition at line 146 of file BayesNet_tpl.h.

                                                                                              {
    if (this != &source) {
      IBayesNet< GUM_SCALAR >::operator=(source);
      _varMap_ = source._varMap_;
 
      _clearTensors_();
      _copyTensors_(source);
    }
 
    return *this;
  }

References _clearTensors_(), _copyTensors_(), _varMap_, and gum::IBayesNet< GUM_SCALAR >::operator=().

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operator==()

template<typename GUM_SCALAR>

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

inherited

This operator compares 2 BNs !

Warning

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

Returns

true if the src and this are equal.

Definition at line 256 of file IBayesNet_tpl.h.

                                                                    {
    if (size() != from.size()) { return false; }
 
    if (sizeArcs() != from.sizeArcs()) { return false; }
 
    for (auto node: nodes()) {
      try {
        const auto& v1 = variable(node);
        const auto& v2 = from.variableFromName(variable(node).name());
        if (v1 != v2) { return false; }
      } catch (NotFound const&) {
        // a name is not found in from
        return false;
      }
    }
 
    for (auto node: nodes()) {
      NodeId fromnode = from.idFromName(variable(node).name());
 
      if (cpt(node).nbrDim() != from.cpt(fromnode).nbrDim()) { return false; }
 
      if (cpt(node).domainSize() != from.cpt(fromnode).domainSize()) { return false; }
 
      for (Idx i = 0; i < cpt(node).nbrDim(); ++i) {
        if (!from.cpt(fromnode).contains(from.variableFromName(cpt(node).variable(i).name()))) {
          return false;
        }
      }
 
      Instantiation i(cpt(node));
      Instantiation j(from.cpt(fromnode));
 
      AlmostDifferent< GUM_SCALAR > cmp;
      for (i.setFirst(); !i.end(); i.inc()) {
        for (Idx indice = 0; indice < cpt(node).nbrDim(); ++indice) {
          const DiscreteVariable* p = &(i.variable(indice));
          j.chgVal(j.pos(from.variableFromName(p->name())), i.val(*p));
        }
 
        if (cmp(cpt(node).get(i), from.cpt(fromnode).get(j))) { return false; }
      }
    }
 
    return true;
  }

References IBayesNet(), and gum::DAGmodel::size().

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parents() [1/4]

INLINE const NodeSet & gum::DAGmodel::parents ( const NodeId id ) const

inherited

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

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

Parameters

id	the node which is the head of an arc with the returned nodes
name	the name of the node the node which is the head of an arc with the returned nodes

Definition at line 75 of file DAGmodel_inl.h.

{ return dag_.parents(id); }

References dag_.

Referenced by gum::IBayesNet< GUM_SCALAR >::check(), gum::BayesNetFragment< GUM_SCALAR >::checkConsistency(), gum::InfluenceDiagram< GUM_SCALAR >::copyStructureAndTables_(), gum::BayesNetFragment< GUM_SCALAR >::installCPT(), gum::BayesNetFragment< GUM_SCALAR >::installCPT_(), parents(), parents(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), and gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot().

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parents() [2/4]

INLINE NodeSet gum::DAGmodel::parents ( const NodeSet & ids ) const

inherited

returns the parents of a set of nodes

Definition at line 99 of file DAGmodel_inl.h.

{ return dag_.children(ids); }

References dag_, and gum::GraphicalModel::ids().

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parents() [3/4]

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

inherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Definition at line 77 of file DAGmodel_inl.h.

                                                                     {
    return parents(idFromName(name));
  }

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

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parents() [4/4]

INLINE NodeSet gum::DAGmodel::parents ( const std::vector< std::string > & names ) const

inherited

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

Parameters

tail	the nodeId (or the name) of the tail in tail->head
head	the nodeId (or the name) of the head in tail->head

Returns

true if the arc exists

Definition at line 101 of file DAGmodel_inl.h.

                                                                            {
    return parents(nodeset(names));
  }

References gum::GraphicalModel::names(), gum::GraphicalModel::nodeset(), and parents().

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properties()

INLINE std::vector< std::string > gum::GraphicalModel::properties ( ) const

inherited

List of all the properties.

Definition at line 81 of file graphicalModel_inl.h.

                                                          {
    std::vector< std::string > prop;
    for (const auto& [p, v]: _propertiesMap_)
      prop.push_back(p);
    return prop;
  }

References _propertiesMap_.

property()

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

inherited

Return the value of the property name of this GraphicalModel.

Exceptions

NotFound

Raised if no name property is found.

Definition at line 60 of file graphicalModel_inl.h.

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

References _properties_(), and GUM_ERROR.

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

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propertyWithDefault()

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

inherited

Return the value of the property name of this GraphicalModel.

return byDefault if the property name is not found

Definition at line 75 of file graphicalModel_inl.h.

                                                                                           {
    return (_propertiesMap_.exists(name)) ? _propertiesMap_[name] : byDefault;
  }

References _propertiesMap_.

reverseArc() [1/3]

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

InvalidArc

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

Definition at line 335 of file BayesNet_tpl.h.

                                                        {
    // check that the arc exists
    if (!_varMap_.exists(arc.tail()) || !_varMap_.exists(arc.head()) || !dag().existsArc(arc)) {
      GUM_ERROR(InvalidArc, "a non-existing arc cannot be reversed")
    }
 
    NodeId tail = arc.tail();
    NodeId head = arc.head();
 
    // check that the reversal does not induce a cycle
    try {
      DAG d = dag();
      d.eraseArc(arc);
      d.addArc(head, tail);
    } catch (Exception const&) {
      GUM_ERROR(InvalidArc, "this arc reversal would induce a directed cycle")
    }
 
    // with the same notations as Shachter (1986), "evaluating influence
    // diagrams", p.878, we shall first compute the product of probabilities:
    // pi_j^old (x_j | x_c^old(j) ) * pi_i^old (x_i | x_c^old(i) )
    Tensor< GUM_SCALAR > prod{cpt(tail) * cpt(head)};
 
    // modify the topology of the graph: add to tail all the parents of head
    // and add to head all the parents of tail
    beginTopologyTransformation();
    NodeSet new_parents;
    for (const auto node: this->parents(tail))
      new_parents.insert(node);
    for (const auto node: this->parents(head))
      new_parents.insert(node);
    // remove arc (head, tail)
    eraseArc(arc);
 
    // add the necessary arcs to the tail
    for (const auto p: new_parents) {
      if ((p != tail) && !dag().existsArc(p, tail)) { addArc(p, tail); }
    }
 
    addArc(head, tail);
    // add the necessary arcs to the head
    new_parents.erase(tail);
 
    for (const auto p: new_parents) {
      if ((p != head) && !dag().existsArc(p, head)) { addArc(p, head); }
    }
 
    endTopologyTransformation();
 
    // update the conditional distributions of head and tail
    gum::VariableSet del_vars;
    del_vars << &(variable(tail));
    Tensor< GUM_SCALAR > new_cpt_head = prod.sumOut(del_vars).putFirst(&variable(head));
 
    auto& cpt_head = const_cast< Tensor< GUM_SCALAR >& >(cpt(head));
    cpt_head       = std::move(new_cpt_head);
 
    Tensor< GUM_SCALAR > new_cpt_tail{(prod / cpt_head).putFirst(&variable(tail))};
    auto&                cpt_tail = const_cast< Tensor< GUM_SCALAR >& >(cpt(tail));
    cpt_tail                      = std::move(new_cpt_tail);
  }

References _varMap_, gum::DAGmodel::dag(), gum::DAGmodel::existsArc(), GUM_ERROR, gum::Arc::head(), and gum::Arc::tail().

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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

InvalidArc

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

Definition at line 485 of file BayesNet.h.

                                                                  {
      reverseArc(idFromName(tail), idFromName(head));
    }

References idFromName(), and reverseArc().

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reverseArc() [3/3]

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

InvalidArc

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

Definition at line 398 of file BayesNet_tpl.h.

                                                                         {
    reverseArc(Arc(tail, head));
  }

References reverseArc().

Referenced by reverseArc(), and reverseArc().

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setProperty()

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

inherited

Add or change a property of this GraphicalModel.

Definition at line 89 of file graphicalModel_inl.h.

                                                                                {
    if (_propertiesMap_.exists(name)) _propertiesMap_[name] = value;
    else _propertiesMap_.insert(name, value);
  }

References _propertiesMap_.

Referenced by gum::IBayesNet< GUM_SCALAR >::IBayesNet(), gum::IMarkovRandomField< GUM_SCALAR >::IMarkovRandomField(), and gum::InfluenceDiagram< GUM_SCALAR >::fastPrototype().

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size()

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

finalvirtualinherited

Returns the number of variables in this Directed Graphical Model.

Implements gum::GraphicalModel.

Definition at line 60 of file DAGmodel_inl.h.

{ return dag().size(); }

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

Referenced by gum::InfluenceDiagram< GUM_SCALAR >::copyStructureAndTables_(), gum::InfluenceDiagram< GUM_SCALAR >::decisionNodeSize(), hasSameStructure(), gum::MarkovBlanket::hasSameStructure(), gum::IBayesNet< GUM_SCALAR >::operator==(), gum::InfluenceDiagram< GUM_SCALAR >::operator==(), gum::prm::ClassBayesNet< GUM_SCALAR >::toDot(), gum::prm::InstanceBayesNet< GUM_SCALAR >::toDot(), and gum::IBayesNet< GUM_SCALAR >::toString().

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sizeArcs()

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

inherited

Returns the number of arcs in this Directed Graphical Model.

Definition at line 63 of file DAGmodel_inl.h.

{ return dag_.sizeArcs(); }

References dag_.

Referenced by hasSameStructure(), gum::MarkovBlanket::hasSameStructure(), and gum::InfluenceDiagram< GUM_SCALAR >::operator==().

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spaceCplxToStream()

void gum::GraphicalModel::spaceCplxToStream ( std::stringstream & s, double dSize, int dim, Size usedMem )

staticinherited

send to the stream the space complexity with 3 parametrs

Parameters

s	the stream
dSize	the log10domainSize
dim	the dimension
usedMem	the memory needed for the params

Definition at line 69 of file graphicalModel.cpp.

                                                                                               {
    if (dSize > 6) s << "domainSize: 10^" << dSize;
    else s << "domainSize: " << std::round(std::pow(10.0, dSize));
 
    s << ", dim: " << dim << ", mem: ";
 
    if (const Size go = usedMem / (1024 * 1024 * 1024); go > 0) s << go << "Go ";
    if (const Size mo = (usedMem / (1024 * 1024)) % 1024; mo > 0) s << mo << "Mo ";
    if (const Size ko = (usedMem / 1024) % 1024; ko > 0) s << ko << "Ko ";
    s << usedMem % 1024 << "o";
  }

Referenced by gum::IBayesNet< GUM_SCALAR >::toString().

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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 179 of file IBayesNet_tpl.h.

                                                 {
    std::stringstream output;
    output << "digraph \"";
 
    std::string bn_name;
 
    try {
      bn_name = this->property("name");
    } catch (NotFound const&) { bn_name = "no_name"; }
 
    output << bn_name << "\" {" << std::endl;
    output << "  graph [bgcolor=transparent,label=\"" << bn_name << "\"];" << std::endl;
    output << "  node [style=filled fillcolor=\"#ffffaa\"];" << std::endl << std::endl;
 
    for (auto node: nodes())
      output << "\"" << variable(node).name() << "\" [comment=\"" << node << ":"
             << variable(node).toStringWithDescription() << "\"];" << std::endl;
 
    output << std::endl;
 
    std::string tab = "  ";
 
    for (auto node: nodes()) {
      if (children(node).size() > 0) {
        for (auto child: children(node)) {
          output << tab << "\"" << variable(node).name() << "\" -> " << "\""
                 << variable(child).name() << "\";" << std::endl;
        }
      } else if (parents(node).size() == 0) {
        output << tab << "\"" << variable(node).name() << "\";" << std::endl;
      }
    }
 
    output << "}" << std::endl;
 
    return output.str();
  }

topologicalOrder()

INLINE Sequence< NodeId > gum::DAGmodel::topologicalOrder ( ) const

inherited

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

Parameters

clear

If false returns the previously created topology.

Definition at line 115 of file DAGmodel_inl.h.

{ return dag().topologicalOrder(); }

Referenced by gum::InfluenceDiagramGenerator< GUM_SCALAR >::_checkTemporalOrder_(), gum::InfluenceDiagram< GUM_SCALAR >::decisionOrder(), and gum::InfluenceDiagram< GUM_SCALAR >::decisionOrderExists().

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toString()

template<typename GUM_SCALAR>

INLINE std::string gum::IBayesNet< GUM_SCALAR >::toString ( ) const

inherited

Returns

Returns a string representation of this IBayesNet.

Definition at line 170 of file IBayesNet_tpl.h.

                                                           {
    std::stringstream s;
    s << "BN{nodes: " << size() << ", arcs: " << dag().sizeArcs() << ", ";
    spaceCplxToStream(s, log10DomainSize(), (int)dim(), memoryFootprint());
    s << "}";
    return s.str();
  }

References gum::DAGmodel::dag(), dim(), gum::GraphicalModel::log10DomainSize(), memoryFootprint(), gum::DAGmodel::size(), and gum::GraphicalModel::spaceCplxToStream().

Referenced by gum::operator<<().

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variable() [1/2]

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 334 of file BayesNet.h.

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

References idFromName(), and variable().

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variable() [2/2]

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

id	The variable's id to return.

Returns

Returns a constant reference of the gum::DiscreteVariable corresponding to id in the gum::BayesNet.

Exceptions

NotFound

Raised if id does not match a a variable in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 167 of file BayesNet_tpl.h.

                                                                                 {
    return _varMap_.get(id);
  }

References _varMap_.

Referenced by add(), addArc(), addWeightedArc(), changeVariableLabel(), erase(), eraseArc(), generateCPT(), gum::getMaxModality(), and variable().

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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

name	The variable's name in the gum::BayesNet.

Returns

Returns the gum::DiscreteVariable named name in the gum::BayesNet.

Exceptions

NotFound

Raised if name does not match a variable in the gum::BayesNet.

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 253 of file BayesNet_tpl.h.

                                                                          {
    return _varMap_.variableFromName(name);
  }

References _varMap_.

Referenced by _copyTensors_().

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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 263 of file BayesNet_tpl.h.

                                                                              {
    return _varMap_;
  }

References _varMap_.

variables() [1/2]

INLINE VariableSet gum::GraphicalModel::variables ( const NodeSet & ids ) const

inherited

transform a vector of NodeId into a VariableeSet

Returns

NodeSet

Definition at line 160 of file graphicalModel_inl.h.

                                                              {
    VariableSet            s;
    const VariableNodeMap& v = variableNodeMap();
    for (const auto& node: l) {
      s.insert(&v.get(node));
    }
    return s;
  }

References gum::VariableNodeMap::get(), gum::Set< Key >::insert(), and variableNodeMap().

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variables() [2/2]

INLINE VariableSet gum::GraphicalModel::variables ( const std::vector< std::string > & l ) const

inherited

transform a vector of names into a VariableeSet

Returns

NodeSet

Definition at line 150 of file graphicalModel_inl.h.

                                                                             {
    VariableSet            s;
    const VariableNodeMap& v = variableNodeMap();
    for (const auto& name: l) {
      s.insert(&v.variableFromName(name));
    }
    return s;
  }

References variableNodeMap().

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Friends And Related Symbol Documentation

BayesNetFactory< GUM_SCALAR >

template<typename GUM_SCALAR>

friend class BayesNetFactory< GUM_SCALAR >

friend

Definition at line 714 of file BayesNet.h.

Member Data Documentation

_probaMap_

template<typename GUM_SCALAR>

NodeProperty< Tensor< GUM_SCALAR >* > gum::BayesNet< GUM_SCALAR >::_probaMap_

private

Mapping between the variable's id and their CPT.

Definition at line 693 of file BayesNet.h.

Referenced by ~BayesNet(), _clearTensors_(), _copyTensors_(), _unsafeChangeTensor_(), add(), addArc(), beginTopologyTransformation(), cpt(), endTopologyTransformation(), erase(), and eraseArc().

_propertiesMap_

HashTable< std::string, std::string > gum::GraphicalModel::_propertiesMap_

privateinherited

The properties of this Directed Graphical Model.

Definition at line 236 of file graphicalModel.h.

Referenced by _properties_(), operator=(), properties(), propertyWithDefault(), and setProperty().

_varMap_

template<typename GUM_SCALAR>

VariableNodeMap gum::BayesNet< GUM_SCALAR >::_varMap_

private

the map between variable and id

Definition at line 690 of file BayesNet.h.

Referenced by BayesNet(), add(), changeVariableName(), erase(), erase(), eraseArc(), idFromName(), operator=(), reverseArc(), variable(), variableFromName(), and variableNodeMap().

dag_

DAG gum::DAGmodel::dag_

protectedinherited

The DAG of this Directed Graphical Model.

Definition at line 272 of file DAGmodel.h.

Referenced by DAGmodel(), gum::prm::ClassBayesNet< GUM_SCALAR >::_get_(), gum::prm::ClassBayesNet< GUM_SCALAR >::_init_(), gum::prm::InstanceBayesNet< GUM_SCALAR >::_init_(), gum::BayesNet< GUM_SCALAR >::add(), gum::BayesNet< GUM_SCALAR >::addArc(), gum::InfluenceDiagram< GUM_SCALAR >::addArc(), gum::InfluenceDiagram< GUM_SCALAR >::addNode_(), arcs(), children(), children(), children(), dag(), gum::BayesNet< GUM_SCALAR >::erase(), gum::InfluenceDiagram< GUM_SCALAR >::erase(), gum::BayesNet< GUM_SCALAR >::eraseArc(), gum::InfluenceDiagram< GUM_SCALAR >::eraseArc(), exists(), existsArc(), gum::InfluenceDiagram< GUM_SCALAR >::existsPathBetween(), family(), family(), gum::InfluenceDiagram< GUM_SCALAR >::getChildrenDecision_(), gum::InfluenceDiagram< GUM_SCALAR >::getDecisionGraph(), gum::InfluenceDiagram< GUM_SCALAR >::getPartialTemporalOrder(), gum::BayesNetFragment< GUM_SCALAR >::installArc_(), gum::BayesNetFragment< GUM_SCALAR >::installNode(), minimalCondSet(), gum::InfluenceDiagram< GUM_SCALAR >::moralGraph_(), nodes(), operator=(), parents(), parents(), gum::InfluenceDiagram< GUM_SCALAR >::removeTables_(), sizeArcs(), gum::InfluenceDiagram< GUM_SCALAR >::toDot(), gum::BayesNetFragment< GUM_SCALAR >::uninstallArc_(), and gum::BayesNetFragment< GUM_SCALAR >::uninstallNode().

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

• agrum/BN/BayesNet.h
• agrum/BN/BayesNet_tpl.h