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

This class decorates an PRMInstance<GUM_SCALAR> as an IBaseBayesNet. More...

#include <agrum/PRM/instanceBayesNet.h>

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

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

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

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

Detailed Description

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

This class decorates an PRMInstance<GUM_SCALAR> as an IBaseBayesNet.

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

Definition at line 56 of file instanceBayesNet.h.

Constructor & Destructor Documentation

InstanceBayesNet() [1/2]

template<typename GUM_SCALAR >

INLINE gum::prm::InstanceBayesNet< GUM_SCALAR >::InstanceBayesNet

(

const PRMInstance< GUM_SCALAR > &

i

)

Default constructor.

Parameters

i	The PRMInstance<GUM_SCALAR> decorated by this InstanceBayesNet.

Definition at line 57 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

                                                                                              :
        IBayesNet< GUM_SCALAR >(), _inst_(&i) {
      GUM_CONSTRUCTOR(InstanceBayesNet);
      _init_(i);
    }

Here is the call graph for this function:

InstanceBayesNet() [2/2]

template<typename GUM_SCALAR >

INLINE gum::prm::InstanceBayesNet< GUM_SCALAR >::InstanceBayesNet

(

const InstanceBayesNet< GUM_SCALAR > &

from

)

Copy constructor.

Definition at line 64 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

                                                                                        :
        IBayesNet< GUM_SCALAR >(from), _varNodeMap_(from._varNodeMap_), _inst_(from._inst_) {
      GUM_CONS_CPY(InstanceBayesNet);
    }

Here is the call graph for this function:

~InstanceBayesNet()

template<typename GUM_SCALAR >

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

virtual

Destructor.

Definition at line 70 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

                                                             {
      GUM_DESTRUCTOR(InstanceBayesNet);
    }

Here is the call graph for this function:

Member Function Documentation

_get_() [1/2]

template<typename GUM_SCALAR >

INLINE const PRMClassElement< GUM_SCALAR > & gum::prm::InstanceBayesNet< GUM_SCALAR >::_get_ ( NodeId  id ) const

private

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

Exceptions

NotFound

Raised if id is not a formal attribute.

Definition at line 119 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

                                                                      {
      return _inst_->get(id);
    }

Here is the call graph for this function:

_get_() [2/2]

template<typename GUM_SCALAR >

INLINE const PRMClassElement< GUM_SCALAR > & gum::prm::InstanceBayesNet< GUM_SCALAR >::_get_ ( const std::string &  name ) const

private

Definition at line 125 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

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

Here is the call graph for this function:

_init_()

template<typename GUM_SCALAR >

void gum::prm::InstanceBayesNet< GUM_SCALAR >::_init_ ( const PRMInstance< GUM_SCALAR > &  i )

private

Definition at line 35 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

                                                                                  {
      for (const auto node: i.type().containerDag().nodes()) {
        try {
          // Adding the attribute
          const PRMAttribute< GUM_SCALAR >& attr = i.get(node);
          this->dag_.addNodeWithId(attr.id());
          _varNodeMap_.insert(&(attr.type().variable()), &attr);
        } catch (NotFound&) {
          // Not an attribute
        }
      }

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

Here is the call graph for this function:

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 96 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

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 98 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                {
    return ancestors(idFromName(name));
  }

Here is the call graph for this function:

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 43 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag_.arcs(); }

Here is the call graph for this function:

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 65 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag_.children(id); }

Here is the call graph for this function:

children() [2/4]

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 66 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

children() [3/4]

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

inherited

returns the children of a set of nodes

Definition at line 70 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag_.children(ids); }

Here is the call graph for this function:

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 72 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                             {
    return children(nodeset(names));
  }

Here is the call graph for this function:

completeInstantiation()

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

inherited

Get an instantiation over all the variables of the model.

Definition at line 84 of file graphicalModel_inl.h.

                                                            {
    Instantiation I;

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

    return I;
  }

cpt()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::cpt().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 87 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

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

Here is the call graph for this function:

dag()

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

inherited

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

Definition at line 35 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag_; }

Here is the call graph for this function:

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 90 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

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 92 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                  {
    return descendants(idFromName(name));
  }

Here is the call graph for this function:

dim()

template<typename GUM_SCALAR >

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

inherited

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

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

Definition at line 78 of file IBayesNet_tpl.h.

                                          {
    Size dim = 0;

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

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

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

    return dim;
  }

empty()

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

virtualinherited

Return true if this graphical model is empty.

Definition at line 94 of file graphicalModel_inl.h.

{ return size() == 0; }

exists() [1/2]

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 82 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag_.exists(node); }

Here is the call graph for this function:

exists() [2/2]

bool gum::GraphicalModel::exists ( const std::string &  name ) const

inlineinherited

Return true if this graphical model is empty.

Definition at line 112 of file graphicalModel.h.

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

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 45 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

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 49 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

family() [1/2]

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

inherited

returns the parents of a node and the 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 59 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag_.family(id); }

Here is the call graph for this function:

family() [2/2]

INLINE NodeSet gum::DAGmodel::family ( 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 61 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

hasSameStructure()

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

inherited

Returns

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

Definition at line 69 of file DAGmodel.cpp.

References gum::Set< Key, Alloc >::emplace().

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

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

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

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

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

    return true;
  }

Here is the call graph for this function:

idFromName()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::idFromName().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 107 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

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

Here is the call graph for this function:

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 117 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;
  }

isIndependent() [1/4]

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 113 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

isIndependent() [2/4]

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 117 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

isIndependent() [3/4]

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 185 of file DAGmodel.h.

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

isIndependent() [4/4]

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 191 of file DAGmodel.h.

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

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 207 of file IBayesNet_tpl.h.

                                                                                   {
    auto value = (GUM_SCALAR)1.0;

    GUM_SCALAR tmp;

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

      value *= tmp;
    }

    return value;
  }

log10DomainSize()

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

inherited

Definition at line 73 of file graphicalModel_inl.h.

                                               {
    double dSize = 0.0;

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

    return dSize;
  }

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 225 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 134 of file IBayesNet_tpl.h.

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

maxParam()

template<typename GUM_SCALAR >

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

inherited

Returns

the biggest value in the CPTs of *this

Definition at line 114 of file IBayesNet_tpl.h.

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

maxVarDomainSize()

template<typename GUM_SCALAR >

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

inherited

Returns

the biggest domainSize among the variables of *this

Definition at line 94 of file IBayesNet_tpl.h.

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

minimalCondSet() [1/2]

template<typename GUM_SCALAR >

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

inherited

Definition at line 332 of file IBayesNet_tpl.h.

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

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

    for (auto fath: dag_.parents(target))
      _minimalCondSetVisitUp_(fath, soids, res, alreadyVisitedUp, alreadyVisitedDn);
    for (auto chil: dag_.children(target))
      _minimalCondSetVisitDn_(chil, soids, res, alreadyVisitedUp, alreadyVisitedDn);
    return res;
  }

minimalCondSet() [2/2]

template<typename GUM_SCALAR >

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

inherited

Definition at line 349 of file IBayesNet_tpl.h.

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

minNonZeroParam()

template<typename GUM_SCALAR >

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

inherited

Returns

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

Warning

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

Definition at line 124 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;
  }

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 104 of file IBayesNet_tpl.h.

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

modalities()

template<typename GUM_SCALAR >

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

(

)

const

See gum::IBaseBayesNet::cpt().

Definition at line 132 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

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

      return _modalities_;
    }

Here is the call graph for this function:

moralGraph()

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

inherited

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

Parameters

clear

If false returns the previously created moral graph.

Definition at line 55 of file DAGmodel.cpp.

References gum::Set< Key, Alloc >::emplace().

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

    return *_mutableMoralGraph_;
  }

Here is the call graph for this function:

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 109 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

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 104 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                    {
    return moralizedAncestralGraph(nodeset(nodenames));
  }

Here is the call graph for this function:

names() [1/2]

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 97 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;
  }

names() [2/2]

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 107 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;
  }

nodeId()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::nodeId().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 102 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

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

Here is the call graph for this function:

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 84 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return (NodeGraphPart&)dag_; }

Here is the call graph for this function:

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 58 of file graphicalModel.cpp.

References gum::Set< Key, Alloc >::emplace().

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

Here is the call graph for this function:

operator!=()

template<typename GUM_SCALAR>

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

inherited

Returns

Returns false if the src and this are equal.

Definition at line 286 of file IBayesNet_tpl.h.

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

operator=()

template<typename GUM_SCALAR >

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

(

const InstanceBayesNet< GUM_SCALAR > &

from

)

Copy operator.

Definition at line 76 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

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

        _varNodeMap_ = from._varNodeMap_;
      }

      return *this;
    }

Here is the call graph for this function:

operator==()

template<typename GUM_SCALAR>

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

inherited

This operator compares 2 BNs !

Warning

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

Returns

true if the src and this are equal.

Definition at line 242 of file IBayesNet_tpl.h.

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

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

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

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

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

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

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

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

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

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

    return true;
  }

parents() [1/4]

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 53 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag_.parents(id); }

Here is the call graph for this function:

parents() [2/4]

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 55 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                     {
    return parents(idFromName(name));
  }

Here is the call graph for this function:

parents() [3/4]

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

inherited

returns the parents of a set of nodes

Definition at line 76 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag_.children(ids); }

Here is the call graph for this function:

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 78 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                            {
    return parents(nodeset(names));
  }

Here is the call graph for this function:

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 38 of file graphicalModel_inl.h.

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

propertyWithDefault()

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

inherited

Return the value of the property name of this GraphicalModel.

return byDefault if the property name is not found

Definition at line 57 of file graphicalModel_inl.h.

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

setProperty()

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

inherited

Add or change a property of this GraphicalModel.

Definition at line 65 of file graphicalModel_inl.h.

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

size()

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

finalvirtualinherited

Returns the number of variables in this Directed Graphical Model.

Implements gum::GraphicalModel.

Definition at line 38 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag().size(); }

Here is the call graph for this function:

sizeArcs()

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

inherited

Returns the number of arcs in this Directed Graphical Model.

Definition at line 41 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

{ return dag_.sizeArcs(); }

Here is the call graph for this function:

toDot()

template<typename GUM_SCALAR >

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

virtual

Returns

Returns a dot representation of this IBayesNet.

Reimplemented from gum::IBayesNet< GUM_SCALAR >.

Definition at line 143 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

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

      for (const auto node: this->nodes()) {
        if (this->children(node).size() > 0) {
          const NodeSet& children = this->children(node);

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

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

Here is the call graph for this function:

topologicalOrder()

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

inherited

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

Parameters

clear

If false returns the previously created topology.

Definition at line 86 of file DAGmodel_inl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                              {
    return dag().topologicalOrder(clear);
  }

Here is the call graph for this function:

toString()

template<typename GUM_SCALAR >

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

inherited

Returns

Returns a string representation of this IBayesNet.

Definition at line 144 of file IBayesNet_tpl.h.

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

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

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

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

    s << ", dim: " << param << "}";

    return s.str();
  }

variable()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::variable().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 97 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

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

Here is the call graph for this function:

variableFromName()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::variableFromName().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 113 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

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

Here is the call graph for this function:

variableNodeMap()

template<typename GUM_SCALAR >

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

virtual

See gum::IBaseBayesNet::variableNodeMap().

Implements gum::IBayesNet< GUM_SCALAR >.

Definition at line 92 of file instanceBayesNet_tpl.h.

References gum::prm::ParamScopeData< GUM_SCALAR >::ParamScopeData().

                                                                                        {
      GUM_ERROR(NotFound, "no VariableNodeMap in an InstanceBayesNet")
    }

Here is the call graph for this function:

Member Data Documentation

_inst_

template<typename GUM_SCALAR >

const PRMInstance< GUM_SCALAR >* gum::prm::InstanceBayesNet< GUM_SCALAR >::_inst_

private

The PRMClassElementContainer decorated by this.

Definition at line 124 of file instanceBayesNet.h.

_modalities_

template<typename GUM_SCALAR >

NodeProperty< Size > gum::prm::InstanceBayesNet< GUM_SCALAR >::_modalities_

mutableprivate

Definition at line 126 of file instanceBayesNet.h.

_varNodeMap_

template<typename GUM_SCALAR >

HashTable< const DiscreteVariable*, const PRMAttribute< GUM_SCALAR >* > gum::prm::InstanceBayesNet< GUM_SCALAR >::_varNodeMap_

private

Mapping between DiscreteVariable and their NodeId.

Definition at line 114 of file instanceBayesNet.h.

dag_

DAG gum::DAGmodel::dag_

protectedinherited

The DAG of this Directed Graphical Model.

Definition at line 222 of file DAGmodel.h.

---

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

• agrum/PRM/instanceBayesNet.h
• agrum/PRM/instanceBayesNet_tpl.h