gum::BayesBall Class Reference

Implementation of Shachter's Bayes Balls algorithm. More...

#include <agrum/BN/inference/BayesBall.h>

Static Public Member Functions
Accessors / Modifiers
static void	requisiteNodes (const DAG &dag, const NodeSet &query, const NodeSet &hardEvidence, const NodeSet &softEvidence, NodeSet &requisite)
	Fill the 'requisite' nodeset with the requisite nodes in dag given a query and evidence.
template<GUM_Numeric GUM_SCALAR, class TABLE>
static void	relevantTensors (const IBayesNet< GUM_SCALAR > &bn, const NodeSet &query, const NodeSet &hardEvidence, const NodeSet &softEvidence, Set< const TABLE * > &tensors)
	update a set of tensors, keeping only those d-connected with query variables given evidence

Private Member Functions
Constructors / Destructors
	BayesBall ()
	Default constructor.
	~BayesBall ()
	Destructor.

Detailed Description

Implementation of Shachter's Bayes Balls algorithm.

Definition at line 67 of file BayesBall.h.

Constructor & Destructor Documentation

BayesBall()

INLINE gum::BayesBall::BayesBall ( )

private

Default constructor.

Definition at line 55 of file BayesBall_inl.h.

{ GUM_CONSTRUCTOR(BayesBall) }

References BayesBall().

Referenced by BayesBall(), and ~BayesBall().

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~BayesBall()

INLINE gum::BayesBall::~BayesBall ( )

private

Destructor.

Definition at line 57 of file BayesBall_inl.h.

{ GUM_DESTRUCTOR(BayesBall) }

References BayesBall().

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Member Function Documentation

relevantTensors()

template<GUM_Numeric GUM_SCALAR, class TABLE>

void gum::BayesBall::relevantTensors ( const IBayesNet< GUM_SCALAR > & bn, const NodeSet & query, const NodeSet & hardEvidence, const NodeSet & softEvidence, Set< const TABLE * > & tensors )

static

update a set of tensors, keeping only those d-connected with query variables given evidence

Definition at line 56 of file BayesBall_tpl.h.

                                                                          {
    const DAG& dag = bn.internalDag();
 
    // create the marks (top = first and bottom = second)
    NodeProperty< std::pair< bool, bool > > marks(dag.size());
    const std::pair< bool, bool >           empty_mark(false, false);

    HashTable< NodeId, Set< const TABLE* > > node2tensors;
    for (const auto pot: tensors) {
      const Sequence< const DiscreteVariable* >& vars = pot->variablesSequence();
      for (const auto var: vars) {
        const NodeId id = bn.nodeId(*var);
        if (!node2tensors.exists(id)) { node2tensors.insert(id, Set< const TABLE* >()); }
        node2tensors[id].insert(pot);
      }
    }
 
    // indicate that we will send the ball to all the query nodes (as children):
    // in list nodes_to_visit, the first element is the next node to send the
    // ball to and the Boolean indicates whether we shall reach it from one of
    // its children (true) or from one parent (false)
    List< std::pair< NodeId, bool > > nodes_to_visit;
    for (const auto node: query) {
      nodes_to_visit.insert(std::pair< NodeId, bool >(node, true));
    }
 
    // perform the bouncing ball until  _node2tensors_ becomes empty (which
    // means that we have reached all the tensors and, therefore, those
    // are d-connected to query) or until there is no node in the graph to send
    // the ball to
    while (!nodes_to_visit.empty() && !node2tensors.empty()) {
      // get the next node to visit
      NodeId node = nodes_to_visit.front().first;
 
      // if the marks of the node do not exist, create them
      if (!marks.exists(node)) marks.insert(node, empty_mark);
 
      // if the node belongs to the query, update  _node2tensors_: remove all
      // the tensors containing the node
      if (node2tensors.exists(node)) {
        auto& pot_set = node2tensors[node];
        for (const auto pot: pot_set) {
          const auto& vars = pot->variablesSequence();
          for (const auto var: vars) {
            const NodeId id = bn.nodeId(*var);
            if (id != node) {
              node2tensors[id].erase(pot);
              if (node2tensors[id].empty()) { node2tensors.erase(id); }
            }
          }
        }
        node2tensors.erase(node);
 
        // if  _node2tensors_ is empty, no need to go on: all the tensors
        // are d-connected to the query
        if (node2tensors.empty()) return;
      }
 
 
      // bounce the ball toward the neighbors
      if (nodes_to_visit.front().second) {   // visit from a child
        nodes_to_visit.popFront();
 
        if (hardEvidence.exists(node)) { continue; }
 
        if (!marks[node].first) {
          marks[node].first = true;   // top marked
          for (const auto par: dag.parents(node)) {
            nodes_to_visit.insert(std::pair< NodeId, bool >(par, true));
          }
        }
 
        if (!marks[node].second) {
          marks[node].second = true;   // bottom marked
          for (const auto chi: dag.children(node)) {
            nodes_to_visit.insert(std::pair< NodeId, bool >(chi, false));
          }
        }
      } else {   // visit from a parent
        nodes_to_visit.popFront();
 
        const bool is_hard_evidence = hardEvidence.exists(node);
        const bool is_evidence      = is_hard_evidence || softEvidence.exists(node);
 
        if (is_evidence && !marks[node].first) {
          marks[node].first = true;
 
          for (const auto par: dag.parents(node)) {
            nodes_to_visit.insert(std::pair< NodeId, bool >(par, true));
          }
        }
 
        if (!is_hard_evidence && !marks[node].second) {
          marks[node].second = true;
 
          for (const auto chi: dag.children(node)) {
            nodes_to_visit.insert(std::pair< NodeId, bool >(chi, false));
          }
        }
      }
    }
 
 
    // here, all the tensors that belong to  _node2tensors_ are d-separated
    // from the query
    for (const auto& elt: node2tensors) {
      for (const auto pot: elt.second) {
        tensors.erase(pot);
      }
    }
  }

References gum::ArcGraphPart::children(), gum::HashTable< Key, Val >::empty(), gum::List< Val >::empty(), gum::HashTable< Key, Val >::erase(), gum::Set< Key >::erase(), gum::HashTable< Key, Val >::exists(), gum::Set< Key >::exists(), gum::List< Val >::front(), gum::HashTable< Key, Val >::insert(), gum::List< Val >::insert(), gum::DAGmodel::internalDag(), gum::DiscreteGraphicalModel::nodeId(), gum::ArcGraphPart::parents(), gum::List< Val >::popFront(), and gum::NodeGraphPart::size().

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requisiteNodes()

void gum::BayesBall::requisiteNodes ( const DAG & dag, const NodeSet & query, const NodeSet & hardEvidence, const NodeSet & softEvidence, NodeSet & requisite )

static

Fill the 'requisite' nodeset with the requisite nodes in dag given a query and evidence.

Requisite nodes are those that are d-connected to at least one of the query nodes given a set of hard and soft evidence

Definition at line 56 of file BayesBall.cpp.

                                                           {
    requisite = graph::requisiteNodes(dag, query, hardEvidence, softEvidence);
  }

References gum::graph::requisiteNodes().

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

• agrum/BN/algorithms/BayesBall.h
• agrum/BN/algorithms/BayesBall.cpp
• agrum/BN/algorithms/BayesBall_inl.h
• agrum/BN/algorithms/BayesBall_tpl.h