gum::IMDDI< AttributeSelection, isScalar > Class Template Reference

#include <agrum/FMDP/planning/FunctionGraph/imddi.h>

Inheritance diagram for gum::IMDDI< AttributeSelection, isScalar >:

Collaboration diagram for gum::IMDDI< AttributeSelection, isScalar >:

Public Member Functions
void	insertSetOfVars (MultiDimFunctionGraph< double > *ret) const
Size	size ()
Constructor & destructor.
	IMDDI (MultiDimFunctionGraph< double > *target, double attributeSelectionThreshold, double pairSelectionThreshold, Set< const DiscreteVariable * > attributeListe, const DiscreteVariable *learnedValue)
	Variable Learner constructor. More...
	IMDDI (MultiDimFunctionGraph< double > *target, double attributeSelectionThreshold, double pairSelectionThreshold, Set< const DiscreteVariable * > attributeListe)
	Reward Learner constructor. More...
	~IMDDI ()
	Default destructor. More...
Visit Methods
NodeId	root () const
bool	isTerminal (NodeId ni) const
const DiscreteVariable *	nodeVar (NodeId ni) const
NodeId	nodeSon (NodeId ni, Idx modality) const
Idx	nodeNbObservation (NodeId ni) const

Protected Attributes
MultiDimFunctionGraph< double > *	_target
	The final diagram we're building. More...
Set< const DiscreteVariable *>	_setOfVars
const DiscreteVariable *	_value
Sequence< ValueType >	_valueAssumed
bool	_needUpdate
Model handling datastructures
NodeGraphPart	_model
	The source of nodeId. More...
NodeId	_root
	The root of the ordered tree. More...
HashTable< NodeId, const DiscreteVariable *>	_nodeVarMap
	Gives for any node its associated variable. More...
HashTable< NodeId, NodeId *>	_nodeSonsMap
	A table giving for any node a table mapping to its son idx is the modality of associated variable. More...
HashTable< const DiscreteVariable *, LinkedList< NodeId > *>	_var2Node
	Associates to any variable the list of all nodes associated to this variable. More...
HashTable< NodeId, NodeDatabase< AttributeSelection, isScalar > *>	_nodeId2Database
	This hashtable binds every node to an associated NodeDatabase which handles every observation that concerns that node. More...
HashTable< NodeId, Set< const Observation *> *>	_leafDatabase
	This hashtable binds to every leaf an associated set of all hte observations compatible with it. More...

Incrementals methods
void	addObservation (const Observation *)
	Adds a new observation to the structure. More...
void	updateGraph ()
	Updates the tree after a new observation has been added. More...
void	_updateNodeWithObservation (const Observation *newObs, NodeId currentNodeId)
	Adds a new observation to the structure. More...
NodeId	_insertLeafNode (NodeDatabase< AttributeSelection, isScalar > *nDB, const DiscreteVariable *boundVar, Set< const Observation * > *sonsMap)
	Adds a new observation to the structure. More...
void	_chgNodeBoundVar (NodeId chgedNodeId, const DiscreteVariable *desiredVar)
	Adds a new observation to the structure. More...
void	_removeNode (NodeId removedNodeId)
	Adds a new observation to the structure. More...
void	__addLeaf (NodeId)
	Adds a new observation to the structure. More...
void	__removeLeaf (NodeId)
	Adds a new observation to the structure. More...

Updating private methods
void	updateFunctionGraph ()
	Computes the score of the given variables for the given node. More...
void	__updateScore (const DiscreteVariable *, NodeId, VariableSelector &vs)
	Computes the score of the given variables for the given node. More...
void	__downdateScore (const DiscreteVariable *, NodeId, VariableSelector &vs)
	Computes the score of the given variables for the given node. More...
void	__updateNodeSet (Set< NodeId > &, const DiscreteVariable *, VariableSelector &)
	For each node in the given set, this methods checks whether or not we should installed the given variable as a test. If so, the node is updated. More...
void	__rebuildFunctionGraph ()
	Computes the score of the given variables for the given node. More...
NodeId	__insertLeafInFunctionGraph (AbstractLeaf *, Int2Type< true >)
	Computes the score of the given variables for the given node. More...
NodeId	__insertLeafInFunctionGraph (AbstractLeaf *, Int2Type< false >)
	Computes the score of the given variables for the given node. More...

Graph Structure update methods
virtual void	updateVar (const DiscreteVariable *)
	If a new modality appears to exists for given variable, call this method to turn every associated node to this variable into leaf. Graph has then indeed to be revised. More...
void	_updateNode (NodeId nody, Set< const DiscreteVariable * > &bestVars)
	From the given sets of node, selects randomly one and installs it on given node. More...
virtual void	_convertNode2Leaf (NodeId)
	Turns the given node into a leaf if not already so. More...
virtual void	_transpose (NodeId, const DiscreteVariable *)
	Installs given variable to the given node, ensuring that the variable is not present in its subtree. More...
virtual NodeId	_insertNode (NodeDatabase< AttributeSelection, isScalar > *nDB, const DiscreteVariable *boundVar)
	inserts a new node in internal graph More...
virtual NodeId	_insertInternalNode (NodeDatabase< AttributeSelection, isScalar > *nDB, const DiscreteVariable *boundVar, NodeId *sonsMap)
	inserts a new internal node in internal graph More...

Detailed Description

template<TESTNAME AttributeSelection, bool isScalar = false>
class gum::IMDDI< AttributeSelection, isScalar >

Definition at line 55 of file imddi.h.

Constructor & Destructor Documentation

IMDDI() [1/2]

template<TESTNAME AttributeSelection, bool isScalar>

gum::IMDDI< AttributeSelection, isScalar >::IMDDI	(	MultiDimFunctionGraph< double > *	target,
		double	attributeSelectionThreshold,
		double	pairSelectionThreshold,
		Set< const DiscreteVariable * >	attributeListe,
		const DiscreteVariable *	learnedValue
	)

Variable Learner constructor.

Definition at line 48 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__addLeaf(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_root.

                                                    :
      IncrementalGraphLearner< AttributeSelection, isScalar >(
         target, attributeListe, learnedValue),
      __lg(&(this->_model), pairSelectionThreshold), __nbTotalObservation(0),
      __attributeSelectionThreshold(attributeSelectionThreshold) {
    GUM_CONSTRUCTOR(IMDDI);
    __addLeaf(this->_root);
  }

Here is the call graph for this function:

IMDDI() [2/2]

template<TESTNAME AttributeSelection, bool isScalar>

gum::IMDDI< AttributeSelection, isScalar >::IMDDI	(	MultiDimFunctionGraph< double > *	target,
		double	attributeSelectionThreshold,
		double	pairSelectionThreshold,
		Set< const DiscreteVariable * >	attributeListe
	)

Reward Learner constructor.

Definition at line 66 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__addLeaf(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_root.

                                                      :
      IncrementalGraphLearner< AttributeSelection, isScalar >(
         target, attributeListe, new LabelizedVariable("Reward", "", 2)),
      __lg(&(this->_model), pairSelectionThreshold), __nbTotalObservation(0),
      __attributeSelectionThreshold(attributeSelectionThreshold) {
    GUM_CONSTRUCTOR(IMDDI);
    __addLeaf(this->_root);
  }

Here is the call graph for this function:

~IMDDI()

template<TESTNAME AttributeSelection, bool isScalar>

gum::IMDDI< AttributeSelection, isScalar >::~IMDDI

(

)

Default destructor.

Definition at line 83 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__leafMap.

                                                {
    GUM_DESTRUCTOR(IMDDI);
    for (HashTableIteratorSafe< NodeId, AbstractLeaf* > leafIter =
            __leafMap.beginSafe();
         leafIter != __leafMap.endSafe();
         ++leafIter)
      delete leafIter.val();
  }

Member Function Documentation

__addLeaf()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::__addLeaf ( NodeId  currentNodeId )

private

Adds a new observation to the structure.

Definition at line 287 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__leafMap, gum::IMDDI< AttributeSelection, isScalar >::__lg, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeId2Database, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_valueAssumed, and gum::LeafAggregator::addLeaf().

Referenced by gum::IMDDI< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IMDDI< AttributeSelection, isScalar >::_insertLeafNode(), and gum::IMDDI< AttributeSelection, isScalar >::IMDDI().

                                                                            {
    __leafMap.insert(currentNodeId,
                     new ConcreteLeaf< AttributeSelection, isScalar >(
                        currentNodeId,
                        this->_nodeId2Database[currentNodeId],
                        &(this->_valueAssumed)));
    __lg.addLeaf(__leafMap[currentNodeId]);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

__downdateScore()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::__downdateScore ( const DiscreteVariable *  var, NodeId  nody, VariableSelector &  vs  )

private

Computes the score of the given variables for the given node.

Definition at line 180 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__nbTotalObservation, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeId2Database, and gum::VariableSelector::downdateScore().

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__updateNodeSet().

                                                                     {
    if (!this->_nodeId2Database[nody]->isTestRelevant(var)) return;
    double weight = (double)this->_nodeId2Database[nody]->nbObservation()
                    / (double)this->__nbTotalObservation;
    vs.downdateScore(var,
                     weight * this->_nodeId2Database[nody]->testValue(var),
                     weight
                        * this->_nodeId2Database[nody]->testOtherCriterion(var));
  }

Here is the call graph for this function:

Here is the caller graph for this function:

__insertLeafInFunctionGraph() [1/2]

template<TESTNAME AttributeSelection, bool isScalar>

NodeId gum::IMDDI< AttributeSelection, isScalar >::__insertLeafInFunctionGraph ( AbstractLeaf *  leaf, Int2Type< true >    )

private

Computes the score of the given variables for the given node.

Definition at line 388 of file imddi_tpl.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_target, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_valueAssumed, gum::MultiDimFunctionGraphManager< GUM_SCALAR, TerminalNodePolicy >::addTerminalNode(), gum::AbstractLeaf::effectif(), gum::MultiDimFunctionGraph< GUM_SCALAR, TerminalNodePolicy >::manager(), gum::AbstractLeaf::nbModa(), and gum::AbstractLeaf::total().

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph().

                                           {
    double value = 0.0;
    for (Idx moda = 0; moda < leaf->nbModa(); moda++) {
      value += (double)leaf->effectif(moda) * this->_valueAssumed.atPos(moda);
    }
    if (leaf->total()) value /= (double)leaf->total();
    return this->_target->manager()->addTerminalNode(value);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

__insertLeafInFunctionGraph() [2/2]

template<TESTNAME AttributeSelection, bool isScalar>

NodeId gum::IMDDI< AttributeSelection, isScalar >::__insertLeafInFunctionGraph ( AbstractLeaf *  leaf, Int2Type< false >    )

private

Computes the score of the given variables for the given node.

Definition at line 403 of file imddi_tpl.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_target, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_value, gum::MultiDimFunctionGraphManager< GUM_SCALAR, TerminalNodePolicy >::addInternalNode(), gum::MultiDimFunctionGraphManager< GUM_SCALAR, TerminalNodePolicy >::addTerminalNode(), gum::DiscreteVariable::domainSize(), gum::AbstractLeaf::effectif(), gum::MultiDimFunctionGraph< GUM_SCALAR, TerminalNodePolicy >::manager(), SOA_ALLOCATE, and gum::AbstractLeaf::total().

                                            {
    NodeId* sonsMap = static_cast< NodeId* >(
       SOA_ALLOCATE(sizeof(NodeId) * this->_value->domainSize()));
    for (Idx modality = 0; modality < this->_value->domainSize(); ++modality) {
      double newVal = 0.0;
      if (leaf->total())
        newVal = (double)leaf->effectif(modality) / (double)leaf->total();
      sonsMap[modality] = this->_target->manager()->addTerminalNode(newVal);
    }
    return this->_target->manager()->addInternalNode(this->_value, sonsMap);
  }

Here is the call graph for this function:

__rebuildFunctionGraph()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph ( )

private

Computes the score of the given variables for the given node.

Definition at line 325 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__insertLeafInFunctionGraph(), gum::IMDDI< AttributeSelection, isScalar >::__lg, gum::IMDDI< AttributeSelection, isScalar >::__varOrder, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeSonsMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_root, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_target, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_value, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_var2Node, gum::MultiDimFunctionGraph< GUM_SCALAR, TerminalNodePolicy >::add(), gum::MultiDimFunctionGraphManager< GUM_SCALAR, TerminalNodePolicy >::addInternalNode(), gum::HashTable< Key, Val, Alloc >::cbeginSafe(), gum::HashTable< Key, Val, Alloc >::cendSafe(), gum::MultiDimFunctionGraphManager< GUM_SCALAR, TerminalNodePolicy >::clean(), gum::MultiDimFunctionGraph< GUM_SCALAR, TerminalNodePolicy >::clear(), gum::HashTable< Key, Val, Alloc >::exists(), gum::HashTable< Key, Val, Alloc >::insert(), gum::LeafAggregator::leavesMap(), gum::MultiDimFunctionGraph< GUM_SCALAR, TerminalNodePolicy >::manager(), gum::MultiDimFunctionGraphManager< GUM_SCALAR, TerminalNodePolicy >::setRootNode(), SOA_ALLOCATE, and gum::LeafAggregator::update().

Referenced by gum::IMDDI< AttributeSelection, isScalar >::updateFunctionGraph().

                                                                     {
    // *******************************************************************************************************
    // Mise à jour de l'aggregateur de feuille
    __lg.update();

    // *******************************************************************************************************
    // Reinitialisation du Graphe de Décision
    this->_target->clear();
    for (auto varIter = __varOrder.beginSafe(); varIter != __varOrder.endSafe();
         ++varIter)
      this->_target->add(**varIter);
    this->_target->add(*this->_value);

    HashTable< NodeId, NodeId > toTarget;

    // *******************************************************************************************************
    // Insertion des feuilles
    HashTable< NodeId, AbstractLeaf* > treeNode2leaf = __lg.leavesMap();
    HashTable< AbstractLeaf*, NodeId > leaf2DGNode;
    for (HashTableConstIteratorSafe< NodeId, AbstractLeaf* > treeNodeIter =
            treeNode2leaf.cbeginSafe();
         treeNodeIter != treeNode2leaf.cendSafe();
         ++treeNodeIter) {
      if (!leaf2DGNode.exists(treeNodeIter.val()))
        leaf2DGNode.insert(treeNodeIter.val(),
                           __insertLeafInFunctionGraph(treeNodeIter.val(),
                                                       Int2Type< isScalar >()));

      toTarget.insert(treeNodeIter.key(), leaf2DGNode[treeNodeIter.val()]);
    }

    // *******************************************************************************************************
    // Insertion des noeuds internes (avec vérification des possibilités de
    // fusion)
    for (SequenceIteratorSafe< const DiscreteVariable* > varIter =
            __varOrder.rbeginSafe();
         varIter != __varOrder.rendSafe();
         --varIter) {
      for (Link< NodeId >* curNodeIter = this->_var2Node[*varIter]->list();
           curNodeIter;
           curNodeIter = curNodeIter->nextLink()) {
        NodeId* sonsMap = static_cast< NodeId* >(
           SOA_ALLOCATE(sizeof(NodeId) * (*varIter)->domainSize()));
        for (Idx modality = 0; modality < (*varIter)->domainSize(); ++modality)
          sonsMap[modality] =
             toTarget[this->_nodeSonsMap[curNodeIter->element()][modality]];
        toTarget.insert(
           curNodeIter->element(),
           this->_target->manager()->addInternalNode(*varIter, sonsMap));
      }
    }

    // *******************************************************************************************************
    // Polish
    this->_target->manager()->setRootNode(toTarget[this->_root]);
    this->_target->manager()->clean();
  }

Here is the call graph for this function:

Here is the caller graph for this function:

__removeLeaf()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::__removeLeaf ( NodeId  currentNodeId )

private

Adds a new observation to the structure.

Definition at line 301 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__leafMap, gum::IMDDI< AttributeSelection, isScalar >::__lg, and gum::LeafAggregator::removeLeaf().

Referenced by gum::IMDDI< AttributeSelection, isScalar >::_chgNodeBoundVar(), and gum::IMDDI< AttributeSelection, isScalar >::_removeNode().

                                                                               {
    __lg.removeLeaf(__leafMap[currentNodeId]);
    delete __leafMap[currentNodeId];
    __leafMap.erase(currentNodeId);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

__updateNodeSet()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::__updateNodeSet ( Set< NodeId > &  nodeSet, const DiscreteVariable *  selectedVar, VariableSelector &  vs  )

private

For each node in the given set, this methods checks whether or not we should installed the given variable as a test. If so, the node is updated.

Definition at line 198 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__attributeSelectionThreshold, gum::IMDDI< AttributeSelection, isScalar >::__downdateScore(), gum::IMDDI< AttributeSelection, isScalar >::__updateScore(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeId2Database, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeSonsMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose(), gum::VariableSelector::begin(), gum::Set< Key, Alloc >::beginSafe(), gum::Set< Key, Alloc >::clear(), gum::VariableSelector::current(), gum::Set< Key, Alloc >::endSafe(), gum::VariableSelector::hasNext(), and gum::VariableSelector::next().

Referenced by gum::IMDDI< AttributeSelection, isScalar >::updateGraph().

                                 {
    Set< NodeId > oldNodeSet(nodeSet);
    nodeSet.clear();
    for (SetIteratorSafe< NodeId > nodeIter = oldNodeSet.beginSafe();
         nodeIter != oldNodeSet.endSafe();
         ++nodeIter) {
      if (this->_nodeId2Database[*nodeIter]->isTestRelevant(selectedVar)
          && this->_nodeId2Database[*nodeIter]->testValue(selectedVar)
                > __attributeSelectionThreshold) {
        this->_transpose(*nodeIter, selectedVar);

        // Then we subtract the from the score given to each variables the
        // quantity given by this node
        for (vs.begin(); vs.hasNext(); vs.next()) {
          __downdateScore(vs.current(), *nodeIter, vs);
        }

        // And finally we add all its child to the new set of nodes
        // and updates the remaining var's score
        for (Idx modality = 0;
             modality < this->_nodeVarMap[*nodeIter]->domainSize();
             ++modality) {
          NodeId sonId = this->_nodeSonsMap[*nodeIter][modality];
          nodeSet << sonId;

          for (vs.begin(); vs.hasNext(); vs.next()) {
            __updateScore(vs.current(), sonId, vs);
          }
        }
      } else {
        nodeSet << *nodeIter;
      }
    }
  }

Here is the call graph for this function:

Here is the caller graph for this function:

__updateScore()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::__updateScore ( const DiscreteVariable *  var, NodeId  nody, VariableSelector &  vs  )

private

Computes the score of the given variables for the given node.

Definition at line 169 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__nbTotalObservation, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeId2Database, and gum::VariableSelector::updateScore().

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__updateNodeSet(), and gum::IMDDI< AttributeSelection, isScalar >::updateGraph().

                                                                     {
    if (!this->_nodeId2Database[nody]->isTestRelevant(var)) return;
    double weight = (double)this->_nodeId2Database[nody]->nbObservation()
                    / (double)this->__nbTotalObservation;
    vs.updateScore(var,
                   weight * this->_nodeId2Database[nody]->testValue(var),
                   weight * this->_nodeId2Database[nody]->testOtherCriterion(var));
  }

Here is the call graph for this function:

Here is the caller graph for this function:

_chgNodeBoundVar()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::_chgNodeBoundVar ( NodeId  chgedNodeId, const DiscreteVariable *  desiredVar  )

protectedvirtual

Adds a new observation to the structure.

Reimplemented from gum::IncrementalGraphLearner< AttributeSelection, isScalar >.

Definition at line 259 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__addLeaf(), gum::IMDDI< AttributeSelection, isScalar >::__removeLeaf(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap, and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_value.

                                                               {
    if (this->_nodeVarMap[currentNodeId] == this->_value)
      __removeLeaf(currentNodeId);

    IncrementalGraphLearner< AttributeSelection, isScalar >::_chgNodeBoundVar(
       currentNodeId, desiredVar);

    if (desiredVar == this->_value) __addLeaf(currentNodeId);
  }

Here is the call graph for this function:

_convertNode2Leaf()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_convertNode2Leaf ( NodeId  currentNodeId )

protectedvirtualinherited

Turns the given node into a leaf if not already so.

Definition at line 208 of file incrementalGraphLearner_tpl.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_leafDatabase, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeSonsMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_value, gum::HashTable< Key, Val, Alloc >::erase(), and SOA_DEALLOCATE.

Referenced by gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNodeWithObservation(), gum::IMDDI< AttributeSelection, isScalar >::updateGraph(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::updateVar().

                           {
    if (_nodeVarMap[currentNodeId] != _value) {
      _leafDatabase.insert(currentNodeId, new Set< const Observation* >());

      // Resolving potential sons issue
      for (Idx modality = 0; modality < _nodeVarMap[currentNodeId]->domainSize();
           ++modality) {
        NodeId sonId = _nodeSonsMap[currentNodeId][modality];
        _convertNode2Leaf(sonId);
        (*_leafDatabase[currentNodeId]) =
           (*_leafDatabase[currentNodeId]) + *(_leafDatabase[sonId]);
        _removeNode(sonId);
      }

      SOA_DEALLOCATE(_nodeSonsMap[currentNodeId],
                     sizeof(NodeId) * _nodeVarMap[currentNodeId]->domainSize());
      _nodeSonsMap.erase(currentNodeId);

      _chgNodeBoundVar(currentNodeId, _value);
    }
  }

Here is the call graph for this function:

Here is the caller graph for this function:

_insertInternalNode()

template<TESTNAME AttributeSelection, bool isScalar>

NodeId gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertInternalNode ( NodeDatabase< AttributeSelection, isScalar > *  nDB, const DiscreteVariable *  boundVar, NodeId *  sonsMap  )

protectedvirtualinherited

inserts a new internal node in internal graph

Parameters

nDB	: the associated database
boundVar	: the associated variable
sonsMap	: a table giving node's sons node

Returns

the newly created node's id

Definition at line 380 of file incrementalGraphLearner_tpl.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeSonsMap, and gum::HashTable< Key, Val, Alloc >::insert().

Referenced by gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNodeWithObservation().

                                                               {
    NodeId newNodeId = this->_insertNode(nDB, boundVar);
    _nodeSonsMap.insert(newNodeId, sonsMap);
    return newNodeId;
  }

Here is the call graph for this function:

Here is the caller graph for this function:

_insertLeafNode()

template<TESTNAME AttributeSelection, bool isScalar>

NodeId gum::IMDDI< AttributeSelection, isScalar >::_insertLeafNode ( NodeDatabase< AttributeSelection, isScalar > *  nDB, const DiscreteVariable *  boundVar, Set< const Observation * > *  sonsMap  )

protectedvirtual

Adds a new observation to the structure.

Reimplemented from gum::IncrementalGraphLearner< AttributeSelection, isScalar >.

Definition at line 241 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__addLeaf(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertLeafNode().

                                                           {
    NodeId currentNodeId =
       IncrementalGraphLearner< AttributeSelection, isScalar >::_insertLeafNode(
          nDB, boundVar, obsSet);

    __addLeaf(currentNodeId);

    return currentNodeId;
  }

Here is the call graph for this function:

_insertNode()

template<TESTNAME AttributeSelection, bool isScalar>

NodeId gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertNode ( NodeDatabase< AttributeSelection, isScalar > *  nDB, const DiscreteVariable *  boundVar  )

protectedvirtualinherited

inserts a new node in internal graph

Parameters

nDB	: the associated database
boundVar	: the associated variable

Returns

the newly created node's id

Reimplemented in gum::ITI< AttributeSelection, isScalar >.

Definition at line 355 of file incrementalGraphLearner_tpl.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_model, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_needUpdate, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeId2Database, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_var2Node, and gum::NodeGraphPart::addNode().

Referenced by gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertInternalNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertLeafNode(), gum::ITI< AttributeSelection, isScalar >::_insertNode(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNodeWithObservation().

                                                             {
    NodeId newNodeId = _model.addNode();
    _nodeVarMap.insert(newNodeId, boundVar);
    _nodeId2Database.insert(newNodeId, nDB);
    _var2Node[boundVar]->addLink(newNodeId);

    _needUpdate = true;

    return newNodeId;
  }

Here is the call graph for this function:

Here is the caller graph for this function:

_removeNode()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::_removeNode ( NodeId  removedNodeId )

protectedvirtual

Adds a new observation to the structure.

Reimplemented from gum::IncrementalGraphLearner< AttributeSelection, isScalar >.

Definition at line 275 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__removeLeaf(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_value.

                                                                              {
    if (this->_nodeVarMap[currentNodeId] == this->_value)
      __removeLeaf(currentNodeId);
    IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(
       currentNodeId);
  }

Here is the call graph for this function:

_transpose()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose ( NodeId  currentNodeId, const DiscreteVariable *  desiredVar  )

protectedvirtualinherited

Installs given variable to the given node, ensuring that the variable is not present in its subtree.

Definition at line 237 of file incrementalGraphLearner_tpl.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::__branchObs(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertInternalNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertLeafNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_leafDatabase, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeId2Database, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeSonsMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_setOfVars, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_value, gum::NodeDatabase< AttributeSelection, isScalar >::addObservation(), gum::DiscreteVariable::domainSize(), gum::Set< Key, Alloc >::insert(), gum::HashTable< Key, Val, Alloc >::insert(), SOA_ALLOCATE, and SOA_DEALLOCATE.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__updateNodeSet(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNode(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNodeWithObservation().

                                                               {
    // **************************************************************************************
    // Si le noeud courant contient déjà la variable qu'on souhaite lui amener
    // Il n'y a rien à faire
    if (_nodeVarMap[currentNodeId] == desiredVar) { return; }

    // **************************************************************************************
    // Si le noeud courant est terminal,
    // Il faut artificiellement insérer un noeud liant à la variable
    if (_nodeVarMap[currentNodeId] == _value) {
      // We turned this leaf into an internal node.
      // This mean that we'll need to install children leaves for each value of
      // desiredVar

      // First We must prepare these new leaves NodeDatabases and Sets<const
      // Observation*>
      NodeDatabase< AttributeSelection, isScalar >** dbMap =
         static_cast< NodeDatabase< AttributeSelection, isScalar >** >(
            SOA_ALLOCATE(sizeof(NodeDatabase< AttributeSelection, isScalar >*)
                         * desiredVar->domainSize()));
      Set< const Observation* >** obsetMap =
         static_cast< Set< const Observation* >** >(SOA_ALLOCATE(
            sizeof(Set< const Observation* >*) * desiredVar->domainSize()));
      for (Idx modality = 0; modality < desiredVar->domainSize(); ++modality) {
        dbMap[modality] =
           new NodeDatabase< AttributeSelection, isScalar >(&_setOfVars, _value);
        obsetMap[modality] = new Set< const Observation* >();
      }
      for (SetIteratorSafe< const Observation* > obsIter =
              _leafDatabase[currentNodeId]->beginSafe();
           _leafDatabase[currentNodeId]->endSafe() != obsIter;
           ++obsIter) {
        dbMap[__branchObs(*obsIter, desiredVar)]->addObservation(*obsIter);
        obsetMap[__branchObs(*obsIter, desiredVar)]->insert(*obsIter);
      }

      // Then we can install each new leaves (and put in place the sonsMap)
      NodeId* sonsMap = static_cast< NodeId* >(
         SOA_ALLOCATE(sizeof(NodeId) * desiredVar->domainSize()));
      for (Idx modality = 0; modality < desiredVar->domainSize(); ++modality)
        sonsMap[modality] =
           _insertLeafNode(dbMap[modality], _value, obsetMap[modality]);

      // Some necessary clean up
      SOA_DEALLOCATE(dbMap,
                     sizeof(NodeDatabase< AttributeSelection, isScalar >*)
                        * desiredVar->domainSize());
      SOA_DEALLOCATE(
         obsetMap, sizeof(Set< const Observation* >*) * desiredVar->domainSize());

      // And finally we can turn the node into an internal node associated to
      // desiredVar
      _chgNodeBoundVar(currentNodeId, desiredVar);
      _nodeSonsMap.insert(currentNodeId, sonsMap);

      return;
    }

    // *************************************************************************************
    // Remains the general case where currentNodeId is an internal node.

    // First we ensure that children node use desiredVar as variable
    for (Idx modality = 0; modality < _nodeVarMap[currentNodeId]->domainSize();
         ++modality)
      _transpose(_nodeSonsMap[currentNodeId][modality], desiredVar);

    //        Sequence<NodeDatabase<AttributeSelection, isScalar>*>
    //        sonsNodeDatabase =
    //        _nodeId2Database[currentNodeId]->splitOnVar(desiredVar);
    NodeId* sonsMap = static_cast< NodeId* >(
       SOA_ALLOCATE(sizeof(NodeId) * desiredVar->domainSize()));

    // Then we create the new mapping
    for (Idx desiredVarModality = 0; desiredVarModality < desiredVar->domainSize();
         ++desiredVarModality) {
      NodeId* grandSonsMap = static_cast< NodeId* >(
         SOA_ALLOCATE(sizeof(NodeId) * _nodeVarMap[currentNodeId]->domainSize()));
      NodeDatabase< AttributeSelection, isScalar >* sonDB =
         new NodeDatabase< AttributeSelection, isScalar >(&_setOfVars, _value);
      for (Idx currentVarModality = 0;
           currentVarModality < _nodeVarMap[currentNodeId]->domainSize();
           ++currentVarModality) {
        grandSonsMap[currentVarModality] =
           _nodeSonsMap[_nodeSonsMap[currentNodeId][currentVarModality]]
                       [desiredVarModality];
        sonDB->operator+=((*_nodeId2Database[grandSonsMap[currentVarModality]]));
      }

      sonsMap[desiredVarModality] =
         _insertInternalNode(sonDB, _nodeVarMap[currentNodeId], grandSonsMap);
    }

    // Finally we clean the old remaining nodes
    for (Idx currentVarModality = 0;
         currentVarModality < _nodeVarMap[currentNodeId]->domainSize();
         ++currentVarModality) {
      _removeNode(_nodeSonsMap[currentNodeId][currentVarModality]);
    }

    // We suppress the old sons map and remap to the new one
    SOA_DEALLOCATE(_nodeSonsMap[currentNodeId],
                   sizeof(NodeId) * _nodeVarMap[currentNodeId]->domainSize());
    _nodeSonsMap[currentNodeId] = sonsMap;

    _chgNodeBoundVar(currentNodeId, desiredVar);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

_updateNode()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNode ( NodeId  updatedNode, Set< const DiscreteVariable * > &  varsOfInterest  )

protectedinherited

From the given sets of node, selects randomly one and installs it on given node.

Chechks of course if node's current variable is not in that set first.

Parameters

nody	: the node we update
bestVars	: the set of interessting vars to be installed here

Chechks of course if node's current variable is not in that set first.

Parameters

nody	: the node we update
bestVar	: the set of interessting vars to be installed here

Definition at line 177 of file incrementalGraphLearner_tpl.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_convertNode2Leaf(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose(), gum::Set< Key, Alloc >::cbeginSafe(), gum::Set< Key, Alloc >::cendSafe(), gum::Set< Key, Alloc >::empty(), gum::Set< Key, Alloc >::exists(), and gum::Set< Key, Alloc >::size().

Referenced by gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNodeWithObservation(), and gum::ITI< AttributeSelection, isScalar >::updateGraph().

                                                                         {
    // If this node has no interesting variable, we turn it into a leaf
    if (varsOfInterest.empty()) {
      _convertNode2Leaf(updatedNode);
      return;
    }

    // If this node has already one of the best variable intalled as test, we
    // move on
    if (_nodeVarMap.exists(updatedNode)
        && varsOfInterest.exists(_nodeVarMap[updatedNode])) {
      return;
    }

    // In any other case we have to install variable as best test
    Idx randy = (Idx)(std::rand() / RAND_MAX) * varsOfInterest.size(), basc = 0;
    SetConstIteratorSafe< const DiscreteVariable* > varIter;
    for (varIter = varsOfInterest.cbeginSafe(), basc = 0;
         varIter != varsOfInterest.cendSafe() && basc < randy;
         ++varIter, basc++)
      ;

    _transpose(updatedNode, *varIter);
  }

Here is the call graph for this function:

Here is the caller graph for this function:

_updateNodeWithObservation()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::_updateNodeWithObservation ( const Observation *  newObs, NodeId  currentNodeId  )

protectedvirtual

Adds a new observation to the structure.

Reimplemented from gum::IncrementalGraphLearner< AttributeSelection, isScalar >.

Definition at line 105 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__leafMap, gum::IMDDI< AttributeSelection, isScalar >::__lg, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_value, and gum::LeafAggregator::updateLeaf().

                                                      {
    IncrementalGraphLearner< AttributeSelection,
                             isScalar >::_updateNodeWithObservation(newObs,
                                                                    currentNodeId);
    if (this->_nodeVarMap[currentNodeId] == this->_value)
      __lg.updateLeaf(__leafMap[currentNodeId]);
  }

Here is the call graph for this function:

addObservation()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::addObservation ( const Observation *  obs )

virtual

Adds a new observation to the structure.

Reimplemented from gum::IncrementalGraphLearner< AttributeSelection, isScalar >.

Definition at line 98 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__nbTotalObservation, and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::addObservation().

                             {
    __nbTotalObservation++;
    IncrementalGraphLearner< AttributeSelection, isScalar >::addObservation(obs);
  }

Here is the call graph for this function:

insertSetOfVars()

template<TESTNAME AttributeSelection, bool isScalar = false>

void gum::IMDDI< AttributeSelection, isScalar >::insertSetOfVars ( MultiDimFunctionGraph< double > *  ret ) const

inlinevirtual

Reimplemented from gum::IncrementalGraphLearner< AttributeSelection, isScalar >.

Definition at line 156 of file imddi.h.

References gum::IMDDI< AttributeSelection, isScalar >::__varOrder, and gum::MultiDimFunctionGraph< GUM_SCALAR, TerminalNodePolicy >::add().

                                                                     {
      for (SequenceIteratorSafe< const DiscreteVariable* > varIter =
              __varOrder.beginSafe();
           varIter != __varOrder.endSafe();
           ++varIter)
        ret->add(**varIter);
    }

Here is the call graph for this function:

isTerminal()

template<TESTNAME AttributeSelection, bool isScalar = false>

bool gum::IncrementalGraphLearner< AttributeSelection, isScalar >::isTerminal ( NodeId  ni ) const

inlinevirtualinherited

Implements gum::IVisitableGraphLearner.

Definition at line 315 of file incrementalGraphLearner.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeSonsMap, and gum::HashTable< Key, Val, Alloc >::exists().

{ return !this->_nodeSonsMap.exists(ni); }

Here is the call graph for this function:

nodeNbObservation()

template<TESTNAME AttributeSelection, bool isScalar = false>

Idx gum::IncrementalGraphLearner< AttributeSelection, isScalar >::nodeNbObservation ( NodeId  ni ) const

inlinevirtualinherited

Implements gum::IVisitableGraphLearner.

Definition at line 334 of file incrementalGraphLearner.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeId2Database.

                                           {
      return this->_nodeId2Database[ni]->nbObservation();
    }

nodeSon()

template<TESTNAME AttributeSelection, bool isScalar = false>

NodeId gum::IncrementalGraphLearner< AttributeSelection, isScalar >::nodeSon ( NodeId  ni, Idx  modality  ) const

inlinevirtualinherited

Implements gum::IVisitableGraphLearner.

Definition at line 327 of file incrementalGraphLearner.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeSonsMap.

                                                  {
      return this->_nodeSonsMap[ni][modality];
    }

nodeVar()

template<TESTNAME AttributeSelection, bool isScalar = false>

const DiscreteVariable* gum::IncrementalGraphLearner< AttributeSelection, isScalar >::nodeVar ( NodeId  ni ) const

inlinevirtualinherited

Implements gum::IVisitableGraphLearner.

Definition at line 320 of file incrementalGraphLearner.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap.

                                                     {
      return this->_nodeVarMap[ni];
    }

root()

template<TESTNAME AttributeSelection, bool isScalar = false>

NodeId gum::IncrementalGraphLearner< AttributeSelection, isScalar >::root ( ) const

inlinevirtualinherited

Implements gum::IVisitableGraphLearner.

Definition at line 310 of file incrementalGraphLearner.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_root.

{ return this->_root; }

size()

template<TESTNAME AttributeSelection, bool isScalar = false>

Size gum::IncrementalGraphLearner< AttributeSelection, isScalar >::size ( )

inlineinherited

Definition at line 299 of file incrementalGraphLearner.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap.

{ return _nodeVarMap.size(); }

updateFunctionGraph()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::updateFunctionGraph ( )

virtual

Computes the score of the given variables for the given node.

Implements gum::IncrementalGraphLearner< AttributeSelection, isScalar >.

Definition at line 313 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_needUpdate.

                                                                  {
    //      if( __lg.needsUpdate() || this->_needUpdate ){
    __rebuildFunctionGraph();
    this->_needUpdate = false;
    //      }
  }

Here is the call graph for this function:

updateGraph()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IMDDI< AttributeSelection, isScalar >::updateGraph ( )

virtual

Updates the tree after a new observation has been added.

Implements gum::IncrementalGraphLearner< AttributeSelection, isScalar >.

Definition at line 119 of file imddi_tpl.h.

References gum::IMDDI< AttributeSelection, isScalar >::__lg, gum::IMDDI< AttributeSelection, isScalar >::__updateNodeSet(), gum::IMDDI< AttributeSelection, isScalar >::__updateScore(), gum::IMDDI< AttributeSelection, isScalar >::__varOrder, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_convertNode2Leaf(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_root, gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_setOfVars, gum::VariableSelector::begin(), gum::Set< Key, Alloc >::beginSafe(), gum::VariableSelector::current(), gum::Set< Key, Alloc >::endSafe(), gum::VariableSelector::hasNext(), gum::Set< Key, Alloc >::insert(), gum::VariableSelector::isEmpty(), gum::LeafAggregator::needsUpdate(), gum::VariableSelector::next(), gum::VariableSelector::select(), and gum::LeafAggregator::update().

                                                          {
    __varOrder.clear();

    // First xe initialize the node set which will give us the scores
    Set< NodeId > currentNodeSet;
    currentNodeSet.insert(this->_root);

    // Then we initialize the pool of variables to consider
    VariableSelector vs(this->_setOfVars);
    for (vs.begin(); vs.hasNext(); vs.next()) {
      __updateScore(vs.current(), this->_root, vs);
    }

    // Then, until there's no node remaining
    while (!vs.isEmpty()) {
      // We select the best var
      const DiscreteVariable* selectedVar = vs.select();
      __varOrder.insert(selectedVar);

      // Then we decide if we update each node according to this var
      __updateNodeSet(currentNodeSet, selectedVar, vs);
    }

    // If there are remaining node that are not leaves after we establish the
    // var order
    // these nodes are turned into leaf.
    for (SetIteratorSafe< NodeId > nodeIter = currentNodeSet.beginSafe();
         nodeIter != currentNodeSet.endSafe();
         ++nodeIter)
      this->_convertNode2Leaf(*nodeIter);


    if (__lg.needsUpdate()) __lg.update();
  }

Here is the call graph for this function:

updateVar()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::updateVar ( const DiscreteVariable *  var )

virtualinherited

If a new modality appears to exists for given variable, call this method to turn every associated node to this variable into leaf. Graph has then indeed to be revised.

Definition at line 155 of file incrementalGraphLearner_tpl.h.

References gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_convertNode2Leaf(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_var2Node, gum::Link< T >::element(), and gum::Link< T >::nextLink().

Referenced by gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNodeWithObservation().

                                  {
    Link< NodeId >* nodIter = _var2Node[var]->list();
    Link< NodeId >* nni = nullptr;
    while (nodIter) {
      nni = nodIter->nextLink();
      _convertNode2Leaf(nodIter->element());
      nodIter = nni;
    }
  }

Here is the call graph for this function:

Here is the caller graph for this function:

Member Data Documentation

__attributeSelectionThreshold

template<TESTNAME AttributeSelection, bool isScalar = false>

double gum::IMDDI< AttributeSelection, isScalar >::__attributeSelectionThreshold

private

The threshold above which we consider variables to be dependant.

Definition at line 175 of file imddi.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__updateNodeSet().

__leafMap

template<TESTNAME AttributeSelection, bool isScalar = false>

HashTable< NodeId, AbstractLeaf* > gum::IMDDI< AttributeSelection, isScalar >::__leafMap

private

Definition at line 169 of file imddi.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__addLeaf(), gum::IMDDI< AttributeSelection, isScalar >::__removeLeaf(), gum::IMDDI< AttributeSelection, isScalar >::_updateNodeWithObservation(), and gum::IMDDI< AttributeSelection, isScalar >::~IMDDI().

__lg

template<TESTNAME AttributeSelection, bool isScalar = false>

LeafAggregator gum::IMDDI< AttributeSelection, isScalar >::__lg

private

Definition at line 167 of file imddi.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__addLeaf(), gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph(), gum::IMDDI< AttributeSelection, isScalar >::__removeLeaf(), gum::IMDDI< AttributeSelection, isScalar >::_updateNodeWithObservation(), and gum::IMDDI< AttributeSelection, isScalar >::updateGraph().

__nbTotalObservation

template<TESTNAME AttributeSelection, bool isScalar = false>

Idx gum::IMDDI< AttributeSelection, isScalar >::__nbTotalObservation

private

The total number of observation added to this tree.

Definition at line 172 of file imddi.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__downdateScore(), gum::IMDDI< AttributeSelection, isScalar >::__updateScore(), and gum::IMDDI< AttributeSelection, isScalar >::addObservation().

__varOrder

template<TESTNAME AttributeSelection, bool isScalar = false>

Sequence< const DiscreteVariable* > gum::IMDDI< AttributeSelection, isScalar >::__varOrder

private

Definition at line 165 of file imddi.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph(), gum::IMDDI< AttributeSelection, isScalar >::insertSetOfVars(), and gum::IMDDI< AttributeSelection, isScalar >::updateGraph().

_leafDatabase

template<TESTNAME AttributeSelection, bool isScalar = false>

HashTable< NodeId, Set< const Observation* >* > gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_leafDatabase

protectedinherited

This hashtable binds to every leaf an associated set of all hte observations compatible with it.

Definition at line 396 of file incrementalGraphLearner.h.

Referenced by gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_convertNode2Leaf(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertLeafNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::addObservation(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::~IncrementalGraphLearner().

_model

template<TESTNAME AttributeSelection, bool isScalar = false>

NodeGraphPart gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_model

protectedinherited

The source of nodeId.

Definition at line 361 of file incrementalGraphLearner.h.

Referenced by gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::IncrementalGraphLearner().

_needUpdate

template<TESTNAME AttributeSelection, bool isScalar = false>

bool gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_needUpdate

protectedinherited

Definition at line 409 of file incrementalGraphLearner.h.

Referenced by gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), and gum::IMDDI< AttributeSelection, isScalar >::updateFunctionGraph().

_nodeId2Database

template<TESTNAME AttributeSelection, bool isScalar = false>

HashTable< NodeId, NodeDatabase< AttributeSelection, isScalar >* > gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeId2Database

protectedinherited

This hashtable binds every node to an associated NodeDatabase which handles every observation that concerns that node.

Definition at line 390 of file incrementalGraphLearner.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__addLeaf(), gum::IMDDI< AttributeSelection, isScalar >::__downdateScore(), gum::ITI< AttributeSelection, isScalar >::__insertTerminalNode(), gum::IMDDI< AttributeSelection, isScalar >::__updateNodeSet(), gum::IMDDI< AttributeSelection, isScalar >::__updateScore(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNodeWithObservation(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::nodeNbObservation(), gum::ITI< AttributeSelection, isScalar >::updateGraph(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::~IncrementalGraphLearner().

_nodeSonsMap

template<TESTNAME AttributeSelection, bool isScalar = false>

HashTable< NodeId, NodeId* > gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeSonsMap

protectedinherited

A table giving for any node a table mapping to its son idx is the modality of associated variable.

Definition at line 377 of file incrementalGraphLearner.h.

Referenced by gum::ITI< AttributeSelection, isScalar >::__insertNodeInFunctionGraph(), gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph(), gum::IMDDI< AttributeSelection, isScalar >::__updateNodeSet(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_convertNode2Leaf(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertInternalNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::addObservation(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::isTerminal(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::nodeSon(), gum::ITI< AttributeSelection, isScalar >::updateGraph(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::~IncrementalGraphLearner().

_nodeVarMap

template<TESTNAME AttributeSelection, bool isScalar = false>

HashTable< NodeId, const DiscreteVariable* > gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_nodeVarMap

protectedinherited

Gives for any node its associated variable.

Definition at line 371 of file incrementalGraphLearner.h.

Referenced by gum::ITI< AttributeSelection, isScalar >::__insertNodeInFunctionGraph(), gum::IMDDI< AttributeSelection, isScalar >::__updateNodeSet(), gum::IMDDI< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::ITI< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_convertNode2Leaf(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertNode(), gum::IMDDI< AttributeSelection, isScalar >::_removeNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_updateNode(), gum::IMDDI< AttributeSelection, isScalar >::_updateNodeWithObservation(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::addObservation(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::nodeVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::size(), gum::ITI< AttributeSelection, isScalar >::updateGraph(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::~IncrementalGraphLearner().

_root

template<TESTNAME AttributeSelection, bool isScalar = false>

NodeId gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_root

protectedinherited

The root of the ordered tree.

Definition at line 366 of file incrementalGraphLearner.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::addObservation(), gum::IMDDI< AttributeSelection, isScalar >::IMDDI(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::IncrementalGraphLearner(), gum::ITI< AttributeSelection, isScalar >::ITI(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::root(), gum::ITI< AttributeSelection, isScalar >::updateFunctionGraph(), gum::IMDDI< AttributeSelection, isScalar >::updateGraph(), and gum::ITI< AttributeSelection, isScalar >::updateGraph().

_setOfVars

template<TESTNAME AttributeSelection, bool isScalar = false>

Set< const DiscreteVariable* > gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_setOfVars

protectedinherited

Definition at line 404 of file incrementalGraphLearner.h.

Referenced by gum::ITI< AttributeSelection, isScalar >::_insertSetOfVars(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::IncrementalGraphLearner(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::insertSetOfVars(), gum::IMDDI< AttributeSelection, isScalar >::updateGraph(), and gum::ITI< AttributeSelection, isScalar >::updateGraph().

_target

template<TESTNAME AttributeSelection, bool isScalar = false>

MultiDimFunctionGraph< double >* gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_target

protectedinherited

The final diagram we're building.

Definition at line 402 of file incrementalGraphLearner.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__insertLeafInFunctionGraph(), gum::ITI< AttributeSelection, isScalar >::__insertNodeInFunctionGraph(), gum::ITI< AttributeSelection, isScalar >::__insertTerminalNode(), gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph(), and gum::ITI< AttributeSelection, isScalar >::updateFunctionGraph().

_value

template<TESTNAME AttributeSelection, bool isScalar = false>

const DiscreteVariable* gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_value

protectedinherited

Definition at line 406 of file incrementalGraphLearner.h.

Referenced by gum::IncrementalGraphLearner< AttributeSelection, isScalar >::__assumeValue(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::__clearValue(), gum::IMDDI< AttributeSelection, isScalar >::__insertLeafInFunctionGraph(), gum::ITI< AttributeSelection, isScalar >::__insertNodeInFunctionGraph(), gum::ITI< AttributeSelection, isScalar >::__insertTerminalNode(), gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph(), gum::IMDDI< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_convertNode2Leaf(), gum::IMDDI< AttributeSelection, isScalar >::_removeNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_transpose(), gum::IMDDI< AttributeSelection, isScalar >::_updateNodeWithObservation(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::IncrementalGraphLearner(), and gum::ITI< AttributeSelection, isScalar >::updateGraph().

_valueAssumed

template<TESTNAME AttributeSelection, bool isScalar = false>

Sequence< ValueType > gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_valueAssumed

protectedinherited

Definition at line 407 of file incrementalGraphLearner.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__addLeaf(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::__assumeValue(), and gum::IMDDI< AttributeSelection, isScalar >::__insertLeafInFunctionGraph().

_var2Node

template<TESTNAME AttributeSelection, bool isScalar = false>

HashTable< const DiscreteVariable*, LinkedList< NodeId >* > gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_var2Node

protectedinherited

Associates to any variable the list of all nodes associated to this variable.

Definition at line 383 of file incrementalGraphLearner.h.

Referenced by gum::IMDDI< AttributeSelection, isScalar >::__rebuildFunctionGraph(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_chgNodeBoundVar(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_insertNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::_removeNode(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::IncrementalGraphLearner(), gum::IncrementalGraphLearner< AttributeSelection, isScalar >::updateVar(), and gum::IncrementalGraphLearner< AttributeSelection, isScalar >::~IncrementalGraphLearner().

---

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

• agrum/FMDP/learning/datastructure/imddi.h
• agrum/FMDP/learning/datastructure/imddi_tpl.h