<agrum/FMDP/learning/datastructure/incrementalGraphLearner> More...

#include <incrementalGraphLearner.h>

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

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

Public Member Functions
Size	size ()

Function Graph Updating methods
virtual void	updateFunctionGraph ()=0
	Updates target to currently learned graph structure. 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

virtual void	insertSetOfVars (MultiDimFunctionGraph< double > *ret) 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...

Constructor & destructor.
	IncrementalGraphLearner (MultiDimFunctionGraph< double > target, Set< const DiscreteVariable > attributesSet, const DiscreteVariable *learnVariable)
	Default constructor. More...

virtual	~IncrementalGraphLearner ()
	Default destructor. More...

void	clearValue__ ()
	Template function dispatcher. More...

void	clearValue__ (Int2Type< true >)
	In the case where we're learning a function of real values this has to be wiped out upon destruction (to be deprecated) More...

void	clearValue__ (Int2Type< false >)
	In case where we're learning function of variable behaviour, this should do nothing. More...

New Observation insertion methods
virtual void	addObservation (const Observation *obs)
	Inserts a new observation. More...

void	assumeValue__ (const Observation *obs)
	Get value assumed by studied variable for current observation. More...

void	assumeValue__ (const Observation *obs, Int2Type< true >)
	Inserts a new observation. More...

void	assumeValue__ (const Observation *obs, Int2Type< false >)
	Inserts a new observation. More...

Idx	branchObs__ (const Observation obs, const DiscreteVariable var)
	Seek modality assumed in obs for given var. More...

Idx	branchObs__ (const Observation obs, const DiscreteVariable var, Int2Type< true >)
	Inserts a new observation. More...

Idx	branchObs__ (const Observation obs, const DiscreteVariable var, Int2Type< false >)
	Inserts a new observation. More...

virtual void	updateNodeWithObservation_ (const Observation *newObs, NodeId currentNodeId)
	Will update internal graph's NodeDatabase of given node with the new observation. 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...

virtual void	updateGraph ()=0
	Updates the tree after a new observation has been added. 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...

virtual NodeId	insertLeafNode_ (NodeDatabase< AttributeSelection, isScalar > nDB, const DiscreteVariable boundVar, Set< const Observation * > *obsSet)
	inserts a new leaf node in internal graohs More...

virtual void	chgNodeBoundVar_ (NodeId chgedNodeId, const DiscreteVariable *desiredVar)
	Changes the associated variable of a node. More...

virtual void	removeNode_ (NodeId removedNodeId)
	Removes a node from the internal graph. More...

Detailed Description

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

Abstract class for incrementaly learn a graphical representation of a function. Can handle both function of real values, and function explaining the behaviour of a variable given set of other variables (as typically in conditionnal probabilities)

Maintains two graph in memory, one which is incrementaly updated and the other one which is updated on demand and is usable by the outside.

Definition at line 65 of file incrementalGraphLearner.h.

Member Typedef Documentation

◆ ValueType

template<TESTNAME AttributeSelection, bool isScalar = false>

typedef ValueSelect< isScalar, double, Idx >::type gum::IncrementalGraphLearner< AttributeSelection, isScalar >::ValueType

private

Definition at line 66 of file incrementalGraphLearner.h.

Constructor & Destructor Documentation

◆ IncrementalGraphLearner()

template<TESTNAME AttributeSelection, bool isScalar>

gum::IncrementalGraphLearner< AttributeSelection, isScalar >::IncrementalGraphLearner	(	MultiDimFunctionGraph< double > *	target,
		Set< const DiscreteVariable * >	varList,
		const DiscreteVariable *	value
	)

Default constructor.

Parameters

target	: the output diagram usable by the outside
attributesSet	: set of variables from which we try to describe the learned function
learnVariable	: if we tried to learn a the behaviour of a variable given variable given another set of variables, this is the one. If we are learning a function of real value, this is just a computationnal trick (and is to be deprecated)

Definition at line 61 of file incrementalGraphLearner_tpl.h.

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

                                              :
       target_(target),
       setOfVars_(varList), value_(value) {
     GUM_CONSTRUCTOR(IncrementalGraphLearner);
 
     for (auto varIter = setOfVars_.cbeginSafe(); varIter != setOfVars_.cendSafe();
          ++varIter)
       var2Node_.insert(*varIter, new LinkedList< NodeId >());
     var2Node_.insert(value_, new LinkedList< NodeId >());
 
     model_.addNode();
     this->root_ = insertLeafNode_(
        new NodeDatabase< AttributeSelection, isScalar >(&setOfVars_, value_),
        value_,
        new Set< const Observation* >());
   }

Here is the call graph for this function:

◆ ~IncrementalGraphLearner()

template<TESTNAME AttributeSelection, bool isScalar>

gum::IncrementalGraphLearner< AttributeSelection, isScalar >::~IncrementalGraphLearner ( )

virtual

Default destructor.

Definition at line 87 of file incrementalGraphLearner_tpl.h.

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

                                                                 {
     for (auto nodeIter = nodeId2Database_.beginSafe();
          nodeIter != nodeId2Database_.endSafe();
          ++nodeIter)
       delete nodeIter.val();
 
     for (auto nodeIter = nodeSonsMap_.beginSafe();
          nodeIter != nodeSonsMap_.endSafe();
          ++nodeIter)
       SOA_DEALLOCATE(nodeIter.val(),
                      sizeof(NodeId) * nodeVarMap_[nodeIter.key()]->domainSize());
 
     for (auto varIter = var2Node_.beginSafe(); varIter != var2Node_.endSafe();
          ++varIter)
       delete varIter.val();
 
     for (auto nodeIter = leafDatabase_.beginSafe();
          nodeIter != leafDatabase_.endSafe();
          ++nodeIter)
       delete nodeIter.val();
 
     clearValue__();
 
     GUM_DESTRUCTOR(IncrementalGraphLearner);
   }

Here is the call graph for this function:

Member Function Documentation

◆ addObservation()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::addObservation ( const Observation * newObs )

virtual

Inserts a new observation.

Parameters

the	new observation to learn

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

Definition at line 125 of file incrementalGraphLearner_tpl.h.

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

                                 {
     assumeValue__(newObs);
 
     // The we go across the tree
     NodeId currentNodeId = root_;
 
     while (nodeSonsMap_.exists(currentNodeId)) {
       // On each encountered node, we update the database
       updateNodeWithObservation_(newObs, currentNodeId);
 
       // The we select the next to go throught
       currentNodeId
          = nodeSonsMap_[currentNodeId]
                        [branchObs__(newObs, nodeVarMap_[currentNodeId])];
     }
 
     // On final insertion into the leave we reach
     updateNodeWithObservation_(newObs, currentNodeId);
     leafDatabase_[currentNodeId]->insert(newObs);
   }

Here is the call graph for this function:

◆ assumeValue__() [1/3]

template<TESTNAME AttributeSelection, bool isScalar = false>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::assumeValue__ ( const Observation * obs )

inlineprivate

Get value assumed by studied variable for current observation.

Definition at line 134 of file incrementalGraphLearner.h.

                                                {
       assumeValue__(obs, Int2Type< isScalar >());
     }

◆ assumeValue__() [2/3]

template<TESTNAME AttributeSelection, bool isScalar = false>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::assumeValue__	(	const Observation *	obs,
		Int2Type< true >
	)

inlineprivate

Inserts a new observation.

Parameters

the	new observation to learn

Definition at line 137 of file incrementalGraphLearner.h.

                                                                  {
       if (!valueAssumed_.exists(obs->reward())) valueAssumed_ << obs->reward();
     }

◆ assumeValue__() [3/3]

template<TESTNAME AttributeSelection, bool isScalar = false>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::assumeValue__	(	const Observation *	obs,
		Int2Type< false >
	)

inlineprivate

Inserts a new observation.

Parameters

the	new observation to learn

Definition at line 140 of file incrementalGraphLearner.h.

                                                                   {
       if (!valueAssumed_.exists(obs->modality(value_)))
         valueAssumed_ << obs->modality(value_);
     }

◆ branchObs__() [1/3]

template<TESTNAME AttributeSelection, bool isScalar = false>

Idx gum::IncrementalGraphLearner< AttributeSelection, isScalar >::branchObs__	(	const Observation *	obs,
		const DiscreteVariable *	var
	)

inlineprivate

Seek modality assumed in obs for given var.

Definition at line 151 of file incrementalGraphLearner.h.

                                                                          {
       return branchObs__(obs, var, Int2Type< isScalar >());
     }

◆ branchObs__() [2/3]

template<TESTNAME AttributeSelection, bool isScalar = false>

Idx gum::IncrementalGraphLearner< AttributeSelection, isScalar >::branchObs__	(	const Observation *	obs,
		const DiscreteVariable *	var,
		Int2Type< true >
	)

inlineprivate

Inserts a new observation.

Parameters

the	new observation to learn

Definition at line 154 of file incrementalGraphLearner.h.

                                       {
       return obs->rModality(var);
     }

◆ branchObs__() [3/3]

template<TESTNAME AttributeSelection, bool isScalar = false>

Idx gum::IncrementalGraphLearner< AttributeSelection, isScalar >::branchObs__	(	const Observation *	obs,
		const DiscreteVariable *	var,
		Int2Type< false >
	)

inlineprivate

Inserts a new observation.

Parameters

the	new observation to learn

Definition at line 159 of file incrementalGraphLearner.h.

                                        {
       return obs->modality(var);
     }

◆ chgNodeBoundVar_()

template<TESTNAME AttributeSelection, bool isScalar>

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

protectedvirtual

Changes the associated variable of a node.

Parameters

chgedNodeId	: the node to change
desiredVar	: its new associated variable

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

Definition at line 424 of file incrementalGraphLearner_tpl.h.

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

                                          {
     if (nodeVarMap_[currentNodeId] == desiredVar) return;
 
     var2Node_[nodeVarMap_[currentNodeId]]->searchAndRemoveLink(currentNodeId);
     var2Node_[desiredVar]->addLink(currentNodeId);
     nodeVarMap_[currentNodeId] = desiredVar;
 
     if (nodeVarMap_[currentNodeId] != value_
         && leafDatabase_.exists(currentNodeId)) {
       delete leafDatabase_[currentNodeId];
       leafDatabase_.erase(currentNodeId);
     }
 
     if (nodeVarMap_[currentNodeId] == value_
         && !leafDatabase_.exists(currentNodeId)) {
       leafDatabase_.insert(currentNodeId, new Set< const Observation* >());
     }
 
     needUpdate_ = true;
   }

Here is the call graph for this function:

◆ clearValue__() [1/3]

template<TESTNAME AttributeSelection, bool isScalar = false>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::clearValue__ ( )

inlineprivate

Template function dispatcher.

Definition at line 99 of file incrementalGraphLearner.h.

99 { clearValue__(Int2Type< isScalar >()); }

gum::IncrementalGraphLearner::clearValue__

void clearValue__()

Template function dispatcher.

Definition: incrementalGraphLearner.h:99

◆ clearValue__() [2/3]

template<TESTNAME AttributeSelection, bool isScalar = false>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::clearValue__ ( Int2Type< true > )

inlineprivate

In the case where we're learning a function of real values this has to be wiped out upon destruction (to be deprecated)

Definition at line 105 of file incrementalGraphLearner.h.

105 { delete value_; }

gum::IncrementalGraphLearner::value_

const DiscreteVariable * value_

Definition: incrementalGraphLearner.h:408

◆ clearValue__() [3/3]

template<TESTNAME AttributeSelection, bool isScalar = false>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::clearValue__ ( Int2Type< false > )

inlineprivate

In case where we're learning function of variable behaviour, this should do nothing.

Definition at line 111 of file incrementalGraphLearner.h.

111 {}

◆ convertNode2Leaf_()

template<TESTNAME AttributeSelection, bool isScalar>

void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::convertNode2Leaf_ ( NodeId currentNodeId )

protectedvirtual

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

Definition at line 212 of file incrementalGraphLearner_tpl.h.

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

                            {
     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:

◆ insertInternalNode_()

template<TESTNAME AttributeSelection, bool isScalar>

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

protectedvirtual

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 386 of file incrementalGraphLearner_tpl.h.

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

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

Here is the call graph for this function:

◆ insertLeafNode_()

template<TESTNAME AttributeSelection, bool isScalar>

NodeId gum::IncrementalGraphLearner< AttributeSelection, isScalar >::insertLeafNode_	(	NodeDatabase< AttributeSelection, isScalar > *	nDB,
		const DiscreteVariable *	boundVar,
		Set< const Observation * > *	obsSet
	)

protectedvirtual

inserts a new leaf node in internal graohs

Parameters

nDB	: the associated database
boundVar	: the associated variable
obsSet	: the set of observation this leaf retains

Returns: the newly created node's id

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

Definition at line 406 of file incrementalGraphLearner_tpl.h.

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

                                                            {
     NodeId newNodeId = this->insertNode_(nDB, boundVar);
     leafDatabase_.insert(newNodeId, obsSet);
     return newNodeId;
   }

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
	)

protectedvirtual

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 361 of file incrementalGraphLearner_tpl.h.

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

                                                              {
     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:

◆ insertSetOfVars()

template<TESTNAME AttributeSelection, bool isScalar = false>

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

inlinevirtual

Implements gum::IVisitableGraphLearner.

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

Definition at line 343 of file incrementalGraphLearner.h.

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

◆ isTerminal()

template<TESTNAME AttributeSelection, bool isScalar = false>

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

inlinevirtual

Implements gum::IVisitableGraphLearner.

Definition at line 317 of file incrementalGraphLearner.h.

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

gum::HashTable::exists

bool exists(const Key &key) const

Checks whether there exists an element with a given key in the hashtable.

Definition: hashTable_tpl.h:744

gum::IncrementalGraphLearner::nodeSonsMap_

HashTable< NodeId, NodeId *> nodeSonsMap_

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

Definition: incrementalGraphLearner.h:379

◆ nodeNbObservation()

template<TESTNAME AttributeSelection, bool isScalar = false>

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

inlinevirtual

Implements gum::IVisitableGraphLearner.

Definition at line 336 of file incrementalGraphLearner.h.

                                            {
       return this->nodeId2Database_[ni]->nbObservation();
     }

◆ nodeSon()

template<TESTNAME AttributeSelection, bool isScalar = false>

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

inlinevirtual

Implements gum::IVisitableGraphLearner.

Definition at line 329 of file incrementalGraphLearner.h.

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

◆ nodeVar()

template<TESTNAME AttributeSelection, bool isScalar = false>

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

inlinevirtual

Implements gum::IVisitableGraphLearner.

Definition at line 322 of file incrementalGraphLearner.h.

                                                      {
       return this->nodeVarMap_[ni];
     }

◆ removeNode_()

template<TESTNAME AttributeSelection, bool isScalar>

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

protectedvirtual

Removes a node from the internal graph.

Parameters

removedNodeId : the node to remove

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

Definition at line 455 of file incrementalGraphLearner_tpl.h.

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

                            {
     // Retriat de l'id
     model_.eraseNode(currentNodeId);
 
     // Retrait du vecteur fils
     if (nodeSonsMap_.exists(currentNodeId)) {
       SOA_DEALLOCATE(nodeSonsMap_[currentNodeId],
                      sizeof(NodeId) * nodeVarMap_[currentNodeId]->domainSize());
       nodeSonsMap_.erase(currentNodeId);
     }
 
     if (leafDatabase_.exists(currentNodeId)) {
       delete leafDatabase_[currentNodeId];
       leafDatabase_.erase(currentNodeId);
     }
 
     // Retrait de la variable
     var2Node_[nodeVarMap_[currentNodeId]]->searchAndRemoveLink(currentNodeId);
     nodeVarMap_.erase(currentNodeId);
 
     // Retrait du NodeDatabase
     delete nodeId2Database_[currentNodeId];
     nodeId2Database_.erase(currentNodeId);
 
     needUpdate_ = true;
   }

Here is the call graph for this function:

◆ root()

template<TESTNAME AttributeSelection, bool isScalar = false>

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

inlinevirtual

Implements gum::IVisitableGraphLearner.

Definition at line 312 of file incrementalGraphLearner.h.

312 { return this->root_; }

gum::IncrementalGraphLearner::root_

NodeId root_

The root of the ordered tree.

Definition: incrementalGraphLearner.h:368

◆ size()

template<TESTNAME AttributeSelection, bool isScalar = false>

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

inline

Definition at line 301 of file incrementalGraphLearner.h.

301 { return nodeVarMap_.size(); }

gum::IncrementalGraphLearner::nodeVarMap_

HashTable< NodeId, const DiscreteVariable *> nodeVarMap_

Gives for any node its associated variable.

Definition: incrementalGraphLearner.h:373

◆ transpose_()

template<TESTNAME AttributeSelection, bool isScalar>

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

protectedvirtual

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

Definition at line 241 of file incrementalGraphLearner_tpl.h.

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

                                          {
     // **************************************************************************************
     // 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:

◆ updateFunctionGraph()

template<TESTNAME AttributeSelection, bool isScalar = false>

virtual void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::updateFunctionGraph ( )

pure virtual

Updates target to currently learned graph structure.

Implemented in gum::ITI< AttributeSelection, isScalar >, and gum::IMDDI< AttributeSelection, isScalar >.

◆ updateGraph()

template<TESTNAME AttributeSelection, bool isScalar = false>

virtual void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::updateGraph ( )

pure virtual

Updates the tree after a new observation has been added.

Implemented in gum::ITI< AttributeSelection, isScalar >, and gum::IMDDI< AttributeSelection, isScalar >.

◆ updateNode_()

template<TESTNAME AttributeSelection, bool isScalar>

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

protected

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 180 of file incrementalGraphLearner_tpl.h.

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

                                                      {
     // 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:

◆ updateNodeWithObservation_()

template<TESTNAME AttributeSelection, bool isScalar = false>

virtual void gum::IncrementalGraphLearner< AttributeSelection, isScalar >::updateNodeWithObservation_	(	const Observation *	newObs,
		NodeId	currentNodeId
	)

inlineprotectedvirtual

Will update internal graph's NodeDatabase of given node with the new observation.

Parameters

newObs
currentNodeId

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

Definition at line 174 of file incrementalGraphLearner.h.

                                                                               {
       nodeId2Database_[currentNodeId]->addObservation(newObs);
     }

◆ updateVar()

template<TESTNAME AttributeSelection, bool isScalar>

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

virtual

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 158 of file incrementalGraphLearner_tpl.h.

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

                                   {
     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:

Member Data Documentation

◆ leafDatabase_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

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

Definition at line 398 of file incrementalGraphLearner.h.

◆ model_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

The source of nodeId.

Definition at line 363 of file incrementalGraphLearner.h.

◆ needUpdate_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

Definition at line 411 of file incrementalGraphLearner.h.

◆ nodeId2Database_

template<TESTNAME AttributeSelection, bool isScalar = false>

HashTable< NodeId, NodeDatabase< AttributeSelection, isScalar >* > gum::IncrementalGraphLearner< AttributeSelection, isScalar >::nodeId2Database_

protected

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

Definition at line 392 of file incrementalGraphLearner.h.

◆ nodeSonsMap_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

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

Definition at line 379 of file incrementalGraphLearner.h.

◆ nodeVarMap_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

Gives for any node its associated variable.

Definition at line 373 of file incrementalGraphLearner.h.

◆ root_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

The root of the ordered tree.

Definition at line 368 of file incrementalGraphLearner.h.

◆ setOfVars_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

Definition at line 406 of file incrementalGraphLearner.h.

◆ target_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

The final diagram we're building.

Definition at line 404 of file incrementalGraphLearner.h.

◆ value_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

Definition at line 408 of file incrementalGraphLearner.h.

◆ valueAssumed_

template<TESTNAME AttributeSelection, bool isScalar = false>

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

protected

Definition at line 409 of file incrementalGraphLearner.h.

◆ var2Node_

template<TESTNAME AttributeSelection, bool isScalar = false>

HashTable< const DiscreteVariable*, LinkedList< NodeId >* > gum::IncrementalGraphLearner< AttributeSelection, isScalar >::var2Node_

protected

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

Definition at line 385 of file incrementalGraphLearner.h.

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

agrum/FMDP/learning/datastructure/incrementalGraphLearner.h
agrum/FMDP/learning/datastructure/incrementalGraphLearner_tpl.h

Public Member Functions

Protected Attributes

Constructor & destructor.

New Observation insertion methods

Graph Structure update methods

Detailed Description

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

Member Typedef Documentation

◆ ValueType

Constructor & Destructor Documentation

◆ IncrementalGraphLearner()

◆ ~IncrementalGraphLearner()

Member Function Documentation

◆ addObservation()

◆ assumeValue__() [1/3]

◆ assumeValue__() [2/3]

◆ assumeValue__() [3/3]

◆ branchObs__() [1/3]

◆ branchObs__() [2/3]

◆ branchObs__() [3/3]

◆ chgNodeBoundVar_()

◆ clearValue__() [1/3]

◆ clearValue__() [2/3]

◆ clearValue__() [3/3]

◆ convertNode2Leaf_()

◆ insertInternalNode_()

◆ insertLeafNode_()

◆ insertNode_()

◆ insertSetOfVars()

◆ isTerminal()

◆ nodeNbObservation()

◆ nodeSon()

◆ nodeVar()

◆ removeNode_()

◆ root()

◆ size()

◆ transpose_()

◆ updateFunctionGraph()

◆ updateGraph()

◆ updateNode_()

◆ updateNodeWithObservation_()

◆ updateVar()

Member Data Documentation

◆ leafDatabase_

◆ model_

◆ needUpdate_

◆ nodeId2Database_

◆ nodeSonsMap_

◆ nodeVarMap_

◆ root_

◆ setOfVars_

◆ target_

◆ value_

◆ valueAssumed_

◆ var2Node_

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