gum::learning::Miic Class Reference

The Miic learning algorithm. More...

#include <Miic.h>

Inheritance diagram for gum::learning::Miic:

Collaboration diagram for gum::learning::Miic:

Public Types
enum class	ApproximationSchemeSTATE : char {   Undefined , Continue , Epsilon , Rate ,   Limit , TimeLimit , Stopped }
	The different state of an approximation scheme. More...

Public Member Functions
Miic &	operator= (const Miic &from)
	copy operator
Miic &	operator= (Miic &&from)
	move operator
void	setForbiddenGraph (gum::DiGraph forbidGraph)
	Set ForbiddenGraph (resp. MadatoryGraph) which contains the forbidden (resp. mandatory) arcs.
void	setMandatoryGraph (gum::DAG mandaGraph)
void	setMaxIndegree (gum::Size n)
Constructors / Destructors
	Miic ()
	default constructor
	Miic (int maxLog)
	default constructor with maxLog
	Miic (const Miic &from)
	copy constructor
	Miic (Miic &&from)
	move constructor
	~Miic () override
	destructor
Accessors / Modifiers
MixedGraph	learnSkeleton (CorrectedMutualInformation &mutualInformation, MixedGraph graph)
	learns the skeleton of a MixedGraph (no orientation).
MixedGraph	learnMixedStructure (CorrectedMutualInformation &mutualInformation, MixedGraph graph)
	learns the structure of a MixedGraph (Meek rules not used here).
PDAG	learnPDAG (CorrectedMutualInformation &mutualInformation, MixedGraph graph)
	learns the structure of an Essential Graph
DAG	learnStructure (CorrectedMutualInformation &I, MixedGraph graph)
	learns the structure of a Bayesian network, i.e. a DAG, by first learning an Essential graph and then directing the remaining edges.
template<typename GUM_SCALAR = double, typename GRAPH_CHANGES_SELECTOR, typename PARAM_ESTIMATOR>
BayesNet< GUM_SCALAR >	learnBN (GRAPH_CHANGES_SELECTOR &selector, PARAM_ESTIMATOR &estimator, DAG initial_dag=DAG())
	learns the structure and the parameters of a BN
const std::vector< Arc >	latentVariables () const
	get the list of arcs hiding latent variables
void	addConstraints (HashTable< std::pair< NodeId, NodeId >, char > constraints)
	Set a ensemble of constraints for the orientation phase.
Getters and setters
void	setEpsilon (double eps) override
	Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|.
double	epsilon () const override
	Returns the value of epsilon.
void	disableEpsilon () override
	Disable stopping criterion on epsilon.
void	enableEpsilon () override
	Enable stopping criterion on epsilon.
bool	isEnabledEpsilon () const override
	Returns true if stopping criterion on epsilon is enabled, false otherwise.
void	setMinEpsilonRate (double rate) override
	Given that we approximate f(t), stopping criterion on d/dt(|f(t+1)-f(t)|).
double	minEpsilonRate () const override
	Returns the value of the minimal epsilon rate.
void	disableMinEpsilonRate () override
	Disable stopping criterion on epsilon rate.
void	enableMinEpsilonRate () override
	Enable stopping criterion on epsilon rate.
bool	isEnabledMinEpsilonRate () const override
	Returns true if stopping criterion on epsilon rate is enabled, false otherwise.
void	setMaxIter (Size max) override
	Stopping criterion on number of iterations.
Size	maxIter () const override
	Returns the criterion on number of iterations.
void	disableMaxIter () override
	Disable stopping criterion on max iterations.
void	enableMaxIter () override
	Enable stopping criterion on max iterations.
bool	isEnabledMaxIter () const override
	Returns true if stopping criterion on max iterations is enabled, false otherwise.
void	setMaxTime (double timeout) override
	Stopping criterion on timeout.
double	maxTime () const override
	Returns the timeout (in seconds).
double	currentTime () const override
	Returns the current running time in second.
void	disableMaxTime () override
	Disable stopping criterion on timeout.
void	enableMaxTime () override
	Enable stopping criterion on timeout.
bool	isEnabledMaxTime () const override
	Returns true if stopping criterion on timeout is enabled, false otherwise.
void	setPeriodSize (Size p) override
	How many samples between two stopping is enable.
Size	periodSize () const override
	Returns the period size.
void	setVerbosity (bool v) override
	Set the verbosity on (true) or off (false).
bool	verbosity () const override
	Returns true if verbosity is enabled.
ApproximationSchemeSTATE	stateApproximationScheme () const override
	Returns the approximation scheme state.
Size	nbrIterations () const override
	Returns the number of iterations.
const std::vector< double > &	history () const override
	Returns the scheme history.
void	initApproximationScheme ()
	Initialise the scheme.
bool	startOfPeriod () const
	Returns true if we are at the beginning of a period (compute error is mandatory).
void	updateApproximationScheme (unsigned int incr=1)
	Update the scheme w.r.t the new error and increment steps.
Size	remainingBurnIn () const
	Returns the remaining burn in.
void	stopApproximationScheme ()
	Stop the approximation scheme.
bool	continueApproximationScheme (double error)
	Update the scheme w.r.t the new error.
Getters and setters
std::string	messageApproximationScheme () const
	Returns the approximation scheme message.

Public Attributes
Signaler4< gum::NodeId, gum::NodeId, std::string, std::string >	onStructuralModification
Signaler3< Size, double, double >	onProgress
	Progression, error and time.
Signaler1< const std::string & >	onStop
	Criteria messageApproximationScheme.

Protected Member Functions
void	findBestContributor_ (NodeId x, NodeId y, const std::vector< NodeId > &ui, const MixedGraph &graph, CorrectedMutualInformation &mutualInformation, Heap< CondRanking, GreaterPairOn2nd > &rank)
	finds the best contributor node for a pair given a conditioning set
std::vector< Ranking >	unshieldedTriples_ (const MixedGraph &graph, CorrectedMutualInformation &mutualInformation, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > &sepSet)
	gets the list of unshielded triples in the graph in decreasing value of |I'(x, y, z|{ui})|
std::vector< ProbabilisticRanking >	unshieldedTriplesMiic_ (const MixedGraph &graph, CorrectedMutualInformation &mutualInformation, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > &sepSet, HashTable< std::pair< NodeId, NodeId >, char > &marks)
	gets the list of unshielded triples in the graph in decreasing value of |I'(x, y, z|{ui})|, prepares the orientation matrix for MIIC
std::vector< ProbabilisticRanking >	updateProbaTriples_ (const MixedGraph &graph, std::vector< ProbabilisticRanking > probaTriples)
	Gets the orientation probabilities like MIIC for the orientation phase.
void	orientDoubleHeadedArcs_ (MixedGraph &mg)
	Orient double headed arcs to avoid cycles.
bool	isForbiddenArc_ (NodeId x, NodeId y) const
	Check constraints.
bool	isForbiddenEdge_ (NodeId x, NodeId y)
bool	isMaxIndegree_ (MixedGraph graph, NodeId x)
bool	isArcValid_ (MixedGraph graph, NodeId x, NodeId y)
Main phases
Set a ensemble of constraints for the learning/orientation phase
void	initiation_ (CorrectedMutualInformation &mutualInformation, MixedGraph &graph, HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > &sepSet, Heap< CondRanking, GreaterPairOn2nd > &rank)
	Initiation phase.
void	iteration_ (CorrectedMutualInformation &mutualInformation, MixedGraph &graph, HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > &sepSet, Heap< CondRanking, GreaterPairOn2nd > &rank)
	Iteration phase.
void	orientationMiic_ (CorrectedMutualInformation &mutualInformation, MixedGraph &graph, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > &sepSet)
	Orientation phase from the MIIC algorithm, returns a mixed graph that may contain circles.

Protected Attributes
gum::MeekRules	meekRules_
	Object that can propagates orientations to non-oriented edges.
double	current_epsilon_
	Current epsilon.
double	last_epsilon_
	Last epsilon value.
double	current_rate_
	Current rate.
Size	current_step_
	The current step.
Timer	timer_
	The timer.
ApproximationSchemeSTATE	current_state_
	The current state.
std::vector< double >	history_
	The scheme history, used only if verbosity == true.
double	eps_
	Threshold for convergence.
bool	enabled_eps_
	If true, the threshold convergence is enabled.
double	min_rate_eps_
	Threshold for the epsilon rate.
bool	enabled_min_rate_eps_
	If true, the minimal threshold for epsilon rate is enabled.
double	max_time_
	The timeout.
bool	enabled_max_time_
	If true, the timeout is enabled.
Size	max_iter_
	The maximum iterations.
bool	enabled_max_iter_
	If true, the maximum iterations stopping criterion is enabled.
Size	burn_in_
	Number of iterations before checking stopping criteria.
Size	period_size_
	Checking criteria frequency.
bool	verbosity_
	If true, verbosity is enabled.

Private Member Functions
void	_orientingVstructureMiic_ (MixedGraph &graph, HashTable< std::pair< NodeId, NodeId >, char > &marks, NodeId x, NodeId y, NodeId z, double p1, double p2)
void	_propagatingOrientationMiic_ (MixedGraph &graph, HashTable< std::pair< NodeId, NodeId >, char > &marks, NodeId x, NodeId y, NodeId z, double p1, double p2)
bool	_isNotLatentCouple_ (NodeId x, NodeId y)
void	stopScheme_ (ApproximationSchemeSTATE new_state)
	Stop the scheme given a new state.

Static Private Member Functions
static bool	_existsNonTrivialDirectedPath_ (const MixedGraph &graph, NodeId n1, NodeId n2)
	checks for directed paths in a graph, considering double arcs like edges, not considering arc as a directed path.
static bool	_existsDirectedPath_ (const MixedGraph &graph, NodeId n1, NodeId n2)
	checks for directed paths in a graph, consider double arcs like edges

Private Attributes
int	_maxLog_ = 100
	Fixes the maximum log that we accept in exponential computations.
const std::vector< NodeId >	_emptySet_
	an empty conditioning set
std::vector< Arc >	_latentCouples_
	an empty vector of arcs
gum::Size	_maxIndegree_
	maximum number of parents
Size	_size_
	size of the database
ArcProperty< double >	_arcProbas_
	Storing the propabilities for each arc set in the graph.
HashTable< std::pair< NodeId, NodeId >, char >	_initialMarks_
	Initial marks for the orientation phase, used to convey constraints.
gum::DAG	_mandatoryGraph_
	Graph that contains the mandatories arcs.
gum::DiGraph	_forbiddenGraph_
	Graph that contains the forbidden arcs.

Detailed Description

The Miic learning algorithm.

The Miic class implements the miic algorithm based on https://doi.org/10.1371/journal.pcbi.1005662. It starts by eliminating edges that correspond to independent variables to build the skeleton of the graph, and then directs the remaining edges to get an essential graph. Latent variables can be detected using bi-directed arcs.

Miic allows the option of adding constraints on the skeleton construction such as: a maximum number of parents, mandatory arcs, forbidden arcs or an order between the variables.

Definition at line 126 of file Miic.h.

Member Enumeration Documentation

ApproximationSchemeSTATE

enum class gum::IApproximationSchemeConfiguration::ApproximationSchemeSTATE : char

stronginherited

The different state of an approximation scheme.

Enumerator
Undefined
Continue
Epsilon
Rate
Limit
TimeLimit
Stopped

Definition at line 86 of file IApproximationSchemeConfiguration.h.

                                        : char {
      Undefined,
      Continue,
      Epsilon,
      Rate,
      Limit,
      TimeLimit,
      Stopped
    };

Constructor & Destructor Documentation

Miic() [1/4]

gum::learning::Miic::Miic

(

)

default constructor

Definition at line 63 of file Miic.cpp.

: _maxLog_(100), _size_(0) { GUM_CONSTRUCTOR(Miic); }

References Miic(), _maxLog_, and _size_.

Referenced by Miic(), Miic(), Miic(), Miic(), ~Miic(), operator=(), and operator=().

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Miic() [2/4]

gum::learning::Miic::Miic ( int maxLog )

explicit

default constructor with maxLog

Definition at line 66 of file Miic.cpp.

: _maxLog_(maxLog), _size_(0) { GUM_CONSTRUCTOR(Miic); }

References Miic(), _maxLog_, and _size_.

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Miic() [3/4]

gum::learning::Miic::Miic

(

const Miic &

from

)

copy constructor

Definition at line 69 of file Miic.cpp.

                               : ApproximationScheme(from), _size_(from._size_) {
      GUM_CONS_CPY(Miic);
    }

References gum::ApproximationScheme::ApproximationScheme(), Miic(), and _size_.

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Miic() [4/4]

gum::learning::Miic::Miic

(

Miic &&

from

)

move constructor

Definition at line 74 of file Miic.cpp.

                          : ApproximationScheme(std::move(from)), _size_(from._size_) {
      GUM_CONS_MOV(Miic);
    }

References gum::ApproximationScheme::ApproximationScheme(), Miic(), and _size_.

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~Miic()

gum::learning::Miic::~Miic ( )

override

destructor

Definition at line 79 of file Miic.cpp.

{ GUM_DESTRUCTOR(Miic); }

References Miic().

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Member Function Documentation

_existsDirectedPath_()

bool gum::learning::Miic::_existsDirectedPath_ ( const MixedGraph & graph, NodeId n1, NodeId n2 )

staticprivate

checks for directed paths in a graph, consider double arcs like edges

Parameters

graph	MixedGraph in which to search the path
n1	tail of the path
n2	head of the path

Definition at line 930 of file Miic.cpp.

                                                                                             {
      // not recursive version => use a FIFO for simulating the recursion
      List< NodeId > nodeFIFO;
      // mark[node] = successor if visited, else mark[node] does not exist
      Set< NodeId > mark;
 
      mark.insert(n2);
      nodeFIFO.pushBack(n2);
 
      NodeId current;
 
      while (!nodeFIFO.empty()) {
        current = nodeFIFO.front();
        nodeFIFO.popFront();
 
        // check the parents
        for (const auto new_one: graph.parents(current)) {
          if (graph.existsArc(current,
                              new_one))   // if there is a double arc, pass
            continue;
 
          if (new_one == n1) { return true; }
 
          if (mark.exists(new_one))   // if this node is already marked, do not
            continue;                 // check it again
 
          mark.insert(new_one);
          nodeFIFO.pushBack(new_one);
        }
      }
 
      return false;
    }

References gum::List< Val >::empty(), gum::Set< Key >::exists(), gum::ArcGraphPart::existsArc(), gum::List< Val >::front(), gum::Set< Key >::insert(), gum::ArcGraphPart::parents(), gum::List< Val >::popFront(), and gum::List< Val >::pushBack().

Referenced by _existsNonTrivialDirectedPath_(), _propagatingOrientationMiic_(), orientationMiic_(), and orientDoubleHeadedArcs_().

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_existsNonTrivialDirectedPath_()

bool gum::learning::Miic::_existsNonTrivialDirectedPath_ ( const MixedGraph & graph, NodeId n1, NodeId n2 )

staticprivate

checks for directed paths in a graph, considering double arcs like edges, not considering arc as a directed path.

Parameters

graph	MixedGraph in which to search the path
n1	tail of the path
n2	head of the path
countArc	bool to know if we

Definition at line 917 of file Miic.cpp.

                                                                    {
      for (const auto parent: graph.parents(n2)) {
        if (graph.existsArc(n2, parent))   // if there is a double arc, pass
          continue;
        if (parent == n1)                  // trivial directed path => not recognized
          continue;
        if (_existsDirectedPath_(graph, n1, parent)) return true;
      }
      return false;
    }

References _existsDirectedPath_(), gum::ArcGraphPart::existsArc(), and gum::ArcGraphPart::parents().

Referenced by _orientingVstructureMiic_().

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_isNotLatentCouple_()

bool gum::learning::Miic::_isNotLatentCouple_ ( NodeId x, NodeId y )

private

Definition at line 964 of file Miic.cpp.

                                                                 {
      const auto& lbeg = _latentCouples_.begin();
      const auto& lend = _latentCouples_.end();
 
      return (std::find(lbeg, lend, Arc(x, y)) == lend)
          && (std::find(lbeg, lend, Arc(y, x)) == lend);
    }

References _latentCouples_.

Referenced by _orientingVstructureMiic_().

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_orientingVstructureMiic_()

void gum::learning::Miic::_orientingVstructureMiic_ ( MixedGraph & graph, HashTable< std::pair< NodeId, NodeId >, char > & marks, NodeId x, NodeId y, NodeId z, double p1, double p2 )

private

Definition at line 420 of file Miic.cpp.

                                                                                             {
      // v-structure discovery
      if (marks[{x, z}] == 'o' && marks[{y, z}] == 'o') {   // If x-z-y
        if (!_existsNonTrivialDirectedPath_(graph, z, x)) {
          if (isArcValid_(graph, x, z)) {
            graph.eraseEdge(Edge(x, z));
            graph.addArc(x, z);
            GUM_SL_EMIT(x, z, "Add Arc " << x << " -> " << z, "V-structure Orientation")
            // GUM_TRACE("1.a Removing edge (" << x << "," << z << ")")
            // GUM_TRACE("1.a Adding arc (" << x << "," << z << ")")
 
            marks[{x, z}] = '>';
            if (graph.existsArc(z, x) && _isNotLatentCouple_(z, x)) {
              // GUM_TRACE("Adding latent couple (" << z << "," << x << ")")
              _latentCouples_.emplace_back(z, x);
            }
            if (!_arcProbas_.exists(Arc(x, z))) _arcProbas_.insert(Arc(x, z), p1);
          }
 
        } else {
          graph.eraseEdge(Edge(x, z));
          // GUM_TRACE("1.b Adding arc (" << x << "," << z << ")")
          if (!_existsNonTrivialDirectedPath_(graph, x, z)) {
            if (isArcValid_(graph, z, x)) {
              graph.addArc(z, x);
              GUM_SL_EMIT(z, x, "Add Arc " << z << " -> " << x, "V-structure Orientation")
              // GUM_TRACE("1.b Removing edge (" << x << "," << z << ")")
              marks[{z, x}] = '>';
            }
          }
        }
 
        if (!_existsNonTrivialDirectedPath_(graph, z, y)) {
          if (isArcValid_(graph, y, z)) {
            graph.eraseEdge(Edge(y, z));
            graph.addArc(y, z);
            GUM_SL_EMIT(y, z, "Add Arc " << y << " -> " << z, "V-structure Orientation")
            // GUM_TRACE("1.c Removing edge (" << y << "," << z << ")")
            // GUM_TRACE("1.c Adding arc (" << y << "," << z << ")")
            marks[{y, z}] = '>';
            if (graph.existsArc(z, y) && _isNotLatentCouple_(z, y)) {
              _latentCouples_.emplace_back(z, y);
            }
            if (!_arcProbas_.exists(Arc(y, z))) _arcProbas_.insert(Arc(y, z), p2);
          }
        } else {
          graph.eraseEdge(Edge(y, z));
          // GUM_TRACE("1.d Removing edge (" << y << "," << z << ")")
          if (!_existsNonTrivialDirectedPath_(graph, y, z)) {
            if (isArcValid_(graph, z, y)) {
              graph.addArc(z, y);
              GUM_SL_EMIT(z, y, "Add Arc " << z << " -> " << y, "V-structure Orientation")
              // GUM_TRACE("1.d Adding arc (" << z << "," << y << ")")
              marks[{z, y}] = '>';
            }
          }
        }
      } else if (marks[{x, z}] == '>' && marks[{y, z}] == 'o') {   // If x->z-y
        if (!_existsNonTrivialDirectedPath_(graph, z, y)) {
          if (isArcValid_(graph, y, z)) {
            graph.eraseEdge(Edge(y, z));
            graph.addArc(y, z);
            GUM_SL_EMIT(y,
                        z,
                        "Add Arc " << y << " -> " << z,
                        "V-structure Orientation | existing "
                            << x << " -> " << z << ", then orienting " << y << " -> " << z)
            // GUM_TRACE("2.a Removing edge (" << y << "," << z << ")")
            // GUM_TRACE("2.a Adding arc (" << y << "," << z << ")")
            marks[{y, z}] = '>';
            if (graph.existsArc(z, y) && _isNotLatentCouple_(z, y)) {
              _latentCouples_.emplace_back(z, y);
            }
            if (!_arcProbas_.exists(Arc(y, z))) _arcProbas_.insert(Arc(y, z), p2);
          }
        } else {
          graph.eraseEdge(Edge(y, z));
          // GUM_TRACE("2.b Removing edge (" << y << "," << z << ")")
          if (!_existsNonTrivialDirectedPath_(graph, y, z)) {
            if (isArcValid_(graph, z, y)) {
              graph.addArc(z, y);
              GUM_SL_EMIT(z,
                          y,
                          "Add Arc " << z << " -> " << y,
                          "V-structure Orientation | existing "
                              << x << " -> " << z << ", then orienting " << z << " -> " << y)
              // GUM_TRACE("2.b Adding arc (" << y << "," << z << ")")
              marks[{z, y}] = '>';
            }
          }
        }
 
      } else if (marks[{y, z}] == '>' && marks[{x, z}] == 'o') {   // If y->z-x
        if (!_existsNonTrivialDirectedPath_(graph, z, x)) {
          if (isArcValid_(graph, x, z)) {
            graph.eraseEdge(Edge(x, z));
            graph.addArc(x, z);
            GUM_SL_EMIT(x, z, "Add Arc " << x << " -> " << z, "V-structure Orientation")
            // GUM_TRACE("3.a Removing edge (" << x << "," << z << ")")
            // GUM_TRACE("3.a Adding arc (" << x << "," << z << ")")
            marks[{x, z}] = '>';
            if (graph.existsArc(z, x) && _isNotLatentCouple_(z, x)) {
              _latentCouples_.emplace_back(z, x);
            }
            if (!_arcProbas_.exists(Arc(x, z))) _arcProbas_.insert(Arc(x, z), p1);
          }
 
        } else {
          graph.eraseEdge(Edge(x, z));
          // GUM_TRACE("3.b Removing edge (" << x << "," << z << ")")
          if (!_existsNonTrivialDirectedPath_(graph, x, z)) {
            if (isArcValid_(graph, z, x)) {
              graph.addArc(z, x);
              GUM_SL_EMIT(z, x, "Add Arc " << z << " -> " << x, "V-structure Orientation")
              // GUM_TRACE("3.b Adding arc (" << x << "," << z << ")")
              marks[{z, x}] = '>';
            }
          }
        }
      }
    }

References _arcProbas_, _existsNonTrivialDirectedPath_(), _isNotLatentCouple_(), _latentCouples_, gum::DiGraph::addArc(), gum::EdgeGraphPart::eraseEdge(), gum::ArcGraphPart::existsArc(), GUM_SL_EMIT, and isArcValid_().

Referenced by orientationMiic_().

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_propagatingOrientationMiic_()

void gum::learning::Miic::_propagatingOrientationMiic_ ( MixedGraph & graph, HashTable< std::pair< NodeId, NodeId >, char > & marks, NodeId x, NodeId y, NodeId z, double p1, double p2 )

private

Definition at line 548 of file Miic.cpp.

                                                                                                {
      // orientation propagation
      if (marks[{x, z}] == '>' && marks[{y, z}] == 'o' && marks[{z, y}] != '-') {
        graph.eraseEdge(Edge(z, y));
        if (!_existsDirectedPath_(graph, y, z) && graph.parents(y).empty()) {
          if (isArcValid_(graph, z, y)) {
            graph.addArc(z, y);
            GUM_SL_EMIT(z,
                        y,
                        "Add Arc " << z << " -> " << y,
                        "Propagation MIIC (919) | existing x -> " << z << " and " << z << " - "
                                                                  << y)
            // GUM_TRACE("4.a Adding arc (" << z << "," << y << ")")
            marks[{z, y}] = '>';
            marks[{y, z}] = '-';
            if (!_arcProbas_.exists(Arc(z, y))) _arcProbas_.insert(Arc(z, y), p2);
          }
 
        } else if (!_existsDirectedPath_(graph, z, y) && graph.parents(z).empty()) {
          if (isArcValid_(graph, y, z)) {
            graph.addArc(y, z);
            GUM_SL_EMIT(y, z, "Add Arc " << y << " -> " << z, "Propagation MIIC line 932 ")
            // GUM_TRACE("4.b Adding arc (" << y << "," << z << ")")
 
            marks[{z, y}] = '-';
            marks[{y, z}] = '>';
            _latentCouples_.emplace_back(y, z);
            if (!_arcProbas_.exists(Arc(y, z))) _arcProbas_.insert(Arc(y, z), p2);
          }
 
        } else if (!_existsDirectedPath_(graph, y, z)) {
          if (isArcValid_(graph, z, y)) {
            graph.addArc(z, y);
            GUM_SL_EMIT(z, y, "Add Arc " << z << "->" << y, "Propagation MIIC 947 ")
            // GUM_TRACE("4.c Adding arc (" << z << "," << y << ")")
            marks[{z, y}] = '>';
            marks[{y, z}] = '-';
            if (!_arcProbas_.exists(Arc(z, y))) _arcProbas_.insert(Arc(z, y), p2);
          }
 
        } else if (!_existsDirectedPath_(graph, z, y)) {
          if (isArcValid_(graph, y, z)) {
            graph.addArc(y, z);
            GUM_SL_EMIT(z, y, "Add Arc " << z << "->" << y, "Propagation MIIC 959")
            // GUM_TRACE("4.d Adding arc (" << y << "," << z << ")")
 
            _latentCouples_.emplace_back(y, z);
            marks[{z, y}] = '-';
            marks[{y, z}] = '>';
            if (!_arcProbas_.exists(Arc(y, z))) _arcProbas_.insert(Arc(y, z), p2);
          }
        }
      } else if (marks[{y, z}] == '>' && marks[{x, z}] == 'o' && marks[{z, x}] != '-') {
        graph.eraseEdge(Edge(z, x));
        // GUM_TRACE("5. Removing edge (" << z << "," << x << ")")
        if (!_existsDirectedPath_(graph, x, z) && graph.parents(x).empty()) {
          if (isArcValid_(graph, z, x)) {
            graph.addArc(z, x);
            GUM_SL_EMIT(z, x, "Add Arc " << z << " -> " << x, "Propagation MIIC 977")
            // GUM_TRACE("5.a Adding arc (" << z << "," << x << ")")
            marks[{z, x}] = '>';
            marks[{x, z}] = '-';
            if (!_arcProbas_.exists(Arc(z, x))) _arcProbas_.insert(Arc(z, x), p1);
          }
 
        } else if (!_existsDirectedPath_(graph, z, x) && graph.parents(z).empty()) {
          if (isArcValid_(graph, x, z)) {
            graph.addArc(x, z);
            GUM_SL_EMIT(x, z, "Add Arc " << x << "->" << z, "Propagation MIIC 990")
            // GUM_TRACE("5.b Adding arc (" << x << "," << z << ")")
            marks[{z, x}] = '-';
            marks[{x, z}] = '>';
            _latentCouples_.emplace_back(x, z);
            if (!_arcProbas_.exists(Arc(x, z))) _arcProbas_.insert(Arc(x, z), p1);
          }
 
        } else if (!_existsDirectedPath_(graph, x, z)) {
          if (isArcValid_(graph, z, x)) {
            graph.addArc(z, x);
            GUM_SL_EMIT(z, x, "Add Arc " << z << " -> " << x, "Propagation MIIC 1004")
            // GUM_TRACE("5.c Adding arc (" << z << "," << x << ")")
            marks[{z, x}] = '>';
            marks[{x, z}] = '-';
            if (!_arcProbas_.exists(Arc(z, x))) _arcProbas_.insert(Arc(z, x), p1);
          }
        } else if (!_existsDirectedPath_(graph, z, x)) {
          if (isArcValid_(graph, x, z)) {
            graph.addArc(x, z);
            GUM_SL_EMIT(x, z, "Add Arc " << x << " -> " << z, "Propagation MIIC 1016")
            // GUM_TRACE("5.d Adding arc (" << x << "," << z << ")")
            marks[{z, x}] = '-';
            marks[{x, z}] = '>';
            _latentCouples_.emplace_back(x, z);
            if (!_arcProbas_.exists(Arc(x, z))) _arcProbas_.insert(Arc(x, z), p1);
          }
        }
      }
    }

References _arcProbas_, _existsDirectedPath_(), _latentCouples_, gum::DiGraph::addArc(), gum::Set< Key >::empty(), gum::EdgeGraphPart::eraseEdge(), GUM_SL_EMIT, isArcValid_(), and gum::ArcGraphPart::parents().

Referenced by orientationMiic_().

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addConstraints()

void gum::learning::Miic::addConstraints

(

HashTable< std::pair< NodeId, NodeId >, char >

constraints

)

Set a ensemble of constraints for the orientation phase.

Definition at line 913 of file Miic.cpp.

                                                                                      {
      this->_initialMarks_ = constraints;
    }

References _initialMarks_.

continueApproximationScheme()

INLINE bool gum::ApproximationScheme::continueApproximationScheme ( double error )

inherited

Update the scheme w.r.t the new error.

Test the stopping criterion that are enabled.

Parameters

error

The new error value.

Returns

false if state become != ApproximationSchemeSTATE::Continue

Exceptions

OperationNotAllowed

Raised if state != ApproximationSchemeSTATE::Continue.

Definition at line 229 of file approximationScheme_inl.h.

                                                                           {
    // For coherence, we fix the time used in the method
 
    double timer_step = timer_.step();
 
    if (enabled_max_time_) {
      if (timer_step > max_time_) {
        stopScheme_(ApproximationSchemeSTATE::TimeLimit);
        return false;
      }
    }
 
    if (!startOfPeriod()) { return true; }
 
    if (current_state_ != ApproximationSchemeSTATE::Continue) {
      GUM_ERROR(
          OperationNotAllowed,
          "state of the approximation scheme is not correct : " << messageApproximationScheme());
    }
 
    if (verbosity()) { history_.push_back(error); }
 
    if (enabled_max_iter_) {
      if (current_step_ > max_iter_) {
        stopScheme_(ApproximationSchemeSTATE::Limit);
        return false;
      }
    }
 
    last_epsilon_    = current_epsilon_;
    current_epsilon_ = error;   // eps rate isEnabled needs it so affectation was
    // moved from eps isEnabled below
 
    if (enabled_eps_) {
      if (current_epsilon_ <= eps_) {
        stopScheme_(ApproximationSchemeSTATE::Epsilon);
        return false;
      }
    }
 
    if (last_epsilon_ >= 0.) {
      if (current_epsilon_ > .0) {
        // ! current_epsilon_ can be 0. AND epsilon
        // isEnabled can be disabled !
        current_rate_ = std::fabs((current_epsilon_ - last_epsilon_) / current_epsilon_);
      }
      // limit with current eps ---> 0 is | 1 - ( last_eps / 0 ) | --->
      // infinity the else means a return false if we isEnabled the rate below,
      // as we would have returned false if epsilon isEnabled was enabled
      else {
        current_rate_ = min_rate_eps_;
      }
 
      if (enabled_min_rate_eps_) {
        if (current_rate_ <= min_rate_eps_) {
          stopScheme_(ApproximationSchemeSTATE::Rate);
          return false;
        }
      }
    }
 
    if (stateApproximationScheme() == ApproximationSchemeSTATE::Continue) {
      if (onProgress.hasListener()) {
        GUM_EMIT3(onProgress, current_step_, current_epsilon_, timer_step);
      }
 
      return true;
    } else {
      return false;
    }
  }

References enabled_max_time_, and timer_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByOrderedArcs_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByRandomOrder_(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

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currentTime()

INLINE double gum::ApproximationScheme::currentTime ( ) const

overridevirtualinherited

Returns the current running time in second.

Returns

Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 136 of file approximationScheme_inl.h.

{ return timer_.step(); }

References timer_.

disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

overridevirtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 74 of file approximationScheme_inl.h.

{ enabled_eps_ = false; }

References enabled_eps_.

Referenced by gum::learning::EMApproximationScheme::EMApproximationScheme(), and gum::learning::EMApproximationScheme::setMinEpsilonRate().

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disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

overridevirtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 115 of file approximationScheme_inl.h.

{ enabled_max_iter_ = false; }

References enabled_max_iter_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing().

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disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

overridevirtualinherited

Disable stopping criterion on timeout.

Returns

Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 139 of file approximationScheme_inl.h.

{ enabled_max_time_ = false; }

References enabled_max_time_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing().

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disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

overridevirtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 95 of file approximationScheme_inl.h.

{ enabled_min_rate_eps_ = false; }

References enabled_min_rate_eps_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::learning::EMApproximationScheme::setEpsilon().

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enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

overridevirtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 77 of file approximationScheme_inl.h.

{ enabled_eps_ = true; }

References enabled_eps_.

enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

overridevirtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 118 of file approximationScheme_inl.h.

{ enabled_max_iter_ = true; }

References enabled_max_iter_.

enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

overridevirtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 142 of file approximationScheme_inl.h.

{ enabled_max_time_ = true; }

References enabled_max_time_.

enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

overridevirtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 98 of file approximationScheme_inl.h.

{ enabled_min_rate_eps_ = true; }

References enabled_min_rate_eps_.

Referenced by gum::learning::EMApproximationScheme::EMApproximationScheme(), and gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

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epsilon()

INLINE double gum::ApproximationScheme::epsilon ( ) const

overridevirtualinherited

Returns the value of epsilon.

Returns

Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 71 of file approximationScheme_inl.h.

{ return eps_; }

References eps_.

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

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findBestContributor_()

void gum::learning::Miic::findBestContributor_ ( NodeId x, NodeId y, const std::vector< NodeId > & ui, const MixedGraph & graph, CorrectedMutualInformation & mutualInformation, Heap< CondRanking, GreaterPairOn2nd > & rank )

protected

finds the best contributor node for a pair given a conditioning set

Parameters

x	first node
y	second node
ui	conditioning set
mutualInformation	A mutual information instance that will do the computations and has loaded the database.
graph	containing the assessed nodes
rank	the heap of ranks of the algorithm

Definition at line 718 of file Miic.cpp.

                                                                                 {
      double maxP = -1.0;
      NodeId maxZ = 0;
 
      // compute N
      // __N = I.N();
      const double Ixy_ui = mutualInformation.score(x, y, ui);
 
      for (const NodeId z: graph) {
        // if z!=x and z!=y and z not in ui
        if (z != x && z != y && std::find(ui.begin(), ui.end(), z) == ui.end()) {
          double Pnv;
          double Pb;
 
          // Computing Pnv
          const double Ixyz_ui    = mutualInformation.score(x, y, z, ui);
          double       calc_expo1 = -Ixyz_ui * M_LN2;
          // if exponential are too high or to low, crop them at _maxLog_
          if (calc_expo1 > _maxLog_) {
            Pnv = 0.0;
          } else if (calc_expo1 < -_maxLog_) {
            Pnv = 1.0;
          } else {
            Pnv = 1 / (1 + std::exp(calc_expo1));
          }
 
          // Computing Pb
          const double Ixz_ui = mutualInformation.score(x, z, ui);
          const double Iyz_ui = mutualInformation.score(y, z, ui);
 
          calc_expo1        = -(Ixz_ui - Ixy_ui) * M_LN2;
          double calc_expo2 = -(Iyz_ui - Ixy_ui) * M_LN2;
 
          // if exponential are too high or to low, crop them at  _maxLog_
          if (calc_expo1 > _maxLog_ || calc_expo2 > _maxLog_) {
            Pb = 0.0;
          } else if (calc_expo1 < -_maxLog_ && calc_expo2 < -_maxLog_) {
            Pb = 1.0;
          } else {
            double expo1, expo2;
            if (calc_expo1 < -_maxLog_) {
              expo1 = 0.0;
            } else {
              expo1 = std::exp(calc_expo1);
            }
            if (calc_expo2 < -_maxLog_) {
              expo2 = 0.0;
            } else {
              expo2 = std::exp(calc_expo2);
            }
            Pb = 1 / (1 + expo1 + expo2);
          }
 
          // Getting max(min(Pnv, pb))
          const double min_pnv_pb = std::min(Pnv, Pb);
          if (min_pnv_pb > maxP) {
            maxP = min_pnv_pb;
            maxZ = z;
          }
        }   // if z not in (x, y)
      }   // for z in graph.nodes
      // storing best z in rank_
      CondRanking final;
      auto        tup = new CondThreePoints{x, y, maxZ, ui};
      final.first     = tup;
      final.second    = maxP;
      rank.insert(final);
    }

References _maxLog_, gum::Heap< Val, Cmp >::insert(), M_LN2, and gum::learning::CorrectedMutualInformation::score().

Referenced by initiation_(), and iteration_().

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history()

INLINE const std::vector< double > & gum::ApproximationScheme::history ( ) const

overridevirtualinherited

Returns the scheme history.

Returns

Returns the scheme history.

Exceptions

OperationNotAllowed

Raised if the scheme did not performed or if verbosity is set to false.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 178 of file approximationScheme_inl.h.

                                                                       {
    if (stateApproximationScheme() == ApproximationSchemeSTATE::Undefined) {
      GUM_ERROR(OperationNotAllowed, "state of the approximation scheme is udefined")
    }
 
    if (!verbosity()) GUM_ERROR(OperationNotAllowed, "No history when verbosity=false")
 
    return history_;
  }

References GUM_ERROR, stateApproximationScheme(), and gum::IApproximationSchemeConfiguration::Undefined.

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initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 189 of file approximationScheme_inl.h.

                                                           {
    current_state_   = ApproximationSchemeSTATE::Continue;
    current_step_    = 0;
    current_epsilon_ = current_rate_ = -1.0;
    history_.clear();
    timer_.reset();
  }

References ApproximationScheme(), gum::IApproximationSchemeConfiguration::Continue, current_epsilon_, current_rate_, current_state_, current_step_, and initApproximationScheme().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), initApproximationScheme(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInference(), and gum::SamplingInference< GUM_SCALAR >::onStateChanged_().

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initiation_()

void gum::learning::Miic::initiation_ ( CorrectedMutualInformation & mutualInformation, MixedGraph & graph, HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > & sepSet, Heap< CondRanking, GreaterPairOn2nd > & rank )

protected

Initiation phase.

We go over all edges and test if the variables are marginally independent. If they are, the edge is deleted. If not, the best contributor is found.

Parameters

mutualInformation	A mutual information instance that will do the computations and has loaded the database.
graph	the MixedGraph we start from for the learning
sepSet	the separation set for independent couples, here set to {}
rank	the heap of ranks of the algorithm

Definition at line 204 of file Miic.cpp.

                                                                                                  {
      NodeId  x, y;
      EdgeSet edges      = graph.edges();
      Size    steps_init = edges.size();
 
      for (const Edge& edge: edges) {
        x = edge.first();
        y = edge.second();
        if (isForbiddenEdge_(x, y)) {   // Erase Forbidden edges
          GUM_SL_EMIT(x, y, "Remove " << x << " - " << y, " Constraints : Forbidden edge")
          graph.eraseEdge(edge);
        }
 
        else {
          double Ixy = mutualInformation.score(x, y);
 
          if (Ixy <= 0) {   //< K
            graph.eraseEdge(edge);
            GUM_SL_EMIT(x,
                        y,
                        "Remove " << x << " - " << y,
                        "Independent based on Mutual Information :" << Ixy)
 
            sepSet.insert(std::make_pair(x, y), _emptySet_);
          } else {
            findBestContributor_(x, y, _emptySet_, graph, mutualInformation, rank);
            GUM_SL_EMIT(x,
                        y,
                        "Keep " << x << " - " << y,
                        "Dependent based on Mutual Information :" << Ixy)
          }
 
          ++current_step_;
          if (onProgress.hasListener()) {
            GUM_EMIT3(onProgress, (current_step_ * 33) / steps_init, 0., timer_.step());
          }
        }
      }
    }

References _emptySet_, gum::ApproximationScheme::current_step_, gum::EdgeGraphPart::edges(), gum::EdgeGraphPart::eraseEdge(), findBestContributor_(), GUM_EMIT3, GUM_SL_EMIT, isForbiddenEdge_(), gum::IApproximationSchemeConfiguration::onProgress, gum::learning::CorrectedMutualInformation::score(), gum::Set< Key >::size(), and gum::ApproximationScheme::timer_.

Referenced by learnMixedStructure(), and learnSkeleton().

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isArcValid_()

bool gum::learning::Miic::isArcValid_ ( MixedGraph graph, NodeId x, NodeId y )

protected

Definition at line 988 of file Miic.cpp.

                                                                     {
      return (isForbiddenArc_(x, y) == false && isMaxIndegree_(graph, y) == false);
    }

References isForbiddenArc_(), and isMaxIndegree_().

Referenced by _orientingVstructureMiic_(), and _propagatingOrientationMiic_().

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isEnabledEpsilon()

INLINE bool gum::ApproximationScheme::isEnabledEpsilon ( ) const

overridevirtualinherited

Returns true if stopping criterion on epsilon is enabled, false otherwise.

Returns

Returns true if stopping criterion on epsilon is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 81 of file approximationScheme_inl.h.

{ return enabled_eps_; }

References enabled_eps_.

isEnabledMaxIter()

INLINE bool gum::ApproximationScheme::isEnabledMaxIter ( ) const

overridevirtualinherited

Returns true if stopping criterion on max iterations is enabled, false otherwise.

Returns

Returns true if stopping criterion on max iterations is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 122 of file approximationScheme_inl.h.

{ return enabled_max_iter_; }

References enabled_max_iter_.

isEnabledMaxTime()

INLINE bool gum::ApproximationScheme::isEnabledMaxTime ( ) const

overridevirtualinherited

Returns true if stopping criterion on timeout is enabled, false otherwise.

Returns

Returns true if stopping criterion on timeout is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 146 of file approximationScheme_inl.h.

{ return enabled_max_time_; }

References enabled_max_time_.

isEnabledMinEpsilonRate()

INLINE bool gum::ApproximationScheme::isEnabledMinEpsilonRate ( ) const

overridevirtualinherited

Returns true if stopping criterion on epsilon rate is enabled, false otherwise.

Returns

Returns true if stopping criterion on epsilon rate is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 102 of file approximationScheme_inl.h.

{ return enabled_min_rate_eps_; }

References enabled_min_rate_eps_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

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isForbiddenArc_()

bool gum::learning::Miic::isForbiddenArc_ ( NodeId x, NodeId y ) const

protected

Check constraints.

Definition at line 984 of file Miic.cpp.

                                                       {
      return (_forbiddenGraph_.existsArc(x, y));
    }

References _forbiddenGraph_.

Referenced by isArcValid_().

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isForbiddenEdge_()

bool gum::learning::Miic::isForbiddenEdge_ ( NodeId x, NodeId y )

protected

Definition at line 992 of file Miic.cpp.

                                                  {
      return (_forbiddenGraph_.existsArc(x, y) && _forbiddenGraph_.existsArc(y, x));
    }

References _forbiddenGraph_.

Referenced by initiation_().

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isMaxIndegree_()

bool gum::learning::Miic::isMaxIndegree_ ( MixedGraph graph, NodeId x )

protected

Definition at line 980 of file Miic.cpp.

                                                              {
      return ((graph.parents(x).size() >= _maxIndegree_));
    }

References _maxIndegree_, gum::ArcGraphPart::parents(), and gum::Set< Key >::size().

Referenced by isArcValid_().

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iteration_()

void gum::learning::Miic::iteration_ ( CorrectedMutualInformation & mutualInformation, MixedGraph & graph, HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > & sepSet, Heap< CondRanking, GreaterPairOn2nd > & rank )

protected

Iteration phase.

As long as we find important nodes for edges, we go over them to see if we can assess the conditional independence of the variables.

Parameters

mutualInformation	A mutual information instance that will do the computations and has loaded the database.
graph	the MixedGraph returned from the previous phase
sepSet	the separation set for independent couples, built during the iterations of the phase
rank	the heap of ranks of the algorithm

Definition at line 253 of file Miic.cpp.

                                                                                                 {
      // if no triples to further examine pass
      CondRanking best;
      Size        steps_init = current_step_;
      Size        steps_iter = rank.size();
 
      try {
        while (rank.top().second > 0.5) {
          best = rank.pop();
 
          const NodeId          x  = std::get< 0 >(*(best.first));
          const NodeId          y  = std::get< 1 >(*(best.first));
          const NodeId          z  = std::get< 2 >(*(best.first));
          std::vector< NodeId > ui = std::move(std::get< 3 >(*(best.first)));
 
          ui.push_back(z);
          const double i_xy_ui = mutualInformation.score(x, y, ui);
          if (i_xy_ui < 0) {
            graph.eraseEdge(Edge(x, y));
            GUM_SL_EMIT(x,
                        y,
                        "Remove " << x << " - " << y,
                        "Independent based on MutualInformation knowing Sep "
                            << ui << "Mutual information:" << i_xy_ui)
 
            sepSet.insert(std::make_pair(x, y), std::move(ui));
          } else {
            findBestContributor_(x, y, ui, graph, mutualInformation, rank);
          }
 
          delete best.first;
 
          ++current_step_;
          if (onProgress.hasListener()) {
            GUM_EMIT3(onProgress,
                      (current_step_ * 66) / (steps_init + steps_iter),
                      0.,
                      timer_.step());
          }
        }
      } catch (...) {}   // here, rank is empty
      current_step_ = steps_init + steps_iter;
      if (onProgress.hasListener()) { GUM_EMIT3(onProgress, 66, 0., timer_.step()); }
      current_step_ = steps_init + steps_iter;
    }

References gum::ApproximationScheme::current_step_, gum::EdgeGraphPart::eraseEdge(), findBestContributor_(), GUM_EMIT3, GUM_SL_EMIT, gum::IApproximationSchemeConfiguration::onProgress, gum::Heap< Val, Cmp >::pop(), gum::learning::CorrectedMutualInformation::score(), gum::Heap< Val, Cmp >::size(), gum::ApproximationScheme::timer_, and gum::Heap< Val, Cmp >::top().

Referenced by learnMixedStructure(), and learnSkeleton().

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latentVariables()

const std::vector< Arc > gum::learning::Miic::latentVariables

(

)

const

get the list of arcs hiding latent variables

Definition at line 911 of file Miic.cpp.

{ return _latentCouples_; }

References _latentCouples_.

learnBN()

template<typename GUM_SCALAR, typename GRAPH_CHANGES_SELECTOR, typename PARAM_ESTIMATOR>

BayesNet< GUM_SCALAR > gum::learning::Miic::learnBN	(	GRAPH_CHANGES_SELECTOR &	selector,
		PARAM_ESTIMATOR &	estimator,
		DAG	initial_dag = DAG() )

learns the structure and the parameters of a BN

Parameters

selector	A selector class that computes the best changes that can be applied and that enables the user to get them very easily. Typically, the selector is a GraphChangesSelector4DiGraph<SCORE, STRUCT_CONSTRAINT, GRAPH_CHANGES_GENERATOR>.
estimator	A estimator.
names	The variables names.
modal	the domain sizes of the random variables observed in the database
translator	The cell translator to use.
initial_dag	the DAG we start from for our learning

Definition at line 190 of file Miic.cpp.

                                                                              {
      return DAG2BNLearner::createBN< GUM_SCALAR >(estimator,
                                                   learnStructure(selector, initial_dag));
    }

References gum::learning::DAG2BNLearner::createBN(), and learnStructure().

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learnMixedStructure()

MixedGraph gum::learning::Miic::learnMixedStructure	(	CorrectedMutualInformation &	mutualInformation,
		MixedGraph	graph )

learns the structure of a MixedGraph (Meek rules not used here).

Parameters

mutualInformation	A mutual information instance that will do the computations and has loaded the database.
graph	the MixedGraph we start from for the learning

Definition at line 147 of file Miic.cpp.

                                                                            {
      // GUM_TRACE_VAR(mutualInformation.score(gum::NodeId (5), gum::NodeId (6)) );
 
      timer_.reset();
      current_step_ = 0;
 
      // clear the vector of latent arcs to be sure
      _latentCouples_.clear();

      Heap< CondRanking, GreaterPairOn2nd > rank;

      HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > sep_set;
 
      initiation_(mutualInformation, graph, sep_set, rank);
 
      iteration_(mutualInformation, graph, sep_set, rank);
 
      orientationMiic_(mutualInformation, graph, sep_set);
      // Propagates existing orientations thanks to Meek rules
 
      return meekRules_.propagate(graph);
      // todo : check meekRules_.choices()
    }

References _latentCouples_, gum::ApproximationScheme::current_step_, initiation_(), iteration_(), meekRules_, orientationMiic_(), and gum::ApproximationScheme::timer_.

Referenced by learnPDAG(), and learnStructure().

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learnPDAG()

PDAG gum::learning::Miic::learnPDAG	(	CorrectedMutualInformation &	mutualInformation,
		MixedGraph	graph )

learns the structure of an Essential Graph

learns the structure of a PDAG from à MixedGraph. It returns a MixedGraph with the constraints of a PDAG, to avoid changing the dependencies in the other methods of the MIIC class.

Parameters

mutualInformation	A mutual information instance that will do the computations and has loaded the database.
graph	the MixedGraph we start from for the learning

Definition at line 178 of file Miic.cpp.

                                                                               {
      return meekRules_.propagateToCPDAG(learnMixedStructure(I, initialGraph));
      // @todo: check the meekRules.choices()
    }

References learnMixedStructure(), and meekRules_.

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learnSkeleton()

MixedGraph gum::learning::Miic::learnSkeleton	(	CorrectedMutualInformation &	mutualInformation,
		MixedGraph	graph )

learns the skeleton of a MixedGraph (no orientation).

learns the skeleton of a Graph from a fully connected graph

Parameters

mutualInformation	A mutual information instance that will do the computations and has loaded the database.
graph	the MixedGraph we start from for the learning

Definition at line 124 of file Miic.cpp.

                                                                      {
      // GUM_TRACE_VAR(mutualInformation.score(gum::NodeId (5), gum::NodeId (6)) );
 
      timer_.reset();
      current_step_ = 0;
 
      // clear the vector of latent arcs to be sure
      _latentCouples_.clear();

      Heap< CondRanking, GreaterPairOn2nd > rank;

      HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > sep_set;
 
      initiation_(mutualInformation, graph, sep_set, rank);
 
      iteration_(mutualInformation, graph, sep_set, rank);
 
      return graph;
    }

References _latentCouples_, gum::ApproximationScheme::current_step_, initiation_(), iteration_(), and gum::ApproximationScheme::timer_.

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learnStructure()

DAG gum::learning::Miic::learnStructure	(	CorrectedMutualInformation &	I,
		MixedGraph	graph )

learns the structure of a Bayesian network, i.e. a DAG, by first learning an Essential graph and then directing the remaining edges.

Parameters

I	A mutual information instance that will do the computations and has loaded the database
graph	the MixedGraph we start from for the learning

Definition at line 183 of file Miic.cpp.

                                                                                   {
      return meekRules_.propagateToDAG(learnMixedStructure(I, initialGraph));
      // @todo: check the meekRules.choices()
    }

References learnMixedStructure(), and meekRules_.

Referenced by learnBN().

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maxIter()

INLINE Size gum::ApproximationScheme::maxIter ( ) const

overridevirtualinherited

Returns the criterion on number of iterations.

Returns

Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 112 of file approximationScheme_inl.h.

{ return max_iter_; }

References max_iter_.

maxTime()

INLINE double gum::ApproximationScheme::maxTime ( ) const

overridevirtualinherited

Returns the timeout (in seconds).

Returns

Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 133 of file approximationScheme_inl.h.

{ return max_time_; }

References max_time_.

messageApproximationScheme()

INLINE std::string gum::IApproximationSchemeConfiguration::messageApproximationScheme ( ) const

inherited

Returns the approximation scheme message.

Returns

Returns the approximation scheme message.

Definition at line 59 of file IApproximationSchemeConfiguration_inl.h.

                                                                                {
    std::stringstream s;
 
    switch (stateApproximationScheme()) {
      case ApproximationSchemeSTATE::Continue : s << "in progress"; break;
 
      case ApproximationSchemeSTATE::Epsilon : s << "stopped with epsilon=" << epsilon(); break;
 
      case ApproximationSchemeSTATE::Rate : s << "stopped with rate=" << minEpsilonRate(); break;
 
      case ApproximationSchemeSTATE::Limit : s << "stopped with max iteration=" << maxIter(); break;
 
      case ApproximationSchemeSTATE::TimeLimit : s << "stopped with timeout=" << maxTime(); break;
 
      case ApproximationSchemeSTATE::Stopped : s << "stopped on request"; break;
 
      case ApproximationSchemeSTATE::Undefined : s << "undefined state"; break;
    };
 
    return s.str();
  }

References Continue, Epsilon, epsilon(), Limit, maxIter(), maxTime(), minEpsilonRate(), Rate, stateApproximationScheme(), Stopped, TimeLimit, and Undefined.

Referenced by gum::credal::InferenceEngine< GUM_SCALAR >::getApproximationSchemeMsg(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::stateApproximationScheme().

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minEpsilonRate()

INLINE double gum::ApproximationScheme::minEpsilonRate ( ) const

overridevirtualinherited

Returns the value of the minimal epsilon rate.

Returns

Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 92 of file approximationScheme_inl.h.

{ return min_rate_eps_; }

References min_rate_eps_.

nbrIterations()

INLINE Size gum::ApproximationScheme::nbrIterations ( ) const

overridevirtualinherited

Returns the number of iterations.

Returns

Returns the number of iterations.

Exceptions

OperationNotAllowed

Raised if the scheme did not perform.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 169 of file approximationScheme_inl.h.

                                                       {
    if (stateApproximationScheme() == ApproximationSchemeSTATE::Undefined) {
      GUM_ERROR(OperationNotAllowed, "state of the approximation scheme is undefined")
    }
 
    return current_step_;
  }

References current_step_, GUM_ERROR, stateApproximationScheme(), and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

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operator=() [1/2]

Miic & gum::learning::Miic::operator=

(

const Miic &

from

)

copy operator

Definition at line 82 of file Miic.cpp.

                                        {
      ApproximationScheme::operator=(from);
      return *this;
    }

References Miic().

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operator=() [2/2]

Miic & gum::learning::Miic::operator=

(

Miic &&

from

)

move operator

Definition at line 88 of file Miic.cpp.

                                   {
      ApproximationScheme::operator=(std::move(from));
      return *this;
    }

References Miic().

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orientationMiic_()

void gum::learning::Miic::orientationMiic_ ( CorrectedMutualInformation & mutualInformation, MixedGraph & graph, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > & sepSet )

protected

Orientation phase from the MIIC algorithm, returns a mixed graph that may contain circles.

The orientation protocol of MIIC.

Parameters

mutualInformation	A mutual information instance that will do the computations and has loaded the database.
graph	the MixedGraph returned from the previous phase
sepSet	the separation set for independent couples, built during the previous phase

Definition at line 309 of file Miic.cpp.

                                                                                   {
      // structure to store the orientations marks -, o, or >,
      // Considers the head of the arc/edge: first node -* second node
      HashTable< std::pair< NodeId, NodeId >, char > marks = _initialMarks_;
 
      // marks always correspond to the head of the arc/edge. - is for a forbidden
      // arc, > for a mandatory arc
      // we start @by adding the mandatory arcs
 
 
      /* for (auto& iter = mandaArcs.begin(); iter != mandaArcs.end(); ++iter) {
      //   if (graph.existsEdge(iter.value_type().head(), iter.tail()) &&
      //   _isMandatoryArc_(iter.head(), iter.tail())) {
      //     graph.eraseEdge(Edge(iter.head(), iter.tail()));
      //     graph.addArc(iter.head(), iter.tail());
      //   }
       }*/
 
      for (auto& arc: _mandatoryGraph_.arcs()) {
        // Check if the edge exists in graph
        if (graph.existsEdge(arc.head(), arc.tail())) {
          graph.eraseEdge(Edge(arc.head(), arc.tail()));
          GUM_SL_EMIT(arc.tail(),
                      arc.head(),
                      "Add Arc" << arc.tail() << "->" << arc.head(),
                      "Mandatory")
          graph.addArc(arc.tail(), arc.head());
          marks.insert({arc.tail(), arc.head()}, '>');
          marks.insert({arc.head(), arc.tail()}, '-');
        } else {
          // If the edge doesn't exist we add the arc as is to graph
          graph.addArc(arc.tail(), arc.head());
          marks.insert({arc.tail(), arc.head()}, '>');
          marks.insert({arc.head(), arc.tail()}, '-');
        }
      }
 
      for (Arc arc: _forbiddenGraph_.arcs()) {
        // Check if the edge exists in graph
        if (graph.existsEdge(Edge(arc.tail(), arc.head()))) {
          graph.eraseEdge(Edge(arc.tail(), arc.head()));
 
          // Arc is forced in the other direction
          graph.addArc(arc.head(), arc.tail());
          GUM_SL_EMIT(arc.head(),
                      arc.tail(),
                      "Add Arc" << arc.head() << "->" << arc.tail(),
                      "Forbidden in the other orientation")
          marks.insert({arc.tail(), arc.head()}, '-');
          marks.insert({arc.head(), arc.tail()}, '>');
        }
      }
 
      std::vector< ProbabilisticRanking > proba_triples
          = unshieldedTriplesMiic_(graph, mutualInformation, sepSet, marks);
 
      const Size steps_orient = proba_triples.size();
      Size       past_steps   = current_step_;
 
      ProbabilisticRanking best;
      if (steps_orient > 0) { best = proba_triples[0]; }
 
      while (!proba_triples.empty() && std::max(std::get< 2 >(best), std::get< 3 >(best)) >= 0.5) {
        const NodeId x = std::get< 0 >(*std::get< 0 >(best));
        const NodeId y = std::get< 1 >(*std::get< 0 >(best));
        const NodeId z = std::get< 2 >(*std::get< 0 >(best));
 
        const double i3 = std::get< 1 >(best);
 
        const double p1 = std::get< 2 >(best);
        const double p2 = std::get< 3 >(best);
 
        if (i3 <= 0) {
          _orientingVstructureMiic_(graph, marks, x, y, z, p1, p2);
        } else {
          _propagatingOrientationMiic_(graph, marks, x, y, z, p1, p2);
        }
 
        delete std::get< 0 >(best);
        proba_triples.erase(proba_triples.begin());
        // actualisation of the list of triples
        proba_triples = updateProbaTriples_(graph, proba_triples);
 
        if (!proba_triples.empty()) best = proba_triples[0];
 
        ++current_step_;
        if (onProgress.hasListener()) {
          GUM_EMIT3(onProgress,
                    (current_step_ * 100) / (steps_orient + past_steps),
                    0.,
                    timer_.step());
        }
      }   // while
 
      // erasing the double headed arcs
      for (auto iter = _latentCouples_.rbegin(); iter != _latentCouples_.rend(); ++iter) {
        graph.eraseArc(Arc(iter->head(), iter->tail()));
        if (_existsDirectedPath_(graph, iter->head(), iter->tail())) {
          // if we find a cycle, we force the competing edge
          graph.addArc(iter->head(), iter->tail());
          graph.eraseArc(Arc(iter->tail(), iter->head()));
          *iter = Arc(iter->head(), iter->tail());
        }
      }
 
      if (onProgress.hasListener()) { GUM_EMIT3(onProgress, 100, 0., timer_.step()); }
    }

References _existsDirectedPath_(), _forbiddenGraph_, _initialMarks_, _latentCouples_, _mandatoryGraph_, _orientingVstructureMiic_(), _propagatingOrientationMiic_(), gum::DiGraph::addArc(), gum::ApproximationScheme::current_step_, gum::ArcGraphPart::eraseArc(), gum::EdgeGraphPart::eraseEdge(), gum::EdgeGraphPart::existsEdge(), GUM_EMIT3, GUM_SL_EMIT, gum::HashTable< Key, Val >::insert(), gum::IApproximationSchemeConfiguration::onProgress, gum::ApproximationScheme::timer_, unshieldedTriplesMiic_(), and updateProbaTriples_().

Referenced by learnMixedStructure().

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orientDoubleHeadedArcs_()

void gum::learning::Miic::orientDoubleHeadedArcs_ ( MixedGraph & mg )

protected

Orient double headed arcs to avoid cycles.

Parameters

mg	the MixedGraph from which the double headed arcs will be oriented.

Definition at line 653 of file Miic.cpp.

                                                     {
      gum::ArcSet L;   // create a set of all double-headed arcs
      for (gum::NodeId x: mg.nodes())
        for (NodeId y: mg.parents(x))
          // If there is a mutual parent-child relationship, add the arc to the set
          if (mg.parents(y).contains(x)) {
            if (x > y) {
              continue;   // Avoid duplicate arcs by considering only one direction
            } else {
              L.insert(gum::Arc(x, y));
            }
          }
 
      // If there are double-headed arcs
      if (not L.empty()) {
        while (true) {
          bool withdrawFlag_L = false;
          for (auto arc: ArcSet(L)) {
            bool tail_head        = _existsDirectedPath_(mg, arc.tail(), arc.head());
            bool head_tail        = _existsDirectedPath_(mg, arc.head(), arc.tail());
            bool withdrawFlag_arc = false;
 
            // Case 1: There is already a path from tail to head and no path from head to tail
            if (tail_head && !head_tail) {
              // Erase the arc from head to tail to avoid cycles
              mg.eraseArc(Arc(arc.head(), arc.tail()));
              withdrawFlag_arc = true;
 
              // Case 2: There is already a path from head to tail and no path from tail to head
            } else if (!tail_head && head_tail) {
              // Erase the arc from tail to head to avoid cycles
              mg.eraseArc(Arc(arc.tail(), arc.head()));
              withdrawFlag_arc = true;
 
              // Case 3: There is no path between tail and head
            } else if (!tail_head && !head_tail) {
              // Choose an arbitrary orientation and erase the corresponding arc
              mg.eraseArc(Arc(arc.head(), arc.tail()));
              withdrawFlag_arc = true;
            }
 
            // Remove the arc from the set if it was processed
            if (withdrawFlag_arc) {
              L.erase(arc);
              withdrawFlag_L = true;
            }
          }
          // If all double-headed arcs are processed, exit the loop
          if (L.empty()) { break; }
 
          // If no arcs were withdrawn, erase an arbitrary double arc in the graph (the first one in
          // L). Hoping the situation will improve. ┐(￣ヘ￣)┌ If we arrive here, it's because the
          // double-headed arc creates cycles in both directions
          if (!withdrawFlag_L) {
            auto it  = L.begin();
            auto arc = *it;
            mg.eraseArc(Arc(arc.head(), arc.tail()));
            mg.eraseArc(Arc(arc.tail(), arc.head()));
            L.erase(arc);
          }
        }
      }
    }

References _existsDirectedPath_(), gum::Set< Key >::begin(), gum::Set< Key >::contains(), gum::Set< Key >::empty(), gum::Set< Key >::erase(), gum::ArcGraphPart::eraseArc(), gum::Set< Key >::insert(), gum::NodeGraphPart::nodes(), and gum::ArcGraphPart::parents().

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periodSize()

INLINE Size gum::ApproximationScheme::periodSize ( ) const

overridevirtualinherited

Returns the period size.

Returns

Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 155 of file approximationScheme_inl.h.

{ return period_size_; }

References period_size_.

remainingBurnIn()

INLINE Size gum::ApproximationScheme::remainingBurnIn ( ) const

inherited

Returns the remaining burn in.

Returns

Returns the remaining burn in.

Definition at line 212 of file approximationScheme_inl.h.

                                                         {
    if (burn_in_ > current_step_) {
      return burn_in_ - current_step_;
    } else {
      return 0;
    }
  }

References burn_in_, and current_step_.

setEpsilon()

INLINE void gum::ApproximationScheme::setEpsilon ( double eps )

overridevirtualinherited

Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|.

If the criterion was disabled it will be enabled.

Parameters

eps	The new epsilon value.

Exceptions

OutOfBounds

Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Reimplemented in gum::learning::EMApproximationScheme.

Definition at line 63 of file approximationScheme_inl.h.

                                                        {
    if (eps < 0.) { GUM_ERROR(OutOfBounds, "eps should be >=0") }
 
    eps_         = eps;
    enabled_eps_ = true;
  }

References enabled_eps_, eps_, and GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::EMApproximationScheme::setEpsilon().

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setForbiddenGraph()

void gum::learning::Miic::setForbiddenGraph

(

gum::DiGraph

forbidGraph

)

Set ForbiddenGraph (resp. MadatoryGraph) which contains the forbidden (resp. mandatory) arcs.

Definition at line 974 of file Miic.cpp.

                                                             {
      this->_forbiddenGraph_ = forbidGraph;
    }

References _forbiddenGraph_.

setMandatoryGraph()

void gum::learning::Miic::setMandatoryGraph

(

gum::DAG

mandaGraph

)

Definition at line 972 of file Miic.cpp.

{ this->_mandatoryGraph_ = mandaGraph; }

References _mandatoryGraph_.

setMaxIndegree()

void gum::learning::Miic::setMaxIndegree

(

gum::Size

n

)

Definition at line 978 of file Miic.cpp.

{ this->_maxIndegree_ = n; }

References _maxIndegree_.

setMaxIter()

INLINE void gum::ApproximationScheme::setMaxIter ( Size max )

overridevirtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfBounds

Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 105 of file approximationScheme_inl.h.

                                                      {
    if (max < 1) { GUM_ERROR(OutOfBounds, "max should be >=1") }
    max_iter_         = max;
    enabled_max_iter_ = true;
  }

References enabled_max_iter_, GUM_ERROR, and max_iter_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

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setMaxTime()

INLINE void gum::ApproximationScheme::setMaxTime ( double timeout )

overridevirtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout

The timeout value in seconds.

Exceptions

OutOfBounds

Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 126 of file approximationScheme_inl.h.

                                                            {
    if (timeout <= 0.) { GUM_ERROR(OutOfBounds, "timeout should be >0.") }
    max_time_         = timeout;
    enabled_max_time_ = true;
  }

References enabled_max_time_, GUM_ERROR, and max_time_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

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setMinEpsilonRate()

INLINE void gum::ApproximationScheme::setMinEpsilonRate ( double rate )

overridevirtualinherited

Given that we approximate f(t), stopping criterion on d/dt(|f(t+1)-f(t)|).

If the criterion was disabled it will be enabled

Parameters

rate	The minimal epsilon rate.

Exceptions

OutOfBounds

if rate<0

Implements gum::IApproximationSchemeConfiguration.

Reimplemented in gum::learning::EMApproximationScheme.

Definition at line 84 of file approximationScheme_inl.h.

                                                                {
    if (rate < 0) { GUM_ERROR(OutOfBounds, "rate should be >=0") }
 
    min_rate_eps_         = rate;
    enabled_min_rate_eps_ = true;
  }

References enabled_min_rate_eps_, GUM_ERROR, and min_rate_eps_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::EMApproximationScheme::setMinEpsilonRate().

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setPeriodSize()

INLINE void gum::ApproximationScheme::setPeriodSize ( Size p )

overridevirtualinherited

How many samples between two stopping is enable.

Parameters

p	The new period value.

Exceptions

OutOfBounds

Raised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 149 of file approximationScheme_inl.h.

                                                       {
    if (p < 1) { GUM_ERROR(OutOfBounds, "p should be >=1") }
 
    period_size_ = p;
  }

References GUM_ERROR, and period_size_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

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setVerbosity()

INLINE void gum::ApproximationScheme::setVerbosity ( bool v )

overridevirtualinherited

Set the verbosity on (true) or off (false).

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 158 of file approximationScheme_inl.h.

{ verbosity_ = v; }

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

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startOfPeriod()

INLINE bool gum::ApproximationScheme::startOfPeriod ( ) const

inherited

Returns true if we are at the beginning of a period (compute error is mandatory).

Returns

Returns true if we are at the beginning of a period (compute error is mandatory).

Definition at line 199 of file approximationScheme_inl.h.

                                                       {
    if (current_step_ < burn_in_) { return false; }
 
    if (period_size_ == 1) { return true; }
 
    return ((current_step_ - burn_in_) % period_size_ == 0);
  }

References burn_in_, and current_step_.

stateApproximationScheme()

INLINE IApproximationSchemeConfiguration::ApproximationSchemeSTATE gum::ApproximationScheme::stateApproximationScheme ( ) const

overridevirtualinherited

Returns the approximation scheme state.

Returns

Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 164 of file approximationScheme_inl.h.

                                                             {
    return current_state_;
  }

References current_state_.

Referenced by history(), and nbrIterations().

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stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 221 of file approximationScheme_inl.h.

                                                           {
    if (current_state_ == ApproximationSchemeSTATE::Continue) {
      stopScheme_(ApproximationSchemeSTATE::Stopped);
    }
  }

Referenced by gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

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stopScheme_()

INLINE void gum::ApproximationScheme::stopScheme_ ( ApproximationSchemeSTATE new_state )

privateinherited

Stop the scheme given a new state.

Parameters

new_state

The scheme new state.

Definition at line 301 of file approximationScheme_inl.h.

                                                                                 {
    if (new_state == ApproximationSchemeSTATE::Continue) { return; }
 
    if (new_state == ApproximationSchemeSTATE::Undefined) { return; }
 
    current_state_ = new_state;
    timer_.pause();
 
    if (onStop.hasListener()) { GUM_EMIT1(onStop, messageApproximationScheme()); }
  }

References gum::IApproximationSchemeConfiguration::Continue, current_state_, and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::disableMaxIter(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::disableMaxTime(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::isEnabledMaxIter(), gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::maxTime(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::setPeriodSize().

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unshieldedTriples_()

std::vector< Ranking > gum::learning::Miic::unshieldedTriples_ ( const MixedGraph & graph, CorrectedMutualInformation & mutualInformation, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > & sepSet )

protected

gets the list of unshielded triples in the graph in decreasing value of |I'(x, y, z|{ui})|

Parameters

graph	graph in which to find the triples
I	mutual information object to compute the scores
sep_set	hashtable storing the separation sets for pairs of variables

Definition at line 794 of file Miic.cpp.

                                                                                   {
      std::vector< Ranking > triples;
      for (NodeId z: graph) {
        for (NodeId x: graph.neighbours(z)) {
          for (NodeId y: graph.neighbours(z)) {
            if (y < x && !graph.existsEdge(x, y)) {
              std::vector< NodeId >       ui;
              std::pair< NodeId, NodeId > key     = {x, y};
              std::pair< NodeId, NodeId > rev_key = {y, x};
              if (sepSet.exists(key)) {
                ui = sepSet[key];
              } else if (sepSet.exists(rev_key)) {
                ui = sepSet[rev_key];
              }
              // remove z from ui if it's present
              const auto iter_z_place = std::find(ui.begin(), ui.end(), z);
              if (iter_z_place != ui.end()) { ui.erase(iter_z_place); }
 
              double  Ixyz_ui = mutualInformation.score(x, y, z, ui);
              Ranking triple;
              auto    tup   = new ThreePoints{x, y, z};
              triple.first  = tup;
              triple.second = Ixyz_ui;
              triples.push_back(triple);
            }
          }
        }
      }
      std::sort(triples.begin(), triples.end(), GreaterAbsPairOn2nd());
      return triples;
    }

References gum::EdgeGraphPart::existsEdge(), gum::EdgeGraphPart::neighbours(), and gum::learning::CorrectedMutualInformation::score().

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unshieldedTriplesMiic_()

std::vector< ProbabilisticRanking > gum::learning::Miic::unshieldedTriplesMiic_ ( const MixedGraph & graph, CorrectedMutualInformation & mutualInformation, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > & sepSet, HashTable< std::pair< NodeId, NodeId >, char > & marks )

protected

gets the list of unshielded triples in the graph in decreasing value of |I'(x, y, z|{ui})|, prepares the orientation matrix for MIIC

Parameters

graph	graph in which to find the triples
I	mutual information object to compute the scores
sep_set	hashtable storing the separation sets for pairs of variables
marks	hashtable containing the orientation marks for edges

Definition at line 831 of file Miic.cpp.

                                                                                    {
      std::vector< ProbabilisticRanking > triples;
      for (NodeId z: graph) {
        for (NodeId x: graph.neighbours(z)) {
          for (NodeId y: graph.neighbours(z)) {
            if (y < x && !graph.existsEdge(x, y)) {
              std::vector< NodeId >       ui;
              std::pair< NodeId, NodeId > key     = {x, y};
              std::pair< NodeId, NodeId > rev_key = {y, x};
              if (sepSet.exists(key)) {
                ui = sepSet[key];
              } else if (sepSet.exists(rev_key)) {
                ui = sepSet[rev_key];
              }
              // remove z from ui if it's present
              const auto iter_z_place = std::find(ui.begin(), ui.end(), z);
              if (iter_z_place != ui.end()) { ui.erase(iter_z_place); }
 
              const double         Ixyz_ui = mutualInformation.score(x, y, z, ui);
              auto                 tup     = new ThreePoints{x, y, z};
              ProbabilisticRanking triple{tup, Ixyz_ui, 0.5, 0.5};
              triples.push_back(triple);
              if (!marks.exists({x, z})) { marks.insert({x, z}, 'o'); }
              if (!marks.exists({z, x})) { marks.insert({z, x}, 'o'); }
              if (!marks.exists({y, z})) { marks.insert({y, z}, 'o'); }
              if (!marks.exists({z, y})) { marks.insert({z, y}, 'o'); }
            }
          }
        }
      }
      triples = updateProbaTriples_(graph, triples);
      std::sort(triples.begin(), triples.end(), GreaterTupleOnLast());
      return triples;
    }

References gum::EdgeGraphPart::existsEdge(), gum::EdgeGraphPart::neighbours(), gum::learning::CorrectedMutualInformation::score(), and updateProbaTriples_().

Referenced by orientationMiic_().

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updateApproximationScheme()

INLINE void gum::ApproximationScheme::updateApproximationScheme ( unsigned int incr = 1 )

inherited

Update the scheme w.r.t the new error and increment steps.

Parameters

incr	The new increment steps.

Definition at line 208 of file approximationScheme_inl.h.

                                                                              {
    current_step_ += incr;
  }

References current_step_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByOrderedArcs_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByRandomOrder_(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

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updateProbaTriples_()

std::vector< ProbabilisticRanking > gum::learning::Miic::updateProbaTriples_ ( const MixedGraph & graph, std::vector< ProbabilisticRanking > probaTriples )

protected

Gets the orientation probabilities like MIIC for the orientation phase.

Parameters

graph	graph in which to find the triples
proba_triples	probabilities for the different triples to update

Definition at line 872 of file Miic.cpp.

                                                                                  {
      for (auto& triple: probaTriples) {
        NodeId x, y, z;
        x                 = std::get< 0 >(*std::get< 0 >(triple));
        y                 = std::get< 1 >(*std::get< 0 >(triple));
        z                 = std::get< 2 >(*std::get< 0 >(triple));
        const double Ixyz = std::get< 1 >(triple);
        double       Pxz  = std::get< 2 >(triple);
        double       Pyz  = std::get< 3 >(triple);
 
        if (Ixyz <= 0) {
          const double expo = std::exp(Ixyz);
          const double P0   = (1 + expo) / (1 + 3 * expo);
          // distinguish between the initialization and the update process
          if (Pxz == Pyz && Pyz == 0.5) {
            std::get< 2 >(triple) = P0;
            std::get< 3 >(triple) = P0;
          } else {
            if (graph.existsArc(x, z) && Pxz >= P0) {
              std::get< 3 >(triple) = Pxz * (1 / (1 + expo) - 0.5) + 0.5;
            } else if (graph.existsArc(y, z) && Pyz >= P0) {
              std::get< 2 >(triple) = Pyz * (1 / (1 + expo) - 0.5) + 0.5;
            }
          }
        } else {
          const double expo = std::exp(-Ixyz);
          if (graph.existsArc(x, z) && Pxz >= 0.5) {
            std::get< 3 >(triple) = Pxz * (1 / (1 + expo) - 0.5) + 0.5;
          } else if (graph.existsArc(y, z) && Pyz >= 0.5) {
            std::get< 2 >(triple) = Pyz * (1 / (1 + expo) - 0.5) + 0.5;
          }
        }
      }
      std::sort(probaTriples.begin(), probaTriples.end(), GreaterTupleOnLast());
      return probaTriples;
    }

References gum::ArcGraphPart::existsArc().

Referenced by orientationMiic_(), and unshieldedTriplesMiic_().

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verbosity()

INLINE bool gum::ApproximationScheme::verbosity ( ) const

overridevirtualinherited

Returns true if verbosity is enabled.

Returns

Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 160 of file approximationScheme_inl.h.

{ return verbosity_; }

References verbosity_.

Referenced by ApproximationScheme(), and gum::learning::EMApproximationScheme::EMApproximationScheme().

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Member Data Documentation

_arcProbas_

ArcProperty< double > gum::learning::Miic::_arcProbas_

private

Storing the propabilities for each arc set in the graph.

Definition at line 355 of file Miic.h.

Referenced by _orientingVstructureMiic_(), and _propagatingOrientationMiic_().

_emptySet_

const std::vector< NodeId > gum::learning::Miic::_emptySet_

private

an empty conditioning set

Definition at line 344 of file Miic.h.

Referenced by initiation_().

_forbiddenGraph_

gum::DiGraph gum::learning::Miic::_forbiddenGraph_

private

Graph that contains the forbidden arcs.

Definition at line 364 of file Miic.h.

Referenced by isForbiddenArc_(), isForbiddenEdge_(), orientationMiic_(), and setForbiddenGraph().

_initialMarks_

HashTable< std::pair< NodeId, NodeId >, char > gum::learning::Miic::_initialMarks_

private

Initial marks for the orientation phase, used to convey constraints.

Definition at line 358 of file Miic.h.

Referenced by addConstraints(), and orientationMiic_().

_latentCouples_

std::vector< Arc > gum::learning::Miic::_latentCouples_

private

an empty vector of arcs

Definition at line 346 of file Miic.h.

Referenced by _isNotLatentCouple_(), _orientingVstructureMiic_(), _propagatingOrientationMiic_(), latentVariables(), learnMixedStructure(), learnSkeleton(), and orientationMiic_().

_mandatoryGraph_

gum::DAG gum::learning::Miic::_mandatoryGraph_

private

Graph that contains the mandatories arcs.

Definition at line 361 of file Miic.h.

Referenced by orientationMiic_(), and setMandatoryGraph().

_maxIndegree_

gum::Size gum::learning::Miic::_maxIndegree_

private

maximum number of parents

Definition at line 349 of file Miic.h.

Referenced by isMaxIndegree_(), and setMaxIndegree().

_maxLog_

int gum::learning::Miic::_maxLog_ = 100

private

Fixes the maximum log that we accept in exponential computations.

Definition at line 342 of file Miic.h.

Referenced by Miic(), Miic(), and findBestContributor_().

_size_

Size gum::learning::Miic::_size_

private

size of the database

Definition at line 352 of file Miic.h.

Referenced by Miic(), Miic(), Miic(), and Miic().

burn_in_

Size gum::ApproximationScheme::burn_in_

protectedinherited

Number of iterations before checking stopping criteria.

Definition at line 423 of file approximationScheme.h.

Referenced by ApproximationScheme(), gum::GibbsBNdistance< GUM_SCALAR >::burnIn(), gum::GibbsSampling< GUM_SCALAR >::burnIn(), remainingBurnIn(), gum::GibbsBNdistance< GUM_SCALAR >::setBurnIn(), gum::GibbsSampling< GUM_SCALAR >::setBurnIn(), and startOfPeriod().

current_epsilon_

double gum::ApproximationScheme::current_epsilon_

protectedinherited

Current epsilon.

Definition at line 378 of file approximationScheme.h.

Referenced by initApproximationScheme().

current_rate_

double gum::ApproximationScheme::current_rate_

protectedinherited

Current rate.

Definition at line 384 of file approximationScheme.h.

Referenced by initApproximationScheme().

current_state_

ApproximationSchemeSTATE gum::ApproximationScheme::current_state_

protectedinherited

The current state.

Definition at line 393 of file approximationScheme.h.

Referenced by ApproximationScheme(), initApproximationScheme(), stateApproximationScheme(), and stopScheme_().

current_step_

Size gum::ApproximationScheme::current_step_

protectedinherited

The current step.

Definition at line 387 of file approximationScheme.h.

Referenced by initApproximationScheme(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::Miic::learnMixedStructure(), gum::learning::SimpleMiic::learnMixedStructure(), gum::learning::Miic::learnSkeleton(), nbrIterations(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), gum::learning::SimpleMiic::orientationMiic_(), remainingBurnIn(), startOfPeriod(), and updateApproximationScheme().

enabled_eps_

bool gum::ApproximationScheme::enabled_eps_

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 402 of file approximationScheme.h.

Referenced by ApproximationScheme(), disableEpsilon(), enableEpsilon(), isEnabledEpsilon(), and setEpsilon().

enabled_max_iter_

bool gum::ApproximationScheme::enabled_max_iter_

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 420 of file approximationScheme.h.

Referenced by ApproximationScheme(), disableMaxIter(), enableMaxIter(), isEnabledMaxIter(), and setMaxIter().

enabled_max_time_

bool gum::ApproximationScheme::enabled_max_time_

protectedinherited

If true, the timeout is enabled.

Definition at line 414 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), disableMaxTime(), enableMaxTime(), isEnabledMaxTime(), and setMaxTime().

enabled_min_rate_eps_

bool gum::ApproximationScheme::enabled_min_rate_eps_

protectedinherited

If true, the minimal threshold for epsilon rate is enabled.

Definition at line 408 of file approximationScheme.h.

Referenced by ApproximationScheme(), disableMinEpsilonRate(), enableMinEpsilonRate(), isEnabledMinEpsilonRate(), and setMinEpsilonRate().

eps_

double gum::ApproximationScheme::eps_

protectedinherited

Threshold for convergence.

Definition at line 399 of file approximationScheme.h.

Referenced by ApproximationScheme(), epsilon(), and setEpsilon().

history_

std::vector< double > gum::ApproximationScheme::history_

protectedinherited

The scheme history, used only if verbosity == true.

Definition at line 396 of file approximationScheme.h.

last_epsilon_

double gum::ApproximationScheme::last_epsilon_

protectedinherited

Last epsilon value.

Definition at line 381 of file approximationScheme.h.

max_iter_

Size gum::ApproximationScheme::max_iter_

protectedinherited

The maximum iterations.

Definition at line 417 of file approximationScheme.h.

Referenced by ApproximationScheme(), maxIter(), and setMaxIter().

max_time_

double gum::ApproximationScheme::max_time_

protectedinherited

The timeout.

Definition at line 411 of file approximationScheme.h.

Referenced by ApproximationScheme(), maxTime(), and setMaxTime().

meekRules_

gum::MeekRules gum::learning::Miic::meekRules_

protected

Object that can propagates orientations to non-oriented edges.

Definition at line 332 of file Miic.h.

Referenced by learnMixedStructure(), learnPDAG(), and learnStructure().

min_rate_eps_

double gum::ApproximationScheme::min_rate_eps_

protectedinherited

Threshold for the epsilon rate.

Definition at line 405 of file approximationScheme.h.

Referenced by ApproximationScheme(), minEpsilonRate(), and setMinEpsilonRate().

onProgress

Signaler3< Size, double, double > gum::IApproximationSchemeConfiguration::onProgress

inherited

Progression, error and time.

Definition at line 80 of file IApproximationSchemeConfiguration.h.

Referenced by gum::learning::IBNLearner::distributeProgress(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), and gum::learning::SimpleMiic::orientationMiic_().

onStop

Signaler1< const std::string& > gum::IApproximationSchemeConfiguration::onStop

inherited

Criteria messageApproximationScheme.

Definition at line 83 of file IApproximationSchemeConfiguration.h.

Referenced by gum::learning::IBNLearner::distributeStop().

onStructuralModification

Signaler4< gum::NodeId, gum::NodeId, std::string, std::string > gum::learning::Miic::onStructuralModification

Definition at line 401 of file Miic.h.

period_size_

Size gum::ApproximationScheme::period_size_

protectedinherited

Checking criteria frequency.

Definition at line 426 of file approximationScheme.h.

Referenced by ApproximationScheme(), periodSize(), and setPeriodSize().

timer_

Timer gum::ApproximationScheme::timer_

protectedinherited

The timer.

Definition at line 390 of file approximationScheme.h.

Referenced by continueApproximationScheme(), currentTime(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::Miic::learnMixedStructure(), gum::learning::SimpleMiic::learnMixedStructure(), gum::learning::Miic::learnSkeleton(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), and gum::learning::SimpleMiic::orientationMiic_().

verbosity_

bool gum::ApproximationScheme::verbosity_

protectedinherited

If true, verbosity is enabled.

Definition at line 429 of file approximationScheme.h.

Referenced by ApproximationScheme(), and verbosity().

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

• agrum/BN/learning/Miic.h
• agrum/BN/learning/Miic.cpp