gum::learning::LocalSearchWithTabuList Class Reference

The local search with tabu list learning algorithm (for directed graphs) More...

#include <localSearchWithTabuList.h>

Inheritance diagram for gum::learning::LocalSearchWithTabuList:

Collaboration diagram for gum::learning::LocalSearchWithTabuList:

Public Attributes
Signaler3< Size, double, double >	onProgress
	Progression, error and time. More...
Signaler1< std::string >	onStop
	Criteria messageApproximationScheme. More...

Public Member Functions
Constructors / Destructors
	LocalSearchWithTabuList ()
	default constructor More...
	LocalSearchWithTabuList (const LocalSearchWithTabuList &from)
	copy constructor More...
	LocalSearchWithTabuList (LocalSearchWithTabuList &&from)
	move constructor More...
virtual	~LocalSearchWithTabuList ()
	destructor More...
Operators
LocalSearchWithTabuList &	operator= (const LocalSearchWithTabuList &from)
	copy operator More...
LocalSearchWithTabuList &	operator= (LocalSearchWithTabuList &&from)
	move operator More...
Accessors / Modifiers
ApproximationScheme &	approximationScheme ()
	returns the approximation policy of the learning algorithm More...
void	setMaxNbDecreasingChanges (Size nb)
	set the max number of changes decreasing the score that we allow to apply More...
template<typename GRAPH_CHANGES_SELECTOR >
DAG	learnStructure (GRAPH_CHANGES_SELECTOR &selector, DAG initial_dag=DAG())
	learns the structure of a Bayes net More...
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 More...
Getters and setters
void	setEpsilon (double eps)
	Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|. More...
double	epsilon () const
	Returns the value of epsilon. More...
void	disableEpsilon ()
	Disable stopping criterion on epsilon. More...
void	enableEpsilon ()
	Enable stopping criterion on epsilon. More...
bool	isEnabledEpsilon () const
	Returns true if stopping criterion on epsilon is enabled, false otherwise. More...
void	setMinEpsilonRate (double rate)
	Given that we approximate f(t), stopping criterion on d/dt(|f(t+1)-f(t)|). More...
double	minEpsilonRate () const
	Returns the value of the minimal epsilon rate. More...
void	disableMinEpsilonRate ()
	Disable stopping criterion on epsilon rate. More...
void	enableMinEpsilonRate ()
	Enable stopping criterion on epsilon rate. More...
bool	isEnabledMinEpsilonRate () const
	Returns true if stopping criterion on epsilon rate is enabled, false otherwise. More...
void	setMaxIter (Size max)
	Stopping criterion on number of iterations. More...
Size	maxIter () const
	Returns the criterion on number of iterations. More...
void	disableMaxIter ()
	Disable stopping criterion on max iterations. More...
void	enableMaxIter ()
	Enable stopping criterion on max iterations. More...
bool	isEnabledMaxIter () const
	Returns true if stopping criterion on max iterations is enabled, false otherwise. More...
void	setMaxTime (double timeout)
	Stopping criterion on timeout. More...
double	maxTime () const
	Returns the timeout (in seconds). More...
double	currentTime () const
	Returns the current running time in second. More...
void	disableMaxTime ()
	Disable stopping criterion on timeout. More...
void	enableMaxTime ()
	Enable stopping criterion on timeout. More...
bool	isEnabledMaxTime () const
	Returns true if stopping criterion on timeout is enabled, false otherwise. More...
void	setPeriodSize (Size p)
	How many samples between two stopping is enable. More...
Size	periodSize () const
	Returns the period size. More...
void	setVerbosity (bool v)
	Set the verbosity on (true) or off (false). More...
bool	verbosity () const
	Returns true if verbosity is enabled. More...
ApproximationSchemeSTATE	stateApproximationScheme () const
	Returns the approximation scheme state. More...
Size	nbrIterations () const
	Returns the number of iterations. More...
const std::vector< double > &	history () const
	Returns the scheme history. More...
void	initApproximationScheme ()
	Initialise the scheme. More...
bool	startOfPeriod ()
	Returns true if we are at the beginning of a period (compute error is mandatory). More...
void	updateApproximationScheme (unsigned int incr=1)
	Update the scheme w.r.t the new error and increment steps. More...
Size	remainingBurnIn ()
	Returns the remaining burn in. More...
void	stopApproximationScheme ()
	Stop the approximation scheme. More...
bool	continueApproximationScheme (double error)
	Update the scheme w.r.t the new error. More...
Getters and setters
std::string	messageApproximationScheme () const
	Returns the approximation scheme message. More...

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

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

Detailed Description

The local search with tabu list learning algorithm (for directed graphs)

The LocalSearchWithTabuList class implements a greedy search in which we allow applying at most N consecutive graph changes that decrease the score. To prevent infinite loops, when using local search, you should use a structural constraint that includes a tabu list of at least N elements.

Definition at line 60 of file localSearchWithTabuList.h.

Member Enumeration Documentation

ApproximationSchemeSTATE

enum gum::IApproximationSchemeConfiguration::ApproximationSchemeSTATE : char

stronginherited

The different state of an approximation scheme.

Enumerator
Undefined
Continue
Epsilon
Rate
Limit
TimeLimit
Stopped

Definition at line 64 of file IApproximationSchemeConfiguration.h.

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

Constructor & Destructor Documentation

LocalSearchWithTabuList() [1/3]

gum::learning::LocalSearchWithTabuList::LocalSearchWithTabuList

(

)

default constructor

LocalSearchWithTabuList() [2/3]

gum::learning::LocalSearchWithTabuList::LocalSearchWithTabuList

(

const LocalSearchWithTabuList &

from

)

copy constructor

LocalSearchWithTabuList() [3/3]

gum::learning::LocalSearchWithTabuList::LocalSearchWithTabuList

(

LocalSearchWithTabuList &&

from

)

move constructor

~LocalSearchWithTabuList()

virtual gum::learning::LocalSearchWithTabuList::~LocalSearchWithTabuList ( )

virtual

destructor

Member Function Documentation

approximationScheme()

ApproximationScheme& gum::learning::LocalSearchWithTabuList::approximationScheme

(

)

returns the approximation policy of the learning algorithm

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 226 of file approximationScheme_inl.h.

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

                                                                           {
    // 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;
    }
  }

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currentTime()

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

virtualinherited

Returns the current running time in second.

Returns

Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 127 of file approximationScheme_inl.h.

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

{ return timer_.step(); }

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disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

virtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 53 of file approximationScheme_inl.h.

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

{ enabled_eps_ = false; }

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disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

virtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 104 of file approximationScheme_inl.h.

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

{ enabled_max_iter_ = false; }

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disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

virtualinherited

Disable stopping criterion on timeout.

Returns

Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 130 of file approximationScheme_inl.h.

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

{ enabled_max_time_ = false; }

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disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

virtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 78 of file approximationScheme_inl.h.

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

                                                         {
    enabled_min_rate_eps_ = false;
  }

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enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

virtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 56 of file approximationScheme_inl.h.

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

{ enabled_eps_ = true; }

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enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

virtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 107 of file approximationScheme_inl.h.

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

{ enabled_max_iter_ = true; }

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enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

virtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 133 of file approximationScheme_inl.h.

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

{ enabled_max_time_ = true; }

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enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

virtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 83 of file approximationScheme_inl.h.

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

                                                        {
    enabled_min_rate_eps_ = true;
  }

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epsilon()

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

virtualinherited

Returns the value of epsilon.

Returns

Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 50 of file approximationScheme_inl.h.

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

{ return eps_; }

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history()

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

virtualinherited

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 172 of file approximationScheme_inl.h.

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

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

    if (verbosity() == false) {
      GUM_ERROR(OperationNotAllowed, "No history when verbosity=false");
    }

    return history_;
  }

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initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 186 of file approximationScheme_inl.h.

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

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

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isEnabledEpsilon()

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

virtualinherited

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 60 of file approximationScheme_inl.h.

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

                                                          {
    return enabled_eps_;
  }

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isEnabledMaxIter()

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

virtualinherited

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 111 of file approximationScheme_inl.h.

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

                                                          {
    return enabled_max_iter_;
  }

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isEnabledMaxTime()

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

virtualinherited

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 137 of file approximationScheme_inl.h.

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

                                                          {
    return enabled_max_time_;
  }

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isEnabledMinEpsilonRate()

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

virtualinherited

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 89 of file approximationScheme_inl.h.

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

                                                                 {
    return enabled_min_rate_eps_;
  }

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learnBN()

template<typename GUM_SCALAR , typename GRAPH_CHANGES_SELECTOR , typename PARAM_ESTIMATOR >

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

learns the structure and the parameters of a BN

Definition at line 201 of file localSearchWithTabuList_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

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

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learnStructure()

template<typename GRAPH_CHANGES_SELECTOR >

DAG gum::learning::LocalSearchWithTabuList::learnStructure	(	GRAPH_CHANGES_SELECTOR &	selector,
		DAG	initial_dag = DAG()
	)

learns the structure of a Bayes net

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>.
initial_dag	the DAG we start from for our learning

Definition at line 38 of file localSearchWithTabuList_tpl.h.

References gum::learning::genericBNLearner::Database::Database().

                                                                             {
      selector.setGraph(dag);

      unsigned int nb_changes_applied                 = 0;
      Idx          applied_change_with_positive_score = 0;
      Idx          current_N                          = 0;

      initApproximationScheme();

      // a vector that indicates which queues have valid scores, i.e., scores
      // that were not invalidated by previously applied changes
      std::vector< bool > impacted_queues(dag.size(), false);

      // the best dag found so far with its score
      DAG    best_dag      = dag;
      double best_score    = 0;
      double current_score = 0;
      double delta_score   = 0;

      do {
        applied_change_with_positive_score = 0;
        delta_score                        = 0;

        std::vector< std::pair< NodeId, double > > ordered_queues
           = selector.nodesSortedByBestScore();

        for (Idx j = 0; j < dag.size(); ++j) {
          NodeId i = ordered_queues[j].first;

          if (!selector.empty(i)
              && (!nb_changes_applied || (selector.bestScore(i) > 0))) {
            // pick up the best change
            const GraphChange& change = selector.bestChange(i);

            // perform the change
            switch (change.type()) {
              case GraphChangeType::ARC_ADDITION:
                if (!impacted_queues[change.node2()]
                    && selector.isChangeValid(change)) {
                  if (selector.bestScore(i) > 0) {
                    ++applied_change_with_positive_score;
                  } else if (current_score > best_score) {
                    best_score = current_score;
                    best_dag   = dag;
                  }

                  // std::cout << "apply arc addition " << change.node1()
                  //           << " -> " << change.node2()
                  //           << "  delta = " << selector.bestScore( i )
                  //           << std::endl;

                  delta_score += selector.bestScore(i);
                  current_score += selector.bestScore(i);
                  dag.addArc(change.node1(), change.node2());
                  impacted_queues[change.node2()] = true;
                  selector.applyChangeWithoutScoreUpdate(change);
                  ++nb_changes_applied;
                }

                break;

              case GraphChangeType::ARC_DELETION:
                if (!impacted_queues[change.node2()]
                    && selector.isChangeValid(change)) {
                  if (selector.bestScore(i) > 0) {
                    ++applied_change_with_positive_score;
                  } else if (current_score > best_score) {
                    best_score = current_score;
                    best_dag   = dag;
                  }

                  // std::cout << "apply arc deletion " << change.node1()
                  //           << " -> " << change.node2()
                  //           << "  delta = " << selector.bestScore( i )
                  //           << std::endl;

                  delta_score += selector.bestScore(i);
                  current_score += selector.bestScore(i);
                  dag.eraseArc(Arc(change.node1(), change.node2()));
                  impacted_queues[change.node2()] = true;
                  selector.applyChangeWithoutScoreUpdate(change);
                  ++nb_changes_applied;
                }

                break;

              case GraphChangeType::ARC_REVERSAL:
                if ((!impacted_queues[change.node1()])
                    && (!impacted_queues[change.node2()])
                    && selector.isChangeValid(change)) {
                  if (selector.bestScore(i) > 0) {
                    ++applied_change_with_positive_score;
                  } else if (current_score > best_score) {
                    best_score = current_score;
                    best_dag   = dag;
                  }

                  // std::cout << "apply arc reversal " << change.node1()
                  //           << " -> " << change.node2()
                  //           << "  delta = " << selector.bestScore( i )
                  //           << std::endl;

                  delta_score += selector.bestScore(i);
                  current_score += selector.bestScore(i);
                  dag.eraseArc(Arc(change.node1(), change.node2()));
                  dag.addArc(change.node2(), change.node1());
                  impacted_queues[change.node1()] = true;
                  impacted_queues[change.node2()] = true;
                  selector.applyChangeWithoutScoreUpdate(change);
                  ++nb_changes_applied;
                }

                break;

              default:
                GUM_ERROR(OperationNotAllowed,
                          "edge modifications are not "
                          "supported by local search");
            }

            break;
          }
        }

        selector.updateScoresAfterAppliedChanges();

        // reset the impacted queue and applied changes structures
        for (auto iter = impacted_queues.begin(); iter != impacted_queues.end();
             ++iter) {
          *iter = false;
        }

        updateApproximationScheme(nb_changes_applied);

        // update current_N
        if (applied_change_with_positive_score) {
          current_N          = 0;
          nb_changes_applied = 0;
        } else {
          ++current_N;
        }

        // std::cout << "current N = " << current_N << std::endl;
      } while ((current_N <= MaxNbDecreasing__)
               && continueApproximationScheme(delta_score));

      stopApproximationScheme();   // just to be sure of the
                                   // approximationScheme has
                                   // been notified of the end of looop

      if (current_score > best_score) {
        return dag;
      } else {
        return best_dag;
      }
    }

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maxIter()

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

virtualinherited

Returns the criterion on number of iterations.

Returns

Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 101 of file approximationScheme_inl.h.

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

{ return max_iter_; }

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maxTime()

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

virtualinherited

Returns the timeout (in seconds).

Returns

Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 124 of file approximationScheme_inl.h.

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

{ return max_time_; }

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messageApproximationScheme()

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

inherited

Returns the approximation scheme message.

Returns

Returns the approximation scheme message.

Definition at line 39 of file IApproximationSchemeConfiguration_inl.h.

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

                                                                         {
    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();
  }

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minEpsilonRate()

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

virtualinherited

Returns the value of the minimal epsilon rate.

Returns

Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 73 of file approximationScheme_inl.h.

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

                                                          {
    return min_rate_eps_;
  }

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nbrIterations()

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

virtualinherited

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 162 of file approximationScheme_inl.h.

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

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

    return current_step_;
  }

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operator=() [1/2]

LocalSearchWithTabuList& gum::learning::LocalSearchWithTabuList::operator=

(

const LocalSearchWithTabuList &

from

)

copy operator

operator=() [2/2]

LocalSearchWithTabuList& gum::learning::LocalSearchWithTabuList::operator=

(

LocalSearchWithTabuList &&

from

)

move operator

periodSize()

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

virtualinherited

Returns the period size.

Returns

Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 148 of file approximationScheme_inl.h.

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

{ return period_size_; }

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remainingBurnIn()

INLINE Size gum::ApproximationScheme::remainingBurnIn ( )

inherited

Returns the remaining burn in.

Returns

Returns the remaining burn in.

Definition at line 209 of file approximationScheme_inl.h.

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

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

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setEpsilon()

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

virtualinherited

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

OutOfLowerBound

Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 42 of file approximationScheme_inl.h.

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

                                                        {
    if (eps < 0.) { GUM_ERROR(OutOfLowerBound, "eps should be >=0"); }

    eps_         = eps;
    enabled_eps_ = true;
  }

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setMaxIter()

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

virtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfLowerBound

Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 94 of file approximationScheme_inl.h.

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

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

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setMaxNbDecreasingChanges()

void gum::learning::LocalSearchWithTabuList::setMaxNbDecreasingChanges

(

Size

nb

)

set the max number of changes decreasing the score that we allow to apply

setMaxTime()

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

virtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout

The timeout value in seconds.

Exceptions

OutOfLowerBound

Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 117 of file approximationScheme_inl.h.

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

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

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setMinEpsilonRate()

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

virtualinherited

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

OutOfLowerBound

if rate<0

Implements gum::IApproximationSchemeConfiguration.

Definition at line 65 of file approximationScheme_inl.h.

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

                                                                {
    if (rate < 0) { GUM_ERROR(OutOfLowerBound, "rate should be >=0"); }

    min_rate_eps_         = rate;
    enabled_min_rate_eps_ = true;
  }

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setPeriodSize()

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

virtualinherited

How many samples between two stopping is enable.

Parameters

p	The new period value.

Exceptions

OutOfLowerBound

Raised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 142 of file approximationScheme_inl.h.

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

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

    period_size_ = p;
  }

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setVerbosity()

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

virtualinherited

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

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 151 of file approximationScheme_inl.h.

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

{ verbosity_ = v; }

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startOfPeriod()

INLINE bool gum::ApproximationScheme::startOfPeriod ( )

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 196 of file approximationScheme_inl.h.

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

                                                 {
    if (current_step_ < burn_in_) { return false; }

    if (period_size_ == 1) { return true; }

    return ((current_step_ - burn_in_) % period_size_ == 0);
  }

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stateApproximationScheme()

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

virtualinherited

Returns the approximation scheme state.

Returns

Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 157 of file approximationScheme_inl.h.

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

                                                             {
    return current_state_;
  }

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stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 218 of file approximationScheme_inl.h.

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

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

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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 205 of file approximationScheme_inl.h.

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

                                                                              {
    current_step_ += incr;
  }

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verbosity()

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

virtualinherited

Returns true if verbosity is enabled.

Returns

Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 153 of file approximationScheme_inl.h.

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

{ return verbosity_; }

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Member Data Documentation

burn_in_

Size gum::ApproximationScheme::burn_in_

protectedinherited

Number of iterations before checking stopping criteria.

Definition at line 413 of file approximationScheme.h.

current_epsilon_

double gum::ApproximationScheme::current_epsilon_

protectedinherited

Current epsilon.

Definition at line 368 of file approximationScheme.h.

current_rate_

double gum::ApproximationScheme::current_rate_

protectedinherited

Current rate.

Definition at line 374 of file approximationScheme.h.

current_state_

ApproximationSchemeSTATE gum::ApproximationScheme::current_state_

protectedinherited

The current state.

Definition at line 383 of file approximationScheme.h.

current_step_

Size gum::ApproximationScheme::current_step_

protectedinherited

The current step.

Definition at line 377 of file approximationScheme.h.

enabled_eps_

bool gum::ApproximationScheme::enabled_eps_

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 392 of file approximationScheme.h.

enabled_max_iter_

bool gum::ApproximationScheme::enabled_max_iter_

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 410 of file approximationScheme.h.

enabled_max_time_

bool gum::ApproximationScheme::enabled_max_time_

protectedinherited

If true, the timeout is enabled.

Definition at line 404 of file approximationScheme.h.

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 398 of file approximationScheme.h.

eps_

double gum::ApproximationScheme::eps_

protectedinherited

Threshold for convergence.

Definition at line 389 of file approximationScheme.h.

history_

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

protectedinherited

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

Definition at line 386 of file approximationScheme.h.

last_epsilon_

double gum::ApproximationScheme::last_epsilon_

protectedinherited

Last epsilon value.

Definition at line 371 of file approximationScheme.h.

max_iter_

Size gum::ApproximationScheme::max_iter_

protectedinherited

The maximum iterations.

Definition at line 407 of file approximationScheme.h.

max_time_

double gum::ApproximationScheme::max_time_

protectedinherited

The timeout.

Definition at line 401 of file approximationScheme.h.

MaxNbDecreasing__

Size gum::learning::LocalSearchWithTabuList::MaxNbDecreasing__ {2}

private

the max number of changes decreasing the score that we allow to apply

Definition at line 128 of file localSearchWithTabuList.h.

min_rate_eps_

double gum::ApproximationScheme::min_rate_eps_

protectedinherited

Threshold for the epsilon rate.

Definition at line 395 of file approximationScheme.h.

onProgress

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

inherited

Progression, error and time.

Definition at line 58 of file IApproximationSchemeConfiguration.h.

onStop

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

inherited

Criteria messageApproximationScheme.

Definition at line 61 of file IApproximationSchemeConfiguration.h.

period_size_

Size gum::ApproximationScheme::period_size_

protectedinherited

Checking criteria frequency.

Definition at line 416 of file approximationScheme.h.

timer_

Timer gum::ApproximationScheme::timer_

protectedinherited

The timer.

Definition at line 380 of file approximationScheme.h.

verbosity_

bool gum::ApproximationScheme::verbosity_

protectedinherited

If true, verbosity is enabled.

Definition at line 419 of file approximationScheme.h.

---

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

• agrum/BN/learning/localSearchWithTabuList.h
• agrum/BN/learning/localSearchWithTabuList_tpl.h