aGrUM  0.13.2
gum::learning::K2 Class Reference

The K2 algorithm. More...

#include <K2.h>

+ Inheritance diagram for gum::learning::K2:
+ Collaboration diagram for gum::learning::K2:

Public Member Functions

Constructors / Destructors
 K2 ()
 default constructor More...
 
 K2 (const K2 &from)
 copy constructor More...
 
 K2 (K2 &&from)
 move constructor More...
 
 ~K2 ()
 destructor More...
 
Operators
K2operator= (const K2 &from)
 copy operator More...
 
K2operator= (K2 &&from)
 move operator More...
 

Accessors / Modifiers

Sequence< NodeId__order
 the order on the variable used for learning More...
 
ApproximationSchemeapproximationScheme ()
 returns the approximation policy of the learning algorithm More...
 
void setOrder (const Sequence< NodeId > &order)
 sets the order on the variables More...
 
void setOrder (const std::vector< NodeId > &order)
 sets the order on the variables More...
 
const Sequence< NodeId > & order () const noexcept
 returns the current order More...
 
template<typename GRAPH_CHANGES_SELECTOR >
DAG learnStructure (GRAPH_CHANGES_SELECTOR &selector, const std::vector< Size > &modal, DAG initial_dag=DAG())
 learns the structure of a Bayes net More...
 
template<typename GUM_SCALAR , typename GRAPH_CHANGES_SELECTOR , typename PARAM_ESTIMATOR , typename CELL_TRANSLATORS >
BayesNet< GUM_SCALAR > learnBN (GRAPH_CHANGES_SELECTOR &selector, PARAM_ESTIMATOR &estimator, const std::vector< std::string > &names, const std::vector< Size > &modal, const CELL_TRANSLATORS &translator, DAG initial_dag=DAG())
 learns the structure and the parameters of a BN More...
 
void __checkOrder (const std::vector< Size > &modal)
 checks that the order passed to K2 is coherent with the variables as specified by their modalities More...
 

Detailed Description

The K2 algorithm.

Definition at line 44 of file K2.h.

Member Enumeration Documentation

The different state of an approximation scheme.

Enumerator
Undefined 
Continue 
Epsilon 
Rate 
Limit 
TimeLimit 
Stopped 

Definition at line 64 of file IApproximationSchemeConfiguration.h.

64  : char {
65  Undefined,
66  Continue,
67  Epsilon,
68  Rate,
69  Limit,
70  TimeLimit,
71  Stopped
72  };

Constructor & Destructor Documentation

gum::learning::K2::K2 ( )

default constructor

gum::learning::K2::K2 ( const K2 from)

copy constructor

gum::learning::K2::K2 ( K2 &&  from)

move constructor

gum::learning::K2::~K2 ( )

destructor

Member Function Documentation

void gum::learning::K2::__checkOrder ( const std::vector< Size > &  modal)
private

checks that the order passed to K2 is coherent with the variables as specified by their modalities

Referenced by learnBN(), and learnStructure().

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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
errorThe new error value.
Returns
false if state become != ApproximationSchemeSTATE::Continue
Exceptions
OperationNotAllowedRaised if state != ApproximationSchemeSTATE::Continue.

Definition at line 225 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_epsilon, gum::ApproximationScheme::_current_rate, gum::ApproximationScheme::_current_state, gum::ApproximationScheme::_current_step, gum::ApproximationScheme::_enabled_eps, gum::ApproximationScheme::_enabled_max_iter, gum::ApproximationScheme::_enabled_max_time, gum::ApproximationScheme::_enabled_min_rate_eps, gum::ApproximationScheme::_eps, gum::ApproximationScheme::_history, gum::ApproximationScheme::_last_epsilon, gum::ApproximationScheme::_max_iter, gum::ApproximationScheme::_max_time, gum::ApproximationScheme::_min_rate_eps, gum::ApproximationScheme::_stopScheme(), gum::ApproximationScheme::_timer, gum::IApproximationSchemeConfiguration::Continue, gum::IApproximationSchemeConfiguration::Epsilon, GUM_EMIT3, GUM_ERROR, gum::IApproximationSchemeConfiguration::Limit, gum::IApproximationSchemeConfiguration::messageApproximationScheme(), gum::IApproximationSchemeConfiguration::onProgress, gum::IApproximationSchemeConfiguration::Rate, gum::ApproximationScheme::startOfPeriod(), gum::ApproximationScheme::stateApproximationScheme(), gum::Timer::step(), gum::IApproximationSchemeConfiguration::TimeLimit, and gum::ApproximationScheme::verbosity().

Referenced by gum::GibbsKL< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

225  {
226  // For coherence, we fix the time used in the method
227 
228  double timer_step = _timer.step();
229 
230  if (_enabled_max_time) {
231  if (timer_step > _max_time) {
233  return false;
234  }
235  }
236 
237  if (!startOfPeriod()) { return true; }
238 
240  GUM_ERROR(OperationNotAllowed,
241  "state of the approximation scheme is not correct : "
243  }
244 
245  if (verbosity()) { _history.push_back(error); }
246 
247  if (_enabled_max_iter) {
248  if (_current_step > _max_iter) {
250  return false;
251  }
252  }
253 
255  _current_epsilon = error; // eps rate isEnabled needs it so affectation was
256  // moved from eps isEnabled below
257 
258  if (_enabled_eps) {
259  if (_current_epsilon <= _eps) {
261  return false;
262  }
263  }
264 
265  if (_last_epsilon >= 0.) {
266  if (_current_epsilon > .0) {
267  // ! _current_epsilon can be 0. AND epsilon
268  // isEnabled can be disabled !
269  _current_rate =
271  }
272  // limit with current eps ---> 0 is | 1 - ( last_eps / 0 ) | --->
273  // infinity the else means a return false if we isEnabled the rate below,
274  // as we would have returned false if epsilon isEnabled was enabled
275  else {
277  }
278 
279  if (_enabled_min_rate_eps) {
280  if (_current_rate <= _min_rate_eps) {
282  return false;
283  }
284  }
285  }
286 
288  if (onProgress.hasListener()) {
290  }
291 
292  return true;
293  } else {
294  return false;
295  }
296  }
ApproximationSchemeSTATE stateApproximationScheme() const
Returns the approximation scheme state.
bool verbosity() const
Returns true if verbosity is enabled.
Signaler3< Size, double, double > onProgress
Progression, error and time.
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.
std::string messageApproximationScheme() const
Returns the approximation scheme message.
bool _enabled_eps
If true, the threshold convergence is enabled.
void _stopScheme(ApproximationSchemeSTATE new_state)
Stop the scheme given a new state.
double _current_epsilon
Current epsilon.
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.
bool startOfPeriod()
Returns true if we are at the beginning of a period (compute error is mandatory). ...
double _eps
Threshold for convergence.
double step() const
Returns the delta time between now and the last reset() call (or the constructor).
Definition: timer_inl.h:39
double _current_rate
Current rate.
bool _enabled_max_time
If true, the timeout is enabled.
Size _current_step
The current step.
std::vector< double > _history
The scheme history, used only if verbosity == true.
double _min_rate_eps
Threshold for the epsilon rate.
double _last_epsilon
Last epsilon value.
Size _max_iter
The maximum iterations.
#define GUM_EMIT3(signal, arg1, arg2, arg3)
Definition: signaler3.h:40
ApproximationSchemeSTATE _current_state
The current state.
double _max_time
The timeout.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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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 126 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_timer, and gum::Timer::step().

Referenced by gum::learning::genericBNLearner::currentTime().

126 { return _timer.step(); }
double step() const
Returns the delta time between now and the last reset() call (or the constructor).
Definition: timer_inl.h:39

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INLINE void gum::ApproximationScheme::disableEpsilon ( )
virtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 52 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

Referenced by gum::learning::genericBNLearner::disableEpsilon().

52 { _enabled_eps = false; }
bool _enabled_eps
If true, the threshold convergence is enabled.

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INLINE void gum::ApproximationScheme::disableMaxIter ( )
virtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 103 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::learning::genericBNLearner::disableMaxIter(), and gum::learning::GreedyHillClimbing::GreedyHillClimbing().

103 { _enabled_max_iter = false; }
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.

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INLINE void gum::ApproximationScheme::disableMaxTime ( )
virtualinherited

Disable stopping criterion on timeout.

Returns
Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 129 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

Referenced by gum::learning::genericBNLearner::disableMaxTime(), and gum::learning::GreedyHillClimbing::GreedyHillClimbing().

129 { _enabled_max_time = false; }
bool _enabled_max_time
If true, the timeout is enabled.

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INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )
virtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 77 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::GibbsKL< GUM_SCALAR >::_computeKL(), gum::learning::genericBNLearner::disableMinEpsilonRate(), and gum::learning::GreedyHillClimbing::GreedyHillClimbing().

77  {
78  _enabled_min_rate_eps = false;
79  }
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.

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INLINE void gum::ApproximationScheme::enableEpsilon ( )
virtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 55 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), and gum::learning::genericBNLearner::enableEpsilon().

55 { _enabled_eps = true; }
bool _enabled_eps
If true, the threshold convergence is enabled.

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INLINE void gum::ApproximationScheme::enableMaxIter ( )
virtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 106 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

Referenced by gum::learning::genericBNLearner::enableMaxIter().

106 { _enabled_max_iter = true; }
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.

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INLINE void gum::ApproximationScheme::enableMaxTime ( )
virtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 132 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), and gum::learning::genericBNLearner::enableMaxTime().

132 { _enabled_max_time = true; }
bool _enabled_max_time
If true, the timeout is enabled.

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INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )
virtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 82 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

Referenced by gum::GibbsKL< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::enableMinEpsilonRate().

82  {
83  _enabled_min_rate_eps = true;
84  }
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.

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

Returns the value of epsilon.

Returns
Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 49 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_eps.

Referenced by gum::ImportanceSampling< GUM_SCALAR >::_onContextualize(), and gum::learning::genericBNLearner::epsilon().

49 { return _eps; }
double _eps
Threshold for convergence.

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

Returns the scheme history.

Returns
Returns the scheme history.
Exceptions
OperationNotAllowedRaised if the scheme did not performed or if verbosity is set to false.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 171 of file approximationScheme_inl.h.

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

Referenced by gum::learning::genericBNLearner::history().

171  {
173  GUM_ERROR(OperationNotAllowed,
174  "state of the approximation scheme is udefined");
175  }
176 
177  if (verbosity() == false) {
178  GUM_ERROR(OperationNotAllowed, "No history when verbosity=false");
179  }
180 
181  return _history;
182  }
ApproximationSchemeSTATE stateApproximationScheme() const
Returns the approximation scheme state.
bool verbosity() const
Returns true if verbosity is enabled.
std::vector< double > _history
The scheme history, used only if verbosity == true.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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INLINE void gum::ApproximationScheme::initApproximationScheme ( )
inherited

Initialise the scheme.

Definition at line 185 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_epsilon, gum::ApproximationScheme::_current_rate, gum::ApproximationScheme::_current_state, gum::ApproximationScheme::_current_step, gum::ApproximationScheme::_history, gum::ApproximationScheme::_timer, gum::IApproximationSchemeConfiguration::Continue, and gum::Timer::reset().

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::GibbsKL< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::SamplingInference< GUM_SCALAR >::_onStateChanged(), gum::learning::GreedyHillClimbing::learnStructure(), and gum::learning::LocalSearchWithTabuList::learnStructure().

185  {
187  _current_step = 0;
189  _history.clear();
190  _timer.reset();
191  }
double _current_epsilon
Current epsilon.
void reset()
Reset the timer.
Definition: timer_inl.h:29
double _current_rate
Current rate.
Size _current_step
The current step.
std::vector< double > _history
The scheme history, used only if verbosity == true.
ApproximationSchemeSTATE _current_state
The current state.

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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 59 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps.

Referenced by gum::learning::genericBNLearner::isEnabledEpsilon().

59  {
60  return _enabled_eps;
61  }
bool _enabled_eps
If true, the threshold convergence is enabled.

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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 110 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter.

Referenced by gum::learning::genericBNLearner::isEnabledMaxIter().

110  {
111  return _enabled_max_iter;
112  }
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.

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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 136 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time.

Referenced by gum::learning::genericBNLearner::isEnabledMaxTime().

136  {
137  return _enabled_max_time;
138  }
bool _enabled_max_time
If true, the timeout is enabled.

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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 88 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps.

Referenced by gum::GibbsKL< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::isEnabledMinEpsilonRate().

88  {
89  return _enabled_min_rate_eps;
90  }
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.

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template<typename GUM_SCALAR , typename GRAPH_CHANGES_SELECTOR , typename PARAM_ESTIMATOR , typename CELL_TRANSLATORS >
BayesNet< GUM_SCALAR > gum::learning::K2::learnBN ( GRAPH_CHANGES_SELECTOR &  selector,
PARAM_ESTIMATOR &  estimator,
const std::vector< std::string > &  names,
const std::vector< Size > &  modal,
const CELL_TRANSLATORS &  translator,
DAG  initial_dag = DAG() 
)

learns the structure and the parameters of a BN

Definition at line 63 of file K2_tpl.h.

References __checkOrder(), and __order.

68  {
69  // check that we used a selector compatible with the K2 algorithm
70  static_assert(
71  std::is_base_of< __GraphChangesGenerator4K2,
72  typename GRAPH_CHANGES_SELECTOR::GeneratorType >::value,
73  "K2 must be called with a K2-compliant Graph Change Generator");
74 
75  // check that the order passed in argument concerns all the nodes
76  __checkOrder(modal);
77 
78  // get the generator and assign the order
79  auto& generator = selector.graphChangeGenerator();
80  generator.setOrder(__order);
81 
82  // use the greedy hill climbing algorithm to perform the search
83  return GreedyHillClimbing::learnBN< GUM_SCALAR >(
84  selector, estimator, names, modal, translator, initial_dag);
85  }
void __checkOrder(const std::vector< Size > &modal)
checks that the order passed to K2 is coherent with the variables as specified by their modalities ...
Sequence< NodeId > __order
the order on the variable used for learning
Definition: K2.h:123

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template<typename GRAPH_CHANGES_SELECTOR >
DAG gum::learning::K2::learnStructure ( GRAPH_CHANGES_SELECTOR &  selector,
const std::vector< Size > &  modal,
DAG  initial_dag = DAG() 
)

learns the structure of a Bayes net

Parameters
selectorA 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>.
modalthe domain sizes of the random variables observed in the database
initial_dagthe DAG we start from for our learning

Definition at line 38 of file K2_tpl.h.

References __checkOrder(), __order, and gum::learning::GreedyHillClimbing::learnStructure().

Referenced by gum::learning::genericBNLearner::__learnDAG().

40  {
41  // check that we used a selector compatible with the K2 algorithm
42  static_assert(
43  std::is_base_of< __GraphChangesGenerator4K2,
44  typename GRAPH_CHANGES_SELECTOR::GeneratorType >::value,
45  "K2 must be called with a K2-compliant Graph Change Generator");
46 
47  // check that the order passed in argument concerns all the nodes
48  __checkOrder(modal);
49 
50  // get the generator and assign the order
51  auto& generator = selector.graphChangeGenerator();
52  generator.setOrder(__order);
53 
54  // use the greedy hill climbing algorithm to perform the search
55  return GreedyHillClimbing::learnStructure(selector, modal, initial_dag);
56  }
DAG learnStructure(GRAPH_CHANGES_SELECTOR &selector, const std::vector< Size > &modal, DAG initial_dag=DAG())
learns the structure of a Bayes net
void __checkOrder(const std::vector< Size > &modal)
checks that the order passed to K2 is coherent with the variables as specified by their modalities ...
Sequence< NodeId > __order
the order on the variable used for learning
Definition: K2.h:123

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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 100 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_iter.

Referenced by gum::learning::genericBNLearner::maxIter().

100 { return _max_iter; }
Size _max_iter
The maximum iterations.

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

Returns the timeout (in seconds).

Returns
Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 123 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_max_time.

Referenced by gum::learning::genericBNLearner::maxTime().

123 { return _max_time; }
double _max_time
The timeout.

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

Returns the approximation scheme message.

Returns
Returns the approximation scheme message.

Definition at line 38 of file IApproximationSchemeConfiguration_inl.h.

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

Referenced by gum::ApproximationScheme::_stopScheme(), gum::ApproximationScheme::continueApproximationScheme(), and gum::credal::InferenceEngine< GUM_SCALAR >::getApproximationSchemeMsg().

38  {
39  std::stringstream s;
40 
41  switch (stateApproximationScheme()) {
42  case ApproximationSchemeSTATE::Continue: s << "in progress"; break;
43 
45  s << "stopped with epsilon=" << epsilon();
46  break;
47 
49  s << "stopped with rate=" << minEpsilonRate();
50  break;
51 
53  s << "stopped with max iteration=" << maxIter();
54  break;
55 
57  s << "stopped with timeout=" << maxTime();
58  break;
59 
60  case ApproximationSchemeSTATE::Stopped: s << "stopped on request"; break;
61 
62  case ApproximationSchemeSTATE::Undefined: s << "undefined state"; break;
63  };
64 
65  return s.str();
66  }
virtual double epsilon() const =0
Returns the value of epsilon.
virtual ApproximationSchemeSTATE stateApproximationScheme() const =0
Returns the approximation scheme state.
virtual double maxTime() const =0
Returns the timeout (in seconds).
virtual Size maxIter() const =0
Returns the criterion on number of iterations.
virtual double minEpsilonRate() const =0
Returns the value of the minimal epsilon rate.

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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 72 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_min_rate_eps.

Referenced by gum::learning::genericBNLearner::minEpsilonRate().

72  {
73  return _min_rate_eps;
74  }
double _min_rate_eps
Threshold for the epsilon rate.

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

Returns the number of iterations.

Returns
Returns the number of iterations.
Exceptions
OperationNotAllowedRaised if the scheme did not perform.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 161 of file approximationScheme_inl.h.

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

Referenced by gum::GibbsKL< GUM_SCALAR >::_computeKL(), and gum::learning::genericBNLearner::nbrIterations().

161  {
163  GUM_ERROR(OperationNotAllowed,
164  "state of the approximation scheme is undefined");
165  }
166 
167  return _current_step;
168  }
ApproximationSchemeSTATE stateApproximationScheme() const
Returns the approximation scheme state.
Size _current_step
The current step.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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K2& gum::learning::K2::operator= ( const K2 from)

copy operator

K2& gum::learning::K2::operator= ( K2 &&  from)

move operator

const Sequence< NodeId >& gum::learning::K2::order ( ) const
noexcept

returns the current order

Referenced by gum::learning::genericBNLearner::__learnDAG().

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

Returns the period size.

Returns
Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 147 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_period_size.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference(), and gum::learning::genericBNLearner::periodSize().

147 { return _period_size; }
Size _period_size
Checking criteria frequency.

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

Returns the remaining burn in.

Returns
Returns the remaining burn in.

Definition at line 208 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_burn_in, and gum::ApproximationScheme::_current_step.

208  {
209  if (_burn_in > _current_step) {
210  return _burn_in - _current_step;
211  } else {
212  return 0;
213  }
214  }
Size _burn_in
Number of iterations before checking stopping criteria.
Size _current_step
The current step.
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
epsThe new epsilon value.
Exceptions
OutOfLowerBoundRaised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 41 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_eps, gum::ApproximationScheme::_eps, and GUM_ERROR.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::__mcInitApproximationScheme(), gum::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setEpsilon().

41  {
42  if (eps < 0.) { GUM_ERROR(OutOfLowerBound, "eps should be >=0"); }
43 
44  _eps = eps;
45  _enabled_eps = true;
46  }
bool _enabled_eps
If true, the threshold convergence is enabled.
double _eps
Threshold for convergence.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters
maxThe maximum number of iterations.
Exceptions
OutOfLowerBoundRaised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 93 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_iter, gum::ApproximationScheme::_max_iter, and GUM_ERROR.

Referenced by gum::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMaxIter().

93  {
94  if (max < 1) { GUM_ERROR(OutOfLowerBound, "max should be >=1"); }
95  _max_iter = max;
96  _enabled_max_iter = true;
97  }
bool _enabled_max_iter
If true, the maximum iterations stopping criterion is enabled.
Size _max_iter
The maximum iterations.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters
timeoutThe timeout value in seconds.
Exceptions
OutOfLowerBoundRaised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 116 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_max_time, gum::ApproximationScheme::_max_time, and GUM_ERROR.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setMaxTime().

116  {
117  if (timeout <= 0.) { GUM_ERROR(OutOfLowerBound, "timeout should be >0."); }
118  _max_time = timeout;
119  _enabled_max_time = true;
120  }
bool _enabled_max_time
If true, the timeout is enabled.
double _max_time
The timeout.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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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
rateThe minimal epsilon rate.
Exceptions
OutOfLowerBoundif rate<0

Implements gum::IApproximationSchemeConfiguration.

Definition at line 64 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_enabled_min_rate_eps, gum::ApproximationScheme::_min_rate_eps, and GUM_ERROR.

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

64  {
65  if (rate < 0) { GUM_ERROR(OutOfLowerBound, "rate should be >=0"); }
66 
67  _min_rate_eps = rate;
68  _enabled_min_rate_eps = true;
69  }
bool _enabled_min_rate_eps
If true, the minimal threshold for epsilon rate is enabled.
double _min_rate_eps
Threshold for the epsilon rate.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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void gum::learning::K2::setOrder ( const Sequence< NodeId > &  order)

sets the order on the variables

Referenced by gum::learning::genericBNLearner::useK2().

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void gum::learning::K2::setOrder ( const std::vector< NodeId > &  order)

sets the order on the variables

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

How many samples between two stopping is enable.

Parameters
pThe new period value.
Exceptions
OutOfLowerBoundRaised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 141 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_period_size, and GUM_ERROR.

Referenced by gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::CNMonteCarloSampling(), gum::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setPeriodSize().

141  {
142  if (p < 1) { GUM_ERROR(OutOfLowerBound, "p should be >=1"); }
143 
144  _period_size = p;
145  }
Size _period_size
Checking criteria frequency.
#define GUM_ERROR(type, msg)
Definition: exceptions.h:66

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

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

Parameters
vIf true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 150 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

Referenced by gum::GibbsKL< GUM_SCALAR >::GibbsKL(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::genericBNLearner::setVerbosity().

150 { _verbosity = v; }
bool _verbosity
If true, verbosity is enabled.

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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 195 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_burn_in, gum::ApproximationScheme::_current_step, and gum::ApproximationScheme::_period_size.

Referenced by gum::ApproximationScheme::continueApproximationScheme().

195  {
196  if (_current_step < _burn_in) { return false; }
197 
198  if (_period_size == 1) { return true; }
199 
200  return ((_current_step - _burn_in) % _period_size == 0);
201  }
Size _burn_in
Number of iterations before checking stopping criteria.
Size _current_step
The current step.
Size _period_size
Checking criteria frequency.

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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 156 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_state.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::history(), gum::ApproximationScheme::nbrIterations(), and gum::learning::genericBNLearner::stateApproximationScheme().

156  {
157  return _current_state;
158  }
ApproximationSchemeSTATE _current_state
The current state.

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INLINE void gum::ApproximationScheme::stopApproximationScheme ( )
inherited

Stop the approximation scheme.

Definition at line 217 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_state, gum::ApproximationScheme::_stopScheme(), gum::IApproximationSchemeConfiguration::Continue, and gum::IApproximationSchemeConfiguration::Stopped.

Referenced by gum::learning::GreedyHillClimbing::learnStructure(), and gum::learning::LocalSearchWithTabuList::learnStructure().

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

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

Parameters
incrThe new increment steps.

Definition at line 204 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_current_step.

Referenced by gum::GibbsKL< GUM_SCALAR >::_computeKL(), gum::SamplingInference< GUM_SCALAR >::_loopApproxInference(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNMonteCarloSampling< GUM_SCALAR, BNInferenceEngine >::makeInference().

204  {
205  _current_step += incr;
206  }
Size _current_step
The current step.

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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 152 of file approximationScheme_inl.h.

References gum::ApproximationScheme::_verbosity.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::ApproximationScheme::history(), and gum::learning::genericBNLearner::verbosity().

152 { return _verbosity; }
bool _verbosity
If true, verbosity is enabled.

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Member Data Documentation

Sequence< NodeId > gum::learning::K2::__order
private

the order on the variable used for learning

Definition at line 123 of file K2.h.

Referenced by learnBN(), and learnStructure().

double gum::ApproximationScheme::_current_epsilon
protectedinherited
double gum::ApproximationScheme::_current_rate
protectedinherited
bool gum::ApproximationScheme::_enabled_max_iter
protectedinherited
bool gum::ApproximationScheme::_enabled_min_rate_eps
protectedinherited
double gum::ApproximationScheme::_eps
protectedinherited
std::vector< double > gum::ApproximationScheme::_history
protectedinherited
double gum::ApproximationScheme::_last_epsilon
protectedinherited

Last epsilon value.

Definition at line 371 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme().

Size gum::ApproximationScheme::_max_iter
protectedinherited
double gum::ApproximationScheme::_max_time
protectedinherited
double gum::ApproximationScheme::_min_rate_eps
protectedinherited
Size gum::ApproximationScheme::_period_size
protectedinherited
bool gum::ApproximationScheme::_verbosity
protectedinherited

If true, verbosity is enabled.

Definition at line 419 of file approximationScheme.h.

Referenced by gum::ApproximationScheme::setVerbosity(), and gum::ApproximationScheme::verbosity().

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

Criteria messageApproximationScheme.

Definition at line 61 of file IApproximationSchemeConfiguration.h.

Referenced by gum::ApproximationScheme::_stopScheme(), and gum::learning::genericBNLearner::distributeStop().


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