54#ifndef GUM_LEARNING_LOCAL_SEARCH_WITH_TABU_LIST_H
55#define GUM_LEARNING_LOCAL_SEARCH_WITH_TABU_LIST_H
131 template <
typename GRAPH_CHANGES_
SELECTOR >
135 template <
typename GUM_SCALAR =
double,
136 typename GRAPH_CHANGES_SELECTOR,
137 typename PARAM_ESTIMATOR >
138 BayesNet< GUM_SCALAR >
learnBN(GRAPH_CHANGES_SELECTOR& selector,
139 PARAM_ESTIMATOR& estimator,
Class representing Bayesian networks.
This file contains general scheme for iteratively convergent algorithms.
ApproximationScheme(bool verbosity=false)
Size _MaxNbDecreasing_
the max number of changes decreasing the score that we allow to apply
ApproximationScheme & approximationScheme()
returns the approximation policy of the learning algorithm
BayesNet< GUM_SCALAR > learnBN(GRAPH_CHANGES_SELECTOR &selector, PARAM_ESTIMATOR &estimator, DAG initial_dag=DAG())
learns the structure and the parameters of a BN
LocalSearchWithTabuList(const LocalSearchWithTabuList &from)
copy constructor
LocalSearchWithTabuList & operator=(LocalSearchWithTabuList &&from)
move operator
DAG learnStructure(GRAPH_CHANGES_SELECTOR &selector, DAG initial_dag=DAG())
learns the structure of a Bayes net
LocalSearchWithTabuList()
default constructor
void setMaxNbDecreasingChanges(Size nb)
set the max number of changes decreasing the score that we allow to apply
LocalSearchWithTabuList & operator=(const LocalSearchWithTabuList &from)
copy operator
LocalSearchWithTabuList(LocalSearchWithTabuList &&from)
move constructor
virtual ~LocalSearchWithTabuList()
destructor
std::size_t Size
In aGrUM, hashed values are unsigned long int.
The local search learning algorithm (for directed graphs).
The local search with tabu list learning algorithm (for directed graphs).
include the inlined functions if necessary
gum is the global namespace for all aGrUM entities