28 #ifndef GUM_LEARNING_K2_H 29 #define GUM_LEARNING_K2_H 93 void setOrder(
const std::vector< NodeId >& order);
105 template <
typename GRAPH_CHANGES_
SELECTOR >
110 template <
typename GUM_SCALAR,
111 typename GRAPH_CHANGES_SELECTOR,
112 typename PARAM_ESTIMATOR >
114 PARAM_ESTIMATOR& estimator,
133 #ifndef GUM_NO_INLINE Class representing a Bayesian Network.
ApproximationScheme & approximationScheme()
returns the approximation policy of the learning algorithm
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
void setOrder(const Sequence< NodeId > &order)
sets the order on the variables
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
const Sequence< NodeId > & order() const noexcept
returns the current order
The greedy hill climbing learning algorithm (for directed graphs)
K2 & operator=(const K2 &from)
copy operator
DAG learnStructure(GRAPH_CHANGES_SELECTOR &selector, DAG initial_dag=DAG())
learns the structure of a Bayes net
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
void __checkOrder(const std::vector< Size > &modal)
checks that the order passed to K2 is coherent with the variables as specified by their modalities ...
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
Sequence< NodeId > __order
the order on the variable used for learning
BayesNet< GUM_SCALAR > learnBN(GRAPH_CHANGES_SELECTOR &selector, PARAM_ESTIMATOR &estimator, DAG initial_dag=DAG())
learns the structure and the parameters of a BN