29 #ifndef DOXYGEN_SHOULD_SKIP_THIS 36 INLINE
K2::K2() { GUM_CONSTRUCTOR(
K2); }
66 __order = std::move(from.__order);
77 for (
const auto node : order) {
90 "the number of elements in the order given " 91 "to K2 is not the same as the number of nodes");
94 for (
const auto node :
__order) {
95 if (node >= __order.size()) {
102 "there exist at least one node in the order " 103 "given to K2 that has no domain size");
ApproximationScheme & approximationScheme()
returns the approximation policy of the learning algorithm
void clear()
Clear the sequence.
Size size() const noexcept
Returns the size of the sequence.
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.
ApproximationScheme(bool verbosity=false)
const Sequence< NodeId > & order() const noexcept
returns the current order
ApproximationScheme & approximationScheme()
returns the approximation policy of the learning algorithm
K2 & operator=(const K2 &from)
copy operator
GreedyHillClimbing()
default constructor
void __checkOrder(const std::vector< Size > &modal)
checks that the order passed to K2 is coherent with the variables as specified by their modalities ...
#define GUM_ERROR(type, msg)
Sequence< NodeId > __order
the order on the variable used for learning
void insert(const Key &k)
Insert an element at the end of the sequence.
GreedyHillClimbing & operator=(const GreedyHillClimbing &from)
copy operator