aGrUM 3.1.1
a C++ library for (probabilistic) graphical models
K2_inl.h
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40
41#pragma once
42
43
49
50#include <agrum/BN/learning/K2.h> // to ease IDE parser
51#ifndef DOXYGEN_SHOULD_SKIP_THIS
52
53namespace gum {
54
55 namespace learning {
56
58 INLINE K2::K2() {
59 GUM_CONSTRUCTOR(K2);
60 ;
61 }
62
64 INLINE K2::K2(const K2& from) : GreedyHillClimbing(from), _order_(from._order_) {
65 GUM_CONS_CPY(K2);
66 }
67
69 INLINE K2::K2(K2&& from) :
70 GreedyHillClimbing(std::move(from)), _order_(std::move(from._order_)) {
71 GUM_CONS_MOV(K2);
72 }
73
75 INLINE K2::~K2() {
76 GUM_DESTRUCTOR(K2);
77 ;
78 }
79
81 INLINE K2& K2::operator=(const K2& from) {
82 if (this != &from) {
83 GreedyHillClimbing::operator=(from);
84 _order_ = from._order_;
85 }
86 return *this;
87 }
88
90 INLINE K2& K2::operator=(K2&& from) {
91 if (this != &from) {
92 GreedyHillClimbing::operator=(std::move(from));
93 _order_ = std::move(from._order_);
94 }
95 return *this;
96 }
97
99 INLINE void K2::setOrder(const Sequence< NodeId >& order) { _order_ = order; }
100
102 INLINE void K2::setOrder(const std::vector< NodeId >& order) {
103 _order_.clear();
104 for (const auto node: order) {
105 _order_.insert(node);
106 }
107 }
108
110 INLINE const Sequence< NodeId >& K2::order() const noexcept { return _order_; }
111
114 INLINE void K2::_checkOrder_(const std::vector< Size >& modal) {
115 if (modal.size() != _order_.size()) {
117 "the number of elements in the order given "
118 "to K2 is not the same as the number of nodes");
119 }
120 bool order_ok = true;
121 for (const auto node: _order_) {
122 if (node >= _order_.size()) {
123 order_ok = false;
124 break;
125 }
126 }
127 if (!order_ok) {
129 "there exist at least one node in the order "
130 "given to K2 that has no domain size");
131 }
132 }
133
135 INLINE ApproximationScheme& K2::approximationScheme() {
136 return GreedyHillClimbing::approximationScheme();
137 }
138
139 } /* namespace learning */
140
141} /* namespace gum */
142
143#endif /* DOXYGEN_SHOULD_SKIP_THIS */
The K2 algorithm.
Exception: at least one argument passed to a function is not what was expected.
The greedy hill climbing learning algorithm (for directed graphs).
The K2 algorithm.
Definition K2.h:63
K2()
default constructor
#define GUM_ERROR(type, msg)
Definition exceptions.h:76
include the inlined functions if necessary
Definition CSVParser.h:55
gum is the global namespace for all aGrUM entities
Definition agrum.h:46
STL namespace.