aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
K2_inl.h
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40#pragma once
41
42
48
49#ifndef DOXYGEN_SHOULD_SKIP_THIS
50
51namespace gum {
52
53 namespace learning {
54
56 INLINE K2::K2() {
57 GUM_CONSTRUCTOR(K2);
58 ;
59 }
60
62 INLINE K2::K2(const K2& from) : GreedyHillClimbing(from), _order_(from._order_) {
63 GUM_CONS_CPY(K2);
64 }
65
67 INLINE K2::K2(K2&& from) :
68 GreedyHillClimbing(std::move(from)), _order_(std::move(from._order_)) {
69 GUM_CONS_MOV(K2);
70 }
71
73 INLINE K2::~K2() {
74 GUM_DESTRUCTOR(K2);
75 ;
76 }
77
79 INLINE K2& K2::operator=(const K2& from) {
80 if (this != &from) {
81 GreedyHillClimbing::operator=(from);
82 _order_ = from._order_;
83 }
84 return *this;
85 }
86
88 INLINE K2& K2::operator=(K2&& from) {
89 if (this != &from) {
90 GreedyHillClimbing::operator=(std::move(from));
91 _order_ = std::move(from._order_);
92 }
93 return *this;
94 }
95
97 INLINE void K2::setOrder(const Sequence< NodeId >& order) { _order_ = order; }
98
100 INLINE void K2::setOrder(const std::vector< NodeId >& order) {
101 _order_.clear();
102 for (const auto node: order) {
103 _order_.insert(node);
104 }
105 }
106
108 INLINE const Sequence< NodeId >& K2::order() const noexcept { return _order_; }
109
112 INLINE void K2::_checkOrder_(const std::vector< Size >& modal) {
113 if (modal.size() != _order_.size()) {
115 "the number of elements in the order given "
116 "to K2 is not the same as the number of nodes");
117 }
118 bool order_ok = true;
119 for (const auto node: _order_) {
120 if (node >= _order_.size()) {
121 order_ok = false;
122 break;
123 }
124 }
125 if (!order_ok) {
127 "there exist at least one node in the order "
128 "given to K2 that has no domain size");
129 }
130 }
131
133 INLINE ApproximationScheme& K2::approximationScheme() {
134 return GreedyHillClimbing::approximationScheme();
135 }
136
137 } /* namespace learning */
138
139} /* namespace gum */
140
141#endif /* DOXYGEN_SHOULD_SKIP_THIS */
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:72
include the inlined functions if necessary
Definition CSVParser.h:54
gum is the global namespace for all aGrUM entities
Definition agrum.h:46
STL namespace.