aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
K2Prior_inl.h
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40#pragma once
41
42
48#ifndef DOXYGEN_SHOULD_SKIP_THIS
49
51
52namespace gum {
53
54 namespace learning {
55
56
58 INLINE K2Prior::K2Prior(const DatabaseTable& database,
59 const Bijection< NodeId, std::size_t >& nodeId2columns) :
60 SmoothingPrior(database, nodeId2columns) {
61 GUM_CONSTRUCTOR(K2Prior);
62 }
63
65 INLINE K2Prior::K2Prior(const K2Prior& from) : SmoothingPrior(from) { GUM_CONS_CPY(K2Prior); }
66
68 INLINE K2Prior::K2Prior(K2Prior&& from) : SmoothingPrior(std::move(from)) {
69 GUM_CONS_MOV(K2Prior);
70 }
71
73 INLINE K2Prior* K2Prior::clone() const { return new K2Prior(*this); }
74
76 INLINE K2Prior::~K2Prior() { GUM_DESTRUCTOR(K2Prior); }
77
79 INLINE K2Prior& K2Prior::operator=(const K2Prior& from) {
80 SmoothingPrior::operator=(from);
81 return *this;
82 }
83
85 INLINE K2Prior& K2Prior::operator=(K2Prior&& from) {
86 SmoothingPrior::operator=(std::move(from));
87 return *this;
88 }
89
91 INLINE void K2Prior::setWeight(const double weight) {}
92
93
94 } /* namespace learning */
95
96} /* namespace gum */
97
98#endif /* DOXYGEN_SHOULD_SKIP_THIS */
the internal prior for the K2 score = Laplace Prior
The class representing a tabular database as used by learning tasks.
K2Prior(const DatabaseTable &database, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
the smooth a priori: adds a weight w to all the counts
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.