aGrUM 3.1.1
a C++ library for (probabilistic) graphical models
K2Prior_inl.h
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40
41#pragma once
42
43
49#include <agrum/BN/learning/priors/K2Prior.h> // to ease IDE parser
50#ifndef DOXYGEN_SHOULD_SKIP_THIS
51
53
54namespace gum {
55
56 namespace learning {
57
58
60 INLINE K2Prior::K2Prior(const DatabaseTable& database,
61 const Bijection< NodeId, std::size_t >& nodeId2columns) :
62 SmoothingPrior(database, nodeId2columns) {
63 GUM_CONSTRUCTOR(K2Prior);
64 }
65
67 INLINE K2Prior::K2Prior(const K2Prior& from) : SmoothingPrior(from) { GUM_CONS_CPY(K2Prior); }
68
70 INLINE K2Prior::K2Prior(K2Prior&& from) : SmoothingPrior(std::move(from)) {
71 GUM_CONS_MOV(K2Prior);
72 }
73
75 INLINE K2Prior* K2Prior::clone() const { return new K2Prior(*this); }
76
78 INLINE K2Prior::~K2Prior() { GUM_DESTRUCTOR(K2Prior); }
79
81 INLINE K2Prior& K2Prior::operator=(const K2Prior& from) = default;
82
84 INLINE K2Prior& K2Prior::operator=(K2Prior&& from) {
85 SmoothingPrior::operator=(std::move(from));
86 return *this;
87 }
88
90 INLINE void K2Prior::setWeight(const double weight) {}
91
92
93 } /* namespace learning */
94
95} /* namespace gum */
96
97#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:55
gum is the global namespace for all aGrUM entities
Definition agrum.h:46
STL namespace.