48#ifndef DOXYGEN_SHOULD_SKIP_THIS
59 const Bijection< NodeId, std::size_t >& nodeId2columns) :
61 GUM_CONSTRUCTOR(K2Prior);
65 INLINE K2Prior::K2Prior(
const K2Prior& from) : SmoothingPrior(from) { GUM_CONS_CPY(K2Prior); }
68 INLINE K2Prior::K2Prior(K2Prior&& from) : SmoothingPrior(
std::move(from)) {
69 GUM_CONS_MOV(K2Prior);
73 INLINE K2Prior* K2Prior::clone()
const {
return new K2Prior(*
this); }
76 INLINE K2Prior::~K2Prior() { GUM_DESTRUCTOR(K2Prior); }
79 INLINE K2Prior& K2Prior::operator=(
const K2Prior& from) {
80 SmoothingPrior::operator=(from);
85 INLINE K2Prior& K2Prior::operator=(K2Prior&& from) {
86 SmoothingPrior::operator=(std::move(from));
91 INLINE
void K2Prior::setWeight(
const double weight) {}
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
gum is the global namespace for all aGrUM entities