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3.2.0
a C++ library for (probabilistic) graphical models
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K2_inl.h
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/****************************************************************************
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* This file is part of the aGrUM/pyAgrum library. *
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* *
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* Copyright (c) 2005-2026 by *
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* - Pierre-Henri WUILLEMIN(_at_LIP6) *
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* - Christophe GONZALES(_at_AMU) *
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* *
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* The aGrUM/pyAgrum library is free software; you can redistribute it *
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* and/or modify it under the terms of either : *
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* *
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* - the GNU Lesser General Public License as published by *
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* the Free Software Foundation, either version 3 of the License, *
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* or (at your option) any later version, *
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* - the MIT license (MIT), *
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* - or both in dual license, as here. *
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* *
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* (see https://agrum.gitlab.io/articles/dual-licenses-lgplv3mit.html) *
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* *
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* This aGrUM/pyAgrum library is distributed in the hope that it will be *
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* useful, but WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, *
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* OTHER DEALINGS IN THE SOFTWARE. *
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* See LICENCES for more details. *
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* *
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* SPDX-FileCopyrightText: Copyright 2005-2026 *
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* - Pierre-Henri WUILLEMIN(_at_LIP6) *
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* - Christophe GONZALES(_at_AMU) *
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* SPDX-License-Identifier: LGPL-3.0-or-later OR MIT *
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* *
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* Contact : info_at_agrum_dot_org *
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* homepage : http://agrum.gitlab.io *
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* gitlab : https://gitlab.com/agrumery/agrum *
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* *
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****************************************************************************/
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#pragma once
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#include <
agrum/BN/learning/K2.h
>
// to ease IDE parser
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#ifndef DOXYGEN_SHOULD_SKIP_THIS
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namespace
gum
{
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namespace
learning
{
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// Constructors, destructor and assignment operators are defined
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// out-of-line in K2.cpp on purpose -- see the comment there.
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INLINE
void
K2::setOrder
(
const
Sequence< NodeId >& order) {
_order_
=
order
; }
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INLINE
void
K2::setOrder
(
const
std::vector< NodeId >& order) {
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_order_
.
clear
();
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for
(
const
auto
node:
order
) {
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_order_
.
insert
(node);
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}
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}
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INLINE
const
Sequence< NodeId >&
K2::order
() const noexcept {
return
_order_
; }
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INLINE
void
K2::_checkOrder_
(
const
std::vector< Size >& modal) {
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if
(modal.size() !=
_order_
.
size
()) {
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GUM_ERROR
(InvalidArgument,
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"the number of elements in the order given "
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"to K2 is not the same as the number of nodes"
);
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}
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bool
order_ok =
true
;
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for
(
const
auto
node:
_order_
) {
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if
(node >=
_order_
.size()) {
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order_ok =
false
;
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break
;
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}
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}
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if
(!order_ok) {
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GUM_ERROR
(InvalidArgument,
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"there exist at least one node in the order "
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"given to K2 that has no domain size"
);
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}
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}
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INLINE ApproximationScheme&
K2::approximationScheme
() {
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return
GreedyHillClimbing::approximationScheme
();
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}
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}
/* namespace learning */
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}
/* namespace gum */
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#endif
/* DOXYGEN_SHOULD_SKIP_THIS */
K2.h
The K2 algorithm.
gum::SequenceImplementation< Key, std::is_scalar< Key >::value >::insert
void insert(const Key &k)
gum::SequenceImplementation< Key, std::is_scalar< Key >::value >::clear
void clear()
gum::SequenceImplementation< Key, std::is_scalar< Key >::value >::size
Size size() const noexcept
gum::learning::GreedyHillClimbing::approximationScheme
ApproximationScheme & approximationScheme()
returns the approximation policy of the learning algorithm
Definition
greedyHillClimbing.cpp:88
gum::learning::K2::order
const Sequence< NodeId > & order() const noexcept
returns the current order
gum::learning::K2::_order_
Sequence< NodeId > _order_
the order on the variable used for learning
Definition
K2.h:132
gum::learning::K2::setOrder
void setOrder(const Sequence< NodeId > &order)
sets the order on the variables
gum::learning::K2::_checkOrder_
void _checkOrder_(const std::vector< Size > &modal)
checks that the order passed to K2 is coherent with the variables as specified by their modalities
gum::learning::K2::approximationScheme
ApproximationScheme & approximationScheme()
returns the approximation policy of the learning algorithm
GUM_ERROR
#define GUM_ERROR(type, msg)
Definition
exceptions.h:76
gum::learning
include the inlined functions if necessary
Definition
CSVParser.h:55
gum
gum is the global namespace for all aGrUM entities
Definition
agrum.h:46
aGrUM
3.2.0
© PHW&CG&others - 2022
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