aGrUM
0.20.3
a C++ library for (probabilistic) graphical models
greedyHillClimbing.cpp
Go to the documentation of this file.
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/**
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*
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* Copyright (c) 2005-2021 by Pierre-Henri WUILLEMIN(@LIP6) & Christophe GONZALES(@AMU)
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* info_at_agrum_dot_org
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*
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* This library is free software: you can redistribute it and/or modify
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* it under the terms of the GNU Lesser General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This library is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public License
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* along with this library. If not, see <http://www.gnu.org/licenses/>.
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*
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*/
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/** @file
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* @brief The greedy hill learning algorithm (for directed graphs)
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*
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* @author Christophe GONZALES(@AMU) and Pierre-Henri WUILLEMIN(@LIP6)
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*/
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#
include
<
agrum
/
BN
/
learning
/
greedyHillClimbing
.
h
>
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namespace
gum
{
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namespace
learning
{
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/// default constructor
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GreedyHillClimbing
::
GreedyHillClimbing
() {
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setEpsilon
(0);
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disableMinEpsilonRate
();
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disableMaxIter
();
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disableMaxTime
();
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GUM_CONSTRUCTOR
(
GreedyHillClimbing
);
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}
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/// copy constructor
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GreedyHillClimbing
::
GreedyHillClimbing
(
const
GreedyHillClimbing
&
from
) :
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ApproximationScheme
(
from
) {
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GUM_CONS_CPY
(
GreedyHillClimbing
);
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}
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/// move constructor
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GreedyHillClimbing
::
GreedyHillClimbing
(
GreedyHillClimbing
&&
from
) :
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ApproximationScheme
(
std
::
move
(
from
)) {
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GUM_CONS_MOV
(
GreedyHillClimbing
);
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}
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/// destructor
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GreedyHillClimbing
::~
GreedyHillClimbing
() {
GUM_DESTRUCTOR
(
GreedyHillClimbing
); }
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/// copy operator
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GreedyHillClimbing
&
GreedyHillClimbing
::
operator
=(
const
GreedyHillClimbing
&
from
) {
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ApproximationScheme
::
operator
=(
from
);
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return
*
this
;
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}
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/// move operator
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GreedyHillClimbing
&
GreedyHillClimbing
::
operator
=(
GreedyHillClimbing
&&
from
) {
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ApproximationScheme
::
operator
=(
std
::
move
(
from
));
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return
*
this
;
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}
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/// returns the approximation policy of the learning algorithm
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ApproximationScheme
&
GreedyHillClimbing
::
approximationScheme
() {
return
*
this
; }
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}
/* namespace learning */
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}
/* namespace gum */
gum::Set::emplace
INLINE void emplace(Args &&... args)
Definition:
set_tpl.h:643
gum::learning::genericBNLearner::Database::Database
Database(const std::string &filename, const BayesNet< GUM_SCALAR > &bn, const std::vector< std::string > &missing_symbols)
Definition:
genericBNLearner_tpl.h:31