aGrUM 3.0.0
a C++ library for (probabilistic) graphical models
ConstraintBasedLearning_tpl.h
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1#pragma once
2
3#include <agrum/BN/BayesNet.h>
4#include <agrum/BN/learning/ConstraintBasedLearning.h> // to ease IDE parser
6
7namespace gum {
8
9 namespace learning {
10
11 template < GUM_Numeric GUM_SCALAR, typename PARAM_ESTIMATOR >
12 BayesNet< GUM_SCALAR > ConstraintBasedLearning::learnBN(PARAM_ESTIMATOR& estimator,
15 }
16
17 } /* namespace learning */
18
19} /* namespace gum */
20
21/****************************************************************************
22 * This file is part of the aGrUM/pyAgrum library. *
23 * *
24 * Copyright (c) 2005-2026 by *
25 * - Pierre-Henri WUILLEMIN(_at_LIP6) *
26 * - Christophe GONZALES(_at_AMU) *
27 * *
28 * The aGrUM/pyAgrum library is free software; you can redistribute it *
29 * and/or modify it under the terms of either : *
30 * *
31 * - the GNU Lesser General Public License as published by *
32 * the Free Software Foundation, either version 3 of the License, *
33 * or (at your option) any later version, *
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35 * - or both in dual license, as here. *
36 * *
37 * (see https://agrum.gitlab.io/articles/dual-licenses-lgplv3mit.html) *
38 * *
39 * This aGrUM/pyAgrum library is distributed in the hope that it will be *
40 * useful, but WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, *
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48 * See LICENCES for more details. *
49 * *
50 * SPDX-FileCopyrightText: Copyright 2005-2026 *
51 * - Pierre-Henri WUILLEMIN(_at_LIP6) *
52 * - Christophe GONZALES(_at_AMU) *
53 * SPDX-License-Identifier: LGPL-3.0-or-later OR MIT *
54 * *
55 * Contact : info_at_agrum_dot_org *
56 * homepage : http://agrum.gitlab.io *
57 * gitlab : https://gitlab.com/agrumery/agrum *
58 * *
59 ****************************************************************************/
Class representing Bayesian networks.
Abstract base class for constraint-based structure learning algorithms.
A class that, given a structure and a parameter estimator returns a full Bayes net.
Base class for mixed graphs.
Definition mixedGraph.h:146
BayesNet< GUM_SCALAR > learnBN(PARAM_ESTIMATOR &estimator, MixedGraph graph)
learns structure then estimates parameters
DAG learnDAG(MixedGraph graph)
learns a DAG
static BayesNet< GUM_SCALAR > createBN(ParamEstimator &estimator, const DAG &dag)
create a BN from a DAG using a one pass generator (typically ML)
include the inlined functions if necessary
Definition CSVParser.h:55
gum is the global namespace for all aGrUM entities
Definition agrum.h:46