aGrUM
3.2.0
a C++ library for (probabilistic) graphical models
Toggle main menu visibility
paramEstimatorML_inl.h
Go to the documentation of this file.
1
/****************************************************************************
2
* This file is part of the aGrUM/pyAgrum library. *
3
* *
4
* Copyright (c) 2005-2026 by *
5
* - Pierre-Henri WUILLEMIN(_at_LIP6) *
6
* - Christophe GONZALES(_at_AMU) *
7
* *
8
* The aGrUM/pyAgrum library is free software; you can redistribute it *
9
* and/or modify it under the terms of either : *
10
* *
11
* - the GNU Lesser General Public License as published by *
12
* the Free Software Foundation, either version 3 of the License, *
13
* or (at your option) any later version, *
14
* - the MIT license (MIT), *
15
* - or both in dual license, as here. *
16
* *
17
* (see https://agrum.gitlab.io/articles/dual-licenses-lgplv3mit.html) *
18
* *
19
* This aGrUM/pyAgrum library is distributed in the hope that it will be *
20
* useful, but WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, *
21
* INCLUDING BUT NOT LIMITED TO THE WARRANTIES MERCHANTABILITY or FITNESS *
22
* FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE *
23
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER *
24
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, *
25
* ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR *
26
* OTHER DEALINGS IN THE SOFTWARE. *
27
* *
28
* See LICENCES for more details. *
29
* *
30
* SPDX-FileCopyrightText: Copyright 2005-2026 *
31
* - Pierre-Henri WUILLEMIN(_at_LIP6) *
32
* - Christophe GONZALES(_at_AMU) *
33
* SPDX-License-Identifier: LGPL-3.0-or-later OR MIT *
34
* *
35
* Contact : info_at_agrum_dot_org *
36
* homepage : http://agrum.gitlab.io *
37
* gitlab : https://gitlab.com/agrumery/agrum *
38
* *
39
****************************************************************************/
40
41
#pragma once
42
43
49
#include <
agrum/BN/learning/paramUtils/paramEstimatorML.h
>
// to ease IDE parser
50
#ifndef DOXYGEN_SHOULD_SKIP_THIS
51
52
namespace
gum
{
53
54
namespace
learning
{
55
56
// Constructors are defined out-of-line in paramEstimatorML.cpp on
57
// purpose -- see the comment there.
58
60
INLINE
ParamEstimatorML
*
ParamEstimatorML::clone
()
const
{
return
new
ParamEstimatorML
(*
this
); }
61
63
INLINE std::vector< double >
64
ParamEstimatorML::parameters
(
const
NodeId
target_node,
65
const
std::vector< NodeId >& conditioning_nodes) {
66
return
_parametersAndLogLikelihood_
(target_node, conditioning_nodes,
false
).first;
67
}
68
70
INLINE std::pair< std::vector< double >,
double
>
ParamEstimatorML::parametersAndLogLikelihood
(
71
const
NodeId
target_node,
72
const
std::vector< NodeId >& conditioning_nodes) {
73
return
_parametersAndLogLikelihood_
(target_node, conditioning_nodes,
true
);
74
}
75
76
}
/* namespace learning */
77
78
}
/* namespace gum */
79
80
#endif
/* DOXYGEN_SHOULD_SKIP_THIS */
gum::learning::ParamEstimatorML
The class for estimating parameters of CPTs using Maximum Likelihood.
Definition
paramEstimatorML.h:65
gum::learning::ParamEstimatorML::parametersAndLogLikelihood
std::pair< std::vector< double >, double > parametersAndLogLikelihood(const NodeId target_node, const std::vector< NodeId > &conditioning_nodes) override
returns the parameters of a CPT as well as its log-likelihood
gum::learning::ParamEstimatorML::clone
ParamEstimatorML * clone() const override
virtual copy constructor
gum::learning::ParamEstimatorML::_parametersAndLogLikelihood_
std::pair< std::vector< double >, double > _parametersAndLogLikelihood_(const NodeId target_node, const std::vector< NodeId > &conditioning_nodes, const bool compute_log_likelihood)
gum::learning::ParamEstimatorML::parameters
std::vector< double > parameters(const NodeId target_node, const std::vector< NodeId > &conditioning_nodes) override
returns the CPT's parameters corresponding to a given nodeset
gum::learning::ParamEstimatorML::ParamEstimatorML
ParamEstimatorML(const DBRowGeneratorParser &parser, const Prior &external_prior, const Prior &_score_internal_prior, const std::vector< std::pair< std::size_t, std::size_t > > &ranges, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
gum::NodeId
Size NodeId
Type for node ids.
Definition
graphElements.h:117
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
paramEstimatorML.h
the class for estimating parameters of CPTs using Maximum Likelihood
aGrUM
3.2.0
© PHW&CG&others - 2022
DoXyGeN 1.18.0