aGrUM 3.1.1
a C++ library for (probabilistic) graphical models
scoreK2.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
50
51#ifndef GUM_LEARNING_SCORE_K2_H
52#define GUM_LEARNING_SCORE_K2_H
53
54#include <string>
55
56#include <agrum/agrum.h>
57
61
62namespace gum {
63
64 namespace learning {
65
79 class ScoreK2: public Score {
80 public:
81 // ##########################################################################
83 // ##########################################################################
85
87
106 const Prior& prior,
107 const std::vector< std::pair< std::size_t, std::size_t > >& ranges,
108 const Bijection< NodeId, std::size_t >& nodeId2columns
110
111
113
126 const Prior& prior,
127 const Bijection< NodeId, std::size_t >& nodeId2columns
129
131 ScoreK2(const ScoreK2& from);
132
135
137 [[nodiscard]] ScoreK2* clone() const override;
138
140 ~ScoreK2() override;
141
143
144
145 // ##########################################################################
147 // ##########################################################################
148
150
152 ScoreK2& operator=(const ScoreK2& from);
153
156
158
159
160 // ##########################################################################
162 // ##########################################################################
164
166
175 std::string isPriorCompatible() const final;
176
178
188 const Prior& internalPrior() const final;
189
191
192
194
196 static std::string isPriorCompatible(PriorType prior_type, double weight = 1.0f);
197
199
200 static std::string isPriorCompatible(const Prior& prior);
201
202
203 protected:
205
208 double score_(const IdCondSet& idset) final;
209
210
211#ifndef DOXYGEN_SHOULD_SKIP_THIS
212
213 private:
215 K2Prior _internal_prior_;
216
218 GammaLog2 _gammalog2_;
219
220
221#endif /* DOXYGEN_SHOULD_SKIP_THIS */
222 };
223
224 } /* namespace learning */
225
226} /* namespace gum */
227
229#ifndef GUM_NO_INLINE
231#endif /* GUM_NO_INLINE */
232
233#endif /* GUM_LEARNING_SCORE_K2_H */
the internal prior for the K2 score = Laplace Prior
The class for computing Log2 (Gamma(x)).
Definition gammaLog2.h:68
the class used to read a row in the database and to transform it into a set of DBRow instances that c...
A class for storing a pair of sets of NodeIds, the second one corresponding to a conditional set.
Definition idCondSet.h:214
the internal prior for the K2 score = Laplace Prior
Definition K2Prior.h:71
the base class for all a priori
Definition prior.h:81
~ScoreK2() override
destructor
ScoreK2(const ScoreK2 &from)
copy constructor
ScoreK2(const DBRowGeneratorParser &parser, const Prior &prior, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
ScoreK2 & operator=(ScoreK2 &&from)
move operator
const Prior & internalPrior() const final
returns the internal prior of the score
ScoreK2(const DBRowGeneratorParser &parser, const Prior &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
ScoreK2(ScoreK2 &&from)
move constructor
double score_(const IdCondSet &idset) final
returns the score for a given IdCondSet
std::string isPriorCompatible() const final
indicates whether the prior is compatible (meaningful) with the score
ScoreK2 & operator=(const ScoreK2 &from)
copy operator
ScoreK2 * clone() const override
virtual copy constructor
const std::vector< std::pair< std::size_t, std::size_t > > & ranges() const
returns the current ranges
Score(const DBRowGeneratorParser &parser, const Prior &external_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
The class for computing Log2 (Gamma(x)).
include the inlined functions if necessary
Definition CSVParser.h:55
gum is the global namespace for all aGrUM entities
Definition agrum.h:46
STL namespace.
the class for computing K2 scores
the base class for all the scores used for learning (BIC, BDeu, etc)