aGrUM
3.2.0
a C++ library for (probabilistic) graphical models
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bdeuPrior.cpp
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/****************************************************************************
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* This file is part of the aGrUM/pyAgrum library. *
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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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* The aGrUM/pyAgrum library is free software; you can redistribute it *
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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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* homepage : http://agrum.gitlab.io *
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****************************************************************************/
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#include <
agrum/base/stattests/priors/bdeuPrior.h
>
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#ifndef DOXYGEN_SHOULD_SKIP_THIS
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# ifdef GUM_NO_INLINE
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# include <
agrum/base/stattests/priors/bdeuPrior_inl.h
>
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# endif
/* GUM_NO_INLINE */
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namespace
gum::learning
{
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void
BDeuPrior::addConditioningPseudoCount
(
const
IdCondSet
& idset,
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std::vector< double >& counts) {
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// if the conditioning set is empty or the weight is equal to zero,
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// the prior is also empty
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if
((idset.size() == idset.nbLHSIds()) || (this->weight_ == 0.0)
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|| (idset.nbLHSIds() == std::size_t(0)))
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return
;
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// add the weight to the counting vector
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const
double
weight
= this->
weight_
/ double(counts.size());
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for
(
auto
& count: counts)
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count +=
weight
;
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}
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}
// namespace gum::learning
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#endif
/* DOXYGEN_SHOULD_SKIP_THIS */
bdeuPrior.h
the internal prior for the BDeu score (N' / (r_i * q_i)
bdeuPrior_inl.h
the internal prior for the BDeu score (N' / (r_i * q_i)
gum::learning::BDeuPrior::addConditioningPseudoCount
void addConditioningPseudoCount(const IdCondSet &idset, std::vector< double > &counts) final
adds the prior to a counting vector defined over the right hand side of the idset
gum::learning::IdCondSet
A class for storing a pair of sets of NodeIds, the second one corresponding to a conditional set.
Definition
idCondSet.h:214
gum::learning::Prior::weight_
double weight_
the weight of the prior
Definition
prior.h:159
gum::learning::Prior::weight
double weight() const
returns the weight assigned to the prior
gum::learning
include the inlined functions if necessary
Definition
CSVParser.h:55
aGrUM
3.2.0
© PHW&CG&others - 2022
DoXyGeN 1.18.0