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3.2.0
a C++ library for (probabilistic) graphical models
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DAG2BNLearner_inl.h
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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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* 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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#pragma once
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#include <
agrum/BN/learning/paramUtils/DAG2BNLearner.h
>
// to ease IDE parser
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#ifndef DOXYGEN_SHOULD_SKIP_THIS
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# include <
agrum/BN/learning/paramUtils/DAG2BNLearner.h
>
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namespace
gum
{
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namespace
learning
{
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INLINE
EMApproximationScheme
&
DAG2BNLearner::approximationScheme
() {
return
*
this
; }
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INLINE
DAG2BNLearner
&
DAG2BNLearner::setNoise
(
const
double
noise) {
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if
((noise < 0.0) || (noise > 1.0))
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GUM_ERROR
(OutOfBounds,
"EM's noise must belong to interval [0,1]"
);
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noiseEM_ = noise;
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return
*
this
;
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}
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}
/* namespace learning */
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}
/* namespace gum */
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#endif
/* DOXYGEN_SHOULD_SKIP_THIS */
DAG2BNLearner.h
A class that, given a structure and a parameter estimator returns a full Bayes net.
gum::learning::DAG2BNLearner
A class that, given a structure and a parameter estimator returns a full Bayes net.
Definition
DAG2BNLearner.h:70
gum::learning::DAG2BNLearner::approximationScheme
EMApproximationScheme & approximationScheme()
returns the approximation policy of the EM learning algorithm
gum::learning::DAG2BNLearner::setNoise
DAG2BNLearner & setNoise(const double noise)
sets the noise amount used to perturb the initial CPTs used by EM
gum::learning::EMApproximationScheme
A class for parameterizing EM's parameter learning approximations.
Definition
EMApproximationScheme.h:68
GUM_ERROR
#define GUM_ERROR(type, msg)
Definition
exceptions.h:76
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
aGrUM
3.2.0
© PHW&CG&others - 2022
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