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a C++ library for (probabilistic) graphical models
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DirichletPriorFromBN.h
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* - Christophe GONZALES(_at_AMU) *
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#ifndef GUM_LEARNING_PRIOR_DIRICHLET_FROM_BN_H
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#define GUM_LEARNING_PRIOR_DIRICHLET_FROM_BN_H
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#include <vector>
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#include <
agrum/agrum.h
>
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#include <
agrum/base/stattests/priors/prior.h
>
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#include <
agrum/BN/inference/lazyPropagation.h
>
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namespace
gum::learning
{
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template
< GUM_Numeric GUM_SCALAR >
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class
DirichletPriorFromBN
:
public
Prior
{
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public
:
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// ##########################################################################
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// ##########################################################################
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DirichletPriorFromBN
(
const
DatabaseTable
& learning_db,
const
BayesNet< GUM_SCALAR >* priorbn);
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DirichletPriorFromBN
(
const
DirichletPriorFromBN
& from);
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DirichletPriorFromBN
(
DirichletPriorFromBN
&& from)
noexcept
;
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[[nodiscard]]
DirichletPriorFromBN
*
clone
() const final;
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~
DirichletPriorFromBN
() override;
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// ##########################################################################
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// ##########################################################################
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DirichletPriorFromBN
& operator=(const
DirichletPriorFromBN
& from);
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DirichletPriorFromBN
& operator=(
DirichletPriorFromBN
&& from);
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// ##########################################################################
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// ##########################################################################
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PriorType
getType
() const final;
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bool
isInformative
() const final;
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void
setWeight
(
double
weight
) final;
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void
addJointPseudoCount
(const
IdCondSet
& idset,
std
::vector<
double
>& counts) final;
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void
addConditioningPseudoCount
(const
IdCondSet
& idset,
std
::vector<
double
>& counts) final;
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private:
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const
BayesNet
< GUM_SCALAR >*
_prior_bn_
;
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void
_addCountsForJoint_
(
Instantiation
& Ijoint,
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const
Set
<
NodeId
>& joint,
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std
::vector<
double
>& counts);
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};
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/* namespace learning */
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}
// namespace gum::learning
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#include <agrum/BN/learning/priors/DirichletPriorFromBN_tpl.h>
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#endif
/* GUM_LEARNING_PRIOR_DIRICHLET_FROM_BN_H */
agrum.h
gum::BayesNet
Class representing a Bayesian network.
Definition
BayesNet.h:99
gum::Instantiation
Class for assigning/browsing values to tuples of discrete variables.
Definition
instantiation.h:102
gum::Set
Representation of a set.
Definition
set.h:129
gum::learning::DatabaseTable
The class representing a tabular database as used by learning tasks.
Definition
databaseTable.h:200
gum::learning::DirichletPriorFromBN::DirichletPriorFromBN
DirichletPriorFromBN(DirichletPriorFromBN &&from) noexcept
move constructor
gum::learning::DirichletPriorFromBN::_addCountsForJoint_
void _addCountsForJoint_(Instantiation &Ijoint, const Set< NodeId > &joint, std::vector< double > &counts)
gum::learning::DirichletPriorFromBN::clone
DirichletPriorFromBN * clone() const final
virtual copy constructor
gum::learning::DirichletPriorFromBN::getType
PriorType getType() const final
returns the type of the prior
gum::learning::DirichletPriorFromBN::addJointPseudoCount
void addJointPseudoCount(const IdCondSet &idset, std::vector< double > &counts) final
adds the prior to a counting vector corresponding to the idset
gum::learning::DirichletPriorFromBN::_prior_bn_
const BayesNet< GUM_SCALAR > * _prior_bn_
Definition
DirichletPriorFromBN.h:150
gum::learning::DirichletPriorFromBN::setWeight
void setWeight(double weight) final
sets the weight of the a prior(kind of virtual sample size)
gum::learning::DirichletPriorFromBN::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::DirichletPriorFromBN::DirichletPriorFromBN
DirichletPriorFromBN(const DirichletPriorFromBN &from)
copy constructor
gum::learning::DirichletPriorFromBN::DirichletPriorFromBN
DirichletPriorFromBN(const DatabaseTable &learning_db, const BayesNet< GUM_SCALAR > *priorbn)
default constructor
gum::learning::DirichletPriorFromBN::isInformative
bool isInformative() const final
indicates whether the prior is potentially informative
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::Prior
Prior(const DatabaseTable &database, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
gum::learning::Prior::weight
double weight() const
returns the weight assigned to the prior
gum::NodeId
Size NodeId
Type for node ids.
Definition
graphElements.h:117
lazyPropagation.h
Implementation of a Shafer-Shenoy's-like version of lazy propagation for inference in Bayesian networ...
gum::learning
include the inlined functions if necessary
Definition
CSVParser.h:55
gum::learning::PriorType
PriorType
Definition
prior.h:59
std
STL namespace.
prior.h
the base class for all a priori
aGrUM
3.2.0
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