aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
DirichletPriorFromBN.h
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47#ifndef GUM_LEARNING_PRIOR_DIRICHLET_FROM_BN_H
48#define GUM_LEARNING_PRIOR_DIRICHLET_FROM_BN_H
49
50#include <vector>
51
52#include <agrum/agrum.h>
53
56
57namespace gum::learning {
58
59
65 template < typename GUM_SCALAR >
67 public:
68 // ##########################################################################
70 // ##########################################################################
72
74
80 DirichletPriorFromBN(const DatabaseTable& learning_db, const BayesNet< GUM_SCALAR >* priorbn);
81
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95
96
97 // ##########################################################################
99 // ##########################################################################
101
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111 // ##########################################################################
113 // ##########################################################################
115
117 PriorType getType() const final;
118
120
127 bool isInformative() const final;
128
130 void setWeight(double weight) final;
131
133
138 void addJointPseudoCount(const IdCondSet& idset, std::vector< double >& counts) final;
139
145 void addConditioningPseudoCount(const IdCondSet& idset, std::vector< double >& counts) final;
146
148
149 private:
150 const BayesNet< GUM_SCALAR >* _prior_bn_;
151
153 const Set< NodeId >& joint,
154 std::vector< double >& counts);
155 };
156
157 /* namespace learning */
158
159} // namespace gum::learning
160
161#include <agrum/BN/learning/priors/DirichletPriorFromBN_tpl.h>
162
163#endif /* GUM_LEARNING_PRIOR_DIRICHLET_FROM_BN_H */
Class representing a Bayesian network.
Definition BayesNet.h:93
Class for assigning/browsing values to tuples of discrete variables.
Representation of a set.
Definition set.h:131
The class representing a tabular database as used by learning tasks.
DirichletPriorFromBN(DirichletPriorFromBN &&from) noexcept
move constructor
void _addCountsForJoint_(Instantiation &Ijoint, const Set< NodeId > &joint, std::vector< double > &counts)
DirichletPriorFromBN * clone() const final
virtual copy constructor
PriorType getType() const final
returns the type of the prior
void addJointPseudoCount(const IdCondSet &idset, std::vector< double > &counts) final
adds the prior to a counting vector corresponding to the idset
const BayesNet< GUM_SCALAR > * _prior_bn_
void setWeight(double weight) final
sets the weight of the a prior(kind of virtual sample size)
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
DirichletPriorFromBN(const DirichletPriorFromBN &from)
copy constructor
DirichletPriorFromBN(const DatabaseTable &learning_db, const BayesNet< GUM_SCALAR > *priorbn)
default constructor
bool isInformative() const final
indicates whether the prior is tensorly informative
A class for storing a pair of sets of NodeIds, the second one corresponding to a conditional set.
Definition idCondSet.h:214
Prior(const DatabaseTable &database, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
double weight() const
returns the weight assigned to the prior
Size NodeId
Type for node ids.
Implementation of a Shafer-Shenoy's-like version of lazy propagation for inference in Bayesian networ...
include the inlined functions if necessary
Definition CSVParser.h:54
STL namespace.
the base class for all a priori