aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
GibbsBNdistance.h
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51
52
53#ifndef GUM_GIBBS_KL2_H
54#define GUM_GIBBS_KL2_H
55
59
60namespace gum {
61
94
95 template < typename GUM_SCALAR >
97 public BNdistance< GUM_SCALAR >,
99 public GibbsOperator< GUM_SCALAR > {
100 public:
101 /* no default constructor */
102
108
109
111
114 explicit GibbsBNdistance(const BNdistance< GUM_SCALAR >& kl);
115
118
124 void setBurnIn(Size b);
125
130 Size burnIn() const;
131
132 protected:
133 void computeKL_() final;
134
135 using BNdistance< GUM_SCALAR >::p_;
136 using BNdistance< GUM_SCALAR >::q_;
137 using BNdistance< GUM_SCALAR >::hellinger_;
138 using BNdistance< GUM_SCALAR >::bhattacharya_;
139 using BNdistance< GUM_SCALAR >::jsd_;
140
141 using BNdistance< GUM_SCALAR >::klPQ_;
142 using BNdistance< GUM_SCALAR >::klQP_;
143
144 using BNdistance< GUM_SCALAR >::errorPQ_;
145 using BNdistance< GUM_SCALAR >::errorQP_;
146 };
147
148
149#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
150 extern template class GibbsBNdistance< double >;
151#endif
152
153} // namespace gum
154
156
157#endif
algorithm for KL divergence between BNs
KL divergence between BNs – implementation using Gibbs sampling.
This file contains general scheme for iteratively convergent algorithms.
ApproximationScheme(bool verbosity=false)
GUM_SCALAR hellinger_
Definition BNdistance.h:165
GUM_SCALAR klPQ_
Definition BNdistance.h:159
BNdistance(const IBayesNet< GUM_SCALAR > &P, const IBayesNet< GUM_SCALAR > &Q)
constructor must give 2 BNs
GUM_SCALAR jsd_
Definition BNdistance.h:167
GUM_SCALAR klQP_
Definition BNdistance.h:160
GUM_SCALAR bhattacharya_
Definition BNdistance.h:166
const IBayesNet< GUM_SCALAR > & q_
Definition BNdistance.h:157
const IBayesNet< GUM_SCALAR > & p_
Definition BNdistance.h:156
GibbsKL computes the KL divergence betweens 2 BNs using an approximation pattern: GIBBS sampling.
Size burnIn() const
Returns the number of burn in.
void computeKL_() final
void setBurnIn(Size b)
Number of burn in for one iteration.
GibbsBNdistance(const IBayesNet< GUM_SCALAR > &P, const IBayesNet< GUM_SCALAR > &Q)
constructor must give 2 BNs
Definition GibbsKL_tpl.h:73
~GibbsBNdistance()
destructor
GibbsOperator(const IBayesNet< GUM_SCALAR > &BN, const NodeProperty< Idx > *hardEv, Size nbr=1, bool atRandom=false)
constructor
Class representing the minimal interface for Bayesian network with no numerical data.
Definition IBayesNet.h:75
This file contains Gibbs sampling (for BNs) class definitions.
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition types.h:74
gum is the global namespace for all aGrUM entities
Definition agrum.h:46