aGrUM  0.16.0
exactBNdistance_tpl.h
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1 
30 #include <agrum/core/math/math.h>
31 #include <agrum/BN/IBayesNet.h>
34 
35 namespace gum {
36  template < typename GUM_SCALAR >
39  BNdistance< GUM_SCALAR >(P, Q) {
40  GUM_CONSTRUCTOR(ExactBNdistance);
41  }
42 
43  template < typename GUM_SCALAR >
45  const BNdistance< GUM_SCALAR >& kl) :
46  BNdistance< GUM_SCALAR >(kl) {
47  GUM_CONSTRUCTOR(ExactBNdistance);
48  }
49 
50  template < typename GUM_SCALAR >
52  GUM_DESTRUCTOR(ExactBNdistance);
53  }
54 
55  template < typename GUM_SCALAR >
57  _klPQ = _klQP = _hellinger = _bhattacharya = _jsd = (GUM_SCALAR)0.0;
58  _errorPQ = _errorQP = 0;
59 
60  auto Ip = _p.completeInstantiation();
61  auto Iq = _q.completeInstantiation();
62 
63  // map between _p variables and _q variables (using name of vars)
65 
66  for (Idx ite = 0; ite < Ip.nbrDim(); ++ite) {
67  map.insert(&Ip.variable(ite), &_q.variableFromName(Ip.variable(ite).name()));
68  }
69  GUM_SCALAR pp, pq, pmid, lpp, lpq, lpmid;
70  for (Ip.setFirst(); !Ip.end(); ++Ip) {
71  Iq.setValsFrom(map, Ip);
72  pp = _p.jointProbability(Ip);
73  pq = _q.jointProbability(Iq);
74  pmid = (pp + pq) / 2.0;
75  lpmid = lpq = lpp = (GUM_SCALAR)0.0;
76  if (pmid != (GUM_SCALAR)0.0) lpmid = log2(pmid);
77  if (pp != (GUM_SCALAR)0.0) lpp = log2(pp);
78  if (pq != (GUM_SCALAR)0.0) lpq = log2(pq);
79 
80 
81  _hellinger += std::pow(std::sqrt(pp) - std::sqrt(pq), 2);
82  _bhattacharya += std::sqrt(pp * pq);
83 
84  if (pp != (GUM_SCALAR)0.0) {
85  if (pq != (GUM_SCALAR)0.0) {
86  _klPQ -= pp * (lpq - lpp); // log2(pq / pp);
87  } else {
88  _errorPQ++;
89  }
90  }
91 
92  if (pq != (GUM_SCALAR)0.0) {
93  if (pp != (GUM_SCALAR)0.0) {
94  _klQP -= pq * (lpp - lpq); // log2(pp / pq);
95  } else {
96  _errorQP++;
97  }
98  }
99  if (pmid != (GUM_SCALAR)0.0) {
100  _jsd +=
101  pp * lpp + pq * lpq
102  - (pp + pq) * lpmid; // pp* log2(pp / pmid) + pq * log2(pq / pmid);
103  }
104  }
105  _jsd /= 2.0;
106  _hellinger = std::sqrt(_hellinger);
107  _bhattacharya = -std::log(_bhattacharya);
108  }
109 
110 } // namespace gum
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
virtual ~ExactBNdistance()
destructor
GUM_SCALAR _klPQ
Definition: BNdistance.h:139
GUM_SCALAR _bhattacharya
Definition: BNdistance.h:142
const IBayesNet< GUM_SCALAR > & _p
Definition: BNdistance.h:136
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
GUM_SCALAR _jsd
Definition: BNdistance.h:143
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
Class representing the minimal interface for Bayesian Network.
Definition: IBayesNet.h:62
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
Definition: agrum.h:25
The class for generic Hash Tables.
Definition: hashTable.h:679
const IBayesNet< GUM_SCALAR > & _q
Definition: BNdistance.h:137
GUM_SCALAR _hellinger
Definition: BNdistance.h:141
Copyright 2005-2019 Pierre-Henri WUILLEMIN et Christophe GONZALES (LIP6) {prenom.nom}_at_lip6.fr.
ExactBNdistance computes exactly the KL divergence betweens 2 BNs.
ExactBNdistance(const IBayesNet< GUM_SCALAR > &P, const IBayesNet< GUM_SCALAR > &Q)
constructor must give 2 BNs
GUM_SCALAR _klQP
Definition: BNdistance.h:140
Size Idx
Type for indexes.
Definition: types.h:53
value_type & insert(const Key &key, const Val &val)
Adds a new element (actually a copy of this element) into the hash table.