aGrUM  0.13.2
MonteCarloSampling_tpl.h
Go to the documentation of this file.
1 /***************************************************************************
2  * Copyright (C) 2005 by Christophe GONZALES et Pierre-Henri WUILLEMIN *
3  * {prenom.nom}_at_lip6.fr *
4  * *
5  * This program is free software; you can redistribute it and/or modify *
6  * it under the terms of the GNU General Public License as published by *
7  * the Free Software Foundation; either version 2 of the License, or *
8  * (at your option) any later version. *
9  * *
10  * This program is distributed in the hope that it will be useful, *
11  * but WITHOUT ANY WARRANTY; without even the implied warranty of *
12  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
13  * GNU General Public License for more details. *
14  * *
15  * You should have received a copy of the GNU General Public License *
16  * along with this program; if not, write to the *
17  * Free Software Foundation, Inc., *
18  * 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. *
19  ***************************************************************************/
30 
31 
32 namespace gum {
33 
35  template < typename GUM_SCALAR >
37  const IBayesNet< GUM_SCALAR >* bn) :
38  SamplingInference< GUM_SCALAR >(bn) {
39  GUM_CONSTRUCTOR(MonteCarloSampling);
40  }
41 
43  template < typename GUM_SCALAR >
45  GUM_DESTRUCTOR(MonteCarloSampling);
46  }
47 
49  template < typename GUM_SCALAR >
52  return I;
53  }
54 
55 
56  template < typename GUM_SCALAR >
58  Instantiation prev) {
59  *w = 1.0f;
60  bool wrong_value = false;
61  do {
62  wrong_value = false;
63  prev.clear();
64  for (const auto nod : this->BN().topologicalOrder()) {
65  this->_addVarSample(nod, &prev);
66  if (this->hardEvidenceNodes().contains(nod)
67  && prev.val(this->BN().variable(nod)) != this->hardEvidence()[nod]) {
68  wrong_value = true;
69  break;
70  }
71  }
72  } while (wrong_value);
73  return prev;
74  }
75 } // namespace gum
This file contains Monte Carlo sampling class definition.
Class representing the minimal interface for Bayesian Network.
Definition: IBayesNet.h:59
gum is the global namespace for all aGrUM entities
Definition: agrum.h:25
const NodeProperty< Idx > & hardEvidence() const
indicate for each node with hard evidence which value it took
Instantiation _burnIn() override
draws a defined number of samples without updating the estimators
void clear()
Erase all variables from an Instantiation.
~MonteCarloSampling() override
Destructor.
Instantiation _draw(float *w, Instantiation prev) override
draws a sample according to classic Monte Carlo sampling
const NodeSet & hardEvidenceNodes() const
returns the set of nodes with hard evidence
Class for assigning/browsing values to tuples of discrete variables.
Definition: instantiation.h:80
virtual void _addVarSample(NodeId nod, Instantiation *I)
adds a node to current instantiation
virtual const IBayesNet< GUM_SCALAR > & BN() const final
Returns a constant reference over the IBayesNet referenced by this class.
Idx val(Idx i) const
Returns the current value of the variable at position i.
MonteCarloSampling(const IBayesNet< GUM_SCALAR > *bn)
Default constructor.