aGrUM 3.1.1
a C++ library for (probabilistic) graphical models
DAG2BNLearner_inl.h
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40
41#pragma once
42
43
50
51#include <agrum/BN/learning/paramUtils/DAG2BNLearner.h> // to ease IDE parser
52#ifndef DOXYGEN_SHOULD_SKIP_THIS
53
55
56namespace gum {
57
58 namespace learning {
59
62
64 INLINE DAG2BNLearner& DAG2BNLearner::setNoise(const double noise) {
65 if ((noise < 0.0) || (noise > 1.0))
66 GUM_ERROR(OutOfBounds, "EM's noise must belong to interval [0,1]");
67 noiseEM_ = noise;
68 return *this;
69 }
70
71 } /* namespace learning */
72
73} /* namespace gum */
74
75#endif /* DOXYGEN_SHOULD_SKIP_THIS */
A class that, given a structure and a parameter estimator returns a full Bayes net.
A class that, given a structure and a parameter estimator returns a full Bayes net.
EMApproximationScheme & approximationScheme()
returns the approximation policy of the EM learning algorithm
DAG2BNLearner & setNoise(const double noise)
sets the noise amount used to perturb the initial CPTs used by EM
A class for parameterizing EM's parameter learning approximations.
#define GUM_ERROR(type, msg)
Definition exceptions.h:76
include the inlined functions if necessary
Definition CSVParser.h:55
gum is the global namespace for all aGrUM entities
Definition agrum.h:46