aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
DAG2BNLearner_inl.h
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40#pragma once
41
42
49
50#ifndef DOXYGEN_SHOULD_SKIP_THIS
51
53
54namespace gum {
55
56 namespace learning {
57
60
62 INLINE DAG2BNLearner& DAG2BNLearner::setNoise(const double noise) {
63 if ((noise < 0.0) || (noise > 1.0))
64 GUM_ERROR(OutOfBounds, "EM's noise must belong to interval [0,1]");
65 noiseEM_ = noise;
66 return *this;
67 }
68
69 } /* namespace learning */
70
71} /* namespace gum */
72
73#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:72
include the inlined functions if necessary
Definition CSVParser.h:54
gum is the global namespace for all aGrUM entities
Definition agrum.h:46