aGrUM 3.1.1
a C++ library for (probabilistic) graphical models
indepTestG2.cpp
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50
51#ifndef DOXYGEN_SHOULD_SKIP_THIS
52
54# ifdef GUM_NO_INLINE
56# endif /* GUM_NO_INLINE */
57
58namespace gum {
59
60 namespace learning {
61
63 IndepTestG2& IndepTestG2::operator=(const IndepTestG2& from) = default;
64
67 IndependenceTest::operator=(std::move(from));
68 return *this;
69 }
70
72 std::pair< double, double > IndepTestG2::statistics_(const IdCondSet& idset) {
73 // computeStatistics_ guarantees margX * margY != 0 before calling this lambda,
74 // so the log argument is well-defined — no need to guard against E == 0 here.
75 return computeStatistics_(idset, [](double O, double margX, double margY, double total) {
76 if (O == 0.0) return 0.0;
77 return 2.0 * O * std::log((O * total) / (margX * margY));
78 });
79 }
80
81 } /* namespace learning */
82
83} /* namespace gum */
84
85#endif /* DOXYGEN_SHOULD_SKIP_THIS */
A class for storing a pair of sets of NodeIds, the second one corresponding to a conditional set.
Definition idCondSet.h:214
the class for computing G2 independence test scores
Definition indepTestG2.h:66
IndepTestG2 & operator=(const IndepTestG2 &from)
copy operator
std::pair< double, double > statistics_(const IdCondSet &idset)
compute the pair <G2 statistic,pvalue>
IndependenceTest & operator=(const IndependenceTest &from)
copy operator
std::pair< double, double > computeStatistics_(const IdCondSet &idset, CellContribFn cellContrib)
shared loop for chi-squared-family statistics
the class for computing G2 scores
the class for computing G2 scores
include the inlined functions if necessary
Definition CSVParser.h:55
gum is the global namespace for all aGrUM entities
Definition agrum.h:46