48#ifndef GUM_LEARNING_INDEP_TEST_G2_H
49#define GUM_LEARNING_INDEP_TEST_G2_H
95 const Prior& external_prior,
96 const std::vector< std::pair< std::size_t, std::size_t > >&
ranges,
148 std::pair< double, double >
158#ifndef DOXYGEN_SHOULD_SKIP_THIS
const std::vector< std::pair< std::size_t, std::size_t > > & ranges() const
returns the current ranges
the class used to read a row in the database and to transform it into a set of DBRow instances that c...
A class for storing a pair of sets of NodeIds, the second one corresponding to a conditional set.
IndepTestG2(const IndepTestG2 &from)
copy constructor
IndepTestG2 * clone() const override
virtual copy constructor
IndepTestG2 & operator=(IndepTestG2 &&from) noexcept
move operator
~IndepTestG2() override
destructor
std::pair< double, double > statistics(NodeId var1, NodeId var2, const std::vector< NodeId > &rhs_ids={}) override
get the pair <G2statistic,pvalue> for a test var1 indep var2 given rhs_ids
IndepTestG2(const DBRowGeneratorParser &parser, const Prior &prior, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
IndepTestG2 & operator=(const IndepTestG2 &from)
copy operator
IndepTestG2(const DBRowGeneratorParser &parser, const Prior &external_prior, const std::vector< std::pair< std::size_t, std::size_t > > &ranges, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
IndepTestG2(IndepTestG2 &&from) noexcept
move constructor
std::pair< double, double > statistics_(const IdCondSet &idset)
compute the pair <G2 statistic,pvalue>
IndependenceTest(const DBRowGeneratorParser &parser, const Prior &external_prior, const std::vector< std::pair< std::size_t, std::size_t > > &ranges, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
default constructor
the base class for all a priori
Size NodeId
Type for node ids.
the class for computing G2 scores
the base class for all the independence tests used for learning
include the inlined functions if necessary
gum is the global namespace for all aGrUM entities
the no a priorclass: corresponds to 0 weight-sample