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aGrUM
0.20.3
a C++ library for (probabilistic) graphical models
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aGrUM
0.20.3
a C++ library for (probabilistic) graphical models
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This is the complete list of members for gum::learning::KNML< ALLOC >, including all inherited members.
| allocator_type typedef | gum::learning::KNML< ALLOC > | |
| apriori_ | gum::learning::IndependenceTest< ALLOC > | private |
| cache_ | gum::learning::IndependenceTest< ALLOC > | private |
| clear() | gum::learning::KNML< ALLOC > | virtual |
| clearCache() | gum::learning::KNML< ALLOC > | virtual |
| clearRanges() | gum::learning::IndependenceTest< ALLOC > | private |
| clone() const | gum::learning::KNML< ALLOC > | virtual |
| clone(const allocator_type &alloc) const | gum::learning::KNML< ALLOC > | virtual |
| counter_ | gum::learning::IndependenceTest< ALLOC > | private |
| database() const | gum::learning::IndependenceTest< ALLOC > | private |
| empty_ids_ | gum::learning::IndependenceTest< ALLOC > | private |
| getAllocator() const | gum::learning::IndependenceTest< ALLOC > | private |
| IndependenceTest(const DBRowGeneratorParser< ALLOC > &parser, const Apriori< ALLOC > &external_apriori, const std::vector< std::pair< std::size_t, std::size_t >, ALLOC< std::pair< std::size_t, std::size_t > > > &ranges, const Bijection< NodeId, std::size_t, ALLOC< std::size_t > > &nodeId2columns=Bijection< NodeId, std::size_t, ALLOC< std::size_t > >(), const allocator_type &alloc=allocator_type()) | gum::learning::IndependenceTest< ALLOC > | private |
| IndependenceTest(const DBRowGeneratorParser< ALLOC > &parser, const Apriori< ALLOC > &external_apriori, const Bijection< NodeId, std::size_t, ALLOC< std::size_t > > &nodeId2columns=Bijection< NodeId, std::size_t, ALLOC< std::size_t > >(), const allocator_type &alloc=allocator_type()) | gum::learning::IndependenceTest< ALLOC > | private |
| IndependenceTest(const IndependenceTest< ALLOC > &from) | gum::learning::IndependenceTest< ALLOC > | private |
| IndependenceTest(const IndependenceTest< ALLOC > &from, const allocator_type &alloc) | gum::learning::IndependenceTest< ALLOC > | private |
| IndependenceTest(IndependenceTest< ALLOC > &&from) | gum::learning::IndependenceTest< ALLOC > | private |
| IndependenceTest(IndependenceTest< ALLOC > &&from, const allocator_type &alloc) | gum::learning::IndependenceTest< ALLOC > | private |
| KNML(const DBRowGeneratorParser< ALLOC > &parser, const Apriori< ALLOC > &apriori, const std::vector< std::pair< std::size_t, std::size_t >, ALLOC< std::pair< std::size_t, std::size_t > > > &ranges, const Bijection< NodeId, std::size_t, ALLOC< std::size_t > > &nodeId2columns=Bijection< NodeId, std::size_t, ALLOC< std::size_t > >(), const allocator_type &alloc=allocator_type()) | gum::learning::KNML< ALLOC > | |
| KNML(const DBRowGeneratorParser< ALLOC > &parser, const Apriori< ALLOC > &apriori, const Bijection< NodeId, std::size_t, ALLOC< std::size_t > > &nodeId2columns=Bijection< NodeId, std::size_t, ALLOC< std::size_t > >(), const allocator_type &alloc=allocator_type()) | gum::learning::KNML< ALLOC > | |
| KNML(const KNML< ALLOC > &from) | gum::learning::KNML< ALLOC > | |
| KNML(const KNML< ALLOC > &from, const allocator_type &alloc) | gum::learning::KNML< ALLOC > | |
| KNML(KNML< ALLOC > &&from) | gum::learning::KNML< ALLOC > | |
| KNML(KNML< ALLOC > &&from, const allocator_type &alloc) | gum::learning::KNML< ALLOC > | |
| marginalize_(const std::size_t node_2_marginalize, const std::size_t X_size, const std::size_t Y_size, const std::size_t Z_size, const std::vector< double, ALLOC< double > > &N_xyz) const | gum::learning::IndependenceTest< ALLOC > | private |
| minNbRowsPerThread() const | gum::learning::IndependenceTest< ALLOC > | privatevirtual |
| nbThreads() const | gum::learning::IndependenceTest< ALLOC > | privatevirtual |
| nodeId2Columns() const | gum::learning::IndependenceTest< ALLOC > | private |
| one_log2_ | gum::learning::IndependenceTest< ALLOC > | private |
| operator=(const KNML< ALLOC > &from) | gum::learning::KNML< ALLOC > | |
| operator=(KNML< ALLOC > &&from) | gum::learning::KNML< ALLOC > | |
| gum::learning::IndependenceTest::operator=(const IndependenceTest< ALLOC > &from) | gum::learning::IndependenceTest< ALLOC > | private |
| gum::learning::IndependenceTest::operator=(IndependenceTest< ALLOC > &&from) | gum::learning::IndependenceTest< ALLOC > | private |
| ranges() const | gum::learning::IndependenceTest< ALLOC > | private |
| score(const NodeId var1, const NodeId var2) | gum::learning::IndependenceTest< ALLOC > | private |
| score(const NodeId var1, const NodeId var2, const std::vector< NodeId, ALLOC< NodeId > > &rhs_ids) | gum::learning::IndependenceTest< ALLOC > | private |
| score_(const IdCondSet< ALLOC > &idset) final | gum::learning::KNML< ALLOC > | protectedvirtual |
| setMaxNbThreads(std::size_t nb) const | gum::learning::IndependenceTest< ALLOC > | privatevirtual |
| setMinNbRowsPerThread(const std::size_t nb) const | gum::learning::IndependenceTest< ALLOC > | privatevirtual |
| setRanges(const std::vector< std::pair< std::size_t, std::size_t >, XALLOC< std::pair< std::size_t, std::size_t > > > &new_ranges) | gum::learning::IndependenceTest< ALLOC > | private |
| use_cache_ | gum::learning::IndependenceTest< ALLOC > | private |
| useCache(const bool on_off) | gum::learning::KNML< ALLOC > | virtual |
| ~IndependenceTest() | gum::learning::IndependenceTest< ALLOC > | privatevirtual |
| ~KNML() | gum::learning::KNML< ALLOC > | virtual |