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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::ScoreAIC< ALLOC >, including all inherited members.
| allocator_type typedef | gum::learning::ScoreAIC< ALLOC > | |
| apriori_ | gum::learning::Score< ALLOC > | protected |
| cache_ | gum::learning::Score< ALLOC > | protected |
| clear() | gum::learning::Score< ALLOC > | |
| clearCache() | gum::learning::Score< ALLOC > | |
| clearRanges() | gum::learning::Score< ALLOC > | |
| clone() const | gum::learning::ScoreAIC< ALLOC > | virtual |
| clone(const allocator_type &alloc) const | gum::learning::ScoreAIC< ALLOC > | virtual |
| counter_ | gum::learning::Score< ALLOC > | protected |
| database() const | gum::learning::Score< ALLOC > | |
| empty_ids_ | gum::learning::Score< ALLOC > | protected |
| getAllocator() const | gum::learning::Score< ALLOC > | |
| internalApriori() const final | gum::learning::ScoreAIC< ALLOC > | virtual |
| isAprioriCompatible() const final | gum::learning::ScoreAIC< ALLOC > | virtual |
| isAprioriCompatible(const std::string &apriori_type, double weight=1.0f) | gum::learning::ScoreAIC< ALLOC > | static |
| isAprioriCompatible(const Apriori< ALLOC > &apriori) | gum::learning::ScoreAIC< ALLOC > | static |
| isUsingCache() const | gum::learning::Score< ALLOC > | |
| marginalize_(const NodeId X_id, const std::vector< double, ALLOC< double > > &N_xyz) const | gum::learning::Score< ALLOC > | protected |
| minNbRowsPerThread() const | gum::learning::Score< ALLOC > | virtual |
| nbThreads() const | gum::learning::Score< ALLOC > | virtual |
| nodeId2Columns() const | gum::learning::Score< ALLOC > | |
| one_log2_ | gum::learning::Score< ALLOC > | protected |
| operator=(const ScoreAIC< ALLOC > &from) | gum::learning::ScoreAIC< ALLOC > | |
| operator=(ScoreAIC< ALLOC > &&from) | gum::learning::ScoreAIC< ALLOC > | |
| gum::learning::Score::operator=(const Score< ALLOC > &from) | gum::learning::Score< ALLOC > | protected |
| gum::learning::Score::operator=(Score< ALLOC > &&from) | gum::learning::Score< ALLOC > | protected |
| ranges() const | gum::learning::Score< ALLOC > | |
| score(const NodeId var) | gum::learning::Score< ALLOC > | |
| score(const NodeId var, const std::vector< NodeId, ALLOC< NodeId > > &rhs_ids) | gum::learning::Score< ALLOC > | |
| Score(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::Score< ALLOC > | |
| Score(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::Score< ALLOC > | |
| Score(const Score< ALLOC > &from) | gum::learning::Score< ALLOC > | protected |
| Score(const Score< ALLOC > &from, const allocator_type &alloc) | gum::learning::Score< ALLOC > | protected |
| Score(Score< ALLOC > &&from) | gum::learning::Score< ALLOC > | protected |
| Score(Score< ALLOC > &&from, const allocator_type &alloc) | gum::learning::Score< ALLOC > | protected |
| score_(const IdCondSet< ALLOC > &idset) final | gum::learning::ScoreAIC< ALLOC > | protectedvirtual |
| ScoreAIC(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::ScoreAIC< ALLOC > | |
| ScoreAIC(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::ScoreAIC< ALLOC > | |
| ScoreAIC(const ScoreAIC< ALLOC > &from) | gum::learning::ScoreAIC< ALLOC > | |
| ScoreAIC(const ScoreAIC< ALLOC > &from, const allocator_type &alloc) | gum::learning::ScoreAIC< ALLOC > | |
| ScoreAIC(ScoreAIC< ALLOC > &&from) | gum::learning::ScoreAIC< ALLOC > | |
| ScoreAIC(ScoreAIC< ALLOC > &&from, const allocator_type &alloc) | gum::learning::ScoreAIC< ALLOC > | |
| setMaxNbThreads(std::size_t nb) const | gum::learning::Score< ALLOC > | virtual |
| setMinNbRowsPerThread(const std::size_t nb) const | gum::learning::Score< ALLOC > | virtual |
| 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::Score< ALLOC > | |
| use_cache_ | gum::learning::Score< ALLOC > | protected |
| useCache(const bool on_off) | gum::learning::Score< ALLOC > | |
| ~Score() | gum::learning::Score< ALLOC > | virtual |
| ~ScoreAIC() | gum::learning::ScoreAIC< ALLOC > | virtual |