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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::ParamEstimator< ALLOC >, including all inherited members.
| allocator_type typedef | gum::learning::ParamEstimator< ALLOC > | |
| clear() | gum::learning::ParamEstimator< ALLOC > | virtual |
| clearRanges() | gum::learning::ParamEstimator< ALLOC > | |
| clone() const =0 | gum::learning::ParamEstimator< ALLOC > | pure virtual |
| clone(const allocator_type &alloc) const =0 | gum::learning::ParamEstimator< ALLOC > | pure virtual |
| counter_ | gum::learning::ParamEstimator< ALLOC > | protected |
| database() const | gum::learning::ParamEstimator< ALLOC > | |
| empty_nodevect_ | gum::learning::ParamEstimator< ALLOC > | protected |
| external_apriori_ | gum::learning::ParamEstimator< ALLOC > | protected |
| getAllocator() const | gum::learning::ParamEstimator< ALLOC > | |
| minNbRowsPerThread() const | gum::learning::ParamEstimator< ALLOC > | virtual |
| nbThreads() const | gum::learning::ParamEstimator< ALLOC > | virtual |
| nodeId2Columns() const | gum::learning::ParamEstimator< ALLOC > | |
| operator=(const ParamEstimator< ALLOC > &from) | gum::learning::ParamEstimator< ALLOC > | protected |
| operator=(ParamEstimator< ALLOC > &&from) | gum::learning::ParamEstimator< ALLOC > | protected |
| ParamEstimator(const DBRowGeneratorParser< ALLOC > &parser, const Apriori< ALLOC > &external_apriori, const Apriori< ALLOC > &_score_internal_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::ParamEstimator< ALLOC > | |
| ParamEstimator(const DBRowGeneratorParser< ALLOC > &parser, const Apriori< ALLOC > &external_apriori, const Apriori< ALLOC > &_score_internal_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::ParamEstimator< ALLOC > | |
| ParamEstimator(const ParamEstimator< ALLOC > &from) | gum::learning::ParamEstimator< ALLOC > | |
| ParamEstimator(const ParamEstimator< ALLOC > &from, const allocator_type &alloc) | gum::learning::ParamEstimator< ALLOC > | |
| ParamEstimator(ParamEstimator< ALLOC > &&from) | gum::learning::ParamEstimator< ALLOC > | |
| ParamEstimator(ParamEstimator< ALLOC > &&from, const allocator_type &alloc) | gum::learning::ParamEstimator< ALLOC > | |
| parameters(const NodeId target_node) | gum::learning::ParamEstimator< ALLOC > | |
| parameters(const NodeId target_node, const std::vector< NodeId, ALLOC< NodeId > > &conditioning_nodes)=0 | gum::learning::ParamEstimator< ALLOC > | pure virtual |
| ranges() const | gum::learning::ParamEstimator< ALLOC > | |
| score_internal_apriori_ | gum::learning::ParamEstimator< ALLOC > | protected |
| setBayesNet(const BayesNet< GUM_SCALAR > &new_bn) | gum::learning::ParamEstimator< ALLOC > | |
| setMaxNbThreads(std::size_t nb) const | gum::learning::ParamEstimator< ALLOC > | virtual |
| setMinNbRowsPerThread(const std::size_t nb) const | gum::learning::ParamEstimator< ALLOC > | virtual |
| setParameters(const NodeId target_node, const std::vector< NodeId, ALLOC< NodeId > > &conditioning_nodes, Potential< GUM_SCALAR > &pot) | gum::learning::ParamEstimator< ALLOC > | |
| 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::ParamEstimator< ALLOC > | |
| ~ParamEstimator() | gum::learning::ParamEstimator< ALLOC > | virtual |