libDAI
Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 1234]
oCdai::BP_dual::_edges_t< T >Convenience label for storing edge properties
oCdai::BBPImplements BBP (Back-Belief-Propagation) [EaG09]
oCdai::BBPCostFunctionPredefined cost functions that can be used with BBP
oCdai::BP_dual::beliefsGroups together the data structures for storing the two types of beliefs and their normalizers
oCdai::BipartiteGraphRepresents the neighborhood structure of nodes in an undirected, bipartite graph
oCdai::BP_dualCalculates both types of BP messages and their normalizers from an InfAlg
oCdai::ClusterGraphA ClusterGraph is a hypergraph with variables as nodes, and "clusters" (sets of variables) as hyperedges
oCdai::DAGRepresents the neighborhood structure of nodes in a directed cyclic graph
oCdai::DEdgeRepresents a directed edge
oCdai::BP::EdgePropType used for storing edge properties
oCdai::EMAlgEMAlg performs Expectation Maximization to learn factor parameters
oCdai::EvidenceStores a data set consisting of multiple samples, where each sample is the observed joint state of some variables
oCdai::ExceptionError handling in libDAI is done by throwing an instance of the Exception class
oCdai::FactorGraphRepresents a factor graph
|\Cdai::RegionGraphA RegionGraph combines a bipartite graph consisting of outer regions (type FRegion) and inner regions (type Region) with a FactorGraph
oCdai::first_less< T1, T2 >Function object that returns true if a.first < b.first
oCdai::fo_abs< T >Function object that takes the absolute value
oCdai::fo_absdiff< T >Function object that returns the absolute difference of x and y
oCdai::fo_divides0< T >Function object similar to std::divides(), but different in that dividing by zero results in zero
oCdai::fo_exp< T >Function object that takes the exponent
oCdai::fo_Hellinger< T >Function object useful for calculating the Hellinger distance
oCdai::fo_id< T >Function object that returns the value itself
oCdai::fo_inv< T >Function object that takes the inverse
oCdai::fo_inv0< T >Function object that takes the inverse, except that 1/0 is defined to be 0
oCdai::fo_KL< T >Function object useful for calculating the KL distance
oCdai::fo_log< T >Function object that takes the logarithm
oCdai::fo_log0< T >Function object that takes the logarithm, except that log(0) is defined to be 0
oCdai::fo_max< T >Function object that returns the maximum of two values
oCdai::fo_min< T >Function object that returns the minimum of two values
oCdai::fo_plog0p< T >Function object that returns p*log0(p)
oCdai::fo_pow< T >Function object that returns x to the power y
oCdai::GraphALRepresents the neighborhood structure of nodes in an undirected graph
oCdai::GraphELRepresents an undirected graph, implemented as a std::set of undirected edges
oCdai::greedyVariableEliminationHelper object for dai::ClusterGraph::VarElim()
oCdai::hash_map< T, U, H >Hash_map is an alias for std::tr1::unordered_map
oCdai::IndexForTool for looping over the states of several variables
oCdai::InfAlgInfAlg is an abstract base class, defining the common interface of all inference algorithms in libDAI
|\Cdai::DAIAlg< GRM >Combines the abstract base class InfAlg with a graphical model (e.g., a FactorGraph or RegionGraph)
| oCdai::BPApproximate inference algorithm "(Loopy) Belief Propagation"
| |oCdai::FBPApproximate inference algorithm "Fractional Belief Propagation" [WiH03]
| |\Cdai::TRWBPApproximate inference algorithm "Tree-Reweighted Belief Propagation" [WJW03]
| oCdai::CBPClass for CBP (Conditioned Belief Propagation) [EaG09]
| oCdai::DecMAPApproximate inference algorithm DecMAP, which constructs a MAP state by decimation
| oCdai::ExactInfExact inference algorithm using brute force enumeration (mainly useful for testing purposes)
| oCdai::GibbsApproximate inference algorithm "Gibbs sampling"
| oCdai::HAKApproximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [HAK03]
| oCdai::JTreeExact inference algorithm using junction tree
| |\Cdai::TreeEPApproximate inference algorithm "Tree Expectation Propagation" [MiQ04]
| oCdai::LCApproximate inference algorithm "Loop Corrected Belief Propagation" [MoK07]
| oCdai::MFApproximate inference algorithm "Mean Field"
| \Cdai::MRApproximate inference algorithm by Montanari and Rizzo [MoR05]
oCdai::BipartiteGraph::levelTypeUsed internally by isTree()
oCdai::MaximizationStepA MaximizationStep groups together several parameter estimation tasks (SharedParameters objects) into a single unit
oCdai::BP_dual::messagesGroups together the data structures for storing the two types of messages and their normalizers
oCdai::multiforMultifor makes it easy to perform a dynamic number of nested for loops
oCdai::NeighborDescribes the neighbor relationship of two nodes in a graph
oCdai::ParameterEstimationBase class for parameter estimation methods
|\Cdai::CondProbEstimationEstimates the parameters of a conditional probability table, using pseudocounts
oCdai::PermuteTool for calculating permutations of linear indices of multi-dimensional arrays
oCdai::CBP::PropertiesParameters for CBP
oCdai::BBP::PropertiesParameters for BBP
oCdai::BP::PropertiesParameters for BP
oCdai::DecMAP::PropertiesParameters for DecMAP
oCdai::ExactInf::PropertiesParameters for ExactInf
oCdai::JTree::PropertiesParameters for JTree
oCdai::Gibbs::PropertiesParameters for Gibbs
oCdai::HAK::PropertiesParameters for HAK
oCdai::LC::PropertiesParameters for LC
oCdai::MF::PropertiesParameters for MF
oCdai::TreeEP::PropertiesParameters for TreeEP
oCdai::MR::PropertiesParameters for MR
oCdai::PropertySetRepresents a set of properties, mapping keys (of type PropertyKey) to values (of type PropertyValue)
oCdai::RootedTreeRepresents a rooted tree, implemented as a vector of directed edges
oCdai::sequentialVariableEliminationHelper object for dai::ClusterGraph::VarElim()
oCdai::SharedParametersRepresents a single factor or set of factors whose parameters should be estimated
oCdai::SmallSet< T >Represents a set; the implementation is optimized for a small number of elements
oCdai::SmallSet< Var >
|\Cdai::VarSetRepresents a set of variables
| \Cdai::RegionA Region is a set of variables with a counting number
oCdai::StateMakes it easy to iterate over all possible joint states of variables within a VarSet
oCdai::TFactor< T >Represents a (probability) factor
|\Cdai::FRegionAn FRegion is a factor with a counting number
oCdai::TFactorSp< T, spvector_type >Represents a (probability) factor
oCdai::TProb< T >Represents a vector with entries of type T
oCdai::TProbSp< T, spvector_type >Represents a vector with entries of type T
oCdai::TreeEP::TreeEPSubTreeStores the data structures needed to efficiently update the approximation of an off-tree factor
oCdai::UEdgeRepresents an undirected edge
oCdai::VarRepresents a discrete random variable
\Cdai::WeightedGraph< T >Represents an undirected weighted graph, with weights of type T, implemented as a std::map mapping undirected edges to weights