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## Uses of Class
cc.mallet.grmm.types.FactorGraph

Packages that use FactorGraph
cc.mallet.grmm.inference |
cc.mallet.grmm.inference.gbp |
cc.mallet.grmm.learning |
cc.mallet.grmm.types |
cc.mallet.grmm.util |

Uses of FactorGraph in cc.mallet.grmm.inference

Fields in cc.mallet.grmm.inference declared as FactorGraph
`protected FactorGraph` | `AbstractBeliefPropagation.mdlCurrent`

Methods in cc.mallet.grmm.inference that return FactorGraph
`static FactorGraph` | `RandomGraphs.createGridWithObs(RandomGraphs.FactorGenerator gridGener, RandomGraphs.FactorGenerator obsGener, int size)`
`static FactorGraph` | `RandomGraphs.createRandomChain(Randoms r, int length)`
`static FactorGraph` | `RandomGraphs.createUniformChain(int length)`
`static FactorGraph` | `RandomGraphs.createUniformGrid(int length)`

Methods in cc.mallet.grmm.inference with parameters of type FactorGraph
`static void` | `RandomGraphs.addRandomNodePotentials(java.util.Random r, FactorGraph mdl)`
`static double[]` | `Utils.allL1MarginalDistance(FactorGraph mdl, Inferencer inf1, Inferencer inf2)`
`static double` | `Utils.avgL1MarginalDistance(FactorGraph mdl, Inferencer inf1, Inferencer inf2)`
`JunctionTree` | `JunctionTreeInferencer.buildJunctionTree(FactorGraph mdl)` Constructs a junction tree from a given factor graph.
`void` | `ResidualBP.computeMarginals(FactorGraph mdl)`
`abstract void` | `AbstractInferencer.computeMarginals(FactorGraph fg)`
`void` | `LoopyBP.computeMarginals(FactorGraph mdl)`
`void` | `TRP.computeMarginals(FactorGraph m)`
`void` | `TreeBP.computeMarginals(FactorGraph fg)`
`void` | `VariableElimination.computeMarginals(FactorGraph m)`
`void` | `Inferencer.computeMarginals(FactorGraph mdl)` Computes marginal distributions for a factor graph.
`void` | `JunctionTreeInferencer.computeMarginals(FactorGraph mdl)`
`void` | `SamplingInferencer.computeMarginals(FactorGraph mdl)`
`void` | `BruteForceInferencer.computeMarginals(FactorGraph mdl)`
`double` | `VariableElimination.computeNormalizationFactor(FactorGraph m)` Computes the normalization constant for a model.
`protected void` | `AbstractBeliefPropagation.doneWithGraph(FactorGraph mdl)`
`protected void` | `AbstractBeliefPropagation.initForGraph(FactorGraph mdl)`
`protected void` | `TRP.initForGraph(FactorGraph m)`
`protected void` | `TreeBP.initForGraph(FactorGraph fg)`
`Factor` | `BruteForceInferencer.joint(FactorGraph model)`
`static double` | `Utils.lookupMinusLogZ(FactorGraph mdl, Inferencer inf)` Returns ths value of -log Z in mdl according to the given inferencer.
`static TRP.TreeListFactory` | `TRP.TreeListFactory.makeFromReaders(FactorGraph fg, java.util.List readerList)`
`static double` | `Utils.maxL1MarginalDistance(FactorGraph mdl, Inferencer inf1, Inferencer inf2)`
`Tree` | `TRP.TreeFactory.nextTree(FactorGraph mdl)`
`Tree` | `TRP.AlmostRandomTreeFactory.nextTree(FactorGraph fullGraph)`
`Tree` | `TRP.TreeListFactory.nextTree(FactorGraph mdl)`
`double` | `AbstractInferencer.query(FactorGraph mdl, Assignment assn)`
`double` | `Inferencer.query(FactorGraph mdl, Assignment assn)` Computes the marginal probability of a given assignment to a small number of model variables.
`static TRP.TreeListFactory` | `TRP.TreeListFactory.readFromFiles(FactorGraph fg, java.util.List fileList)`
`Assignment` | `Sampler.sample(FactorGraph mdl, int N)` Samples from the distribution of a given undirected model.
`Assignment` | `GibbsSampler.sample(FactorGraph mdl, int N)`
`Assignment` | `ExactSampler.sample(FactorGraph mdl, int N)`
`protected void` | `AbstractBeliefPropagation.sendMessage(FactorGraph mdl, Factor from, Variable to)`
`void` | `AbstractBeliefPropagation.MessageStrategy.sendMessage(FactorGraph mdl, Factor from, Variable to)`
`void` | `AbstractBeliefPropagation.SumProductMessageStrategy.sendMessage(FactorGraph mdl, Factor from, Variable to)`
`void` | `AbstractBeliefPropagation.MaxProductMessageStrategy.sendMessage(FactorGraph mdl, Factor from, Variable to)`
`protected void` | `AbstractBeliefPropagation.sendMessage(FactorGraph mdl, Variable from, Factor to)`
`void` | `AbstractBeliefPropagation.MessageStrategy.sendMessage(FactorGraph mdl, Variable from, Factor to)`
`void` | `AbstractBeliefPropagation.SumProductMessageStrategy.sendMessage(FactorGraph mdl, Variable from, Factor to)`
`void` | `AbstractBeliefPropagation.MaxProductMessageStrategy.sendMessage(FactorGraph mdl, Variable from, Factor to)`
`Factor` | `VariableElimination.unnormalizedMarginal(FactorGraph model, Variable query)` The bulk of the variable-elimination algorithm.

Constructors in cc.mallet.grmm.inference with parameters of type FactorGraph
`MessageArray(FactorGraph fg)`

Uses of FactorGraph in cc.mallet.grmm.inference.gbp

Methods in cc.mallet.grmm.inference.gbp with parameters of type FactorGraph
`static void` | `ClusterVariationalRegionGenerator.addAllFactors(FactorGraph mdl, java.util.List regions)`
`java.util.List` | `ClusterVariationalRegionGenerator.BaseRegionComputer.computeBaseRegions(FactorGraph mdl)` Returns a list of top-level regions for use in the cluster variational method.
`java.util.List` | `ClusterVariationalRegionGenerator.ByFactorRegionComputer.computeBaseRegions(FactorGraph mdl)`
`java.util.List` | `ClusterVariationalRegionGenerator.Grid2x2RegionComputer.computeBaseRegions(FactorGraph mdl)`
`void` | `ParentChildGBP.computeMarginals(FactorGraph mdl)`
`cc.mallet.grmm.inference.gbp.RegionGraph` | `RegionGraphGenerator.constructRegionGraph(FactorGraph mdl)` Construct a region graph from an artbitrary model.
`cc.mallet.grmm.inference.gbp.RegionGraph` | `BPRegionGenerator.constructRegionGraph(FactorGraph mdl)`
`cc.mallet.grmm.inference.gbp.RegionGraph` | `ClusterVariationalRegionGenerator.constructRegionGraph(FactorGraph mdl)`
`cc.mallet.grmm.inference.gbp.RegionGraph` | `Kikuchi4SquareRegionGenerator.constructRegionGraph(FactorGraph mdl)`

Uses of FactorGraph in cc.mallet.grmm.learning

Subclasses of FactorGraph in cc.mallet.grmm.learning
`static class` | `ACRF.UnrolledGraph`

Uses of FactorGraph in cc.mallet.grmm.types

Subclasses of FactorGraph in cc.mallet.grmm.types
`class` | `DirectedModel` Class for directed graphical models.
`class` | `UndirectedGrid` A grid-shaped undirected graphical model.
`class` | `UndirectedModel` Class for pairwise undirected graphical models, also known as pairwise Markov random fields.

Constructors in cc.mallet.grmm.types with parameters of type FactorGraph
`Assignment(FactorGraph mdl, int[] outcomes)` Creates an assignment over all Variables in a model.

Uses of FactorGraph in cc.mallet.grmm.util

Methods in cc.mallet.grmm.util that return FactorGraph
`static FactorGraph` | `Models.addEvidence(FactorGraph mdl, Assignment assn)` Returns a new factor graph, the same as a given one, except that all the nodes in the given Assignment are clamped as evidence.
`static FactorGraph` | `Models.addEvidence(FactorGraph mdl, Assignment assn, java.util.Map toSlicedMap)`
`FactorGraph` | `ModelReader.readModel(java.io.BufferedReader in)`

Methods in cc.mallet.grmm.util with parameters of type FactorGraph
`static FactorGraph` | `Models.addEvidence(FactorGraph mdl, Assignment assn)` Returns a new factor graph, the same as a given one, except that all the nodes in the given Assignment are clamped as evidence.
`static FactorGraph` | `Models.addEvidence(FactorGraph mdl, Assignment assn, java.util.Map toSlicedMap)`
`static Assignment` | `Models.bestAssignment(FactorGraph mdl, Inferencer inf)` Returns the highest-score Assignment in a model according to a given inferencer.
`static double` | `Models.entropy(FactorGraph mdl)` Computes the exact entropy of a factor graph distribution using the junction tree algorithm.
`static double` | `Models.KL(FactorGraph mdl1, FactorGraph mdl2)` Computes the KL divergence `KL(mdl1||mdl2)`.
`static org._3pq.jgrapht.UndirectedGraph` | `Graphs.mdlToGraph(FactorGraph fg)` Converts a FactorGraph into a plain graph where each Variable is a vertex, and two Variables are connected by an edge if they are arguments to the same factor.
`static void` | `Models.removeConstantFactors(FactorGraph sliced)`

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