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DistributedFactorGraphs.jl provides a flexible factor graph API for use in the Caesar.jl ecosystem. The package supplies:
- A standardized API for interacting with factor graphs
- Implementations of the API for in-memory and database-driven operation
- Visualization extensions to validate the underlying graph
Note this package is still under initial development, and will adopt parts of the functionality currently contained in IncrementalInference.jl.
DistributedFactorGraphs can be installed from Julia packages using:
The in-memory implementation is the default, using LightGraphs.jl.
It is recommended to use
IncrementalInference to create factor graphs as they will be solvable.
using DistributedFactorGraphs using IncrementalInference
Both drivers support the same functions, so choose which you want to use when creating your initial DFG. For example:
# In-memory DFG # Initialize the default in-memory factor graph with default solver parameters. dfg = initfg() # add 2 ContinuousScalar variable types to the new factor graph addVariable!(dfg, :a, ContinuousScalar) addVariable!(dfg, :b, ContinuousScalar) # add a LinearConditional factor addFactor!(dfg, [:a, :b], LinearConditional(Normal(10.0,1.0)))