Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
src
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

DistributedFactorGraphs.jl

Click on badges to follow links:

Release v0.9 Release v0.10 Dev Coverage Documentation
Build Status Build Status Build Status
Average time to resolve an issue
Codecov Status
Percentage of issues still open
docs
docs

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.

Documentation

Please see the documentation and the unit tests for examples on using DistributedFactorGraphs.jl.

Installation

DistributedFactorGraphs can be installed from Julia packages using:

add DistributedFactorGraphs

Usage

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)))

About

Abstraction layer for crossing factor graphs over various technologies

Resources

License

Packages

No packages published

Languages

You can’t perform that action at this time.