Skip to main content

The sum-product algorithm. (Loopy) Belief Propagation (message passing) for factor graphs

Project description

`sumproduct <>`__

|Build Status| |Downloads|

An implementation of Belief Propagation for factor graphs, also known as
the sum-product algorithm
(`Reference <>`__).


pip install sumproduct

.. figure::
:alt: Simple factor graph

Simple factor graph
The factor graph used in ```` (image made with
`yEd <>`__).

Basic Usage

Create a factor graph


from sumproduct import Variable, Factor, FactorGraph
import numpy as np

g = FactorGraph(silent=True) # init the graph without message printouts
x1 = Variable('x1', 2) # init a variable with 2 states
x2 = Variable('x2', 3) # init a variable with 3 states
f12 = Factor('f12', np.array([
])) # create a factor, node potential for p(x1 | x2)
# connect the parents to their children
g.append('f12', x2) # order must be the same as dimensions in factor potential!
g.append('f12', x1) # note: f12 potential's shape is (3,2), i.e. (x2,x1)

Run Inference

sum-product algorithm


>>> g.compute_marginals()
>>> g.nodes['x1'].marginal()
array([ 0.5, 0.5])

Brute force marginalization and conditioning

The sum-product algorithm can only compute exact marginals for acyclic
graphs. Check against the brute force method (at great computational
expense) if you have a loopy graph.


>>> g.brute_force()
>>> g.nodes['x1'].bfmarginal
array([ 0.5, 0.5])

Condition on Observations


>>> g.observe('x2', 2) # observe state 1 (middle of above f12 potential)
>>> g.compute_marginals(max_iter=500, tolerance=1e-6)
>>> g.nodes['x1'].marginal()
array([ 0.2, 0.8])
>>> g.brute_force()
>>> g.nodes['x1'].bfmarginal
array([ 0.2, 0.8])

Additional Information

``sumproduct`` implements a parallel message passing schedule: Message
passing alternates between Factors and Variables sending messages to all
their neighbors until the convergence of marginals.

Check ```` for a detailed example.

Implementation Details

See block comments in the code's methods for details, but the
implementation strategy comes from Chapter 5 of `David Barber's
book <>`__.

.. |Build Status| image::
.. |Downloads| image::

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sumproduct-0.0.7.tar.gz (8.1 kB view hashes)

Uploaded Source

Built Distribution

sumproduct-0.0.7-py2.py3-none-any.whl (9.5 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page