Skip to main content

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

Project description

sumproduct
==========

An implementation of Belief Propagation for factor graphs, also known as
the sum product algorithm.

::

pip install sumproduct

.. figure:: http://f.cl.ly/items/2P021j2y3A2Q191F451h/unnamed0.png
:alt: Simple factor graph

Simple factor graph
The factor graph used in ``test.py``.

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([
[0.8,0.2],
[0.2,0.8],
[0.5,0.5]
])) # create a factor node potentials for p(x1 | x2)
# connect the parents to their children
g.add(f12)
g.append('f12', x2)
g.append('f12', 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])

Additional Information
^^^^^^^^^^^^^^^^^^^^^^

Check ``test.py`` 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 <http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage>`__.

Project details


Download files

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

Filename, size & hash SHA256 hash help File type Python version Upload date
sumproduct-0.0.3-py2.py3-none-any.whl (8.4 kB) Copy SHA256 hash SHA256 Wheel 2.7 Dec 29, 2014
sumproduct-0.0.3.tar.gz (7.3 kB) Copy SHA256 hash SHA256 Source None Dec 29, 2014

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page