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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.

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

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

Usage
-----

Check ``test.py`` for details, but:

Create a factor graph
~~~~~~~~~~~~~~~~~~~~~

::

g = FactorGraph() # init the graph
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(max_iter=500, tolerance=1e-6)
g.nodes['x1'].marginal()

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.

::

res = g.brute_force()
g.nodes['x1'].bfmarginal

Implementation Details
----------------------

See block comments in the code's methods for details, but 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>`__.

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