Python implementation of Sum-product (aka Belief-Propagation) for discrete Factor Graphs.

See this paper for more details on the Factor Graph framework and the sum-product algorithm. This code was originally written as part of a grad student seminar taught by Erik Sudderth at Brown University; the seminar web page is an excellent resource for learning more about graphical models.

Requires NumPy and future.

To use:

from graph import Graph
import numpy as np

G = Graph()

# add variable nodes
a = G.addVarNode('a',3)
b = G.addVarNode('b',2)

# add factors
# unary factor
Pb = np.array([[0.3],[0.7]])
G.addFacNode(Pb, b)

# connecting factor
Pab = np.array([[0.2, 0.8], [0.4, 0.6], [0.1, 0.9]])
G.addFacNode(Pab, a, b)

# factors can connect an arbitrary number of variables

# run sum-product and get marginals for variables
marg = G.marginals()
distA = marg['a']
distB = marg['b']

# reset before altering graph further
G.reset()

# condition on variables
G.var['a'].condition(0)

# disable and enable to run sum-product on subgraphs
G.var['b'].disable()
G.var['b'].enable()
G.disableAll()
# reset automatically enables all variables and removes conditioning