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        "author": "Ilya Kavalerov",
        "author_email": "ilyakavalerov@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "`sumproduct <https://pypi.python.org/pypi/sumproduct>`__\n========================================================\n\n|Build Status| |Downloads|\n\nAn implementation of Belief Propagation for factor graphs, also known as\nthe sum-product algorithm\n(`Reference <http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage>`__).\n\n::\n\n    pip install sumproduct\n\n.. figure:: http://f.cl.ly/items/2P021j2y3A2Q191F451h/unnamed0.png\n   :alt: Simple factor graph\n\n   Simple factor graph\nThe factor graph used in ``test.py`` (image made with\n`yEd <http://www.yworks.com/en/products_yed_applicationfeatures.html>`__).\n\nBasic Usage\n-----------\n\nCreate a factor graph\n~~~~~~~~~~~~~~~~~~~~~\n\n::\n\n    from sumproduct import Variable, Factor, FactorGraph\n    import numpy as np\n\n    g = FactorGraph(silent=True) # init the graph without message printouts\n    x1 = Variable('x1', 2) # init a variable with 2 states\n    x2 = Variable('x2', 3) # init a variable with 3 states\n    f12 = Factor('f12', np.array([\n      [0.8,0.2],\n      [0.2,0.8],\n      [0.5,0.5]\n    ])) # create a factor, node potential for p(x1 | x2)\n    # connect the parents to their children\n    g.add(f12)\n    g.append('f12', x2) # order must be the same as dimensions in factor potential!\n    g.append('f12', x1) # note: f12 potential's shape is (3,2), i.e. (x2,x1)\n\nRun Inference\n~~~~~~~~~~~~~\n\nsum-product algorithm\n^^^^^^^^^^^^^^^^^^^^^\n\n::\n\n    >>> g.compute_marginals()\n    >>> g.nodes['x1'].marginal()\n    array([ 0.5,  0.5])\n\nBrute force marginalization and conditioning\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nThe sum-product algorithm can only compute exact marginals for acyclic\ngraphs. Check against the brute force method (at great computational\nexpense) if you have a loopy graph.\n\n::\n\n    >>> g.brute_force()\n    >>> g.nodes['x1'].bfmarginal\n    array([ 0.5,  0.5])\n\nCondition on Observations\n^^^^^^^^^^^^^^^^^^^^^^^^^\n\n::\n\n    >>> g.observe('x2', 2) # observe state 1 (middle of above f12 potential)\n    >>> g.compute_marginals(max_iter=500, tolerance=1e-6)\n    >>> g.nodes['x1'].marginal()\n    array([ 0.2,  0.8])\n    >>> g.brute_force()\n    >>> g.nodes['x1'].bfmarginal\n    array([ 0.2,  0.8])\n\nAdditional Information\n^^^^^^^^^^^^^^^^^^^^^^\n\n``sumproduct`` implements a parallel message passing schedule: Message\npassing alternates between Factors and Variables sending messages to all\ntheir neighbors until the convergence of marginals.\n\nCheck ``test.py`` for a detailed example.\n\nImplementation Details\n----------------------\n\nSee block comments in the code's methods for details, but the\nimplementation strategy comes from Chapter 5 of `David Barber's\nbook <http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage>`__.\n\n.. |Build Status| image:: https://travis-ci.org/ilyakava/sumproduct.svg?branch=master\n   :target: https://travis-ci.org/ilyakava/sumproduct\n.. |Downloads| image:: https://pypip.in/download/sumproduct/badge.svg\n   :target: https://pypi.python.org/pypi/sumproduct/\n",
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