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        "author": "Jan Olszak",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: C++",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Information Analysis",
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        "description": "Information Flow Analysis\n=========================\n\nIFA is a simple and fast library for information theory research and information flow analysis. It's a Python module written C++, Cython.\n\n\nInstallation\n============\nDependencies:\n* numpy\n\nIf you have Cython some cpp files will get regenerated during installation\n```bash\n    pip install ifa\n\n```\n\nOr if you want the developmen version:\n```bash\n    git clone https://github.com/janekolszak/ifa.git;\n    cd ifa;\n    sudo make install;\n```\nUsage\n=====\nComputing Jensen\u2013Shannon divergence:\n```python\n    from ifa.distribution import Distribution\n    from ifa.divergence import jsd\n\n    from numpy.testing import assert_allclose\n\n    p = Distribution([\"A\", \"B\"], [0.5, 0.5])\n    q = Distribution([\"A\", \"C\"], [0.5, 0.5])\n\n    assert_allclose(jsd(p, 0.5, q, 0.5), [0.5])\n```\nWhat's inside:\n==============\n* Distribution class with some basic operations\n* Divergences:\n * Jensen\u2013Shannon divergence\n * Kullback\u2013Leibler divergence\n* Functions to compute information flow between distributions\n",
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        "license": "MIT License",
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        "name": "ifa",
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