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        "author": "Edward Newell",
        "author_email": "edward.newell@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Topic :: Software Development :: Build Tools"
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        "description": "Categorical Sampler\n-----\n\nInstall from pip: `pip install categorical-sampler`\n\nLet’s generate a probability distribution to get us started.  First, sample a bunch of random numbers to determine probability “scores”.\n\n\n    >>> from random import random\n    >>> k = 10**6\n    >>> scores = [random() for i in range(k)]\n    >>> total = sum(scores)\n    >>> probabilities = [s / total for s in scores]\n\n\nWe've normalized the scores to sum to 1, i.e. make\nthem into proper probabilities, but actually the categorical sampler will do that for us, so it’s not necessary:\n\n    >>> from categorical import Categorical as C\n    >>> my_sampler = C(scores)\n    >>> print my_sampler.sample()\n    487702\n\nComparing to numpy, assuming we draw 1000 individual samples *individually*:\n\n\n    >>> from numpy.random import choice\n    >>> import time\n    >>> \n    >>> def time_numpy():\n    >>>     start = time.time()\n    >>>     for i in range(1000):\n    >>>         choice(k, p=probabilities)\n    >>>     print time.time() - start\n    >>> \n    >>> def time_my_alias():\n    >>>     start = time.time()\n    >>>     for i in range(1000):\n    >>>         my_sampler.sample()\n    >>>     print time.time() - start\n    >>> \n    >>> time_numpy()\n    31.0555009842\n    >>> time_my_alias()\n    0.0127031803131\n\nGet the actual probability of a given outcome:\n\n    >>> my_sampler.get_probability(487702)\n    1.0911282101090306e-06",
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