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        "author": "Will McCutchen",
        "author_email": "will@mccutch.org",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Information Technology",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "======\nVokram\n======\n\nVokram is a toy `Markov chain`_ library that is most likely implemented\nincorrectly and extremely inefficiently.\n\n\nInstallation\n============\n\nUse `pip`_ to install::\n\n    pip install vokram\n\n\nUsage\n=====\n\nCommand Line Usage\n------------------\n\nPipe a body of text into ``vokram`` and it will generate some (hopefully)\nplausible sentences synthesized from that body of text::\n\n    $ cat the_art_of_war.txt | vokram\n    Spies cannot be obtained inductively from experience, nor by any danger.\n\nYou can control the maximum number of words in the output and the n-gram size\nused when building the Markov model. All command line options are given below::\n\n    $ vokram --help\n\nOutputs::\n\n    usage: vokram [-h] [-w NUM_WORDS] [-n NGRAM_SIZE]\n\n    Generates plausible new sentences from a corpus provided on STDIN.\n\n    optional arguments:\n      -h, --help            show this help message and exit\n      -w NUM_WORDS, --num-words NUM_WORDS\n                            Maximum number of words in the resulting sentence.\n      -n NGRAM_SIZE, --ngram-size NGRAM_SIZE\n\nLibrary Usage\n-------------\n\nVokram can also be used as a plain old Python library::\n\n    >>> import vokram\n    >>> corpus = open('the_art_of_war.txt')\n    >>> model = vokram.build_word_model(corpus, 2)\n    >>> vokram.markov_words(model, 25))\n    'Hence it is not supreme excellence; supreme excellence consists in breaking the enemy's few.'\n\n\nCredits\n=======\n\nVokram was made with inspiration from this simple and approachable\n`Python implementation and explanation`_.\n\n.. _Markov chain: http://en.wikipedia.org/wiki/Markov_chain\n.. _Python implementation and explanation: http://code.activestate.com/recipes/194364-the-markov-chain-algorithm/\n.. _pip: http://www.pip-installer.org/",
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        "license": "MIT",
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        "name": "vokram",
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