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        "author": "Christopher Chen",
        "author_email": "christopher.chen1995@gmail.com",
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        "classifiers": [
            "Operating System :: OS Independent",
            "Programming Language :: Python",
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        "description": "Originally a proof of concept, I've used this in enough projects that\nI've decided to publish it tomake it easier to import. The name is a\nplay on words similar to Markup/Markdown. Hey, I'm funny sometimes,\n\nBasic Use\n=========\n\nUse the included ``from_sentences()`` and ``from_words()`` if your data\nalready behaves nicely. These methods return a ``Markov`` object which\nis the workhorse of this whole thing. Your input sequences of words or\nsentences should be delimited by newlines. Use ``next_word()`` and\n``next_sentence()`` to generate your desired forat of output.\n\nAdvanced Use\n============\n\nInstantiate with ``m = markoff.Markov(seeds)`` where ``seeds`` is an\niterable of sub-iterables. That could be a list of lists of words or\nletters if you want to emulate the functionality of ``from_sentences()``\nand ``from_words()``. Each sub-iterable being a chain in the set of\nchains you want to model.\n\nYou can supply it with just one chain or many.\n\nThen use ``m.generate(max_length=100)`` to produce a single chain\nlimited to ``max_length`` automatically terminating at known ending\nstate (usually a word ending with punctuation).\n\nExamples\n========\n\nDon't forget to begin with ``import pymarkoff`` ## Generating Sentences\n##\n\nInput\n~~~~~\n\n::\n\n    m = pymarkoff.from_sentences(\n    \"\"\"The quick brown fox jumped over the lazy dog.\n    Jack and Jill ran up the hill to fetch a pail of water.\n    Whenever the black fox jumped the squirrel gazed suspiciously.\"\"\"\n    )\n\n    print([m.next_sentence() for i in range(10)])\n\nOutput\n~~~~~~\n\n::\n\n    [\n        'The quick brown fox jumped over the black fox jumped the lazy dog.',\n        'The quick brown fox jumped the squirrel gazed suspiciously.',\n        'Whenever the squirrel gazed suspiciously.',\n        'Jack and Jill ran up the lazy dog.',\n        'Jack and Jill ran up the hill to fetch a pail of water.',\n        'Jack and Jill ran up the black fox jumped the hill to fetch a pail of water.',\n        'Whenever the lazy dog.',\n        'The quick brown fox jumped over the lazy dog.',\n        'Jack and Jill ran up the hill to fetch a pail of water.',\n        'Jack and Jill ran up the squirrel gazed suspiciously.'\n     ]\n\nGenerating Words\n----------------\n\nInput\n~~~~~\n\n::\n\n    seeds = \"\"\"Ana\n    Bastion\n    D.Va\n    Genji\n    Hanzo\n    Junkrat\n    L\u00facio\n    McCree\n    Mei\n    Mercy\n    Pharah\n    Reaper\n    Reinhardt\n    Roadhog\n    Soldier: 76\n    Symmetra\n    Torbj\u00f6rn\n    Tracer\n    Widowmaker\n    Winston\n    Zarya\n    Zenyatta\"\"\"\n\n    brain = pymarkoff.from_words(mystr)\n    print([brain.next_word() for i in range(10)])\n\nOutput\n~~~~~~\n\n::\n\n    ['Zen', 'D.Vaperein', 'Za', 'To', 'Merya', 'Metrdo', 'So', 'Junj\u00f6r', 'Ph', 'Mera']\n\nMore advanced use\n=================\n\nThe ``Markov.feed()`` method can be used to add more data into the model\nof a ``Markov`` object. This lets you add sentences or words one at a\ntime, if you want.\n\nNotes\n=====\n\nThis module is still under development and is mostly for me to play\naround with and learn Markov Chains. Cheers.\n",
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