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        "author": "greenify",
        "author_email": "greenify@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
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        "description": "smarkov\n=======\n\nSimple, lightweight and easy to read implementation of Markov chains and\nHMMs.\n\nThis is a toy project, don't expect any exciting speeds or robustness.\n\nHappy hacking!\n\nInstalling\n----------\n\n::\n\n    pip3 install git+git://github.com/greenify/smarkov.git\n\nHacking\n-------\n\n::\n\n    git clone https://github.com/greenify/smarkov\n    cd smarkov\n    python3 setup.py develop\n\nTrain with a corpus\n-------------------\n\n::\n\n    from smarkov import Markov\n    chain = Markov([\"AGACAGACGAC\"])\n\nAttributes\n~~~~~~~~~~\n\n| corpus: given corpus (a corpus\\_entry needs to be a tuple or array)\n| order: maximal order to look back for a given state (default 1)\n  tokenize: function how to split an element of the corpus (e.g\n  sentences into words)\n\nGenerate text from a chain\n--------------------------\n\n::\n\n    print(\"\".join(chain.generate_text()))\n\n``Generate_text()`` generates exactly one element from the Markov chain.\nIn other words: It goes in the Markov chain the universal start state to\nuniversal end state.\n\nMore Examples\n-------------\n\nSee ``examples``\n\nComing\n------\n\nDocumentation how to use it with HMM.\n\nLicense\n-------\n\nMIT",
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