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            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Utilities"
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        "description": "simplebayes\n===========\nA memory-based, optional-persistence na\u00efve bayesian text classifier.\n--------------------------------------------------------------------\n::\n\n    This work is heavily inspired by the python \"redisbayes\" module found here:\n    [https://github.com/jart/redisbayes] and [https://pypi.python.org/pypi/redisbayes]\n\n    I've elected to write this to alleviate the network/time requirements when\n    using the bayesian classifier to classify large sets of text, or when\n    attempting to train with very large sets of sample data.\n\nBuild Status\n------------\n.. image:: https://travis-ci.org/hickeroar/simplebayes.svg?branch=master\n.. image:: https://img.shields.io/badge/coverage-100%-brightgreen.svg?style=flat\n.. image:: https://img.shields.io/badge/pylint-10.00/10-brightgreen.svg?style=flat\n.. image:: https://img.shields.io/badge/flake8-passing-brightgreen.svg?style=flat\n\nInstallation\n------------\n::\n\n    sudo pip install simplebayes\n\nBasic Usage\n-----------\n.. code-block:: python\n\n    import simplebayes\n    bayes = simplebayes.SimpleBayes()\n\n    bayes.train('good', 'sunshine drugs love sex lobster sloth')\n    bayes.train('bad', 'fear death horror government zombie')\n\n    assert bayes.classify('sloths are so cute i love them') == 'good'\n    assert bayes.classify('i would fear a zombie and love the government') == 'bad'\n\n    print bayes.score('i fear zombies and love the government')\n\n    bayes.untrain('bad', 'fear death')\n\n    assert bayes.tally('bad') == 3\n\nCache Usage\n-----------\n.. code-block:: python\n\n    import simplebayes\n    bayes = simplebayes.SimpleBayes(cache_path='/my/cache/')\n    # Cache file is '/my/cache/_simplebayes.pickle'\n    # Default cache_path is '/tmp/'\n\n    if not bayes.cache_train():\n        # Unable to load cache data, so we're training it\n        bayes.train('good', 'sunshine drugs love sex lobster sloth')\n        bayes.train('bad', 'fear death horror government zombie')\n\n        # Saving the cache so next time the training won't be needed\n        bayes.persist_cache()\n\nTokenizer Override\n------------------\n.. code-block:: python\n\n    import simplebayes\n\n    def my_tokenizer(sample):\n        return sample.split()\n\n    bayes = simplebayes.SimpleBayes(tokenizer=my_tokenizer)\n\nLicense\n-------\n::\n\n    The MIT License (MIT)\n\n    Copyright (c) 2015 Ryan Vennell\n\n    Permission is hereby granted, free of charge, to any person obtaining a copy\n    of this software and associated documentation files (the \"Software\"), to deal\n    in the Software without restriction, including without limitation the rights\n    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n    copies of the Software, and to permit persons to whom the Software is\n    furnished to do so, subject to the following conditions:\n\n    The above copyright notice and this permission notice shall be included in all\n    copies or substantial portions of the Software.\n\n    THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n    SOFTWARE.\n\nAPI Documentation\n-----------------\n`<http://hickeroar.github.io/simplebayes/simplebayes.html>`_",
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