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    "info": {
        "author": "Didip Kerabat",
        "author_email": "didipk@gmail.com",
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        "description": "# What is BayesOnRedis?\n\nBayesian classifier on top of Redis\n\n## Why on Redis?\n\n[Redis](http://code.google.com/p/redis) is a persistent in-memory database with supports for various data structures such as lists, sets, and ordered sets.\nAll this data types can be manipulated with atomic operations to push/pop elements, add/remove elements, perform server side union, intersection, difference between sets, and so forth.\n\nBecause of Redis properties:\n\n * It is extremely easy to implement simple algorithm such as bayesian filter.\n\n * The persistence of Redis means that the Bayesian implementation can be used in real production environment.\n\n * Even though I don't particularly care about performance at the moment. Redis benchmarks give me confidence that the implementation can scale to relatively large training data.\n\n## How to install? (Ruby version)\n\n    gem install bayes_on_redis\n\n## Getting started\n\n    # Create instance of BayesOnRedis and pass your Redis information.\n    # Of course, use real sentences for much better accuracy.\n    # Unless if you want to train spam related things.\n    bor = BayesOnRedis.new(:redis_host => '127.0.0.1', :redis_port => 6379, :redis_db => 5)\n\n    # Teach it\n    bor.train \"good\", \"sweet awesome kick-ass cool pretty smart\"\n    bor.train \"bad\", \"sucks lame boo death bankrupt loser sad\"\n\n    # Then ask it to classify text.\n    bor.classify(\"awesome kick-ass ninja can still be lame.\")\n\n## for Pythonistas\n\nBayesOnRedis is also available in Python. With the same API.\n\n## Contributing\n\n[Fork http://github.com/didip/bayes_on_redis](http://github.com/didip/bayes_on_redis) and send pull requests.",
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        "keywords": "bayesian filter redis",
        "license": "haven't decided",
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        "summary": "bayes_on_redis library provides bayesian classification on a given text similar to many SPAM/HAM filtering technique.",
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