This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!
Project Description

# What is BayesOnRedis?

Bayesian classifier on top of Redis

## Why on Redis?

[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. All 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.

Because of Redis properties:

  • It is extremely easy to implement simple algorithm such as bayesian filter.
  • The persistence of Redis means that the Bayesian implementation can be used in real production environment.
  • 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.

## How to install? (Ruby version)

gem install bayes_on_redis

## Getting started

# Create instance of BayesOnRedis and pass your Redis information. # Of course, use real sentences for much better accuracy. # Unless if you want to train spam related things. bor = BayesOnRedis.new(:redis_host => ‘127.0.0.1’, :redis_port => 6379, :redis_db => 5)

# Teach it bor.train “good”, “sweet awesome kick-ass cool pretty smart” bor.train “bad”, “sucks lame boo death bankrupt loser sad”

# Then ask it to classify text. bor.classify(“awesome kick-ass ninja can still be lame.”)

## for Pythonistas

BayesOnRedis is also available in Python. With the same API.

## Contributing

[Fork http://github.com/didip/bayes_on_redis](http://github.com/didip/bayes_on_redis) and send pull requests.

Release History

Release History

0.1.9

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
bayes_on_redis-0.1.9-py2.6.egg (7.8 kB) Copy SHA256 Checksum SHA256 2.6 Egg Jun 28, 2011

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting