Bayesian classifier on top of Redis
Redis is a persistent, in-memory, key-value store with support for various data structures such as lists, sets, and ordered sets. All these 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.
gem install bayes_on_redis
# Require BayesOnRedis and RubyGems require "rubygems" require "bayes_on_redis" # 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 => 0) # 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.")
BayesOnRedis is also available in Python. With the same API.
Fork http://github.com/didip/bayes_on_redis and send pull requests.