A memory-based, non-persistent naïve bayesian text classifier.

This work is heavily inspired by the python "redisbayes" module found here:
[https://github.com/jart/redisbayes] and [https://pypi.python.org/pypi/redisbayes]

I've elected to write this to alleviate the network/time requirements when
using the bayesian classifier to classify large sets of text, or when
attempting to train with very large sets of sample data.

Installation

PIP:

sudo pip install simplebayes

GIT:

sudo pip install git+git://github.com/hickeroar/simplebayes.git

Basic Usage

import simplebayes
bayes = simplebayes.SimpleBayes()

bayes.train('good', 'sunshine drugs love sex lobster sloth')
bayes.train('bad', 'fear death horror government zombie')

assert bayes.classify('sloths are so cute i love them') == 'good'
assert bayes.classify('i would fear a zombie and love the government') == 'bad'

print bayes.score('i fear god and love the government')