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')
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