Reverend is a simple Bayesian classifier. It is designed to be easy to adapt and extend for your application.
A simple example would look like:
from reverend.thomas import Bayes
guesser = Bayes()
guesser.train('fish', 'salmon trout cod carp')
guesser.train('fowl', 'hen chicken duck goose')
guesser.guess('chicken tikka marsala')
# Returns
# [('fowl', 0.9999)]You can also "forget" some training:
guesser.untrain('fish', 'salmon carp')The first argument of train is the bucket or class that
you want associated with the training. If the bucket does not
exist, Bayes will create it.
The second argument is the object that you want Bayes to be
trained on. By default, Bayes expects a string and uses something
like string.split to break it into individual tokens (words).
It uses these tokens as the basis of its bookkeeping.
The two ways to extend it are:
- Pass in a function as the tokenizer when creating your Bayes. The function should expect one argument which will be whatever you pass to the train() method. The function should return a list of strings, which are the tokens that are relevant to your app.
- Subclass Bayes and override the method get_tokens to return a list of string tokens relevant to your app.
- Not compatible with Python 3
- Amir Bakhtiar <amir@divmod.org>
- Jean-Paul Calderone
- Tristan Seligmann
- Allen Short
- Dave Lasley <dave@laslabs.com>
This module is maintained by LasLabs Inc.
It was migrated from the Launchpad Divmod Project, which is no longer maintained.