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Latest commit 32872ce
Nov 30, 2009
Fixes: #2991 Update version numbers, news files, and setup.py files for this round of releases. git-svn-id: http://divmod.org/svn/Divmod/trunk/Reverend@17880 866e43f7-fbfc-0310-8f2a-ec88d1da2979
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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') 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 exists, 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 indidual tokens (words). It uses these tokens as the basis of its bookkeeping. The two ways to extend it are: 1. 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. 2. Subclass Bayes and override the method getTokens to return a list of string tokens relevant to your app. I hope all you guesses are right, firstname.lastname@example.org