https://github.com/jnrowe/reverend

Reverend - Simple Bayesian classifier
https://github.com/jnrowe/reverend

Last synced: 5 days ago
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Reverend - Simple Bayesian classifier

• Host: GitHub
• URL: https://github.com/jnrowe/reverend
• Owner: JNRowe
• License: lgpl-2.1
• Created: 2010-05-06T17:58:58.000Z (almost 15 years ago)
• Default Branch: master
• Last Pushed: 2010-05-06T18:10:51.000Z (almost 15 years ago)
• Last Synced: 2023-04-14T17:08:48.262Z (almost 2 years ago)
• Language: Python
• Homepage: http://divmod.org/trac/wiki/DivmodReverend
• Size: 113 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.txt
  • Changelog: changelog.txt
  • License: COPYING

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README

        
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,

[email protected]