This is a Ruby interface to the CRM114 Controllable Regex Mutilator, an advanced and fast text classifier that uses sparse binary polynomial matching with a Bayesian Chain Rule evaluator and a hidden Markov model to categorize data with up to a 99.87% accuracy.

• http://crm114.rubyforge.org/
• http://github.com/bendiken/crm114
• http://ar.to/2006/07/spam-filters-alien-technology-and-ruby-on-rails

• http://crm114.sourceforge.net/
• http://en.wikipedia.org/wiki/CRM114
• http://en.wikipedia.org/wiki/Dr_Strangelove
• http://www.paulgraham.com/wsy.html

The CRM114 library interface is very similar to that of the Classifier project.

Here follows a brief example:

require 'crm114'

crm = Classifier::CRM114.new([:interesting, :boring])

crm.train! :interesting, 'Some data set with a decent signal to noise ratio.'
crm.train! :boring, 'Pig latin, as in lorem ipsum dolor sit amet.'

crm.classify 'Lorem ipsum'       => [:boring, 0.99]
crm.interesting? 'Lorem ipsum'   => false
crm.boring? 'Lorem ipsum'        => true

Have a look at the included unit tests for more comprehensive examples.

Requires the CRM114 binaries to be installed. Specifically, the crm binary should be accessible in the current user's PATH environment variable.

To get a local working copy of the development repository, do:

% git clone git://github.com/bendiken/crm114.git

Alternatively, you can download the latest development version as a tarball as follows:

% wget http://github.com/bendiken/crm114/tarball/master

The recommended installation method is via RubyGems. To install the latest official release from Gemcutter, do:

% [sudo] gem install crm114

• http://gemcutter.org/gems/crm114
• http://rubyforge.org/projects/crm114
• http://raa.ruby-lang.org/project/crm114/
• http://www.ohloh.net/p/crm114-ruby
• http://www.elegantchaos.com/node/129 (crm.py)
• http://rubyforge.org/projects/classifier
• http://rubyforge.org/projects/bishop

• Arto Bendiken - http://ar.to/

CRM114.rb is free and unencumbered public domain software. For more information, see http://unlicense.org/ or the accompanying UNLICENSE file.