Welcome to Classifier Reborn

Classifier is a general module to allow Bayesian and other types of classifications.

Classifier Reborn is a fork of cardmagic/classifier under more active development.

Download

Add this line to your application's Gemfile:

gem 'classifier-reborn'

And then execute:

$ bundle

Or install it yourself as:

$ gem install classifier-reborn

Dependencies

The only runtime dependency you'll need to install is Roman Shterenzon's fast-stemmer gem:

gem install fast-stemmer

This should install automatically with RubyGems.

If you would like to speed up LSI classification by at least 10x, please install the following libraries:

• GNU GSL
• rb-gsl

Notice that LSI will work without these libraries, but as soon as they are installed, Classifier will make use of them. No configuration changes are needed, we like to keep things ridiculously easy for you.

Bayes

A Bayesian classifier by Lucas Carlson. Bayesian Classifiers are accurate, fast, and have modest memory requirements.

Usage

require 'classifier-reborn'
b = ClassifierReborn::Bayes.new 'Interesting', 'Uninteresting'
b.train_interesting "here are some good words. I hope you love them"
b.train_uninteresting "here are some bad words, I hate you"
b.classify "I hate bad words and you" # returns 'Uninteresting'

require 'madeleine' # use madeline to persist the data
m = SnapshotMadeleine.new("bayes_data") {
  ClassifierReborn::Bayes.new 'Interesting', 'Uninteresting'
}
m.system.train_interesting "here are some good words. I hope you love them"
m.system.train_uninteresting "here are some bad words, I hate you"
m.take_snapshot
m.system.classify "I love you" # returns 'Interesting'

Using Madeleine, your application can persist the learned data over time.

Bayesian Classification

• http://www.process.com/precisemail/bayesian_filtering.htm
• http://en.wikipedia.org/wiki/Bayesian_filtering
• http://www.paulgraham.com/spam.html

LSI

A Latent Semantic Indexer by David Fayram. Latent Semantic Indexing engines are not as fast or as small as Bayesian classifiers, but are more flexible, providing fast search and clustering detection as well as semantic analysis of the text that theoretically simulates human learning.

Usage

require 'classifier-reborn'
lsi = ClassifierReborn::LSI.new
strings = [ ["This text deals with dogs. Dogs.", :dog],
            ["This text involves dogs too. Dogs! ", :dog],
            ["This text revolves around cats. Cats.", :cat],
            ["This text also involves cats. Cats!", :cat],
            ["This text involves birds. Birds.",:bird ]]
strings.each {|x| lsi.add_item x.first, x.last}

lsi.search("dog", 3)
# returns => ["This text deals with dogs. Dogs.", "This text involves dogs too. Dogs! ",
#             "This text also involves cats. Cats!"]

lsi.find_related(strings[2], 2)
# returns => ["This text revolves around cats. Cats.", "This text also involves cats. Cats!"]

lsi.classify "This text is also about dogs!"
# returns => :dog

Please see the ClassifierReborn::LSI documentation for more information. It is possible to index, search and classify with more than just simple strings.

Latent Semantic Indexing

• http://www.c2.com/cgi/wiki?LatentSemanticIndexing
• http://www.chadfowler.com/index.cgi/Computing/LatentSemanticIndexing.rdoc
• http://en.wikipedia.org/wiki/Latent_semantic_analysis

Authors

• Lucas Carlson (lucas@rufy.com)
• David Fayram II (dfayram@gmail.com)
• Cameron McBride (cameron.mcbride@gmail.com)
• Ivan Acosta-Rubio (ivan@softwarecriollo.com)
• Parker Moore (email@byparker.com)

This library is released under the terms of the GNU LGPL. See LICENSE for more details.