Project

classifier

0.35
No release in over 3 years
Low commit activity in last 3 years
There's a lot of open issues
A general classifier module to allow Bayesian and other types of classifications.
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
 Dependencies

Development

>= 0

Runtime

~> 1.0.0
>= 0
>= 0
 Project Readme

Welcome to Classifier

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

Download

Dependencies

If you install Classifier from source, you'll need to install Roman Shterenzon's fast-stemmer gem with RubyGems as follows:

gem install fast-stemmer

If you would like to speed up LSI classification by at least 10x, please install the following libraries: GNU GSL:: http://www.gnu.org/software/gsl rb-gsl:: https://github.com/SciRuby/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'
b = Classifier::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'
m = SnapshotMadeleine.new("bayes_data") {
    Classifier::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

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'
lsi = Classifier::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 Classifier::LSI documentation for more information. It is possible to index, search and classify with more than just simple strings.

Latent Semantic Indexing

Authors

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