Welcome to Classifier
Classifier is a general module to allow Bayesian and other types of classifications.
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.
Changes in this branch
I made this branch to fix a TypeError on untrain (classifier-1.3.1), then francois branch for jeweler and all the changes yuri made on his branch (specially the use of ruby-stemmer, and the incompatibility fix on Array#sum, which I needed). After that I added support for loading the stopwords of certain language from a file (before the list was embedded on the source code) and a stopword list for Spanish. This branch only works with Ruby 1.9
Bayes
A Bayesian classifier by Lucas Carlson. Bayesian Classifiers are accurate, fast, and have modest memory requirements.
Usage
require 'classifier'
b = Classifier::Bayes.new :categories => ['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.
Stemmer configuration
You can specify language and encoding for internal stemmer:
b = Classifier::Bayes.new :categories => [‘Interesting’, ‘Uninteresting’],
:language => 'ro', :encoding => 'ISO_8859_2'
The default values are ‘en’ for language and ‘UTF-8’ for the encoding.
Each language uses a word list to exclude certain words (stopwords). classifier comes with three included stopword lists, for English, Russian and Spanish. The English list is the list that comes with the original gem (don’t know where it was taken from) and the Russian and Spanish are from snowball.
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.(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.
The configuration for the stemmer is the same used for Bayes:
lsi = Classifier::LSI.new :language => ‘ro’, :encoding => ‘ISO_8859_2’
Latent Semantic Indexing
Authors
-
Lucas Carlson ([email protected])
-
David Fayram II ([email protected])
-
Cameron McBride ([email protected])
-
Yury Korolev ([email protected])
-
Luis Parravicini ([email protected])
This library is released under the terms of the GNU LGPL. See LICENSE for more details.