libsvm-ruby-swig

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= libsvm-ruby-swig

• Ruby interface to LIBSVM (using SWIG)
• http://www.tomzconsulting.com
• http://tweetsentiments.com

== DESCRIPTION:

This is the Ruby port of the LIBSVM Python SWIG (Simplified Wrapper and Interface Generator) interface.

A slightly modified version of LIBSVM 2.9 is included, it allows turrning on/off the debug log. You don't need your own copy of SWIG to use this library - all needed files are generated using SWIG already.

Look for the README file in the ruby subdirectory for instructions. The binaries included were built under Ubuntu Linux 2.6.28-18-generic x86_64, you should run make under the libsvm-2.9 and libsvm-2.9/ruby directories to regenerate the executables for your environment.

LIBSVM is in use at http://tweetsentiments.com - A Twitter / Tweet sentiment analysis application

== INSTALL: Currently the gem is available on linux only(tested on Ubuntu 8-9 and Fedora 9-12, and on OS X by danielsdeleo), and you will need g++ installed to compile the native code.

sudo gem sources -a http://gems.github.com (you only have to do this once) sudo gem install tomz-libsvm-ruby-swig

== SYNOPSIS:

Quick Interactive Tutorial using irb (adopted from the python code from Toby Segaran's "Programming Collective Intelligence" book):

irb(main):001:0> require 'svm' => true irb(main):002:0> prob = Problem.new([1,-1],[[1,0,1],[-1,0,-1]]) irb(main):003:0> param = Parameter.new(:kernel_type => LINEAR, :C => 10) irb(main):004:0> m = Model.new(prob,param) irb(main):005:0> m.predict([1,1,1]) => 1.0 irb(main):006:0> m.predict([0,0,1]) => 1.0 irb(main):007:0> m.predict([0,0,-1]) => -1.0 irb(main):008:0> m.save("test.model") irb(main):009:0> m2 = Model.new("test.model") irb(main):010:0> m2.predict([0,0,-1]) => -1.0

== AUTHOR:

Tom Zeng

• http://twitter.com/tomzeng
• http://www.tomzconsulting.com
• http://www.linkedin.com/in/tomzeng
• tom.z.zeng at gmail dot com