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• Ruby interface to LIBSVM (using SWIG)

• www.tomzconsulting.com

• tweetsentiments.com

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 attweetsentiments.com - A Twitter / Tweet sentiment analysis application

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

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

irb(main):001:0> require 'svm'=> trueirb(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])=> 1.0irb(main):006:0> m.predict([0,0,1])=> 1.0irb(main):007:0> m.predict([0,0,-1])=> -1.0irb(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

Tom Zeng

• twitter.com/tomzeng

• www.tomzconsulting.com

• www.linkedin.com/in/tomzeng

• tom.z.zengat gmaildot com