libsvm-ruby-swig

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 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