README.md in rb-libsvm-1.0.7 vs README.md in rb-libsvm-1.0.8
- old
+ new
@@ -1,54 +1,55 @@
# rb-libsvm -- Ruby language bindings for LIBSVM
Spec Status: [](http://travis-ci.org/febeling/rb-libsvm)
-This is a module which provides a Ruby API to the LIBSVM [1] library.
-SVM is a machine learning and classification algorithm, and LIBSVM is
-a popular free implementation of it, written by Chih-Chung Chang and
+This package provides a Ruby bindings to the LIBSVM [1] library. SVM
+is a machine learning and classification algorithm, and LIBSVM is a
+popular free implementation of it, written by Chih-Chung Chang and
Chih-Jen Lin, of National Taiwan University, Taipei. See "Programming
Collective Intelligence," [2] among others, for a usage example.
-Note: There exists another Ruby binding for LIBSVM, named Ruby SVM,
-[3] written by Rudi Cilibrasi. (That's the one mentioned in the
-libsvm documentation.) This package is not related but written
-independently and from scratch.
+Note: There exist two other Ruby bindings for LIBSVM. One is named
+Ruby SVM, written by Rudi Cilibrasi. It is hard to find now. The
+other, more actively developed one is libsvm-ruby-swig by Tom Zeng
+[3], which is built using SWIG.
## Dependencies
None. Libsvm is bundled with the project. Just install and go!
-Currently using libsvm version 3.12
+Currently includes libsvm version 3.12.
## Installation
gem install rb-libsvm
## Usage
- require 'libsvm'
+```ruby
+require 'libsvm'
- # This library is namespaced.
- problem = Libsvm::Problem.new
- parameter = Libsvm::SvmParameter.new
+# This library is namespaced.
+problem = Libsvm::Problem.new
+parameter = Libsvm::SvmParameter.new
- parameter.cache_size = 1 # in megabytes
+parameter.cache_size = 1 # in megabytes
- parameter.eps = 0.001
- parameter.c = 10
+parameter.eps = 0.001
+parameter.c = 10
- examples = [ [1,0,1], [-1,0,-1] ].map {|ary| Libsvm::Node.features(ary) }
- labels = [1, -1]
+examples = [ [1,0,1], [-1,0,-1] ].map {|ary| Libsvm::Node.features(ary) }
+labels = [1, -1]
- problem.set_examples(labels, examples)
+problem.set_examples(labels, examples)
- model = Libsvm::Model.train(problem, parameter)
+model = Libsvm::Model.train(problem, parameter)
- pred = model.predict(Libsvm::Node.features(1, 1, 1))
- puts "Example [1, 1, 1] - Predicted #{pred}"
+pred = model.predict(Libsvm::Node.features(1, 1, 1))
+puts "Example [1, 1, 1] - Predicted #{pred}"
+```
-
## Author, License
Written by C. Florian Ebeling. This software can be freely used under
the terms of the MIT license, see file MIT-LICENSE.
@@ -66,10 +67,11 @@
[http://neovintage.blogspot.com/2011/11/text-classification-using-support.html](http://neovintage.blogspot.com/2011/11/text-classification-using-support.html)
[http://www.igvita.com/2008/01/07/support-vector-machines-svm-in-ruby/](http://www.igvita.com/2008/01/07/support-vector-machines-svm-in-ruby/)
-### Notes
-[http://www.csie.ntu.edu.tw/~cjlin/libsvm/](http://www.csie.ntu.edu.tw/~cjlin/libsvm/)
+[1]: [http://www.csie.ntu.edu.tw/~cjlin/libsvm/](http://www.csie.ntu.edu.tw/~cjlin/libsvm/)
-[http://books.google.com/books?id=fEsZ3Ey-Hq4C](http://books.google.com/books?id=fEsZ3Ey-Hq4C)
+[2]: [http://books.google.com/books?id=fEsZ3Ey-Hq4C](http://books.google.com/books?id=fEsZ3Ey-Hq4C)
+
+[3]: http://github.com/tomz/libsvm-ruby-swig/tree/master