Machine Learning & Data Mining with JRuby
Machine Learning & Data Mining with JRuby based on the
Weka Java library.
Add this line to your application’s Gemfile:
gem 'weka'
And then execute:
$ bundle install
Or install it yourself as:
$ gem install weka
Use Weka’s Machine Learning and Data Mining algorithms by requiring the gem:
require 'weka'
The weka gem tries to carry over the namespaces defined in Weka and enhances
some interfaces in order to allow a more Ruby-ish programming style when using
the Weka library.
The idea behind keeping the namespaces is, that you can also use the
Weka documentation for looking up
functionality and classes.
Please refer to the gem’s Wiki
for detailed information about how to use weka with JRuby and some examplary
code snippets.
git clone [email protected]:paulgoetze/weka-jruby.git
.export JARS_VENDOR=false
. This willbin/setup
or bundle install
to install the dependencies.Then, run rake spec
to run the tests. You can also run bin/console
or
rake irb
for an interactive prompt that will allow you to experiment.
Bug reports and pull requests are welcome on GitHub at
https://github.com/paulgoetze/weka-jruby. This project is intended to be a safe,
welcoming space for collaboration, and contributors are expected to adhere to
the
Contributor Covenant Code of Conduct.
For development we use the
git branching model
described by nvie.
Here’s how to contribute:
git checkout -b feature/my-new-feature develop
)git commit -am 'Add some feature'
)git push origin feature/my-new-feature
)Please try to add RSpec tests along with your new feature. This will ensure that
your code does not break existing functionality and that your feature is working
as expected.
We use Rubocop for code style
recommendations. Please make sure your contributions comply with the project’s
Rubocop config.
The original ideas for wrapping Weka in JRuby come from
@arrigonialberto86 and his
ruby-band gem. Great thanks!
The gem is available as open source under the terms of the
MIT License.