a tiny ruby library for one-dimensional clustering methods.
A dynamic programming algorithm for optimal one-dimensional k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to the standard heuristic k-means algorithm, this algorithm guarantees optimality and repeatability.
https://cran.r-project.org/web/packages/Ckmeans.1d.dp/index.html
require 'onedclusterer'
data = [1259.61,2024.82,1855.75,1559.04,1707.65,1107.1,2155.8]
ckmeans = OnedClusterer::Ckmeans.new(data, 1, 7) # chooses an optimal number clusters between 1 and 7
p ckmeans.bounds # => [0, 1259.61, 1855.75, 2155.8]
p ckmeans.clusters # => [[1107.1, 1259.61], [1559.04, 1707.65, 1855.75], [2024.82, 2155.8]]
# exact number of clusters can be requested instead of min and max
ckmeans = OnedClusterer::Ckmeans.new(data, 4)
p ckmeans.bounds # => [0, 1259.61, 1559.04, 1855.75, 2155.8]
p ckmeans.clusters # => [[1107.1, 1259.61], [1559.04], [1707.65, 1855.75], [2024.82, 2155.8]] The Jenks natural breaks classification method seeks to reduce the variance within classes and maximize the variance between classes.
http://en.wikipedia.org/wiki/Jenks_natural_breaks_optimization
http://www.macwright.org/2013/02/18/literate-jenks.html
require 'onedclusterer'
data = [1259.61,2024.82,1855.75,1559.04,1707.65,1107.1,2155.8]
jenks = OnedClusterer::Jenks.new(data, 4)
p jenks.bounds # => [0, 1259.61, 1559.04, 1855.75, 2155.8]
p jenks.clusters # => [[1107.1, 1259.61], [1559.04], [1707.65, 1855.75], [2024.82, 2155.8]]Add this line to your application's Gemfile:
gem 'onedclusterer'And then execute:
$ bundle
Or install it yourself as:
$ gem install onedclusterer
After checking out the repo, run bin/setup to install dependencies. Then, run rake test to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.
To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.
Bug reports and pull requests are welcome on GitHub at https://github.com/Hamdiakoguz/onedclusterer. 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.