t-SNE Ruby
High performance t-SNE for Ruby, powered by Multicore t-SNE
Installation
Add this line to your application’s Gemfile:
gem 'tsne'Getting Started
Prep your data
x = [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]]Run
tsne = TSNE.new
tsne.fit_transform(x)Get Kullback-Leibler divergence
tsne.kl_divergenceFull Example
Install the matplotlib gem and download the optdigits.tes from the Optical Recognition of Handwritten Digits Data Set.
require "csv"
require "matplotlib/pyplot"
require "tsne"
data = []
target = []
CSV.foreach("optdigits.tes", converters: :numeric) do |row|
data << row[0...-1]
target << row[-1]
end
tsne = TSNE.new(n_jobs: 4)
embeddings = tsne.fit_transform(data)
vis_x = embeddings[true, 0]
vis_y = embeddings[true, 1]
plt = Matplotlib::Pyplot
plt.scatter(vis_x.to_a, vis_y.to_a, c: target, cmap: plt.cm.get_cmap("jet", 10), marker: ".")
plt.colorbar(ticks: 10.times.to_a)
plt.clim(-0.5, 9.5)
plt.showParameters
TSNE.new(
n_components: 2,
perplexity: 30.0,
early_exaggeration: 12,
learning_rate: 200,
n_iter: 1000,
n_iter_early_exag: 250,
verbose: 0,
random_state: -1,
angle: 0.5,
n_jobs: 1,
cheat_metric: true
)Data
Data can be a Ruby array
[[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]]Or a Numo array
Numo::DFloat.new(4, 3).randHistory
View the changelog
Contributing
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/ankane/tsne-ruby.git
cd tsne-ruby
bundle install
bundle exec rake vendor:all
bundle exec rake test