RandSVD

RandSVD is a class that performs truncated singular value decomposition using a randomized algorithm. To implement, I referred to the following papers:

  • P.-G. Martinsson, A. Szlam, M. Tygert, "Normalized power iterations for the computation of SVD," Proc. of NIPS Workshop on Low-Rank Methods for Large-Scale Machine Learning, 2011.
  • P.-G. Martinsson, V. Rokhlin, M. Tygert, "A randomized algorithm for the approximation of matrices," Tech. Rep., 1361, Yale University Department of Computer Science, 2006.

Installation

Add this line to your application's Gemfile:

gem 'randsvd'

And then execute:

$ bundle

Or install it yourself as:

$ gem install randsvd

Usage

require 'randsvd'

# Initialize some variables.
input_matrix = NMatrix.rand([1000, 100])
nb_singular_values = 10

# Perform the randomized singular value decomposition.
u, s, vt = RandSVD.gesvd(input_matrix, nb_singular_values)

# Reconstruct the matrix with the singular values and vectors.
reconstructed_matrix = u.dot(NMatrix.diag(s).dot(vt))

Development

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec 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.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/[USERNAME]/randsvd. 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.

License

The gem is available as open source under the terms of the MIT License.