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.
Add this line to your application's Gemfile:
And then execute:
Or install it yourself as:
$ gem install randsvd
require 'randsvd' # Initialize some variables. input_matrix = NMatrix.rand([1000, 100]) nb_singular_values = 10 # Perform the randomized singular value decomposition. u, s, vt = .(input_matrix, nb_singular_values) # Reconstruct the matrix with the singular values and vectors. reconstructed_matrix = u.dot(NMatrix.diag(s).dot(vt))
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.
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.
The gem is available as open source under the terms of the MIT License.