{"id":31688429,"url":"https://github.com/giuse/machine_learning_workbench","last_synced_at":"2025-10-08T10:55:54.712Z","repository":{"id":30155877,"uuid":"123904389","full_name":"giuse/machine_learning_workbench","owner":"giuse","description":"Workbench for practical machine learning in Ruby.","archived":false,"fork":false,"pushed_at":"2021-11-02T03:50:26.000Z","size":117,"stargazers_count":19,"open_issues_count":1,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-09-25T18:15:52.384Z","etag":null,"topics":["black-box-optimization","evolution-strategies","evolutionary-algorithm","evolutionary-algorithms","evolutionary-computation","evolutionary-strategy","machine-learning","machine-learning-algorithms","machine-learning-workbench","modeling","natural-evolution-strategies","neural-network","neural-networks","neuroevolution","optimization","optimization-algorithms","reinforcement-learning","rubyml"],"latest_commit_sha":null,"homepage":null,"language":"Ruby","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/giuse.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-03-05T10:36:15.000Z","updated_at":"2025-08-22T21:36:49.000Z","dependencies_parsed_at":"2022-08-20T13:00:49.800Z","dependency_job_id":null,"html_url":"https://github.com/giuse/machine_learning_workbench","commit_stats":null,"previous_names":[],"tags_count":19,"template":false,"template_full_name":null,"purl":"pkg:github/giuse/machine_learning_workbench","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giuse%2Fmachine_learning_workbench","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giuse%2Fmachine_learning_workbench/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giuse%2Fmachine_learning_workbench/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giuse%2Fmachine_learning_workbench/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/giuse","download_url":"https://codeload.github.com/giuse/machine_learning_workbench/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giuse%2Fmachine_learning_workbench/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278861032,"owners_count":26058632,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-07T02:00:06.786Z","response_time":59,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["black-box-optimization","evolution-strategies","evolutionary-algorithm","evolutionary-algorithms","evolutionary-computation","evolutionary-strategy","machine-learning","machine-learning-algorithms","machine-learning-workbench","modeling","natural-evolution-strategies","neural-network","neural-networks","neuroevolution","optimization","optimization-algorithms","reinforcement-learning","rubyml"],"created_at":"2025-10-08T10:55:49.758Z","updated_at":"2025-10-08T10:55:54.707Z","avatar_url":"https://github.com/giuse.png","language":"Ruby","readme":"# [Machine Learning Workbench](https://github.com/giuse/machine_learning_workbench)\n\n[![Gem Version](https://badge.fury.io/rb/machine_learning_workbench.svg)](https://badge.fury.io/rb/machine_learning_workbench)\n[![Build Status](https://travis-ci.org/giuse/machine_learning_workbench.svg?branch=master)](https://travis-ci.org/giuse/machine_learning_workbench)\n[![Code Climate](https://codeclimate.com/github/giuse/machine_learning_workbench/badges/gpa.svg)](https://codeclimate.com/github/giuse/machine_learning_workbench)\n\nThis workbench holds a collection of machine learning methods in Ruby. Rather than specializing on a single task or method, this gem aims at providing an encompassing framework for any machine learning application.\n\n## Installation\n\nAdd this line to your application's Gemfile:\n\n```ruby\ngem 'machine_learning_workbench'\n```\n\nAnd then execute:\n\n    $ bundle\n\nOr install it yourself as:\n\n    $ gem install machine_learning_workbench\n\n## Usage\n\nTLDR: Check out [the `examples` directory](examples), e.g. [this script](examples/neuroevolution.rb).\n\nThis library is thought as a practical workbench: there is plenty of tools hanging, each has multiple uses and applications, and as such it is built as atomic and flexible as possible. Folders [in the lib structure](lib/machine_learning_workbench) categorize them them.\n\nThe [systems directory](lib/machine_learning_workbench/systems) holds few examples of how to bring them together in higher abstractions, i.e. as _compound tools_.\nFor example, a [neuroevolution setup](lib/machine_learning_workbench/systems/neuroevolution.rb) brings together evolutionary computation and neural networks.\n\nFor an example of how to build it from scratch, check this [neuroevolution script](examples/neuroevolution.rb). To run it, use `bundle exec ruby examples/neuroevolution.rb`\n\n\n## Development\n\nAfter cloning 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.\n\nTo 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](https://rubygems.org).\n\n\n## Contributing\n\nBug reports and pull requests are welcome on GitHub at https://github.com/giuse/machine_learning_workbench.\n\n## License\n\nThe gem is available as open source under the terms of the [MIT License](https://opensource.org/licenses/MIT).\n\n## References\n\nPlease feel free to contribute to this list (see `Contributing` above).\n\n- **NES** stands for Natural Evolution Strategies. Check its [Wikipedia page](https://en.wikipedia.org/wiki/Natural_evolution_strategy) for more info.\n- **CMA-ES** stands for Covariance Matrix Adaptation Evolution Strategy. Check its [Wikipedia page](https://en.wikipedia.org/wiki/CMA-ES) for more info.\n- **UL-ELR** stands for Unsupervised Learning plus Evolutionary Reinforcement Learning, from the paper _\"Intrinsically Motivated Neuroevolution for Vision-Based Reinforcement Learning\" (ICDL2011)_. Check [here](https://exascale.info/members/giuseppe-cuccu/) for citation reference and pdf.\n- **BD-NES** stands for Block Diagonal Natural Evolution Strategy, from the homonymous paper _\"Block Diagonal Natural Evolution Strategies\" (PPSN2012)_. Check [here](https://exascale.info/members/giuseppe-cuccu/) for citation reference and pdf.\n- **RNES** stands for Radial Natural Evolution Strategy, from the paper _\"Novelty-Based Restarts for Evolution Strategies\" (CEC2011)_. Check [here](https://exascale.info/members/giuseppe-cuccu/) for citation reference and pdf.\n- **DLR-VQ** stands for Decaying Learning Rate Vector Quantization, from the algorithm originally named _*Online VQ*_ in the paper _\"Intrinsically Motivated Neuroevolution for Vision-Based Reinforcement Learning\" (ICDL2011)_. Check [here](https://exascale.info/members/giuseppe-cuccu/) for citation reference and pdf.\n","funding_links":[],"categories":["Machine Learning Libraries"],"sub_categories":["Frameworks"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgiuse%2Fmachine_learning_workbench","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgiuse%2Fmachine_learning_workbench","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgiuse%2Fmachine_learning_workbench/lists"}