https://github.com/giuse/machine_learning_workbench
Workbench for practical machine learning in Ruby.
https://github.com/giuse/machine_learning_workbench
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
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Workbench for practical machine learning in Ruby.
- Host: GitHub
- URL: https://github.com/giuse/machine_learning_workbench
- Owner: giuse
- License: mit
- Created: 2018-03-05T10:36:15.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2021-11-02T03:50:26.000Z (about 4 years ago)
- Last Synced: 2025-09-25T18:15:52.384Z (3 months ago)
- 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
- Language: Ruby
- Size: 114 KB
- Stars: 19
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- machine-learning-with-ruby - machine_learning_workbench - (Machine Learning Libraries / Frameworks)
README
# [Machine Learning Workbench](https://github.com/giuse/machine_learning_workbench)
[](https://badge.fury.io/rb/machine_learning_workbench)
[](https://travis-ci.org/giuse/machine_learning_workbench)
[](https://codeclimate.com/github/giuse/machine_learning_workbench)
This 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.
## Installation
Add this line to your application's Gemfile:
```ruby
gem 'machine_learning_workbench'
```
And then execute:
$ bundle
Or install it yourself as:
$ gem install machine_learning_workbench
## Usage
TLDR: Check out [the `examples` directory](examples), e.g. [this script](examples/neuroevolution.rb).
This 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.
The [systems directory](lib/machine_learning_workbench/systems) holds few examples of how to bring them together in higher abstractions, i.e. as _compound tools_.
For example, a [neuroevolution setup](lib/machine_learning_workbench/systems/neuroevolution.rb) brings together evolutionary computation and neural networks.
For 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`
## Development
After 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.
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](https://rubygems.org).
## Contributing
Bug reports and pull requests are welcome on GitHub at https://github.com/giuse/machine_learning_workbench.
## License
The gem is available as open source under the terms of the [MIT License](https://opensource.org/licenses/MIT).
## References
Please feel free to contribute to this list (see `Contributing` above).
- **NES** stands for Natural Evolution Strategies. Check its [Wikipedia page](https://en.wikipedia.org/wiki/Natural_evolution_strategy) for more info.
- **CMA-ES** stands for Covariance Matrix Adaptation Evolution Strategy. Check its [Wikipedia page](https://en.wikipedia.org/wiki/CMA-ES) for more info.
- **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.
- **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.
- **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.
- **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.