Description
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recom- mendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib). The engine aims to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms.
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Based on the "Machine Learning" category.
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CodeRabbit: AI Code Reviews for Developers
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They vary from L1 to L5 with "L5" being the highest.
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README
Crab - A Recommendation Engine library for Python
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recom- mendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib). The engine aims to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms.
Usage
For Usage and Instructions checkout the Crab Wiki
History
The project was started in 2010 by Marcel Caraciolo as a M.S.C related project, and since then many people interested joined to help in the project. It is currently maintained by a team of volunteers, members of the Muriçoca Labs.
Authors
Marcel Caraciolo ([email protected])
Bruno Melo ([email protected])
Ricardo Caspirro ([email protected])
Rodrigo Alves ([email protected])
Bugs, Feedback
Please submit bugs you might encounter, as well Patches and Features Requests to the Issues Tracker located at GitHub.
Contributions
If you want to submit a patch to this project, it is AWESOME. Follow this guide:
- Fork Crab
- Make your alterations and commit
- Create a topic branch - git checkout -b my_branch
- Push to your branch - git push origin my_branch
- Create a Pull Request from your branch.
- You just contributed to the Crab project!
Wiki
Please check our Wiki wiki, for further information on how to start developing or use Crab in your projects.
LICENCE (BSD)
Copyright (c) 2011, Muriçoca Labs
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the Muriçoca Labs nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL MURIÇOCA LABS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.