README.org

CL Machine-Learning

CL Machine-Learning is high performance and large scale statistical machine learning package written in Common Lisp developed at MSI.

This repository contains is a authorized fork of the original CLML with the following goals in mind:

• Remove dependent libraries available from the Quicklisp repository
• Re-factor code to support Quicklisp packaging
• Organize code into independent systems based on functional category
• Support for SBCL, CCL, LispWorks and Allegro Common Lisp
• Improve documentation

Author(s):

Original

• Salvi Péter
• Naganuma Shigeta
• Tada Masashi
• Abe Yusuke
• Jianshi Huang
• Fujii Ryo
• Abe Seika
• Kuroda Hisao

Current Branch Maintainer(s)/Authors(s):

• Mike Maul

Contributors:

• Graham Dobbins

[#B] Installation

Documentation

User and API Documentation

• User and API documentation may be found on line at http://mmaul.github.io/clml/

and also in the project directories docs/clml-manual.html.

• Tutorials may be found on the clml.tutorials blog at https://mmaul.github.io/clml.tutorials/
• Usage examples can be found in the docs/sample project directory
• Some notes and algorithmic details and background information can be

found in the project directory docs/notes files in memo, notes and docs

Related Repositories

• clml.extras - https://github.com/mmaul/clml.extras Extended features for clml
  • cl-plplot integration
  • cl-ana integration
  • r-datasets - Library for seamless access to R datasets
  • eazy-gnuplot integration
• clml.tutorials - https://github.com/mmaul/clml.tutorials Tutorials for CLML

Contributing

All contributions are welcome. If the contribution is to resolve and problem with CLML, please open an issue in the github repository accompanied by a pull request.

If you would like to contribute new functionality, again open an issue at the clml github repository, describe the proposed functionality and we will go from there. There is a separate repository clml.extras (https://github.com/mmaul/clml.extras) which is used for functionality that is not core to CLML but adds features and capabilities, such as integration layers with other libraries. If this describes your contribution please open an issue on clml.extras github repository.

If you are interesting in helping to maintain CLML, please contact me via email.