https://github.com/jhumphry/regressions

A Python package that implements various regression algorithms, including Partial Least Squares and Principal Components Regression
https://github.com/jhumphry/regressions

Last synced: 1 day ago
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A Python package that implements various regression algorithms, including Partial Least Squares and Principal Components Regression

• Host: GitHub
• URL: https://github.com/jhumphry/regressions
• Owner: jhumphry
• License: isc
• Created: 2015-11-08T21:52:07.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2023-03-01T21:49:56.000Z (over 1 year ago)
• Last Synced: 2024-08-03T15:17:46.245Z (3 months ago)
• Language: Python
• Homepage:
• Size: 72.3 KB
• Stars: 22
• Watchers: 2
• Forks: 18
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Regressions

This package provides various forms of regression. The aim of these modules is

to achieve clarity of implementation with a clear connection to the

mathematical descriptions of the algorithms. The motivation for creating the

package was the desire to learn about and explore the use of Principal

Components Regression, Partial Least Squares regression and non-linear

kernel-based Partial Least Squares regression.

Python 3.5 and Numpy 1.10 or greater are required as the new '@' matrix

multiplication operator is used. If SciPy is available some linear algebra

routines may be used as they can sometimes be faster than the routines in

Numpy - however SciPy is not required. Matplotlib is used by the examples to

display the results.

Full documentation of the API is maintained using Sphinx - see the `doc`

directory.