https://github.com/gully/bivariate_practice
This is bivariate practice from the astroML textbook chapter 3.0 figures
https://github.com/gully/bivariate_practice
Last synced: about 1 year ago
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This is bivariate practice from the astroML textbook chapter 3.0 figures
- Host: GitHub
- URL: https://github.com/gully/bivariate_practice
- Owner: gully
- Created: 2013-12-16T22:32:07.000Z (over 12 years ago)
- Default Branch: master
- Last Pushed: 2013-12-26T06:41:30.000Z (over 12 years ago)
- Last Synced: 2025-02-16T16:25:30.643Z (over 1 year ago)
- Language: Python
- Size: 215 KB
- Stars: 0
- Watchers: 4
- Forks: 2
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
bivariate_practice
==================
December 20, 2013
gully; Austin, TX
This is bivariate practice from the astroML textbook chapter 3.0 figures
The original code is located at:
http://www.astroml.org/book_figures/chapter3/fig_robust_pca.html
The file fig_robust_pca.py is the original.
The file fig_robust_pca_gull.py is an edit by gully.
The key idea was that I explored the dependence of the robust and non-robust fitting techniques by trying contamination fraction from 0.001 to 0.70. The original example only looked at 5% and 15% outliers.
Hope you enjoyed the example, and that you can get it to run.
Best wishes,
-gully