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ECDF-based Distance Measure Algorithms

version 1.1 (47.9 KB) by Koorosh Aslansefat
A set of functions for well-known Empirical cumulative distribution function (ECDF)-based distance measures.

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Updated 29 Apr 2020

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View ECDF-based Distance Measure Algorithms  on File Exchange License: MIT Standard - \Python Style Guide

ECDF-based Distance Measure

A set of functions for well-known Empirical Cumulative Distribution Function (CDF)-based distance measure.

Statistical/Probabilistic distance measure algorithms can be categorized into two main categories I) Cumulative Distribution Function (CDF)-based and Probability Density Function (PDF)-based. The following algorithms have been implemented:

  • Wasserstein Distance
  • Anderson-Darling Distance
  • Kolmogorov Smirnov Distance
  • Cramer von Mises Distance
  • Kuiper Distance
  • Wasserstein-Anderson-Darling Distance

Related Works

The code has been converted to MATLAB from "twosamples" library of R (https://github.com/cdowd/twosamples).

License

This framework is available under an MIT License.

Acknowledgments

We would like to thank EDF Energy R&D UK Centre and University of Hull for their support.

Cite As

Koorosh Aslansefat (2020). ECDF-based Distance Measure Algorithms (https://www.github.com/koo-ec/CDF-based-Distance-Measure), GitHub. Retrieved April 29, 2020.

Cite As

Koorosh Aslansefat (2021). ECDF-based Distance Measure Algorithms (https://github.com/koo-ec/ECDF-based-Distance-Measure/releases/tag/v1.1), GitHub. Retrieved .

Aslansefat, Koorosh, et al. “SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures.” Model-Based Safety and Assessment, Springer International Publishing, 2020, pp. 197–211, doi:10.1007/978-3-030-58920-2_13.

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MATLAB Release Compatibility
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.