ECDF-based Distance Measure Algorithms

Version 1.1 (150 KB) by Koorosh Aslansefat

A set of functions for well-known Empirical cumulative distribution function (ECDF)-based distance measures.

https://github.com/koo-ec/ECDF-based-Distance-Measure

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Updated 29 Apr 2020

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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 (2025). ECDF-based Distance Measure Algorithms (https://github.com/koo-ec/ECDF-based-Distance-Measure/releases/tag/v1.1), GitHub. Retrieved February 17, 2025.

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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Version	Published	Release Notes
1.1	29 Apr 2020	See release notes for this release on GitHub: https://github.com/koo-ec/ECDF-based-Distance-Measure/releases/tag/v1.1	Download

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

MLA	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.
APA	Aslansefat, K., Sorokos, I., Whiting, D., Kolagari, R. T., & Papadopoulos, Y. (2020). SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures. In Model-Based Safety and Assessment (pp. 197–211). Springer International Publishing. Retrieved from https://doi.org/10.1007%2F978-3-030-58920-2_13
BibTeX	@incollection{Aslansefat_2020, doi = {10.1007/978-3-030-58920-2_13}, url = {https://doi.org/10.1007%2F978-3-030-58920-2_13}, year = 2020, publisher = {Springer International Publishing}, pages = {197--211}, author = {Koorosh Aslansefat and Ioannis Sorokos and Declan Whiting and Ramin Tavakoli Kolagari and Yiannis Papadopoulos}, title = {{SafeML}: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures}, booktitle = {Model-Based Safety and Assessment} }

ECDF-based Distance Measure Algorithms

ECDF-based Distance Measure

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