ECDF-based Distance Measure Algorithms
A set of functions for well-known Empirical cumulative distribution function (ECDF)-based distance measures.
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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 (2026). 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.
General Information
- Version 1.1 (150 KB)
-
View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.1 | See release notes for this release on GitHub: https://github.com/koo-ec/ECDF-based-Distance-Measure/releases/tag/v1.1 |
