https://github.com/o-laurent/multivariate-ks-test

Python implementation of an extension of the Kolmogorov-Smirnov test for multivariate samples
https://github.com/o-laurent/multivariate-ks-test

kolmogorov-smirnov kolmogorov-smirnov-tests multivariate python statistical-tests

Last synced: 5 days ago
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Python implementation of an extension of the Kolmogorov-Smirnov test for multivariate samples

• Host: GitHub
• URL: https://github.com/o-laurent/multivariate-ks-test
• Owner: o-laurent
• License: mit
• Created: 2023-07-31T17:01:19.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-06T09:01:11.000Z (over 1 year ago)
• Last Synced: 2023-08-06T10:19:18.215Z (over 1 year ago)
• Topics: kolmogorov-smirnov, kolmogorov-smirnov-tests, multivariate, python, statistical-tests
• Language: Python
• Homepage:
• Size: 8.79 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Multivariate extension of the Kolmogorov-Smirnov test

This repository implements the extension of the KS test proposed by Michael Naaman in 

[On the tight constant in the multivariate Dvoretzky–Kiefer–Wolfowitz inequality](https://www.sciencedirect.com/science/article/pii/S016771522100050X/pdf)

in the one and two-sample cases.

This package was translated from the original MATLAB code provided by the author.

Please feel free to open an issue if you have any problems or questions.

## Installation

Clone the repository and install it with pip:

```bash

cd multivariate-ks-test && pip install .

```

Please raise an issue if you want to install it from PyPI.

## Usage example

```python

import numpy as np

from mks_test import mkstest

# Generate two samples from a 5D Normal distribution

n = 100

d = 5

mu = np.zeros(d)

sigma = np.eye(d)

X = np.random.multivariate_normal(mu, sigma, n)

Y = np.random.multivariate_normal(mu, sigma, n)

# Compute the test statistic

mkstest(X, Y, alpha=0.05, verbose=True)

# returns False - you can't reject that the two samples are drawn from the same distribution

```

## Reference

If you find this code useful, you may cite the following paper:

```latex

@article{naaman2021tight,

  title={On the tight constant in the multivariate Dvoretzky--Kiefer--Wolfowitz inequality},

  author={Naaman, Michael},

  journal={Statistics \& Probability Letters},

  year={2021},

}

```