pompetzki/metriX

A library containing a collection of distance and similarity measures for data analysis

A lightweight JAX-based library offering a collection of distance and similarity measures for data analysis. Designed for scalability and accelerator support, it includes high-performance, parallelizable implementations of a wide range of commonly used metrics.

Installation

pip install -e .

Implemented Metrics

This library is still in development and more metrics will be added over time. The following metrics are currently implemented.

Distance Measures

• Minkowski Distance
• Euclidean Distance
• Cosine Distance
• Mahalanobis Distance
• Dynamic Time Warping
• Discrete Frechet Distance
• Sinkhorn Distance

Statistical Measures

• Relative Entropy (Kullback-Leibler Divergence)
• Frechet Inception Distance
• Maximum Mean Discrepancy
• Wassersteim Distance

Examples

To test, there are two examples: Either compare batches of particles

python examples/example_particle_data.py

or batches of time series data

python examples/example_time_series_data.py

Citation

If you use this libarary in your work, please consider citing it as follows:

@software{metrix2024github,
  author = {Pompetzki, Kay and Gruner, Theo and Al-Hafez, Firas, and Peters, Jan},
  title = {MetriX: A JAX-Based Collection of Similarity and Statistical Measures for Accelerated Data Analysis.},
  url = {https://github.com/pompetzki/metriX},
  year = {2024},
}