semvec/pypi-packages huggingface dataset:
netbuild-Blizzarus 0.0.1
A small example package
Requires Python: >=3.6

# netbuild
 The Python Network Building Tool (or 'netbuilder' package) is a terrific tool for students and networking
 enthusiasts alike, which provides helpful functions for IP schema creation and subnetting.  It has four
 primary modes, including a guide/walkthrough and a quick start variatn for both IPv4 and IPv6.
 
 You can use this tool to create a subnet plan for an IPv4 network, check address classes, check public and
 private addresses (as well as special addresses), expand and/or compress an IPv6 address, and check IP ranges
 or /64 network counts for an IPv6 network.
BanditPAM-beta 0.0.20
BanditPAM: A state-of-the-art, high-performance k-medoids algorithm.

BanditPAM: A state-of-the-art, high-performance k-medoids algorithm
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Introduction
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Clustering algorithms such as k-means are ubiquitous in modern data science applications. Despite its popularity, however, k-means has several drawbacks. K-means only supports certain distance metrics, and its cluster centers can lack interpretability. Alternatively, k-medoids uses the points in the dataset itself -- the medoids -- as cluster centers. This enables interpretability of the cluster centers. Furthermore, k-medoids supports arbitrary dissimilarity functions in place of a distance metric.

Details
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In this package, we provide a high-performance implementation of BanditPAM , a state-of-the-art k-medoids algorithm. BanditPAM matches state-of-the-art in clustering quality but improves the runtime of previous approaches from O(n^2) to O(nlogn) in each iteration. Furthermore, BanditPAM supports arbitrarily dissimilarity functions between points -- these functions need not even be metrics. This implementation is written in C++ for performance, but is callable from Python via Python bindings. For further details, please see the paper at https://arxiv.org/abs/2006.06856 for the full paper and the code at https://github.com/ThrunGroup/BanditPAM/blob/main/README.md

If you use this software, please cite:
Mo Tiwari, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony. "Bandit-PAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" Advances in Neural Information Processing Systems (NeurIPS) 2020.