How to Install and Uninstall python3-pynndescent Package on Ubuntu 21.10 (Impish Indri)

Last updated: November 22,2024

1. Install "python3-pynndescent" package

Please follow the steps below to install python3-pynndescent on Ubuntu 21.10 (Impish Indri)

$ sudo apt update $ sudo apt install python3-pynndescent

2. Uninstall "python3-pynndescent" package

Please follow the guidelines below to uninstall python3-pynndescent on Ubuntu 21.10 (Impish Indri):

$ sudo apt remove python3-pynndescent $ sudo apt autoclean && sudo apt autoremove

3. Information about the python3-pynndescent package on Ubuntu 21.10 (Impish Indri)

Package: python3-pynndescent
Architecture: amd64
Version: 0.5.2+dfsg-1
Priority: optional
Section: universe/science
Source: python-pynndescent
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 260
Depends: python3-joblib, python3-llvmlite, python3-numba, python3-scipy, python3-sklearn, python3:any
Filename: pool/universe/p/python-pynndescent/python3-pynndescent_0.5.2+dfsg-1_amd64.deb
Size: 39164
MD5sum: b6b48de68ec6b3125d58a77214a47b42
SHA1: 98b90fdf334491934fd2cac7b183b7487fec68a2
SHA256: 8be48229f91341e426ae2fa3d0fcf72a552ec728569b270a1e6be0f6296d61a6
SHA512: 4c76689c1b1dd517e0cb74112a840b440fdb56b9387b6686e1e4437e1077630c978424eae7510e26553ee53e8fb7eb41f1214a7584b9e5fa60969be9a887b32f
Homepage: https://pypi.org/project/pynndescent/
Description-en: nearest neighbor descent for approximate nearest neighbors
PyNNDescent is a Python nearest neighbor descent for approximate nearest
neighbors. It provides a Python implementation of Nearest Neighbor
Descent for k-neighbor-graph construction and approximate nearest
neighbor search, as per the paper:
.
Dong, Wei, Charikar Moses, and Kai Li. "Efficient k-nearest neighbor
graph construction for generic similarity measures." Proceedings of the
20th international conference on World wide web. ACM, 2011.
.
This library supplements that approach with the use of random projection
trees for initialisation. This can be particularly useful for the
metrics that are amenable to such approaches (euclidean, minkowski,
angular, cosine, etc.). Graph diversification is also performed, pruning
the longest edges of any triangles in the graph.
.
Currently this library targets relatively high accuracy (80%-100%
accuracy rate) approximate nearest neighbor searches.
Description-md5: 7bae7d98c624322f0c1ad77f603f50cd