MinimalKNN: minimal package to construct k-NN Graph
Project description
A Minimal k-Nearest Neighbor Graph Construction Library
Overview
This package will provide a function to construct an approximated k-Nearest Neighbor graph from a list of three dimensional points. The graph construction algorithm is based on NN-descent presented in Dong, Moses, & Li (2011)[^DML2011]. The Euclidean and Manhattan metrics are implemented in the current version, while only the Euclidean one is available in Python. The algorithm efficiently constructs an approximated k-Nearest Neighbor graph. This provides a portable C++11 header and a Python interface.
Dependencies
The library is written in C++11 and do not depends on any library outside of the STL
. The Python interface is depends on NumPy
, and functional test procedures depend on Matplotlib
. The library is developed on g++
version 5.4 installed in Linux Mint 18.1 (serena). The Python interface is developed on Python 3.7.1 and Numpy 1.18.1.
References
[^DML2011]: Wei Dong, Charikar Moses, & Kai Li, WWW'11: Proceedings of the 20th international conference on World wide web (2011), 577--586 (doi: 10.1145/1963405.1963487)
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for minimalKNN-0.5-py3.9-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | f58be9401ce09fd0d90004f1d13a7c8fdc3deca13a4937e1af8e6830a5d746ad |
|
MD5 | 1293b486da91cd2adc5a81ae07384150 |
|
BLAKE2b-256 | cf99742cf7742fbe66f3ab3dd5de9f7ba2baf2a7fb96ef3c479eda2f64431175 |
Hashes for minimalKNN-0.5-cp37-cp37m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4d6c31d5e0bae0d09fabf9c6978755d726ba79739f0aa06a23089cb4f70d8b83 |
|
MD5 | b7a1feade3b593548d7371dc5920b4d8 |
|
BLAKE2b-256 | 5d68ba7e1764bac4ae3d0788c119ad4b33bf22c72ce5f34487a14c348486e39b |