1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
|
Source: python-pynndescent
Maintainer: Debian Python Team <team+python@tracker.debian.org>
Uploaders: Andreas Tille <tille@debian.org>
Section: python
Priority: optional
Build-Depends: debhelper-compat (= 13),
dh-sequence-python3,
python3-all,
python3-joblib <!nocheck>,
python3-llvmlite <!nocheck>,
python3-numba <!nocheck>,
python3-pytest <!nocheck>,
python3-scipy <!nocheck>,
python3-setuptools,
python3-sklearn <!nocheck>
Standards-Version: 4.7.2
Vcs-Browser: https://salsa.debian.org/python-team/packages/python-pynndescent
Vcs-Git: https://salsa.debian.org/python-team/packages/python-pynndescent.git
Homepage: https://github.com/lmcinnes/pynndescent/
Rules-Requires-Root: no
Package: python3-pynndescent
Architecture: any-amd64 alpha arm64 ia64 mips64el ppc64 ppc64el riscv64 s390x sparc64
Depends: ${misc:Depends}, ${python3:Depends}
Description: 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.
|
