python-pynndescent 0.5.11-1 source package in Ubuntu
Changelog
python-pynndescent (0.5.11-1) unstable; urgency=medium [ Andreas Tille ] * Team upload. * New upstream version (Closes: #1057598) * Build-Depends: s/dh-python/dh-sequence-python3/ (routine-update) [ Benjamin Drung ] * test: load pynndescent_bug_np.npz from relative path * Hard-code __version__ during build to fix running test during build -- Benjamin Drung <email address hidden> Sat, 01 Jun 2024 00:37:17 +0200
Upload details
- Uploaded by:
- Debian Python Team
- Uploaded to:
- Sid
- Original maintainer:
- Debian Python Team
- Architectures:
- any-amd64 alpha arm64 ia64 mips64el ppc64 ppc64el riscv64 s390x sparc64
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Oracular | release | universe | misc |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| python-pynndescent_0.5.11-1.dsc | 2.4 KiB | 561d5c076afdd0527dc6619b284af730bda2f12d5a3d65c2532eab3d69158643 |
| python-pynndescent_0.5.11.orig.tar.xz | 2.7 MiB | 6cd27ce8bd8a08ca5d17071c42e124d982beb5227a2984d3f9234e98d90db505 |
| python-pynndescent_0.5.11-1.debian.tar.xz | 5.2 KiB | ca3bf1f9bcf1b6f64c08a590fd4e0dcbbc97f2f044f5b8511147290d6a289dbb |
No changes file available.
Binary packages built by this source
- python3-pynndescent: 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.
