Your message dated Tue, 28 Sep 2021 14:50:06 +0100 with message-id <20210928145006.376cec9d@felix.codehelp> and subject line Already exists in the archive has caused the Debian Bug report #984796, regarding ITP: pynndescent -- nearest neighbor descent for approximate nearest neighbors to be marked as done. This means that you claim that the problem has been dealt with. If this is not the case it is now your responsibility to reopen the Bug report if necessary, and/or fix the problem forthwith. (NB: If you are a system administrator and have no idea what this message is talking about, this may indicate a serious mail system misconfiguration somewhere. Please contact owner@bugs.debian.org immediately.) -- 984796: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=984796 Debian Bug Tracking System Contact owner@bugs.debian.org with problems
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- To: Debian Bug Tracking System <submit@bugs.debian.org>
- Subject: ITP: pynndescent -- nearest neighbor descent for approximate nearest neighbors
- From: Sebastien Delafond <seb@debian.org>
- Date: Mon, 8 Mar 2021 14:31:08 +0100 (CET)
- Message-id: <20210308133108.13293424EC6@hulk.befour.org>
Package: wnpp Severity: wishlist Owner: Sebastien Delafond <seb@debian.org> X-Debbugs-Cc: debian-devel@lists.debian.org * Package name : pynndescent Version : 0.5.2 Upstream Author : Leland McInnes <leland.mcinnes@gmail.com> * URL : https://github.com/lmcinnes/pynndescent * License : BSD-2 Programming Lang: python 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.
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--- Begin Message ---
- To: 984796-done@bugs.debian.org
- Subject: Already exists in the archive
- From: Neil Williams <codehelp@debian.org>
- Date: Tue, 28 Sep 2021 14:50:06 +0100
- Message-id: <20210928145006.376cec9d@felix.codehelp>
https://tracker.debian.org/pkg/python-pynndescent https://packages.debian.org/unstable/python3-pynndescent From https://github.com/lmcinnes/pynndescent/ Closing the ITP due to an overlap with the Debian Med team. -- Neil Williams ============= https://linux.codehelp.co.uk/Attachment: pgprErP4aGbqM.pgp
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