umap-learn 0.5.4+dfsg-1 source package in Ubuntu
Changelog
umap-learn (0.5.4+dfsg-1) unstable; urgency=medium * New upstream version * Build-Depends: s/dh-python/dh-sequence-python3/ (routine-update) -- Andreas Tille <email address hidden> Wed, 08 Nov 2023 21:35:44 +0100
Upload details
- Uploaded by:
- Debian Med
- Uploaded to:
- Sid
- Original maintainer:
- Debian Med
- Architectures:
- all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Noble | proposed | universe | misc |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| umap-learn_0.5.4+dfsg-1.dsc | 2.3 KiB | abf2c9ce144e7e84b1b0026f34d79d2089c90f0d73e91800d36fbdb91ec2e8c4 |
| umap-learn_0.5.4+dfsg.orig.tar.xz | 1.4 MiB | 2609156df2d9dd634938a577db9818fa0a9eed3e388713487f5e08d38a825a63 |
| umap-learn_0.5.4+dfsg-1.debian.tar.xz | 6.3 KiB | 69950dd08071a9a768c7e3e178ca092b7af745879bc735138965e55d053f743f |
Available diffs
- diff from 0.5.3+dfsg-2 to 0.5.4+dfsg-1 (29.1 KiB)
No changes file available.
Binary packages built by this source
- umap-learn: Uniform Manifold Approximation and Projection
Uniform Manifold Approximation and Projection (UMAP) is a dimension
reduction technique that can be used for visualisation similarly to t-
SNE, but also for general non-linear dimension reduction. The algorithm
is founded on three assumptions about the data:
.
1. The data is uniformly distributed on a Riemannian manifold;
2. The Riemannian metric is locally constant (or can be
approximated as such);
3. The manifold is locally connected.
.
From these assumptions it is possible to model the manifold with a fuzzy
topological structure. The embedding is found by searching for a low
dimensional projection of the data that has the closest possible
equivalent fuzzy topological structure.
