deb_control_files:
- control
- md5sums
- postinst
- prerm
deb_fields:
Architecture: all
Depends: python3-numba, python3-numpy, python3-pynndescent, python3-scipy, python3-sklearn,
python3-tqdm, python3:any, python3-pandas
Description: |-
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.
Homepage: https://github.com/lmcinnes/umap
Installed-Size: '523'
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Package: umap-learn
Priority: optional
Section: science
Version: 0.5.3+dfsg-2
srcpkg_name: umap-learn
srcpkg_version: 0.5.3+dfsg-2