Package: umap-learn
Version: 0.5.4+dfsg-1
Architecture: all
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Installed-Size: 545
Depends: python3-numba, python3-numpy, python3-pynndescent, python3-scipy, python3-sklearn, python3-tqdm, python3:any, python3-pandas
Section: science
Priority: optional
Homepage: https://github.com/lmcinnes/umap
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.
