Source: umap-learn
Standards-Version: 4.7.4
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders:
 Andreas Tille <tille@debian.org>,
Section: science
Testsuite: autopkgtest-pkg-pybuild
Build-Depends:
 debhelper-compat (= 14),
 dh-sequence-python3,
 pybuild-plugin-pyproject,
 python3,
 python3-numba,
 python3-numpy,
 python3-pynndescent (>= 0.5.13) <!nocheck>,
 python3-pytest <!nocheck>,
 python3-pytest-benchmark <!nocheck>,
 python3-scipy,
 python3-setuptools,
 python3-sklearn,
 python3-tqdm <!nocheck>,
Vcs-Browser: https://salsa.debian.org/med-team/umap-learn
Vcs-Git: https://salsa.debian.org/med-team/umap-learn.git
Homepage: https://github.com/lmcinnes/umap

Package: python3-umap-learn
Section: python
Architecture: all
Depends:
 python3-numba,
 python3-numpy,
 python3-pandas,
 python3-scipy,
 python3-sklearn,
 ${misc:Depends},
 ${python3:Depends},
Breaks:
 umap-learn (<= 0.5.12+dfsg-1),
Provides:
 umap-learn,
Replaces:
 umap-learn,
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.