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Source: umap-learn
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders: Andreas Tille <tille@debian.org>
Section: science
Priority: optional
Build-Depends: debhelper-compat (= 13),
dh-sequence-python3,
python3,
python3-setuptools,
python3-numpy,
python3-scipy,
python3-sklearn,
python3-numba,
python3-pynndescent (>= 0.5.13) <!nocheck>,
python3-tqdm <!nocheck>,
python3-pytest <!nocheck>
Standards-Version: 4.7.2
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
Rules-Requires-Root: no
Package: umap-learn
Architecture: all
Depends: ${python3:Depends},
${misc:Depends},
python3-numpy,
python3-scipy,
python3-sklearn,
python3-numba,
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.
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