## File: control

package info (click to toggle)
umap-learn 0.4.5%2Bdfsg-2
 `123456789101112131415161718192021222324252627282930313233343536373839404142434445` ``````Source: umap-learn Maintainer: Debian Med Packaging Team Uploaders: Andreas Tille , Nilesh Patra Section: science Priority: optional Build-Depends: debhelper-compat (= 13), dh-python, python3, python3-setuptools, python3-numpy, python3-scipy, python3-sklearn, python3-numba, python3-nose , python3-pytest Standards-Version: 4.5.0 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 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. ``````