How to Install and Uninstall umap-learn Package on Kali Linux
Last updated: November 05,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "umap-learn" package
In this section, we are going to explain the necessary steps to install umap-learn on Kali Linux
$
sudo apt update
Copied
$
sudo apt install
umap-learn
Copied
2. Uninstall "umap-learn" package
This guide let you learn how to uninstall umap-learn on Kali Linux:
$
sudo apt remove
umap-learn
Copied
$
sudo apt autoclean && sudo apt autoremove
Copied
3. Information about the umap-learn package on Kali Linux
Package: umap-learn
Version: 0.4.5+dfsg-3
Installed-Size: 339
Maintainer: Debian Med Packaging Team
Architecture: all
Depends: python3-numba, python3-numpy, python3-scipy, python3-sklearn, python3:any
Size: 55448
SHA256: bfe8a31625aa503b5ee8f142e3e3c942196ac597043f62a78fc036a623312761
SHA1: 86664df1bf47c339da584020e9ef222d0e31374d
MD5sum: af7210f4c9cd4f1e998262a21368dc95
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.
Description-md5: 949d5d3304fc30065a0bd753c8886c71
Homepage: https://github.com/lmcinnes/umap
Section: science
Priority: optional
Filename: pool/main/u/umap-learn/umap-learn_0.4.5+dfsg-3_all.deb
Version: 0.4.5+dfsg-3
Installed-Size: 339
Maintainer: Debian Med Packaging Team
Architecture: all
Depends: python3-numba, python3-numpy, python3-scipy, python3-sklearn, python3:any
Size: 55448
SHA256: bfe8a31625aa503b5ee8f142e3e3c942196ac597043f62a78fc036a623312761
SHA1: 86664df1bf47c339da584020e9ef222d0e31374d
MD5sum: af7210f4c9cd4f1e998262a21368dc95
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.
Description-md5: 949d5d3304fc30065a0bd753c8886c71
Homepage: https://github.com/lmcinnes/umap
Section: science
Priority: optional
Filename: pool/main/u/umap-learn/umap-learn_0.4.5+dfsg-3_all.deb