Complete Guide: Installing umap-learn on Ubuntu 21.10 (Impish Indri) 2024

1. Install "umap-learn" package

Please follow the step by step instructions below to install umap-learn on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                umap-learn
            
                    
    
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2. Uninstall "umap-learn" package

Here is a brief guide to show you how to uninstall umap-learn on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                umap-learn
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the umap-learn package on Ubuntu 21.10 (Impish Indri)

Package: umap-learn
Architecture: all
Version: 0.4.5+dfsg-2
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 342
Depends: python3-numba, python3-numpy, python3-scipy, python3-sklearn, python3:any
Filename: pool/universe/u/umap-learn/umap-learn_0.4.5+dfsg-2_all.deb
Size: 55392
MD5sum: 8e9e4c86d581cda6556885164ebc1320
SHA1: 1fb6f5bfc6f6f70e42df73d2c9bdd18e6207a7f9
SHA256: b7630711524f149ee4cce17fcdad92d877a089a60eeec6c4bd9c719fa692eda6
SHA512: bf6c7e308ef25abcee93175b032789f19170384ab555fbca074a202dfdbade3b9eb3f14cd3832a481b102c1b69b0fb94ab2dcae66079c9fe6413d3cecacc91b2
Homepage: https://github.com/lmcinnes/umap
Description-en: 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:
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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