How to Install and Uninstall python38-fastcluster Package on openSuSE Tumbleweed

Last updated: June 26,2024

Deprecated! Installation of this package may no longer be supported.

1. Install "python38-fastcluster" package

This is a short guide on how to install python38-fastcluster on openSuSE Tumbleweed


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                python38-fastcluster
            
                    
    
    Copied

2. Uninstall "python38-fastcluster" package

Please follow the guidance below to uninstall python38-fastcluster on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                python38-fastcluster
            
                    
    
    Copied

3. Information about the python38-fastcluster package on openSuSE Tumbleweed

Information for package python38-fastcluster:
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Repository : openSUSE-Tumbleweed-Oss
Name : python38-fastcluster
Version : 1.2.0-1.2
Arch : x86_64
Vendor : openSUSE
Installed Size : 131,7 KiB
Installed : No
Status : not installed
Source package : python-fastcluster-1.2.0-1.2.src
Summary : Hierarchical clustering routines for Python
Description :
This library provides Python functions for hierarchical clustering.
It generates hierarchical clusters from distance matrices or from
vector data.
Part of this module is intended to replace the functions
linkage, single, complete, average, weighted, centroid, median, ward
in the module scipy.cluster.hierarchy with the same functionality but
much faster algorithms. Moreover, the function 'linkage_vector'
provides memory-efficient clustering for vector data.
The interface is very similar to MATLAB's Statistics Toolbox API to
make code easier to port from MATLAB to Python/Numpy. The core
implementation of this library is in C++ for efficiency.