How to Install and Uninstall python3-munkres.noarch Package on AlmaLinux 8
Last updated: November 01,2024
1. Install "python3-munkres.noarch" package
Please follow the steps below to install python3-munkres.noarch on AlmaLinux 8
$
sudo dnf update
Copied
$
sudo dnf install
python3-munkres.noarch
Copied
2. Uninstall "python3-munkres.noarch" package
Please follow the step by step instructions below to uninstall python3-munkres.noarch on AlmaLinux 8:
$
sudo dnf remove
python3-munkres.noarch
Copied
$
sudo dnf autoremove
Copied
3. Information about the python3-munkres.noarch package on AlmaLinux 8
Last metadata expiration check: 1:04:26 ago on Mon Sep 5 03:22:42 2022.
Available Packages
Name : python3-munkres
Version : 1.1.2
Release : 1.el8
Architecture : noarch
Size : 24 k
Source : python-munkres-1.1.2-1.el8.src.rpm
Repository : epel
Summary : A Munkres algorithm for Python
URL : http://software.clapper.org/munkres/
License : BSD
Description : The Munkres module provides an implementation of the Munkres algorithm (also
: called the Hungarian algorithm or the Kuhn-Munkres algorithm). The algorithm
: models an assignment problem as an NxM cost matrix, where each element
: represents the cost of assigning the ith worker to the jth job, and it figures
: out the least-cost solution, choosing a single item from each row and column in
: the matrix, such that no row and no column are used more than once.
Available Packages
Name : python3-munkres
Version : 1.1.2
Release : 1.el8
Architecture : noarch
Size : 24 k
Source : python-munkres-1.1.2-1.el8.src.rpm
Repository : epel
Summary : A Munkres algorithm for Python
URL : http://software.clapper.org/munkres/
License : BSD
Description : The Munkres module provides an implementation of the Munkres algorithm (also
: called the Hungarian algorithm or the Kuhn-Munkres algorithm). The algorithm
: models an assignment problem as an NxM cost matrix, where each element
: represents the cost of assigning the ith worker to the jth job, and it figures
: out the least-cost solution, choosing a single item from each row and column in
: the matrix, such that no row and no column are used more than once.