How to Install and Uninstall golang-github-vividcortex-ewma-dev Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 21,2024

1. Install "golang-github-vividcortex-ewma-dev" package

In this section, we are going to explain the necessary steps to install golang-github-vividcortex-ewma-dev on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                golang-github-vividcortex-ewma-dev
            
                    
    
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2. Uninstall "golang-github-vividcortex-ewma-dev" package

This guide let you learn how to uninstall golang-github-vividcortex-ewma-dev on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                golang-github-vividcortex-ewma-dev
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the golang-github-vividcortex-ewma-dev package on Ubuntu 20.10 (Groovy Gorilla)

Package: golang-github-vividcortex-ewma-dev
Architecture: all
Version: 1.1.1-1
Priority: extra
Section: universe/devel
Source: golang-github-vividcortex-ewma
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Go Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 22
Filename: pool/universe/g/golang-github-vividcortex-ewma/golang-github-vividcortex-ewma-dev_1.1.1-1_all.deb
Size: 5252
MD5sum: de557268d2aa11e3628cf4bf748f09e1
SHA1: 92d5965cd7b8feb8b4afe44dce9d3c17c2ee8a47
SHA256: 051bdaf0f24aee0d97d245ba91bd82559b29654c327881f3826782e2aded6391
SHA512: 785b3b78cf9bf13ba8c4e14ca9c590d4fc58ca362652718b84bb6be616bc125dfacccfc7b028558526ceb9a4c97b2ee22f3217f4334156e21a5a83cfd28bd01c
Homepage: https://github.com/vividcortex/ewma
Description-en: Exponentially Weighted Moving Average algorithms for Go
An exponentially weighted moving average is a way to continuously
compute a type of average for a series of numbers, as the numbers
arrive. After a value in the series is added to the average, its
weight in the average decreases exponentially over time. This biases
the average towards more recent data. EWMAs are useful for several
reasons, chiefly their inexpensive computational and memory cost, as
well as the fact that they represent the recent central tendency of
the series of values.
Description-md5: 73a2f7e0eeb3ebdd3634eb8bb6792b1c