How to Install and Uninstall python-shogun Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: May 21,2024

1. Install "python-shogun" package

Please follow the guidelines below to install python-shogun on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                python-shogun
            
                    
    
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2. Uninstall "python-shogun" package

This guide covers the steps necessary to uninstall python-shogun on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
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                sudo apt remove
            
                        
                python-shogun
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the python-shogun package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: python-shogun
Priority: optional
Section: universe/python
Installed-Size: 18424
Maintainer: Ubuntu Developers
Original-Maintainer: Soeren Sonnenburg
Architecture: amd64
Version: 3.2.0-5.2
Provides: python2.7-shogun
Depends: libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libpython2.7 (>= 2.7), libshogun16, libstdc++6 (>= 4.5), python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8)
Recommends: python-matplotlib, python-scipy
Filename: pool/universe/p/python-shogun/python-shogun_3.2.0-5.2_amd64.deb
Size: 2563344
MD5sum: f4fa950e6fece56a9e19e1a4c9eb6e6a
SHA1: 0e0591f1e7d5cd0e2ff810fa2e59e4b91cf765bd
SHA256: d10d0ff8bf6e5ad78647c95de97c3df535d9b890167cdb5223e3408e72d323f3
Description-en: Large Scale Machine Learning Toolbox
SHOGUN - is a new machine learning toolbox with focus on large scale kernel
methods and especially on Support Vector Machines (SVM) with focus to
bioinformatics. It provides a generic SVM object interfacing to several
different SVM implementations. Each of the SVMs can be combined with a variety
of the many kernels implemented. It can deal with weighted linear combination
of a number of sub-kernels, each of which not necessarily working on the same
domain, where an optimal sub-kernel weighting can be learned using Multiple
Kernel Learning. Apart from SVM 2-class classification and regression
problems, a number of linear methods like Linear Discriminant Analysis (LDA),
Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to
train hidden markov models are implemented. The input feature-objects can be
dense, sparse or strings and of type int/short/double/char and can be
converted into different feature types. Chains of preprocessors (e.g.
substracting the mean) can be attached to each feature object allowing for
on-the-fly pre-processing.
.
SHOGUN comes in different flavours, a stand-a-lone version and also with
interfaces to Matlab(tm), R, Octave, Readline and Python. This package contains
the static and the modular Python interfaces.
Description-md5: 5b94f29b021a8bdc343c6ffa0b259ffd
Homepage: http://www.shogun-toolbox.org
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu