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

Last updated: May 21,2024

1. Install "python-shogun-dbg" package

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


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

Please follow the guidance below to uninstall python-shogun-dbg on Ubuntu 16.04 LTS (Xenial Xerus):


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

Package: python-shogun-dbg
Priority: extra
Section: universe/debug
Installed-Size: 5070
Maintainer: Ubuntu Developers
Original-Maintainer: Soeren Sonnenburg
Architecture: amd64
Source: python-shogun
Version: 3.2.0-5.2
Depends: python-shogun (= 3.2.0-5.2)
Filename: pool/universe/p/python-shogun/python-shogun-dbg_3.2.0-5.2_amd64.deb
Size: 2662158
MD5sum: efa996561ea18573098dbe5d7736659f
SHA1: 713723812db3759558866ac00dbfc9f83e682295
SHA256: d4549eac8e5843c933c7c0f471d4ede884ca15cbe96ae4b4d101b6240b58c7e0
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 debug symbols for the static and the modular Python interfaces.
Description-md5: 3979e7348b2d7ed916b630fe648d7189
Homepage: http://www.shogun-toolbox.org
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu