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

Last updated: December 24,2024

1. Install "python-mlpy" package

This is a short guide on how to install python-mlpy on Ubuntu 16.04 LTS (Xenial Xerus)


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

Learn how to uninstall python-mlpy on Ubuntu 16.04 LTS (Xenial Xerus):


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

Package: python-mlpy
Priority: optional
Section: universe/python
Installed-Size: 280
Maintainer: Ubuntu Developers
Original-Maintainer: NeuroDebian Team
Architecture: all
Source: mlpy
Version: 2.2.0~dfsg1-3build2
Provides: python2.7-mlpy
Depends: python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~), python, python-mlpy-lib (>= 2.2.0~dfsg1-3build2)
Suggests: python-mvpa
Filename: pool/universe/m/mlpy/python-mlpy_2.2.0~dfsg1-3build2_all.deb
Size: 46940
MD5sum: 004f7a38b2427e58334acba2379dc208
SHA1: 62dab1dd0ad23014981f2323feffda7baf8e354e
SHA256: ef9141777d471d84fb60c56dd245ac199708e0960ea03f6ddfb9f42cc899b46d
Description-en: high-performance Python package for predictive modeling
mlpy provides high level procedures that support, with few lines of
code, the design of rich Data Analysis Protocols (DAPs) for
preprocessing, clustering, predictive classification and feature
selection. Methods are available for feature weighting and ranking,
data resampling, error evaluation and experiment landscaping.
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mlpy includes: SVM (Support Vector Machine), KNN (K Nearest
Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression,
Penalized, Diagonal Linear Discriminant Analysis) for classification
and feature weighting, I-RELIEF, DWT and FSSun for feature weighting,
RFE (Recursive Feature Elimination) and RFS (Recursive Forward
Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated,
Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time
Warping), Hierarchical Clustering, k-medoids, Resampling Methods,
Metric Functions, Canberra indicators.
Description-md5: 8aa02b039fb76de9e138e063b8e10fcd
Homepage: https://mlpy.fbk.eu/
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu