How to Install and Uninstall libghc-hierarchical-clustering-prof Package on Ubuntu 16.04 LTS (Xenial Xerus)
Last updated: December 24,2024
1. Install "libghc-hierarchical-clustering-prof" package
This guide let you learn how to install libghc-hierarchical-clustering-prof on Ubuntu 16.04 LTS (Xenial Xerus)
$
sudo apt update
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$
sudo apt install
libghc-hierarchical-clustering-prof
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2. Uninstall "libghc-hierarchical-clustering-prof" package
Please follow the instructions below to uninstall libghc-hierarchical-clustering-prof on Ubuntu 16.04 LTS (Xenial Xerus):
$
sudo apt remove
libghc-hierarchical-clustering-prof
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the libghc-hierarchical-clustering-prof package on Ubuntu 16.04 LTS (Xenial Xerus)
Package: libghc-hierarchical-clustering-prof
Priority: extra
Section: universe/haskell
Installed-Size: 479
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Haskell Group
Architecture: amd64
Source: haskell-hierarchical-clustering
Version: 0.4.6-1
Provides: libghc-hierarchical-clustering-prof-0.4.6-b8c18
Depends: libghc-hierarchical-clustering-dev (= 0.4.6-1), libghc-array-prof-0.5.1.0-960bf, libghc-base-prof-4.8.2.0-0d6d1, libghc-containers-prof-0.5.6.2-59326
Filename: pool/universe/h/haskell-hierarchical-clustering/libghc-hierarchical-clustering-prof_0.4.6-1_amd64.deb
Size: 60454
MD5sum: cb783ebf9e2a4bb16ae7b6698fbfa675
SHA1: 7601a383b305ba5ae6ce8b777df3fba65363061a
SHA256: edd2214b5b4afd122db642fbc441f6a31c18bbe29484e2cd6f6ade9437bd7f39
Description-en: Fast algorithms for single, average/UPGMA and complete linkage clustering.; profiling libraries
This package provides a function to create a dendrogram from a
list of items and a distance function between them. Initially
a singleton cluster is created for each item, and then new,
bigger clusters are created by merging the two clusters with
least distance between them. The distance between two clusters
is calculated according to the linkage type. The dendrogram
represents not only the clusters but also the order on which
they were created.
.
This package has many implementations with different
performance characteristics. There are SLINK and CLINK
algorithm implementations that are optimal in both space and
time. There are also naive implementations using a distance
matrix. Using the dendrogram function from
Data.Clustering.Hierarchical automatically chooses the best
implementation we have.
.
This package provides a library for the Haskell programming language, compiled
for profiling. See http://www.haskell.org/ for more information on Haskell.
Description-md5: d876fad6cb7b23a24ab59794c97ba045
Homepage: http://hackage.haskell.org/package/hierarchical-clustering
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu
Priority: extra
Section: universe/haskell
Installed-Size: 479
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Haskell Group
Architecture: amd64
Source: haskell-hierarchical-clustering
Version: 0.4.6-1
Provides: libghc-hierarchical-clustering-prof-0.4.6-b8c18
Depends: libghc-hierarchical-clustering-dev (= 0.4.6-1), libghc-array-prof-0.5.1.0-960bf, libghc-base-prof-4.8.2.0-0d6d1, libghc-containers-prof-0.5.6.2-59326
Filename: pool/universe/h/haskell-hierarchical-clustering/libghc-hierarchical-clustering-prof_0.4.6-1_amd64.deb
Size: 60454
MD5sum: cb783ebf9e2a4bb16ae7b6698fbfa675
SHA1: 7601a383b305ba5ae6ce8b777df3fba65363061a
SHA256: edd2214b5b4afd122db642fbc441f6a31c18bbe29484e2cd6f6ade9437bd7f39
Description-en: Fast algorithms for single, average/UPGMA and complete linkage clustering.; profiling libraries
This package provides a function to create a dendrogram from a
list of items and a distance function between them. Initially
a singleton cluster is created for each item, and then new,
bigger clusters are created by merging the two clusters with
least distance between them. The distance between two clusters
is calculated according to the linkage type. The dendrogram
represents not only the clusters but also the order on which
they were created.
.
This package has many implementations with different
performance characteristics. There are SLINK and CLINK
algorithm implementations that are optimal in both space and
time. There are also naive implementations using a distance
matrix. Using the dendrogram function from
Data.Clustering.Hierarchical automatically chooses the best
implementation we have.
.
This package provides a library for the Haskell programming language, compiled
for profiling. See http://www.haskell.org/ for more information on Haskell.
Description-md5: d876fad6cb7b23a24ab59794c97ba045
Homepage: http://hackage.haskell.org/package/hierarchical-clustering
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu