haskell-hierarchical-clustering 0.4.6-6 source package in Ubuntu
Changelog
haskell-hierarchical-clustering (0.4.6-6) unstable; urgency=medium * Patch for newer deps -- Gianfranco Costamagna <email address hidden> Sat, 03 Aug 2019 12:15:26 +0200
Upload details
- Uploaded by:
- Debian Haskell Group
- Uploaded to:
- Sid
- Original maintainer:
- Debian Haskell Group
- Architectures:
- any all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
Series | Published | Component | Section | |
---|---|---|---|---|
Focal | release | universe | misc |
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
haskell-hierarchical-clustering_0.4.6-6.dsc | 2.5 KiB | 3aa8b1f4febfcb372638f91b6e20393acd9381c14e149f882d1a5080d4fed5a1 |
haskell-hierarchical-clustering_0.4.6.orig.tar.gz | 10.4 KiB | 75f17f09b9c38d51a208edee10da2f4706ee784b5cdfe8efc31f7f86bbcdccb1 |
haskell-hierarchical-clustering_0.4.6-6.debian.tar.xz | 3.1 KiB | c482da74d8927ea1cb38bf2d75bec68ef69e56b523d4d238a88d985798654a64 |
Available diffs
- diff from 0.4.6-5 to 0.4.6-6 (543 bytes)
- diff from 0.4.6-5build2 (in Ubuntu) to 0.4.6-6 (392 bytes)
No changes file available.
Binary packages built by this source
- libghc-hierarchical-clustering-dev: No summary available for libghc-hierarchical-clustering-dev in ubuntu eoan.
No description available for libghc-
hierarchical- clustering- dev in ubuntu eoan.
- libghc-hierarchical-clustering-doc: No summary available for libghc-hierarchical-clustering-doc in ubuntu groovy.
No description available for libghc-
hierarchical- clustering- doc in ubuntu groovy.
- libghc-hierarchical-clustering-prof: 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.