ghmm 0.9~rc3-2 source package in Ubuntu

Changelog

ghmm (0.9~rc3-2) unstable; urgency=medium

  [ Chris Lamb ]
  * Enable reproducible build by removing absolute build path from ghmm-config
    Closes: #929791

  [ Andreas Tille ]
  * Standards-Version: 4.3.0

 -- Andreas Tille <email address hidden>  Sun, 02 Jun 2019 15:13:30 +0200

Upload details

Uploaded by:
Debian Med on 2019-06-02
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Focal release on 2019-10-18 universe misc
Eoan release on 2019-06-02 universe misc

Downloads

File Size SHA-256 Checksum
ghmm_0.9~rc3-2.dsc 2.0 KiB 55648c468e32cc0e8f2b8159ad29018e21b128bd70c8667f4bc6ab81c4e65e14
ghmm_0.9~rc3.orig.tar.gz 752.5 KiB c42462ff4f87aadd5efe7bb6adb59657786ed71046ba924111bc28c7cbb588d0
ghmm_0.9~rc3-2.debian.tar.xz 4.0 KiB 3020d8c0a3f2cd2f176d98efa8eb1e83177e68f92e3a949971a4e85c2717e697

Available diffs

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Binary packages built by this source

ghmm: General Hidden-Markov-Model library - tools

 The General Hidden Markov Model Library (GHMM) is a C library with
 additional Python bindings implementing a wide range of types of
 Hidden Markov Models and algorithms: discrete, continuous emissions,
 basic training, HMM clustering, HMM mixtures.
 .
 This package contains some tools using the library.

ghmm-dbgsym: debug symbols for ghmm
libghmm-dev: General Hidden-Markov-Model library - header files

 The General Hidden Markov Model Library (GHMM) is a C library with
 additional Python bindings implementing a wide range of types of
 Hidden Markov Models and algorithms: discrete, continuous emissions,
 basic training, HMM clustering, HMM mixtures.
 .
 Header files and static library to compile against the library.

libghmm1: General Hidden-Markov-Model library

 The General Hidden Markov Model Library (GHMM) is a C library with
 additional Python bindings implementing a wide range of types of
 Hidden Markov Models and algorithms: discrete, continuous emissions,
 basic training, HMM clustering, HMM mixtures.
 .
 The dynamic library.

libghmm1-dbgsym: debug symbols for libghmm1