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python-pomegranate package

python-pomegranate 0.12.0+dfsg-1 source package in Ubuntu

Changelog

python-pomegranate (0.12.0+dfsg-1) unstable; urgency=medium

  [ Andreas Tille ]
  * Team upload.
  * Properly renamed version due to removal of autogenerated files (+dfsg suffix)

  [ Michael R. Crusoe ]
  * Switch to downloading from GitHub
  * Add Testsuite: autopkgtest-pkg-python
  * Mark the -doc package as Multi-Arch: foreign
  * Added build-dep on python3-pandas for the tests

 -- Michael R. Crusoe <email address hidden>  Thu, 02 Jan 2020 08:57:53 +0100

Upload details

Uploaded by:
Debian Python Modules Team
Uploaded to:
Sid
Original maintainer:
Debian Python Modules Team
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section

Downloads

File Size SHA-256 Checksum
python-pomegranate_0.12.0+dfsg-1.dsc 2.5 KiB 71ef75ea904bb4fb433cfaa96076e51bf09c608048b9322e847c869bcd4d5d81
python-pomegranate_0.12.0+dfsg.orig.tar.xz 13.1 MiB a1a00b01b5f782b43165163b02f88ef23c697599163af4c09f0afbf56c3cc9a3
python-pomegranate_0.12.0+dfsg-1.debian.tar.xz 3.0 KiB 09f92bb8c300ecd0426c2c6ad7cf2b23276949aa43c8c3c3373a3ed7957c6fc1

Available diffs

No changes file available.

Binary packages built by this source

python-pomegranate-doc: documentation accompanying probabilistic modelling library

 pomegranate is a package for probabilistic models in Python that is
 implemented in cython for speed. It's focus is on merging the easy-to-use
 scikit-learn API with the modularity that comes with probabilistic
 modeling to allow users to specify complicated models without needing to
 worry about implementation details. The models are built from the ground
 up with big data processing in mind and so natively support features
 like out-of-core learning and parallelism.
 .
 This is the common documentation package.

python3-pomegranate: Fast, flexible and easy to use probabilistic modelling

 pomegranate is a package for probabilistic models in Python that is
 implemented in cython for speed. It's focus is on merging the easy-to-use
 scikit-learn API with the modularity that comes with probabilistic
 modeling to allow users to specify complicated models without needing to
 worry about implementation details. The models are built from the ground
 up with big data processing in mind and so natively support features
 like out-of-core learning and parallelism.

python3-pomegranate-dbgsym: debug symbols for python3-pomegranate