python-pomegranate 0.13.5-2 source package in Ubuntu

Changelog

python-pomegranate (0.13.5-2) unstable; urgency=medium

  [ Debian Janitor ]
  * Remove constraints unnecessary since buster:
    + Build-Depends: Drop versioned constraint on cython3, python3-joblib,
      python3-networkx and python3-scipy.
    + python3-pomegranate: Drop versioned constraint on python3-joblib,
      python3-networkx and python3-scipy in Depends.

  [ Michael R. Crusoe ]
  * Fix watchfile to detect new versions on github
  * Standards-Version: 4.6.0 (routine-update)

 -- Michael R. Crusoe <email address hidden>  Sun, 07 Nov 2021 17:52:25 +0100

Upload details

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

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File Size SHA-256 Checksum
python-pomegranate_0.13.5-2.dsc 2.5 KiB f436650a45b74ddbf37fec8823df220cad51c2018912025f60079171160e607e
python-pomegranate_0.13.5.orig.tar.gz 26.1 MiB 16ffa0b835007e33432ae08ce79a4da26c5ed32afbb38f5356b27c0e143a9aa5
python-pomegranate_0.13.5-2.debian.tar.xz 3.7 KiB 75e4a628dbaaf72b2ac4a016d3b5b5335599a28fc1409e6da57864fc748c5cb6

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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