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Source: python-pomegranate
Maintainer: Debian Python Team <team+python@tracker.debian.org>
Uploaders: Steffen Moeller <moeller@debian.org>, Michael R. Crusoe <crusoe@debian.org>
Section: science
Priority: optional
Testsuite: autopkgtest-pkg-python
Build-Depends: debhelper-compat (= 13),
dh-python,
python3-all,
python3-all-dev,
python3-setuptools,
cython3 (>= 0.22.1),
python3-numpy,
python3-scipy (>= 0.17.0),
python3-nose <!nocheck>,
python3-joblib (>= 0.9.0b4) <!nocheck>,
python3-networkx (>= 2.0) <!nocheck>,
python3-yaml <!nocheck>,
python3-pandas <!nocheck>
# for documentation - packaged raw for now
# sphinx >= 1.6.0
# sphinx-rtd-theme >= 0.2.0, < 0.3.0
Standards-Version: 4.5.1
Vcs-Browser: https://salsa.debian.org/python-team/packages/python-pomegranate
Vcs-Git: https://salsa.debian.org/python-team/packages/python-pomegranate.git
Homepage: https://github.com/jmschrei/pomegranate
Rules-Requires-Root: no
Package: python3-pomegranate
Architecture: any
Section: python
Depends: ${python3:Depends},
${shlibs:Depends},
${misc:Depends},
python3-numpy,
python3-scipy (>= 0.17.0),
python3-joblib (>= 0.9.0b4),
python3-networkx (>= 2.0),
python3-yaml
Suggests: python-pomegranate-doc
Description: 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.
Package: python-pomegranate-doc
Architecture: all
Multi-Arch: foreign
Section: doc
Depends: ${sphinxdoc:Depends},
${misc:Depends}
Description: 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.
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