python-pomegranate 0.14.8-2 source package in Ubuntu
Changelog
python-pomegranate (0.14.8-2) unstable; urgency=medium
* debian/patches/nose2pytest: Convert to pytest.
Closes: #1024045, #1018555, #1001659
-- Michael R. Crusoe <email address hidden> Wed, 16 Nov 2022 09:54:20 +0100
Upload details
- Uploaded by:
- Debian Python Team
- Uploaded to:
- Sid
- Original maintainer:
- Debian Python Team
- Architectures:
- any all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| python-pomegranate_0.14.8-2.dsc | 2.5 KiB | 0f987b7bfe2b984bc4a1db81baadc530364fbb28f2358fc3b13170e5ca94ecda |
| python-pomegranate_0.14.8.orig.tar.gz | 26.1 MiB | a34595fca1a269f454f7b5d10b91e0279e69bb21e75815803e16c7df4780987d |
| python-pomegranate_0.14.8-2.debian.tar.xz | 37.7 KiB | 09d3a8acae39ec3e505b21d610d865138121948c208ffce8c2447a67a6543abf |
Available diffs
- diff from 0.14.8-1 to 0.14.8-2 (43.0 KiB)
- diff from 0.14.8-1build1 (in Ubuntu) to 0.14.8-2 (43.1 KiB)
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
