ppc64le/linux/: hmmlearn-0.3.3 metadata and description
Hidden Markov Models in Python with scikit-learn like API
| author | David Cournapeau, Fabian Pedregosa, Gael Varoquaux, Sergei Lebedev, Antony Lee, Matthew Danielson |
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| description_content_type | text/x-rst |
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| metadata_version | 2.4 |
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| requires_python | >=3.8 |
| File | Tox results | History |
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hmmlearn-0.3.3-cp310-cp310-manylinux_2_34_ppc64le.whl
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hmmlearn-0.3.3-cp311-cp311-manylinux_2_34_ppc64le.whl
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hmmlearn-0.3.3-cp312-cp312-manylinux_2_34_ppc64le.whl
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hmmlearn-0.3.3-cp313-cp313-manylinux_2_34_ppc64le.whl
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hmmlearn-0.3.3-cp314-cp314-manylinux_2_34_ppc64le.whl
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hmmlearn
hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models. For supervised learning learning of HMMs and similar models see seqlearn.
Note: This package is under limited-maintenance mode.
Important links
Official source code repo: https://github.com/hmmlearn/hmmlearn
HTML documentation (stable release): https://hmmlearn.readthedocs.org/en/stable
HTML documentation (development version): https://hmmlearn.readthedocs.org/en/latest
Dependencies
The required dependencies to use hmmlearn are
Python >= 3.6
NumPy >= 1.10
scikit-learn >= 0.16
You also need Matplotlib >= 1.1.1 to run the examples and pytest >= 2.6.0 to run the tests.
Installation
Requires a C compiler and Python headers.
To install from PyPI:
pip install --upgrade --user hmmlearn
To install from the repo:
pip install --user git+https://github.com/hmmlearn/hmmlearn