hmmlearn

Hidden Markov Models in Python with scikit-learn like API

These details have not been verified by PyPI

Project links

Repository

Development Status
- 3 - Alpha
Intended Audience
- Developers
- Science/Research
License
- OSI Approved :: BSD License
Programming Language
- Python
- Python :: 3
Topic
- Scientific/Engineering
- Software Development

Project description

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

Project details

These details have not been verified by PyPI

Project links

Repository

Development Status
- 3 - Alpha
Intended Audience
- Developers
- Science/Research
License
- OSI Approved :: BSD License
Programming Language
- Python
- Python :: 3
Topic
- Scientific/Engineering
- Software Development

Release history Release notifications | RSS feed

This version

0.3.3

Oct 31, 2024

0.3.2

Mar 2, 2024

0.3.0

Apr 18, 2023

0.2.8

Sep 26, 2022

0.2.7

Feb 10, 2022

0.2.6

Jul 18, 2021

0.2.5

Feb 3, 2021

0.2.4

Sep 12, 2020

0.2.3

Dec 17, 2019

0.2.2

May 6, 2019

0.2.1

Oct 17, 2018

0.2.0

Mar 1, 2016

0.1.1

Feb 9, 2015

0.1.0

Feb 8, 2015

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SHA256	`498ad4758e069dac03ec15a9c1424cae2b6fa0422139f910d89c8757fed1363a`
MD5	`2e3bc96daee60a0c11353aea67ea1f15`
BLAKE2b-256	`8672b5d860ea97c944bed95d3d9020a38cc6da9fa0623a534f1e0edc5d281e4e`

Hashes for hmmlearn-0.3.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for hmmlearn-0.3.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`1fcbf1438a5313b7da03ac380ff36d1578ebf515eb47bb269a1c1ecbf9bd59e2`
MD5	`31bf8d532b3f4c3ed1d60ffbf176586a`
BLAKE2b-256	`cf642faaf4bb429745dbf4923b921d314fa79d03a19d6632248a2b2c9e933b03`

Hashes for hmmlearn-0.3.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Hashes for hmmlearn-0.3.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm	Hash digest
SHA256	`a1e5398e43141f70232de799a08d4a4eab19ca7db86f56cea8509f68857146e9`
MD5	`fcf4556390fb1a4831d3b8181d1cf3db`
BLAKE2b-256	`d33a82e67cb262ca3c290da1362751409973ab07c342d0da3f7a83a78c865929`

Hashes for hmmlearn-0.3.3-cp38-cp38-macosx_10_9_x86_64.whl

Hashes for hmmlearn-0.3.3-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`517fdb17711181933b253f6d1970e2b2b6f2ec1bd7239de6f88d83c8e6a28fc8`
MD5	`aa8208724aac732be82ff9c862fbde99`
BLAKE2b-256	`d5fe09e94b3d1d7e067fbc6f2143d2b0b2c3a48e9c998ce0b8d48e1cd2eb9941`

Hashes for hmmlearn-0.3.3-cp38-cp38-macosx_10_9_universal2.whl

Hashes for hmmlearn-0.3.3-cp38-cp38-macosx_10_9_universal2.whl
Algorithm	Hash digest
SHA256	`ade68e4dfed1e60233afe0169de101f6b670eb83ec66f3c0691230dd7276c0bf`
MD5	`5b5c8e5df319ea66676d728db7f5c2f6`
BLAKE2b-256	`27fbf809d8e3d8c699eb755de147a051f6d90dbe3822290efb40da75a94d8d33`