An(other) implementation of Explicit Duration Hidden Semi-Markov Models in Python 3
Project description
edhsmm
An(other) implementation of Explicit Duration Hidden Semi-Markov Models in Python 3
The EM algorithm is based on Yu (2010) (Section 3.1), while the Viterbi algorithm is based on Benouareth et al. (2008).
The code style is inspired from hmmlearn and jvkersch/hsmmlearn.
Implemented so far
- EM algorithm
- Scoring (log-likelihood of observation under the model)
- Viterbi algorithm
- Generate samples
- Support for multivariate Gaussian emissions
- Support for multiple observation sequences
- Support for multinomial (discrete) emissions
Dependencies
- python >= 3.5
- numpy >= 1.17
- scikit-learn >= 0.16
- scipy >= 0.19
Installation & Tutorial
Via pip:
pip install edhsmm
Via setup.py:
python setup.py install
Test in venv (Windows):
python -m venv venv
venv\Scripts\activate
pip install --upgrade -r requirements.txt
python setup.py install
Note: Also run pip install notebook matplotlib
to run the notebooks.
For tutorial, see the notebooks. This also serves as some sort of "documentation".
Found a bug? Suggest a feature? Please post on issues.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
edhsmm-0.2.1.tar.gz
(127.5 kB
view hashes)
Built Distributions
edhsmm-0.2.1-py3.10-win-amd64.egg
(91.0 kB
view hashes)
Close
Hashes for edhsmm-0.2.1-py3.10-win-amd64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | cd965a19d24496c70a0da91ed169c49131bb72936eb153682d09fa972fd5cfb1 |
|
MD5 | 59d32e9290c8ac7dba4fd9babab5414c |
|
BLAKE2b-256 | dda7bf56f57b40953c5d0f8e5af32289e24c94a5c33046c14a6b1db13a22cf78 |
Close
Hashes for edhsmm-0.2.1-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 968369a0e79851cb1525d0096e8e8bdc8816c8ddd5248deb2dfe5e8629a29759 |
|
MD5 | c8d026450b836adbc7b711b7be9a901d |
|
BLAKE2b-256 | 56aee9028333a1ef5b046823b5f54d35eda4e7862adb6a78f9787a913225deb8 |