semvec/pypi-packages huggingface dataset:
oramask 0.2.3
A simple converter from Oracle date/time masks to strftime format
License: MIT
Requires Python: >=3.8

[![build status](https://github.com/andzhi4/raws/actions/workflows/matrix-test.yml/badge.svg)](https://github.com/andzhi4/raws/actions/workflows/matrix-test.yml)
# oramask

Oramask is a simplistic converter of Oracle specific date/time masks to Python's strftime masks.

## Installation

You can install the oramask by cloning the repository and running pip install:
```shell
git clone git@github.com:andzhi4/oramask
cd oramask && pip install .
```

or using PyPi:
```shell
python3 -m pip install oramask
```

## Examples
Here is an example of usage:
```python
from oramask import ora_to_sft
from datetime import datetime

now = datetime.now()
formatted = now.strftime(ora_to_sft('DD-MON-YYYY HH24:MI:SS'))
print(formatted)
# Output: 23-Feb-2023 17:51:15
```

## Contributing

If you find a bug or have an idea for a new feature, feel free to create an issue or submit a pull request.

## License

Oramask is released under the MIT License.
edhsmm 0.2.2
An(other) implementation of Explicit Duration Hidden Semi-Markov Models in Python 3
License: MIT
Requires Python: >=3.5

**Warning:** I made this repo when I was an undergrad, but was not even part of my undergrad project. Correctness of implementation not guaranteed, so use at your own risk.

# edhsmm
An(other) implementation of Explicit Duration Hidden Semi-Markov Models in Python 3

The EM algorithm is based on [Yu (2010)](https://www.sciencedirect.com/science/article/pii/S0004370209001416) (Section 3.1, 2.2.1 & 2.2.2), while the Viterbi algorithm is based on [Benouareth et al. (2008)](https://link.springer.com/article/10.1155/2008/247354).

The code style is inspired from [hmmlearn](https://github.com/hmmlearn/hmmlearn) and [jvkersch/hsmmlearn](https://github.com/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*:
```console
pip install edhsmm
```

Via *setup.py*:
```console
python setup.py install
```

Test in *venv* (Windows):
```console
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](notebooks). This also serves as some sort of "documentation".

Found a bug? Suggest a feature? Please post on [issues](https://github.com/poypoyan/edhmm/issues).