semvec/pypi-packages huggingface dataset:
hmm-py 0.0.2
A python module to implement Hidden Markov hidden_markov for financial times series.
Requires Python: >=3.8

HMMpy
======
[![Build Status](https://travis-ci.com/Cstolborg/HMMpy.svg?branch=main)](https://travis-ci.com/Cstolborg/HMMpy)
[![Documentation Status](https://readthedocs.org/projects/hmmpy/badge/?version=latest)](https://hmmpy.readthedocs.io/en/latest/?badge=latest)

**The HMMpy documentation is at [https://hmmpy.readthedocs.io/en/latest/](https://hmmpy.readthedocs.io/en/latest/).**

- [Installation](#installation)
- [Getting started](#getting-started)

HMMpy is a Python-embedded modeling language for hidden markov models. It currently supports training of 2-state models using either maximum-likelihood or jump estimation, and uses and API that is very similar to scikit-learn.

HMMpy began as a University project at Copenhagen Business School, where it was used for financial times series forecasting in an asset allocation project. 


## Installation
HMMpy is available on PyPI, and can be installed with (only for windows)
```
pip install hmm-py
```

HMMpy has the following dependencies:

- Python >= 3.8
- Cython >= 0.29
- NumPy >= 1.20.1
- Pandas >= 1.2.0
- SciPy >= 1.5.4
- tqdm


## Getting started
The following code samples some data, and then trains a hidden markov model using the JumpHMM class:

```python3
from hmmpy.jump import JumpHMM
from hmmpy.sampler import SampleHMM

# Instantiate the HMM model
hmm = JumpHMM(random_state=42)

# Instantiate the sampler with user defined HMM model parameters
hmm_params = {'mu': [0.1, -0.05],
              'std': [0.1, 0.2],
              'tpm': [[1-0.0021, 0.0021],
                      [0.0120, 1-0.0120]]
             }
sampler = SampleHMM(hmm_params=hmm_params, random_state=42)

# Simulate data
observations, state_sequence = sampler.sample(n_samples=2000, n_sequences=1)  # Outputs 2000 observations and the underlying states

# Fit the model
hmm.fit(observations)

# Inspect model parameters
print(hmm.mu)
print(hmm.std)
print(hmm.tpm)
```
gamest-plugins-diablo-iii 2.0.4
Report on Diablo III games.

# Gamest Reporter Plugin: Diablo III

This plugin enables [gamest](https://github.com/sopoforic/gamest) to report on
a running game of *Diablo III*.

## Installation

Install with pip:

```
pip install gamest-plugins-diablo-iii
```

## License

Copyright (C) 2018  Tracy Poff

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.