semvec/pypi-packages huggingface dataset:
chainsail-helpers-etjean 0.1.2
Probability distribution interfaces, examples, and utilities for the Chainsail sampling service
License: MIT
Requires Python: >=3.8,<3.11
Keywords: probabilistic programming,sampling,MCMC

# Chainsail PDF interfaces and utilities

This small package complements the [Chainsail](https://chainsail.io) sampling service. It
- defines the general interface for objects representing Chainsail-consumable probability distributions,
- provides implementations thereof for popular probabilistic programming languages,
- and contains a few helper scripts for post-processing.

## Installation
```bash
$ pip install chainsail-helpers
```
If you'd like implement a probability density using [Stan](https://mc-stan.org) or [PyMC3](https://docs.pymc.io), install the corresponding extra dependencies like so: `poetry install --extras pymc3` and similarly for `stan`. 
When using Chainsail, this package will be automatically installed, so no need to add it to the list of dependencies in the job submission form.
If you like to develop this package, best use [Poetry](https://python-poetry.org):
```bash
$ poetry install
$ poetry shell
```
and you will be dropped into a virtual environment with all dependencies installed.

## Contributing
Contributions, for example PDF implementations for other probabilistic programming languages, are highly welcome!
Just open a pull request and we'll be happy to work with you to make Chainsail even more useful.

## License
&copy; 2021 [Tweag](https://tweag.io). `chainsail_helpers` is open-source software and licensed under the [MIT license](https://opensource.org/licenses/MIT).
shapeGMM 0.0.5
Gaussian Mixture Model clustering in size-and-shape space
License: MIT
Requires Python: >=3.6

# GMM Positions

## Overview

This is a package to perform Gaussian Mixture Model (GMM) clustering on particle positions (in <img src="https://render.githubusercontent.com/render/math?math=\mathbb{R}^3">). Like other GMM schemes, the user must specify the number of clusters and a cluster initialization scheme (defaults to random).  This is specified in the object initialization line, analagous to how it is done for the sklean GaussianMixture package.  There are two choices for the form of the covariance but those are specified by calling different fit functions.  See preprint (https://arxiv.org/abs/2112.11424) for additional details.

## Installation

The package can be installed using pip

`pip install shapeGMM`

or downloaded and installed with 

`python setup.py install`

## Usage 

This package is designed to mimic the usage of the sklearn package.  You first initiliaze the object and then fit.  Predict can be done once the model is fit.  Fit and ppredict functions take particle position trajectories as input in the form of a (n_frames, n_atoms, 3) numpy array.

### Initialize:

`from shapeGMM import gmm_shapes`

`sgmm_object = gmm_shapes.ShapeGMM(n_clusters,verbose=True)`

### Fit:

Uniform (spherical, uncorrelated) covariance:

`aligned_trajectory = sgmm_object.fit_uniform(training_set_positions)`

Weighted (Kronecker product) covariance:

`aligned_trajectory = sgmm_object.fit_weighted(training_set_positions)`

### Predict:

Uniform (spherical, uncorrelated) covariance:

`clusters, aligned_traj, log_likelihood = sgmm_object.predict_uniform(full_trajectory_positions)`

Weighted (Kronecker product) covariance:

`clusters, aligned_traj, log_likelihood = sgmm_object.predict_weighted(full_trajectory_positions)`

## Description of Contents

## Test Cases