https://github.com/cbg-ethz/jnotype

Probabilistic modeling of high-dimensional binary data in JAX
https://github.com/cbg-ethz/jnotype

bayesian-statistics cancer-genomics clustering ideal-point-estimation jax probabilistic-machine-learning unsupervised-learning

Last synced: about 1 month ago
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Probabilistic modeling of high-dimensional binary data in JAX

• Host: GitHub
• URL: https://github.com/cbg-ethz/jnotype
• Owner: cbg-ethz
• License: mit
• Created: 2023-02-20T12:19:40.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-10-27T21:35:44.000Z (about 2 months ago)
• Last Synced: 2024-10-28T01:39:48.678Z (about 2 months ago)
• Topics: bayesian-statistics, cancer-genomics, clustering, ideal-point-estimation, jax, probabilistic-machine-learning, unsupervised-learning
• Language: Python
• Homepage:
• Size: 207 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Jnotype

[JAX](https://github.com/google/jax)-powered Python package for exploratory analysis of binary data.

This includes genotype data, binary images, and data sets used in ecology.

**Note:** this package is in early development stage.

  - **Source code:** [https://github.com/cbg-ethz/Jnotype](https://github.com/cbg-ethz/Jnotype)

  - **Bug reports:** [https://github.com/cbg-ethz/Jnotype/issues](https://github.com/cbg-ethz/Jnotype/issues)

  - **PyPI package**: [https://pypi.org/project/jnotype/](https://pypi.org/project/jnotype/)

## Installation

**Note:** The package has not reached a stable API yet. Frequent changes may appear.

We recommend setting up a new [virtual environment](https://docs.python.org/3/library/venv.html).

You can install the released version of the package from PyPI:

```bash

$ python -m pip install jnotype

```

or install the development version from GitHub:

```bash

$ python -m pip install 'jnotype @ git+https://github.com/cbg-ethz/jnotype'

```

### Using GPU

Instructions above install the CPU version of JAX.

To use GPU, you may need to follow the [official JAX installation tutorial](https://github.com/google/jax#pip-installation-gpu-cuda-installed-via-pip-easier).

## Getting started

Directory `examples/` contains [Quarto](https://quarto.org/) notebooks, which demonstrate basic functionalities of the package.