Metadata-Version: 2.2
Name: tsnex
Version: 0.0.3
Summary: Minimal t-distributed stochastic neighbor embedding (t-SNE) implementation in JAX.
Author-email: Albert Alonso <alonfnt@pm.me>
License: MIT License
        
        Copyright (c) 2023 Albert Alonso
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/alonfnt/tsnex
Project-URL: Documentation, https://github.com/alonfnt/tsnex
Project-URL: Source, https://github.com/alonfnt/tsnex
Project-URL: Bug Tracker, https://github.com/alonfnt/tsnex/issues
Keywords: jax,tsne,python,data-visualization
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: jax
Requires-Dist: pcax

# tsnex

**t-SNEx** is a high-performance Python library for t-SNE, built on JAX for fast, scalable dimensionality reduction.
It utilizes JIT compilation, automatic differentiation, and hardware acceleration to efficiently process high-dimensional data for visualization and clustering.

## Installation
Use the package manager [pip](https://pypi.org/project/tsnex/) to install `tsnex`.
```bash
pip install tsnex
```

## Usage
```python
import tsnex

# Generate some high-dimensional data
key = jax.random.key(0)
X = jax.random.normal(key, shape=(10_000, 50))

# Perform t-SNE dimensionality reduction
X_embedded = tsnex.transform(X, n_components=2)
```

## Contributing
We welcome contributions to **TSNEx**! Whether it's adding new features, improving documentation, or reporting issues, please feel free to make a pull request and/or open an issue.

## Citation
If you use `tsnex` in your research and need to reference it, please cite it as follows:
```
@software{alonso_tsnex,
  author = {Alonso, Albert},
  title = {tsnex: Minimal t-distributed stochastic neighbor embedding (t-SNE) implementation in JAX},
  url = {https://github.com/alonfnt/tsnex},
  version = {0.0.2}
}
```

## License
TSNEx is licensed under the MIT License. See the ![LICENSE](LICENSE) file for more details.

