Name | tsnex JSON |
Version |
0.0.1
JSON |
| download |
home_page | |
Summary | Minimal t-distributed stochastic neighbor embedding (t-SNE) implementation in JAX. |
upload_time | 2024-03-12 17:34:37 |
maintainer | |
docs_url | None |
author | Antonio Matas Gil |
requires_python | >=3.9 |
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. |
keywords |
jax
tsne
python
data-visualization
|
VCS |
|
bugtrack_url |
|
requirements |
jax
jaxlib
pcax
|
Travis-CI |
No Travis.
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coveralls test coverage |
No coveralls.
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# TSNEx
**TSNEx** is a lightweight, high-performance Python library for t-Distributed Stochastic Neighbor Embedding (t-SNE) built on top of JAX. Leveraging the power of JAX, `tsnex` offers JIT compilation, automatic differentiation, and hardware acceleration support to efficiently handle high-dimensional data for visualization and clustering tasks.
## 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.
## License
TSNEx is licensed under the MIT License. See the ![LICENSE](LICENSE) file for more details.
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