DistributionalPrincipalAutoencoder

Name	DistributionalPrincipalAutoencoder JSON
Version	0.0.0.dev0 JSON
	download
home_page	https://github.com/xwshen51/DistributionalPrincipalAutoencoder
Summary	Distributional Principal Autoencoder
upload_time	2024-04-20 12:57:04
maintainer	None
docs_url	None
author	Xinwei Shen and Nicolai Meinshausen
requires_python	None
license	BSD 3-Clause License
keywords
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # Distributional Principal Autoencoder

Distributional Principal Autoencoder (DPA) is a nonlinear dimension reduction method proposed in the paper "[*Distributional Principal Autoencoders*]()" by Xinwei Shen and Nicolai Meinshausen. This directory contains the Python implementation of DPA.


## Installation
The latest release of the Python package can be installed through pip:
```sh
pip install DistributionalPrincipalAutoencoder
```

The development version can be installed from github:

```sh
pip install -e "git+https://github.com/xwshen51/DistributionalPrincipalAutoencoder" 
```


## Usage Example

See [this tutorial](https://github.com/xwshen51/DistributionalPrincipalAutoencoder/blob/main/examples/scurve.ipynb) for an example on S-curve.


## Contact information
If you meet any problems with the code, please submit an issue or contact [Xinwei Shen](mailto:xinwei.shen@stat.math.ethz.ch).

            

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