This release is a pre-release and may not be stable for production use.
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:
pip install DistributionalPrincipalAutoencoder
The development version can be installed from github:
pip install -e "git+https://github.com/xwshen51/DistributionalPrincipalAutoencoder"
Usage Example
See this tutorial for an example on S-curve.
Contact information
If you meet any problems with the code, please submit an issue or contact Xinwei Shen.
1 maintainer
Release files for DistributionalPrincipalAutoencoder 0.0.0.dev0
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Source distribution (sdist)
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| DistributionalPrincipalAutoencoder-0.0.0.dev0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 21.4 kB
Release files / DistributionalPrincipalAutoencoder-0.0.0.dev0.tar.gz
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| Size | 11.3 kB |
| Tags | Python 3 |
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