Metadata-Version: 2.4
Name: deep-tensor-py
Version: 0.0.1
Summary: A PyTorch implementation of the deep inverse Rosenblatt transport (DIRT) algorithm.
Author-email: Alex de Beer <adeb0907@uni.sydney.edu.au>
License-Expression: GPL-3.0-or-later
Project-URL: Homepage, https://github.com/DeepTransport/deep-tensor-py
Project-URL: Documentation, https://deeptransport.github.io/deep-tensor-py/
Project-URL: Issues, https://github.com/DeepTransport/deep-tensor-py/issues
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: COPYING
Requires-Dist: h5py
Requires-Dist: torch
Dynamic: license-file

<h1 align="center"> deep-tensor-py </h1>

<div align="center">

[![Unit tests](https://github.com/alexgdebeer/deep-tensor-py/actions/workflows/run_tests.yaml/badge.svg)](https://github.com/alexgdebeer/deep-tensor-py/actions/workflows/run_tests.yaml)
[![Docs build](https://github.com/DeepTransport/deep-tensor-py/actions/workflows/publish.yaml/badge.svg)](https://github.com/DeepTransport/deep-tensor-py/actions/workflows/publish.yaml)

</div>

This package contains a [PyTorch](https://pytorch.org) implementation of the deep inverse Rosenblatt transport (DIRT) algorithm introduced by Cui and Dolgov [[1](#1)].

## Installation

Coming soon...

## Examples and Documentation

Examples and documentation are available on the package [website](https://deeptransport.github.io/deep-tensor-py/).

## References

[<a id="1">1</a>] 
Cui, T and Dolgov, S (2022). 
*[Deep composition of tensor-trains using squared inverse Rosenblatt transports](https://doi.org/10.1007/s10208-021-09537-5).* 
Foundations of Computational Mathematics **22**, 1863–1922.
