# IPOT

**Repository Path**: gravifer/IPOT

## Basic Information

- **Project Name**: IPOT
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-02
- **Last Updated**: 2021-06-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# IPOT
Implementation of Inexact Proximal point method for Optimal Transport from paper "A Fast Proximal Point Method for Computing Exact Wasserstein Distance" (https://arxiv.org/abs/1802.04307).

--------------------
Package dependencies
--------------------

It requires the following Python packages:

Python 3.5
numpy
matplotlib 
pot 0.4.0
tensorflow 1.1.0

-------------------
Included modules
--------------------

ipot.py: include function ipot_WD that computes the Wasserstein distance, a function ipot_barycenter that computes the Wasserstein barycenter.

ipot_demo.py: Demo for computing Wasserstein distance using ipot.ipot_WD.

learning_demo.py: 1D demo for learning generative model using ipot.ipot_WD. This file is the only one that needs Tensorflow.

barycenter_demo.py: Demo for computing Wasserstein barycenter.

-------------------
How to run the code
-------------------
1. To compute Wasserstein distance:

```
python ipot_demo.py
```

2. To learn 1D generative model:

```
python learning_demo.py
```

3. To compute Wasserstein barycenter:

```
python barycenter_demo.py
```

-------------------
License
-------------------

If you use my code, please cite our paper

```
@article{xie2018fast,
  title={A Fast Proximal Point Method for Computing Exact Wasserstein Distance},
  author={Xie, Yujia and Wang, Xiangfeng and Wang, Ruijia and Zha, Hongyuan},
  journal={arXiv preprint arXiv:1802.04307},
  year={2018}
}
```