Metadata-Version: 2.4
Name: MLGeometry-tf
Version: 1.2.2
Summary: Higher dimensional computational geometry using machine learning software
Maintainer-email: Yidi Qi <qiyidi2012@gmail.com>
License: MIT License
        
        Copyright (c) 2020 Michael R. Douglas, Subramanian Lakshminarasimhan and Yidi Qi
        
        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.
        
Project-URL: Homepage, https://github.com/yidiq7/MLGeometry
Project-URL: Changelog, https://github.com/yidiq7/MLGeometry/blob/master/CHANGELOG.md
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: tensorflow-probability[tf]
Requires-Dist: sympy
Requires-Dist: matplotlib
Dynamic: license-file

# MLGeometry

Higher dimensional computational geometry using machine learning software 

- Kahler geometry and Kahler-Einstein metrics

More to come.

## Recent Changes

MLGeometry has been updated to be compatible with the lastest version of TensorFlow and Keras 3, and it can now be installed directly from PyPI. If you prefer the older version, please check the 'Using and Older Version' section below.

## Installation

### Prerequisites

MLGeometry requires Python 3.11 and TensorFlow (>=2.16).

Install TensorFlow by following the official installation guide: [TensorFlow Installation](https://www.tensorflow.org/install). 

On Linux with GPU, TensorFlow can be installed by

    pip install 'tensorflow[and-cuda]'

### Installing MLGeometry

You can install MLGeometry using one of the following methods:

#### Via PyPI

    pip install MLGeometry-tf

*Note: Use "MLGeometry-tf" with a suffix when installing via pip.*

#### Directly from Github

    pip install git+https://github.com/yidiq7/MLGeometry.git

#### Using an Older Version

If you prefer to use an older version of MLGeometry based on Tensorflow 2.12 and Keras 2, you can check out the previous release (v1.1.0) here: [Version 1.1.0 Release](https://github.com/yidiq7/MLGeometry/releases/tag/v1.1.0). Follow the installation instructions provided in that release's documentation. The compatible versions of Python and CUDA can be found [here](https://www.tensorflow.org/install/source#gpu).


## [Sample jupyter notebook](https://github.com/yidiq7/MLGeometry/blob/main/Guide.ipynb)

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yidiq7/MLGeometry/blob/main/Guide.ipynb)

## Citation

You can find our paper on [arxiv](https://arxiv.org/abs/2012.04797) or [PMLR](https://proceedings.mlr.press/v145/douglas22a.html). 
If you find our paper or package useful in your research or project, please cite it as follows:

```
@InProceedings{pmlr-v145-douglas22a,
  title =    {Numerical Calabi-Yau metrics from holomorphic networks},
  author =       {Douglas, Michael and Lakshminarasimhan, Subramanian and Qi, Yidi},
  booktitle =    {Proceedings of the 2nd Mathematical and Scientific Machine Learning Conference},
  pages =    {223--252},
  year =     {2022},
  editor =   {Bruna, Joan and Hesthaven, Jan and Zdeborova, Lenka},
  volume =   {145},
  series =   {Proceedings of Machine Learning Research},
  month =    {16--19 Aug},
  publisher =    {PMLR},
  pdf =      {https://proceedings.mlr.press/v145/douglas22a/douglas22a.pdf},
  url =      {https://proceedings.mlr.press/v145/douglas22a.html},
}
```
