Metadata-Version: 2.1
Name: VayDiff
Version: 0.0.2
Summary: Python package for Automatic Differentiation
Home-page: https://github.com/cs207-group-11/cs207-FinalProject
Author: Abhimanyu Vasishth, Zheyu Wu, Yiming Xu
Author-email: 
License: MIT
Keywords: Python,Automatic differentiation
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown

# VayDiff [![Build Status](https://travis-ci.com/cs207-group-11/cs207-FinalProject.svg?branch=master)](https://travis-ci.com/cs207-group-11/cs207-FinalProject)[![Coverage Status](https://coveralls.io/repos/github/cs207-group-11/cs207-FinalProject/badge.svg?branch=master)](https://coveralls.io/github/cs207-group-11/cs207-FinalProject?branch=master)

An Automatic Differentiation Library for Python 3. This project was done for CS207 at Harvard University, taught by Professor David Sondak. Check out our [Documentation](./docs/Final/documentation.md) for more details!

## How to Install

### Installing via PyPI (for end-users)

Download our project on [PyPI](https://pypi.org/project/VayDiff/) using the following command:

```
pip install VayDiff
```

### Manual Installation (for developers)

Clone or download our [GitHub repository](https://github.com/HIPS/autograd) and navigate into this directory in your terminal.

Optional: create a virtual environment using `virtualenv`. This can be downloaded using `pip3` or `easy_install` as follows:

```
pip3 install virtualenv
```

or

```
sudo easy_install virtualenv
```

Then, create a virtual environment (using Python3), activate this virtual environment, and install the dependencies as follows:

```
virtualenv -p python3 my_env
source my_env/bin/activate
pip3 install -r requirements.txt
```

In order to deactivate the virtual environment, use the following command

```
deactivate
```

## Example

```python
from VayDiff.VayDiff import Variable
from VayDiff.VayDiff import Diff

def user_function(a):
  return a**2

x = Variable(3, name='x')
t = Diff().auto_diff(user_function, [x])
print(t.val, t.der['x'])
9 6.0
```

## Fractals!

![newton_fractal](./docs/Final/Figures/fractal.gif)

Look at our [Feature](./Feature) section for examples and more fractals.

## Made By:

1. Abhimanyu Vasishth
2. Zheyu Wu
3. Yiming Xu


