Metadata-Version: 2.1
Name: funAD
Version: 0.1.3
Summary: an automatic differentiation package created by AC207 students
Author-email: Hanlin Zhu <hzhu@g.harvard.edu>, Xu Tang <xutang@g.harvard.edu>, Tiantong Li <tiantongli@g.harvard.edu>, Zhecheng Yao <zhechengyao@g.harvard.edu>
Project-URL: Homepage, https://code.harvard.edu/CS107/team21
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Software Development :: Testing
Requires-Python: >=3.5
Description-Content-Type: text/markdown
License-File: LICENSE

# team21
[![Continuous Integration Test Coverage for Milestone 2](https://code.harvard.edu/CS107/team21/actions/workflows/coverage.yml/badge.svg?branch=milestone2_dev)](https://code.harvard.edu/CS107/team21/actions/workflows/coverage.yml)
[![test AD for Milestone 2](https://code.harvard.edu/CS107/team21/actions/workflows/test.yml/badge.svg?branch=milestone2_dev)](https://code.harvard.edu/CS107/team21/actions/workflows/test.yml)

## Brief Introduction

funAD is a PyPi-distributed package that executes forward-mode of automatic differentiation, enabling users to solve functional derivatives with high computational efficiency and machine precision.

This project/package is the fruit of Harvard CS107/AC207 class final project in 2022 Fall. Our package utilize forward mode and dual number. Additionally, we also allow users to define their own seeds vector for the Jacobian Matrix and the option to calculate local maxima and minima through gradient descent.

To install, run the following command in your terminal

`pip install funAD`

For details instruction on how to use this package, please follow the steps in the [usage page](https://code.harvard.edu/CS107/team21/tree/main/docs/documentation.ipynb).

**Group Number:**

Group 21

**Group Members:**

_Zhecheng Yao zhechengyao@g.harvard.edu_

_Hanlin Zhu hzhu@g.harvard.edu_

_Xu Tang xutang@g.harvard.edu_

_Tiantong Li tiantongli@g.harvard.edu_

Harvard University, Fall 2022
