{
    "info": {
        "author": "Will Claybaugh, Fan (Bruce) Xiong, Erin Williams",
        "author_email": "erinwilliams@g.harvard.edu",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "[![Build Status](https://travis-ci.com/cs207-project-erin-bruce-will/cs207-FinalProject.svg?branch=master)](https://travis-ci.com/cs207-project-erin-bruce-will/cs207-FinalProject)\n\n[![Coverage Status](https://coveralls.io/repos/github/cs207-project-erin-bruce-will/cs207-FinalProject/badge.svg)](https://coveralls.io/github/cs207-project-erin-bruce-will/cs207-FinalProject)\n\n# AutoDiff\n\nDeveloped by: Will Claybaugh, Bruce Xiong, Erin Williams  \nGroup #3, CS207 Fall 2018\n\n\n## Introduction\nAutodiff finds the derivatives of a function (to machine precision!) at the same time it finds the value of the function.\n```\nimport autodiff.autodiff as ad\n\nx = ad.DualNumber('x', 2)\ny = ad.DualNumber('y', 3)\n\nout = x/y\nout.value # 0.66666, the value of 2 divided by 3\nout.derivatives #{x: 1/3, y: -2/(3**2)}, the gradient of x/y at (2,3)\n```\nAutodiff works for functions and expressions with any number of inputs. Just pass those functions DualNumbers instead of regular ints/floats (and upgrade any math module functions to their autodiff equvalents)\n\n## Installation\nAutodiff is on [PyPi](https://pypi.org/project/AutoDiff-group3/) and can be installed using the command ```pip install AutoDiff-group3```. To import, use ```import autodiff.autodiff as ad```.\n\nAutodiff can also be installed by downloading from [github](https://github.com/cs207-project-erin-bruce-will/cs207-FinalProject). Becuase it has no dependencies, you can simply add the repo folder to your python path (```import sys\nsys.path.insert(0, '/path_to_repo/')```) and import as normal. \n\n## Examples\n\nUsing autodiff is very simple:\n```\nimport autodiff.autodiff as ad\n\ndef f(a,b):\n    return 3*a/b*ad.sin(a*b+2)\n\nout = f(ad.DualNumber('x',2),ad.DualNumber('y',3))\n\nprint(out.value)\n1.978716\n\nprint(out.derivatives['x'])\n0.116358\n\nprint(out.derivatives['y'])\n-1.24157\n\n# get the value and derifative of f at a different point\nout = f(ad.DualNumber('x',0),ad.DualNumber('y',1))\n```\n\nA Python 3 notebook containing more in-depth examples and usage is available [HERE](https://github.com/cs207-project-erin-bruce-will/cs207-FinalProject/blob/master/docs/Demo.ipynb)\n\n## Documentation\n\nClick [HERE](https://github.com/cs207-project-erin-bruce-will/cs207-FinalProject/blob/master/docs/documentation.md) for full documentation.\n\n## Dependencies\n\nClick [HERE](https://github.com/cs207-project-erin-bruce-will/cs207-FinalProject/blob/master/docs/requirements.txt) for a full listing of dependencies.\n\n## License\n\nClick [HERE](https://github.com/cs207-project-erin-bruce-will/cs207-FinalProject/blob/master/LICENSE) to view our MIT License.\n\n\n\n\n",
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