AutoDiff-group3 0.0.2

Automatic differentiation with dual numbers

AutoDiff

Developed by: Will Claybaugh, Bruce Xiong, Erin Williams
Group #3, CS207 Fall 2018

Introduction

Autodiff finds the derivatives of a function (to machine precision!) at the same time it finds the value of the function.

import autodiff as ad

x = ad.DualNumber('x', 2)
y = ad.DualNumber('y', 3)

out = x/y
out.value # 0.66666, the value of 2 divided by 3
out.derivatives #{x: 1/3, y: -2/(3**2)}, the gradient of x/y at (2,3)

Autodiff 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)

Installation

Autodiff can be installed using pip install AutoDiff-group3.

Autodiff can also be installed by downloading from github. Becuase it has no dependencies, you can simply add the repo folder to your python path (import sys sys.path.insert(0, '/path_to_repo/')) and import as normal.

Examples

Using autodiff is very simple:

import sys
sys.path.insert(0, "C:/Users/erina/cs207-FinalProject/")
import autodiff as ad

def f(a,b):
    return 3*a/b*ad.sin(a*b+2)

out = f(ad.DualNumber('x',2),ad.DualNumber('y',3))

print(out.value)
1.978716

print(out.derivatives['x'])
0.116358

print(out.derivatives['y'])
-1.24157

# get the value and derifative of f at a different point
out = f(ad.DualNumber('x',0),ad.DualNumber('y',1))

A Python 3 notebook containing more in-depth examples and usage is available HERE

Documentation

Click HERE for full documentation.

Dependencies

Click HERE for a full listing of dependencies.

License

Click HERE to view our MIT License.