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A python package to do auto differentiation.

## Project description

[![Build Status](https://travis-ci.org/amartya18x/easyGrad.svg?branch=master)](https://travis-ci.org/amartya18x/easyGrad)
# Easy Grad

This library aims to provide an easy implementation of doing symbolic operations in python
## Operators allowed

- *exp(x)* and *log(x)*
Python
def testOps():
x = Integer('x')
y = ops.log(x)
z = ops.exp(y)
graph = GradGraph(z)
graph.getOutput({x: 1})
graph.getGradients(wrt=x)
print x.gradient

- *Sigmoid(x)*
Python

def activ_fns():
x = Double('x')
z = ops.sigmoid(x)
graph = GradGraph(z)
graph.getOutput({x: 110.5})
graph.getGradients(wrt=x)
print x.gradient

- *Tanh(x)*
Python

def activ_fns():
x = Double('x')
z = ops.tanh(x)
graph = GradGraph(z)
graph.getOutput({x: 110.5})
graph.getGradients(wrt=x)
print x.gradient


## Testing

Python
def gradTestSimple():
a = Integer("a")
b = Integer("b")
c = a + b
d = b + 6
e = c * d
graph = GradGraph(e)
graph.getOutput({a: 32,
b: 11})
graph.getGradients(wrt=b)
print a.gradient, b.gradient

### Here is a more complex example.
Python
def gradTest():
x = Integer("Int1x")
y = Integer("Int2y")
z = Integer("Int3z")
p = Integer("Int4p")
k = p * z
t = y * k
m = k + t
n = m * z
graph = GradGraph(n)
graph.getOutput({x: 9,
y: 9,
z: 9,
p: 2})
graph.getGradients(wrt=z)
print x.gradient, y.gradient, z.gradient, p.gradient

### This is the same examples as above but the commands are not three op commands.

Python
def gradTestLong():
x = Integer("Int1x")
y = Integer("Int2y")
z = Integer("Int3z")
p = Integer("Int4p")
k = p * z
n = (k + (y * p * z)) * z
graph = GradGraph(n)
graph.getOutput({x: 9,
y: 9,
z: 9,
p: 2})
graph.getGradients(wrt=z)
print x.gradient, y.gradient, z.gradient, p.gradient

Tensor Operations
Python

def dotProduct():
x = DoubleTensor("Tensor1")
y = x.dot([3, 4])
z = y.dot([4, 5])
graph = GradGraph(z)
output = graph.getOutput({x: [3, 4]})
graph.getGradients(wrt=x)
assert(np.all(output == [100, 125]))
assert(np.all(x.gradient == [[ 12., 16.], [ 15., 20.]]))

def TensorOp():
x = DoubleTensor("Tensor1")
y = x - [3, 4]
z = ops.log(y * x)
graph = GradGraph(z)
output = graph.getOutput({x: [10]})
assert(np.all(np.isclose(output, np.log(10 * (10 - np.asarray([3, 4]))))))
graph.getGradients(wrt=x)
a = 2 * 10 - np.asarray([3, 4])
b = 1.0/np.exp(np.asarray(output))
assert(np.all(np.isclose(x.gradient, a * b)))



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### Source Distribution

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