import numpy as np

def nonlin(x,deriv=False):
    if(deriv==True):
        return x*(1-x)

    return 1/(1+np.exp(-x))
    
X = np.array([[.5,-.5],
            [.3, -.4],
            [-.1,1]])
y = np.array([[1.0],
			[-2.0],
			[.5]])
np.random.seed(1)

# randomly initialize our weights with mean 0
syn0 = 2*np.random.random((2,3)) - 1
syn1 = 2*np.random.random((3,1)) - 1

for j in xrange(60000):
    # Feed forward through layers 0, 1, and 2
    l0 = X
    l1 = nonlin(np.dot(l0,syn0))
    l2 = nonlin(np.dot(l1,syn1))

    # Compute Error
    l2_error = y - l2
    if (j% 10000) == 0:
        print "Error:" + str(np.mean(np.abs(l2_error)))

    l2_delta = l2_error*nonlin(l2,deriv=True)
    # how much did each l1 value contribute to the l2 error (according to the weights)?
    l1_error = l2_delta.dot(syn1.T)
    l1_delta = l1_error * nonlin(l1,deriv=True)
    syn1 += l1.T.dot(l2_delta)
    syn0 += l0.T.dot(l1_delta)

# print l2
