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([[0, 0, 1],
              [0, 1, 1],
              [1, 0, 1],
              [1, 0, 0],
              [1, 1, 1]])

y = np.array([[0],
              [1],
              [1],
              [1],
              [0]])

np.random.seed(1)

# randomly initialize our weights with mean 0
syn0 = 2*np.random.random((3, 4)) - 1
syn1 = 2*np.random.random((4, 1)) - 1

for j in range(100000):

	# Feed forward through layers 0, 1, and 2
    l0 = X
    l1 = nonlin(np.dot(l0, syn0))
    l2 = nonlin(np.dot(l1, syn1))

    # how much did we miss the target value?
    l2_error = y - l2

    if (j % 10000) == 0:
        print("Error:" + str(np.mean(np.abs(l2_error))))

    # in what direction is the target value?
    # were we really sure? if so, don't change too much.
    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)

    # in what direction is the target l1?
    # were we really sure? if so, don't change too much.
    l1_delta = l1_error * nonlin(l1, deriv=True)

    syn1 += l1.T.dot(l2_delta)
    syn0 += l0.T.dot(l1_delta)


def predict(input):
    l1 = nonlin(np.dot(input, syn0))
    l2 = nonlin(np.dot(l1, syn1))
    print(l2)


# print(syn0)
# print(syn1)
predict([0,1,1])
predict([1,0,1])
predict([1,0,0])
predict([0,0,1])