from NeuralNetwork import NeuralNetwork
import numpy as np
layout = [3, 2]

def cap(x):
    return min(1, max(-1, x))

def sigmoid(x):
    return 1 / (1 + np.exp(x)) 

NN = NeuralNetwork(layout, sigmoid)

data = [
    [[0, 0, 0], [0, 0]],
    [[0, 0, 1], [0, 1]],
    [[0, 1, 0], [1, 0]],
    [[0, 1, 1], [1, 1]],
    [[1, 0, 0], [0, 0]],
    [[1, 0, 1], [0, 1]],
    [[1, 1, 0], [1, 0]],
]

# NN.train(data)

print(NN.toString())
print("")
# print(NN.evaluate(data[0][0]))
for i in range(10):
    print(NN.fit(data, (10-i) / 20))
print(NN.evaluate([1, 1, 1]))
print(NN.toString())