import numpy as np

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

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

traning_outputs = np.array([[0,1,1,0]]).T

np.random.seed(1)

synaptic_weights = 2 * np.random.random((3,1))-1

print("Случайные что-то там:")
print(synaptic_weights)
for i in range(20000):
    input_layer=traning_inputs
    outputs = sigmoid(np.dot(input_layer,synaptic_weights))
    err = training_outputs-outputs
    adjustents = np.dot(input_layer.T,err * (outputs * (1-outputs)))

    synaptic_weights+= adjustents
print("Ещё 123")
print(synaptic_weights)


input_layer = traning_inputs
outputs = sigmoid( np.dot(imput_layer, synaptic_weights))

print("Результат:")
print(outputs)