from numpy import exp, array, random, dot
class NeuralNetwork():
    def __init__(self):
        random.seed(1)
        self.synaptic_weights = 2 * random.random((3, 1)) - 1
    def __sigmoid(self, x):
        return 1 / (1 + exp(-x))
    def __sigmoid_derivative(self, x):
        return x * (1 - x)
    def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
        for iteration in range(number_of_training_iterations):
            output = self.think(training_set_inputs)
            error = training_set_outputs - output
            adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output))
            self.synaptic_weights += adjustment
    def think(self, inputs):
        return self.__sigmoid(dot(inputs, self.synaptic_weights))
neural_network = NeuralNetwork()
training_set_inputs = array([[0, 0, 0], [1, 0, 0]])
training_set_outputs = array([[0, 1]]).T
neural_network.train(training_set_inputs, training_set_outputs, 10000)
number = neural_network.think(array([1, 0, 0]))
number = str(number*117)
print(number[1:4])