class Neuron:

    def __init__(self, size):
        self.bias = 0.0
        self.spin = 1.0

        self.weights = np.zeros(size)

    def updateSpin(spins):
        for i in range(len(spins)):
            value = self.weights[i] * spins[i] + self.bias

        oldSpin = self.spin

        # if value >= 0:
        #     self.spin = 1.0
        # else:
        #     self.spin = -1.0

        self.spin = 1 if value >= 0 else -1
        return self.spin != oldSpin

class Network:
    def __init__(self, size):
        self.stableCount = 10000

        self.neurons = []
        self.size = size


        for i in range(self.size):
            self.neurons.append(Neuron(self.size))


    def learn(self, examples):
        example = []
        result = []

        for e in examples:
            for i in range(len(e)):
                result.append(np.multiply(e[i], e))

        for i, row in enumerate(result):
            self.neurons[i].weights = row

examples = [
    [1, 2, 3, 4],
]

net = Network(4)
net.learn(examples)

for neuron in net.neurons:
    print(neuron.weights)