Training perceptron for OR gate

# input[0] works has bias

input = [
    [1, 0, 0],
    [1, 0, 1],
    [1, 1, 0],
    [1, 1, 1]
]

output = [
    0,
    1,
    1,
    1
]

weights = [
    0.0,
    0.0,
    0.0
]

learning_tax = 0.5

remaining_error = 0.0


def calculate_output(c_input, desired_output):
    obtained_output = weights[0] * c_input[0] + \
        weights[1] * c_input[1] + \
        weights[2] * c_input[2]

    output_error = desired_output - obtained_output

    if output_error != 0.0:
        for s in range(0, 3):
            weights[s] += learning_tax * output_error * c_input[s]

    return output_error

for b in range(0, 100):
    for c in range(0, 4):
        remaining_error = calculate_output(input[c], output[c])

    if b % 10 == 0:
        print('\nWeights: {} - {} - {}\nRemainingError: {}'
              .format(weights[0], weights[1], weights[2], remaining_error))