解决方案

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

def output_formula(features, weights, bias):
    return sigmoid(np.dot(features, weights) + bias)

def error_formula(y, output):
    return - y*np.log(output) - (1 - y) * np.log(1-output)

def update_weights(x, y, weights, bias, learnrate):
    output = output_formula(x, weights, bias)
    d_error = (y - output)
    weights += learnrate * d_error * x
    bias += learnrate * d_error
    return weights, bias