为什么Wikipedia的感知器正确地分离了XOR？

Question

我知道感知器只能在线性可分离集上正确工作，比如NAND、AND、OR函数的输出。我一直在读Wikipedia's entry on the perceptron，并开始研究它的代码。在

当它是一个可分离的层时，它不应该是线性的。在

#xor
print ("xor")
t_s           = [((1, 1, 1), 0), ((1, 0, 1), 1), ((1, 1, 0), 1), ((1, 1, 1), 0)] 


threshold     = 0.5
learning_rate = 0.1
w             = [0, 0, 0]

def dot_product(values, weights):
    return sum(value * weight for value, weight in zip(values, weights))

def train_perceptron(threshold, learning_rate, weights, training_set):
    while True:
        #print('-' * 60)
        error_count = 0

        for input_vector, desired_output in training_set:
            #print(weights)
            result = dot_product(input_vector, weights) > threshold
            error  = desired_output - result

            if error != 0:
                error_count += 1
                for index, value in enumerate(input_vector):
                    weights[index] += learning_rate * error * value

        if error_count == 0: #iterate till there's no error 
            break
    return training_set

t_s = train_perceptron(threshold, learning_rate, w, t_s)

t_s = [(a[1:], b) for a, b in t_s]

for a, b in t_s:
    print "input: " + str(a) + ", output: " + str(b)

The output for this Ideone run is correct for XOR。怎么会？在

^{pr2}$

Answer 1

试着改变你的训练计划：

t_s = [((1, 1, 1), 0), ((1, 0, 1), 1), ((1, 1, 0), 1), ((0, 0, 0), 0)]