信息安全管理作业-L

import math
import random

def dot_product(v1, v2):
    return sum(x*y for x, y in zip(v1, v2))

def vector_add(v1, v2):
    return [x+y for x, y in zip(v1, v2)]

def scalar_vector_product(scalar, vector):
    return [scalar * x for x in vector]

def matrix_vector_product(matrix, vector):
    return [dot_product(row, vector) for row in matrix]

def transpose(matrix):
    return list(map(list, zip(*matrix)))
def output(ans):
    a=int(ans*100+0.5)
    print('%.2f' % (a/100))
class NeuralNet(object):
    def __init__(self):
        self.synaptic_weights = [0.5] * 6

    def __sigmoid(self, x):
        return 1 / (1 + math.exp(-x))

    def __sigmoid_derivative(self, x):
        return x * (1 - x)

    def train(self, inputs, outputs, training_iterations):
        for iteration in range(training_iterations):
            output = self.learn(inputs)
            error = [o - out for o, out in zip(outputs, output)]
            adjustments = []
            fo