# http://web.cs.ucla.edu/~rosen/161/notes/hopfield.html

attractors = np.array([[0, 1, 1, 0, 1], [1, 0, 1, 0, 1]])

print('attractors:\n', attractors)

weight_matrix = np.zeros((5, 5))

print('weight_matrix:\n', weight_matrix)

# 第一个向量表示第一个节点到各个节点的权值

# 初始化网络，权值是对称的，例如w12 = w21，而w11 w22都是0

# for i in range(len(weight_matrix)):

def reflect_about_the_diagonal(matrix):

    # 将矩阵上三角翻转拷贝到下三角

    matrix += matrix.T - np.diag(matrix.diagonal())

for attractor in attractors:

    temp_matrix = np.zeros((5, 5))

    for i in range(0, 4):

        for j in range(i + 1, 5):

            temp_matrix[i, j] = (2 * attractor[i] - 1) * (2 * attractor[j] - 1)

    weight_matrix += temp_matrix

reflect_about_the_diagonal(weight_matrix)

print('weight_matrix:\n', weight_matrix)

# print(weight_matrix[2].dot(attractors[0]))

# 类似于bp里的预测,som里的map

def xxxx(input_vector):

    vector = input_vector.copy()

    stable_state = False

    random_order = np.arange(len(attractors[0]))

    while not stable_state:

        # 生成一个随机序列，以随机的顺序更新节点

        np.random.shuffle(random_order)

        stable_state = True

        for i in random_order:

            original_value = vector[i]

            vector[i] = weight_matrix[i].dot(vector)

            if (vector[i] >= 0):

                vector[i] = 1

            else:

                vector[i] = 0

            if (vector[i] != original_value):

                print(i, "change ", original_value, '->', vector[i])

                stable_state = False

    return vector

x = [1, 1, 1, 1, 1]

print('test:', x, '->', xxxx(np.array(x)))