def add(x,y): return x+y a,b=[float(i) for i in input().split()] print(".2f"%(add(a,b)))

As an AI language model, I don't have a physical body, emotions or consciousness, so I don't have any personal opinions or feelings about anything. However, I can provide information and answer questions to the best of my abilities.

相关问题

详细注释以下代码class Graph: def init(self, n, e): self.n = n self.e = e self.arc = [[float('inf')]*n for _ in range(n)] self.freq = [0]*n self.name = ['']n def add_edge(self, v1, v2, weight): self.arc[v1][v2] = weight self.arc[v2][v1] = weight def set_freq(self, v, freq): self.freq[v] = freq def set_name(self, v, name): self.name[v] = name def floyd(graph): n = graph.n arc = graph.arc for k in range(n): for i in range(n): for j in range(n): if arc[i][j] > arc[i][k] + arc[k][j]: arc[i][j] = arc[i][k] + arc[k][j] return arc n = int(input('请输入图中顶点个数：').strip()) e = int(input('请输入图中边的条数：').strip()) t = Graph(n, e) for i in range(n): name = input('请输入第%d个单位的名称：' % (i+1)) t.set_name(i, name) freq = int(input('请输入第%d个单位的去超市的频度：' % (i+1))) t.set_freq(i, freq) for i in range(e): while True: v1, v2, weight = input('请输入存在路径的两个单位以及相通两个单位间的距离(用空格隔开)：').split() v1, v2, weight = int(v1), int(v2), float(weight) if v1 < n and v2 < n: t.add_edge(v1, v2, weightt.freq[v2]) break else: print('输入的节点编号超出范围，请重新输入。') arc = floyd(t) min_row = float('inf') min_row_index = 0 for i in range(n): row_sum = sum(arc[i]) if row_sum < min_row: min_row = row_sum min_row_index = i print('学校超市最佳选址是：', t.name[min_row_index])

注释如下：

class Graph:
    def __init__(self, n, e):  # 创建图类，初始化节点数量、边数量、边权值矩阵、节点频度列表和节点名称列表
        self.n = n
        self.e = e
        self.arc = [[float('inf')]*n for _ in range(n)]  # 边权值矩阵，初始化为无穷大
        self.freq = [0]*n  # 节点频度列表，初始化为0
        self.name = ['']*n  # 节点名称列表，初始化为空

    def add_edge(self, v1, v2, weight):  # 添加边，更新边权值矩阵
        self.arc[v1][v2] = weight
        self.arc[v2][v1] = weight

    def set_freq(self, v, freq):  # 设置节点频度
        self.freq[v] = freq

    def set_name(self, v, name):  # 设置节点名称
        self.name[v] = name

def floyd(graph):  # Floyd算法求解最短路径
    n = graph.n
    arc = graph.arc
    for k in range(n):
        for i in range(n):
            for j in range(n):
                if arc[i][j] > arc[i][k] + arc[k][j]:
                    arc[i][j] = arc[i][k] + arc[k][j]
    return arc

n = int(input('请输入图中顶点个数：').strip())  # 输入节点数量
e = int(input('请输入图中边的条数：').strip())  # 输入边数量
t = Graph(n, e)  # 创建Graph类的实例
for i in range(n):
    name = input('请输入第%d个单位的名称：' % (i+1))
    t.set_name(i, name)  # 逐一输入节点的名称和频度
    freq = int(input('请输入第%d个单位的去超市的频度：' % (i+1)))
    t.set_freq(i, freq)
for i in range(e):
    while True:
        v1, v2, weight = input('请输入存在路径的两个单位以及相通两个单位间的距离(用空格隔开)：').split()
        v1, v2, weight = int(v1), int(v2), float(weight)
        if v1 < n and v2 < n:
            t.add_edge(v1, v2, weightt.freq[v2])  # 逐一输入边的两个节点和边权值，并添加到Graph类的实例中
            break
        else:
            print('输入的节点编号超出范围，请重新输入。')

arc = floyd(t)  # 调用floyd()函数求解最短路径
min_row = float('inf')
min_row_index = 0
for i in range(n):
    row_sum = sum(arc[i])
    if row_sum < min_row:  # 在最短路径矩阵中找到总路径长度最小的行
        min_row = row_sum
        min_row_index = i
print('学校超市最佳选址是：', t.name[min_row_index])  # 输出最佳选址的名称

总体来说，这段代码实现了一个使用 Floyd 算法求解最短路径的图(Graph)类，并在控制台中进行了交互式的输入和输出。其中，Graph类封装了节点数量、边数量、边权值矩阵、节点频度列表和节点名称列表等信息，而floyd()函数则实现了 Floyd 算法的核心代码，通过遍历和更新边权值矩阵求解最短路径。最后，通过在最短路径矩阵中找到总路径长度最小的行，输出最佳选址的名称。

arr0 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]) arr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]) arr2 = np.array(input("请输入连续24个月的车辆销售数据，元素之间用空格隔开：").split(), dtype=float) arr3 = np.array(input("请输入连续24个月的配件销售数据，元素之间用空格隔开：").split(), dtype=float) data_array = np.vstack((arr0, arr1, arr2, arr3)) data_matrix = data_array.T data = pd.DataFrame(data_matrix, columns=['num', 'month', 'car sales', 'sales']) data = data[['month', 'car sales', 'sales']] train_data, test_data = train_test_split(data, test_size=0.3) scaler = MinMaxScaler(feature_range=(0, 1)) data_scaled = scaler.fit_transform(data) train_size = int(len(data_scaled) * 0.7) test_size = len(data_scaled) - train_size train, test = data_scaled[0:train_size,:], data_scaled[train_size:len(data_scaled),:] def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset)-look_back): X.append(dataset[i:(i+look_back), :]) Y.append(dataset[i+look_back, :]) return np.array(X), np.array(Y) look_back = 3 X_train, Y_train = create_dataset(train, look_back) X_test, Y_test = create_dataset(test, look_back) model = Sequential() model.add(LSTM(4, input_shape=(look_back, 3))) model.add(Dense(3)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X_train, Y_train, epochs=100, batch_size=1, verbose=0) train_predict = model.predict(X_train) test_predict = model.predict(X_test) train_predict = scaler.inverse_transform(train_predict) Y_train = scaler.inverse_transform(Y_train) test_predict = scaler.inverse_transform(test_predict) Y_test = scaler.inverse_transform(Y_test) last_month = data_scaled[-look_back:] last_month = last_month.reshape((1, look_back, 3))#1，12，3 next_month = model.predict(last_month) next_month = scaler.inverse_transform(next_month) print('下个月的预测结果是:', round(next_month[0][2])),如何将以下代码插入，def comput_acc(real,predict,level): num_error=0 for i in range(len(real)): if abs(real[i]-predict[i])/real[i]>level: num_error+=1 return 1-num_error/len(real) a=np.array(test_data[label]) real_y=a real_predict=test_predict print("置信水平：{},预测准确率：{}".format(0.2,round(comput_acc(real_y,real_predict,0.2)* 100,2)),"%")

可以将以下代码插入到代码中：

def comput_acc(real, predict, level):
    num_error = 0
    for i in range(len(real)):
        if abs(real[i] - predict[i]) / real[i] > level:
            num_error += 1
    return 1 - num_error / len(real)

a = np.array(test_data['sales'])
real_y = a
real_predict = test_predict[:, 2]
print("置信水平：{},预测准确率：{}".format(0.2, round(comput_acc(real_y, real_predict, 0.2) * 100, 2)), "%")

其中，`test_data['sales']` 是测试集中的实际值（即真实销售数据），`test_predict[:, 2]` 则是模型预测的销售数据。`comput_acc` 函数用于计算预测准确率。在这里，我们将置信水平设为 0.2，表示预测结果与实际结果的相对误差在 20% 以内视为准确。