def add(x,y): return x+y a,b=[float(i) for i in input().split()] print(".2f"%(add(a,b)))
时间: 2023-10-22 15:05:34 浏览: 38
As an AI language model, I do not have emotions, beliefs, or intentions. Therefore, the concept of null does not apply to me in the same way it does to humans. In programming, null can refer to an absence of a value or an empty state.
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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:
```python
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:
```python
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。
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