给下面代码加上注释import numpy as np import pickle def solve_tag(answer,sentence): result = [] hidden_state = ["B", "M", "E", "S"] for i in range(len(answer)): if(hidden_state[int(answer[i])]=='E' or hidden_state[int(answer[i])]=='S'): result.append(sentence[i]) result.append(" ") else: result.append(sentence[i]) return result
时间: 2024-04-26 09:20:01 浏览: 12
# 导入 numpy 和 pickle 库
import numpy as np
import pickle
def solve_tag(answer,sentence):
# 定义隐藏状态列表
hidden_state = ["B", "M", "E", "S"]
# 初始化结果列表
result = []
# 循环遍历答案列表
for i in range(len(answer)):
# 判断当前标记是否为"E"或"S"
if(hidden_state[int(answer[i])]=='E' or hidden_state[int(answer[i])]=='S'):
# 将当前字符添加到结果列表中
result.append(sentence[i])
# 添加空格
result.append(" ")
else:
# 将当前字符添加到结果列表中
result.append(sentence[i])
# 返回结果列表
return result
相关问题
import numpy as np import imageio def preprocess_input(x, v2=True): x = x.astype('float32') x = x / 255.0 if v2: x = x - 0.5 x = x * 2.0 return x
这是一段Python代码,用于数据预处理。下面是对代码的解释:
```
import numpy as np
import imageio
def preprocess_input(x, v2=True):
x = x.astype('float32') # 将输入数据类型转换为浮点型
x = x / 255.0 # 将数据归一化到[0, 1]范围内
if v2: # 如果v2为True
x = x - 0.5 # 将数据中心化到0附近
x = x * 2.0 # 将数据放大到[-1, 1]范围内
return x # 返回处理后的数据
```
这段代码依赖于NumPy和imageio库。`preprocess_input`函数的输入`x`是一组数据,输出也是一组处理后的数据。函数将输入数据的类型转换为浮点型,然后将它们归一化到[0, 1]的范围内。如果`v2`为True,则将数据中心化到0附近,然后将其放大到[-1, 1]的范围内。最后,函数返回处理后的数据。这个函数可以用于图像处理、深度学习等领域。
import numpy as np from scipy.optimize import linprog def integer_cutting_plane(c, A, b, bounds): relaxed_A = A relaxed_b = b while True: res = linprog(c=c, A_ub=relaxed_A, b_ub=relaxed_b, bounds=bounds) x = res.x if all(int(val) == val for val in x): return x.astype(int) new_constraint = (relaxed_A @ x <= relaxed_b) relaxed_A = np.vstack((relaxed_A, new_constraint)) def get_bounds(): return [(0, None), (0, None)] def get_c(): return np.array([40, 90]) def get_A(): return np.array([[-9, -7], [-7, -20]]) def get_b(): return np.array([-56, -70]) if __name__ == '__main__': bounds = get_bounds() integer_cutting_plane(get_c(), get_A(), get_b(), bounds)以上代码运行报错ValueError: Invalid input for linprog: b_ub must be a 1-D array; b_ub must not have more than one non-singleton dimension and the number of rows in A_ub must equal the number of values in b_ub 请解决
在你的代码中,报错是因为在调用linprog函数时,传递给参数b_ub的参数relaxed_b是一个二维数组。linprog函数要求b_ub是一个一维数组。
要解决这个问题,你可以使用numpy库的flatten()方法将relaxed_b转换为一维数组。flatten()方法会将多维数组展平为一维数组。
以下是修改后的代码:
```python
import numpy as np
from scipy.optimize import linprog
def integer_cutting_plane(c, A, b, bounds):
relaxed_A = A
relaxed_b = b
while True:
res = linprog(c=c, A_ub=relaxed_A, b_ub=relaxed_b.flatten(), bounds=bounds)
x = res.x
if all(int(val) == val for val in x):
return x.astype(int)
new_constraint = (relaxed_A @ x <= relaxed_b)
relaxed_A = np.vstack((relaxed_A, new_constraint))
def get_bounds():
return [(0, None), (0, None)]
def get_c():
return np.array([40, 90])
def get_A():
return np.array([[-9, -7], [-7, -20]])
def get_b():
return np.array([-56, -70])
if __name__ == '__main__':
bounds = get_bounds()
integer_cutting_plane(get_c(), get_A(), get_b(), bounds)
```
通过使用flatten()方法将relaxed_b转换为一维数组,你可以解决报错问题。请注意,这里使用的是numpy库的flatten()方法,而不是reshape()方法。