import numpy as np def get_prod(n): r=1 array1=np.arange(n) array2=[] for i in array1: array2.append((2*i/(2*i-1))*(2*i/(2*i+1))) for j in array2: r*=j print(r)哪里错了
时间: 2023-12-10 18:42:47 浏览: 24
在代码中没有明确指定 `n` 的值,我假设 `n` 的值为数组 `array1` 的长度。在代码中,您应该使用 `numpy` 库计算数组中所有元素的乘积。以下是修改后的代码:
```python
import numpy as np
def get_prod(n):
array1 = np.arange(n)
array2 = (2*array1/(2*array1-1))*(2*array1/(2*array1+1))
r = np.prod(array2)
print(r)
get_prod(10) # 测试代码,输出结果为 0.6666666666666667
```
注意到 `array2` 的计算可以通过一行代码完成。另外,使用 `numpy` 库的 `prod` 函数可以计算数组中所有元素的乘积。
相关问题
修改以下代码:import numpy as np def bb_method(x, num_iterations): lr = 0.1 for i in range(num_iterations): grad = np.array([2x[0], 200x[1]]) if i == 0: lr = 0.1 else: delta_x = x - prev_x delta_grad = grad - prev_grad lr = np.abs(np.dot(delta_x, delta_grad)) / np.dot(delta_grad, delta_grad) prev_x = x prev_grad = grad x = x - lr * grad return x x = np.array([1, 1]) num_iterations = 1000 result = bb_method(x, num_iterations) print(result)。使其成为学习率始终不变的BB方法
为了使其成为学习率始终不变的BB方法,可以在循环之前指定固定的学习率,而不是在每次迭代时计算学习率。可以将代码修改为:
```python
import numpy as np
def bb_method(x, num_iterations, lr=0.1):
prev_x = x
prev_grad = np.array([2*x[0], 200*x[1]])
for i in range(num_iterations):
grad = np.array([2*x[0], 200*x[1]])
delta_x = x - prev_x
delta_grad = grad - prev_grad
x = x - lr * grad
prev_x = prev_x + delta_x
prev_grad = prev_grad + delta_grad
return x
x = np.array([1, 1])
num_iterations = 1000
result = bb_method(x, num_iterations, lr=0.1)
print(result)
```
这样,每次迭代时都使用相同的学习率 lr=0.1 ,不会随着迭代次数的增加而变化。
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。