如何通过更改函数参数来最小化函数的值

Question

import numpy as np
x = np.array([1,2,3,4,5,6,7])
f1= np.array([1,2,3,4,5,6,7])
f2= np.array([1,2,3,4,5,6,7])
def func(w1,w2,x,f1,f2):
w1=1-w2
return np.std(x/(w1*f1+w2*f2))

我需要我的代码通过改变w1和w2来最小化func(w1，w2，x，f1，f2(，然后给我w1和w2的值。w1+w2应该等于1。

Answer 1

您可能需要这样的东西：

x = np.random.randint(1, 10, 7)
f1 = np.random.randint(1, 10, 7)
f2 = np.random.randint(1, 10, 7)
def func(w, x, f1, f2):  # no need to pass w1 and w2 separately
return np.std(x / (w[0] * f1 + (1 - w[0]) * f2))
res = scipy.optimize.minimize(func, x0=[0.5], args=(x, f1, f2), bounds=[(0, 1)])
w1 = res.x[0]
w2 = 1 - w1
print("Optimal weights are", w1, w2)