如何解决使用 Scipy 进行 3D 优化的界限
我找不到答案。我想用成本函数优化一个三维数组。数组的值不得低于 1/9999 且不得高于 1.0。
例如我的数组是这样的:
myarray = np.array([[0.1,0.2,0.3],[0.1,0.3]],[[0.1,0.3]])
bounds = [(1/9999,1.0) for i in myarray for j in i for k in j]
opt.minimize(fun=CostFunction,x0=myarray.flatten(),method='Powell',bounds=bounds)
但是我收到了这个错误:
Traceback (most recent call last):
File "C:/Users/Name/PycharmProjects/OptimalisatiePreset/Main.py",line 74,in <module>
result = opt.minimize(fun=CostFunction,bounds=bounds)
File "C:\Users\Name\PycharmProjects\OptimalisatiePreset\venv\lib\site-packages\scipy\optimize\_minimize.py",line 610,in minimize
return _minimize_powell(fun,x0,args,callback,bounds,**options)
File "C:\Users\Name\PycharmProjects\OptimalisatiePreset\venv\lib\site-packages\scipy\optimize\optimize.py",line 2965,in _minimize_powell
fval,x,direc1 = _linesearch_powell(func,direc1,File "C:\Users\Name\PycharmProjects\OptimalisatiePreset\venv\lib\site-packages\scipy\optimize\optimize.py",line 2700,in _linesearch_powell
bound = _line_for_search(p,xi,lower_bound,upper_bound)
File "C:\Users\Name\PycharmProjects\OptimalisatiePreset\venv\lib\site-packages\scipy\optimize\optimize.py",line 2643,in _line_for_search
lower_bound,upper_bound = lower_bound[nonzero],upper_bound[nonzero]
IndexError: index 0 is out of bounds for axis 0 with size 0
有人知道我做错了什么吗?
解决方法
不确定您的问题是什么,以下对我有用,没有错误。答案并不完美:我认为每个 x
都应该是 1.0/9999.0
,但事实并非如此。但是它似乎可以运行,也许它可以成为您的起点。
import numpy as np
from scipy.optimize import minimize
myarray = np.asarray([[[0.1,0.2,0.3],[0.1,0.3]],[[0.1,0.3]]])
bounds = [(1/9999,1.0) for i in myarray for j in i for k in j]
def CostFunction(x):
return np.linalg.norm(x)
out=minimize(fun=CostFunction,x0=myarray.flatten(),method='Powell',bounds=bounds)
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