scipy.optimize.shgo值错误:具有多个元素的数组的真值不明确,请使用.any()或.all()

ddhy6vgd  于 2022-11-10  发布在  Go
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我试图拟合一个函数y(x,T,p)来得到系数abcdefp是已知的。使用全局优化器,我想找到一个好的起点。shgo似乎是唯一一个接受constraints的。

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import shgo

# test data

x = np.array([0.1,0.2,0.3,1])
T = np.array([300,300,300,300])
p = np.array([67.2,67.2,67.2,67.2])
y = np.array([30,50,55,67.2])

# function

def func(pars,x,T,p):
    a,b,c,d,e,f = pars
    return x*p+x*(1-x)*(a+b*T+c*T**2+d*x+e*x*T+f*x*T**2)*p

# residual

def resid(pars):
    return ((func(pars,x,T,p) - y)**2).sum()

# constraint: derivation is positive in every data point

def der(pars):
    a,b,c,d,e,f = pars
    return -p*((3*f*T**2+3*e*T+3*d)*x**2+((2*c-2*f)*T**2+(2*b-2*e)*T-2*d+2*a)*x-c*T**2-b*T-a-1)

con1 = ({'type':'ineq', 'fun':der})

# minimizer shgo

bounds = [(-1,1),(-1,1),(-1,1),(-1,1),(-1,1),(-1,1)]
res = shgo(resid, bounds, constraints=con1)
print("a = %f , b = %f, c = %f, d = %f, e = %f, f = %f" % (res[0], res[1], res[2], res[3], res[4], res[5]))

# plotting

x0 = np.linspace(0, 1, 100)
fig, ax = plt.subplots()
fig.dpi = 80
ax.plot(x,y,'ro',label='data')
for i,txt in enumerate(T):
    ax.annotate(txt,(x[i],y[i]))
ax.plot(x0, func(res.x, x0, 300,67.2), '-', label='fit1')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()

有了这个,我得到ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()我不知道这个错误意味着什么,其他线程有同样的错误并没有真正帮助我理解.当我使用一个局部极小化(scipy.optimize.minimize与方法cobyla)的错误没有出现.
有没有人能帮助我了解我的问题,甚至帮助解决它?谢谢
编辑:

Traceback (most recent call last):
  File "C:\Users\...\Python\Python36\site-packages\scipy\optimize\_shgo_lib\triangulation.py", line 759, in __getitem__
    return self.cache[x]
KeyError: (0, 0, 0, 0, 0, 0)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:/Users/.../test.py", line 70, in <module>
    res = shgo(resid, bounds, constraints=con1)
  File "C:\Users\...\Python\Python36\site-packages\scipy\optimize\_shgo.py", line 423, in shgo
    shc.construct_complex()
  File "C:\Users\...\Python\Python36\site-packages\scipy\optimize\_shgo.py", line 726, in construct_complex
    self.iterate()
  File "C:\Users\...\Python\Python36\site-packages\scipy\optimize\_shgo.py", line 869, in iterate
    self.iterate_complex()
  File "C:\Users\...\Python\Python36\site-packages\scipy\optimize\_shgo.py", line 890, in iterate_hypercube
    self.g_args)
  File "C:\Users\...\Python\Python36\site-packages\scipy\optimize\_shgo_lib\triangulation.py", line 121, in __init__
    self.n_cube(dim, symmetry=symmetry)
  File "C:\Users\...\Python\Python36\site-packages\scipy\optimize\_shgo_lib\triangulation.py", line 172, in n_cube
    self.C0.add_vertex(self.V[origintuple])
  File "C:\Users\...\Python\Python36\site-packages\scipy\optimize\_shgo_lib\triangulation.py", line 767, in __getitem__
    index=self.index)
  File "C:\Users\...\Python\Python36\site-packages\scipy\optimize\_shgo_lib\triangulation.py", line 681, in __init__
    if g(self.x_a, *args) < 0.0:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
5gfr0r5j

5gfr0r5j1#

问题是,der返回的是数组而不是标量值。

con1 = ({'type':'ineq', 'fun':der})

con_list = [{'type':'ineq', 'fun': lambda x: der(x)[i_out]} for i_out in range(T.shape[0])]

移除错误。这会将der的每个输出转换成它自己的不等式限制。

zdwk9cvp

zdwk9cvp2#

此外,由于您的约束都被写为der(x)>=0,因此可以简单地保留具有向量输出的oyur约束的定义,然后获取输出的最小值,即,取标量值约束x -> \min (der(x))

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