我正在尝试绘制一个3D图，使用一个重新存在的函数来生成Z值。然而，这产生了误差"；具有一个以上元素的数组的真值是不明确的"；。这似乎很奇怪，因为我可以使用相同的函数和y，x值生成一个Z值列表，但一旦我包含了3D绘图代码，就会出现错误。
我的绘图代码是：
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
from matplotlib import cm
def f(tau,tau_b): #re-use society welfare function of tau & tau_b, using corr=0.6
Z = society_welfare2 (0.6, tau, tau_b)
return Z
xgrid=np.linspace(1e-5, 1-1e-5,100) #tau grid
ygrid=np.linspace(1e-5, 1-1e-5,100) #tau_b grid
tau,tau_b=np.meshgrid(xgrid,ygrid)
fig=plt.figure(figsize=(8,6))
ax=fig.add_subplot(111,projection='3d')
ax.plot_surface(tau,
tau_b,
f(tau,tau_b),
rstride=2,cstride=2,
cmap=cm.jet,
alpha=0.7,
linewidth=0.25)
ax.set_zlim(-0.5,1.0)
plt.show()
我的社会_福利2功能代码：
def society_welfare2 (corr, tau, tau_b):
cov = [[1,corr], [corr,1]] #covariance
epsilon_start,b_start = np.random.multivariate_normal(mean, cov, sample_N).T
epsilon = np.exp(epsilon_start) #to ensure epsilon positive
b = np.exp(b_start) #to ensure b positive
indv_welfares = []
def GBC (t_o):
taxes_paid = []
for i in range(sample_N): #loop over all agents to find their C1,C2,L
def consumption_functions(Lguess,epsilon=epsilon,b=b):
C2 = (((1-tau)*epsilon[i]*w*Lguess) +(1-tau_b)*b[i] + ((t_o)/(1+r)))/((1/((beta**(1/gamma))*((1+r)**(1/gamma)))) + (1/(1+r)))
C1 = C2 /((beta**(1/gamma))*(1+r)**(1/gamma))
return -Utility(C1,C2,Lguess)
result = minimize_scalar(consumption_functions,bounds=(0,1),method='bounded', args=(epsilon, b))
opt_L = result.x
opt_C1=(((1-tau)*(epsilon[i])*w)/(opt_L**sigma))**(1/gamma)
opt_C2=(opt_C1)*((beta**(1/gamma))*(1+r)**(1/gamma))
income_tax = tau*(epsilon[i])*w*opt_L
bequest_tax = tau_b*(b[i])
taxes_paid.append(income_tax)
taxes_paid.append(bequest_tax)
welfare_func = opt_C1**(1-gamma)/(1-gamma)-opt_L**(1+sigma)/(1+sigma) + beta*(opt_C2**(1-gamma)/(1-gamma))
indv_welfares.append(welfare_func)
total_tax_revenue = sum(taxes_paid)
return total_tax_revenue - (10000*t_o)
result1 = minimize_scalar(GBC,bounds=(1e-5, 100000),method='bounded')
opt_t_o = result1.x
total_welfare = sum(indv_welfares)
return total_welfare
完整的回溯错误代码：
ValueError Traceback (most recent call last)
<ipython-input-19-3633f4a9db76> in <module>
18 ax.plot_surface(tau,
19 tau_b,
---> 20 f(tau,tau_b),
21 rstride=2,cstride=2,
22 cmap=cm.jet,
<ipython-input-19-3633f4a9db76> in f(tau, tau_b)
7
8 def f(tau,tau_b): #re-use society welfare function of tau & tau_b, using corr=0.6
----> 9 Z = society_welfare2 (0.6, tau, tau_b)
10 return Z
11
<ipython-input-17-321a709b9684> in society_welfare2(corr, tau, tau_b)
61 return total_tax_revenue - (10000*t_o)
62
---> 63 result1 = minimize_scalar(GBC,bounds=(1e-5, 100000),method='bounded')
64
65 opt_t_o = result1.x
/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/_minimize.py in minimize_scalar(fun, bracket, bounds, args, method, tol, options)
798 if isinstance(disp, bool):
799 options['disp'] = 2 * int(disp)
--> 800 return _minimize_scalar_bounded(fun, bounds, args, **options)
801 elif meth == 'golden':
802 return _minimize_scalar_golden(fun, bracket, args, **options)
/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/optimize.py in _minimize_scalar_bounded(func, bounds, args, xatol, maxiter, disp, **unknown_options)
1956 rat = e = 0.0
1957 x = xf
-> 1958 fx = func(x, *args)
1959 num = 1
1960 fmin_data = (1, xf, fx)
<ipython-input-17-321a709b9684> in GBC(t_o)
41 return -Utility(C1,C2,Lguess)
42
---> 43 result = minimize_scalar(consumption_functions,bounds=(0,1),method='bounded', args=(epsilon, b))
44
45 opt_L = result.x
/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/_minimize.py in minimize_scalar(fun, bracket, bounds, args, method, tol, options)
798 if isinstance(disp, bool):
799 options['disp'] = 2 * int(disp)
--> 800 return _minimize_scalar_bounded(fun, bounds, args, **options)
801 elif meth == 'golden':
802 return _minimize_scalar_golden(fun, bracket, args, **options)
/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/optimize.py in _minimize_scalar_bounded(func, bounds, args, xatol, maxiter, disp, **unknown_options)
2015 print("%5.0f %12.6g %12.6g %s" % (fmin_data + (step,)))
2016
-> 2017 if fu <= fx:
2018 if x >= xf:
2019 a = xf
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
回溯的最低点是
if fu <= fx:
这是比较if中的两个变量。如果fu和fx是标量或单值数组，这将起作用。但如果其中一个是多值数组，则if将引发此错误。
此时，我们的任务是将这些变量追溯到您的代码中。我怀疑您正在为某个参数提供数组，其中they/is应该是标量。
看看顶部。当你要求绘图时，它会发生，但参数是一个函数调用：
f(tau,tau_b)
并且通过对CCD_ 5函数上的CCD_。我认为GBC就是中的func
fx = func(x, *args)
这就提出了一个问题，GBC究竟返回了什么？它正在_minimize_scalar中使用，所以它应该只返回一个值。
它的返回表达式是什么？
return total_tax_revenue - (10000*t_o)
你认为你可以从那里进行分析吗？
现在你明白我们为什么坚持要看traceback了吗。错误在您的代码中，但到达那里的顺序很长，而且从简单地读取代码来看并不明显。
编辑
哦，我看到了minimize的另一个级别，一个使用的级别
consumption_functions
它有几个参数，epsilon和b。我想我们可以推断出它们是什么。但是是什么
Utility
fu <= fx似乎正在针对绑定的fu测试fx返回值。假设边界是标量，则值fx必须是一个数组。是吗？？？