Python错误：“numpy.ndarray”对象不可调用-如何修复？

Question

这是我的密码。当我运行它时，我会发现python错误的一个问题：'numpy.ndarray‘对象是不可调用的。我认为问题是因为我在find_k，find_w的函数中调用了find_w，但是我不知道怎么做

import numpy as np
import copy as cp 
import scipy as sp
from scipy import optimize as optimize
from scipy.interpolate import PchipInterpolator as pchip

#primatives
#beta R <1 means that the 
beta = .8
R = 1.02
phi = .8

#define a grid
size = 100
w_grid = np.linspace(0.001,5,num = size)

#set up functions

def utility(c):
    return np.log(c)

def u_prime(c):
    return 1/c

def production(k):
    return k**(1/3)

def production_prime(k):
    return 1/3*k**(-2/3)

def production_2prime(k):
    return (-2/9)*k**(-5/3)

def inv_prod_prime(x):
    return (3*x)**(-2/3)

#define functions to get threshold value wbar and optimal policy b, k

def find_w(V_function_prime, k_star, capital_evolution):
    w_bar = (1-phi)*k_star + 1/(beta*R*V_function_prime(capital_evolution))
    return w_bar
#find_w(phi, R)

#takes in value w and current guess of v_prime and returns optimal bond choice (b)
def find_b(b, w, v_function_prime, k_star):
    foc = u_prime(w - b - k_star) - beta*R*v_function_prime(production(k_star) + R*b)
    return foc

#takes in value w and current guess of v_prime and returns optimal capital choice (k)
def find_k(k, w, v_function_prime):
    foc = (1-phi)*u_prime(w - (1-phi)*k) - beta*v_function_prime(production(k) - R*phi*k)(production_prime(k)-R*phi)
    return foc

#value function iteration function

def vfi(R, phi, beta, size, tol):
    #use known info ab optimum--add explanation here 
    k_star = inv_prod_prime(R)
    capital_evolution = production(k_star)-R*phi*k_star
    
    #inital guess of value function is utility
    VV = utility(w_grid)
    #V_prime = u_prime(w_grid)
    
    #params of loop
    err = tol + 1
    epsilon = 1e-5
    
    while err > tol:
        V_previous = cp.copy(VV) 
        V_function = pchip(w_grid, VV)
        #V_w is value function evaluated at w_grid
        V_w = V_function(w_grid)
        V_function_prime = V_function.derivative(1)
        V_prime_w = V_function_prime(w_grid)
        
        w_bar = find_w(V_function_prime, k_star, capital_evolution)
        
        k_prime = np.zeros(size)
        b_prime = np.zeros(size)
        
        for i in range(size):
            #solve unconstrained region of state-space
            if w_grid[i] >= w_bar: 
                k_choice = k_star
                #limits set based on natural bounds for borrowing given in the SP
                b_choice = optimize.brentq(find_b, (-phi*k_star), (w_grid[i] - k_star - epsilon), args = (w_grid[i], V_function_prime, k_star))
            #solve constrained region of state-space
            else:
                bound = w_grid[i]/(1-phi) - epsilon
                k_choice = optimize.brentq(find_k, (epsilon), (bound), args = (w_grid[i], V_function_prime))
                b_choice = -phi*k_choice
            #add in new guesses for optimal b, k, and update value function vector
            k_prime[i] = k_choice
            b_prime[i] = b_choice
            VV[i] = utility(w_grid[i] - b_prime[i] - k_prime[i]) + beta*V_function(production(k_prime[i]) + R*b_prime[i])
        V_function_update = pchip(w_grid, VV)
        err = np.max(np.abs(V_function_update(w_grid) - V_w))
        print(err)
        V_function = V_function_update
    return V_function, b_prime, k_prime     
                     
                
vfi(R, phi, beta, size = 100, tol = 1e-3)  

我知道bc会发生这种情况，我有一个函数V_function_prime，我正在传递给另一个函数，但我不太清楚如何解决这个问题

Answer 1

#takes in value w and current guess of v_prime and returns optimal capital choice (k)
def find_k(k, w, v_function_prime):
    foc = (1-phi)*u_prime(w - (1-phi)*k) - beta*v_function_prime(production(k) - R*phi*k)(production_prime(k)-R*phi) ## Two times repeated function call
    return foc

检查上面的街区。v_function_prime(**args)将返回一个数组。你又用一些其他的论点来调用它。你可能需要移除其中一个。