scipy.optimize.curve_fit ValueError: The truth value of an array with more than one element is ambiguous

Question

我正在尝试使用 scipy.optimize.curve_fit 将 S 形曲线拟合到我的数据集，但出现以下错误：

Traceback (most recent call last):
  File "<input>", line 1, in <module>
  File "Y:\WORK\code\venv\lib\site-packages\scipy\optimize\minpack.py", line 784, in curve_fit
    res = leastsq(func, p0, Dfun=jac, full_output=1, **kwargs)
  File "Y:\WORK\code\venv\lib\site-packages\scipy\optimize\minpack.py", line 410, in leastsq
    shape, dtype = _check_func('leastsq', 'func', func, x0, args, n)
  File "Y:\WORK\code\venv\lib\site-packages\scipy\optimize\minpack.py", line 24, in _check_func
    res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
  File "Y:\WORK\code\venv\lib\site-packages\scipy\optimize\minpack.py", line 484, in func_wrapped
    return func(xdata, *params) - ydata
  File "<input>", line 7, in func
  File "Y:\WORK\code\venv\lib\site-packages\scipy\integrate\quadpack.py", line 348, in quad
    flip, a, b = b < a, min(a, b), max(a, b)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

我尝试拟合的 S 型函数包含从负无穷大到自变量函数的积分。这是我的代码：

import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.integrate import quad
import numpy as np
import math

x_data = np.array([ 2.5,  7.5, 12.5, 17.5, 22.5, 27.5, 32.5, 37.5, 42.5, 47.5, 52.5, 57.5, 62.5, 67.5, 72.5, 77.5])
y_data = np.array([0.05, 0.09, 0.13, 0.15, 0.2, 0.35, 0.45, 0.53, 0.68, 0.8, 0.9, 0.92, 0.99, 1, 0.95, 0.97])

#Constructing the sigmoidal function
def integrand(x):
    return np.exp(-np.square(x)/2)

def func(x, a, b):
    t = (x-a)/(b*a)
    integral = quad(integrand, -math.inf, t)[0]
    return math.sqrt(1/(2*math.pi))*integral

# Initial guess for parameters a, b
initialGuess = [35, 0.25]

# Perform the curve-fit
popt, pcov = curve_fit(func, x_data, y_data, initialGuess)

问题好像出在积分部分。在其他类似的帖子中，他们的函数包含一个布尔参数，如果自变量超过一个值，则提供另一个函数。但不是我的情况。我很困惑...

Answer 1

感谢 @hpaulj：quad 函数只接受标量，而我的 t 是一个数组。我按如下方式更改了函数，错误消失了

def func(x, a, b):
    sigmoid_arr = np.array([])
    for i in x:
        t = (i- a)/(b*a)
        integral = quad(integrand, -math.inf, t)[0]
        sigmoid = math.sqrt(1/(2*math.pi))*integral
        sigmoid_arr = np.append(sigmoid_arr, sigmoid)
    return sigmoid_arr

scipy.optimize.curve_fit ValueError: The truth value of an array with more than one element is ambiguous

scipy.optimize.curve_fit ValueError: The truth value of an array with more than one element is ambiguous

curve-fitting

scipy

quad

valueerror

scipy-optimize