曲线拟合的问题，exp遇到溢出

Question

我想用以下数据拟合曲线，但出现错误：

ipython-input>:2: RuntimeWarning: overflow encountered in exp

有谁知道这个问题的原因是什么？我用 Matlab 的不同数据类型拟合了这条曲线，它运行良好。我使用了我的 Matlab 代码中的初始条件。两条曲线相同，但在这种情况下 y 轴的值要高得多。

import numpy as np
import scipy.optimize
#sympy.init_printing()
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

list_R1_fit = [
    19.53218114920747, 42.52167990454083, 60.95540646861309,
    70.10646960395906, 73.99897337091254, 75.36736639556727,
    75.69578522881915, 75.62147077733012, 75.42605227692485,
    75.21657113589387, 75.04519265636262, 74.94144261816007,
    74.92153132015117, 74.99475606015201, 75.15746897265564
]
tau_list = [
    0.052, 0.12, 0.252,
    0.464, 0.792, 1.264,
    1.928, 2.824, 4,
    5.600, 7.795, 10.806,
    14.928, 20.599, 28.000
]

array_R1_fit = np.asarray(list_R1_fit)
tau_array = np.asarray(tau_list)
plt.plot(tau_array, array_R1_fit, 'o') 

def func_R1_fit( t, a0, a1, a2, a3, a4):
    R1_fit_Curve = (
        a0 * np.exp(
            a1 * (1 - (28 / t)**(4 / 5))
        ) + 
        a2 * (t / ((a3)**a4 + t**a4))
    )
    return R1_fit_Curve

pars, cov = curve_fit(
    f=func_R1_fit, 
    xdata=tau_array, ydata=array_R1_fit, 
    p0=[
        0.249714296337621, 0.101851223776512,
        0.209343265573669, 0.306273529630680,
        1.511897539010256
    ],
    bounds=(-np.inf, np.inf),
    maxfev=100000
)

我在图表的第一部分生成了更多数据。现在我得到另一个错误。

<ipython-input-7-f61377d66140>:2: RuntimeWarning:
 invalid value encountered in double_scalars
  R1_fit_Curve=a0*np.exp(a1*(1-(28/t)**(4/5)))+a2*(t/((a3)**a4+t**a4))

新榜单如下：

list_R1_fit=[8.889450414508385, 13.832704635961235, 3.0955553517738656, 6.944672155278666, 19.53218114920747, 23.06912497313617, 32.92595485184, 42.52167990454083, 54.23640835031421, 60.95540646861309, 66.91996368676925, 70.10646960395906, 72.69136093289741, 73.99897337091254, 74.93277916119311, 75.36736639556727, 75.62190347933995, 75.69578522881915, 75.68268608294542, 75.62147077733012, 75.52270979845973, 75.42605227692485, 75.21657113589387, 75.04519265636262, 74.94144261816007, 74.92153132015117, 74.99475606015201, 75.15746897265564]
tau_list=[0.03,0.04,0.052/3,0.052/2,0.052,0.12/2,(0.052+0.12)/2,0.12,(0.252+0.12)/2,0.252,(0.464+0.252)/2,0.464,(0.464+0.792)/2,0.792,(1.264+0.792)/2,1.264,(1.264+1.928)/2,1.928,(2.824+1.928)/2,2.824,(2.824+4)/2,4,5.600,7.795,10.806,14.928,20.599,28.000]

Answer 1

指数通常会导致“较小”数据类型的曲线拟合出现问题。所以你想要做的是将你的输入转换为更大的表示形式，比如 longdouble，它应该是你的平台自然支持的最大的。

array_R1_fit = np.asarray(list_R1_fit).astype(np.longdouble)
tau_array =np.asarray(tau_list).astype(np.longdouble)

Answer 2

如果将 array_R1_fit 和 tau_array 的数据类型设置为 np.longdouble 或 np.float64 应该修复 RuntimeWarning: overflow encountered in exp 即：

array_R1_fit = np.asarray(list_R1_fit, dtype=np.longdouble)
tau_array =np.asarray(tau_list, dtype=np.longdouble)

请注意，如果您使用的是 Windows 64 位计算机，np.longdouble 将不会生成 float128，而是定义为 float64。您可以尝试在 linux 系统上运行它。

曲线拟合的问题，exp遇到溢出

Problem with curve fitting, overflow encountered in exp

python

curve-fitting