在曲线拟合过程中，参数的协方差无法估计。
在曲线拟合过程中，参数的协方差无法估计。
提问于 2022-07-11 09:17:11
我试图使用scipy.optimize.curve_fit函数根据函数表达式求解两个未知的参数。我使用的公式如下：在这里输入图像描述
我的代码如下：
p_freqs =np.array(0.,8.19672131,16.39344262,24.59016393,32.78688525,
40.98360656,49.18032787,57.37704918,65.57377049,73.7704918,
81.96721311,90.16393443,98.36065574,106.55737705,114.75409836,
122.95081967,131.14754098,139.3442623, 147.54098361,155.73770492,
163.93442623,172.13114754,180.32786885,188.52459016,196.72131148,
204.91803279,213.1147541, 221.31147541,229.50819672,237.70491803,
245.90163934)
p_fft_amp1 = np.array(3.34278536e-08,5.73549829e-08,1.94897033e-08,1.59088184e-08,
9.23948302e-09,3.71198908e-09,1.85535722e-09,1.86064653e-09,
1.52149363e-09,1.33626573e-09,1.19468040e-09,1.08304535e-09,
9.96594475e-10,9.25671797e-10,8.66775330e-10,8.17287132e-10,
7.75342888e-10,7.39541296e-10,7.08843676e-10,6.82440637e-10,
6.59712650e-10,6.40169517e-10,6.23422124e-10,6.09159901e-10,
5.97134297e-10,5.87146816e-10,5.79040074e-10,5.72691200e-10,
5.68006964e-10,5.64920239e-10,5.63387557e-10)
def cal_omiga_tstar(omiga,tstar,f):
return omiga*np.exp(-np.pi*f*tstar)/(1+(f/18.15)**2)
omiga,tstar = optimize.curve_fit(cal_omiga_tstar,p_freqs,p_fft_amp1)[0]当我运行代码时，我得到以下提示：OptimizeWarning: Covariance of the parameters could not be estimated warnings.warn('Covariance of the parameters could not be estimated'
回答 1
Stack Overflow用户
回答已采纳
发布于 2022-07-11 11:01:43
我无法准确确定错误消息的原因，因为您的代码在此之前有一些错误。首先，两个数组的构造具有无效的语法，然后cal_omiga_tstar的定义有错误的参数顺序。在修复这些问题时，我确实收到过一次您的错误消息，但我没有能够复制它，非常奇怪。不过，我确实满足了你的要求。您应该提供对参数的初始猜测，特别是考虑到您的y有这么多低值。这里没有魔法，只需绘制模型和数据，直到它相对接近。然后，让算法拿起轮子。
这是我的密码：
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import numpy as np
# Changed here, was "np.array(0.,..."
p_freqs =np.array([0.,8.19672131,16.39344262,24.59016393,32.78688525,
40.98360656,49.18032787,57.37704918,65.57377049,73.7704918,
81.96721311,90.16393443,98.36065574,106.55737705,114.75409836,
122.95081967,131.14754098,139.3442623, 147.54098361,155.73770492,
163.93442623,172.13114754,180.32786885,188.52459016,196.72131148,
204.91803279,213.1147541, 221.31147541,229.50819672,237.70491803,
245.90163934])
p_fft_amp1 = np.array([3.34278536e-08,5.73549829e-08,1.94897033e-08,1.59088184e-08,
9.23948302e-09,3.71198908e-09,1.85535722e-09,1.86064653e-09,
1.52149363e-09,1.33626573e-09,1.19468040e-09,1.08304535e-09,
9.96594475e-10,9.25671797e-10,8.66775330e-10,8.17287132e-10,
7.75342888e-10,7.39541296e-10,7.08843676e-10,6.82440637e-10,
6.59712650e-10,6.40169517e-10,6.23422124e-10,6.09159901e-10,
5.97134297e-10,5.87146816e-10,5.79040074e-10,5.72691200e-10,
5.68006964e-10,5.64920239e-10,5.63387557e-10])
# Changed sequence from "omiga, tstar, f" to "f, omiga, tstar".
def cal_omiga_tstar(f, omiga,tstar):
return omiga*np.exp(-np.pi*f*tstar)/(1+(f/18.15)**2)
# Changed this call to get popt, pcov, and supplied the initial guesses
popt, pcov = curve_fit(cal_omiga_tstar,p_freqs,p_fft_amp1, p0=(1E-5, 1E-2))这是popt：array([ 4.51365934e-08, -1.48124194e-06])和pcov：array([[1.35757744e-17, 3.54656128e-12],[3.54656128e-12, 2.90508007e-06]])。如您所见，在这种情况下可以估计协方差矩阵。
下面是x型数据曲线：
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/72936204
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