curve.fit过拟合

Question

有人能帮我解决curve.fit的贴身问题吗？我想把我的数据拟合成二阶方程。但我得到了一个类似于线性方程的结果。

这是我的代码：

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

def func(x, a, b, c):
    f = a*np.power(x, 2) + b*x + c
    return f

xdata_prime=[3.0328562996216282, 3.101784841139168, 3.1707134502066894, 3.2396419917242292, 3.308570533241769, 3.3774990747593088, 3.3774990747593088, 3.4337789932367149, 3.4900589392912855, 3.5463388577686916, 3.6026187762460977, 3.6588987223006684]
ydata_prime=[6.344300000000002, 6.723900000000002, 7.080399999999999, 7.399800000000001, 7.649099999999999, 7.753100000000002, 7.753100000000002, 7.658600000000002, 7.442100000000002, 7.180100000000001, 6.902700000000001, 6.6211]

plt.plot(xdata_prime, ydata_prime, 'b-', label='data')
popt, pcov = curve_fit(func, xdata_prime, ydata_prime)
popt
plt.plot(xdata_prime, func(xdata_prime, *popt), 'r-',label='fit')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()

Answer 1

您的数组需要是numpy数组，因为您的函数正在执行矢量化操作(即a*np.power(x，2))。因此，这样您的代码就可以工作了：

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

def func(x, a, b, c):
    f = a*np.power(x, 2) + b*x + c
    return f

xdata_prime=np.array([3.0328562996216282, 3.101784841139168, 3.1707134502066894, 3.2396419917242292, 3.308570533241769, 3.3774990747593088, 3.3774990747593088, 3.4337789932367149, 3.4900589392912855, 3.5463388577686916, 3.6026187762460977, 3.6588987223006684])
ydata_prime=np.array([6.344300000000002, 6.723900000000002, 7.080399999999999, 7.399800000000001, 7.649099999999999, 7.753100000000002, 7.753100000000002, 7.658600000000002, 7.442100000000002, 7.180100000000001, 6.902700000000001, 6.6211])

plt.plot(xdata_prime, ydata_prime, 'b-', label='data')
popt, pcov = curve_fit(func, xdata_prime, ydata_prime)
plt.plot(xdata_prime, func(xdata_prime, *popt), 'r-',label='fit')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()

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