对python指数、幂数拟合curve_fit详解

from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import numpy as np
 
def func(x, a, b, c):
 return a * np.exp(-b * x) + c
 
xdata = np.linspace(0, 4, 50)
y = func(xdata, 2.5, 1.3, 0.5)
ydata = y + 0.2 * np.random.normal(size=len(xdata))
plt.plot(xdata,ydata,'b-')
popt, pcov = curve_fit(func, xdata, ydata)
#popt数组中，三个值分别是待求参数a,b,c
y2 = [func(i, popt[0],popt[1],popt[2]) for i in xdata]
plt.plot(xdata,y2,'r--')
print popt

def fund(x, a, b):
 return x**a + b
 
xdata = np.linspace(0, 4, 50)
y = fund(xdata, 2.5, 1.3)
ydata = y + 4 * np.random.normal(size=len(xdata))
plt.plot(xdata,ydata,'b-')
popt, pcov = curve_fit(fund, xdata, ydata)
#popt数组中，三个值分别是待求参数a,b,c
y2 = [fund(i, popt[0],popt[1]) for i in xdata]
plt.plot(xdata,y2,'r--')
print popt