少量数据集的情况通过scipy优化来拟合函数

import numpy as np
 import matplotlib.pyplot as plt
 from scipy.optimize import curve_fit
def func(x, a, b, c, d):
     return 1/(1+np.exp(-x*a+b)) # + c*x + d
xdata = np.linspace(0, 4, 50)
 y = func(xdata, 2.5, 1.3, 0.5,0.2)
 rng = np.random
 y_noise = 0.1 * rng.normal(size=xdata.size)
 ydata = y + y_noise
 plt.plot(xdata, ydata, 'b-', label='data')
popt, pcov = curve_fit(func, xdata, ydata)
 popt
 # array([2.56274217, 1.37268521, 0.47427475])
 plt.plot(xdata, func(xdata, *popt), 'r-',
          label='fit: a=%5.3f, b=%5.3f, c=%5.3f, d=%5.3f' % tuple(popt))
 popt, pcov = curve_fit(func, xdata, ydata, bounds=(0, [3., 1.2, 0.5,0.2]))
 plt.plot(xdata, func(xdata, *popt), 'g--',
          label='fit: a=%5.3f, b=%5.3f, c=%5.3f, d=%5.3f' % tuple(popt))
 plt.xlabel('x')
 plt.ylabel('y')
 plt.legend()
 plt.show()