python数据拟合之scipy.optimize.curve_fit解读

# 导入模块
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

# 导入数据
x = np.linspace(0,1.0,11)
y = np.array([-0.447, 1.978, 3.28, 6.16, 7.08, 7.34, 7.66, 9.56, 9.48, 9.30, 11.2])

# 拟合二次多项式
f = lambda x, a, b, c: a*x**2 + b*x + c
 
# popt为拟合参数，pcov为参数的协方差矩阵
popt, pcov = curve_fit(f, x, y)
print("拟合的参数值a,b,c分别为：", popt)
 
# x=0.25, 0.35的预测值
print("x=0.25, 0.35的预测值为", f(np.array([0.25, 0.35]), *popt))

plt.rc('font', size=15)
plt.plot(x, y, '*')    # 观测值数据的散点图
# 拟合数据的折线图
plt.plot(np.linspace(0.0, 1.0, 50), f(np.linspace(0.0, 1.0, 50), *popt), '-')
plt.show()