使用非线性最小平方差来拟合一个函数
参数 | Value |
---|---|
f | 函数,它必须以xdata为第一个入参 |
xdata | 测量的独立数据 |
ydata | 相关的数据,名义上是 f(xdata,…)的结果 |
输出
输出 | Value |
---|---|
popt | 最优值,即拟合函数根据x输出的值 |
pcov | popt的协方差矩阵 |
infodict | a dictionary of optional outputs with the keys (returned only if full_output is True) |
mesg | 相关的信息 (returned only if full_output is True) |
ier | An integer flag. If it is equal to 1, 2, 3 or 4, the solution was found. Otherwise, the solution was not found. In either case, the optional output variable mesg gives more information. (returned only if full_output is True) |
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def official_demo_func(x, a, b, c):
return a * np.exp(-b * x) + c
def official_demo():
x = np.linspace(0, 4, 50)
y = official_demo_func(x, 2.5, 1.3, 0.5)
rng = np.random.default_rng()
y_noise = 0.2 * rng.normal(size=x.size)
ydata = y + y_noise
plt.plot(x, ydata, 'b-', label='data')
popt, pcov = curve_fit(official_demo_func, x, ydata)
print(popt)
plt.plot(x, official_demo_func(x, *popt), 'g--',
label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()
official_demo()
[2.61499295 1.35033395 0.51541771]
它与输入的值 [2.5, 1.3, 0.5],还是很相近的。