【python】scipy.optimize.curve_fit

功能

使用非线性最小平方差来拟合一个函数

功能介绍

​​官方文档​​

输入

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)

例子

官方的例子

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()

输出拟合结果为

[2.61499295 1.35033395 0.51541771]

它与输入的值 [2.5, 1.3, 0.5]，还是很相近的。