最小二乘法曲线拟合python

最小二乘法是一种数学优化技术，用于通过最小化误差的平方和，寻找数据的最佳函数匹配。在Python中，可以使用NumPy和SciPy库来实现最小二乘法曲线拟合。

以下是一个使用最小二乘法拟合曲线的Python代码示例：

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

# 自定义函数
def func(x, a, b, c, d):
    return -a * x - b + c - d

# 定义x、y散点坐标
x = np.array([0, 4, 8, 12, 16, 20, 24, 28])
y = np.array([0.1, 0.255, 0.15, 0.31, 0.1, 0.3, 0.2, 0.3])

# 非线性最小二乘法拟合
popt, pcov = curve_fit(func, x, y)

# 获取拟合系数
a = popt<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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