python画图拟合

你可以使用自行实现的最小二乘法函数或者使用scikit-learn提供的线性回归类来进行拟合和绘图。下面是一个示例代码，展示了如何使用matplotlib库来进行线性拟合和绘图。

import numpy as np
import matplotlib.pyplot as plt

def linear_regression(data):
    x = np.array([d for d in data])  # 提取自变量x
    y = np.array＊＊＊    plt.scatter(x, y, label='Data')  # 绘制散点图
    plt.plot(x, w*x + b, color='red', label='Fitted line')  # 绘制拟合直线
    plt.xlabel('x')
    plt.ylabel('y')
    plt.legend()
    plt.show()

    return round(w, 2), round(b, 2)

# 示例数据
data = [[5.06, 5.79], [4.92, 6.61], [4.67, 5.48], [4.54, 6.11], [4.26, 6.39], [4.07, 4.81], [4.01, 4.16], [4.01, 5.55], [3.66, 5.05], [3.43, 4.34], [3.12, 3.24], [3.02, 4.80], [2.87, 4.01], [2.64, 3.17], [2.48, 1.61], [2.48, 2.62], [2.02, 2.50], [1.95, 3.59], [1.79, 1.49], [1.54, 2.10]]

w, b = linear_regression(data)

这段代码将根据给定的数据样本进行线性拟合，并使用散点图和拟合直线进行可视化。返回的w和b分别表示拟合直线的斜率和截距项。<span class="em">1</span><span class="em">2</span>
#### 引用[.reference_title]
- *1* *2* [Python实现线性回归拟合并绘图](https://blog.csdn.net/zbp_12138/article/details/113939744)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
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