如何使用matplotlib绘制具有2个特征的3D多元线性回归？

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.linear_model import LinearRegression

# 输入特征
X1 = np.random.rand(100)
X2 = np.random.rand(100)

# 目标变量
y = 2 + 3*X1 + 4*X2 + np.random.randn(100)

# 特征矩阵
X = np.column_stack((X1, X2))

# 目标向量
y = y.reshape(-1, 1)

# 创建线性回归模型
model = LinearRegression()

# 拟合模型
model.fit(X, y)

# 生成预测结果
y_pred = model.predict(X)

# 创建3D图形
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制数据点
ax.scatter(X1, X2, y, c='r', marker='o')

# 绘制回归平面
x1_grid, x2_grid = np.meshgrid(X1, X2)
y_pred_grid = model.intercept_ + model.coef_[0][0]*x1_grid + model.coef_[0][1]*x2_grid
ax.plot_surface(x1_grid, x2_grid, y_pred_grid, alpha=0.5)

# 设置坐标轴标签
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('y')

# 显示图形
plt.show()