如何在Python中绘制超平面？

Question

我的代码：

import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import SGDRegressor

alpha_lst = [0.0001,1,100]

outlier = [(0,2),(21, 13), (-23, -15), (22,14), (23, 14)]

for i in range(len(alpha_lst)):
    plt.figure(figsize = (17,14))
    k = 0
    X= b * np.sin(phi)
    Y= a * np.cos(phi)
    for j in outlier:
        plt.subplot(3,5,k+1)
        k+=1 
        X = np.append(X,j[0]).reshape(-1,1)
        Y = np.append(Y,j[1]).reshape(-1,1)
        clf = SGDRegressor(alpha=alpha_lst[i], eta0=0.001, learning_rate='constant',random_state=0)
        clf.fit(X,Y)
        coef = clf.coef_
        intercept = clf.intercept_
        y_min = np.amin(X)
        y_max = np.amax(X)
        hyper_plane = draw_hyper_plane(coef,intercept,y_min,y_max)

        plt.scatter(X,Y,color='blue')

    plt.show()

我的绘图功能：

def draw_hyper_plane(coef,intercept,y_max,y_min):
    points=np.array([[((-coef*y_min - intercept)/coef), y_min],[((-coef*y_max - intercept)/coef), y_max]])
    plt.plot(points[:,0], points[:,1])

实际输出：

所需输出：

我的问题：

• 如何修改代码以获得所需的输出？

• 离群点对超平面位置的影响是什么？