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:
我如何修改我的代码以获得所需的输出?
离群值对超平面位置有什么影响?
什么参数影响平面的位置?
我的代码:将matplotlib.pyplot导入为plt,从sklearn.linear_model导入numpy为np导入SGDRegressor alpha_lst = [0.0001,1,100]离群值= [(0,2),(21,13),(-23,-15) ,(22,14),(23,14)]表示i ...
您开始知道如何改变超平面的角度..如果可以,请向她解释