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python - 如何在 Python 中修正超平面的位置?

转载 作者:行者123 更新时间:2023-12-05 02:58:06 27 4
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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: output from this code snippet

期望的输出: Desired output

我的问题:

  • 如何修改我的代码以获得所需的输出?

  • 异常值对超平面位置有什么影响?

  • 什么参数影响平面的位置?

最佳答案

你可以试试这段代码:

hypers = [0.001,1,100]

plt.figure(figsize = (20,16))

for j,lr in enumerate(hypers):

outlier_points = [(0,2),(21, 13), (-23, -15), (22,14), (23, 14)]
X = b * np.sin(phi)
Y = a * np.cos(phi)

for c,k in enumerate(range(5*j+1, 5*(j+1)+1)):
X= np.append(X,outlier_points[c][0])
Y= np.append(Y,outlier_points[c][1])


#training the model afte updating the outliers
clf = SGDRegressor(alpha = lr, random_state=12)
clf.fit(X.reshape(-1,1), Y)
Y_pred =clf.predict(X.reshape(-1,1))
plt.subplot(4,5,k)
plt.scatter(X,Y)
plt.plot(X,Y_pred, color ='red')
plt.title(str(lr))

plt.show()

关于python - 如何在 Python 中修正超平面的位置?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/59367939/

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