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python - 如何在 Python 中纠正超平面的位置？

问题描述

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： 此代码段的输出

期望的输出： 期望的输出

我的问题：

标签： pythonmatplotliblinear-regression

解决方案

那么你可以试试这个代码：

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()

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