SVC分类的对分类决策边界

import mglearn
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
import numpy as np
from sklearn.svm import LinearSVC


X,Y=make_blobs(random_state=42)
linear_svm=LinearSVC().fit(X,Y)

mglearn.discrete_scatter(X[:,0],X[:,1],Y)
coef=linear_svm.coef_ #shape(3个类别，2个特征)
intercept=linear_svm.intercept_ #shape(3个类别)
color1=['b','r','g']
for c,i,co in zip(coef,intercept,color1):
    plt.plot(line,-(line*c[0]+i)/c[1],c=co) #就是决策边界 C[0]就是第一个类别的第一个特征
plt.ylim(-10,15)
plt.xlim(-10,8)
plt.xlabel('feature 0')
plt.ylabel('feature 1')