SVC中线性核和高斯核的效果展示

源码

import matplotlib.pyplot as plt
import matplotlib
import numpy as np
 
from sklearn import svm
 
plt.figure(figsize=(5, 4), dpi=140)
plt.subplot(1, 1, 1)

# 生成随机点
# 图1
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=200,
                  n_features=2,
                  centers=[(1, 1), (2,1.5)],
                  random_state=4,
                  shuffle=False,
                  cluster_std=0.6) # 生成随机点
xmax, xmin = 3 , 0
ymax, ymin = 3 , 0

'''
# 图2
from sklearn.datasets import make_circles
X, y = make_circles(n_samples=100,
                    shuffle=True,  # 打乱
                    noise=0.05,    # 噪声0-1
                    random_state=4,  
                    factor=0.3 )   # 小圆相对大圆的倍数
xmax, xmin = 1.5 , -1.5
ymax, ymin = 1.5 , -1.5 
'''

'''
# 图3
from sklearn.datasets import make_moons
X, y = make_moons(n_samples = 100,
                  shuffle = True,
                  noise = 0.1,      # 噪声0-1
                  random_state = 2)
xmax, xmin = 2.5, -1.5
ymax, ymin = 2 , -1.5
'''

plt.title('linear_SVC and rbf_SVC')

plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
ax = plt.gca()                                  # gca 代表当前坐标轴,即 'get current axis'
ax.spines['right'].set_color('none')            # 隐藏坐标轴
ax.spines['top'].set_color('none')

############高斯核#########
rbf_svc = svm.SVC(kernel='rbf', C=1.5)
rbf_svc.fit(X, y)

temp_X,temp_Y=np.mgrid[xmin:xmax:200j, ymin:ymax:200j ] #生成网络采样点
grid_test=np.stack((temp_X.flat,temp_Y.flat) ,axis=1) #测试点
grid_hat = rbf_svc.predict(grid_test)       # 预测分类值
grid_hat = grid_hat.reshape(temp_X.shape)  # 使之与输入的形状相同

cm_light=matplotlib.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])

plt.pcolormesh(temp_X, temp_Y, grid_hat, cmap=cm_light)     # 预测值的显示

############训练点的散点图############
plt.scatter(X[y==0][:, 0], X[y==0][:, 1], marker='o')
plt.scatter(X[y==1][:, 0], X[y==1][:, 1], marker='s')


############线性核############
linear_svc = svm.SVC(kernel='linear', C=1.5)
linear_svc.fit(X, y)

w = linear_svc.coef_  
b = linear_svc.intercept_
# print(w)
# print(b)

line_x = np.linspace(xmin, xmax,30)
line_y = -(w[0,0]*line_x + b)/w[0,1]

plt.plot(line_x,line_y)

plt.show()

效果1

效果2

效果3

结论

高斯核牛逼！

参考

sklearn中SVM的可视化
【机器学习】python使用支持向量机SVM
SVM支持向量机python可视化