《Web安全之机器学习入门》笔记:第九章 9.2 支持向量机SVM hello world

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本小结是通过随机创建40个点,构造超平面,使用svm处理

1.数据集处理

随机创40个点,前20个标记为10,后20个标记为1

X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
Y = [0] * 20 + [1] * 20

打印数据集X与Y

[[-0.23594765 -1.59984279]
 [-1.02126202  0.2408932 ]
 [-0.13244201 -2.97727788]
 [-1.04991158 -2.15135721]
 [-2.10321885 -1.5894015 ]
 [-1.85595643 -0.54572649]
 [-1.23896227 -1.87832498]
 [-1.55613677 -1.66632567]
 [-0.50592093 -2.20515826]
 [-1.6869323  -2.85409574]
 [-4.55298982 -1.3463814 ]
 [-1.1355638  -2.74216502]
 [ 0.26975462 -3.45436567]
 [-1.95424148 -2.18718385]
 [-0.46722079 -0.53064123]
 [-1.84505257 -1.62183748]
 [-2.88778575 -3.98079647]
 [-2.34791215 -1.84365103]
 [-0.76970932 -0.79762015]
 [-2.38732682 -2.30230275]
 [ 0.95144703  0.57998206]
 [ 0.29372981  3.9507754 ]
 [ 1.49034782  1.5619257 ]
 [ 0.74720464  2.77749036]
 [ 0.38610215  1.78725972]
 [ 1.10453344  2.3869025 ]
 [ 1.48919486  0.81936782]
 [ 1.97181777  2.42833187]
 [ 2.06651722  2.3024719 ]
 [ 1.36567791  1.63725883]
 [ 1.32753955  1.64044684]
 [ 1.18685372  0.2737174 ]
 [ 2.17742614  1.59821906]
 [ 0.36980165  2.46278226]
 [ 1.09270164  2.0519454 ]
 [ 2.72909056  2.12898291]
 [ 3.13940068  0.76517418]
 [ 2.40234164  1.31518991]
 [ 1.12920285  1.42115034]
 [ 1.68844747  2.05616534]]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

2.训练数据集

构造超平面,使用svm处理

# fit the model
clf = svm.SVC(kernel='linear')
clf.fit(X, Y)

# get the separating hyperplane
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - (clf.intercept_[0]) / w[1]

# plot the parallels to the separating hyperplane that pass through the
# support vectors
b = clf.support_vectors_[0]
yy_down = a * xx + (b[1] - a * b[0])
b = clf.support_vectors_[-1]
yy_up = a * xx + (b[1] - a * b[0])

3.完整代码

源码如下

        

print(__doc__)

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm

# we create 40 separable points
np.random.seed(0)
X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
Y = [0] * 20 + [1] * 20

# fit the model
clf = svm.SVC(kernel='linear')
clf.fit(X, Y)

# get the separating hyperplane
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - (clf.intercept_[0]) / w[1]

# plot the parallels to the separating hyperplane that pass through the
# support vectors
b = clf.support_vectors_[0]
yy_down = a * xx + (b[1] - a * b[0])
b = clf.support_vectors_[-1]
yy_up = a * xx + (b[1] - a * b[0])

# plot the line, the points, and the nearest vectors to the plane
plt.plot(xx, yy, 'k-')
plt.plot(xx, yy_down, 'k--')
plt.plot(xx, yy_up, 'k--')

plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
            s=80, facecolors='none')
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)

plt.axis('tight')
plt.show()

4.运行结果

img

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