FilesExpand file tree

 master

/

SVM_scikit-learn.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

71 lines (56 loc) · 2.02 KB

 master

/

SVM_scikit-learn.py

Copy path

Top

File metadata and controls

• Code
• Blame

71 lines (56 loc) · 2.02 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

import numpy as np

from scipy import io as spio

from matplotlib import pyplot as plt

from sklearn import svm

def SVM():

'''data1——线性分类'''

data1 = spio.loadmat('data1.mat')

X = data1['X']

y = data1['y']

y = np.ravel(y)

plot_data(X,y)

model = svm.SVC(C=1.0,kernel='linear').fit(X,y) # 指定核函数为线性核函数

plot_decisionBoundary(X, y, model) # 画决策边界

'''data2——非线性分类'''

data2 = spio.loadmat('data2.mat')

X = data2['X']

y = data2['y']

y = np.ravel(y)

plt = plot_data(X,y)

plt.show()

model = svm.SVC(gamma=100).fit(X,y) # gamma为核函数的系数，值越大拟合的越好

plot_decisionBoundary(X, y, model,class_='notLinear') # 画决策边界

# 作图

def plot_data(X,y):

plt.figure(figsize=(10,8))

pos = np.where(y==1) # 找到y=1的位置

neg = np.where(y==0) # 找到y=0的位置

p1, = plt.plot(np.ravel(X[pos,0]),np.ravel(X[pos,1]),'ro',markersize=8)

p2, = plt.plot(np.ravel(X[neg,0]),np.ravel(X[neg,1]),'g^',markersize=8)

plt.xlabel("X1")

plt.ylabel("X2")

plt.legend([p1,p2],["y==1","y==0"])

return plt

# 画决策边界

def plot_decisionBoundary(X,y,model,class_='linear'):

plt = plot_data(X, y)

# 线性边界

if class_=='linear':

w = model.coef_

b = model.intercept_

xp = np.linspace(np.min(X[:,0]),np.max(X[:,1]),100)

yp = -(w[0,0]*xp+b)/w[0,1]

plt.plot(xp,yp,'b-',linewidth=2.0)

plt.show()

else: # 非线性边界

x_1 = np.transpose(np.linspace(np.min(X[:,0]),np.max(X[:,0]),100).reshape(1,-1))

x_2 = np.transpose(np.linspace(np.min(X[:,1]),np.max(X[:,1]),100).reshape(1,-1))

X1,X2 = np.meshgrid(x_1,x_2)

vals = np.zeros(X1.shape)

for i in range(X1.shape[1]):

this_X = np.hstack((X1[:,i].reshape(-1,1),X2[:,i].reshape(-1,1)))

vals[:,i] = model.predict(this_X)

plt.contour(X1,X2,vals,[0,1],color='blue')

plt.show()

if __name__ == "__main__":

SVM()