-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathfisher_judge.py
160 lines (128 loc) · 4.31 KB
/
fisher_judge.py
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
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#w1中数据点的坐标
x1=[0.2331,1.5207,0.6499,0.7757,1.0524,1.1974,
0.2908,0.2518,0.6682,0.5622,0.9023,0.1333,
-0.5431,0.9407,-0.2126,0.0507,-0.0810,0.7315,
0.3345,1.0650,-0.0247,0.1043,0.3122,0.6655,
0.5838,1.1653,1.2653,0.8137,-0.3399,0.5152,
0.7226,-0.2015,0.4070,-0.1717,-1.0573,-0.2099]
x2=[2.3385,2.1946,1.6730,1.6365,1.7844,2.0155,
2.0681,2.1213,2.4797,1.5118,1.9692,1.8340,
1.8704,2.2948,1.7714,2.3939,1.5648,1.9329,
2.2027,2.4568,1.7523,1.6991,2.4883,1.7259,
2.0466,2.0226,2.3757,1.7987,2.0828,2.0798,
1.9449,2.3801,2.2373,2.1614,1.9235,2.2604]
x3=[0.5338,0.8514,1.0831,0.4164,1.1176,0.5536,
0.6071,0.4439,0.4928,0.5901,1.0927,1.0756,
1.0072,0.4272,0.4353,0.9869,0.4841,1.0992,
1.0299,0.7127,1.0124,0.4576,0.8544,1.1275,
0.7705,0.4129,1.0085,0.7676,0.8418,0.8784,
0.9751,0.7840,0.4158,1.0315,0.7533,0.9548]
#将x1，x2，x3变为行向量
x1=np.array(x1)
x2=np.array(x2)
x3=np.array(x3)
#第一类样本均值m1
m1=np.zeros(3)
m1[0]=np.mean(x1)
m1[1]=np.mean(x2)
m1[2]=np.mean(x3)
#第一类样本类内离散度矩阵S1
S1=np.zeros((3,3))
for i in range(36):
temp=np.array([-m1[0]+x1[i],-m1[1]+x2[i],-m1[2]+x3[i]]).reshape(1,3)
temp_T=temp.reshape(3,1)
S1+=np.dot(temp_T,temp)
#w2的数据点坐标
x4=[1.4010,1.2301,2.0814,1.1655,1.3740,1.1829,
1.7632,1.9739,2.4152,2.5890,2.8472,1.9539,
1.2500,1.2864,1.2614,2.0071,2.1831,1.7909,
1.3322,1.1466,1.7087,1.5920,2.9353,1.4664,
2.9313,1.8349,1.8340,2.5096,2.7198,2.3148,
2.0353,2.6030,1.2327,2.1465,1.5673,2.9414]
x5=[1.0298,0.9611,0.9154,1.4901,0.8200,0.9399,
1.1405,1.0678,0.8050,1.2889,1.4601,1.4334,
0.7091,1.2942,1.3744,0.9387,1.2266,1.1833,
0.8798,0.5592,0.5150,0.9983,0.9120,0.7126,
1.2833,1.1029,1.2680,0.7140,1.2446,1.3392,
1.1808,0.5503,1.4708,1.1435,0.7679,1.1288]
x6=[0.6210,1.3656,0.5498,0.6708,0.8932,1.4342,
0.9508,0.7324,0.5784,1.4943,1.0915,0.7644,
1.2159,1.3049,1.1408,0.9398,0.6197,0.6603,
1.3928,1.4084,0.6909,0.8400,0.5381,1.3729,
0.7731,0.7319,1.3439,0.8142,0.9586,0.7379,
0.7548,0.7393,0.6739,0.8651,1.3699,1.1458]
x4=np.array(x4)
x5=np.array(x5)
x6=np.array(x6)
#第二类样本均值m2
m2=np.zeros(3)
m2[0]=np.mean(x4)
m2[1]=np.mean(x5)
m2[2]=np.mean(x6)
#计算第二类样本类内离散度矩阵S2
S2=np.zeros((3,3))
for i in range(36):
temp=np.array([-m2[0]+x4[i],-m2[1]+x5[i],-m2[2]+x6[i]]).reshape(1,3)
temp_T=temp.reshape(3,1)
S2+=np.dot(temp_T,temp)
#总类内离散度矩阵Sw
Sw=np.zeros((3,3))
Sw=S1+S2
#样本类间离散度矩阵Sb
Sb=np.zeros((3,3))
m12=m1-m2
m12=m12.reshape(1,3)
Sb=np.dot(np.transpose(m12),m12)
#最优解W
W=np.dot(np.linalg.inv(Sw),np.transpose(m12))
W/=np.sqrt(np.sum(W**2))
#计算一维Y空间中各类样本均值M1及M2
y=np.zeros(36)
for i in range(36):
temp=np.array([x1[i],x2[i],x3[i]]).reshape(1,3)
y[i]=np.dot(np.transpose(W),np.transpose(temp))
M1=np.mean(y)
for i in range(36):
temp=np.array([x4[i],x5[i],x6[i]]).reshape(1,3)
y[i]=np.dot(np.transpose(W),np.transpose(temp))
M2=np.mean(y)
#计算W0
p1,p2=0.6,0.4
W0=-(M1+M2)/2+(np.log(p2/p1))/(36+36-2)
X1=x1*W[0]+x2*W[1]+x3*W[2]
X2=x4*W[0]+x5*W[1]+x6*W[2]
X1=X1.reshape(1,36)
X2=X2.reshape(1,36)
XX1=np.concatenate((W[0]*X1,W[1]*X1,W[2]*X1),axis=0)
XX2=np.concatenate((W[0]*X2,W[1]*X2,W[2]*X2),axis=0)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x1,x2,x3,c='r',marker='o')
ax.scatter(x4,x5,x6,c='b',marker='o')
W1=5*W
w1=np.linspace(-W1[0],W1[0],50).ravel()
w2=np.linspace(-W1[1],W1[1],50).ravel()
w3=np.linspace(-W1[2],W1[2],50).ravel()
ax.plot(w1,w2,w3)
#判别
a1=np.array([1,1.5,0.6]).reshape(3,1)
a2=np.array([1.2,1.0,0.55]).reshape(3,1)
a3=np.array([2.0,0.9,0.68]).reshape(3,1)
a4=np.array(([1.2,1.5,0.89])).reshape(3,1)
a5=np.array([0.23,2.33,1.43]).reshape(3,1)
A=np.concatenate((a1,a2,a3,a4,a5),axis=1)
n=A.shape[1]
for k in range(n):
A1=np.dot(np.transpose(A[:,k]),W)
A11=W*A1
y=np.dot(np.transpose(W),A[:,k])+W0
if y>0:
ax.scatter(A[0,k],A[1,k],A[2,k],c='r',marker='o')
ax.scatter(A11[0], A11[1], A11[2], c='r', marker='o')
else:
ax.scatter(A[0, k], A[1, k], A[2, k], c='b', marker='o')
ax.scatter(A11[0], A11[1], A11[2], c='b', marker='o')
plt.show()