forked from supportingvector/VAEHRRP
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_f.py
74 lines (57 loc) · 1.83 KB
/
test_f.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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 10 20:48:03 2018
@author: xms
"""
import sys
import numpy as np
sys.path.append(r'C:\Program Files\Anaconda3\Lib\site-packages\libsvm\python')
from libsvm.python.svmutil import *
from libsvm.python.svm import *
#y, x = np.array([1,-1]), np.array([[1,2,2], [1,2,3]])
#prob = svm_problem(y, x)
##param = svm_parameter('-t 0 -c 4 -b 1')
#print("((((((((((((((((")
#print(prob)
#model = svm_train(y,x)
#yt = [1]
#xt = [{1:1, 2:1}]
#p_label, p_acc, p_val = svm_predict(yt, xt, model)
#print(p_label)
def test_accuracy():
z1=np.load('save\\z1.npy').tolist()
z2=np.load('save\\z2.npy').tolist()
z3=np.load('save\\z3.npy').tolist()
x=z1+z2+z3
#x=z1+z2
y1=[0 for i in range(52000)]
y2=[1 for i in range(52000)]
y3=[2 for i in range(36000)]
y=y1+y2+y3
#y=y1+y2
model = svm_train(y,x)
z1test=np.load('save\\z1t.npy').tolist()
z2test=np.load('save\\z2t.npy').tolist()
z3test=np.load('save\\z3t.npy').tolist()
xtest=z1test+z2test+z3test
#xtest=z1test+z2test
yt1=[0 for i in range(2000)]
yt2=[1 for i in range(2000)]
yt3=[2 for i in range(1200)]
ytest=yt1+yt2+yt3
#ytest=yt1+yt2
#p_label, p_acc, p_val = svm_predict(y, x, model)
p_label, p_acc, p_val = svm_predict(ytest, xtest, model)
c1_1=p_label[0:2000].count(0.0)/2000
c1_2=p_label[0:2000].count(1.0)/2000
c1_3=p_label[0:2000].count(2.0)/2000
c2_1=p_label[2000:4000].count(0.0)/2000
c2_2=p_label[2000:4000].count(1.0)/2000
c2_3=p_label[2000:4000].count(2.0)/2000
c3_1=p_label[4000:5200].count(0.0)/2000
c3_2=p_label[4000:5200].count(1.0)/2000
c3_3=p_label[4000:5200].count(2.0)/1200
acc=(c1_1+c2_2+c3_3)/3
#p_label, p_acc, p_val = svm_predict(y, x, model)
#print(p_label)