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
|
#!/usr/bin/python
import sys
import numpy as np
import matplotlib.pyplot as plt
import math
from sets import ImmutableSet
def distance(a,b):
return math.sqrt(np.square(a-b).sum())
def gen_pairs(var,sk_data):
np.random.shuffle(sk_data)
sk_data = sk_data[:,1:]
noise1 = np.random.normal(0,var,sk_data.shape)
noise2 = np.random.normal(0,var,sk_data.shape)
sk1 = sk_data+noise1
sk2 = sk_data+noise2
randoms = np.random.randint(0,sk_data.shape[0],(2000,2))
dict = {}
u_pairs = []
i = 0
while len(u_pairs) < sk_data.shape[0]:
pair = randoms[i]
key = ImmutableSet(pair)
i += 1
if pair[0] != pair[1] and key not in dict:
dict[key] = True
u_pairs += [(pair[0],pair[1])]
m_pairs = zip(range(sk_data.shape[0]),range(sk_data.shape[0]))
result = []
for j in range(sk_data.shape[0]):
result += [(distance(sk1[m_pairs[j][0]],sk2[m_pairs[j][1]]),
distance(sk1[u_pairs[j][0]],sk2[u_pairs[j][1]]))]
return result
if __name__ == "__main__":
ap = np.loadtxt("associatepredict.txt",delimiter=",")
indices = [i for i in range(ap.shape[0]) if ap[i,1]<0.1]
ap_false = ap[:,1][indices]
ap_true = ap[:,0][indices]
plt.plot(ap_false,ap_true,label="Face recognition")
plt.xlabel("False positive rate [\%]")
plt.ylabel("True positive rate [\%]")
np.random.seed()
std = map(float,sys.argv[2].split(","))
sk_data = np.loadtxt(sys.argv[1],comments="#",delimiter=",")
for s in std:
result = gen_pairs(s,sk_data)
thresholds = np.square(np.arange(0,10,0.001))
true_pos = []
false_pos = []
for threshold in thresholds:
true = 0
false = 0
for j in range(len(result)):
if result[j][0] < threshold:
true += 1
if result[j][1] < threshold:
false += 1
true_pos += [float(true)/len(result)]
false_pos += [float(false)/len(result)]
indices = [i for i in range(len(false_pos)) if false_pos[i]<0.1]
false_pos = np.array(false_pos)
false_pos = false_pos[indices]
true_pos = np.array(true_pos)
true_pos = true_pos[indices]
plt.plot(false_pos,true_pos,label="$\sigma$ = "+str(s))
plt.legend(loc="lower right")
plt.savefig("roc.pdf")
plt.show()
|
