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#!/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__":
# eg = np.loadtxt("eigenfaces.txt",delimiter=" ")
ap = np.loadtxt("associatepredict.txt",delimiter=",")
# plt.plot(eg[:,0],eg[:,1])
plt.plot(ap[:,1],ap[:,0])
plt.xlabel("False positive %")
plt.ylabel("True positive %")
np.random.seed()
var = map(float,sys.argv[2].split(","))
sk_data = np.loadtxt(sys.argv[1],comments="#",delimiter=",")
for v in var:
result = gen_pairs(v,sk_data)
thresholds = np.square(np.arange(0,10,0.01))
true_pos = []
false_pos = []
for threshold in thresholds:
true_values = []
false_values = []
for i in range(4):
true = 0
false = 0
min_j = i*300
max_j = min(min_j+300,sk_data.shape[0])
for j in range(min_j,max_j):
if result[j][0] < threshold:
true += 1
if result[j][1] < threshold:
false += 1
true_values += [float(true)/(max_j-min_j)]
false_values += [float(false)/(max_j-min_j)]
true_pos += [sum(true_values)/4]
false_pos += [sum(false_values)/4]
plt.plot(false_pos,true_pos)
plt.show()
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