-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathdata_format.py
97 lines (93 loc) · 2.39 KB
/
data_format.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
import numpy as np
from sklearn.preprocessing import StandardScaler
import RBF_networks
import matplotlib.pyplot as plt
dataset = []
with open('iris.txt', 'r') as f:
for line in f:
line = line.replace("\n", "")
line = line.split(',')
l=len(line)
flower=line[l-1]
line=line[:l-1]
if flower=='Iris-setosa':
line.append(1)
line.append(0)
line.append(0)
elif flower=='Iris-versicolor':
line.append(0)
line.append(1)
line.append(0)
else:
line.append(0)
line.append(0)
line.append(1)
vector = list(map(float, line[:len(line)]))
dataset.append(vector)
dataset = np.array(dataset, dtype=float)
#
scaler = StandardScaler().fit(dataset[:, :4])
dataset[:, :4] = scaler.transform(dataset[:, :4])
#
#
# test = dataset[2799:, :7]
# dataset = dataset[:2799, :]
#
network=RBF_networks.RBF_Network()
#
# benign_dataset=[]
# malign_dataset=[]
# for vector in dataset:
# if vector[7]==0:
# benign_dataset.append(vector[:7])
# else:
# malign_dataset.append(vector[:7])
#
# benign_dataset=np.array(benign_dataset)
# malign_dataset=np.array(malign_dataset)
# print(benign_dataset.shape)
# print(malign_dataset.shape)
# plt.plot(benign_dataset[2], benign_dataset[6], 'g.')
# plt.plot(malign_dataset[2], malign_dataset[6], 'r.')
# plt.show()
#
#
# malign_centroids=network.k_means(malign_dataset, 2)
# benign_centroids=network.k_means(benign_dataset, 2)
#
#
# for input in dataset[2695:,:]:
# if input[7]==1:
# malign_test=input
# break
#
# for input in dataset[2695:,:]:
# if input[7] == 0:
# benign_test = input
# break
#
# print(malign_test)
# print(benign_test)
# benign_distance=[]
# malign_distance=[]
#
# for centre in benign_centroids:
# benign_distance.append(np.linalg.norm(benign_test[0]-centre))
#
# for centre in malign_centroids:
# malign_distance.append(np.linalg.norm(benign_test[0] - centre))
#
# print(np.sum(benign_distance))
# print(np.sum(malign_distance))
#
# benign_distance=[]
# malign_distance=[]
#
# for centre in benign_centroids:
# benign_distance.append(np.linalg.norm(malign_test[0] - centre))
#
# for centre in malign_centroids:
# malign_distance.append(np.linalg.norm(malign_test[0] - centre))
#
# print(np.sum(malign_distance))
# print(np.sum(benign_distance))