fddl-mnist/synthetic.py at main · nickembrey/fddl-mnist · GitHub

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
import csv
import numpy as np

path = '/Users/nick/dev/mfml/project/1k-data-sets/'

csvfile = path + '1k-Labels.csv'
n1000_labels = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-Clean.csv'
n1000_clean = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-Cor-0_5ENR.csv'
n1000_cor0_5ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-Cor-1ENR.csv'
n1000_cor1ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-Cor-2ENR.csv'
n1000_cor2ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-SinCor-0_5ENR.csv'
n1000_sincor0_5ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-SinCor-1ENR.csv'
n1000_sincor1ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-SinCor-2ENR.csv'
n1000_sincor2ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-WGN-0_5ENR.csv'
n1000_WGN0_5ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-WGN-1ENR.csv'
n1000_WGN1ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '1k-WGN-2ENR.csv'
n1000_WGN2ENR = np.genfromtxt(csvfile, delimiter=',')

path = '/Users/nick/dev/mfml/project/3k-data-sets/'

csvfile = path + '3k-Labels.csv'
n3000_labels = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-Clean.csv'
n3000_clean = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-Cor-0_5ENR.csv'
n3000_cor0_5ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-Cor-1ENR.csv'
n3000_cor1ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-Cor-2ENR.csv'
n3000_cor2ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-SinCor-0_5ENR.csv'
n3000_sincor0_5ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-SinCor-1ENR.csv'
n3000_sincor1ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-SinCor-2ENR.csv'
n3000_sincor2ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-WGN-0_5ENR.csv'
n3000_WGN0_5ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-WGN-1ENR.csv'
n3000_WGN1ENR = np.genfromtxt(csvfile, delimiter=',')

csvfile = path + '3k-WGN-2ENR.csv'
n3000_WGN2ENR = np.genfromtxt(csvfile, delimiter=',')

def get_training_set(n_per_class, training_images):
    all_images = []
    all_labels = []
    for i in range(10):
        class_images = []
        class_labels = []
        for j in range(len(n1000_labels)):
            if n1000_labels[j] == i:
                class_images.append(training_images[:, j])
                class_labels.append(n1000_labels[j])
            if len(class_images) == n_per_class:
                break
        all_images += class_images
        all_labels += class_labels
    return np.array(all_images).T, all_labels