-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py.old
71 lines (51 loc) · 1.58 KB
/
main.py.old
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
from image import Image;
import ai;
import os;
from sklearn.model_selection import train_test_split;
from sklearn.naive_bayes import GaussianNB;
import file_mgr as fm
tomAI = ai.PixelArrayAi()
exit()
X, y = fm.loadDatas("Percent_AI")
sum = 0
for i in range(1000):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10);
classifieur = GaussianNB();
classifieur.fit(X_train, y_train);
y_preditected = classifieur.predict(X_test);
print("Real classes :");
print(y_test);
print("Predicted classes :");
print(y_preditected);
print("Score: ")
score = classifieur.score(X_test, y_test)
sum += score
print(score)
print()
print("Average score :")
print(sum / 1000)
exit()
images = []
mer = []
ailleurs = []
listMer = os.listdir('./Data/Mer')
listAilleurs = os.listdir('./Data/Ailleurs')
print("Loading images...")
for file in listMer:
mer.append(Image("./Data/Mer/" + file))
for file in listAilleurs:
ailleurs.append(Image("./Data/Ailleurs/" + file))
images.append(mer)
images.append(ailleurs)
print("Done.")
orange = Image("./orange.jpg");
sea = Image("./sea.jpg");
percentColorsAI = ai.PercentColorsAI()
percentColorsAI.fit(1, images)
print(orange.name + ": " + str(percentColorsAI.evaluate(orange)));
print(sea.name + ": " + str(percentColorsAI.evaluate(sea)));
percentColorsAI.save_data('Percent_AI');
percentColorsAIClone = ai.PercentColorsAI()
percentColorsAIClone.fit(0, "Percent_AI")
print(orange.name + ": " + str(percentColorsAIClone.evaluate(orange)));
print(sea.name + ": " + str(percentColorsAIClone.evaluate(sea)));