-
Notifications
You must be signed in to change notification settings - Fork 1
/
set_file_func.py
87 lines (70 loc) · 3.09 KB
/
set_file_func.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
__author__ = 'inctrl'
from numpy import *
import pickle
import test_set
import test_set_val
import finish_alarm
from tts import *
from requests.exceptions import ConnectionError
def set_data(open_data,test_set_num, valid_set_num, speak):
'''
set_file.py
This python file divides pickle data into Train set, Valid set, Test set.
'''
#############################################################################
# open_data = 'data_j1_01010101_SR48kHz'
# open_data = 'data_j1_11111111_SR48kHz'
# open_data = 'class13_data_j1_01010101_SR48kHz'
#############################################################################
pickle_path = 'pickle_folder/'
with open(pickle_path + open_data + '.pickle') as f:
sum_mat_X_data, sum_mat_y_data, sum_mat_k_data, sum_mat_D_data, jump_num, FN, nb_classes = pickle.load(f)
print "The feature data per one inst sample is (=jump_num) :", jump_num
## Set the key for test set and validation set.
## The whole data is divided into 10 different data sets.
## You can choose a key number from 1 to 10.
# val_key = [[8],[9]]
# # val_key = []
# tst_key = [[7],[1]]
val_key = [valid_set_num]
# val_key =[]
tst_key = [test_set_num]
# print "shape of sum_mat_X_data", shape(sum_mat_X_data)
# print "shape of sum_mat_y_data", shape(sum_mat_y_data)
# print "shape of sum_mat_k_data", shape(sum_mat_k_data)
# print "shape of sum_mat_D_data", shape(sum_mat_D_data)
# print sum_mat_k_data
## Call "test_set.set_train_data" function to divide data
X_train, X_test, X_valid, y_train, y_test, y_valid, k_train, k_test, k_valid, D_train, D_test, D_valid \
= test_set_val.set_train_data(sum_mat_X_data,sum_mat_y_data,sum_mat_k_data, sum_mat_D_data, tst_key, val_key)
# print "k_test:", k_test.T
# print "y_test:", y_test.T
# print "y_train:", y_train.T
# print "shape of X_train", shape(X_train).
# print "shape of X_test", shape(X_test).
# print "shape of X_valid", shape(X_valid)
# print "shape of X_train", shape(X_train)
# print "shape of X_test", shape(X_test)
# print "shape of X_valid", shape(X_valid)
#
# print "shape of y_train", shape(y_train)
# print "shape of y_test", shape(y_test)
# print "shape of y_valid", shape(y_valid)
#
#
# print "shape of k_train", shape(k_train)
# print "shape of k_test", shape(k_test)
# print "shape of k_valid", shape(k_valid)
#
# print "shape of D_train", shape(D_train)
# print "shape of D_test", shape(D_test)
# print "shape of D_valid", shape(D_valid)
print "\n" + open_data + " : DATA set ready! \n"
# speak_str('Data seperation for test set '+ str(test_set_num) +' is complete.')
if speak == 1 :
try:
speak_str('Initiate classification for test set '+ str(test_set_num)+ '.'+'\n')
except ConnectionError as e:
print ('ConnectionError \n')
finish_alarm.ring('guitar_c3_04')
return X_train, X_test, X_valid, y_train, y_test, y_valid, D_train, D_test, D_valid, jump_num, FN, nb_classes, open_data