Created
February 8, 2020 06:24
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import os | |
import random | |
from tqdm import tqdm | |
import numpy as np | |
def batch_loader(iterable, batch_size, shuffle=False): | |
length = len(iterable) | |
if shuffle: | |
random.shuffle(iterable) | |
for start_idx in range(0, length, batch_size): | |
yield iterable[start_idx: min(length, start_idx + batch_size)] | |
dataset_list = [] | |
num_files = 4 | |
dir_name = "./data" | |
fw = open("./data/eeg.txt", "w") | |
for i in tqdm(range(1, num_files + 1)): | |
file_x_name = os.path.join(dir_name, "st_triple_{}_x.npy".format(i)) | |
file_y_name = os.path.join(dir_name, "st_triple_{}_y.npy".format(i)) | |
xs = np.load(file_x_name) | |
ys = np.load(file_y_name) | |
for x, y in zip(xs, ys): | |
flatten_x = np.reshape(x, -1).tolist() | |
str_list = [str(x) for x in flatten_x] | |
str_x = " ".join(str_list) | |
str_y = str(y) | |
fw.write("{}\t{}\n".format(str_x, y)) | |
fw.close() |
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