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import numpy as np
from tqdm import tqdm
import scipy.io as scio
def intoBins(data, n_bins):
k_labels = np.zeros((len(data), n_bins+1))
bin_size = (np.max(data)+0.0001-np.min(data)) / n_bins
for i in tqdm(range(len(data))):
index = (data[i]-np.min(data)) // bin_size
k_labels[i, int(index)] = 1
return k_labels
def preprocessing(n_data, data_length, degrees_of_freedom, n_labels, n_bins, path='2500\\2500'):
# save input
all_data = np.zeros((n_data, degrees_of_freedom+n_labels+1+1, data_length))
for i in tqdm(range(2500)):
file_name = f'{path}\\Data{i+1}.mat'
data = scio.loadmat(file_name)['Data'][:data_length, :]
data = np.transpose(data)
all_data[i] = data
f_and_xs = all_data[:, 1:2+degrees_of_freedom, :]
mean = np.mean(f_and_xs, (0, 2))
std = np.std(f_and_xs, (0, 2))
f_and_xs = f_and_xs - np.reshape(mean, (1, -1, 1))
f_and_xs = f_and_xs / np.reshape(std, (1, -1, 1))
dict = {'f_and_xs': f_and_xs}
# save labels
for i in range(n_labels):
label = all_data[:, 2+degrees_of_freedom+i, 0]
bins = intoBins(label, n_bins)[:, :-1]
dict[f'l_{i}'] = bins
np.save('dataset.npy', dict)
# preprocessing(2500, 10000, 6, 3, 10, path='2500\\2500')
def load_dataset(path='dataset.npy'):
"""
:return:
f_and_xs: numpy array of size [sample_number, channels, sample_length]
label_0, label_1, label_2: one-hot encodes of size [sample_number, number_bins]
"""
r = np.load(path, allow_pickle=True).item()
f_and_xs = r['f_and_xs']
label_0 = r['l_0']
label_1 = r['l_1']
label_2 = r['l_2']
return f_and_xs, label_0, label_1, label_2
f_and_xs, label_0, label_1, label_2 = load_dataset()
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