WeixuanYuan
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7209646
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Parent(s):
552a78c
Upload 2 files
Browse files- data_preprocessing.py +60 -0
- dataset.npy +3 -0
data_preprocessing.py
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import numpy as np
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from tqdm import tqdm
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import scipy.io as scio
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def intoBins(data, n_bins):
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k_labels = np.zeros((len(data), n_bins+1))
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bin_size = (np.max(data)+0.0001-np.min(data)) / n_bins
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for i in tqdm(range(len(data))):
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index = (data[i]-np.min(data)) // bin_size
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k_labels[i, int(index)] = 1
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return k_labels
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def preprocessing(n_data, data_length, degrees_of_freedom, n_labels, n_bins, path='2500\\2500'):
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# save input
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all_data = np.zeros((n_data, degrees_of_freedom+n_labels+1+1, data_length))
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for i in tqdm(range(2500)):
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file_name = f'{path}\\Data{i+1}.mat'
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data = scio.loadmat(file_name)['Data'][:data_length, :]
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data = np.transpose(data)
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all_data[i] = data
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f_and_xs = all_data[:, 1:2+degrees_of_freedom, :]
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mean = np.mean(f_and_xs, (0, 2))
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std = np.std(f_and_xs, (0, 2))
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f_and_xs = f_and_xs - np.reshape(mean, (1, -1, 1))
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f_and_xs = f_and_xs / np.reshape(std, (1, -1, 1))
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dict = {'f_and_xs': f_and_xs}
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# save labels
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for i in range(n_labels):
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label = all_data[:, 2+degrees_of_freedom+i, 0]
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bins = intoBins(label, n_bins)[:, :-1]
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dict[f'l_{i}'] = bins
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np.save('dataset.npy', dict)
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# preprocessing(2500, 10000, 6, 3, 10, path='2500\\2500')
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def load_dataset(path='dataset.npy'):
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"""
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:return:
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f_and_xs: numpy array of size [sample_number, channels, sample_length]
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label_0, label_1, label_2: one-hot encodes of size [sample_number, number_bins]
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"""
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r = np.load(path, allow_pickle=True).item()
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f_and_xs = r['f_and_xs']
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label_0 = r['l_0']
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label_1 = r['l_1']
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label_2 = r['l_2']
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return f_and_xs, label_0, label_1, label_2
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f_and_xs, label_0, label_1, label_2 = load_dataset()
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dataset.npy
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version https://git-lfs.github.com/spec/v1
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oid sha256:8e0db601e34cb2f8c440462432a526bdf6d57eec79801bdd6b0b5e920c8ab9e8
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size 1400600545
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