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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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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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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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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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