WeixuanYuan commited on
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7209646
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  1. data_preprocessing.py +60 -0
  2. dataset.npy +3 -0
data_preprocessing.py ADDED
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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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+
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+
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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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+
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+
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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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+
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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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+
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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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+
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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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+
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+ np.save('dataset.npy', dict)
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+
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+
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+ # preprocessing(2500, 10000, 6, 3, 10, path='2500\\2500')
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+
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+
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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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+
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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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+
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+
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+ f_and_xs, label_0, label_1, label_2 = load_dataset()
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+
dataset.npy ADDED
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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