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data_preprocessing.py

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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()
+

dataset.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8e0db601e34cb2f8c440462432a526bdf6d57eec79801bdd6b0b5e920c8ab9e8
+size 1400600545