| |
| |
| |
| |
|
|
| import numpy as np |
| from PyEMD import EMD |
|
|
| def emd_augment(data, sequence_length, n_IMF = 500): |
| n_imf, channel_num = n_IMF, data.shape[1] |
| emd_data = np.zeros((n_imf,data.shape[0],channel_num)) |
| max_imf = 0 |
| for ci in range(channel_num): |
| s = data[:, ci] |
| IMF = EMD().emd(s) |
| r_s = np.zeros((n_imf, data.shape[0])) |
| if len(IMF) > max_imf: |
| max_imf = len(IMF) |
| for i in range(len(IMF)): |
| r_s[i] = IMF[len(IMF)-1-i] |
| if(len(IMF)==0): r_s[0] = s |
| emd_data[:,:,ci] = r_s |
| if max_imf < n_imf: |
| emd_data = emd_data[:max_imf,:,:] |
| train_data_new = np.zeros((len(data)-sequence_length+1,max_imf,sequence_length,channel_num)) |
| for i in range(len(data)-sequence_length+1): |
| train_data_new[i] = emd_data[:,i:i+sequence_length,:] |
| return train_data_new |
|
|
