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- from DeepKoopmanModel import DeepKoopMan, DeepKoopManConfig
- import torch
- import numpy as np
- import matplotlib.pyplot as plt
- Model = torch.load('./ModelLib/DeepKoopmanModel.pt', map_location="cpu")
- encoder = Model.encoder
- decoder = Model.decoder
- Data = np.loadtxt('./DataLib/DataNor.csv', delimiter=',')
- Latent = encoder(torch.from_numpy(Data).float())
- DataPre = decoder(Latent).detach().numpy()
- MSE = np.linalg.norm(Data - DataPre, ord='fro') ** 2 / np.prod(Data.shape)
- print(MSE)
- for fea in np.arange(13):
- plt.figure(fea)
- plt.plot(Data[:, fea])
- plt.plot(DataPre[:, fea])
- plt.show()
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