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- import torch
- import numpy as np
- import matplotlib.pyplot as plt
- DeepKoopmanModel = torch.load('./ModelLib/DeepKoopmanModel.pt', map_location="cpu")
- DataNor = np.loadtxt('./DataLib/DataNor.csv', delimiter=',')
- DataNor = torch.from_numpy(DataNor).float()
- Latent = DeepKoopmanModel.encoder(DataNor)
- Latent = Latent.detach().numpy()
- Max = np.max(Latent, axis=0)
- Min = np.min(Latent, axis=0)
- LatentMaxMin = np.vstack([Max, Min])
- LatentNor = 2*(Latent-Min)/(Max-Min)-1
- np.savetxt('./DataLib/Latent.csv', Latent, delimiter=',')
- np.savetxt('./DataLib/LatentNor.csv', LatentNor, delimiter=',')
- np.savetxt('./DataLib/LatentMaxMin', LatentMaxMin, delimiter=',')
- plt.figure()
- plt.plot(LatentNor[:, 30])
- plt.show()
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