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()