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- import numpy as np
- from KoopmanESNModel import KoopmanESNConfig
- def DataLoad(Path, config: KoopmanESNConfig):
- Data = np.loadtxt(Path, delimiter=',')
- train_start = config.train_start
- train_len = config.train_len
- warmup_len = config.predict_warmup
- predict_len = config.predict_len
- state_dim = config.state_dim
- Data = Data[train_start:train_start + train_len + predict_len, :state_dim].reshape(-1, state_dim)
- Max = np.max(Data, axis=0)
- Min = np.min(Data, axis=0)
- DataNor = 2*(Data-Min)/(Max-Min)-1
- DataTrain = DataNor[:train_len, :]
- DataWarm = DataTrain[-warmup_len:, :]
- DataVal = DataNor[train_len:, :]
- return DataNor, DataTrain, DataWarm, DataVal
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