dmlc--dgl
Hongzhi (Steve), Chen
be8763fa65
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>
30 行
845 B
Python
30 行
845 B
Python
import numpy as np
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import pandas as pd
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import torch
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def load_data(file_path, len_train, len_val):
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df = pd.read_csv(file_path, header=None).values.astype(float)
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train = df[:len_train]
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val = df[len_train : len_train + len_val]
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test = df[len_train + len_val :]
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return train, val, test
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def data_transform(data, n_his, n_pred, device):
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# produce data slices for training and testing
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n_route = data.shape[1]
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l = len(data)
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num = l - n_his - n_pred
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x = np.zeros([num, 1, n_his, n_route])
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y = np.zeros([num, n_route])
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cnt = 0
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for i in range(l - n_his - n_pred):
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head = i
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tail = i + n_his
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x[cnt, :, :, :] = data[head:tail].reshape(1, n_his, n_route)
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y[cnt] = data[tail + n_pred - 1]
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cnt += 1
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return torch.Tensor(x).to(device), torch.Tensor(y).to(device)
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