71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
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import configparser
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import torch
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import numpy as np
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from torch.utils.data import Dataset
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from query_probability import load_ucr
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import warnings
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warnings.filterwarnings('ignore')
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class UcrDataset(Dataset):
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def __init__(self, txt_file, channel_last, normalize):
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'''
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:param txt_file: path of file
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:param channel_last
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'''
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# self.data = np.loadtxt(txt_file)
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self.data = load_ucr(txt_file, normalize)
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self.channel_last = channel_last
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if self.channel_last:
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self.data = np.reshape(self.data, [self.data.shape[0], self.data.shape[1], 1])
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else:
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self.data = np.reshape(self.data, [self.data.shape[0], 1, self.data.shape[1]])
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def __len__(self):
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return len(self.data)
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def __getitem__(self, idx):
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if not self.channel_last:
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return self.data[idx, :, 1:], self.data[idx, :, 0]
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else:
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return self.data[idx, 1:, :], self.data[idx, 0, :]
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def get_seq_len(self):
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if self.channel_last:
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return self.data.shape[1] - 1
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else:
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return self.data.shape[2] - 1
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class AdvDataset(Dataset):
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def __init__(self, txt_file):
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self.data = np.loadtxt(txt_file)
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self.data = np.reshape(self.data, [self.data.shape[0], self.data.shape[1], 1])
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def __len__(self):
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return len(self.data)
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def __getitem__(self, idx):
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return self.data[idx, 2:, :], self.data[idx, 1, :]
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def get_seq_len(self):
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return self.data.shape[1] - 2
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def UCR_dataloader(dataset, batch_size):
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data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0)
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return data_loader
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def read_config(file_name):
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# 创建一个配置解析器
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config = configparser.ConfigParser()
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# 从INI文件读取配置
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config.read(file_name)
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return config
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