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- import torch
- import torch.nn as nn
- from torch.utils.data import Dataset,ConcatDataset,DataLoader
- import numpy as np
- from data import *
- import os
- import pandas as pd
- class epDataset(Dataset):
- """Sensoring dataset."""
- def __init__(self,label,num_repeats,path,sensor,Normalise=True):
- self.list_IDs = np.arange(num_repeats)
- self.label=label
- self.path = path
- self.sensor = sensor
- self.transform=Normalise
- def __len__(self):
- return len(self.list_IDs)
- def __getitem__(self, idx):
- data_dir = os.path.join(self.path, str(self.label),
- str(idx + 1), self.sensor)
- # print("data_dir:",data_dir)
- sdata=pd.read_csv(data_dir, header=None)
- sdata=prep(sdata)
- label=self.label
- return sdata, label
- class epvDataset(Dataset):
- """Sensoring dataset."""
- def __init__(self,label,num_repeats,num,path,sensor,Normalise=True):
- self.list_IDs = np.arange(num_repeats)
- self.label=label
- self.path = path
- self.sensor = sensor
- self.num = num
- self.transform=Normalise
- def __len__(self):
- return len(self.list_IDs)
- def __getitem__(self, idx):
- data_dir = os.path.join(self.path, str(self.label),
- str(idx + 1 + self.num), self.sensor)
- sdata=pd.read_csv(data_dir, header=None)
- sdata=prep(sdata)
- label=self.label
- return sdata, label
- class eptDataset(Dataset):
- """Sensoring dataset."""
- def __init__(self,label,num_repeats,num1,num2,path,sensor,Normalise=True):
- self.list_IDs = np.arange(num_repeats)
- self.label=label
- self.path = path
- self.sensor = sensor
- self.num1 = num1
- self.num2 = num2
- self.transform=Normalise
- def __len__(self):
- return len(self.list_IDs)
- def __getitem__(self, idx):
- data_dir = os.path.join(self.path, str(self.label),
- str(idx + 1 + self.num1 + self.num2), self.sensor)
- sdata=pd.read_excel(data_dir)
- sdata=prep(sdata)
- label=self.label
- return sdata, label
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