--- a
+++ b/dataset.py
@@ -0,0 +1,97 @@
+# -*- coding: utf-8 -*-
+'''
+@time: 2019/9/8 19:47
+
+@ author: javis
+'''
+import pywt, os, copy
+import torch
+import numpy as np
+import pandas as pd
+from config import config
+from torch.utils.data import Dataset
+from sklearn.preprocessing import scale
+from scipy import signal
+
+
+def resample(sig, target_point_num=None):
+    '''
+    对原始信号进行重采样
+    :param sig: 原始信号
+    :param target_point_num:目标型号点数
+    :return: 重采样的信号
+    '''
+    sig = signal.resample(sig, target_point_num) if target_point_num else sig
+    return sig
+
+def scaling(X, sigma=0.1):
+    scalingFactor = np.random.normal(loc=1.0, scale=sigma, size=(1, X.shape[1]))
+    myNoise = np.matmul(np.ones((X.shape[0], 1)), scalingFactor)
+    return X * myNoise
+
+def verflip(sig):
+    '''
+    信号竖直翻转
+    :param sig:
+    :return:
+    '''
+    return sig[::-1, :]
+
+def shift(sig, interval=20):
+    '''
+    上下平移
+    :param sig:
+    :return:
+    '''
+    for col in range(sig.shape[1]):
+        offset = np.random.choice(range(-interval, interval))
+        sig[:, col] += offset
+    return sig
+
+
+def transform(sig, train=False):
+    # 前置不可或缺的步骤
+    sig = resample(sig, config.target_point_num)
+    # # 数据增强
+    if train:
+        if np.random.randn() > 0.5: sig = scaling(sig)
+        if np.random.randn() > 0.5: sig = verflip(sig)
+        if np.random.randn() > 0.5: sig = shift(sig)
+    # 后置不可或缺的步骤
+    sig = sig.transpose()
+    sig = torch.tensor(sig.copy(), dtype=torch.float)
+    return sig
+
+
+class ECGDataset(Dataset):
+    """
+    A generic data loader where the samples are arranged in this way:
+    dd = {'train': train, 'val': val, "idx2name": idx2name, 'file2idx': file2idx}
+    """
+
+    def __init__(self, data_path, train=True):
+        super(ECGDataset, self).__init__()
+        dd = torch.load(config.train_data)
+        self.train = train
+        self.data = dd['train'] if train else dd['val']
+        self.idx2name = dd['idx2name']
+        self.file2idx = dd['file2idx']
+        self.wc = 1. / np.log(dd['wc'])
+
+    def __getitem__(self, index):
+        fid = self.data[index]
+        file_path = os.path.join(config.train_dir, fid)
+        df = pd.read_csv(file_path, sep=' ').values
+        x = transform(df, self.train)
+        target = np.zeros(config.num_classes)
+        target[self.file2idx[fid]] = 1
+        target = torch.tensor(target, dtype=torch.float32)
+        return x, target
+
+    def __len__(self):
+        return len(self.data)
+
+
+if __name__ == '__main__':
+    d = ECGDataset(config.train_data)
+    print(d[0])