--- a
+++ b/data_load_abandon.py
@@ -0,0 +1,63 @@
+"""
+本DataLoad将所有train文件下的csv文件进行训练，但没有将所有csv文件进行合并。
+从每个csv中获取一次batchsize数据进行训练，这样并没有保证数据的随机性，所以训练效果会差。
+摒弃这个版本。
+"""
+import os
+import random
+import numpy as np
+import pandas as pd
+
+class DataLoad(object):
+
+    _data_file_list = None
+    _current_file_index = 0
+    _extract_data_size = 0
+    _class_num = 0
+    _epoch = 0
+
+    def __init__(self, data_path, time_step, class_num):
+        if not os.path.exists(data_path):
+            print('%s is not found'%(data_path))
+            raise FileExistsError
+        self._time_step = time_step
+        self._extract_data_size = self._time_step
+        self._class_num = class_num
+        self._data_file_list = [os.path.join(data_path, file) for file in os.listdir(data_path)]
+
+    def get_next_batch(self, batchsize):
+        if self._current_file_index == len(self._data_file_list):
+            self._current_file_index = 0
+            self._epoch += 1
+
+        data = pd.read_csv(self._data_file_list[self._current_file_index])
+        self._current_file_index += 1
+        data_size = len(data.acc_x.values)
+
+        train_x = []
+        label_y = []
+        for i in range(batchsize):
+            start = random.randint(1, data_size-self._extract_data_size)
+            train_x.append(data.iloc[start:start+self._extract_data_size, 0:3].values)
+            label = [[0 for i in range(self._class_num)] for _ in range(self._extract_data_size)]
+
+            for s in range(self._extract_data_size):
+                j = data.iloc[start + s:start + s + 1, 6].values[0]
+                label[s][j] = 1
+            label_y.append(label)
+
+        return np.array(train_x), np.array(label_y)
+
+    @property
+    def epoch(self):
+        return self._epoch
+
+    @property
+    def data_file_list(self):
+        return self._data_file_list
+
+if __name__ == '__main__':
+    data = DataLoad('./dataset/train/', time_step=10, class_num=2)
+    x,y = data.get_next_batch(1)
+    print(x)
+    print(y)
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