diff --git a/myutils.py b/myutils.py
index afc5200..2121f7d 100644
--- a/myutils.py
+++ b/myutils.py
@@ -14,36 +14,32 @@
 # statistical analysis
 from scipy.stats import wilcoxon
 
-'''
-整合一些常用的函数
-'''
 class Utils():
     def __init__(self):
         pass
 
-    # remove randomness,固定结果
+    # remove randomness
     def set_seed(self, seed):
         # os.environ['PYTHONHASHSEED'] = str(seed)
         # os.environ['TF_CUDNN_DETERMINISTIC'] = 'true'
         # os.environ['TF_DETERMINISTIC_OPS'] = 'true'
 
-        #basic seed
+        # basic seed
         np.random.seed(seed)
         random.seed(seed)
 
-        #tensorflow seed
+        # tensorflow seed
         try:
             tf.random.set_seed(seed) # for tf >= 2.0
         except:
             tf.set_random_seed(seed)
             tf.random.set_random_seed(seed)
 
-        #pytorch seed
+        # pytorch seed
         torch.manual_seed(seed)
         torch.backends.cudnn.deterministic = True
         torch.backends.cudnn.benchmark = False
 
-    # 检测是否有GPU
     def get_device(self):
         # if torch.cuda.is_available():
         #     n_gpu = torch.cuda.device_count()
@@ -58,7 +54,7 @@ def get_device(self):
         device = torch.device("cpu")
         return device
 
-    #根据两个实数生成唯一的实数
+    # generate unique value
     def unique(self, a, b):
         u = 0.5 * (a + b) * (a + b + 1) + b
         return int(u)
@@ -72,14 +68,14 @@ def data_description(self, X, y):
 
         print(des_dict)
 
-    #返回异常检测中常用的两个metric结果: AUC-ROC以及AUC-PR
+    # metric
     def metric(self, y_true, y_score, pos_label=1):
         aucroc = roc_auc_score(y_true=y_true, y_score=y_score)
         aucpr = average_precision_score(y_true=y_true, y_score=y_score, pos_label=1)
 
         return {'aucroc':aucroc, 'aucpr':aucpr}
 
-    #重采样函数
+    # resampling function
     def sampler(self, X_train, y_train, batch_size):
         index_u = np.where(y_train == 0)[0]
         index_a = np.where(y_train == 1)[0]
@@ -147,23 +143,23 @@ def sampler_pairs(self, X_train_tensor, y_train, epoch, batch_num, batch_size, s
         for i in range(batch_num):  # i.e., drop_last = True
             index = []
 
-            # 分别是(a,a); (a,u); (u,u)共6部分样本
+            # pairs of (a,a); (a,u); (u,u)
             for j in range(6):
                 # generate unique seed and set seed
                 # seed = self.unique(epoch, i)
                 # seed = self.unique(seed, j)
                 # self.set_seed(seed)
 
-                if j < 3:  # 其中batch size // 4与原论文中一致
+                if j < 3:
                     index_sub = np.random.choice(index_a, batch_size // 4, replace=True)
                     index.append(list(index_sub))
 
                 if j == 3:
-                    index_sub = np.random.choice(index_u, batch_size // 4, replace=True)  # unlabel部分可以变为False
+                    index_sub = np.random.choice(index_u, batch_size // 4, replace=True)
                     index.append(list(index_sub))
 
                 if j > 3:
-                    index_sub = np.random.choice(index_u, batch_size // 2, replace=True)  # unlabel部分可以变为False
+                    index_sub = np.random.choice(index_u, batch_size // 2, replace=True)
                     index.append(list(index_sub))
 
             # index[0] + index[1] = (a,a), batch / 4
@@ -189,12 +185,12 @@ def sampler_pairs(self, X_train_tensor, y_train, epoch, batch_num, batch_size, s
             y_train_new = y_train_new[index_shuffle]
 
             # save
-            data_loader_X.append([X_train_tensor_left, X_train_tensor_right])  # 注意left和right顺序
+            data_loader_X.append([X_train_tensor_left, X_train_tensor_right])
             data_loader_y.append(y_train_new)
 
         return data_loader_X, data_loader_y
 
-    #返回梯度
+    # gradient norm
     def grad_norm(self, grad_tuple):
 
         grad = torch.tensor([0.0])
@@ -224,7 +220,7 @@ def plot_grad_flow(self, named_parameters):
     #     # Compute the first Wasserstein distance between two 1D distributions.
     #     return (torch_cdf_loss(tensor_a, tensor_b, p=1))
 
-    #Calculate the First Wasserstein Distance
+    # Calculate the First Wasserstein Distance
     def torch_cdf_loss(self, tensor_a, tensor_b, p=1):
         # last-dimension is weight distribution
         # p is the norm of the distance, p=1 --> First Wasserstein Distance
@@ -249,7 +245,7 @@ def torch_cdf_loss(self, tensor_a, tensor_b, p=1):
         cdf_loss = cdf_distance.mean()
         return cdf_loss
 
-    #Calculate the loss like devnet in PyTorch
+    # Calculate the loss like devnet in PyTorch
     def cal_loss(self, y, y_pred, mode='devnet'):
         if mode == 'devnet':
             y_pred.squeeze_()
diff --git a/run.py b/run.py
index f4ebdd5..9329edb 100644
--- a/run.py
+++ b/run.py
@@ -145,7 +145,6 @@ def dataset_filter(self):
         dataset_list_org = [os.path.splitext(_)[0] for _ in os.listdir(os.path.join(os.getcwd(), 'datasets'))
                             if os.path.splitext(_)[1] != '']
 
-        # 将不符合标准的数据集筛除
         dataset_list = []
         dataset_size = []