diff --git a/function.py b/function.py
index 7aa8180..a0b4a97 100644
--- a/function.py
+++ b/function.py
@@ -58,6 +58,8 @@ def predict_logreg(inX, weights):
 
 import numpy as np
 
+import numpy as np
+
 def load_data(filepath):
     """
     从文件中加载数据，处理缺失数据，并将 '?' 替换为列的均值。
@@ -82,10 +84,9 @@ def load_data(filepath):
             # 将标签提取出来
             label = int(line[-1])
 
-            # 将特征转换为浮动数组
+            # 将特征转换为浮动数组，'?' 替换为 np.nan
             features = line[:-1]  # 去除标签部分
-            # 用特征中的均值替换 '?'，并转换为 float
-            features = [float(f) if f != '?' else None for f in features]
+            features = [float(f) if f != '?' else np.nan for f in features]
             
             # 将数据和标签分别加入到列表
             data.append(features)
@@ -95,15 +96,16 @@ def load_data(filepath):
     data = np.array(data)
     labels = np.array(labels)
 
-    # 计算每列的均值，并用均值替换 None
+    # 计算每列的均值，并用均值替换 NaN
     col_means = np.nanmean(data, axis=0)  # 计算每列的均值，忽略 NaN
-    # 将 None 替换为对应的列均值
+    # 将 NaN 替换为对应的列均值
     for i in range(data.shape[1]):
         data[:, i] = np.where(np.isnan(data[:, i]), col_means[i], data[:, i])
 
     return data, labels
 
 
+
 if __name__ == "__main__":
     # 加载训练集
     X_train, y_train = load_data("./data/horse-colic.data")