diff --git a/function.py b/function.py
index dad4e09..6ebd621 100644
--- a/function.py
+++ b/function.py
@@ -25,7 +25,6 @@ id = "202210423222"
 name = "邓龙树"
 
 import numpy as np
-import pandas as pd
 
 def sigmoid(z):
     """计算sigmoid函数"""
@@ -79,15 +78,15 @@ def load_data(filepath):
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
-    # 使用pandas读取数据，处理不规则行
-    data = pd.read_csv(filepath, delim_whitespace=True, header=None, na_values='?')
+    # 使用dtype=str先将所有数据加载为字符串
+    data = np.loadtxt(filepath, delimiter=' ', dtype=str)
 
-    # 替换缺失值为列的均值
-    data.fillna(data.mean(), inplace=True)
+    # 替换'?'为np.nan，并转换为浮点数
+    data[data == '?'] = np.nan
+    data = data.astype(float)
 
-    # 将数据转换为numpy数组
-    X = data.iloc[:, :-1].values  # 特征数据
-    y = data.iloc[:, -1].values    # 标签数据
+    X = data[:, :-1]  # 假设最后一列是标签
+    y = data[:, -1]
     return X, y
 
 if __name__ == "__main__":
@@ -97,6 +96,10 @@ if __name__ == "__main__":
     # 加载测试集
     X_test, y_test = load_data("./data/horse-colic.test")
 
+    # 处理缺失值，例如用列的均值替代
+    X_train = np.nan_to_num(X_train, nan=np.nanmean(X_train, axis=0))
+    X_test = np.nan_to_num(X_test, nan=np.nanmean(X_train, axis=0))  # 用训练集的均值替代测试集
+
     # 确保标签为浮点型
     y_train = y_train.astype(float)
     y_test = y_test.astype(float)