diff --git a/function.py b/function.py
index 92c3b6f..f10c9f2 100644
--- a/function.py
+++ b/function.py
@@ -1,9 +1,10 @@
-# function.py
 import os
 import numpy as np
 import pandas as pd
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.model_selection import train_test_split
+from sklearn.impute import SimpleImputer
+
 """
 实验说明:
 - 自行编写实现数据加载与预测病马死亡的对率回归算法。
@@ -17,14 +18,16 @@ from sklearn.model_selection import train_test_split
 - Python: 3.10.12
 - numpy: 2.1.1
 """
-# Sigmoid 函数  
-def sigmoid(z):  
-    return 1 / (1 + np.exp(-z)) 
+# Sigmoid 函数
+def sigmoid(z):
+    return 1 / (1 + np.exp(-z))
+
 """
 请在这里填写学号和姓名，不填写即成绩无效。
 """
 id = "202210423223"
 name = "李顺潮"
+
 # 对率回归训练函数
 def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     X = np.hstack((np.ones((X.shape[0], 1)), X))  # 在特征前添加一列全为1的列
@@ -33,50 +36,56 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000):
 
     for _ in range(iterations):
         z = np.dot(X, weights)
-        predictions = 1 / (1 + np.exp(-z))
+        predictions = sigmoid(z)  # 使用sigmoid函数
         gradient = np.dot(X.T, (predictions - y)) / m
         weights -= learning_rate * gradient
 
     return weights
 
-# 对率回归预测函数  
-# 对率回归预测函数，支持多类分类
+# 对率回归预测函数
 def predict_logreg(inX, weights):
     inX = np.insert(inX, 0, 1)  # 在特征前添加1作为偏置项
-    prob = 1 / (1 + np.exp(-np.dot(inX, weights)))
+    prob = sigmoid(np.dot(inX, weights)) # 使用sigmoid函数
 
-    if prob < 0.5:
-        return 0
-    elif prob >= 0.5 and prob < 1.0:
-        return 1
-    else:
-        return 2  # 确保返回3个类别中的一个
+    return 1 if prob >= 0.5 else 0
 
-# 从文件中加载数据  
+
+# 从文件中加载数据
 def load_data(filepath):
     data = pd.read_csv(filepath, header=None, na_values='?', delim_whitespace=True)
-    data.fillna(0, inplace=True)
+
+    # 使用更合理的缺失值填充策略，例如用均值或中位数填充
+    imputer = SimpleImputer(strategy='mean') # 使用均值填充
+    data = pd.DataFrame(imputer.fit_transform(data))
+
+
     X = data.iloc[:, :-1].to_numpy()  # 特征
-    y = data.iloc[:, -1].to_numpy()    # 标签
-    return X, y 
+    y = data.iloc[:, -1].to_numpy().astype(int)    # 标签,确保y是整数类型
+    return X, y
+
 # 准确率计算函数
 def calculate_accuracy(X, y, weights):
     predictions = np.array([predict_logreg(x, weights) for x in X])
     accuracy = np.mean(predictions == y)
     return accuracy
 
-if __name__ == "__main__":  
-    # 加载训练集和测试集  
+if __name__ == "__main__":
+    # 加载训练集和测试集
     X_train, y_train = load_data("E:\\Edge站内文件下载\\病马数据集\\horse-colic.data")
-    X_test, y_test = load_data( "E:\\Edge站内文件下载\\病马数据集\\horse-colic.test")
-    
-    # 训练对率回归模型 
-    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
-  
-    # 测试单个数据  
-    y_pred = predict_logreg(X_test[11], weights)  
-    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")  
+    X_test, y_test = load_data("E:\\Edge站内文件下载\\病马数据集\\horse-colic.test")
 
+    # 特征缩放  (可选，但建议尝试)
+    scaler = MinMaxScaler()
+    X_train = scaler.fit_transform(X_train)
+    X_test = scaler.transform(X_test)
+
+
+    # 训练对率回归模型
+    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=10000) #增加迭代次数
+
+    # 测试单个数据
+    y_pred = predict_logreg(X_test[11], weights)
+    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
 
     # 计算并输出模型的准确率
     accuracy = calculate_accuracy(X_test, y_test, weights)