diff --git a/function.py b/function.py
index f189af5..5466123 100644
--- a/function.py
+++ b/function.py
@@ -1,111 +1,65 @@
-import numpy as np
+import numpy as np  
 
-"""
-请在这里填写学号和姓名，不填写即成绩无效。
-"""
-id = "202210423224"
-name = "吴杨洋"
+# 学号和姓名  
+id = "202210423224"  
+name = "吴杨洋"  
 
+def sigmoid(z):  
+    return 1 / (1 + np.exp(-z))  
 
-def sigmoid(z):
-    """
-    Sigmoid 激活函数。
-    """
-    return 1 / (1 + np.exp(-z))
+def train_logreg(X, y, num_iter=1000, learning_rate=0.01, lambda_reg=0.01):  
+    m, n = X.shape  
+    weights = np.zeros(n)  
+    
+    for _ in range(num_iter):  
+        z = np.dot(X, weights)  
+        h = sigmoid(z)  
+        error = y - h  
+        
+        # 添加正则化项  
+        gradient = np.dot(X.T, error) / m + lambda_reg * weights / m  
+        weights += learning_rate * gradient  
+        
+    return weights  
 
+def predict_logreg(X, weights):  
+    probabilities = sigmoid(np.dot(X, weights))  
+    return (probabilities >= 0.5).astype(int)  
 
-def train_logreg(X, y, numIter=1000, learning_rate=0.01, lambda_reg=0.01):
-    m, n = X.shape
-    weights = np.zeros(n)
-    for j in range(numIter):
-        for i in range(m):
-            z = np.dot(X[i], weights)
-            h = sigmoid(z)
-            error = y[i] - h
-            gradient = X[i] * error
-            reg_term = lambda_reg * weights
-            gradient += reg_term
-            weights += learning_rate * gradient
-    return weights
+def load_data(filepath):  
+    data = np.genfromtxt(filepath, delimiter=',', dtype=str, filling_values=np.nan)  
+    # 处理缺失值，转换数据类型  
+    data = np.where(data == '?', np.nan, data).astype(np.float)  
+    data = data[~np.isnan(data).any(axis=1)]  # 删除含有NaN的行  
 
+    # 特征与标签分离  
+    X = np.delete(data, [22, 24, 25, 26, -1], axis=1)  # 删除不需要的列  
+    y = np.array([1 if row[22] in ['2', '3'] else 0 for row in data])  # 生成标签  
+    X = np.insert(X, 0, 1, axis=1)  # 添加偏置项  
 
-def predict_logreg(inX, weights):
-    """
-    对率回归预测函数。
+    return X, y  
 
-    Args:
-    - inX (numpy array): 输入待分类的样本。
-    - weights (numpy array): 训练得到的权重。
+def standardize(X):  
+    mean = X.mean(axis=0)  
+    std = X.std(axis=0)  
+    return (X - mean) / std, mean, std  
 
-    Returns:
-    - int: 预测的分类标签。
-    """
-    z = np.dot(inX, weights)
-    prob = sigmoid(z)
-    return 1 if prob >= 0.5 else 0
+def main():  
+    # 加载训练集和测试集  
+    X_train, y_train = load_data("./data/horse-colic.data")  
+    X_test, y_test = load_data("./data/horse-colic.test")  
 
+    # 数据标准化  
+    X_train, mean, std = standardize(X_train)  
+    X_test = (X_test - mean) / std  
 
-def load_data(filepath):
-    """
-    从文件中加载数据，若不自行正确实现，则无法通过评测，不记录成绩。
+    # 训练对率回归模型  
+    weights = train_logreg(X_train, y_train, num_iter=1000, learning_rate=0.01, lambda_reg=0.1)  
 
-    Args:
-    - filepath (str): 数据文件路径。
+    # 测试模型  
+    y_pred_test = predict_logreg(X_test, weights)  
+    accuracy_test = np.mean(y_pred_test == y_test)  
+    print(f"Test Accuracy: {accuracy_test:.2f}")  
 
-    Returns:
-    - X (np.array): 特征数据。
-    - y (np.array): 标签数据。
-    """
-    file = open(filepath)
-    fr = file.readlines()
-    data_list = []
-    y = []
-    for data in fr:
-        data = data.strip()
-        data = data.split(' ')
-        data_25 = data[24]
-        y.append(data[22])
-        del data[-1]
-        del data[26]
-        del data[25]
-        del data[24]
-        del data[22]
-        del data[2]
-        for i in data_25:
-            data.append(i)
-        data = [0 if data_elem == '?' else float(data_elem) for data_elem in data]
-        data_list.append(data)
-    data_number = len(y)
-    y_train = np.array([1 if y[j] == '2' or y[j] == '3' else 0 for j in range(data_number)])
-    X = np.array(data_list)
-    X_train = np.insert(X, 0, 1, axis=1)  # 添加偏置项
-    return X_train, y_train
-
-
-def standardize(X):
-    mean = X.mean(axis=0)
-    std = X.std(axis=0)
-    return (X - mean) / std, mean, std
-
-
-def main():
-    # 加载训练集
-    X_train, y_train = load_data("./data/horse-colic.data")
-    # 加载测试集
-    X_test, y_test = load_data("./data/horse-colic.test")
-
-    # 标准化特征数据
-    X_train, mean, std = standardize(X_train)
-    X_test = (X_test - mean) / std
-
-    # 训练对率回归模型
-    weights = train_logreg(X_train, y_train, numIter=1000, learning_rate=0.01, lambda_reg=0.1)
-
-    # 测试单个数据
-    y_pred_test = [predict_logreg(x, weights) for x in X_test]
-    accuracy_test = np.mean(y_pred_test == y_test)
-    print(f"Test Accuracy: {accuracy_test:.2f}")
-
-
-if __name__ == "__main__":
+if __name__ == "__main__":  
     main()
\ No newline at end of file