diff --git a/function.py b/function.py
index 61f82d1..7337ce1 100644
--- a/function.py
+++ b/function.py
@@ -4,60 +4,130 @@ from sklearn.feature_selection import SelectKBest, f_classif
 
 # 填写学号和姓名
 id = "202210423213"
-name = "熊浩燃"
+name = " 熊浩燃"
 
 def load_data(filepath, k_features=20):
-    data, labels = [], []
+    """
+    从文件中加载数据并进行预处理，包括缺失值填充和特征选择。
+
+    Args:
+    - filepath (str): 数据文件路径。
+    - k_features (int): 选择的特征数量。
+
+    Returns:
+    - np.array: 特征数据。
+    - np.array: 标签数据。
+    """
+    data = []
+    labels = []
     with open(filepath, 'r') as file:
         for line in file:
             line = line.strip().split()
             if line:
-                features = [float(feature) if feature.replace('.', '', 1).isdigit() else np.nan for feature in line[:-1]]
+                features = []
+                for feature in line[:-1]:  # 最后一个元素是标签
+                    try:
+                        features.append(float(feature))
+                    except ValueError:
+                        features.append(np.nan)  # 用 NaN 表示缺失值
                 label = int(line[-1])
                 data.append(features)
                 labels.append(label)
 
-    data, labels = np.array(data), np.array(labels)
+    # 转换为 numpy 数组
+    data = np.array(data)
+    labels = np.array(labels)
+
+    # 使用SimpleImputer填充NaN
     imputer = SimpleImputer(strategy='mean')
     data = imputer.fit_transform(data)
-    
-    mean, std_dev = data.mean(axis=0), data.std(axis=0)
-    std_dev[std_dev == 0] = 1
+
+    # 特征标准化，避免除零错误
+    mean = data.mean(axis=0)
+    std_dev = data.std(axis=0)
+    std_dev[std_dev == 0] = 1  # 如果标准差为0，将其设为1，避免除以零
     data = (data - mean) / std_dev
 
+    # 特征选择
     selector = SelectKBest(f_classif, k=k_features)
     data = selector.fit_transform(data, labels)
 
     return data, labels
 
 def sigmoid(z):
+    """
+    Sigmoid激活函数，用于对率回归模型。
+
+    Args:
+    - z (numpy array): 输入数据。
+
+    Returns:
+    - numpy array: Sigmoid函数输出值。
+    """
     return 1 / (1 + np.exp(-z))
 
 def train_logreg(X, y, initial_lr=0.005, iterations=5000, regularization=0.01, decay=0.0001):
+    """
+    对率回归训练函数，包含学习率衰减策略。
+
+    Args:
+    - X (numpy array): 特征数据。
+    - y (numpy array): 标签数据。
+    - initial_lr (float): 初始学习率。
+    - iterations (int): 迭代次数。
+    - regularization (float): 正则化强度。
+    - decay (float): 学习率衰减系数。
+
+    Returns:
+    - numpy array: 训练得到的权重。
+    """
     m, n = X.shape
     weights = np.zeros(n)
     learning_rate = initial_lr
 
     for i in range(iterations):
-        predictions = sigmoid(np.dot(X, weights))
+        z = np.dot(X, weights)
+        predictions = sigmoid(z)
         errors = y - predictions
-        gradient = (np.dot(X.T, errors) - regularization * weights) / m
+        gradient = np.dot(X.T, errors) / m - regularization * weights / m
         weights += learning_rate * gradient
+
+        # 学习率衰减
         learning_rate = initial_lr / (1 + decay * i)
 
     return weights
 
-def predict_logreg(X, weights, threshold=0.5):
-    probabilities = sigmoid(np.dot(X, weights))
-    return (probabilities >= threshold).astype(int)
+def predict_logreg(inX, weights, threshold=0.5):
+    """
+    对率回归预测函数，包含可调阈值。
+
+    Args:
+    - inX (numpy array): 输入待分类的样本。
+    - weights (numpy array): 训练得到的权重。
+    - threshold (float): 判定阈值，默认为0.5。
+
+    Returns:
+    - int: 预测的分类标签。
+    """
+    probability = sigmoid(np.dot(inX, weights))
+    return 1 if probability >= threshold else 2
 
 # 测试代码
 if __name__ == "__main__":
+    # 加载数据并选择前20个最重要的特征
     X_train, y_train = load_data("./data/horse-colic.data", k_features=20)
     X_test, y_test = load_data("./data/horse-colic.test", k_features=20)
 
+    # 训练模型
     weights = train_logreg(X_train, y_train, initial_lr=0.005, iterations=5000, regularization=0.01, decay=0.0001)
 
-    y_pred = predict_logreg(X_test, weights, threshold=0.5)
-    accuracy = np.mean(y_pred == y_test)
-    print(f"测试集准确率: {accuracy:.2f}")
+    # 模型评估
+    correct_predictions = 0
+    for i in range(len(X_test)):
+        y_pred = predict_logreg(X_test[i], weights, threshold=0.5)
+        if y_pred == y_test[i]:
+            correct_predictions += 1
+        print(f"预测结果: {y_pred}, 实际结果: {y_test[i]}")
+
+    accuracy = correct_predictions / len(X_test)
+    print(f"测试集准确率: {accuracy:.2f}")
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