diff --git a/function.py b/function.py
index 01add2a..390a34c 100644
--- a/function.py
+++ b/function.py
@@ -31,35 +31,38 @@ name = "严锡好"
 
 
 # 这里写自己的代码实现
+# sigmoid函数，用于逻辑回归模型中将线性输出转换为概率
 def sigmoid(z):
     return 1.0 / (1 + np.exp(-z))
 
+# compute_gradient函数，用于计算逻辑回归的梯度
 def compute_gradient(X_batch, y_batch, weights, lambda_reg):
-    z = np.dot(X_batch, weights)
-    h = sigmoid(z)
-    gradient = np.dot(X_batch.T, (h - y_batch)) / len(y_batch)
-    gradient += (lambda_reg / len(y_batch)) * weights  # L2正则化
+    z = np.dot(X_batch, weights)  # 计算当前权重下的线性组合
+    h = sigmoid(z)  # 应用sigmoid函数将线性组合转换为概率
+    gradient = np.dot(X_batch.T, (h - y_batch)) / len(y_batch)  # 计算梯度
+    gradient += (lambda_reg / len(y_batch)) * weights  # 添加L2正则化项
     return gradient
 
+# train_logreg函数，用于训练逻辑回归模型
 def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):
-    m, n = X.shape
-    weights = np.random.randn(n) * np.sqrt(2. / n)  # He初始化
-    best_weights = weights.copy()
-    best_accuracy = 0
+    m, n = X.shape  # 获取样本数量和特征数量
+    weights = np.random.randn(n) * np.sqrt(2. / n)  # 使用He初始化权重
+    best_weights = weights.copy()  # 存储最佳权重
+    best_accuracy = 0  # 存储最佳准确率
 
-    for i in range(iterations):
-        indices = np.random.permutation(m)
+    for i in range(iterations):  # 迭代指定次数
+        indices = np.random.permutation(m)  # 随机打乱数据
         X_shuffled = X[indices]
         y_shuffled = y[indices]
 
-        for j in range(0, m, batch_size):
+        for j in range(0, m, batch_size):  # 批量梯度下降
             X_batch = X_shuffled[j:j + batch_size]
             y_batch = y_shuffled[j:j + batch_size]
-            gradient = compute_gradient(X_batch, y_batch, weights, lambda_reg)
-            weights -= learning_rate * gradient
+            gradient = compute_gradient(X_batch, y_batch, weights, lambda_reg)  # 计算梯度
+            weights -= learning_rate * gradient  # 更新权重
 
-        accuracy = accuracy_score(y, predict_logreg(X, weights))
-        if accuracy > best_accuracy:
+        accuracy = accuracy_score(y, predict_logreg(X, weights))  # 计算当前准确率
+        if accuracy > best_accuracy:  # 更新最佳准确率和最佳权重
             best_accuracy = accuracy
             best_weights = weights.copy()
 
@@ -68,32 +71,35 @@ def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lam
 
     return best_weights
 
+# predict_logreg函数，用于使用训练好的权重进行预测
 def predict_logreg(X, weights):
-    z = np.dot(X, weights)
-    h = sigmoid(z)
-    return np.where(h >= 0.5, 1, 0)
+    z = np.dot(X, weights)  # 计算线性组合
+    h = sigmoid(z)  # 应用sigmoid函数
+    return np.where(h >= 0.5, 1, 0)  # 返回预测结果
 
+# preprocess_data函数，用于预处理数据
 def preprocess_data(X, y):
-    y[y == 2] = 0
-    y[y == 3] = 0
-    X = np.delete(X, [3], axis=1)
-    X = X[:, :23]
+    y[y == 2] = 0  # 将类别2转换为0
+    y[y == 3] = 0  # 将类别3转换为0
+    X = np.delete(X, [3], axis=1)  # 删除特征3
+    X = X[:, :23]  # 删除特征24~28
     return X, y
 
+# load_data函数，用于加载数据
 def load_data(filepath):
     try:
-        data = pd.read_csv(filepath, header=None, sep=r'\s+', on_bad_lines='skip')
+        data = pd.read_csv(filepath, header=None, sep=r'\s+', on_bad_lines='skip')  # 读取数据
     except pd.errors.ParserError as e:
         print(f"Error parsing file: {e}")
         return None, None
-    data.replace('?', np.nan, inplace=True)
-    imputer = SimpleImputer(missing_values=np.nan, strategy='constant', fill_value=0)
+    data.replace('?', np.nan, inplace=True)  # 将'?'替换为np.nan
+    imputer = SimpleImputer(missing_values=np.nan, strategy='constant', fill_value=0)  # 填充缺失值
     data.iloc[:, :-1] = imputer.fit_transform(data.iloc[:, :-1])
     for col in data.columns[:-1]:
-        data[col] = pd.to_numeric(data[col], errors='coerce')
-    X = data.iloc[:, :-1].values
-    y = data.iloc[:, -1].values.astype(int)
-    X, y = preprocess_data(X, y)
+        data[col] = pd.to_numeric(data[col], errors='coerce')  # 转换为数值类型
+    X = data.iloc[:, :-1].values  # 特征数据
+    y = data.iloc[:, -1].values.astype(int)  # 目标变量
+    X, y = preprocess_data(X, y)  # 预处理数据
     return X, y
 
 if __name__ == "__main__":
@@ -115,7 +121,6 @@ if __name__ == "__main__":
     y_pred = predict_logreg(X_val, final_weights)
     val_accuracy = accuracy_score(y_val, y_pred)
     print(f"验证集的准确率: {val_accuracy:.4f}")
-    print(classification_report(y_val, y_pred))
 
     # 加载测试集
     X_test, y_test = load_data("./data/horse-colic.test")
@@ -124,5 +129,4 @@ if __name__ == "__main__":
     # 测试模型
     y_test_pred = predict_logreg(X_test, final_weights)
     test_accuracy = accuracy_score(y_test, y_test_pred)
-    print(f"最终测试集的准确率: {test_accuracy:.4f}")
-    print(classification_report(y_test, y_test_pred))
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+    print(f"最终测试集的准确率: {test_accuracy:.4f}")
\ No newline at end of file