diff --git a/function.py b/function.py
index 2966450..61f82d1 100644
--- a/function.py
+++ b/function.py
@@ -1,137 +1,63 @@
 import numpy as np
 from sklearn.impute import SimpleImputer
-from sklearn.feature_selection import SelectKBest, mutual_info_classif
-from sklearn.preprocessing import RobustScaler
+from sklearn.feature_selection import SelectKBest, f_classif
 
 # 填写学号和姓名
 id = "202210423213"
 name = "熊浩燃"
 
 def load_data(filepath, k_features=20):
-    """
-    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 = []
+    data, labels = [], []
     with open(filepath, 'r') as file:
         for line in file:
             line = line.strip().split()
             if line:
-                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
+                features = [float(feature) if feature.replace('.', '', 1).isdigit() else np.nan for feature in line[:-1]]
                 label = int(line[-1])
                 data.append(features)
                 labels.append(label)
 
-    # 转换为 numpy 数组
-    data = np.array(data)
-    labels = np.array(labels)
-
-    # 使用SimpleImputer填充NaN
-    imputer = SimpleImputer(strategy='median')
+    data, labels = np.array(data), np.array(labels)
+    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
+    data = (data - mean) / std_dev
 
-    # 使用RobustScaler进行数据标准化，减少异常值的影响
-    scaler = RobustScaler()
-    data = scaler.fit_transform(data)
-
-    # 特征选择，使用互信息
-    selector = SelectKBest(mutual_info_classif, k=k_features)
+    selector = SelectKBest(f_classif, k=k_features)
     data = selector.fit_transform(data, labels)
 
     return data, labels
 
 def sigmoid(z):
-    """Sigmoid激活函数"""
     return 1 / (1 + np.exp(-z))
 
-def train_logreg(X, y, initial_lr=0.01, iterations=5000, regularization=0.02, decay=0.00005, patience=200):
-    """
-    对率回归训练函数，包含学习率衰减和早停机制。
-
-    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。
-    - patience (int): 早停的等待次数。
-
-    Returns:
-    - numpy array: 训练得到的权重。
-    """
+def train_logreg(X, y, initial_lr=0.005, iterations=5000, regularization=0.01, decay=0.0001):
     m, n = X.shape
     weights = np.zeros(n)
-    best_loss = float('inf')
-    stop_counter = 0
+    learning_rate = initial_lr
 
     for i in range(iterations):
-        # 前向传播和损失计算
-        z = np.dot(X, weights)
-        predictions = sigmoid(z)
-        loss = -np.mean(y * np.log(predictions + 1e-9) + (1 - y) * np.log(1 - predictions + 1e-9)) + regularization * np.sum(weights ** 2) / (2 * m)
-        
-        # 早停机制
-        if loss < best_loss:
-            best_loss = loss
-            stop_counter = 0
-        else:
-            stop_counter += 1
-            if stop_counter >= patience:
-                print("早停：在第 {} 轮迭代停止".format(i))
-                break
-        
-        # 反向传播更新权重
+        predictions = sigmoid(np.dot(X, weights))
         errors = y - predictions
-        gradient = np.dot(X.T, errors) / m - regularization * weights / m
-        weights += initial_lr * gradient / (1 + decay * i)  # 学习率衰减
+        gradient = (np.dot(X.T, errors) - regularization * weights) / m
+        weights += learning_rate * gradient
+        learning_rate = initial_lr / (1 + decay * i)
 
     return weights
 
-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
+def predict_logreg(X, weights, threshold=0.5):
+    probabilities = sigmoid(np.dot(X, weights))
+    return (probabilities >= threshold).astype(int)
 
 # 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=0.01, iterations=5000, regularization=0.02, decay=0.00005, patience=200)
+    weights = train_logreg(X_train, y_train, initial_lr=0.005, iterations=5000, regularization=0.01, decay=0.0001)
 
-    # 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)
+    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}")