diff --git a/function.py b/function.py
index 2959942..0d6661c 100644
--- a/function.py
+++ b/function.py
@@ -1,140 +1,57 @@
-import os
 import numpy as np
 
-"""
-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。
-
-注意事项:
-- 在 function.py 中实现对率回归算法。
-- 可以自行导入其他库，需要在 requirements.txt 添加依赖，若未在 requirements.txt 中添加使用到的依赖，则无法自动评测，成绩为零。
-- 自动评测脚本仅会加载 train_logreg、predict_logreg 和 load_data 函数，请勿修改函数名。
-
-测评环境:
-- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
-- OS: Ubuntu 22.04.4 LTS
-- Python: 3.10.12
-- numpy: 2.1.1
-"""
-
-"""
-请在这里填写学号和姓名，不填写即成绩无效。
-"""
 id = "202210423105"
 name = "魏明帅"
 
-# 这里写自己的代码实现
-import numpy as np
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.model_selection import train_test_split
-
-
-def sigmoid(z):
-    return 1.0 / (1 + np.exp(-z))
-
-
-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
-    return gradient
-
-
-def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):
+def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     m, n = X.shape
-    weights = np.random.randn(n) * np.sqrt(2. / n)  # He初始化
-    best_weights = weights.copy()
-    best_accuracy = 0
-
+    # 添加偏置项
+    X = np.c_[np.ones((m, 1)), X]
+    weights = np.zeros(n + 1)
+    
     for _ in range(iterations):
-        indices = np.random.permutation(m)
-        X_shuffled = X[indices]
-        y_shuffled = y[indices]
-
-        for j in range(0, m, batch_size):
-            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
-
-        # 计算训练集准确率
-        train_accuracy = accuracy(X, y, weights)
-        if train_accuracy > best_accuracy:
-            best_accuracy = train_accuracy
-            best_weights = weights.copy()
-
-        # 动态调整学习率
-        if _ % 100 == 0 and train_accuracy > 0.95:  # 达到高准确率时减小学习率
-            learning_rate *= 0.9
-
-    return best_weights
-
-
-def predict_logreg(X, weights):
-    z = np.dot(X, weights)
-    h = sigmoid(z)
-    return np.where(h >= 0.5, 1, 0)
+        for i in range(m):
+            x_i = X[i]
+            y_i = y[i]
+            z = np.dot(x_i, weights)
+            h = 1 / (1 + np.exp(-z))
+            error = y_i - h
+            weights += learning_rate * error * x_i
+    
+    return weights
 
+def predict_logreg(inX, weights):
+    z = np.dot(inX, weights)
+    h = 1 / (1 + np.exp(-z))
+    return 1 if h >= 0.5 else 0
 
 def load_data(filepath):
-    data, labels = [], []
+    X = []
+    y = []
     with open(filepath, 'r') as f:
         for line in f:
-            line = line.strip().split()
-            if len(line) >= 1 and line[-1].isdigit():
-                line_data = [0 if x == '?' else float(x) for x in line[:-1]]
-                data.append(line_data)
-                labels.append(int(line[-1]))
-
-    data = np.array(data)
-    labels = np.array(labels)
-
+            values = line.strip().split()
+            # 跳过包含 '?' 的行
+            if '?' in values:
+                continue
+            # 将标签和特征转换为浮点数
+            y.append(float(values[-1]))
+            X.append([float(x) for x in values[:-1]])
     
-    num_features = data.shape[1]
-
+    X_array = np.array(X)
+    y_array = np.array(y).reshape(-1, 1)
     
-    indices_to_delete = [2]  
-    if num_features > 24:  
-        indices_to_delete += list(range(24, 27)) 
-
+    # 特征缩放，处理标准差为零的特征
+    means = np.mean(X_array, axis=0)
+    stds = np.std(X_array, axis=0)
+    X_scaled = (X_array - means) / np.where(stds == 0, 1, stds)
     
-    data = np.delete(data, indices_to_delete, axis=1)
-
-    
-    target_variable = np.where((labels == 2) | (labels == 3), 0, 1)
-
-    return data, target_variable
-
-
-def accuracy(X, y, weights):
-    predictions = predict_logreg(X, weights)
-    return np.mean(predictions == y)
-
+    return X_scaled, y_array
 
 if __name__ == "__main__":
-    # 加载数据
-    X, y = load_data("./data/horse-colic.data")
-
-    # 拆分训练集和验证集
-    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
-
-    # 数据归一化
-    scaler = MinMaxScaler()
-    X_train = scaler.fit_transform(X_train)
-    X_val = scaler.transform(X_val)
-
-    # 训练模型
-    final_weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=5000, batch_size=64, lambda_reg=0.1)
-
-    # 验证模型
-    val_accuracy = accuracy(X_val, y_val, final_weights)
-    print(f"验证集的准确率: {val_accuracy:.4f}")
-
-    # 加载测试集
+    X_train, y_train = load_data("./data/horse-colic.data")
     X_test, y_test = load_data("./data/horse-colic.test")
-    X_test = scaler.transform(X_test)
 
-    # 测试模型
-    final_test_accuracy = accuracy(X_test, y_test, final_weights)
-    print(f"最终测试集的准确率: {final_test_accuracy:.4f}")
\ No newline at end of file
+    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
+    y_pred = predict_logreg(X_test[11], weights)
+    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
\ No newline at end of file