diff --git a/function.py b/function.py
index 72412a1..22e411b 100644
--- a/function.py
+++ b/function.py
@@ -1,6 +1,3 @@
-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。
@@ -23,16 +20,13 @@ import numpy as np
 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)
@@ -40,7 +34,6 @@ def compute_gradient(X_batch, y_batch, weights, lambda_reg):
     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):
     m, n = X.shape
     weights = np.random.randn(n) * np.sqrt(2. / n)  # He初始化
@@ -70,13 +63,11 @@ def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lam
 
     return best_weights
 
-
 def predict_logreg(X, weights):
     z = np.dot(X, weights)
     h = sigmoid(z)
     return np.where(h >= 0.5, 1, 0)
 
-
 def load_data(filepath):
     data, labels = [], []
     with open(filepath, 'r') as f:
@@ -106,12 +97,10 @@ def load_data(filepath):
 
     return data, target_variable
 
-
 def accuracy(X, y, weights):
     predictions = predict_logreg(X, weights)
     return np.mean(predictions == y)
 
-
 # 其余代码保持不变
 if __name__ == "__main__":
     # 加载数据