更新 function.py

function.py

@@ -1,6 +1,5 @@
+# function.py
 import numpy as np
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.model_selection import train_test_split
 
 """
 实验说明:
@@ -18,9 +17,17 @@ from sklearn.model_selection import train_test_split
 - numpy: 2.1.1
 """
 
+
+"""
+请在这里填写学号和姓名，不填写即成绩无效。
+"""
 id = "202210423130"
 name = "董秋"
 
+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))
 
@@ -68,11 +75,8 @@ def predict_logreg(X, weights):
 def load_data(filepath):
     data, labels = [], []
     with open(filepath, 'r') as f:
-        for i, line in enumerate(f):
+        for line in f:
             line = line.strip().split()
-            # 只处理数据行，跳过第3行和第24-28行
-            if i in [2] or (24 <= i <= 28):
-                continue
             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)
@@ -80,7 +84,7 @@ def load_data(filepath):
 
     data = np.array(data)
     labels = np.array(labels)
-
+    
     # 将标签二分类化，2和3为0，其余为1
     target_variable = np.where((labels == 2) | (labels == 3), 0, 1)