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function.py

@@ -16,13 +16,8 @@ import numpy as np
 - numpy: 2.1.1
 """
 
-"""
-请在这里填写学号和姓名，不填写即成绩无效。
-姓名请直接**填写中文**
-例如: name = "张三"
-"""
-id = "202210423xxx"
-name = "test_name"
+id = "202210423104"
+name = "张钦诚"
 
 
 # 训练对率回归模型
@@ -46,9 +41,7 @@ def predict_logreg(inX, weights):
 
 # 数据加载函数
 def load_data(filepath):
-    data = np.genfromtxt(filepath, delimiter=' ', dtype=float, filling_values=np.nan)
-
-    # 去掉包含NaN的行
+    data = np.genfromtxt(filepath, delimiter=',', dtype=float, filling_values=np.nan)  # 根据实际分隔符调整
     data = data[~np.isnan(data).any(axis=1)]
 
     X = data[:, :-1]  # 特征
@@ -67,12 +60,8 @@ if __name__ == "__main__":
     print(f"X_train shape: {X_train.shape}, y_train shape: {y_train.shape}")
     print(f"X_test shape: {X_test.shape}, y_test shape: {y_test.shape}")
 
-    # 确保测试集有足够的数据进行预测
     if X_test.shape[0] > 0:
-        # 训练对率回归模型
         weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
-
-        # 测试单个数据，确保索引有效
         index_to_test = 0 if X_test.shape[0] == 1 else 11
         y_pred = predict_logreg(X_test[index_to_test], weights)
         print(f"预测结果: {y_pred}, 实际结果: {y_test[index_to_test]}")