更新 function.py

function.py

@@ -34,19 +34,29 @@ def load_data(filepath):
     - np.array: 标签数据。
     """
     # Load the data, treating missing values as NaN
-    data = np.genfromtxt(filepath, delimiter=',', missing_values='?', filling_values=np.nan)
+    try:
+        data = np.genfromtxt(filepath, delimiter=',', missing_values='?', filling_values=np.nan)
+        
+        # Check if data was loaded correctly
+        if data.ndim == 1 and len(data) == 1 and np.isnan(data).any():
+            print(f"数据加载错误: 文件 '{filepath}' 可能为空或格式不正确。")
+            return None, None
 
-    # Check if data was loaded correctly
+        # Remove rows with NaN values
-    if data.ndim == 1:
+        data = data[~np.isnan(data).any(axis=1)]  
-        print(f"数据加载错误: 文件 '{filepath}' 可能为空或格式不正确。")
+        
+        if data.size == 0:
+            print(f"数据加载错误: 文件 '{filepath}' 中没有有效数据。")
+            return None, None
+
+        X = data[:, :-1]  # All columns except the last one
+        y = data[:, -1]   # Last column
+
+        return X, y
+    except Exception as e:
+        print(f"数据加载错误: {e}")
         return None, None
 
-    data = data[~np.isnan(data).any(axis=1)]  # Remove rows with NaN values
-    X = data[:, :-1]  # All columns except the last one
-    y = data[:, -1]   # Last column
-
-    return X, y
-
 def sigmoid(z):
     """计算 sigmoid 函数"""
     return 1 / (1 + np.exp(-z))
@@ -112,4 +122,3 @@ if __name__ == "__main__":
     # 测试单个数据
     y_pred = predict_logreg(X_test[11], weights)
     print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
-