更新 function.py

function.py

@@ -33,10 +33,18 @@ def load_data(filepath):
     - np.array: 特征数据。
     - 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)
+
+    # Check if data was loaded correctly
+    if data.ndim == 1:
+        print(f"数据加载错误: 文件 '{filepath}' 可能为空或格式不正确。")
+        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):
@@ -83,10 +91,20 @@ def predict_logreg(inX, weights):
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改
     # 加载训练集
-    X_train, y_train = load_data("/data/horse-colic.data")
+    X_train, y_train = load_data("./data/horse-colic.data")
+
+    # 检查加载结果
+    if X_train is None or y_train is None:
+        print("训练集加载失败。请检查文件路径和文件内容。")
+        exit(1)
 
     # 加载测试集
-    X_test, y_test = load_data("/data/horse-colic.test")
+    X_test, y_test = load_data("./data/horse-colic.test")
+
+    # 检查加载结果
+    if X_test is None or y_test is None:
+        print("测试集加载失败。请检查文件路径和文件内容。")
+        exit(1)
 
     # 训练对率回归模型
     weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)