diff --git a/function.py b/function.py
index a6761cd..963c095 100644
--- a/function.py
+++ b/function.py
@@ -50,10 +50,21 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, regularization=0.0):
   
     return weights, bias   
 
-def predict_logreg(inX, weights):
+def predict_logreg(inX, weights, bias):
+    # 确保 inX 是一维数组  
+    if inX.ndim == 2 and inX.shape[1] == 1:  
+        inX = inX.flatten()  
+    elif inX.ndim > 1:  
+        raise ValueError("Input data should be one-dimensional or a column vector.")  
+      
+    # 计算线性输出  
     linear_output = np.dot(inX, weights) + bias  
-    y_predicted = 1 if linear_output >= 0 else 0  # 简化判断条件，因为逻辑回归输出已经通过sigmoid函数处理  
-    return y_predicted
+      
+    # 应用sigmoid函数（如果需要的话）  
+    # sigmoid_output = 1 / (1 + np.exp(-linear_output))  
+      
+    # 这里可以返回线性输出或sigmoid输出，取决于您的需求  
+    return linear_output
 
 # 这里也写自己的代码实现
 def load_data(filepath):