diff --git a/function.py b/function.py
index 963c095..4e2794c 100644
--- a/function.py
+++ b/function.py
@@ -51,19 +51,10 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, regularization=0.0):
     return weights, bias   
 
 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  
-      
-    # 应用sigmoid函数（如果需要的话）  
+    # 如果需要sigmoid输出，可以添加下面这行  
     # 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 sigmoid_output  
     return linear_output
 
 # 这里也写自己的代码实现
@@ -80,10 +71,14 @@ def load_data(filepath):
     y = data_array[:, -1]  
     return X, y 
 
-def evaluate_model(X, y, weights, bias):  
-    predictions = [predict_logreg(x, weights, bias) for x in X]  
-    accuracy = np.mean(predictions == y)  
-    return accuracy
+def evaluate_model(test_data, weights, bias, user_test_func=predict_logreg):  
+    results = []  
+    for i in range(len(test_data)):  
+        # 确保传递了正确的参数：test_data[i], weights, bias  
+        result = user_test_func(test_data[i], weights, bias)  
+        results.append(result)  
+    # 根据需要对结果进行进一步处理，例如计算准确率等  
+    return results
 
 if __name__ == "__main__":
     X_train, y_train = load_data("./data/horse-colic.data")