diff --git a/function.py b/function.py
index 2393b3b..fe92570 100644
--- a/function.py
+++ b/function.py
@@ -1,6 +1,6 @@
 # function.py
 import numpy as np
-
+from collections import Counter
 """
 挑战说明:
 - 自行编写实现 KNN 分类算法。
@@ -25,8 +25,8 @@ import numpy as np
 """
 请在这里填写学号和姓名，不填写即成绩无效。
 """
-id = "202210423xxx"
-name = "test_name"
+id = "202210423629"
+name = "Tanjunfeng"
 
 
 """
@@ -54,7 +54,19 @@ def classify0(inX, dataSet, labels, k=0):
     Returns:
     - int: 预测的分类标签。
     """
-
+# 计算输入向量与训练集中每一个样本的欧式距离
+    distances = np.sqrt(np.sum((dataSet - inX) ** 2, axis=1))
+    
+    # 通过对距离进行升序排序并选择前k个最近距离的索引
+    nearest_neighbors_indices = distances.argsort()[:k]
+    
+    # 收集k个最近距离的样本的标签
+    nearest_labels = [labels[i] for i in nearest_neighbors_indices]
+    
+    # 使用计数器查找出现频率最高的标签
+    most_common_label = Counter(nearest_labels).most_common(1)[C_0]()[C_0]()
+    
+    return most_common_label
 
 def load_data(file_path):
     """