diff --git a/function.py b/function.py
index b1f8c16..e5f3050 100644
--- a/function.py
+++ b/function.py
@@ -1,4 +1,3 @@
-# function.py
 import numpy as np
 import os
 
@@ -21,12 +20,11 @@ import os
 - numpy: 2.1.1
 """
 
-
 """
 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 = "20221042303"
+name = "赵昊龙"
 
 
 """
@@ -41,7 +39,7 @@ name = "test_name"
 
 
 # 这里写自己的代码实现
-def classify(input_vector, dataSet, labels, k=0):
+def classify(input_vector, dataSet, labels, k=3):
     """
     KNN 分类算法。
 
@@ -54,6 +52,22 @@ def classify(input_vector, dataSet, labels, k=0):
     Returns:
     - int: 预测的分类标签。
     """
+    # 计算输入向量与数据集中每个向量的欧式距离
+    distances = []
+    for i in range(dataSet.shape[0]):
+        dist = np.sqrt(np.sum((input_vector - dataSet[i]) ** 2))
+        distances.append((dist, labels[i]))
+    
+    # 根据距离进行排序，取前k个最近邻
+    distances.sort(key=lambda x: x[0])
+    k_nearest_labels = [label for _, label in distances[:k]]
+    
+    # 统计前k个最近邻中出现次数最多的标签
+    from collections import Counter
+    label_counts = Counter(k_nearest_labels)
+    most_common_label = label_counts.most_common(1)[0][0]
+    
+    return most_common_label
 
 
 # 这里也写自己的代码实现
@@ -68,6 +82,17 @@ def load_dataset(directory):
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
+    training_file_list = os.listdir(directory)
+    training_matrix = []
+    training_labels = []
+    
+    for file in training_file_list:
+        class_num = int(file.split('_')[0])
+        vector = img_to_vector(os.path.join(directory, file))
+        training_matrix.append(vector)
+        training_labels.append(class_num)
+    
+    return np.array(training_matrix), np.array(training_labels)
 
 
 # 这里还写自己的代码实现
@@ -82,6 +107,7 @@ def img_to_vector(filename):
     Returns:
     - np.array: 二值图像向量。
     """
+    return np.array([int(pixel) for pixel in open(filename).read().split()])
 
 
 if __name__ == "__main__":