diff --git a/function.py b/function.py
index a840fee..31e1330 100644
--- a/function.py
+++ b/function.py
@@ -1,9 +1,7 @@
-#function.py
+# function.py
 import numpy as np
 import os
-from collections import Counter
-id="202210423115"
-name="李煜锋"
+
 def img_to_vector(filename):
     """
     将二值图像转换为向量。
@@ -33,7 +31,6 @@ def load_dataset(directory):
     feature_list = []
     label_list = []
     
-    print(f"正在加载目录: {directory}")
     # 遍历目录，加载数据
     for filename in os.listdir(directory):
         if filename.endswith('.txt'):
@@ -52,6 +49,7 @@ def load_dataset(directory):
     labels = np.array(label_list)
     
     return features, labels
+from collections import Counter
 
 def knn_classify(input_vector, dataSet, labels, k=3):
     """
@@ -79,29 +77,14 @@ def knn_classify(input_vector, dataSet, labels, k=3):
     label_count = Counter(top_k_labels)
     
     return label_count.most_common(1)[0][0]
+# 加载训练数据
+train_data, train_labels = load_dataset('C:\\Users\\Administrator\\Desktop\\trainingDigits')
 
-# 主程序
-if __name__ == '__main__':
-    # 使用绝对路径替换为你数据集的路径
-    train_directory = r'C:\Users\Administrator\Desktop\trainingDigits'
-    test_directory = r'C:\Users\Administrator\Desktop\testDigits'
-    
-    # 加载训练数据
-    train_data, train_labels = load_dataset(train_directory)
-    
-    # 加载测试数据
-    test_data, test_labels = load_dataset(test_directory)
-    
-    # 测试 kNN 分类器
-    correct_predictions = 0
-    
-    for i in range(len(test_data)):
-        predicted_label = knn_classify(test_data[i], train_data, train_labels, k=3)
-        true_label = test_labels[i]
-        print(f"预测标签: {predicted_label}, 真实标签: {true_label}")
-        
-        if predicted_label == true_label:
-            correct_predictions += 1
-            
-    accuracy = correct_predictions / len(test_data) * 100
-    print(f"分类准确率: {accuracy:.2f}%")
+# 加载测试数据
+test_data, test_labels = load_dataset('C:\\Users\\Administrator\\Desktop\\testDigits')
+
+# 选择一个测试样本，进行分类
+for i in range(len(test_data)):
+    predicted_label = knn_classify(test_data[i], train_data, train_labels, k=3)
+    true_label = test_labels[i]
+    print(f"预测标签: {predicted_label}, 真实标签: {true_label}")