diff --git a/function.py b/function.py
index b1f8c16..29bbaea 100644
--- a/function.py
+++ b/function.py
@@ -41,7 +41,7 @@ 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。
 
@@ -54,7 +54,19 @@ def classify(input_vector, 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个样本之间的欧氏距离  
+    distances = [np.sqrt(np.sum((x - input_vector) ** 2)) for x in dataSet]  
+      
+    # 获取距离最近的 k 个样本的索引  
+    k_indices = np.argsort(distances)[:k]  
+      
+    # 获取这 k 个样本的标签  
+    k_nearest_labels = [labels[i] for i in k_indices]  
+      
+    # 返回出现次数最多的标签  
+    from collections import Counter  
+    most_common = Counter(k_nearest_labels).most_common(1)  
+    return most_common[0][0]
 
 # 这里也写自己的代码实现
 def load_dataset(directory):
@@ -68,7 +80,19 @@ def load_dataset(directory):
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
-
+      data = []  
+    labels = []  
+      
+    for filename in os.listdir(directory):  
+        if filename.endswith(".txt"):  
+            label = int(filename.split('_')[0])  # 假设文件名格式为 "label_xxx.txt"  
+            with open(os.path.join(directory, filename), 'r') as f:  
+                img_str = f.read().strip()  
+                img_vector = np.array([int(pixel) for pixel in img_str]).reshape(1, -1)  
+                data.append(img_vector.flatten())  # 将图像向量展平  
+                labels.append(label)  
+      
+    return np.array(data), np.array(labels)
 
 # 这里还写自己的代码实现
 def img_to_vector(filename):
@@ -82,7 +106,11 @@ def img_to_vector(filename):
     Returns:
     - np.array: 二值图像向量。
     """
-
+  
+    with open(filename, 'r') as f:  
+        img_str = f.read().strip()  
+        img_vector = np.array([int(pixel) for pixel in img_str]).reshape(1, -1)  
+    return img_vector.flatten()
 
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改