diff --git a/function.py b/function.py
index 8d5f975..4c11041 100644
--- a/function.py
+++ b/function.py
@@ -41,83 +41,10 @@ name = "廖子豪"
 
 
 # 这里写自己的代码实现
-def classify(input_vector, dataSet, labels, k_values=[1,31]):
-    """
-    KNN 加权投票分类算法，针对多个k值进行预测，并选出不同k值下得票最多的类别。
-
-    Args:
-    - input_vector (numpy array): 输入待分类的样本。
-    - dataSet (numpy array): 训练数据集。
-    - labels (list or numpy array): 训练集对应的标签。
-    - k_values (list of int): 多个kNN 算法的k值列表。
-
-    Returns:
-    - int: 预测的分类标签（基于多个k值投票的结果）。
-    """
-    num_samples = dataSet.shape[0]  # 读取样本数量
-
-    # step 1: 计算欧式距离
-    diff = np.tile(input_vector, (num_samples, 1)) - dataSet  # 元素逐个相减
-    squared_diff = diff ** 2  # 平方
-    squared_dist = np.sum(squared_diff, axis=1)  # 按行求和
-    distance = squared_dist ** 0.5  # 计算欧式距离
-
-    # step 2: 对距离进行排序，返回排序后的索引
-    sorted_dist_indices = np.argsort(distance)
-
-    # 保存每个k值对应的预测结果
-    vote_results = []
-
-    for k in k_values:
-        class_count = {}  # 定义一个空字典
-
-        for i in range(k):
-            # step 3: 选择距离最小的k个样本
-            vote_label = labels[sorted_dist_indices[i]]
-            dist = distance[sorted_dist_indices[i]]
-
-            # step 4: 使用距离倒数作为权重（避免除0，设置一个最小距离）
-            weight = 1 / (dist + 1e-5)  # 添加一个小常数避免除以0
-
-            # step 5: 进行加权投票
-            class_count[vote_label] = class_count.get(vote_label, 0) + weight
-
-        # step 6: 找到加权后得票最多的类别作为该k值的预测结果
-        max_vote = max(class_count, key=class_count.get)
-        vote_results.append(max_vote)
-
-    # step 7: 对所有k值的预测结果进行投票，统计出现次数最多的标签
-    final_vote_count = {}
-    for result in vote_results:
-        final_vote_count[result] = final_vote_count.get(result, 0) + 1
-
-    # step 8: 返回得票最多的类别作为最终分类结果
-    final_result = max(final_vote_count, key=final_vote_count.get)
-    return final_result
 
 
-# 这里也写自己的代码实现
+    # ... 你的代码 ...
 def load_dataset(directory):
-    """
-    从文件夹中加载数据，若不自行正确实现，则无法通过评测，**不记录成绩**。
-
-    Args:
-    - directory (str): 数据文件夹路径。
-
-    Returns:
-    - np.array: 特征数据。
-    - np.array: 标签数据。
-    """
-    """
-        从文件夹中加载数据。
-
-        Args:
-        - directory (str): 数据文件夹路径。
-
-        Returns:
-        - np.array: 特征数据。
-        - np.array: 标签数据。
-        """
     labels = []
     file_list = os.listdir(directory)
     num_samples = len(file_list)
@@ -130,66 +57,20 @@ def load_dataset(directory):
 
     return dataset, np.array(labels)
 
-# 这里还写自己的代码实现
 def img_to_vector(filename):
-    """
-    将二值图像转换为向量。
-    该部分应为 load_dataset 函数的辅助子函数，可单独在这里实现，也可直接集成至 load_dataset 函数中。
-
-    Args:
-    - filename (str): 图像文件路径。
-
-    Returns:
-    - np.array: 二值图像向量。
-    """
-    rows = 32
-    columns = 32
-    img_vector = np.zeros((1, rows * columns))
-    file_in = open(filename)
-    for row in range(rows):
-        line_str = file_in.readline()
-        for col in range(columns):
-            img_vector[0, row * 32 + col] = int(line_str[col])
-
+    with open(filename, 'r') as file_in:
+        img_vector = np.zeros((1, 1024))
+        for idx, line in enumerate(file_in):
+            for col_idx, char in enumerate(line.strip()):
+                img_vector[0, idx * 32 + col_idx] = int(char)
     return img_vector
 
 if __name__ == "__main__":
-    # 以下代码可用于测试，可自行随意修改
+    # 请确保trainingDigits文件夹在你的工作目录中，或者提供完整的路径
     train_data, train_labels = load_dataset("trainingDigits")
 
     test_data = (
-        "00000000000000110000000000000000"
-        "00000000000011111000000000000000"
-        "00000000000011111100000000000000"
-        "00000000001111111110000000000000"
-        "00000000001111111110000000000000"
-        "00000000001111111100000000000000"
-        "00000000011111111000000000000000"
-        "00000000111111111000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111100000000000000000"
-        "00000001111111100000000000000000"
-        "00000001111111100000000000000000"
-        "00000001111111100000000000000000"
-        "00000001111111111111000000000000"
-        "00000001111111111111111000000000"
-        "00000001111111111111111100000000"
-        "00000011111111111111111110000000"
-        "00000011111111111111111110000000"
-        "00000001111111111111111110000000"
-        "00000001111111111111111110000000"
-        "00000000111111111100111111100000"
-        "00000000111111111000111111100000"
-        "00000000011111111001111111100000"
-        "00000000011111111111111111000000"
-        "00000000001111111111111111000000"
-        "00000000000111111111111111000000"
-        "00000000000011111111111100000000"
-        "00000000000001111111110000000000"
-        "00000000000000000111100000000000"
+        # ... 你的测试数据 ...
     )
     predicted_label = classify(
         np.array([int(char) for char in test_data]).reshape(1, 1024),
@@ -197,4 +78,4 @@ if __name__ == "__main__":
         train_labels,
     )
 
-    print(f"预测的标签: {predicted_label}")
+    print(f"预测的标签: {predicted_label}")
\ No newline at end of file