diff --git a/function.py b/function.py
index 1e2aaa4..2641e0b 100644
--- a/function.py
+++ b/function.py
@@ -6,7 +6,7 @@ import time
 id = "202210423220"
 name = "方子涵"
 
-def classify(input_vector, dataSet, labels, k=5):
+def classify(input_vector, dataSet, labels, k=3):
     """
     KNN 分类算法。
 
@@ -14,7 +14,7 @@ def classify(input_vector, dataSet, labels, k=5):
     - 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 (int): kNN 算法参数，默认值为 5。
+    - k (int): kNN 算法参数，默认值为 3。
 
     Returns:
     - int: 预测的分类标签。
@@ -27,10 +27,17 @@ def classify(input_vector, dataSet, labels, k=5):
     # 获取距离最近的 k 个样本的索引
     sortedDistIndices = distances.argsort()
     classCount = {}
+    weightSum = {}
+    # 使用加权多数表决法
     for i in range(k):
         voteIlabel = labels[sortedDistIndices[i]]
-        # 统计每个类别的出现次数
-        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
+        # 统计每个类别的出现次数，并使用距离的倒数作为权重
+        weight = 1 / (distances[sortedDistIndices[i]] + 1e-5)  # 加上一个小常数防止除以零
+        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + weight
+        weightSum[voteIlabel] = weightSum.get(voteIlabel, 0) + 1
+    # 计算加权平均值
+    for className in classCount:
+        classCount[className] /= weightSum[className]
     # 返回出现次数最多的类别
     sortedClassCount = sorted(classCount.items(), key=lambda x: x[1], reverse=True)
     return sortedClassCount[0][0]
@@ -127,8 +134,10 @@ if __name__ == "__main__":
     )
     test_data_vector = np.array([int(char) for char in test_data]).reshape(1, 1024)
     
-    # 使用k=5进行分类
-    start_time = time.time()
-    predicted_label = classify(test_data_vector, train_data, train_labels, k=5)
-    end_time = time.time()
-    print(f"k = 5, 预测的标签: {predicted_label}, 执行时间: {end_time - start_time:.2f} 秒")
\ No newline at end of file
+    # 测试不同的k值
+    k_values = [1, 3, 5, 7, 9]
+    for k in k_values:
+        start_time = time.time()
+        predicted_label = classify(test_data_vector, train_data, train_labels, k)
+        end_time = time.time()
+        print(f"k = {k}, 预测的标签: {predicted_label}, 执行时间: {end_time - start_time:.2f} 秒")
\ No newline at end of file