diff --git a/function.py b/1fu1nction.py
similarity index 89%
rename from function.py
rename to 1fu1nction.py
index 3d7ae56..497c826 100644
--- a/function.py
+++ b/1fu1nction.py
@@ -45,26 +45,18 @@ import numpy as np
 import os
 from collections import Counter
 
-def classify(input_vector, dataSet, labels, k=1):
-    """使用 KNN 算法对输入样本进行分类，使用加权多数表决法。"""
+def classify(input_vector, dataSet, labels, k=1):  # 设置默认值 k=1
+    """使用 KNN 算法对输入样本进行分类。"""
     # 计算输入向量与训练集之间的欧氏距离
     distances = np.linalg.norm(dataSet - input_vector, axis=1)
     # 根据距离排序并获取索引
     sorted_indices = distances.argsort()
     # 选择距离最近的k个点
     k_nearest_labels = labels[sorted_indices[:k]]
-    k_nearest_distances = distances[sorted_indices[:k]]
-
-    # 计算权重：距离越小，权重越大
-    weights = 1 / (k_nearest_distances + 1e-10)  # 加小常数防止除零
-
-    # 使用加权计数
-    count = Counter()
-    for label, weight in zip(k_nearest_labels, weights):
-        count[label] += weight
-
-    # 返回加权计数最多的类别标签
-    return count.most_common(1)[0][0]
+    # 统计k个点中各类别的数量
+    count = Counter(k_nearest_labels)
+    # 返回数量最多的类别标签
+return count.most_common(1)[0][0]
 
 # 为评测系统创建别名
 classify = classify