diff --git a/function.py b/function.py
index 420ecfb..932af46 100644
--- a/function.py
+++ b/function.py
@@ -25,8 +25,8 @@ import numpy as np
 """
 请在这里填写学号和姓名，不填写即成绩无效。
 """
-id = "202210423xxx"
-name = "test_name"
+id = "202210423230"
+name = "尹家立"
 
 
 """
@@ -53,6 +53,30 @@ def classify0(inX, dataSet, labels, k=0):
     Returns:
     - int: 预测的分类标签。
     """
+    # Step 1: 计算协方差矩阵的逆矩阵
+    covMat = np.cov(dataSet, rowvar=False)  # 计算协方差矩阵
+    invCovMat = np.linalg.inv(covMat)  # 计算协方差矩阵的逆
+
+    # Step 2: 计算马氏距离
+    diffMat = dataSet - inX  # 计算差值矩阵
+    left = np.dot(diffMat, invCovMat)  # 左乘协方差矩阵的逆
+    mahalanobisDist = np.sqrt(np.sum(left * diffMat, axis=1))  # 计算马氏距离
+
+    # Step 3: 按距离排序，得到索引
+    sortedDistIndices = mahalanobisDist.argsort()
+
+    # Step 4: 选择距离最近的 k 个点的标签，并采用权重投票
+    classCount = {}
+    for i in range(k):
+        voteLabel = labels[sortedDistIndices[i]]
+        distance = mahalanobisDist[sortedDistIndices[i]]
+        weight = 1 / (distance + 1e-5)  # 距离越小权重越大，防止除零
+
+        classCount[voteLabel] = classCount.get(voteLabel, 0) + weight
+
+    # Step 5: 按照加权后的类别得票数排序并返回得票最多的类别
+    sortedClassCount = sorted(classCount.items(), key=lambda item: item[1], reverse=True)
+    return sortedClassCount[0][0]
 
 
 def load_data(file_path):