diff --git a/function.py b/function.py
index 9ce8bed..7cd9ea9 100644
--- a/function.py
+++ b/function.py
@@ -53,33 +53,35 @@ def classify0(inX, dataSet, labels, k=3):
     Returns:
     - int: 预测的分类标签。
     """
+    # 确保输入样本 inX 是二维数组
+    inX = np.mat(inX)
     # 计算样本数量和特征数量
     numSamples, numFeatures = dataSet.shape
     
-    # 计算马氏距离
-    # 首先计算训练集的均值和协方差矩阵
+    # 计算训练集的均值和协方差矩阵
     meanVals = np.mean(dataSet, axis=0)
     covMat = np.cov(dataSet, rowvar=False)
     
+    # 添加一个小的正则化项以确保协方差矩阵可逆
+    covMat += np.eye(numFeatures) * 0.0001
+    
     # 计算待分类样本与训练集中每个样本的马氏距离
     inXMinusMean = inX - meanVals
-    dist = np.sqrt(np.dot(inXMinusMean.T, np.linalg.inv(covMat)).dot(inXMinusMean))
+    dist = np.sqrt(np.sum(inXMinusMean * np.linalg.inv(covMat) * inXMinusMean.T))
     
     # 获取距离最近的k个样本的索引
     sortedDistIndices = np.argsort(dist)
     classCount = {}
     
     # 遍历距离最近的k个样本
-    for i in range(k):
+    for i in range(min(k, numSamples)):  # 确保不会超出索引范围
         voteIlabel = labels[sortedDistIndices[i]]
-        # 统计每个类别的票数
         classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
     
     # 选择票数最多的类别作为预测标签
     sortedClassCount = sorted(classCount.items(), key=lambda x: x[1], reverse=True)
     return sortedClassCount[0][0]
 
-
 def load_data(file_path):
     """
     加载 TSV 格式数据文件。