diff --git a/function.py b/function.py
index 430755f..ade67d3 100644
--- a/function.py
+++ b/function.py
@@ -38,50 +38,48 @@ name = "Tanjunfeng"
 返回值:  
 - int: 预测的分类标签。  
 """  
-def normalize_data(dataSet):  
-    """标准化数据集使每个特征列的均值为0方差为1。"""  
-    mean = np.mean(dataSet, axis=0)  
-    std = np.std(dataSet, axis=0)  
-    normalized_data = (dataSet - mean) / std  
-    return normalized_data  
 
 # 这里写自己的代码实现  
-def classify0(inX, dataSet, labels, k=3):  
+def classify0(inX, dataSet, labels, k=5):  
     """  
     KNN 分类算法。  
-    
+
     Args:  
     - inX (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，请**自行设置默认值**，评测时仅会使用其默认值，不修改默认值将**无法通过**评测，**成绩为零**。  
-    
+    - k (int): kNN 算法参数。  
+
     Returns:  
     - int: 预测的分类标签。  
     """  
-    # 对数据集进行标准化  
-    normalized_dataSet = normalize_data(dataSet)  
-    normalized_inX = (inX - np.mean(dataSet, axis=0)) / np.std(dataSet, axis=0)  
-    
-    # 计算欧式距离  
-    dataSetSize = normalized_dataSet.shape[0]  
-    diffMat = np.tile(normalized_inX, (dataSetSize, 1)) - normalized_dataSet  
+    # **步骤1数据归一化**  
+    minVals = dataSet.min(0)  
+    maxVals = dataSet.max(0)  
+    ranges = maxVals - minVals  
+
+    normDataSet = (dataSet - minVals) / ranges  
+    normInX = (inX - minVals) / ranges  
+
+    # **步骤2计算欧氏距离**  
+    diffMat = normDataSet - normInX  
     distances = np.sqrt(np.sum(diffMat ** 2, axis=1))  
-    
-    # 按距离从小到大排序  
+
+    # **步骤3按距离排序**  
     sortedDistIndices = distances.argsort()  
-    
-        # **步骤4选择距离最小的k个点进行加权投票**  
+
+    # **步骤4选择距离最小的k个点，进行加权投票**  
     classCount = {}  
     for i in range(k):  
         voteIlabel = labels[sortedDistIndices[i]]  
-        # 使用距离的倒数作为权重防止距离为0时除以零  
+        # 使用距离的倒数作为权重，防止除以零  
         weight = 1 / (distances[sortedDistIndices[i]] + 1e-8)  
-        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + weight
-    
-    # 按类别票数从大到小排序  
+        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + weight  
+
+    # **步骤5按投票结果排序**  
     sortedClassCount = sorted(classCount.items(), key=lambda x: x[1], reverse=True)  
-    
+
+    # **步骤6返回权重和最高的标签**  
     return sortedClassCount[0][0]  
 
 
@@ -102,5 +100,14 @@ if __name__ == "__main__":
 
     train_data, train_labels = load_data(train_file)  
 
+    # 测试样本  
+    test_sample = np.array([65915, 3.868359, 0.128078])  
+
+    # 调用 classify0 函数进行预测  
+    result = classify0(test_sample, train_data, train_labels)  
+
+    print("预测的分类标签为：", result) 
+    train_data, train_labels = load_data(train_file)  
+
     # 进行测试  
     print(classify0([65915, 3.868359, 0.128078], train_data, train_labels))  
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