diff --git a/function.py b/function.py
index f3f6268..4be52bc 100644
--- a/function.py
+++ b/function.py
@@ -40,27 +40,83 @@ name = "章程鸿"
 
 
 # 这里写自己的代码实现
+import numpy as np
+from collections import Counter
 
-def normalize_data(data):  
-    """  
-    对数据进行Min-Max归一化。  
-      
-    Args:  
-    - data (numpy array): 原始数据。  
-      
-    Returns:  
-    - numpy array: 归一化后的数据。  
-    """  
-    min_vals = np.min(data, axis=0)  
-    max_vals = np.max(data, axis=0)  
-    ranges = max_vals - min_vals  
-    norm_data = np.where(ranges == 0, 0, (data - min_vals) / ranges)  
-    return norm_data  
-  
-# 在加载数据后调用归一化函数  
-train_data_normalized = normalize_data(train_data)  
-test_point_normalized = normalize_data(test_point.reshape(1, -1))[0]  # 注意test_point需要reshape为二维数组  
-  
-# 使用归一化后的数据进行分类  
-predicted_label_normalized = classify0(test_point_normalized, train_data_normalized, train_labels)  
-print("归一化后预测的分类标签是:", predicted_label_normalized)
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+def classify0(inX, dataSet, labels, k=1):
+    """
+    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，默认为3。
+    
+    Returns:
+    - int: 预测的分类标签。
+    """
+    # 计算距离
+    diffMat = inX - dataSet
+    sqDiffMat = diffMat ** 2
+    sqDistances = sqDiffMat.sum(axis=1)
+    distances = sqDistances ** 0.5
+    
+    # 排序并选取前k个最小距离
+    sortedDistIndices = distances.argsort()
+    classCount = {}
+
+    for i in range(k):
+        voteIlabel = labels[sortedDistIndices[i]]
+        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
+
+    # 返回出现频率最高的类别
+    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。
+    """
+    data = np.loadtxt(file_path, delimiter="\t")
+    features = data[:, :-1]
+    labels = data[:, -1].astype(int)
+    return features, labels
+
+def normalize_min_max(data):
+    """
+    对数据进行最小-最大缩放归一化。
+    
+    Args:
+    - data (numpy array): 待归一化的数据。
+    
+    Returns:
+    - numpy array: 归一化后的数据。
+    """
+    min_vals = data.min(0)
+    max_vals = data.max(0)
+    ranges = max_vals - min_vals
+    # 避免除以零的情况
+    ranges[ranges == 0.0] = 1.0
+    m = data.shape[0]
+    norm_data = data - np.tile(min_vals, (m, 1))
+    norm_data = norm_data / np.tile(ranges, (m, 1))
+    return norm_data, min_vals, max_vals
+
+if __name__ == "__main__":
+    # 以下代码可用于测试，可自行随意修改
+    train_file = "datingTrainSet2.tsv"
+    
+    # 加载训练数据
+    train_data, train_labels = load_data(train_file)
+
+    # 归一化训练数据，并获取最小值和最大值
+    train_data_normalized, min_vals, max_vals = normalize_min_max(train_data)
+
+    # 测试数据点也需要用同样的方式归一化
+    test_point = np.array([65915, 3.868359, 0.128078])
+    test_point_normalized = (test_point - min_vals) / (max_vals - min_vals)
+
+    # 使用归一化后的数据进行预测
+    predicted_label = classify0(test_point_normalized, train_data_normalized, train_labels)
+    print("预测的分类标签是:", predicted_label)
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