From 80744d7adad598f36932ad0084c44e5f8b1619af Mon Sep 17 00:00:00 2001
From: zhe <2904040094@qq.com>
Date: Wed, 16 Oct 2024 20:44:26 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 32 +++++++++++++++++++++++++++-----
 1 file changed, 27 insertions(+), 5 deletions(-)

diff --git a/function.py b/function.py
index 2393b3b..a6272dc 100644
--- a/function.py
+++ b/function.py
@@ -1,6 +1,7 @@
 # function.py
 import numpy as np
-
+from collections import Counter
+from sklearn.preprocessing import StandardScaler  # 用于数据标准化 
 """
 挑战说明:
 - 自行编写实现 KNN 分类算法。
@@ -25,8 +26,8 @@ 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 = "202210423111"
+name = "李劲"
 
 
 """
@@ -54,7 +55,28 @@ def classify0(inX, dataSet, labels, k=0):
     Returns:
     - int: 预测的分类标签。
     """
-
+    # 确保输入样本inX是numpy数组  
+    inX = np.array(inX)  
+      
+    # 标准化数据（使用训练数据的均值和标准差来转换输入数据和训练数据）  
+    scaler = StandardScaler().fit(dataSet)  
+    dataSet_scaled = scaler.transform(dataSet)  
+    inX_scaled = scaler.transform([inX])  
+      
+    # 计算输入样本与所有训练样本之间的欧几里得距离  
+    distances = [np.sqrt(np.sum((inX_scaled[0] - x_train) ** 2)) for x_train in dataSet_scaled]  
+      
+    # 根据距离对训练样本进行排序，并获取前k个最近邻的索引  
+    k_indices = np.argsort(distances)[:k]  
+      
+    # 根据索引获取前k个最近邻的标签  
+    k_nearest_labels = [labels[i] for i in k_indices]  
+      
+    # 使用Counter找出出现次数最多的标签  
+    most_common = Counter(k_nearest_labels).most_common(1)  
+      
+    # 返回出现次数最多的标签（即预测的分类标签）  
+    return most_common[0][0]
 
 def load_data(file_path):
     """
@@ -70,7 +92,7 @@ if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改
 
     train_file = "datingTrainSet2.tsv"
-
+    test_file = "datingTestSet2.tsv" 
     train_data, train_labels = load_data(train_file)
 
     print(classify0([65915, 3.868359, 0.128078], train_data, train_labels))