From 6b2538788ee8e7e9eaad38edfd6ef77299a3b4d5 Mon Sep 17 00:00:00 2001
From: llll <2992513220@qq.com>
Date: Thu, 21 Nov 2024 09:07:02 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/function.py b/function.py
index 2393b3b..a09f8b2 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 = "202210423219"
+name = "李燕如"
 
 
 """
@@ -41,7 +41,7 @@ name = "test_name"
 
 
 # 这里写自己的代码实现
-def classify0(inX, dataSet, labels, k=0):
+def classify0(inX, dataSet, labels, k=3):
     """
     KNN 分类算法。
 
@@ -54,6 +54,26 @@ def classify0(inX, dataSet, labels, k=0):
     Returns:
     - int: 预测的分类标签。
     """
+    # 计算待分类样本与训练集中每个样本的距离
+    distances = np.sqrt(np.sum((dataSet - inX) ** 2, axis=1))
+    
+    # 获取距离最近的 k 个样本的索引
+    k_indices = np.argsort(distances)[:k]
+    
+    # 获取这些样本对应的标签
+    k_nearest_labels = labels[k_indices]
+    
+    # 统计每个类别出现的次数
+    count = {}
+    for label in k_nearest_labels:
+        if label in count.keys():
+            count[label] += 1
+        else:
+            count[label] = 1
+    
+    # 找到出现次数最多的类别
+    sorted_count = sorted(count.items(), key=lambda x: x[1], reverse=True)
+    return sorted_count[0][0]
 
 
 def load_data(file_path):
@@ -73,4 +93,4 @@ if __name__ == "__main__":
 
     train_data, train_labels = load_data(train_file)
 
-    print(classify0([65915, 3.868359, 0.128078], train_data, train_labels))
+    print(classify0([65915, 3.868359, 0.128078], train_data, train_labels))
\ No newline at end of file