From 04fccf817952ea69191db026a19e82d1d7d740ca Mon Sep 17 00:00:00 2001
From: wujunjie <297081150@qq.com>
Date: Thu, 26 Sep 2024 10:31:39 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/function.py b/function.py
index 7f551ad..03376ee 100644
--- a/function.py
+++ b/function.py
@@ -50,15 +50,24 @@ def classify0(inX, dataSet, labels,k=3):
     Returns:
     - int: 预测的分类标签。
     """
-    distances = [np.sqrt(np.sum((inX - x) ** 2)) for x in dataSet]  
+    dataSetSize = dataSet.shape[0]  
+    diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet  
+    sqDiffMat = diffMat ** 2  
+    sqDistances = sqDiffMat.sum(axis=1)  
+    distances = sqDistances ** 0.5  
       
-    k_indices = np.argsort(distances)[:k]   
-    k_nearest_labels = [labels[i] for i in k_indices]  
-        
-    from collections import Counter  
-    vote_result = Counter(k_nearest_labels).most_common(1)  
-       
-    return vote_result[0][0]
+    # 对距离进行排序，并获取索引  
+    sortedDistIndicies = distances.argsort()  
+      
+    # 选择距离最小的k个点  
+    classCount = {}  
+    for i in range(k):  
+        voteIlabel = labels[sortedDistIndicies[i]]  
+        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1  
+      
+    # 多数投票  
+    sortedClassCount = sorted(classCount.items(), key=lambda item: item[1], reverse=True)  
+    return sortedClassCount[0][0]
 
 def load_data(file_path):
     """