From 332a5a7afb60eccb937018eea7acdaa3ad105b1c Mon Sep 17 00:00:00 2001
From: wujunjie <297081150@qq.com>
Date: Thu, 26 Sep 2024 10:49:12 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 29 ++++++++++-------------------
 1 file changed, 10 insertions(+), 19 deletions(-)

diff --git a/function.py b/function.py
index 697dd29..ce7aea6 100644
--- a/function.py
+++ b/function.py
@@ -38,7 +38,7 @@ name = "吴俊煯"
 
 
 # 这里写自己的代码实现
-def classify0(inX, dataSet, labels,k=5):
+def classify0(inX, dataSet, labels,k=4):
     """
     KNN 分类算法。
 
@@ -50,24 +50,15 @@ def classify0(inX, dataSet, labels,k=5):
     Returns:
     - int: 预测的分类标签。
     """
-    dataSetSize = dataSet.shape[0]  
-    diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet  
-    sqDiffMat = diffMat ** 2  
-    sqDistances = sqDiffMat.sum(axis=1)  
-    distances = sqDistances ** 0.5  
-      
-    # 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]
+    if dataSet.shape[0] == 0:  
+        raise ValueError("数据集为空")  
+    distances = get_neighbors(dataSet, inX, k)  
+    classVotes = {}  
+    for x in range(len(distances)):  
+        response = labels[distances[x][0][-1]]  # 假设最后一列是标签  
+        classVotes[response] = classVotes.get(response, 0) + 1  
+    sortedVotes = sorted(classVotes.items(), key=lambda x: x[1], reverse=True)  
+    return sortedVotes[0][0]
 
 def load_data(file_path):
     """