From 65a57d4383b691008d628b32b83eb603a14b7dd8 Mon Sep 17 00:00:00 2001
From: wujunjie <297081150@qq.com>
Date: Sun, 6 Oct 2024 18:12:39 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/function.py b/function.py
index 409b95e..d5db875 100644
--- a/function.py
+++ b/function.py
@@ -39,7 +39,7 @@ name = "吴俊煯"
 
 
 # 这里写自己的代码实现
-def classify0(inX, dataSet, labels,k=8):
+def classify0(inX, dataSet, labels):
     """
     KNN 分类算法。
 
@@ -51,19 +51,24 @@ def classify0(inX, dataSet, labels,k=8):
     Returns:
     - int: 预测的分类标签。
     """
-        # 确保 k 不大于数据集的大小  
-    # 计算数据集的协方差矩阵的逆
-    VI = np.linalg.inv(np.cov(dataset.T))
-# 计算 inX 与数据集的差值
-    diff = dataSet -inX
-# 使用矩阵运算计算马氏距离
-    distances =np.sqrt(np,sum(diff @ VI * diff,axis=1))
+# 获取数据集的大小
+    dataSetSize =dataSet.shape[0]
+# 计算数据集的协方差矩阵的逆
+    VI = np.linalg.inv(np.cov(dataSet.T))
+# 计算 Mahalanobis 距离
+    distances =npzeros(dataSetSize)
+    for i in range(dataSetSize):
+        distances[i]=mahalanobis(inX,dataSeti],VI)
 # 对距离排序，获取前 k 个最近的样本索引
-    sortedDistIndices=np.argsort(distances)
+    sortedDistIndices=distances.argsort)
 # 统计前 k 个样本的标签
-    classCount =np.bincount(labels[sortedDistIndices[:k]])
+    classCount ={}
+    for i in range(9):
+        voteLabel=labels[sortedDistIndices[i]]
+        classCount[voteLabel]=classCount.get(voteLabel，0)+1
 # 返回出现次数最多的标签
-    return np.argmax(classCount)
+    sortedClasscount=sorted(classCount.items(),key=operator,itemgetter(1),reverse=True
+    return sortedclassCount[0][0]
 
 
 def load_data(file_path):