From 3be0dbc035f6c5d0eb6a9803fc1f9177c93a8885 Mon Sep 17 00:00:00 2001
From: Yijuehen <1906265724@qq.com>
Date: Fri, 11 Oct 2024 20:13:45 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 function.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/function.py b/function.py
index 932af46..83ce202 100644
--- a/function.py
+++ b/function.py
@@ -1,6 +1,6 @@
 # function.py
 import numpy as np
-
+from scipy.spatial.distance import mahalanobis
 """
 挑战说明:
 - 自行编写实现 KNN 分类算法。
@@ -40,7 +40,7 @@ name = "尹家立"
 
 
 # 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。
 
@@ -58,9 +58,9 @@ def classify0(inX, dataSet, labels, k=0):
     invCovMat = np.linalg.inv(covMat)  # 计算协方差矩阵的逆
 
     # Step 2: 计算马氏距离
-    diffMat = dataSet - inX  # 计算差值矩阵
-    left = np.dot(diffMat, invCovMat)  # 左乘协方差矩阵的逆
-    mahalanobisDist = np.sqrt(np.sum(left * diffMat, axis=1))  # 计算马氏距离
+    mahalanobisDist = np.array([
+        mahalanobis(inX, dataSet[i], invCovMat) for i in range(dataSet.shape[0])
+    ])  # 使用 scipy 的 mahalanobis 函数计算马氏距离
 
     # Step 3: 按距离排序，得到索引
     sortedDistIndices = mahalanobisDist.argsort()