From 870aa2fc2d75564592a68fdc151b1e19979f8965 Mon Sep 17 00:00:00 2001
From: zhangchenghong <1598349064@qq.com>
Date: Sun, 20 Oct 2024 14:05:01 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/function.py b/function.py
index 03f3774..80ff2f8 100644
--- a/function.py
+++ b/function.py
@@ -45,15 +45,21 @@ import numpy as np
 import os  
 from collections import Counter  
 
-def classify(input_vector, dataSet, labels, k=1):  
+def classify(input_vector, dataSet, labels, k=3):  
     """使用 KNN 算法对输入样本进行分类。"""
-    distances = np.sum((input_vector - dataSet) ** 2, axis=1)
+    # 计算协方差矩阵和其逆矩阵
+    cov_matrix = np.cov(dataSet, rowvar=False)
+    inv_cov_matrix = np.linalg.inv(cov_matrix + 1e-5 * np.eye(cov_matrix.shape[0]))  # 添加小常数以防止奇异矩阵
+
+    # 计算马氏距离
+    diffs = input_vector - dataSet
+    distances = np.sqrt(np.einsum('ij,jk->i', diffs, np.dot(inv_cov_matrix, diffs.T)))
+
     sorted_indices = distances.argsort()
-    k_nearest_distances = distances[sorted_indices[:k]]
     k_nearest_labels = labels[sorted_indices[:k]]
     
     # 计算权重（距离的倒数），避免除以零
-    weights = 1 / (k_nearest_distances + 1e-5)
+    weights = 1 / (distances[sorted_indices[:k]] + 1e-5)
     # 归一化权重
     weights /= weights.sum()