From 2c70be6d1c0731606c9963a33691e51a81fb4b03 Mon Sep 17 00:00:00 2001
From: Dengddd <3149942468@qq.com>
Date: Tue, 22 Oct 2024 20:51:36 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/function.py b/function.py
index 6b18f60..3d7ae56 100644
--- a/function.py
+++ b/function.py
@@ -45,21 +45,30 @@ import numpy as np
 import os
 from collections import Counter
 
-def classify(input_vector, dataSet, labels, k=1):  # 设置默认值 k=1
-    """使用 KNN 算法对输入样本进行分类。"""
+def classify(input_vector, dataSet, labels, k=1):
+    """使用 KNN 算法对输入样本进行分类，使用加权多数表决法。"""
     # 计算输入向量与训练集之间的欧氏距离
     distances = np.linalg.norm(dataSet - input_vector, axis=1)
     # 根据距离排序并获取索引
     sorted_indices = distances.argsort()
     # 选择距离最近的k个点
     k_nearest_labels = labels[sorted_indices[:k]]
-    # 统计k个点中各类别的数量
-    count = Counter(k_nearest_labels)
-    # 返回数量最多的类别标签
-return count.most_common(1)[0][0]
+    k_nearest_distances = distances[sorted_indices[:k]]
+
+    # 计算权重：距离越小，权重越大
+    weights = 1 / (k_nearest_distances + 1e-10)  # 加小常数防止除零
+
+    # 使用加权计数
+    count = Counter()
+    for label, weight in zip(k_nearest_labels, weights):
+        count[label] += weight
+
+    # 返回加权计数最多的类别标签
+    return count.most_common(1)[0][0]
 
 # 为评测系统创建别名
-classify = classify_mahalanobis
+classify = classify
+
 
 def load_dataset(directory):
     """加载数据集中的图像，并将其转换为特征向量和标签。"""