From 133eabcf372814a5deba9186d223e247e66f8c9c Mon Sep 17 00:00:00 2001
From: heart20003 <1789588961@qq.com>
Date: Thu, 17 Oct 2024 16:45:17 +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 | 23 ++++++++++-------------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/function.py b/function.py
index 6a2631e..b1d0793 100644
--- a/function.py
+++ b/function.py
@@ -59,23 +59,20 @@ def classify0(inX, dataSet,labels, k=3):
     distances = np.sum(diff @ inv_cov_matrix * diff, axis=1)
 
     # 获取距离最小的 k 个样本的索引
-    sorted_indices = np.argsort(distances)[:k]
+    sorted_indices = np.argsort(distances)[:k] 
 
-    # 获取 k 个最近邻的标签和对应的距离
+    # 获取 k 个最近邻的标签
     nearest_labels = [labels[i] for i in sorted_indices]
-    nearest_distances = distances[sorted_indices]
 
-    # 计算每个标签的加权投票
-    label_weight = {}
-    for i, label in enumerate(nearest_labels):
-        # 反距离作为权重，避免距离为0的情况
-        weight = 1 / (nearest_distances[i] + 1e-10)  # 加一个小值避免除以零
-        if label in label_weight:
-            label_weight[label] += weight
+    # 统计 k 个最近邻中每个类别的出现次数
+    label_count = {}
+    for label in nearest_labels:
+        if label in label_count:
+            label_count[label] += 1
         else:
-            label_weight[label] = weight
+            label_count[label] = 1
 
-    # 找到加权投票中出现次数最多的标签
-    predicted_label = max(label_weight, key=label_weight.get)
+    # 找到出现次数最多的标签
+    predicted_label = max(label_count, key=label_count.get)
 
     return predicted_label