From eb4fef4ac5793b5433128ff7b5c2af7e77c0e219 Mon Sep 17 00:00:00 2001
From: wzy <675163717@qq.com>
Date: Mon, 14 Oct 2024 21:00:53 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/function.py b/function.py
index 6141d77..eca04c2 100644
--- a/function.py
+++ b/function.py
@@ -1,7 +1,6 @@
 # function.py
 import numpy as np
 import os
-from scipy.spatial.distance import euclidean
 from collections import Counter
 
 """
@@ -43,6 +42,9 @@ name = "万子玉"
 
 
 # 这里写自己的代码实现
+def euclidean_distance(a, b):
+    return np.sqrt(np.sum((a - b) ** 2))
+
 def classify(input_vector, dataSet, labels, k=3):
     """
     KNN 分类算法。
@@ -56,21 +58,18 @@ def classify(input_vector, dataSet, labels, k=3):
     Returns:
     - int: 预测的分类标签。
     """
-    dists = [euclidean(input_vector, data) for data in dataSet]
+    dists = [euclidean_distance(input_vector, data) for data in dataSet]
     k_indices = np.argsort(dists)[:k]
     k_nearest_labels = [labels[i] for i in k_indices]
     
-    # 确保至少有一个近邻
     if not k_nearest_labels:
         raise ValueError("No neighbors found; possible causes: k is too small or all distances are the same.")
     
     most_common = Counter(k_nearest_labels).most_common(1)
     
-    # 检查most_common是否为空
     if most_common:
         return most_common[0][0]
     else:
-        # 如果为空，返回一个默认值或者抛出异常
         return None  # 或者 raise ValueError("No common labels found")