From afe2db0ff8411d8a59f692ab2dd9606d0698e2b7 Mon Sep 17 00:00:00 2001
From: Xionghaoran <719929980@qq.com>
Date: Tue, 22 Oct 2024 19:45:38 +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 | 25 -------------------------
 1 file changed, 25 deletions(-)

diff --git a/function.py b/function.py
index e86ac2c..bf7ac7c 100644
--- a/function.py
+++ b/function.py
@@ -1,6 +1,5 @@
 import numpy as np
 import os
-from sklearn.neighbors import KDTree
 
 """
 实验说明:
@@ -58,26 +57,6 @@ def classify(input_vector, dataSet, labels, k=1, distance_metric='euclidean', we
     return predicted_label
 
 
-def classify_with_kdtree(input_vector, dataSet, labels, k=1):
-    """
-    使用 KD-tree 进行加速的 kNN 分类算法。
-
-    Args:
-    - input_vector (numpy array): 输入待分类的样本。
-    - dataSet (numpy array): 训练数据集。
-    - labels (list or numpy array): 训练集对应的标签。
-    - k (int): kNN 算法参数，默认值为1。
-
-    Returns:
-    - int: 预测的分类标签。
-    """
-    tree = KDTree(dataSet)  # 构建 KD-tree
-    dist, indices = tree.query(input_vector.reshape(1, -1), k=k)  # 查询最近邻
-    nearest_labels = np.array(labels)[indices[0]]
-    predicted_label = np.bincount(nearest_labels).argmax()
-    return predicted_label
-
-
 def load_dataset(directory):
     """
     从文件夹中加载数据，若不自行正确实现，则无法通过评测，不记录成绩。
@@ -162,7 +141,3 @@ if __name__ == "__main__":
     # 使用默认的欧氏距离和不加权的投票机制
     predicted_label = classify(test_vector, train_data, train_labels)
     print(f"预测的标签（普通分类）: {predicted_label}")
-
-    # 使用 KD-tree 进行加速的分类
-    predicted_label_kdtree = classify_with_kdtree(test_vector, train_data, train_labels)
-    print(f"预测的标签KD-tree 加速: {predicted_label_kdtree}")