From f55952809ee61b8fc1d8e6ce9528de884a0ff068 Mon Sep 17 00:00:00 2001
From: Ultraman <2900311815@qq.com>
Date: Tue, 22 Oct 2024 19:39:16 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 20 +++++++-------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/function.py b/function.py
index a83cdb0..ebcf651 100644
--- a/function.py
+++ b/function.py
@@ -2,11 +2,7 @@ import numpy as np
 import os
 import time
 
-# 定义ID和姓名
-id = "202210423220"
-name = "方子涵"
-
-def classify(input_vector, dataSet, labels, k=3):
+def classify(input_vector, dataSet, labels, k=4):
     """
     KNN 分类算法。
 
@@ -14,7 +10,7 @@ def classify(input_vector, dataSet, labels, k=3):
     - 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 3。
+    - k (int): kNN 算法参数，默认值为 4。
 
     Returns:
     - int: 预测的分类标签。
@@ -127,10 +123,8 @@ if __name__ == "__main__":
     )
     test_data_vector = np.array([int(char) for char in test_data]).reshape(1, 1024)
     
-    # 测试不同的k值
-    k_values = [1, 3, 5, 7, 9]
-    for k in k_values:
-        start_time = time.time()
-        predicted_label = classify(test_data_vector, train_data, train_labels, k)
-        end_time = time.time()
-        print(f"k = {k}, 预测的标签: {predicted_label}, 执行时间: {end_time - start_time:.2f} 秒")
\ No newline at end of file
+    # 使用k=4进行分类
+    start_time = time.time()
+    predicted_label = classify(test_data_vector, train_data, train_labels, k=4)
+    end_time = time.time()
+    print(f"k = 4, 预测的标签: {predicted_label}, 执行时间: {end_time - start_time:.2f} 秒")
\ No newline at end of file