From 658256c822209e3a876cab34cf21ea72cfd9a1d1 Mon Sep 17 00:00:00 2001
From: LiYufeng <2371258046@qq.com>
Date: Wed, 16 Oct 2024 22:44:39 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 94 +++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 91 insertions(+), 3 deletions(-)

diff --git a/function.py b/function.py
index 71b0fe5..1937345 100644
--- a/function.py
+++ b/function.py
@@ -1,3 +1,46 @@
+# function.py
+import numpy as np
+import os
+
+"""
+实验说明:
+- 自行编写实现数据加载与 kNN 的手写数字分类算法。
+- 函数输入: 待分类的样本 input_vector, 训练数据集 dataSet, 训练集对应的标签 labels。
+- 函数返回: 预测的分类标签。
+
+注意事项:
+- 在 function.py 中实现 kNN 分类算法。
+- 可以自行导入其他库，需要在 requirements.txt 添加依赖，若未在 requirements.txt 中添加使用到的依赖，则无法自动评测，成绩为零。
+- 自动评测脚本仅会加载 classify 和 load_data 函数，请勿修改函数名。
+- 训练集和测试集需手动下载解压，并放入当前目录下。
+
+测评环境:
+- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
+- OS: Ubuntu 22.04.4 LTS
+- Python: 3.10.12
+- numpy: 2.1.1
+"""
+
+
+"""
+请在这里填写学号和姓名，不填写即成绩无效。
+"""
+id = "202210423115"
+name = "李煜锋"
+
+
+"""
+参数说明:
+- 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，请**自行设置默认值**，评测时仅会使用其默认值，不修改默认值将**无法通过**评测，**成绩为零**。
+返回值:
+- int: 预测的分类标签。
+"""
+
+
+# 这里写自己的代码实现
 def classify(input_vector, dataSet, labels, k=3):
     """
     KNN 分类算法。
@@ -14,8 +57,6 @@ def classify(input_vector, dataSet, labels, k=3):
     """
     使用欧式距离的 kNN 分类算法。
     """
-    id="202210423115"
-    name="李煜锋"
      # 计算欧式距离
     distances = np.linalg.norm(dataSet - input_vector, axis=1)
     
@@ -80,4 +121,51 @@ def img_to_vector(filename):
         lines = file.readlines()
         image_vector = np.array([int(char) for line in lines for char in line.strip()])
     
-    return image_vector
\ No newline at end of file
+    return image_vector
+
+
+if __name__ == "__main__":
+    # 以下代码可用于测试，可自行随意修改
+    train_data, train_labels = load_dataset("trainingDigits")
+
+    test_data = (
+        "00000000000000110000000000000000"
+        "00000000000011111000000000000000"
+        "00000000000011111100000000000000"
+        "00000000001111111110000000000000"
+        "00000000001111111110000000000000"
+        "00000000001111111100000000000000"
+        "00000000011111111000000000000000"
+        "00000000111111111000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111100000000000000000"
+        "00000001111111100000000000000000"
+        "00000001111111100000000000000000"
+        "00000001111111100000000000000000"
+        "00000001111111111111000000000000"
+        "00000001111111111111111000000000"
+        "00000001111111111111111100000000"
+        "00000011111111111111111110000000"
+        "00000011111111111111111110000000"
+        "00000001111111111111111110000000"
+        "00000001111111111111111110000000"
+        "00000000111111111100111111100000"
+        "00000000111111111000111111100000"
+        "00000000011111111001111111100000"
+        "00000000011111111111111111000000"
+        "00000000001111111111111111000000"
+        "00000000000111111111111111000000"
+        "00000000000011111111111100000000"
+        "00000000000001111111110000000000"
+        "00000000000000000111100000000000"
+    )
+    predicted_label = classify(
+        np.array([int(char) for char in test_data]).reshape(1, 1024),
+        train_data,
+        train_labels,
+    )
+
+    print(f"预测的标签: {predicted_label}")
\ No newline at end of file