commit 8572160a297e435cae21a3eb76847158af520b2e
Author: kxz <2897100651@qq.com>
Date:   Tue Oct 8 18:13:00 2024 +0800

    Initial commit

diff --git a/.gitea/workflows/test.yaml b/.gitea/workflows/test.yaml
new file mode 100644
index 0000000..256fc25
--- /dev/null
+++ b/.gitea/workflows/test.yaml
@@ -0,0 +1,27 @@
+name: Gitea Actions Challenge Test
+run-name: 挑战赛自动评测 Actions 🚀
+on: [push]
+
+jobs:
+  Challenge-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - name: 检出代码
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: 安装依赖
+        run: |
+          python -m venv ./.venv
+          . ./.venv/bin/activate
+          curl -s -o pip_cache.tar.gz ${{ vars.BACKEND_URL }}/download/pip_cache.tar.gz
+          tar -zxf pip_cache.tar.gz
+          pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+          rm -rf datingTrainSet2.tsv datingTestSet2.tsv main.py
+          curl -s -o main.py ${{ vars.BACKEND_URL }}/public/challenge/knn/main.py
+          curl -s -o datingTrainSet2.tsv ${{ vars.BACKEND_URL }}/public/challenge/knn/datingTrainSet2.tsv
+          curl -s -o datingTestSet2.tsv ${{ vars.BACKEND_URL }}/public/challenge/knn/datingTestSet2.tsv
+      - name: 评测结果
+        run: |
+          . ./.venv/bin/activate
+          python main.py
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..bee8a64
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+__pycache__
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..3c0bbf3
--- /dev/null
+++ b/README.md
@@ -0,0 +1,89 @@
+# 挑战一：kNN 算法
+
+## 挑战说明
+
+- 自行在 [`function.py`](./function.py) 中编写实现 KNN 分类算法。
+- 函数输入: 待分类的样本 `inX`, 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。
+
+成功上传的评测结果将在 [rank.intcplab.cn](https://rank.intcplab.cn/) 展示。
+
+## 注意事项
+
+挑战开始前必须在 [`function.py`](./function.py#L28-L29) 中填写自己的学号和姓名！
+
+- 可以自行导入其他库，需要在 [`requirements.txt`](./requirements.txt) 添加依赖，若未在 [`requirements.txt`](./requirements.txt) 中添加使用到的依赖，则无法自动评测，成绩为零。
+- 需要在 [`function.py`](./function.py) 中实现完整的 KNN 分类算法。
+- 自动评测脚本仅会加载 [`classify0`](./function.py#L43) 函数，请勿修改函数名。
+- 训练集在本目录下的 [`datingTrainSet2.tsv`](./datingTrainSet2.tsv) 中。
+- 评测数据集在本目录下的 [`datingTestSet2.tsv`](./datingTestSet2.tsv) 中。
+- 本地调试完成后，**需要更新提交代码**，否则无法自动评测，**没有挑战成绩**。
+- 评测的运行时间受机器性能影响，运行时间仅供参考。
+- 若有评测相关问题，请在[项目工单](https://git.intcplab.cn/LufsX/Evaluation-Backend/issues)处提出，或联系 QQ: `1968647927`。
+
+代码提交后会进行自动评测，评测所用的环境如下：
+
+- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
+- OS: Ubuntu 22.04.4 LTS
+- Python: 3.10.12
+- numpy: 2.1.1
+
+## 挑战操作
+
+挑战赛与实验为两个**不同**的仓库，注意不要混淆！
+
+---
+
+[注册平台帐号](https://git.intcplab.cn/user/sign_up)，若有平台帐号相关问题，联系 QQ: `1968647927`。
+
+![注册平台帐号](./img/sign_up.png)
+
+进入对应仓库的页面，点击页面的「使用模版」按钮。
+
+![创建仓库](./img/create.png)
+
+填写仓库名称（建议和对应仓库一致，例如本仓库名 `Challenge-kNN`），勾选模版选项中的「Git 数据（默认分支）」后，点击「创建仓库」按钮。
+
+![创建仓库](./img/create2.png)
+
+可以选择拉取到本地写代码，也可以直接在线编辑。
+
+---
+
+下面以在线编辑为例。
+
+打开对应的文件，点击编辑按钮即可。
+
+![在线编辑](./img/edito.png)
+
+编辑完成后，点击「提交变更」按钮提交修改。
+
+![在线编辑](./img/edito2.png)
+
+---
+
+下面以拉取到本地写代码为例。
+
+![拉取到本地](./img/edit.png)
+
+点击后会叫你选个地方保存代码，选择目录后继续。
+
+然后编辑器就会打开，你可以开始写代码了。
+
+![编辑代码](./img/edit2.png)
+
+编辑完成后，点侧栏的分叉图标，选择你要提交的变更，再点提交按钮。
+
+![提交变更](./img/commit.png)
+
+接下来会弹出一个窗口，填写你平台帐号的用户名和密码后即可。
+
+---
+
+提交完成后，会自动开始评测。
+
+这里就可以看到你评测的实时情况与最终得分。
+
+![评测结果](./img/result.png)
+
+![评测结果](./img/result2.png)
diff --git a/datingTestSet2.tsv b/datingTestSet2.tsv
new file mode 100644
index 0000000..3e55fca
--- /dev/null
+++ b/datingTestSet2.tsv
@@ -0,0 +1,100 @@
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diff --git a/datingTrainSet2.tsv b/datingTrainSet2.tsv
new file mode 100644
index 0000000..91d7680
--- /dev/null
+++ b/datingTrainSet2.tsv
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diff --git a/function.py b/function.py
new file mode 100644
index 0000000..420ecfb
--- /dev/null
+++ b/function.py
@@ -0,0 +1,75 @@
+# function.py
+import numpy as np
+
+"""
+挑战说明:
+- 自行编写实现 KNN 分类算法。
+- 函数输入: 待分类的样本 inX, 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:
+- 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。
+- 在 function.py 中实现 KNN 分类算法。
+- 自动评测脚本仅会加载 classify0 函数，请勿修改函数名。
+- 训练集在本目录下的 datingTrainSet2.tsv 中。
+- 评测数据集在本目录下的 datingTestSet2.tsv 中。
+
+挑战环境:
+- 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 = "202210423xxx"
+name = "test_name"
+
+
+"""
+参数说明:
+- inX (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。
+返回值:
+- int: 预测的分类标签。
+"""
+
+
+# 这里写自己的代码实现
+def classify0(inX, dataSet, labels, k=0):
+    """
+    KNN 分类算法。
+
+    Args:
+    - inX (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。
+
+    Returns:
+    - int: 预测的分类标签。
+    """
+
+
+def load_data(file_path):
+    """
+    加载 TSV 格式数据文件。
+    """
+    data = np.loadtxt(file_path, delimiter="\t")
+    features = data[:, :-1]
+    labels = data[:, -1].astype(int)
+    return features, labels
+
+
+if __name__ == "__main__":
+    # 以下代码可用于测试，可自行随意修改
+
+    train_file = "datingTrainSet2.tsv"
+
+    train_data, train_labels = load_data(train_file)
+
+    print(classify0([65915, 3.868359, 0.128078], train_data, train_labels))
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--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+numpy==2.1.1
+requests==2.32.3