Train set/test set: We would like to point out that most prior work in unsupervised classification use both the train and test set during training. We believe this is bad practice and therefore propose to only train on the train set. The final numbers should be reported on the test set (see table 3 of our paper). This also allows us to directly compare with supervised and semi-supervised methods in the literature. We encourage future work to do the same. We observe around 2% improvement over the reported numbers when including the test set. Reproducibility: We noticed that prior work is very initialization sensitive. So, we don't think reporting a single number is therefore fair. We report our results as the mean and standard deviation over 10 runs. Please follow the instructions underneath to perform semantic clustering with SCAN.，这段话是什么意思

这段话是在介绍一个名为SCAN的无监督分类方法的实验设置和结果报告。其中，作者指出之前的一些无监督分类方法在训练时同时使用了训练集和测试集，这种做法是不好的，因为会导致模型泛化能力的过度估计。因此，作者建议只在训练集上进行训练，并在测试集上报告最终结果，以便与其他监督和半监督方法进行比较。作者还观察到，之前的一些方法非常依赖于初始化，因此为了保证结果的可重复性，作者进行了10次实验，并报告了平均值和标准差。最后，作者提供了使用SCAN进行语义聚类的指南。

相关问题

PS C:\WINDOWS\system32> Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1')) Forcing web requests to allow TLS v1.2 (Required for requests to Chocolatey.org) Getting latest version of the Chocolatey package for download. Not using proxy. Getting Chocolatey from https://community.chocolatey.org/api/v2/package/chocolatey/2.0.0. Downloading https://community.chocolatey.org/api/v2/package/chocolatey/2.0.0 to C:\Users\27748\AppData\Local\Temp\chocolatey\chocoInstall\chocolatey.zip Not using proxy. Extracting C:\Users\27748\AppData\Local\Temp\chocolatey\chocoInstall\chocolatey.zip to C:\Users\27748\AppData\Local\Temp\chocolatey\chocoInstall Installing Chocolatey on the local machine Creating ChocolateyInstall as an environment variable (targeting 'Machine') Setting ChocolateyInstall to 'C:\ProgramData\chocolatey' WARNING: It's very likely you will need to close and reopen your shell before you can use choco. Restricting write permissions to Administrators We are setting up the Chocolatey package repository. The packages themselves go to 'C:\ProgramData\chocolatey\lib' (i.e. C:\ProgramData\chocolatey\lib\yourPackageName). A shim file for the command line goes to 'C:\ProgramData\chocolatey\bin' and points to an executable in 'C:\ProgramData\chocolatey\lib\yourPackageName'. Creating Chocolatey folders if they do not already exist. chocolatey.nupkg file not installed in lib. Attempting to locate it from bootstrapper. PATH environment variable does not have C:\ProgramData\chocolatey\bin in it. Adding... 警告: Not setting tab completion: Profile file does not exist at 'C:\Users\27748\Documents\WindowsPowerShell\Microsoft.PowerShell_profile.ps1'. Chocolatey (choco.exe) is now ready. You can call choco from anywhere, command line or powershell by typing choco. Run choco /? for a list of functions. You may need to shut down and restart powershell and/or consoles first prior to using choco. Ensuring Chocolatey commands are on the path Ensuring chocolatey.nupkg is in the lib folder PS C:\WINDOWS\system32>

这段代码是在 Windows PowerShell 中安装 Chocolatey 的过程，Chocolatey 是一个 Windows 包管理器，可以用来方便地安装、更新和卸载各种软件。这段代码的作用是：

1. 设置 PowerShell 的执行策略为 Bypass，即不限制脚本的执行。
2. 设置 .NET Framework 的安全协议为 TLS 1.2，以便能够从 Chocolatey 官网下载安装包。
3. 使用 WebClient 类下载 Chocolatey 安装脚本，保存到临时文件夹。
4. 解压安装脚本，并执行其中的命令来安装 Chocolatey。
5. 将 Chocolatey 的安装路径加入 PATH 环境变量，以便在命令行中可以直接使用 choco 命令。

注意：在运行此脚本之前，建议先了解脚本的内容，确保它是安全的并符合你的需求。

RuntimeError: ./data/Prior.ckpt is a zip archive (did you mean to use torch.jit.load()?)

这个错误通常发生在使用 PyTorch 加载预训练模型时，当您尝试使用 `torch.load()` 加载一个被压缩的 Checkpoint 文件时会出现此错误。要解决这个问题，您可以使用以下两种方法之一：

1. 使用 `torch.jit.load()` 加载模型，如下所示：

import torch.jit

model = torch.jit.load('./data/Prior.ckpt')

2. 将 Checkpoint 文件解压缩并使用 `torch.load()` 加载模型，如下所示：

import zipfile
import torch

# 解压缩 Checkpoint 文件
with zipfile.ZipFile('./data/Prior.ckpt', 'r') as zip_ref:
    zip_ref.extractall('./data/')

# 加载模型
model = torch.load('./data/Prior.ckpt')

注意，如果您选择使用第二种方法，请确保解压缩后的文件名与原始文件名相同，并且解压缩后的文件位于正确的路径中。