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})(window)</script><meta name="generator" content="Hexo 6.3.0"></head><body><div id="web_bg"></div><div id="sidebar"><div id="menu-mask"></div><div id="sidebar-menus"><div class="avatar-img is-center"><img src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/Aliyunimg/2023/202309081559764.jpg" onerror="onerror=null;src='/img/friend_404.gif'" alt="avatar"/></div><div class="sidebar-site-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">38</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">44</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">22</div></a></div><hr class="custom-hr"/><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> 首页</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> 归档</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> 标签</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> 分类</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fa-solid fa-comment"></i><span> 留言板</span></a></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> 友链</span></a></div></div></div></div><div class="page" id="body-wrap"><header class="full_page" id="page-header" style="background-image: url('https://img.chen0495.top/img2022/202207290206838.png')"><nav id="nav"><span id="blog-info"><a href="/" title="Chen0495的空间站"><span class="site-name">Chen0495的空间站</span></a></span><div id="menus"><div id="search-button"><a class="site-page social-icon search" href="javascript:void(0);"><i class="fas fa-search fa-fw"></i><span> 搜索</span></a></div><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> 首页</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> 归档</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> 标签</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> 分类</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fa-solid fa-comment"></i><span> 留言板</span></a></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> 友链</span></a></div></div><div id="toggle-menu"><a class="site-page" href="javascript:void(0);"><i class="fas fa-bars fa-fw"></i></a></div></div></nav><div id="site-info"><h1 id="site-title">Chen0495的空间站</h1><div id="site-subtitle"><span id="subtitle"></span></div><div id="site_social_icons"><a class="social-icon" href="https://github.com/chen0495" target="_blank" title="Github"><i class="fab fa-github"></i></a><a class="social-icon" href="mailto:chenwein612@gmail" target="_blank" title="Email"><i class="fas fa-envelope"></i></a></div></div><div id="scroll-down"><i class="fas fa-angle-down scroll-down-effects"></i></div></header><main class="layout" id="content-inner"><div class="recent-posts" id="recent-posts"><div class="recent-post-item"><div class="post_cover left"><a href="/posts/26c1dbaf/" title="【公告】"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img2022/202207290218017.jpeg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【公告】"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/26c1dbaf/" title="【公告】">【公告】</a><div class="article-meta-wrap"><span class="article-meta"><i class="fas fa-thumbtack sticky"></i><span class="sticky">置顶</span><span class="article-meta-separator">|</span></span><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-07-25T05:46:03.000Z" title="发表于 2022-07-25 13:46:03">2022-07-25</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.542Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%97%A5%E5%BF%97/">日志</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E6%97%A5%E5%BF%97/%E5%85%AC%E5%91%8A/">公告</a></span></div><div class="content">¶最新公告 - 8/20/2022
修复了文章内点击目录h1标题不跳转的问题.
¶以往公告
¶7/29/2022
七牛对象存储作为图床用HTTPS费用有点高啊(按量付费)，图床又转回github了，国内访问看不到图
¶7/28/2022
原主题实现一些功能过于麻烦，故更换博客主题为Butterfly
¶7/27/2022
改版后尝试coding+github双线部署
jsDelivr被墙，所有样式源替换为360前端静态资源库lib.baomitu.com。
¶7/26/2022
评论区系统默认头像更新、添加IP显示功能。
¶7/25/2022
支持评论区系统。
</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/61c9a793/" title="机器学习常用算法汇总"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img2022/202301061516378.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="机器学习常用算法汇总"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/61c9a793/" title="机器学习常用算法汇总">机器学习常用算法汇总</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2023-01-06T01:20:00.000Z" title="发表于 2023-01-06 09:20:00">2023-01-06</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.513Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/">学习</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">¶KNN
分类算法
思想：
计算待预测点X到所有样本点xi的距离
认为距离最近的k个样本点中占比最大的类别为预测类别
优化：
kd树（类似二叉搜索树）
构建
设当前特征值x，y，z，将其按照方差由大到小排序（设z>y>x）
选择方差最大的特征z为当前kd树根节点，对数据项按照z的大小进行排序并按中位数进行划分为左右子kd树
递归进行划分，kd树的当前层特征选择不与上一层相同。
重复直到不可再进行划分
查找
得到一条查找路径，途径节点入栈
回溯节点：令r=预测点X到叶节点的距离，以预测点X为圆心，r为半径作圆，回溯途中根据圆是否涉及其他区域点来决定是否跳到其他子树上查找
返回叶节点
ball tree算法
¶Linear Regression
回归算法
思想：
正规方程法
对于方程y=w^T x，w为权重矩阵
设置损失函数
求令损失函数最小的w，即对损失函数求导等于零的w，推导如下
其中X右乘X^T使之成为方阵，可逆性由特征工程保证(不可逆说明有多余特征)
特征值多的样本计算量过大
梯度下降法
全梯度下降法(FG)：使用所有样本数 ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/posts/6c3f9019/" title="解决QQ邮箱三方登录授权码密保问题"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img2022/202209171056068.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="解决QQ邮箱三方登录授权码密保问题"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/6c3f9019/" title="解决QQ邮箱三方登录授权码密保问题">解决QQ邮箱三方登录授权码密保问题</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-09-17T02:57:00.000Z" title="发表于 2022-09-17 10:57:00">2022-09-17</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.978Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%97%A5%E5%BF%97/">日志</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E6%97%A5%E5%BF%97/%E9%9A%8F%E7%AC%94/">随笔</a></span></div><div class="content">¶问题描述
在尝试通过IMAP登录QQ邮箱时，会提示需要输入授权码，而授权码的设置需要进行密保验证，且提示要求设置二代密保。看到提示想当然的就会以为自己没设置二代密保，但登录qq安全中心后显示密保已设置，且无二代密保相关入口或提示，腾讯相关文档搜索只有相关文字描述也没有链接入口(疑似页面改版后文档未更新)。网络查找关键字“QQ邮箱二代密保”后，发现都是些二代密保申诉的教程，都不涉及邮箱授权码。
¶问题解决
尝试了最新版QQ、QQ邮箱等软件的安全中心入口，均无法找到二代密保相关信息，误打误撞之下发现了二代密保其实就是动态安全密码！！！腾讯的帮助文档真的写的和shi💩一样下载腾讯安全中心（等待漫长的初始化）即可设置，此时等待一会重试即可。
</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/f85198ba/" title="知识管理方案探讨&主流笔记软件体验"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img2022/202209131209904.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="知识管理方案探讨&主流笔记软件体验"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/f85198ba/" title="知识管理方案探讨&主流笔记软件体验">知识管理方案探讨&主流笔记软件体验</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-09-13T04:10:00.000Z" title="发表于 2022-09-13 12:10:00">2022-09-13</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.908Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%97%A5%E5%BF%97/">日志</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E6%97%A5%E5%BF%97/%E9%9A%8F%E7%AC%94/">随笔</a></span></div><div class="content">¶前言
平常本人使用hexo博客系统，用于记录学习笔记、生活随笔等，但是随着笔记的增多，一些碎片化的、不成体系的知识点又不足以单独发文章，了解到双链类笔记对知识体系的串联，于是开始寻找更好的知识管理方案。
以下体验仅代表个人意见。
¶软件体验
可以看看下了31个markdown编辑器，我就不信选不出一个好用的,这里介绍用过的几个印象深刻的笔记软件。
¶Obsidian
Obsidian 几乎所有功能都免费，仅仅针对官方数据同步、发布至网页端这两个功能进行收费。
主攻本地而不是云端也对我口味，双链的操作逻辑 [[双链]] 简单易记，好评（能用键盘不用鼠标，快捷键也要担心冲突和难记）。然而本地文件封闭，所以东西都集中在库里，文件交互不友好。
优点：数据本地储存、丰富的插件市场、md语法支持友好
缺点：本地文件系统封闭，所有文件保存于“库”中，不方便和其它软件产生的文件交互
¶Notion
可以说是一个非常全面的知识管理工具，UI也令人惊艳，如果我没有用过博客系统可能会用这个。
然而导入md代码缩进一堆问题，行内代码都能识别成代码块，公式也识别不了（关键是甚至不保留原始字符），体验极差，直接 ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/posts/285f4694/" title="【Pytorch学习 - 3】模型构建-聚类"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img2022/202208192013831.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【Pytorch学习 - 3】模型构建-聚类"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/285f4694/" title="【Pytorch学习 - 3】模型构建-聚类">【Pytorch学习 - 3】模型构建-聚类</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-08-21T12:15:03.000Z" title="发表于 2022-08-21 20:15:03">2022-08-21</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.298Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/">学习</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/Pytorch/">Pytorch</a></span></div><div class="content">¶什么是神经网络？
神经网络是由神经元通过层连接形成的，每个神经元是一个小的计算单元，每个神经元接受一个或多个带权重的输入，并通过激活函数(如sigmoid)判断是否产生一个有效输出。
如果我们希望能够控制神经元被激活的容易程度，可以对神经元加一个偏置值。
该神经元的输出结果公式即为下图所示，且由激活函数f(x)判断是否激活该神经元。
神经网络即是由这些神经元通过层连接形成的网络。不同神经元组成了3个层(输入层、隐藏层、输出层)，其中隐藏层可以有多层。
¶建立神经网络
torch.nn提供了自定义神经网络的几乎所有模块，在Pytorch中每个神经网络模型都是nn.Module的子类，神经网络模型在实战往往中通过“层”甚至“子神经网络”来构建。
1234567891011# 调包%matplotlib inlineimport osimport torchfrom torch import nnfrom torch.utils.data import DataLoaderfrom torchvision import datasets, transforms# 指定训练平台device ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/37c7b13e/" title="【Pytorch学习 - 2】数据集及其规范化"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img2022/202208192013831.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【Pytorch学习 - 2】数据集及其规范化"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/37c7b13e/" title="【Pytorch学习 - 2】数据集及其规范化">【Pytorch学习 - 2】数据集及其规范化</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-08-20T11:00:03.000Z" title="发表于 2022-08-20 19:00:03">2022-08-20</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.535Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/">学习</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/Pytorch/">Pytorch</a></span></div><div class="content">¶数据集来源
可使用torch.utils.data.Dataset来检索数据集中单个数据项，使用torch.utils.data.DataLoader来定义数据集迭代器(单次批量batch个)。
这里用到来自torchvision的Fashion-MNIST数据集，其中包含60000个训练图像和10000个测试图像。每个图像都是28x28的灰度图像，其中每个像素值是0到255之间的整数，共有10个类别。示例：
12345678910111213141516171819202122232425262728293031323334353637383940414243import torchfrom torch.utils.data import Datasetfrom torchvision import datasetsfrom torchvision.transforms import ToTensor, Lambdaimport matplotlib.pyplot as plt%matplotlib inlinetraining_data = datasets.FashionMNI ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/posts/1cc7acd9/" title="【Pytorch学习 - 1】何为Tensors?"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img2022/202208192013831.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【Pytorch学习 - 1】何为Tensors?"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/1cc7acd9/" title="【Pytorch学习 - 1】何为Tensors?">【Pytorch学习 - 1】何为Tensors?</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-08-19T11:46:03.000Z" title="发表于 2022-08-19 19:46:03">2022-08-19</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.275Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/">学习</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/Pytorch/">Pytorch</a></span></div><div class="content">¶Tensors介绍
Tensors可以简单地看成数组或矩阵(类似Numpy中的ndarrays)，在Pytorch中可用tensors来表示一个模型的输入、输出和参数.
可用tensor.shape tensor.dtype tensor.device来查看tensor的维度、数据类型和设备.
123%matplotlib inlineimport torchimport numpy as np
¶Tensor的初始化 - 1
Pytorch中的tensor结构有多种初始化的方法, 可以直接由List或者Numpy数组初始化, 但要注意由numpy array转tensor是一个原地操作, 两者共享存储地址, 只改变解释方式，改变tensor的同时会改变numpy array, 例如：
123456789101112131415# From Datadata = [[1, 2],[3, 4]]x_data = torch.tensor(data)# From Numpy arraynp_array = np.array(data)x_np = torch.from_numpy(np_ ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/25ac6164/" title="博客换主题啦~"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img2022/202207290213095.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="博客换主题啦~"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/25ac6164/" title="博客换主题啦~">博客换主题啦~</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-07-27T16:00:03.000Z" title="发表于 2022-07-28 00:00:03">2022-07-28</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.615Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%97%A5%E5%BF%97/">日志</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E6%97%A5%E5%BF%97/%E9%9A%8F%E7%AC%94/">随笔</a></span></div><div class="content">¶正文
原博客使用 Fluid主题 因为实现说说功能比较麻烦，折腾半天不够满意，故干脆更换主题为 Butterfly 。
安装及配置比较简单,跟着教程 Butterfly - Jerry 做就行了。
注意几个坑点：
全局背景图片链接要严格按照格式输入，不能直接输入图片地址，要加上url()。
网站底部信息背景的配置在footer_bg,推荐和全局背景相同，直接true会与文章的头图相同。
¶效果展示
下面为本人博客效果展示：
[{"url":"https://img.chen0495.top/img2022/202207290214448.png","alt":"封面"},{"url":"https://img.chen0495.top/img2022/202207290214638.png","alt":"主页"},{"url":"https://img.chen0495.top/img2022/202207290217312.png","alt":"留言板"},{"url":"https://img.chen0495.top/img2022/20220729 ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/posts/764c611d/" title="NGA论坛IP数据分析"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img2022/202207211552310.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="NGA论坛IP数据分析"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/764c611d/" title="NGA论坛IP数据分析">NGA论坛IP数据分析</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-07-25T19:10:00.000Z" title="发表于 2022-07-26 03:10:00">2022-07-26</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.180Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%95%99%E7%A8%8B/">教程</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E6%95%99%E7%A8%8B/%E7%88%AC%E8%99%AB/">爬虫</a></span></div><div class="content">¶前言
NGA论坛刚刚开放了用户IP显示功能，早就想查查泥潭精英充分的我连夜花费数个小时写了个IP爬虫出来，看看都是哪些人在泥潭大漩涡板块活跃
¶爬虫
¶包与headers
首先是配置headers：
12345678910111213141516import requests as reqfrom lxml import etreeimport numpy as npimport timeimport reheaders = { # 在浏览器中，network查看 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.5060.114 Safari/537.36 Edg/103.0.1264.62', 'Cookie': '', 'Connection':'close', ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/992a10c1/" title="高数知识点汇总(新)"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/img/20210508174550.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="高数知识点汇总(新)"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/992a10c1/" title="高数知识点汇总(新)">高数知识点汇总(新)</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-05-10T11:25:49.000Z" title="发表于 2022-05-10 19:25:49">2022-05-10</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-08T06:19:09.339Z" title="更新于 2023-09-08 14:19:09">2023-09-08</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/">学习</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/%E5%AD%A6%E4%B9%A0/%E9%AB%98%E6%95%B0/">高数</a></span></div><div class="content">¶函数、极限、连续
¶函数
sgn符号函数、[x]取整函数
复合函数：能否复合要看内层值域与外层定义域的交集是否为空易错点：复合后定义域应该取交集
易错点：f(x+1)定义域为[0,a]指的是x的范围，此时f(x)的定义域是x_2 = x_1+1的范围即[1,a+1]例题：
反函数：
单调函数一定有反函数，反之不一定
有无反函数看：对于任意y∈值域，有唯一的一个x与之对应
双曲正弦函数及其反函数
应该立马想到双曲正弦函数及其反函数均是奇函数
初等函数：初等函数的复合仍是初等函数
函数性质： 单调性、奇偶性、周期性、有界性
重点：奇偶性补充
周期函数的导函数是周期函数、易错点：周期函数的原函数不一定是周期函数
重点：周期性补充
重点：有界性补充
¶极限
题型：
极限的概念、性质及其存在准则
求极限
无穷小量的阶的比较
数列极限的定义（记忆）
易错点：数列有界性与其前面项没有关系
重点：局部有界性
数列极限：
加绝对值减少收敛性
数列极限趋向于0 == 数列极限绝对值趋于0
函数极限的定义（记忆）
±∞极限均存在且相等才能 ➡ ∞极限 ...</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/#content-inner">2</a><span class="space">…</span><a class="page-number" href="/page/4/#content-inner">4</a><a class="extend next" rel="next" href="/page/2/#content-inner"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://img.chen0495.top/Aliyunimg/2023/202309081559764.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">Chen0495</div><div class="author-info__description">古之立大事者，不惟有超世之才，亦必有坚韧不拔之志。</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">38</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">44</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">22</div></a></div><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/chen0495" target="_blank" title="Github"><i class="fab fa-github"></i></a><a class="social-icon" href="mailto:chenwein612@gmail" target="_blank" title="Email"><i class="fas fa-envelope"></i></a></div></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">因图床问题，国内访问看不到图 - 07/29/2022</div></div><div class="sticky_layout"><div class="card-widget card-categories"><div class="item-headline">
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