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    <article
  id="post-上海抗疫日记"
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    >上海抗疫日记</a> 
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  <time datetime="2022-04-03T12:00:12.000Z" itemprop="datePublished">2022-04-03</time>
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  <p>[全文约9000字]</p>
<h1 id="序"><a href="#序" class="headerlink" title="序"></a>序</h1><p>当日是2022年3月28日，是传统意义中上海封城（半封城）状态的第一天，已是上海交通大学封闭管理的第20天。</p>
<p>从3月9日早上得知封校，已经快三周了，校园管理正逐渐接近正常，但是校外似乎逐渐变了天。</p>
<p>回顾本次上海疫情至今，大概有三个时机，感受到上海疫情的逐渐严重：</p>
<ul>
<li>第一次是3月9日前，微博上广为流传的一则笑话：“上海此次疫情严重到什么程度呢，如果你的朋友圈没有人被隔离，那一定是因为你的朋友太少了。”然后3月9日就封校了，可能对校内很多学生来说，都是2020年以来，离疫情最近的一次，因为这次校内有了确诊病例。</li>
<li>第二次是3月12日前后，当时天天看上海疫情新闻发布会，最开始晚上8点开，后来下午4点开，再后来早上10点开，时间的变化模模糊糊地与两个事件的时间重合：<ol>
<li>确认嘉定和闵行的疫情来源主要是徐汇区的华亭宾馆，第二天发布会时间提前，而且邬惊雷主任约有2天没有来开发布会，我们都称“惊雷故事会”；</li>
<li>从大约18日开始，上海市疫情人数明显上升，发布会时间再次提前，晨报不再公告各病例的具体地址，而是按照区给出病例数和涉及的地址，如此一来，我们也难以统计学校后来有多少人确诊了。</li>
</ol>
</li>
<li>第三次是3月26-28日，逐渐有亲戚来主动询问上海疫情。封校2天后，我才告知父母；直到26日，还没有告诉过爷爷奶奶和其他亲戚，当亲戚朋友主动来问时，可想而知上海疫情在新闻上的曝光情况已经充分引起了千里之外的人们的注意。</li>
</ul>
<p>从个人视角来看，这是一个从校内抗疫逐渐转变为全市抗疫的过程。面对疾病及其传播速度，人们不由自主地会感到了恐惧；获取信息的途径几乎完全转为线上；衣食住行受到极大的阻碍，原有计划被一一打乱。疫情终究会过去，在这个可能数日、数月的抗疫过程中，我想通过主观视角厘清思绪，不被纷杂的谩骂、讽刺、自嘲、鼓吹所左右。</p> 
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  <time datetime="2021-10-24T14:29:43.000Z" itemprop="datePublished">2021-10-24</time>
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  <p>seaborn（以下简称 sns） 是以 matplotlib 为基础的绘图包，有非常丰富的绘图类型，而且形式精美，确实简化了画图的流程，非常适合统计领域的绘图。</p>
<p>目前尝试过的绘图方法有：scatter 散点图、regplot 拟合图、boxplot 箱线图、jointplot 联合分布图、distplot 分布图、heatmap热力图等。它们的画图风格普遍比直接用 matplotlib.pyplot 画图要好一点点，使用也非常方便，只是需要对其中的参数做一点点认识。</p> 
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    >python多子图绘制</a> 
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  <p>一张图往往不能容纳所有，多子图绘制是数据展示中非常重要的方法，网上资料零零散散，查阅起来不方便也不过瘾，现将所学整理成文档，力求简单易懂易上手。</p>
<p>Matplotlib 汲取了 MATLAB 和 R 语言中的绘图特点和方法，在多子图绘制方面有强大的功能，本文使用该库作多子图绘制方面的介绍。</p> 
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    >使用python实现数据滤波</a> 
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  <time datetime="2021-10-19T07:01:13.000Z" itemprop="datePublished">2021-10-19</time>
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  <p>针对高采集率、含噪数据，滤波可能是不可避免的处理方法。</p>
<p>滤波可能会导致一定程度的失真、丧失了频域信息等，但优点是关注了核心内容，合理使用会产生不错的效果。</p>
<p>除 MATLAB 外，Python 函数所带的 seaborn、pandas、scipy 等包均有一些基础的滤波方法。本文使用 scipy 包实现低通和带通滤波器，并对数据进行滤波。</p>
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    >波动数据的特征值提取</a> 
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  <time datetime="2021-10-18T06:56:51.000Z" itemprop="datePublished">2021-10-18</time>
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  <p>在试验中，类似位移、声压、声音之类的数据，数据值会有正有负，并且波动强烈，位移数据本身也常常用于振动信号的分析。怎样提取数据更加合理可靠呢？</p>
<p>比如某个试验中的位移信号，如下所示：</p>
<p><img src="https://typoraim.oss-cn-shanghai.aliyuncs.com/image/image-20211017104337223.png" alt="image-20211017104337223"></p>
<p>如果按照低通滤波（单纯的平滑滤波），那么所得结果就会是上图的蓝线所示。</p>
<p>相比原始数据（灰线），一方面丢失了高频信息，另一方面也丢失了原本的波动信息（变化情况）。</p> 
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    >写在25岁时</a> 
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  <p>写在25岁生日前一周，发布于2021年9月10日——25岁生日时。</p> 
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  <time datetime="2021-09-09T03:29:26.816Z" itemprop="datePublished">2021-09-09</time>
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  <p>Welcome to <a target="_blank" rel="noopener" href="https://hexo.io/">Hexo</a>! This is your very first post. Check <a target="_blank" rel="noopener" href="https://hexo.io/docs/">documentation</a> for more info. If you get any problems when using Hexo, you can find the answer in <a target="_blank" rel="noopener" href="https://hexo.io/docs/troubleshooting.html">troubleshooting</a> or you can ask me on <a target="_blank" rel="noopener" href="https://github.com/hexojs/hexo/issues">GitHub</a>.</p>
<h2 id="Quick-Start"><a href="#Quick-Start" class="headerlink" title="Quick Start"></a>Quick Start</h2><h3 id="Create-a-new-post"><a href="#Create-a-new-post" class="headerlink" title="Create a new post"></a>Create a new post</h3><figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">$ hexo new <span class="string">&quot;My New Post&quot;</span></span><br></pre></td></tr></table></figure>

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    >【统计学习方法】10-隐马尔可夫模型</a> 
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  <p><strong>一句话介绍：关于时序过程中含有不可观测的过程时，从观测结果反推模型参数的问题。</strong></p>
<hr>
<p>隐马尔可夫模型（hidden Markov model， HMM）</p>
<ul>
<li>属于生成模型</li>
<li>描述由隐藏的马尔可夫链随机生成观测序列的过程</li>
</ul> 
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    >【统计学习方法】9-EM算法及其推广</a> 
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  <p>一句话介绍：用于<strong>含有隐变量的概率模型参数的极大似然估计</strong>，是一种<strong>迭代算法</strong>，每次迭代有E步和M步两步，又称期望极大算法。</p>
<hr>
<p><strong>E：</strong>expectation</p>
<p><strong>M</strong>：maximization</p>
<p><strong>EM算法</strong>：用于<strong>含有隐变量的概率模型参数的极大似然估计</strong>，是一种<strong>迭代算法</strong>，每次迭代有E步和M步两步，又称期望极大算法。</p> 
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  <p>一句话介绍：对加法模型，使用前向分布算法实现从弱分类器到强分类器的提升。</p>
<hr>
<h1 id="0-提升方法"><a href="#0-提升方法" class="headerlink" title="0 提升方法"></a>0 提升方法</h1><p>分类问题中，通过改变训练样本的权重，学习多个分类器，并将这些分类器进行线性组合，可以提高分类的性能。</p>
<p>抽象一下：找一些比较粗糙的分类规则（弱学习方法、弱分类器）是相对容易的，那么可以从这些弱学习方法出发，将一系列弱分类器组合，构成一个强分类器。</p>
<h2 id="两个关键问题"><a href="#两个关键问题" class="headerlink" title="两个关键问题"></a>两个关键问题</h2><ul>
<li>在每一轮中如何改变训练数据的权值或概率分布</li>
<li>如何将弱分类器组合成强分类器</li>
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