fix: fix pxory network article details.
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@@ -161,7 +161,7 @@
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</div>
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<ul class="card-category-list" id="aside-cat-list">
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<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
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</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:19:31.591Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
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</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:26:08.219Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
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@@ -54,7 +54,7 @@
|
||||
isHome: false,
|
||||
isHighlightShrink: undefined,
|
||||
isToc: false,
|
||||
postUpdate: '2025-10-16 11:19:31'
|
||||
postUpdate: '2025-10-16 11:26:08'
|
||||
}</script><script>(win=>{
|
||||
win.saveToLocal = {
|
||||
set: (key, value, ttl) => {
|
||||
|
||||
@@ -54,7 +54,7 @@
|
||||
isHome: false,
|
||||
isHighlightShrink: undefined,
|
||||
isToc: false,
|
||||
postUpdate: '2025-10-16 11:19:31'
|
||||
postUpdate: '2025-10-16 11:26:08'
|
||||
}</script><script>(win=>{
|
||||
win.saveToLocal = {
|
||||
set: (key, value, ttl) => {
|
||||
|
||||
+8
-8
@@ -54,7 +54,7 @@
|
||||
isHome: true,
|
||||
isHighlightShrink: undefined,
|
||||
isToc: false,
|
||||
postUpdate: '2025-10-16 11:19:31'
|
||||
postUpdate: '2025-10-16 11:26:08'
|
||||
}</script><script>(win=>{
|
||||
win.saveToLocal = {
|
||||
set: (key, value, ttl) => {
|
||||
@@ -215,11 +215,7 @@ k值
|
||||
配置yum源
|
||||
下载 repo 文件：Centos-7.repo并上传到/tmp，进入到/tmp
|
||||
备份并且替换系统的repo文件1234 cp Centos-7.repo /etc/yum.repos.d/ cd /etc/yum.repos.d/ mv CentOS-Base.repo CentOS-Base.repo.bak mv Centos-7.repo CentOS-Base.repo
|
||||
将nn1上的CentOS-Base.repo拷贝到其他主机12345scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.dscp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.dscp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.dscp /et ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/61252.html" title="Hadoop集群HDFS配置">Hadoop集群HDFS配置</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 datetime="2024-09-11T14:45:40.000Z" title="发表于 2024-09-11 22:45:40">2024-09-11</time></span></div><div class="content">上传hadoop-3.1.4.tar.gz到/tmp，解压
|
||||
注意在六台机器均上传到/tmp
|
||||
|
||||
12345# 在6台机器执行sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/# 分发到其他主机ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop
|
||||
配置环境变量123echo 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/m ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/61251.html" title="Hadoop集群Zookeeper配置">Hadoop集群Zookeeper配置</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 datetime="2024-09-11T14:45:40.000Z" title="发表于 2024-09-11 22:45:40">2024-09-11</time></span></div><div class="content">Zookeeper脚本配置
|
||||
将nn1上的CentOS-Base.repo拷贝到其他主机12345scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.dscp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.dscp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.dscp /et ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/61251.html" title="Hadoop集群Zookeeper配置">Hadoop集群Zookeeper配置</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 datetime="2024-09-11T14:45:40.000Z" title="发表于 2024-09-11 22:45:40">2024-09-11</time></span></div><div class="content">Zookeeper脚本配置
|
||||
拷贝ips1cp ips ips_zookeeper
|
||||
修改为123nn1nn2nn3
|
||||
拷贝三个脚本123cp scp_all.sh scp_all_zookeeper.sh cp ssh_all.sh ssh_all_zookeeper.sh cp ssh_root.sh ssh_root_zookeeper.sh
|
||||
@@ -228,7 +224,11 @@ k值
|
||||
将三个脚本中的ips改为ips_zookeeper
|
||||
|
||||
Zookeeper安装
|
||||
上传到/tmp目录下，解压 12345sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/ssh_root_zookeepe ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/16107.html" title="Hello World">Hello World</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 datetime="2024-09-11T00:01:10.419Z" title="发表于 2024-09-11 08:01:10">2024-09-11</time></span></div><div class="content">Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.
|
||||
上传到/tmp目录下，解压 12345sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/ssh_root_zookeepe ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/61252.html" title="Hadoop集群HDFS配置">Hadoop集群HDFS配置</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 datetime="2024-09-11T14:45:40.000Z" title="发表于 2024-09-11 22:45:40">2024-09-11</time></span></div><div class="content">上传hadoop-3.1.4.tar.gz到/tmp，解压
|
||||
注意在六台机器均上传到/tmp
|
||||
|
||||
12345# 在6台机器执行sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/# 分发到其他主机ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop
|
||||
配置环境变量123echo 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/m ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/16107.html" title="Hello World">Hello World</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 datetime="2024-09-11T00:01:10.419Z" title="发表于 2024-09-11 08:01:10">2024-09-11</time></span></div><div class="content">Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.
|
||||
Quick StartCreate a new post1$ hexo new "My New Post"
|
||||
|
||||
More info: Writing
|
||||
@@ -248,7 +248,7 @@ More info: Deployment
|
||||
</div>
|
||||
<ul class="card-category-list" id="aside-cat-list">
|
||||
<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:19:31.591Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"><script>window.typedJSFn = {
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||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:26:08.219Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"><script>window.typedJSFn = {
|
||||
init: (str) => {
|
||||
window.typed = new Typed('#subtitle', Object.assign({
|
||||
strings: str,
|
||||
|
||||
@@ -196,7 +196,7 @@
|
||||
</div>
|
||||
<ul class="card-category-list" id="aside-cat-list">
|
||||
<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:19:31.591Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
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</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:26:08.219Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
|
||||
POWERMODE.shake = true;
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||||
POWERMODE.mobile = false;
|
||||
document.body.addEventListener('input', POWERMODE);
|
||||
|
||||
@@ -160,7 +160,7 @@
|
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</div>
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<ul class="card-category-list" id="aside-cat-list">
|
||||
<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
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</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:19:31.591Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
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</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:26:08.219Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
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POWERMODE.shake = true;
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POWERMODE.mobile = false;
|
||||
document.body.addEventListener('input', POWERMODE);
|
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|
||||
@@ -160,7 +160,7 @@
|
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</div>
|
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<ul class="card-category-list" id="aside-cat-list">
|
||||
<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:19:31.591Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:26:08.219Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
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||||
POWERMODE.shake = true;
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||||
POWERMODE.mobile = false;
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||||
document.body.addEventListener('input', POWERMODE);
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||||
|
||||
@@ -54,7 +54,7 @@
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||||
isHome: true,
|
||||
isHighlightShrink: undefined,
|
||||
isToc: false,
|
||||
postUpdate: '2025-10-16 11:19:31'
|
||||
postUpdate: '2025-10-16 11:26:08'
|
||||
}</script><script>(win=>{
|
||||
win.saveToLocal = {
|
||||
set: (key, value, ttl) => {
|
||||
@@ -219,7 +219,7 @@ deploy
|
||||
</div>
|
||||
<ul class="card-category-list" id="aside-cat-list">
|
||||
<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:19:31.591Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"><script>window.typedJSFn = {
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:26:08.219Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"><script>window.typedJSFn = {
|
||||
init: (str) => {
|
||||
window.typed = new Typed('#subtitle', Object.assign({
|
||||
strings: str,
|
||||
|
||||
@@ -166,7 +166,7 @@
|
||||
<h3 id="Deploy-to-remote-sites"><a href="#Deploy-to-remote-sites" class="headerlink" title="Deploy to remote sites"></a>Deploy to remote sites</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 deploy</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<p>More info: <a target="_blank" rel="noopener" href="https://hexo.io/docs/one-command-deployment.html">Deployment</a></p>
|
||||
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</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn">shenjianZ</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn/posts/16107.html">https://rq.shenjianl.cn/posts/16107.html</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas fa-circle-exclamation fa-fw"></i>版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://qr.shenjianl.cn/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://rq.shenjianl.cn" target="_blank">QuickReference</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"></div><div class="post_share"><div class="social-share" data-image="/img/avatar.jpg" data-sites="facebook,twitter,wechat,weibo,qq"></div><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/css/share.min.css" media="print" onload="this.media='all'"><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/js/social-share.min.js" defer></script></div></div><nav class="pagination-post" id="pagination"><div class="prev-post pull-left"><a href="/posts/61252.html" title="Hadoop集群HDFS配置"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">Hadoop集群HDFS配置</div></div></a></div><div class="next-post pull-right"><a href="/posts/34849.html" title="script"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">下一篇</div><div class="next_info">script</div></div></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="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细节</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/shenjianz"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></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">一个简单快捷的文档知识点查阅网站</div></div><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-2"><a class="toc-link" href="#Quick-Start"><span class="toc-number">1.</span> <span class="toc-text">Quick Start</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#Create-a-new-post"><span class="toc-number">1.1.</span> <span class="toc-text">Create a new post</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Run-server"><span class="toc-number">1.2.</span> <span class="toc-text">Run server</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Generate-static-files"><span class="toc-number">1.3.</span> <span class="toc-text">Generate static files</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Deploy-to-remote-sites"><span class="toc-number">1.4.</span> <span class="toc-text">Deploy to remote sites</span></a></li></ol></li></ol></div></div><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item"><a class="thumbnail" href="/posts/8816.html" title="集成学习"><img src="/img/machinelearning/ensemble-learning.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="集成Line truncated
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<figure class="highlight shell"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta prompt_">#</span><span class="language-bash">启动zookeeper客户端，并连接zookeeper集群</span></span><br><span class="line">/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">可以简化为:</span></span><br><span class="line">zkCli.sh</span><br></pre></td></tr></table></figure>
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</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn">shenjianZ</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn/posts/61251.html">https://rq.shenjianl.cn/posts/61251.html</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas fa-circle-exclamation fa-fw"></i>版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://qr.shenjianl.cn/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://rq.shenjianl.cn" target="_blank">QuickReference</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"></div><div class="post_share"><div class="social-share" data-image="/img/avatar.jpg" data-sites="facebook,twitter,wechat,weibo,qq"></div><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/css/share.min.css" media="print" onload="this.media='all'"><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/js/social-share.min.js" defer></script></div></div><nav class="pagination-post" id="pagination"><div class="prev-post pull-left"><a href="/posts/61252.html" title="Hadoop集群HDFS配置"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">Hadoop集群HDFS配置</div></div></a></div><div class="next-post pull-right"><a href="/posts/16107.html" title="Hello World"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">下一篇</div><div class="next_info">Hello World</div></div></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="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细节</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/shenjianz"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></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">一个简单快捷的文档知识点查阅网站</div></div><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-3"><a class="toc-link" href="#Zookeeper%E8%84%9A%E6%9C%AC%E9%85%8D%E7%BD%AE"><span class="toc-number">1.</span> <span class="toc-text">Zookeeper脚本配置</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Zookeeper%E5%AE%89%E8%A3%85"><span class="toc-number">2.</span> <span class="toc-text">Zookeeper安装</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Zookeeper%E9%85%8D%E7%BD%AE"><span class="toc-number">3.</span> <span class="toc-text">Zookeeper配置</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Zookeeper%E7%9A%84%E5%91%BD%E4%BB%A4"><span class="toc-number">4.</span> <span class="toc-text">Zookeeper的命令</span></a></li></ol></div></div><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item"><a class="thumbnail" href="/posts/8816.html" title="集成学习"><img src="/img/machinelearning/ensemble-learning.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="集成学习"/></a><div class="content"><a class="title" href="/posts/8816.html" title="集成学习">集成学习</a><time dLine truncated
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</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn">shenjianZ</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn/posts/61251.html">https://rq.shenjianl.cn/posts/61251.html</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas fa-circle-exclamation fa-fw"></i>版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://qr.shenjianl.cn/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://rq.shenjianl.cn" target="_blank">QuickReference</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"></div><div class="post_share"><div class="social-share" data-image="/img/avatar.jpg" data-sites="facebook,twitter,wechat,weibo,qq"></div><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/css/share.min.css" media="print" onload="this.media='all'"><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/js/social-share.min.js" defer></script></div></div><nav class="pagination-post" id="pagination"><div class="prev-post pull-left"><a href="/posts/61253.html" title="Hadoop集群搭建基础环境"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">Hadoop集群搭建基础环境</div></div></a></div><div class="next-post pull-right"><a href="/posts/61252.html" title="Hadoop集群HDFS配置"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">下一篇</div><div class="next_info">Hadoop集群HDFS配置</div></div></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="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细节</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/shenjianz"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></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">一个简单快捷的文档知识点查阅网站</div></div><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-3"><a class="toc-link" href="#Zookeeper%E8%84%9A%E6%9C%AC%E9%85%8D%E7%BD%AE"><span class="toc-number">1.</span> <span class="toc-text">Zookeeper脚本配置</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Zookeeper%E5%AE%89%E8%A3%85"><span class="toc-number">2.</span> <span class="toc-text">Zookeeper安装</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Zookeeper%E9%85%8D%E7%BD%AE"><span class="toc-number">3.</span> <span class="toc-text">Zookeeper配置</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Zookeeper%E7%9A%84%E5%91%BD%E4%BB%A4"><span class="toc-number">4.</span> <span class="toc-text">Zookeeper的命令</span></a></li></ol></div></div><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item"><a class="thumbnail" href="/posts/8816.html" title="集成学习"><img src="/img/machinelearning/ensemble-learning.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="集成学习"/></a><div class="content"><a class="title" href="/posts/8816.html" title=Line truncated
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<li><p>启动datanode节点，在s1、s2、s3执行</p>
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<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#启动各个节点的datanode</span></span><br><span class="line">hadoop-daemons.sh start datanode</span><br></pre></td></tr></table></figure></li>
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</ul>
|
||||
<h3 id="集群启动"><a href="#集群启动" class="headerlink" title="集群启动"></a>集群启动</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">start-dfs.sh</span><br></pre></td></tr></table></figure></article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn">shenjianZ</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn/posts/61252.html">https://rq.shenjianl.cn/posts/61252.html</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas fa-circle-exclamation fa-fw"></i>版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://qr.shenjianl.cn/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://rq.shenjianl.cn" target="_blank">QuickReference</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"></div><div class="post_share"><div class="social-share" data-image="/img/avatar.jpg" data-sites="facebook,twitter,wechat,weibo,qq"></div><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/css/share.min.css" media="print" onload="this.media='all'"><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/js/social-share.min.js" defer></script></div></div><nav class="pagination-post" id="pagination"><div class="prev-post pull-left"><a href="/posts/61253.html" title="Hadoop集群搭建基础环境"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">Hadoop集群搭建基础环境</div></div></a></div><div class="next-post pull-right"><a href="/posts/61251.html" title="Hadoop集群Zookeeper配置"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">下一篇</div><div class="next_info">Hadoop集群Zookeeper配置</div></div></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="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细节</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/shenjianz"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></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">一个简单快捷的文档知识点查阅网站</div></div><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%B8%8A%E4%BC%A0hadoop-3-1-4-tar-gz%E5%88%B0-tmp%EF%BC%8C%E8%A7%A3%E5%8E%8B"><span class="toc-number">1.</span> <span class="toc-text">上传hadoop-3.1.4.tar.gz到/tmp，解压</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E9%85%8D%E7%BD%AE%E7%8E%AF%E5%A2%83%E5%8F%98%E9%87%8F"><span class="toc-number">2.</span> <span class="toc-text">配置环境变量</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BF%AE%E6%94%B9core-site-xml"><span class="toc-number">3.</span> <span class="toc-text">修改core-site.xml</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BF%AE%E6%94%B9hdfs-site-xml"><span class="toc-number">4.</span> <span class="toc-text">修改hdfs-site.xml</span></a></li><li class="toc-item toc-level-3"><a class="toc-linkLine truncated
|
||||
<h3 id="集群启动"><a href="#集群启动" class="headerlink" title="集群启动"></a>集群启动</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">start-dfs.sh</span><br></pre></td></tr></table></figure></article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn">shenjianZ</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn/posts/61252.html">https://rq.shenjianl.cn/posts/61252.html</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas fa-circle-exclamation fa-fw"></i>版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://qr.shenjianl.cn/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://rq.shenjianl.cn" target="_blank">QuickReference</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"></div><div class="post_share"><div class="social-share" data-image="/img/avatar.jpg" data-sites="facebook,twitter,wechat,weibo,qq"></div><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/css/share.min.css" media="print" onload="this.media='all'"><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/js/social-share.min.js" defer></script></div></div><nav class="pagination-post" id="pagination"><div class="prev-post pull-left"><a href="/posts/61251.html" title="Hadoop集群Zookeeper配置"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">Hadoop集群Zookeeper配置</div></div></a></div><div class="next-post pull-right"><a href="/posts/16107.html" title="Hello World"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">下一篇</div><div class="next_info">Hello World</div></div></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="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细节</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/shenjianz"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></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">一个简单快捷的文档知识点查阅网站</div></div><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%B8%8A%E4%BC%A0hadoop-3-1-4-tar-gz%E5%88%B0-tmp%EF%BC%8C%E8%A7%A3%E5%8E%8B"><span class="toc-number">1.</span> <span class="toc-text">上传hadoop-3.1.4.tar.gz到/tmp，解压</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E9%85%8D%E7%BD%AE%E7%8E%AF%E5%A2%83%E5%8F%98%E9%87%8F"><span class="toc-number">2.</span> <span class="toc-text">配置环境变量</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BF%AE%E6%94%B9core-site-xml"><span class="toc-number">3.</span> <span class="toc-text">修改core-site.xml</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BF%AE%E6%94%B9hdfs-site-xml"><span class="toc-number">4.</span> <span class="toc-text">修改hdfs-site.xml</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BF%AE%E6%94%B9hadoop-env-Line truncated
|
||||
POWERMODE.shake = true;
|
||||
POWERMODE.mobile = false;
|
||||
document.body.addEventListener('input', POWERMODE);
|
||||
|
||||
@@ -208,7 +208,7 @@
|
||||
<li><p>将 <code>/home/hadoop/bin</code>添加到hadoop用户的环境变量，需要切换到<code>hadoop</code>用户</p>
|
||||
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">echo</span> <span class="string">'export PATH=$PATH:/home/hadoop/bin'</span> >> ~/.bashrc && <span class="built_in">source</span> ~/.bashrc</span><br><span class="line">scp_all.sh /home/hadoop/.bashrc /home/hadoop/</span><br><span class="line">ssh_all.sh <span class="built_in">source</span> ~/.bashrc </span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn">shenjianZ</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn/posts/61253.html">https://rq.shenjianl.cn/posts/61253.html</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas fa-circle-exclamation fa-fw"></i>版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://qr.shenjianl.cn/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://rq.shenjianl.cn" target="_blank">QuickReference</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"></div><div class="post_share"><div class="social-share" data-image="/img/avatar.jpg" data-sites="facebook,twitter,wechat,weibo,qq"></div><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/css/share.min.css" media="print" onload="this.media='all'"><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/js/social-share.min.js" defer></script></div></div><nav class="pagination-post" id="pagination"><div class="prev-post pull-left"><a href="/posts/29139.html" title="k近邻算法（K-Nearest Neighbors）KNN"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">k近邻算法（K-Nearest Neighbors）KNN</div></div></a></div><div class="next-post pull-right"><a href="/posts/61252.html" title="Hadoop集群HDFS配置"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">下一篇</div><div class="next_info">Hadoop集群HDFS配置</div></div></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="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细节</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/shenjianz"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></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">一个简单快捷的文档知识点查阅网站</div></div><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-3"><a class="toc-link" href="#%E9%98%B2%E7%81%AB%E5%A2%99%E5%85%B3%E9%97%AD"><span class="toc-number">1.</span> <span class="toc-text">防火墙关闭</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E9%85%8D%E7%BD%AEyum%E6%BA%90"><span class="toc-number">2.</span> <span class="toc-text">配置yum源</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#JDK%E5%AE%89%E8%A3%85"><span class="toc-number">3.</span> <span class="toc-text">JDK安装</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BF%AE%E6%94%B9%E4%B8%BB%E6%9C%BA%E5%90%8D%E5%92%8C%E4%B8%BB%E6%9C%BA%E5%90%8D%E6%98%A0%E5%B0%84"><span class="toc-number">4.</span> <span class="toc-text">修改主机名和主机名映射</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%88%9B%E5%BB%BAhadoop%E7%94%A8%E6%88%B7"><span class="toc-number">5.</span> <span class="toc-text">创建hadoop用户</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%A6%81%E6%AD%A2%E9%9D%9E-wheel-%E7%BB%84%E7%94%A8%E6%88%B7%E5%88%87%E6%8D%A2%E5%88%B0root%EF%BC%8C%E9%85%8D%E7%BD%AE%E5%85%Line truncated
|
||||
</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn">shenjianZ</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="https://rq.shenjianl.cn/posts/61253.html">https://rq.shenjianl.cn/posts/61253.html</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas fa-circle-exclamation fa-fw"></i>版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://qr.shenjianl.cn/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://rq.shenjianl.cn" target="_blank">QuickReference</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"></div><div class="post_share"><div class="social-share" data-image="/img/avatar.jpg" data-sites="facebook,twitter,wechat,weibo,qq"></div><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/css/share.min.css" media="print" onload="this.media='all'"><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/js/social-share.min.js" defer></script></div></div><nav class="pagination-post" id="pagination"><div class="prev-post pull-left"><a href="/posts/29139.html" title="k近邻算法（K-Nearest Neighbors）KNN"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">k近邻算法（K-Nearest Neighbors）KNN</div></div></a></div><div class="next-post pull-right"><a href="/posts/61251.html" title="Hadoop集群Zookeeper配置"><div class="cover" style="background: var(--default-bg-color)"></div><div class="pagination-info"><div class="label">下一篇</div><div class="next_info">Hadoop集群Zookeeper配置</div></div></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="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细节</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/shenjianz"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></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">一个简单快捷的文档知识点查阅网站</div></div><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-3"><a class="toc-link" href="#%E9%98%B2%E7%81%AB%E5%A2%99%E5%85%B3%E9%97%AD"><span class="toc-number">1.</span> <span class="toc-text">防火墙关闭</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E9%85%8D%E7%BD%AEyum%E6%BA%90"><span class="toc-number">2.</span> <span class="toc-text">配置yum源</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#JDK%E5%AE%89%E8%A3%85"><span class="toc-number">3.</span> <span class="toc-text">JDK安装</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BF%AE%E6%94%B9%E4%B8%BB%E6%9C%BA%E5%90%8D%E5%92%8C%E4%B8%BB%E6%9C%BA%E5%90%8D%E6%98%A0%E5%B0%84"><span class="toc-number">4.</span> <span class="toc-text">修改主机名和主机名映射</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%88%9B%E5%BB%BAhadoop%E7%94%A8%E6%88%B7"><span class="toc-number">5.</span> <span class="toc-text">创建hadoop用户</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%A6%81%E6%AD%A2%E9%9D%9E-wheel-%E7%BB%84%E7%94%A8%E6%88%B7%E5%88%87%E6%8D%A2%E5%88%B0root%EF%BC%8C%E9%85%8D%E7%BDLine truncated
|
||||
POWERMODE.shake = true;
|
||||
POWERMODE.mobile = false;
|
||||
document.body.addEventListener('input', POWERMODE);
|
||||
|
||||
+300
-300
@@ -10,22 +10,13 @@
|
||||
]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Hello World</title>
|
||||
<url>/posts/16107.html</url>
|
||||
<content><![CDATA[<p>Welcome to <a href="https://hexo.io/">Hexo</a>! This is your very first post. Check <a 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 href="https://hexo.io/docs/troubleshooting.html">troubleshooting</a> or you can ask me on <a 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="code"><pre><span class="line">$ hexo new <span class="string">"My New Post"</span></span><br></pre></td></tr></table></figure>
|
||||
<title>script</title>
|
||||
<url>/posts/34849.html</url>
|
||||
<content><![CDATA[<h3 id="查看CPU、内存使用率"><a href="#查看CPU、内存使用率" class="headerlink" title="查看CPU、内存使用率"></a>查看CPU、内存使用率</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="meta">#!/bin/bash</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 定义颜色</span></span><br><span class="line">RED=<span class="string">'\033[0;31m'</span></span><br><span class="line">GREEN=<span class="string">'\033[0;32m'</span></span><br><span class="line">YELLOW=<span class="string">'\033[0;33m'</span></span><br><span class="line">BLUE=<span class="string">'\033[0;34m'</span></span><br><span class="line">NC=<span class="string">'\033[0m'</span> <span class="comment"># 无颜色</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">while</span> <span class="literal">true</span>; <span class="keyword">do</span></span><br><span class="line"> <span class="comment"># 获取所有进程的CPU使用率和内存使用率</span></span><br><span class="line"> cpu_usage=$(ps aux | awk <span class="string">'{sum_cpu += $3} END {print sum_cpu}'</span>)</span><br><span class="line"> mem_usage=$(ps aux | awk <span class="string">'{sum_mem += $4} END {print sum_mem}'</span>)</span><br><span class="line"> </span><br><span class="line"> <span class="comment"># 打印结果，带有时间戳、分隔线和颜色高亮</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${BLUE}</span>==============================<span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${YELLOW}</span>Timestamp: <span class="subst">$(date)</span><span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${BLUE}</span>==============================<span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${GREEN}</span>Total CPU usage: <span class="variable">${RED}</span><span class="variable">$cpu_usage</span>%<span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${GREEN}</span>Total Memory usage: <span class="variable">${RED}</span><span class="variable">$mem_usage</span>%<span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${BLUE}</span>==============================<span class="variable">${NC}</span>"</span></span><br><span class="line"> </span><br><span class="line"> <span class="comment"># 等待5秒后再次执行</span></span><br><span class="line"> <span class="built_in">sleep</span> 5</span><br><span class="line"><span class="keyword">done</span></span><br><span class="line"></span><br></pre></td></tr></table></figure>
|
||||
<p><strong>保存脚本到/usr/local/bin目录下</strong></p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">mv</span> usage.sh /usr/local/bin/usage</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<p>More info: <a href="https://hexo.io/docs/writing.html">Writing</a></p>
|
||||
<h3 id="Run-server"><a href="#Run-server" class="headerlink" title="Run server"></a>Run server</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">$ hexo server</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<p>More info: <a href="https://hexo.io/docs/server.html">Server</a></p>
|
||||
<h3 id="Generate-static-files"><a href="#Generate-static-files" class="headerlink" title="Generate static files"></a>Generate static files</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">$ hexo generate</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<p>More info: <a href="https://hexo.io/docs/generating.html">Generating</a></p>
|
||||
<h3 id="Deploy-to-remote-sites"><a href="#Deploy-to-remote-sites" class="headerlink" title="Deploy to remote sites"></a>Deploy to remote sites</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">$ hexo deploy</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<p>More info: <a href="https://hexo.io/docs/one-command-deployment.html">Deployment</a></p>
|
||||
]]></content>
|
||||
<h3 id="Shell脚本编写的基本信息"><a href="#Shell脚本编写的基本信息" class="headerlink" title="Shell脚本编写的基本信息"></a>Shell脚本编写的基本信息</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="meta">#! /bin/bash</span></span><br><span class="line"><span class="comment"># -------------------------------------------------</span></span><br><span class="line"><span class="comment"># Filename: test.sh</span></span><br><span class="line"><span class="comment"># Version: 1.0</span></span><br><span class="line"><span class="comment"># Date: 2024/05/02</span></span><br><span class="line"><span class="comment"># Author: shenjianZ</span></span><br><span class="line"><span class="comment"># Email: shenjianZLT@gmail.com</span></span><br><span class="line"><span class="comment"># Website: https://blog.shenjianl.cn</span></span><br><span class="line"><span class="comment"># Description: this is a test shell</span></span><br><span class="line"><span class="comment"># CopyRight: 2024 All rights reserved shenjianZ</span></span><br><span class="line"><span class="comment"># License GPL</span></span><br><span class="line"><span class="comment"># ------------------------------------------------</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># Your script logic goes here</span></span><br></pre></td></tr></table></figure>]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>C lang</title>
|
||||
@@ -47,6 +38,24 @@
|
||||
<tag>C C++</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Hello World</title>
|
||||
<url>/posts/16107.html</url>
|
||||
<content><![CDATA[<p>Welcome to <a href="https://hexo.io/">Hexo</a>! This is your very first post. Check <a 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 href="https://hexo.io/docs/troubleshooting.html">troubleshooting</a> or you can ask me on <a 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="code"><pre><span class="line">$ hexo new <span class="string">"My New Post"</span></span><br></pre></td></tr></table></figure>
|
||||
|
||||
<p>More info: <a href="https://hexo.io/docs/writing.html">Writing</a></p>
|
||||
<h3 id="Run-server"><a href="#Run-server" class="headerlink" title="Run server"></a>Run server</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">$ hexo server</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<p>More info: <a href="https://hexo.io/docs/server.html">Server</a></p>
|
||||
<h3 id="Generate-static-files"><a href="#Generate-static-files" class="headerlink" title="Generate static files"></a>Generate static files</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">$ hexo generate</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<p>More info: <a href="https://hexo.io/docs/generating.html">Generating</a></p>
|
||||
<h3 id="Deploy-to-remote-sites"><a href="#Deploy-to-remote-sites" class="headerlink" title="Deploy to remote sites"></a>Deploy to remote sites</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">$ hexo deploy</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<p>More info: <a href="https://hexo.io/docs/one-command-deployment.html">Deployment</a></p>
|
||||
]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>决策树算法</title>
|
||||
<url>/posts/95.html</url>
|
||||
@@ -91,67 +100,6 @@
|
||||
<tag>decisiontree</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>集成学习</title>
|
||||
<url>/posts/8816.html</url>
|
||||
<content><![CDATA[<h3 id="Bagging"><a href="#Bagging" class="headerlink" title="Bagging"></a>Bagging</h3><h3 id="随机森林"><a href="#随机森林" class="headerlink" title="随机森林"></a>随机森林</h3><blockquote>
|
||||
<p><code>Random-Forest</code> 就是<code>Bagging + Decisiontree</code></p>
|
||||
</blockquote>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> seaborn <span class="keyword">as</span> sns</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split,GridSearchCV</span><br><span class="line"><span class="keyword">from</span> sklearn.feature_extraction <span class="keyword">import</span> DictVectorizer</span><br><span class="line"><span class="keyword">from</span> sklearn.ensemble <span class="keyword">import</span> RandomForestClassifier</span><br><span class="line"><span class="comment"># 1.获取数据集 - 加载 Titanic 数据集</span></span><br><span class="line">titanic = sns.load_dataset(<span class="string">'titanic'</span>)</span><br><span class="line">missing_age_count = titanic[<span class="string">'age'</span>].isna().<span class="built_in">sum</span>()</span><br><span class="line"><span class="comment"># print(f"缺失的 age 数量: {missing_age_count}")</span></span><br><span class="line"><span class="comment"># 2. 数据基本处理</span></span><br><span class="line"><span class="comment"># 2.1 确认特征值、目标值</span></span><br><span class="line">X = titanic[[<span class="string">'pclass'</span>,<span class="string">'age'</span>,<span class="string">'sex'</span>]]</span><br><span class="line">y = titanic[<span class="string">'survived'</span>]</span><br><span class="line"><span class="comment"># 2.2 缺失值处理</span></span><br><span class="line">X.loc[:, <span class="string">'age'</span>] = X[<span class="string">'age'</span>].fillna(value=X[<span class="string">'age'</span>].mean()) <span class="comment"># 使用 .loc 进行修改</span></span><br><span class="line"><span class="comment"># 2.3 划分数据集</span></span><br><span class="line">X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=<span class="number">22</span>)</span><br><span class="line"><span class="comment"># 3. 特征工程（字典特征提取）</span></span><br><span class="line">X_train = X_train.to_dict(orient=<span class="string">"records"</span>)</span><br><span class="line">X_test= X_test.to_dict(orient=<span class="string">"records"</span>)</span><br><span class="line">transfer = DictVectorizer()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test)</span><br><span class="line"><span class="comment"># 4. 机器学习 随机森林</span></span><br><span class="line">rf = RandomForestClassifier()</span><br><span class="line">gc = GridSearchCV(estimator=rf ,param_grid={<span class="string">"n_estimators"</span>:[<span class="number">100</span>,<span class="number">120</span>,<span class="number">300</span>],<span class="string">"max_depth"</span>:[<span class="number">3</span>,<span class="number">7</span>,<span class="number">11</span>]},cv=<span class="number">3</span>)</span><br><span class="line">gc.fit(X_train,y_train)</span><br><span class="line">y_pred = gc.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型的测试集的预测值：<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line">ret = gc.score(X_test,y_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳模型在测试集上的评分:<span class="subst">{ret}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳模型的参数：<span class="subst">{gc.best_estimator_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳模型在训练集上的评分：<span class="subst">{gc.best_score_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(X_test.toarray())</span><br></pre></td></tr></table></figure>
|
||||
<p><img src="/img/machinelearning/random-forest.png"></p>
|
||||
<h3 id="ott案例"><a href="#ott案例" class="headerlink" title="ott案例"></a>ott案例</h3><figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"><span class="keyword">from</span> imblearn.under_sampling <span class="keyword">import</span> RandomUnderSampler</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> LabelEncoder</span><br><span class="line"><span class="keyword">from</span> sklearn.ensemble <span class="keyword">import</span> RandomForestClassifier</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> log_loss</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> OneHotEncoder</span><br><span class="line"><span class="comment"># 1. 获取数据集</span></span><br><span class="line">data = pd.read_csv(<span class="string">'./data/train.csv'</span>)</span><br><span class="line"><span class="comment"># 查看目标值分类</span></span><br><span class="line"><span class="keyword">import</span> seaborn <span class="keyword">as</span> sns</span><br><span class="line">sns.countplot(data=data, x=<span class="string">'target'</span>, hue=<span class="string">'target'</span>, palette=<span class="string">"Set2"</span>, legend=<span class="literal">False</span>) <span class="comment"># 使用 hue='target' 替代 palette</span></span><br><span class="line">plt.show()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 数据集的基本处理</span></span><br><span class="line"><span class="comment"># 2.1 确定特征值、目标值</span></span><br><span class="line">x = data.drop([<span class="string">"id"</span>, <span class="string">"target"</span>], axis=<span class="number">1</span>)</span><br><span class="line">y = data[<span class="string">'target'</span>]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.2 使用随机欠采样进行平衡</span></span><br><span class="line">undersampler = RandomUnderSampler(sampling_strategy=<span class="string">'auto'</span>, random_state=<span class="number">0</span>)</span><br><span class="line">x_resampled, y_resampled = undersampler.fit_resample(x, y)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 查看欠采样后的类别分布</span></span><br><span class="line"><span class="comment"># print(f"欠采样后训练集中的类别分布：\n{y_train_resampled.value_counts()}")</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.3. 将标签转换为数字</span></span><br><span class="line">le = LabelEncoder()</span><br><span class="line">y_resampled = le.fit_transform(y_resampled)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.4. 划分训练集和测试集</span></span><br><span class="line">x_train, x_test, y_train, y_test = train_test_split(x_resampled, y_resampled, test_size=<span class="number">0.2</span>)</span><br><span class="line"></span><br><span class="line"> <span class="comment"># 3. 机器学习</span></span><br><span class="line">rf = RandomForestClassifier(oob_score = <span class="literal">True</span>)</span><br><span class="line">rf.fit(x_train,y_train)</span><br><span class="line">y_pred = rf.predict(x_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"预测值:<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"评分：<span class="subst">{rf.score(x_test,y_test)}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># # 4. 模型评估 （解决二分类预测问题）</span></span><br><span class="line"><span class="comment"># import numpy as np</span></span><br><span class="line"><span class="comment"># from sklearn.metrics import log_loss</span></span><br><span class="line"><span class="comment"># # 假设 y_pred_prob 是通过 predict_proba 得到的预测概率</span></span><br><span class="line"><span class="comment"># # 对预测概率进行裁剪，将其限制在 [eps, 1-eps] 范围内</span></span><br><span class="line"><span class="comment"># Line truncated
|
||||
<p><img src="/img/machinelearning/ott.png"></p>
|
||||
]]></content>
|
||||
<categories>
|
||||
<category>machinelearning</category>
|
||||
</categories>
|
||||
<tags>
|
||||
<tag>ensemble-learning</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>线性回归</title>
|
||||
<url>/posts/52662.html</url>
|
||||
<content><![CDATA[<h3 id="线性回归简介"><a href="#线性回归简介" class="headerlink" title="线性回归简介"></a>线性回归简介</h3><blockquote>
|
||||
<p>用于预测一个连续的目标变量（因变量），与一个或多个特征（自变量）之间存在线性关系。</p>
|
||||
</blockquote>
|
||||
<p>假设函数：<br>$$y = w_1x_1 + w_2x_2 + \cdot\cdot\cdot+w_nx_n$$</p>
|
||||
<ul>
|
||||
<li>$y$ 是目标变量（因变量），即我们希望预测的值。</li>
|
||||
<li>$x1,x2,…,xn$ 是特征变量（自变量），即输入的值。</li>
|
||||
</ul>
|
||||
<h3 id="损失函数"><a href="#损失函数" class="headerlink" title="损失函数"></a>损失函数</h3><p>为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 <strong>均方误差（MSE）</strong>：<br>$$J(\theta) = \frac{1}{2N} \sum_{i=1}^{N} (y_i - f_\theta(x_i))^2$$</p>
|
||||
<ul>
|
||||
<li>N 是样本的数量。</li>
|
||||
<li>$y_i$ 是第 i 个样本的真实值。</li>
|
||||
<li>$f_\theta(x_i)$ 是模型预测的第 i 个样本的值。</li>
|
||||
</ul>
|
||||
<h3 id="线性回归优化"><a href="#线性回归优化" class="headerlink" title="线性回归优化"></a>线性回归优化</h3><ul>
|
||||
<li><p>梯度下降法</p>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> fetch_california_housing</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> SGDRegressor</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_squared_error</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1. 获取数据集</span></span><br><span class="line">housing = fetch_california_housing()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 数据集处理</span></span><br><span class="line"><span class="comment"># 2.1 分割数据集</span></span><br><span class="line">X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程</span></span><br><span class="line"><span class="comment"># 3.1 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test) <span class="comment"># 使用 transform() 而不是 fit_transform()</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 4.机器学习- 梯度下降法</span></span><br><span class="line">estimater = SGDRegressor(max_iter=<span class="number">1000</span>, eta0=<span class="number">0.01</span>)</span><br><span class="line">estimater.fit(X_train, y_train)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"SGD模型的偏置是：<span class="subst">{estimater.intercept_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"SGD模型的系数是：<span class="subst">{estimater.coef_}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line">y_pred = estimater.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"SGD模型预测值：<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line">mse = mean_squared_error(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"SGD模型均方误差:<span class="subst">{mse}</span>"</span>)</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>正规方程</p>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> fetch_california_housing</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> LinearRegression</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_squared_error</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1. 获取数据集</span></span><br><span class="line">housing = fetch_california_housing()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 数据集处理</span></span><br><span class="line"><span class="comment"># 2.1 分割数据集</span></span><br><span class="line">X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程</span></span><br><span class="line"><span class="comment"># 3.1 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.fit_transform(X_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 4.机器学习- 线性回归</span></span><br><span class="line">estimater = LinearRegression()</span><br><span class="line">estimater.fit(X_train, y_train)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型的偏置是：<span class="subst">{estimater.intercept_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型的系数是：<span class="subst">{estimater.coef_}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line">y_pred = estimater.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型预测值：<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line">mse = mean_squared_error(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型均方误差:<span class="subst">{mse}</span>"</span>)</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>岭回归</p>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> fetch_california_housing</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> Ridge, RidgeCV</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_squared_error</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1. 获取数据集</span></span><br><span class="line">housing = fetch_california_housing()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 数据集处理</span></span><br><span class="line"><span class="comment"># 2.1 分割数据集</span></span><br><span class="line">X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程</span></span><br><span class="line"><span class="comment"># 3.1 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test) <span class="comment"># 使用 transform() 而不是 fit_transform()</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 4.机器学习- 岭回归 使用了Ridge的alpha的搜索</span></span><br><span class="line"><span class="comment"># estimater = Ridge(alpha=1.0)</span></span><br><span class="line">estimater = RidgeCV(alphas=[<span class="number">0.001</span>, <span class="number">0.01</span>, <span class="number">0.1</span>, <span class="number">1</span>, <span class="number">10</span>, <span class="number">100</span>])</span><br><span class="line">estimater.fit(X_train, y_train)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"Ridge模型的偏置是：<span class="subst">{estimater.intercept_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"Ridge模型的系数是：<span class="subst">{estimater.coef_}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 查看最佳 alpha</span></span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳 alpha 值是：<span class="subst">{estimater.alpha_}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line">y_pred = estimater.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"Ridge模型预测值：<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line">mse = mean_squared_error(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"Ridge模型均方误差:<span class="subst">{mse}</span>"</span>)</span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
<p>这样每个代码块的缩进保持一致，便于阅读和理解。如果有其他优化需求，随时告诉我！</p>
|
||||
<p><img src="/img/machinelearning/linear.png"></p>
|
||||
<p><img src="/img/machinelearning/fitting.png"></p>
|
||||
<h3 id="模型保存和加载"><a href="#模型保存和加载" class="headerlink" title="模型保存和加载"></a>模型保存和加载</h3><figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> fetch_california_housing</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> Ridge, RidgeCV</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_squared_error</span><br><span class="line"><span class="keyword">import</span> joblib</span><br><span class="line"></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">save_model</span>():</span><br><span class="line"> <span class="comment"># 1. 获取数据集</span></span><br><span class="line"> housing = fetch_california_housing()</span><br><span class="line"> <span class="comment"># 2. 数据集处理</span></span><br><span class="line"> <span class="comment"># 2.1 分割数据集</span></span><br><span class="line"> X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"> <span class="comment"># 3. 特征工程</span></span><br><span class="line"> <span class="comment"># 3.1 标准化</span></span><br><span class="line"> transfer = StandardScaler()</span><br><span class="line"> X_train = transfer.fit_transform(X_train)</span><br><span class="line"> X_test = transfer.transform(X_test) <span class="comment"># 使用 transform() 而不是 fit_transform()</span></span><br><span class="line"> <span class="comment"># 4. 机器学习 - 岭回归 使用了Ridge的alpha的搜索</span></span><br><span class="line"> estimater = RidgeCV(alphas=[<span class="number">0.001</span>, <span class="number">0.01</span>, <span class="number">0.1</span>, <span class="number">1</span>, <span class="number">10</span>, <span class="number">100</span>])</span><br><span class="line"> estimater.fit(X_train, y_train)</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Ridge模型的偏置是：<span class="subst">{estimater.intercept_}</span>"</span>)</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Ridge模型的系数是：<span class="subst">{estimater.coef_}</span>"</span>)</span><br><span class="line"> <span class="comment"># 保存模型</span></span><br><span class="line"> joblib.dump(estimater, <span class="string">'ridge_model.pkl'</span>)</span><br><span class="line"> <span class="comment"># 查看最佳 alpha</span></span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"最佳 alpha 值是：<span class="subst">{estimater.alpha_}</span>"</span>)</span><br><span class="line"> <span class="comment"># 5. 模型评估</span></span><br><span class="line"> y_pred = estimater.predict(X_test)</span><br><span class="line"> mse = mean_squared_error(y_test, y_pred)</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Ridge模型均方误差:<span class="subst">{mse}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">load_model</span>():</span><br><span class="line"> <span class="comment"># 1. 获取数据集</span></span><br><span class="line"> housing = fetch_california_housing()</span><br><span class="line"> <span class="comment"># 2. 数据集处理</span></span><br><span class="line"> <span class="comment"># 2.1 分割数据集</span></span><br><span class="line"> X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"> <span class="comment"># 3. 特征工程</span></span><br><span class="line"> <span class="comment"># 3.1 标准化</span></span><br><span class="line"> transfer = StandardScaler()</span><br><span class="line"> X_train = transfer.fit_transform(X_train)</span><br><span class="line"> X_test = transfer.transform(X_test) <span class="comment"># 使用 transform() 而不是 fit_transform()</span></span><br><span class="line"> <span class="comment"># 加载模型</span></span><br><span class="line"> estimater = joblib.load(<span class="string">'ridge_model.pkl'</span>)</span><br><span class="lineLine truncated
|
||||
<categories>
|
||||
<category>machinelearning</category>
|
||||
</categories>
|
||||
<tags>
|
||||
<tag>linear-regression</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>网络相关</title>
|
||||
<url>/posts/41168.html</url>
|
||||
<content><![CDATA[]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>k近邻算法（K-Nearest Neighbors）KNN</title>
|
||||
<url>/posts/29139.html</url>
|
||||
@@ -218,13 +166,196 @@
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>script</title>
|
||||
<url>/posts/34849.html</url>
|
||||
<content><![CDATA[<h3 id="查看CPU、内存使用率"><a href="#查看CPU、内存使用率" class="headerlink" title="查看CPU、内存使用率"></a>查看CPU、内存使用率</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="meta">#!/bin/bash</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 定义颜色</span></span><br><span class="line">RED=<span class="string">'\033[0;31m'</span></span><br><span class="line">GREEN=<span class="string">'\033[0;32m'</span></span><br><span class="line">YELLOW=<span class="string">'\033[0;33m'</span></span><br><span class="line">BLUE=<span class="string">'\033[0;34m'</span></span><br><span class="line">NC=<span class="string">'\033[0m'</span> <span class="comment"># 无颜色</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">while</span> <span class="literal">true</span>; <span class="keyword">do</span></span><br><span class="line"> <span class="comment"># 获取所有进程的CPU使用率和内存使用率</span></span><br><span class="line"> cpu_usage=$(ps aux | awk <span class="string">'{sum_cpu += $3} END {print sum_cpu}'</span>)</span><br><span class="line"> mem_usage=$(ps aux | awk <span class="string">'{sum_mem += $4} END {print sum_mem}'</span>)</span><br><span class="line"> </span><br><span class="line"> <span class="comment"># 打印结果，带有时间戳、分隔线和颜色高亮</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${BLUE}</span>==============================<span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${YELLOW}</span>Timestamp: <span class="subst">$(date)</span><span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${BLUE}</span>==============================<span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${GREEN}</span>Total CPU usage: <span class="variable">${RED}</span><span class="variable">$cpu_usage</span>%<span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${GREEN}</span>Total Memory usage: <span class="variable">${RED}</span><span class="variable">$mem_usage</span>%<span class="variable">${NC}</span>"</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">"<span class="variable">${BLUE}</span>==============================<span class="variable">${NC}</span>"</span></span><br><span class="line"> </span><br><span class="line"> <span class="comment"># 等待5秒后再次执行</span></span><br><span class="line"> <span class="built_in">sleep</span> 5</span><br><span class="line"><span class="keyword">done</span></span><br><span class="line"></span><br></pre></td></tr></table></figure>
|
||||
<p><strong>保存脚本到/usr/local/bin目录下</strong></p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">mv</span> usage.sh /usr/local/bin/usage</span><br></pre></td></tr></table></figure>
|
||||
<title>集成学习</title>
|
||||
<url>/posts/8816.html</url>
|
||||
<content><![CDATA[<h3 id="Bagging"><a href="#Bagging" class="headerlink" title="Bagging"></a>Bagging</h3><h3 id="随机森林"><a href="#随机森林" class="headerlink" title="随机森林"></a>随机森林</h3><blockquote>
|
||||
<p><code>Random-Forest</code> 就是<code>Bagging + Decisiontree</code></p>
|
||||
</blockquote>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> seaborn <span class="keyword">as</span> sns</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split,GridSearchCV</span><br><span class="line"><span class="keyword">from</span> sklearn.feature_extraction <span class="keyword">import</span> DictVectorizer</span><br><span class="line"><span class="keyword">from</span> sklearn.ensemble <span class="keyword">import</span> RandomForestClassifier</span><br><span class="line"><span class="comment"># 1.获取数据集 - 加载 Titanic 数据集</span></span><br><span class="line">titanic = sns.load_dataset(<span class="string">'titanic'</span>)</span><br><span class="line">missing_age_count = titanic[<span class="string">'age'</span>].isna().<span class="built_in">sum</span>()</span><br><span class="line"><span class="comment"># print(f"缺失的 age 数量: {missing_age_count}")</span></span><br><span class="line"><span class="comment"># 2. 数据基本处理</span></span><br><span class="line"><span class="comment"># 2.1 确认特征值、目标值</span></span><br><span class="line">X = titanic[[<span class="string">'pclass'</span>,<span class="string">'age'</span>,<span class="string">'sex'</span>]]</span><br><span class="line">y = titanic[<span class="string">'survived'</span>]</span><br><span class="line"><span class="comment"># 2.2 缺失值处理</span></span><br><span class="line">X.loc[:, <span class="string">'age'</span>] = X[<span class="string">'age'</span>].fillna(value=X[<span class="string">'age'</span>].mean()) <span class="comment"># 使用 .loc 进行修改</span></span><br><span class="line"><span class="comment"># 2.3 划分数据集</span></span><br><span class="line">X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=<span class="number">22</span>)</span><br><span class="line"><span class="comment"># 3. 特征工程（字典特征提取）</span></span><br><span class="line">X_train = X_train.to_dict(orient=<span class="string">"records"</span>)</span><br><span class="line">X_test= X_test.to_dict(orient=<span class="string">"records"</span>)</span><br><span class="line">transfer = DictVectorizer()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test)</span><br><span class="line"><span class="comment"># 4. 机器学习 随机森林</span></span><br><span class="line">rf = RandomForestClassifier()</span><br><span class="line">gc = GridSearchCV(estimator=rf ,param_grid={<span class="string">"n_estimators"</span>:[<span class="number">100</span>,<span class="number">120</span>,<span class="number">300</span>],<span class="string">"max_depth"</span>:[<span class="number">3</span>,<span class="number">7</span>,<span class="number">11</span>]},cv=<span class="number">3</span>)</span><br><span class="line">gc.fit(X_train,y_train)</span><br><span class="line">y_pred = gc.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型的测试集的预测值：<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line">ret = gc.score(X_test,y_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳模型在测试集上的评分:<span class="subst">{ret}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳模型的参数：<span class="subst">{gc.best_estimator_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳模型在训练集上的评分：<span class="subst">{gc.best_score_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(X_test.toarray())</span><br></pre></td></tr></table></figure>
|
||||
<p><img src="/img/machinelearning/random-forest.png"></p>
|
||||
<h3 id="ott案例"><a href="#ott案例" class="headerlink" title="ott案例"></a>ott案例</h3><figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"><span class="keyword">from</span> imblearn.under_sampling <span class="keyword">import</span> RandomUnderSampler</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> LabelEncoder</span><br><span class="line"><span class="keyword">from</span> sklearn.ensemble <span class="keyword">import</span> RandomForestClassifier</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> log_loss</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> OneHotEncoder</span><br><span class="line"><span class="comment"># 1. 获取数据集</span></span><br><span class="line">data = pd.read_csv(<span class="string">'./data/train.csv'</span>)</span><br><span class="line"><span class="comment"># 查看目标值分类</span></span><br><span class="line"><span class="keyword">import</span> seaborn <span class="keyword">as</span> sns</span><br><span class="line">sns.countplot(data=data, x=<span class="string">'target'</span>, hue=<span class="string">'target'</span>, palette=<span class="string">"Set2"</span>, legend=<span class="literal">False</span>) <span class="comment"># 使用 hue='target' 替代 palette</span></span><br><span class="line">plt.show()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 数据集的基本处理</span></span><br><span class="line"><span class="comment"># 2.1 确定特征值、目标值</span></span><br><span class="line">x = data.drop([<span class="string">"id"</span>, <span class="string">"target"</span>], axis=<span class="number">1</span>)</span><br><span class="line">y = data[<span class="string">'target'</span>]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.2 使用随机欠采样进行平衡</span></span><br><span class="line">undersampler = RandomUnderSampler(sampling_strategy=<span class="string">'auto'</span>, random_state=<span class="number">0</span>)</span><br><span class="line">x_resampled, y_resampled = undersampler.fit_resample(x, y)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 查看欠采样后的类别分布</span></span><br><span class="line"><span class="comment"># print(f"欠采样后训练集中的类别分布：\n{y_train_resampled.value_counts()}")</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.3. 将标签转换为数字</span></span><br><span class="line">le = LabelEncoder()</span><br><span class="line">y_resampled = le.fit_transform(y_resampled)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.4. 划分训练集和测试集</span></span><br><span class="line">x_train, x_test, y_train, y_test = train_test_split(x_resampled, y_resampled, test_size=<span class="number">0.2</span>)</span><br><span class="line"></span><br><span class="line"> <span class="comment"># 3. 机器学习</span></span><br><span class="line">rf = RandomForestClassifier(oob_score = <span class="literal">True</span>)</span><br><span class="line">rf.fit(x_train,y_train)</span><br><span class="line">y_pred = rf.predict(x_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"预测值:<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"评分：<span class="subst">{rf.score(x_test,y_test)}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># # 4. 模型评估 （解决二分类预测问题）</span></span><br><span class="line"><span class="comment"># import numpy as np</span></span><br><span class="line"><span class="comment"># from sklearn.metrics import log_loss</span></span><br><span class="line"><span class="comment"># # 假设 y_pred_prob 是通过 predict_proba 得到的预测概率</span></span><br><span class="line"><span class="comment"># # 对预测概率进行裁剪，将其限制在 [eps, 1-eps] 范围内</span></span><br><span class="line"><span class="comment"># Line truncated
|
||||
<p><img src="/img/machinelearning/ott.png"></p>
|
||||
]]></content>
|
||||
<categories>
|
||||
<category>machinelearning</category>
|
||||
</categories>
|
||||
<tags>
|
||||
<tag>ensemble-learning</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>线性回归</title>
|
||||
<url>/posts/52662.html</url>
|
||||
<content><![CDATA[<h3 id="线性回归简介"><a href="#线性回归简介" class="headerlink" title="线性回归简介"></a>线性回归简介</h3><blockquote>
|
||||
<p>用于预测一个连续的目标变量（因变量），与一个或多个特征（自变量）之间存在线性关系。</p>
|
||||
</blockquote>
|
||||
<p>假设函数：<br>$$y = w_1x_1 + w_2x_2 + \cdot\cdot\cdot+w_nx_n$$</p>
|
||||
<ul>
|
||||
<li>$y$ 是目标变量（因变量），即我们希望预测的值。</li>
|
||||
<li>$x1,x2,…,xn$ 是特征变量（自变量），即输入的值。</li>
|
||||
</ul>
|
||||
<h3 id="损失函数"><a href="#损失函数" class="headerlink" title="损失函数"></a>损失函数</h3><p>为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 <strong>均方误差（MSE）</strong>：<br>$$J(\theta) = \frac{1}{2N} \sum_{i=1}^{N} (y_i - f_\theta(x_i))^2$$</p>
|
||||
<ul>
|
||||
<li>N 是样本的数量。</li>
|
||||
<li>$y_i$ 是第 i 个样本的真实值。</li>
|
||||
<li>$f_\theta(x_i)$ 是模型预测的第 i 个样本的值。</li>
|
||||
</ul>
|
||||
<h3 id="线性回归优化"><a href="#线性回归优化" class="headerlink" title="线性回归优化"></a>线性回归优化</h3><ul>
|
||||
<li><p>梯度下降法</p>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> fetch_california_housing</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> SGDRegressor</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_squared_error</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1. 获取数据集</span></span><br><span class="line">housing = fetch_california_housing()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 数据集处理</span></span><br><span class="line"><span class="comment"># 2.1 分割数据集</span></span><br><span class="line">X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程</span></span><br><span class="line"><span class="comment"># 3.1 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test) <span class="comment"># 使用 transform() 而不是 fit_transform()</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 4.机器学习- 梯度下降法</span></span><br><span class="line">estimater = SGDRegressor(max_iter=<span class="number">1000</span>, eta0=<span class="number">0.01</span>)</span><br><span class="line">estimater.fit(X_train, y_train)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"SGD模型的偏置是：<span class="subst">{estimater.intercept_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"SGD模型的系数是：<span class="subst">{estimater.coef_}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line">y_pred = estimater.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"SGD模型预测值：<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line">mse = mean_squared_error(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"SGD模型均方误差:<span class="subst">{mse}</span>"</span>)</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>正规方程</p>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> fetch_california_housing</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> LinearRegression</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_squared_error</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1. 获取数据集</span></span><br><span class="line">housing = fetch_california_housing()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 数据集处理</span></span><br><span class="line"><span class="comment"># 2.1 分割数据集</span></span><br><span class="line">X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程</span></span><br><span class="line"><span class="comment"># 3.1 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.fit_transform(X_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 4.机器学习- 线性回归</span></span><br><span class="line">estimater = LinearRegression()</span><br><span class="line">estimater.fit(X_train, y_train)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型的偏置是：<span class="subst">{estimater.intercept_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型的系数是：<span class="subst">{estimater.coef_}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line">y_pred = estimater.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型预测值：<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line">mse = mean_squared_error(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型均方误差:<span class="subst">{mse}</span>"</span>)</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>岭回归</p>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> fetch_california_housing</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> Ridge, RidgeCV</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_squared_error</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1. 获取数据集</span></span><br><span class="line">housing = fetch_california_housing()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 数据集处理</span></span><br><span class="line"><span class="comment"># 2.1 分割数据集</span></span><br><span class="line">X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程</span></span><br><span class="line"><span class="comment"># 3.1 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test) <span class="comment"># 使用 transform() 而不是 fit_transform()</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 4.机器学习- 岭回归 使用了Ridge的alpha的搜索</span></span><br><span class="line"><span class="comment"># estimater = Ridge(alpha=1.0)</span></span><br><span class="line">estimater = RidgeCV(alphas=[<span class="number">0.001</span>, <span class="number">0.01</span>, <span class="number">0.1</span>, <span class="number">1</span>, <span class="number">10</span>, <span class="number">100</span>])</span><br><span class="line">estimater.fit(X_train, y_train)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"Ridge模型的偏置是：<span class="subst">{estimater.intercept_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"Ridge模型的系数是：<span class="subst">{estimater.coef_}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 查看最佳 alpha</span></span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳 alpha 值是：<span class="subst">{estimater.alpha_}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line">y_pred = estimater.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"Ridge模型预测值：<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line">mse = mean_squared_error(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"Ridge模型均方误差:<span class="subst">{mse}</span>"</span>)</span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
<p>这样每个代码块的缩进保持一致，便于阅读和理解。如果有其他优化需求，随时告诉我！</p>
|
||||
<p><img src="/img/machinelearning/linear.png"></p>
|
||||
<p><img src="/img/machinelearning/fitting.png"></p>
|
||||
<h3 id="模型保存和加载"><a href="#模型保存和加载" class="headerlink" title="模型保存和加载"></a>模型保存和加载</h3><figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> fetch_california_housing</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> Ridge, RidgeCV</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_squared_error</span><br><span class="line"><span class="keyword">import</span> joblib</span><br><span class="line"></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">save_model</span>():</span><br><span class="line"> <span class="comment"># 1. 获取数据集</span></span><br><span class="line"> housing = fetch_california_housing()</span><br><span class="line"> <span class="comment"># 2. 数据集处理</span></span><br><span class="line"> <span class="comment"># 2.1 分割数据集</span></span><br><span class="line"> X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"> <span class="comment"># 3. 特征工程</span></span><br><span class="line"> <span class="comment"># 3.1 标准化</span></span><br><span class="line"> transfer = StandardScaler()</span><br><span class="line"> X_train = transfer.fit_transform(X_train)</span><br><span class="line"> X_test = transfer.transform(X_test) <span class="comment"># 使用 transform() 而不是 fit_transform()</span></span><br><span class="line"> <span class="comment"># 4. 机器学习 - 岭回归 使用了Ridge的alpha的搜索</span></span><br><span class="line"> estimater = RidgeCV(alphas=[<span class="number">0.001</span>, <span class="number">0.01</span>, <span class="number">0.1</span>, <span class="number">1</span>, <span class="number">10</span>, <span class="number">100</span>])</span><br><span class="line"> estimater.fit(X_train, y_train)</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Ridge模型的偏置是：<span class="subst">{estimater.intercept_}</span>"</span>)</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Ridge模型的系数是：<span class="subst">{estimater.coef_}</span>"</span>)</span><br><span class="line"> <span class="comment"># 保存模型</span></span><br><span class="line"> joblib.dump(estimater, <span class="string">'ridge_model.pkl'</span>)</span><br><span class="line"> <span class="comment"># 查看最佳 alpha</span></span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"最佳 alpha 值是：<span class="subst">{estimater.alpha_}</span>"</span>)</span><br><span class="line"> <span class="comment"># 5. 模型评估</span></span><br><span class="line"> y_pred = estimater.predict(X_test)</span><br><span class="line"> mse = mean_squared_error(y_test, y_pred)</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Ridge模型均方误差:<span class="subst">{mse}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">load_model</span>():</span><br><span class="line"> <span class="comment"># 1. 获取数据集</span></span><br><span class="line"> housing = fetch_california_housing()</span><br><span class="line"> <span class="comment"># 2. 数据集处理</span></span><br><span class="line"> <span class="comment"># 2.1 分割数据集</span></span><br><span class="line"> X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=<span class="number">0.25</span>)</span><br><span class="line"> <span class="comment"># 3. 特征工程</span></span><br><span class="line"> <span class="comment"># 3.1 标准化</span></span><br><span class="line"> transfer = StandardScaler()</span><br><span class="line"> X_train = transfer.fit_transform(X_train)</span><br><span class="line"> X_test = transfer.transform(X_test) <span class="comment"># 使用 transform() 而不是 fit_transform()</span></span><br><span class="line"> <span class="comment"># 加载模型</span></span><br><span class="line"> estimater = joblib.load(<span class="string">'ridge_model.pkl'</span>)</span><br><span class="lineLine truncated
|
||||
<categories>
|
||||
<category>machinelearning</category>
|
||||
</categories>
|
||||
<tags>
|
||||
<tag>linear-regression</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>逻辑回归</title>
|
||||
<url>/posts/60504.html</url>
|
||||
<content><![CDATA[<h3 id="logistic-regression-code"><a href="#logistic-regression-code" class="headerlink" title="logistic regression code"></a>logistic regression code</h3><figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> load_breast_cancer</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> LogisticRegression</span><br><span class="line"><span class="comment"># 1. 加载乳腺癌数据集</span></span><br><span class="line">data = load_breast_cancer()</span><br><span class="line"><span class="comment"># 2.1 数据集基本处理</span></span><br><span class="line">df = pd.DataFrame(data.data, columns=data.feature_names)</span><br><span class="line">df[<span class="string">'target'</span>] = data.target</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> df.columns:</span><br><span class="line"> <span class="comment"># 检查列是否有缺失值</span></span><br><span class="line"> <span class="keyword">if</span> np.<span class="built_in">any</span>(pd.isnull(df[i])):</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Filling missing values in column: <span class="subst">{i}</span>"</span>)</span><br><span class="line"><span class="comment">#2.2 确认特征值、目标值</span></span><br><span class="line">X = df.iloc[:,<span class="number">0</span>:df.shape[<span class="number">1</span>] - <span class="number">1</span>]</span><br><span class="line">y = df.loc[:,<span class="string">"target"</span>]</span><br><span class="line"><span class="comment"># 2.3 分割数据</span></span><br><span class="line">X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=<span class="number">0.3</span>)</span><br><span class="line"><span class="comment"># 显示前几行数据</span></span><br><span class="line">df.head(<span class="number">1</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 4 机器学习 逻辑回归</span></span><br><span class="line">estimator = LogisticRegression()</span><br><span class="line">estimator.fit(X_train,y_train)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型准确率：<span class="subst">{estimator.score(X_test,y_test)}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型预测值为:\n<span class="subst">{estimator.predict(X_test)}</span>"</span>)</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<h3 id="Shell脚本编写的基本信息"><a href="#Shell脚本编写的基本信息" class="headerlink" title="Shell脚本编写的基本信息"></a>Shell脚本编写的基本信息</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="meta">#! /bin/bash</span></span><br><span class="line"><span class="comment"># -------------------------------------------------</span></span><br><span class="line"><span class="comment"># Filename: test.sh</span></span><br><span class="line"><span class="comment"># Version: 1.0</span></span><br><span class="line"><span class="comment"># Date: 2024/05/02</span></span><br><span class="line"><span class="comment"># Author: shenjianZ</span></span><br><span class="line"><span class="comment"># Email: shenjianZLT@gmail.com</span></span><br><span class="line"><span class="comment"># Website: https://blog.shenjianl.cn</span></span><br><span class="line"><span class="comment"># Description: this is a test shell</span></span><br><span class="line"><span class="comment"># CopyRight: 2024 All rights reserved shenjianZ</span></span><br><span class="line"><span class="comment"># License GPL</span></span><br><span class="line"><span class="comment"># ------------------------------------------------</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># Your script logic goes here</span></span><br></pre></td></tr></table></figure>]]></content>
|
||||
<h3 id="分类评估的参数"><a href="#分类评估的参数" class="headerlink" title="分类评估的参数"></a>分类评估的参数</h3><ul>
|
||||
<li><p>准确率<br>准确率是所有预测正确的样本占总样本的比例<br>$$Accuracy = \frac{TP+TN}{TP+FN+FP+TN}$$</p>
|
||||
</li>
|
||||
<li><p>精准率<br>精准率（又称查准率）是指所有被预测为正类的样本中，真正为正类的比例<br>$$Precision = \frac{TP}{TP+FP}$$</p>
|
||||
</li>
|
||||
<li><p>召回率<br>召回率（又称查全率）是指所有实际为正类的样本中，被正确预测为正类的比例<br>$$Recall = \frac{TP}{TP+FN}$$</p>
|
||||
</li>
|
||||
<li><p>F1-score<br>F1 值（F1 Score）是精准率和召回率的调和平均数，综合考虑了精准率和召回率的影响。<br>$$ F1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} $$</p>
|
||||
</li>
|
||||
<li><p>roc曲线<br>tpr、fpr来衡量不平衡的二分类问题</p>
|
||||
</li>
|
||||
</ul>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> load_breast_cancer</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> LogisticRegression</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> classification_report, roc_auc_score</span><br><span class="line"><span class="comment"># 1. 加载乳腺癌数据集</span></span><br><span class="line">data = load_breast_cancer()</span><br><span class="line"><span class="comment"># 2.1 数据集基本处理</span></span><br><span class="line">df = pd.DataFrame(data.data, columns=data.feature_names)</span><br><span class="line">df[<span class="string">'target'</span>] = data.target</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> df.columns:</span><br><span class="line"> <span class="comment"># 检查列是否有缺失值</span></span><br><span class="line"> <span class="keyword">if</span> np.<span class="built_in">any</span>(pd.isnull(df[i])):</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Filling missing values in column: <span class="subst">{i}</span>"</span>)</span><br><span class="line"><span class="comment"># 2.2 确认特征值、目标值</span></span><br><span class="line">X = df.iloc[:, <span class="number">0</span>:df.shape[<span class="number">1</span>] - <span class="number">1</span>]</span><br><span class="line">y = df.loc[:, <span class="string">"target"</span>]</span><br><span class="line"><span class="comment"># 2.3 分割数据</span></span><br><span class="line">X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span class="number">0.3</span>)</span><br><span class="line"><span class="comment"># 显示前几行数据</span></span><br><span class="line">df.head(<span class="number">1</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 4 机器学习 逻辑回归</span></span><br><span class="line">estimator = LogisticRegression()</span><br><span class="line">estimator.fit(X_train, y_train)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型准确率：<span class="subst">{estimator.score(X_test, y_test)}</span>"</span>)</span><br><span class="line">y_pred = estimator.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型预测值为:\n<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line"><span class="comment"># 5.1 精确率、召回率</span></span><br><span class="line">ret = classification_report(y_test, y_pred, labels=[<span class="number">1</span>, <span class="number">0</span>], target_names=[<span class="string">"良性"</span>, <span class="string">"恶性"</span>])</span><br><span class="line">roc_score = roc_auc_score(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"准确率、召回率：<span class="subst">{ret}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"roc_score:<span class="subst">{roc_score}</span>"</span>)</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<h3 id="类别不平衡的处理"><a href="#类别不平衡的处理" class="headerlink" title="类别不平衡的处理"></a>类别不平衡的处理</h3><p>先准备类别不平衡的数据</p>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> imblearn.over_sampling <span class="keyword">import</span> RandomOverSampler,SMOTE</span><br><span class="line"><span class="keyword">from</span> imblearn.under_sampling <span class="keyword">import</span> RandomUnderSampler</span><br><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> make_classification</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"><span class="keyword">from</span> collections <span class="keyword">import</span> Counter</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1.准备类别不平衡的数据</span></span><br><span class="line">X, y = make_classification(</span><br><span class="line"> n_samples=<span class="number">5000</span>,</span><br><span class="line"> n_features=<span class="number">2</span>,</span><br><span class="line"> n_informative=<span class="number">2</span>,</span><br><span class="line"> n_redundant=<span class="number">0</span>,</span><br><span class="line"> n_repeated=<span class="number">0</span>,</span><br><span class="line"> n_classes=<span class="number">3</span>,</span><br><span class="line"> n_clusters_per_class=<span class="number">1</span>,</span><br><span class="line"> weights=[<span class="number">0.01</span>, <span class="number">0.05</span>, <span class="number">0.94</span>],</span><br><span class="line"> random_state=<span class="number">0</span>,</span><br><span class="line">)</span><br><span class="line">counter = Counter(y)</span><br><span class="line">plt.scatter(X[:,<span class="number">0</span>],X[:,<span class="number">1</span>],c=y)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<ul>
|
||||
<li>过采样<br>增加训练集的少数的类别的样本，使得正反例样本数据接近 <ul>
|
||||
<li>随机过采样（RandomOverSampler)<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">ros = RandomOverSampler()</span><br><span class="line">X_resampled,y_resampled = ros.fit_resample(X,y)</span><br><span class="line"><span class="built_in">print</span>(Counter(y_resampled))</span><br><span class="line">plt.scatter(X_resampled[:,<span class="number">0</span>],X_resampled[:,<span class="number">1</span>],c=y_resampled)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
|
||||
<img src="/img/machinelearning/over_random_sampling.png"></li>
|
||||
<li><code>SMOTE</code>过采样（SMOTE）<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">smote = SMOTE()</span><br><span class="line">X_resampled,y_resampled = smote.fit_resample(X,y)</span><br><span class="line"><span class="built_in">print</span>(Counter(y_resampled))</span><br><span class="line">plt.scatter(X_resampled[:,<span class="number">0</span>],X_resampled[:,<span class="number">1</span>],c=y_resampled)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
|
||||
<img src="/img/machinelearning/over_smote_sampling.png"></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>欠采样<br>减少训练集的多数的类别的样本，使得正反例样本数据接近<ul>
|
||||
<li>随机欠采样（RandomUnderSampler）<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">rus = RandomUnderSampler(random_state=<span class="number">0</span>)</span><br><span class="line">X_resampled,y_resampled = rus.fit_resample(X,y)</span><br><span class="line"><span class="built_in">print</span>(Counter(y_resampled))</span><br><span class="line">plt.scatter(X_resampled[:,<span class="number">0</span>],X_resampled[:,<span class="number">1</span>],c=y_resampled)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
|
||||
<img src="/img/machinelearning/under_sampling.png"></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
]]></content>
|
||||
<categories>
|
||||
<category>machinelearning</category>
|
||||
</categories>
|
||||
<tags>
|
||||
<tag>logistic-regression</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>网络相关</title>
|
||||
<url>/posts/41168.html</url>
|
||||
<content><![CDATA[]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Docker被墙，如何继续使用？</title>
|
||||
<url>/posts/47807.html</url>
|
||||
<content><![CDATA[<h2 id="Docker-Download"><a href="#Docker-Download" class="headerlink" title="Docker Download"></a>Docker Download</h2><blockquote>
|
||||
<p>自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。<br>解决docker安装问题：<a href="https://github.com/shenjianZ/docker_installer">https://github.com/shenjianZ/docker_installer</a></p>
|
||||
</blockquote>
|
||||
<ol>
|
||||
<li><p>安装docker </p>
|
||||
<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>启动docker</p>
|
||||
<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">sudo systemctl start docker</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>设置开机自启</p>
|
||||
<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">sudo systemctl enable docker</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>Docker pull images</p>
|
||||
<blockquote>
|
||||
<p>将image下载到阿里云镜像仓库中<br>解决docker pull 镜像问题:<a href="https://github.com/shenjianZ/docker_image_pusher">https://github.com/shenjianZ/docker_image_pusher</a></p>
|
||||
</blockquote>
|
||||
<ol>
|
||||
<li><p><strong>登录阿里云镜像服务</strong> <a href="https://cr.console.aliyun.com/">https://cr.console.aliyun.com/</a>,</p>
|
||||
<p>启用个人实例，创建一个命名空间（<code>ALIYUN_NAME_SPACE</code>）</p>
|
||||
</li>
|
||||
<li><p>在<strong>访问凭证</strong> 可以看到账号 用户名（<code>ALIYUN_REGISTRY_USER</code>)</p>
|
||||
<p>密码（<code>ALIYUN_REGISTRY_PASSWORD</code>)</p>
|
||||
<p>仓库地址（<code>ALIYUN_REGISTRY</code>）</p>
|
||||
</li>
|
||||
<li><p><strong>启动Action</strong><br>进入您自己的项目，点击<code>Action</code>，启用<code>Github Action</code>功能</p>
|
||||
</li>
|
||||
<li><p><strong>配置环境变量</strong><br>进入Settings->Secret and variables->Actions->New Repository secret<br> 将上一步的四个值<br> <code>ALIYUN_NAME_SPACE</code>,</p>
|
||||
<p> <code> ALIYUN_REGISTRY_USER</code>，</p>
|
||||
<p> <code>ALIYUN_REGISTRY_PASSWORD</code>，</p>
|
||||
<p> <code>ALIYUN_REGISTRY</code><br> 配置成环境变量</p>
|
||||
</li>
|
||||
<li><p><strong>添加镜像</strong><br> 打开<code>images.txt</code>文件，添加你想要的镜像 可以加tag</p>
|
||||
</li>
|
||||
<li><p>使用镜像<br>回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)</p>
|
||||
</li>
|
||||
</ol>
|
||||
</li>
|
||||
</ol>
|
||||
]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>组件使用</title>
|
||||
<url>/posts/33957.html</url>
|
||||
<content><![CDATA[<h3 id="组件自动导入"><a href="#组件自动导入" class="headerlink" title="组件自动导入"></a>组件自动导入</h3><figure class="highlight json"><table><tr><td class="code"><pre><span class="line"><span class="attr">"easycom"</span><span class="punctuation">:</span><span class="punctuation">{</span></span><br><span class="line"> <span class="attr">"autoscan"</span><span class="punctuation">:</span> <span class="literal"><span class="keyword">true</span></span><span class="punctuation">,</span></span><br><span class="line"> <span class="attr">"custom"</span><span class="punctuation">:</span> <span class="punctuation">{</span></span><br><span class="line"> <span class="attr">"^tui-(.*)"</span><span class="punctuation">:</span> <span class="string">"@/components/thorui/tui-$1/tui-$1.vue"</span> <span class="comment">// 匹配components目录内的vue文件</span></span><br><span class="line"> <span class="punctuation">}</span></span><br><span class="line"><span class="punctuation">}</span></span><br></pre></td></tr></table></figure>
|
||||
|
||||
<h3 id="tui-sticky-吸顶容器"><a href="#tui-sticky-吸顶容器" class="headerlink" title="tui-sticky 吸顶容器"></a><code>tui-sticky 吸顶容器</code></h3><blockquote>
|
||||
<p>包含 以下 <code>tui</code> 组件 :</p>
|
||||
<ul>
|
||||
<li>tui-sticky</li>
|
||||
<li>tui-list-view</li>
|
||||
<li>tui-list-cell</li>
|
||||
</ul>
|
||||
</blockquote>
|
||||
<figure class="highlight html"><table><tr><td class="code"><pre><span class="line"><span class="tag"><<span class="name">tui-sticky</span> <span class="attr">:scrollTop</span>=<span class="string">"scrollTop"</span> <span class="attr">stickyHeight</span>=<span class="string">"104rpx"</span> <span class="attr">container</span>></span></span><br><span class="line"> <span class="comment"><!-- header start --></span></span><br><span class="line"> <span class="tag"><<span class="name">template</span> <span class="attr">v-slot:header</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">view</span> <span class="attr">class</span>=<span class="string">"sticky-item"</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">view</span> <span class="attr">class</span>=<span class="string">"setting"</span>></span>设置<span class="tag"></<span class="name">view</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">view</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">template</span>></span></span><br><span class="line"> <span class="comment"><!-- header end --></span></span><br><span class="line"> <span class="comment"><!--内容 start--></span></span><br><span class="line"> <span class="tag"><<span class="name">template</span> <span class="attr">v-slot:content</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">tui-list-view</span> <span class="attr">class</span>=<span class="string">"content"</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">tui-list-cell</span> <span class="attr">:arrow</span>=<span class="string">"false"</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">switch</span> <span class="attr">class</span>=<span class="string">'switch'</span> <span class="attr">checked</span> <span class="attr">color</span>=<span class="string">"#FFCC33"</span> /></span></span><br><span class="line"> <span class="tag"></<span class="name">tui-list-cell</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">tui-list-view</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">template</span>></span></span><br><span class="line"> <span class="comment"><!--内容 end--></span></span><br><span class="line"><span class="tag"></<span class="name">tui-sticky</span>></span></span><br><span class="line"></span><br><span class="line"><span class="tag"><<span class="name">script</span> <span class="attr">setup</span>></span><span class="language-javascript"></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">import</span> { ref } <span class="keyword">from</span> <span class="string">'vue'</span></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">import</span> { onPageScroll } <span class="keyword">from</span> <span class="string">'@dcloudio/uni-app'</span></span></span><br><span class="line"><span class="language-javascript"></span></span><br><span class="line"><span class="language-javascript"> <span class="comment">// 定义 scrollTop 响应式变量</span></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">const</span> scrollTop = <span class="title function_">ref</span>(<span class="number">0</span>)</span></span><br><span class="line"><span class="language-javascript"> <span class="comment">// 监听页面滚动事件</span></span></span><br><span class="line"><span class="language-javascript"> <span class="title function_">onPageScroll</span>(<span class="function">(<span class="params">e</span>) =></span> {</span></span><br><span class="line"><span class="language-javascript"> scrollTop.<span class="property">value</span> = e.<span class="property">scrollTop</span></span></span><br><span class="line"><span class="language-javascript"> })</span></span><br><span class="line"><span class="language-javascript"></span><span class="tag"></<span class="name">script</span>></span></span><br></pre></td></tr></table></figure>
|
||||
]]></content>
|
||||
<tags>
|
||||
<tag>uniapp</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>uniapp 开发</title>
|
||||
<url>/posts/58817.html</url>
|
||||
<content><![CDATA[<ul>
|
||||
<li><a href="../component1">uniapp component</a></li>
|
||||
</ul>
|
||||
]]></content>
|
||||
<tags>
|
||||
<tag>uniapp</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>郑伯克段于鄢</title>
|
||||
<url>/posts/58638.html</url>
|
||||
<content><![CDATA[<p>原文如下：</p>
|
||||
<p> 初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。</p>
|
||||
]]></content>
|
||||
<categories>
|
||||
<category>古文观止</category>
|
||||
</categories>
|
||||
<tags>
|
||||
<tag>古文观止</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Hadoop集群搭建基础环境</title>
|
||||
@@ -286,224 +417,6 @@
|
||||
</ul>
|
||||
]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>逻辑回归</title>
|
||||
<url>/posts/60504.html</url>
|
||||
<content><![CDATA[<h3 id="logistic-regression-code"><a href="#logistic-regression-code" class="headerlink" title="logistic regression code"></a>logistic regression code</h3><figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> load_breast_cancer</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> LogisticRegression</span><br><span class="line"><span class="comment"># 1. 加载乳腺癌数据集</span></span><br><span class="line">data = load_breast_cancer()</span><br><span class="line"><span class="comment"># 2.1 数据集基本处理</span></span><br><span class="line">df = pd.DataFrame(data.data, columns=data.feature_names)</span><br><span class="line">df[<span class="string">'target'</span>] = data.target</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> df.columns:</span><br><span class="line"> <span class="comment"># 检查列是否有缺失值</span></span><br><span class="line"> <span class="keyword">if</span> np.<span class="built_in">any</span>(pd.isnull(df[i])):</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Filling missing values in column: <span class="subst">{i}</span>"</span>)</span><br><span class="line"><span class="comment">#2.2 确认特征值、目标值</span></span><br><span class="line">X = df.iloc[:,<span class="number">0</span>:df.shape[<span class="number">1</span>] - <span class="number">1</span>]</span><br><span class="line">y = df.loc[:,<span class="string">"target"</span>]</span><br><span class="line"><span class="comment"># 2.3 分割数据</span></span><br><span class="line">X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=<span class="number">0.3</span>)</span><br><span class="line"><span class="comment"># 显示前几行数据</span></span><br><span class="line">df.head(<span class="number">1</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 4 机器学习 逻辑回归</span></span><br><span class="line">estimator = LogisticRegression()</span><br><span class="line">estimator.fit(X_train,y_train)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型准确率：<span class="subst">{estimator.score(X_test,y_test)}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型预测值为:\n<span class="subst">{estimator.predict(X_test)}</span>"</span>)</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<h3 id="分类评估的参数"><a href="#分类评估的参数" class="headerlink" title="分类评估的参数"></a>分类评估的参数</h3><ul>
|
||||
<li><p>准确率<br>准确率是所有预测正确的样本占总样本的比例<br>$$Accuracy = \frac{TP+TN}{TP+FN+FP+TN}$$</p>
|
||||
</li>
|
||||
<li><p>精准率<br>精准率（又称查准率）是指所有被预测为正类的样本中，真正为正类的比例<br>$$Precision = \frac{TP}{TP+FP}$$</p>
|
||||
</li>
|
||||
<li><p>召回率<br>召回率（又称查全率）是指所有实际为正类的样本中，被正确预测为正类的比例<br>$$Recall = \frac{TP}{TP+FN}$$</p>
|
||||
</li>
|
||||
<li><p>F1-score<br>F1 值（F1 Score）是精准率和召回率的调和平均数，综合考虑了精准率和召回率的影响。<br>$$ F1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} $$</p>
|
||||
</li>
|
||||
<li><p>roc曲线<br>tpr、fpr来衡量不平衡的二分类问题</p>
|
||||
</li>
|
||||
</ul>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> load_breast_cancer</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> LogisticRegression</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> classification_report, roc_auc_score</span><br><span class="line"><span class="comment"># 1. 加载乳腺癌数据集</span></span><br><span class="line">data = load_breast_cancer()</span><br><span class="line"><span class="comment"># 2.1 数据集基本处理</span></span><br><span class="line">df = pd.DataFrame(data.data, columns=data.feature_names)</span><br><span class="line">df[<span class="string">'target'</span>] = data.target</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> df.columns:</span><br><span class="line"> <span class="comment"># 检查列是否有缺失值</span></span><br><span class="line"> <span class="keyword">if</span> np.<span class="built_in">any</span>(pd.isnull(df[i])):</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f"Filling missing values in column: <span class="subst">{i}</span>"</span>)</span><br><span class="line"><span class="comment"># 2.2 确认特征值、目标值</span></span><br><span class="line">X = df.iloc[:, <span class="number">0</span>:df.shape[<span class="number">1</span>] - <span class="number">1</span>]</span><br><span class="line">y = df.loc[:, <span class="string">"target"</span>]</span><br><span class="line"><span class="comment"># 2.3 分割数据</span></span><br><span class="line">X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span class="number">0.3</span>)</span><br><span class="line"><span class="comment"># 显示前几行数据</span></span><br><span class="line">df.head(<span class="number">1</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 特征工程 标准化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 4 机器学习 逻辑回归</span></span><br><span class="line">estimator = LogisticRegression()</span><br><span class="line">estimator.fit(X_train, y_train)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5. 模型评估</span></span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型准确率：<span class="subst">{estimator.score(X_test, y_test)}</span>"</span>)</span><br><span class="line">y_pred = estimator.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"模型预测值为:\n<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line"><span class="comment"># 5.1 精确率、召回率</span></span><br><span class="line">ret = classification_report(y_test, y_pred, labels=[<span class="number">1</span>, <span class="number">0</span>], target_names=[<span class="string">"良性"</span>, <span class="string">"恶性"</span>])</span><br><span class="line">roc_score = roc_auc_score(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"准确率、召回率：<span class="subst">{ret}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"roc_score:<span class="subst">{roc_score}</span>"</span>)</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<h3 id="类别不平衡的处理"><a href="#类别不平衡的处理" class="headerlink" title="类别不平衡的处理"></a>类别不平衡的处理</h3><p>先准备类别不平衡的数据</p>
|
||||
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> imblearn.over_sampling <span class="keyword">import</span> RandomOverSampler,SMOTE</span><br><span class="line"><span class="keyword">from</span> imblearn.under_sampling <span class="keyword">import</span> RandomUnderSampler</span><br><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> make_classification</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"><span class="keyword">from</span> collections <span class="keyword">import</span> Counter</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1.准备类别不平衡的数据</span></span><br><span class="line">X, y = make_classification(</span><br><span class="line"> n_samples=<span class="number">5000</span>,</span><br><span class="line"> n_features=<span class="number">2</span>,</span><br><span class="line"> n_informative=<span class="number">2</span>,</span><br><span class="line"> n_redundant=<span class="number">0</span>,</span><br><span class="line"> n_repeated=<span class="number">0</span>,</span><br><span class="line"> n_classes=<span class="number">3</span>,</span><br><span class="line"> n_clusters_per_class=<span class="number">1</span>,</span><br><span class="line"> weights=[<span class="number">0.01</span>, <span class="number">0.05</span>, <span class="number">0.94</span>],</span><br><span class="line"> random_state=<span class="number">0</span>,</span><br><span class="line">)</span><br><span class="line">counter = Counter(y)</span><br><span class="line">plt.scatter(X[:,<span class="number">0</span>],X[:,<span class="number">1</span>],c=y)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<ul>
|
||||
<li>过采样<br>增加训练集的少数的类别的样本，使得正反例样本数据接近 <ul>
|
||||
<li>随机过采样（RandomOverSampler)<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">ros = RandomOverSampler()</span><br><span class="line">X_resampled,y_resampled = ros.fit_resample(X,y)</span><br><span class="line"><span class="built_in">print</span>(Counter(y_resampled))</span><br><span class="line">plt.scatter(X_resampled[:,<span class="number">0</span>],X_resampled[:,<span class="number">1</span>],c=y_resampled)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
|
||||
<img src="/img/machinelearning/over_random_sampling.png"></li>
|
||||
<li><code>SMOTE</code>过采样（SMOTE）<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">smote = SMOTE()</span><br><span class="line">X_resampled,y_resampled = smote.fit_resample(X,y)</span><br><span class="line"><span class="built_in">print</span>(Counter(y_resampled))</span><br><span class="line">plt.scatter(X_resampled[:,<span class="number">0</span>],X_resampled[:,<span class="number">1</span>],c=y_resampled)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
|
||||
<img src="/img/machinelearning/over_smote_sampling.png"></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>欠采样<br>减少训练集的多数的类别的样本，使得正反例样本数据接近<ul>
|
||||
<li>随机欠采样（RandomUnderSampler）<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">rus = RandomUnderSampler(random_state=<span class="number">0</span>)</span><br><span class="line">X_resampled,y_resampled = rus.fit_resample(X,y)</span><br><span class="line"><span class="built_in">print</span>(Counter(y_resampled))</span><br><span class="line">plt.scatter(X_resampled[:,<span class="number">0</span>],X_resampled[:,<span class="number">1</span>],c=y_resampled)</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
|
||||
<img src="/img/machinelearning/under_sampling.png"></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
]]></content>
|
||||
<categories>
|
||||
<category>machinelearning</category>
|
||||
</categories>
|
||||
<tags>
|
||||
<tag>logistic-regression</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>郑伯克段于鄢</title>
|
||||
<url>/posts/58638.html</url>
|
||||
<content><![CDATA[<p>原文如下：</p>
|
||||
<p> 初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。</p>
|
||||
]]></content>
|
||||
<categories>
|
||||
<category>古文观止</category>
|
||||
</categories>
|
||||
<tags>
|
||||
<tag>古文观止</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Hadoop集群HDFS配置</title>
|
||||
<url>/posts/61252.html</url>
|
||||
<content><![CDATA[<h3 id="上传hadoop-3-1-4-tar-gz到-tmp，解压"><a href="#上传hadoop-3-1-4-tar-gz到-tmp，解压" class="headerlink" title="上传hadoop-3.1.4.tar.gz到/tmp，解压"></a>上传<code>hadoop-3.1.4.tar.gz</code>到<code>/tmp</code>，解压</h3><blockquote>
|
||||
<p>注意在六台机器均上传到<code>/tmp</code></p>
|
||||
</blockquote>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 在6台机器执行</span></span><br><span class="line"><span class="built_in">sudo</span> tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/</span><br><span class="line"><span class="comment"># 分发到其他主机</span></span><br><span class="line">ssh_root.sh <span class="built_in">chown</span> -R hadoop:hadoop /usr/local/hadoop-3.1.4</span><br><span class="line">ssh_root.sh <span class="built_in">ln</span> -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop</span><br></pre></td></tr></table></figure>
|
||||
<h3 id="配置环境变量"><a href="#配置环境变量" class="headerlink" title="配置环境变量"></a>配置环境变量</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">echo</span> <span class="string">'export HADOOP_HOME=/usr/local/hadoop'</span> >> /etc/profile.d/myEnv.sh</span><br><span class="line"><span class="built_in">echo</span> <span class="string">'export PATH=$PATH:$HADOOP_HOME/bin'</span> >> /etc/profile.d/myEnv.sh</span><br><span class="line"><span class="built_in">echo</span> <span class="string">'export PATH=$PATH:$HADOOP_HOME/sbin'</span> >> /etc/profile.d/myEnv.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 分发到nn2、nn3、s1、s2、s3</span></span><br><span class="line">scp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/</span><br><span class="line"><span class="comment"># source 环境变量</span></span><br><span class="line">ssh_root.sh <span class="built_in">source</span> /etc/profile</span><br></pre></td></tr></table></figure>
|
||||
<blockquote>
|
||||
<p>还需要创建 <code>/data</code>这个目录，由于nn1、nn2、nn3已经创建<code>/data</code>，其他三台需要创建一下</p>
|
||||
</blockquote>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment">### 在s1、s2、s3执行</span></span><br><span class="line"><span class="built_in">sudo</span> <span class="built_in">mkdir</span> /data</span><br><span class="line"><span class="built_in">sudo</span> <span class="built_in">chown</span> -R hadoop:hadoop /data</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<h3 id="修改core-site-xml"><a href="#修改core-site-xml" class="headerlink" title="修改core-site.xml"></a>修改core-site.xml</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">vim /usr/local/hadoop/etc/hadoop/core-site.xml </span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight xml"><table><tr><td class="code"><pre><span class="line"><span class="tag"><<span class="name">configuration</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>fs.defaultFS<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>hdfs://ns1<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>hadoop.tmp.dir<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>/data/tmp<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>数据存储目录<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>hadoop.proxyuser.root.groups<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>hadoop<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span></span><br><span class="line"> hdfs dfsadmin –refreshSuperUserGroupsConfiguration,</span><br><span class="line"> yarn rmadmin –refreshSuperUserGroupsConfiguration</span><br><span class="line"> 使用这两个命令不用重启就能刷新</span><br><span class="line"> <span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>hadoop.proxyuser.root.hosts<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>localhost<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>本地代理<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="comment"><!-- zkfc的配置 --></span></span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span> </span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>ha.zookeeper.quorum<span class="tag"></<span class="name">name</span>></span> </span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn1:2181,nn2:2181,nn3:2181<span class="tag"></<span class="name">value</span>></span> </span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>HA使用的zookeeper地址<span class="tag"></<span class="name">description</span>></span> </span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"><span class="tag"></<span class="name">configuration</span>></span></span><br></pre></td></tr></table></figure>
|
||||
<h3 id="修改hdfs-site-xml"><a href="#修改hdfs-site-xml" class="headerlink" title="修改hdfs-site.xml"></a>修改<code>hdfs-site.xml</code></h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">vim /usr/local/hadoop/etc/hadoop/hdfs-site.xml </span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight xml"><table><tr><td class="code"><pre><span class="line"><span class="tag"><<span class="name">configuration</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.namenode.name.dir<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>/data/namenode<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>namenode本地文件存放地址<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.nameservices<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>ns1<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>提供服务的NS逻辑名称，与core-site.xml里的对应<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="comment"><!-- namenode的配置 --></span></span><br><span class="line"> <span class="comment"><!-- 主要的 --></span></span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.ha.namenodes.ns1<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn1,nn2,nn3<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>列出该逻辑名称下的NameNode逻辑名称<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.namenode.rpc-address.ns1.nn1<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn1:9000<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>指定NameNode的RPC位置<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.namenode.http-address.ns1.nn1<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn1:50070<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>指定NameNode的Web Server位置<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.namenode.rpc-address.ns1.nn2<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn2:9000<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">descriLine truncated
|
||||
<h3 id="修改hadoop-env-sh"><a href="#修改hadoop-env-sh" class="headerlink" title="修改hadoop-env.sh"></a>修改<code>hadoop-env.sh</code></h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">vim /usr/local/hadoop/etc/hadoop/hadoop-env.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 添加这两行</span></span><br><span class="line"><span class="built_in">source</span> /etc/profile </span><br><span class="line"><span class="built_in">export</span> HADOOP_HEAPSIZE_MAX=512</span><br></pre></td></tr></table></figure>
|
||||
<h3 id="分发这些配置文件"><a href="#分发这些配置文件" class="headerlink" title="分发这些配置文件"></a>分发这些配置文件</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">scp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/</span><br><span class="line">scp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/</span><br><span class="line">scp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/</span><br></pre></td></tr></table></figure>
|
||||
<h3 id="集群初始化"><a href="#集群初始化" class="headerlink" title="集群初始化"></a>集群初始化</h3><ul>
|
||||
<li><p>需要先启动zookeeper集群</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 第一次启动先启动journalnode，便于3个namenode的元数据同步</span></span><br><span class="line">ssh_all_zookeeper.sh hadoop-daemon.sh start journalnode</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p><code>zkfc</code>搭建</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment">#在nn1节点执行 </span></span><br><span class="line">hdfs zkfc -formatZK</span><br><span class="line"><span class="comment">#nn1 nn2 nn3启动zkfc </span></span><br><span class="line">hadoop-daemon.sh start zkfc</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>初始化nn1的namenode，在nn1执行</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">hdfs namenode -format </span><br><span class="line">hadoop-daemon.sh start namenode</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>格式化第二台和第三台namenode，并且启动namenode,在nn2、nn3执行</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">hdfs namenode -bootstrapStandby </span><br><span class="line">hadoop-daemon.sh start namenode</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>修改<strong>workers</strong></p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">vim /usr/local/hadoop/etc/hadoop/workers</span><br></pre></td></tr></table></figure>
|
||||
<p> 修改为</p>
|
||||
<figure class="highlight text"><table><tr><td class="code"><pre><span class="line">s1</span><br><span class="line">s2</span><br><span class="line">s3</span><br></pre></td></tr></table></figure>
|
||||
<p> 分发给其他机器</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">scp_all.sh /usr/local/hadoop/etc/hadoop/workers /usr/local/hadoop/etc/hadoop</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>启动datanode节点，在s1、s2、s3执行</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment">#启动各个节点的datanode</span></span><br><span class="line">hadoop-daemons.sh start datanode</span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
<h3 id="集群启动"><a href="#集群启动" class="headerlink" title="集群启动"></a>集群启动</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">start-dfs.sh</span><br></pre></td></tr></table></figure>]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Hadoop集群Zookeeper配置</title>
|
||||
<url>/posts/61251.html</url>
|
||||
<content><![CDATA[<h3 id="Zookeeper脚本配置"><a href="#Zookeeper脚本配置" class="headerlink" title="Zookeeper脚本配置"></a>Zookeeper脚本配置</h3><ul>
|
||||
<li>拷贝ips<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">cp</span> ips ips_zookeeper</span><br></pre></td></tr></table></figure>
|
||||
修改为<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">nn1</span><br><span class="line">nn2</span><br><span class="line">nn3</span><br></pre></td></tr></table></figure></li>
|
||||
<li>拷贝三个脚本<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">cp</span> scp_all.sh scp_all_zookeeper.sh </span><br><span class="line"><span class="built_in">cp</span> ssh_all.sh ssh_all_zookeeper.sh </span><br><span class="line"><span class="built_in">cp</span> ssh_root.sh ssh_root_zookeeper.sh</span><br></pre></td></tr></table></figure></li>
|
||||
<li>修改脚本<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">vim scp_all_zookeeper.sh </span><br><span class="line">vim ssh_all_zookeeper.sh </span><br><span class="line">vim ssh_root_zookeeper.sh </span><br></pre></td></tr></table></figure>
|
||||
|
||||
将三个脚本中的ips改为ips_zookeeper</li>
|
||||
</ul>
|
||||
<h3 id="Zookeeper安装"><a href="#Zookeeper安装" class="headerlink" title="Zookeeper安装"></a>Zookeeper安装</h3><ul>
|
||||
<li>上传到<code>/tmp</code>目录下，解压 <figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">sudo</span> tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/</span><br><span class="line">scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/</span><br><span class="line">scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/</span><br><span class="line">ssh_root_zookeeper.sh <span class="built_in">chown</span> -R hadoop:hadoop /usr/local/zookeeper-3.4.8</span><br><span class="line">ssh_root_zookeeper.s <span class="built_in">ln</span> -s /usr/local/zookeeper-3.4.8/ /usr/local/zookeeper</span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
<h3 id="Zookeeper配置"><a href="#Zookeeper配置" class="headerlink" title="Zookeeper配置"></a>Zookeeper配置</h3><ul>
|
||||
<li><p>zoo.cfg配置</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">cd</span> /usr/local/zookeeper/conf/</span><br><span class="line"><span class="built_in">cp</span> zoo_sample.cfg zoo.cfg</span><br></pre></td></tr></table></figure>
|
||||
<p>然后<code>vim zoo.cfg</code>,修改如下：</p>
|
||||
<figure class="highlight properties"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 修改dataDir</span></span><br><span class="line"><span class="attr">dataDir</span>=<span class="string">/data/zookeeper</span></span><br><span class="line"><span class="comment"># 添加一下内容</span></span><br><span class="line"><span class="attr">server.1</span>=<span class="string">nn1:2888:3888 </span></span><br><span class="line"><span class="attr">server.2</span>=<span class="string">nn2:2888:3888 </span></span><br><span class="line"><span class="attr">server.3</span>=<span class="string">nn3:2888:3888</span></span><br></pre></td></tr></table></figure>
|
||||
<p>分发给nn2、nn3<br> <code>scp_all_zookeeper.sh /usr/local/zookeeper/conf/zoo.cfg /usr/local/zookeeper/conf/</code></p>
|
||||
</li>
|
||||
<li><p><code>zkEnv.sh</code>配置<br><code>vim /usr/local/zookeeper/bin/zkEnv.sh</code><br><img src="/../../../img/000001.png" alt="000001.png"><br>分发到nn2、nn3</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">scp_all_zookeeper.sh /usr/local/zookeeper/bin/zkEnv.sh /usr/local/zookeeper/bin/</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>创建zookeeper数据目录</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">ssh_root_zookeeper.sh <span class="built_in">mkdir</span> -p /data/zookeeper</span><br><span class="line">ssh_root_zookeeper.sh <span class="built_in">chown</span> -R hadoop:hadoop /data</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>创建myid文件</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">ssh nn1 <span class="string">'echo "1" > /data/zookeeper/myid'</span></span><br><span class="line">ssh nn2 <span class="string">'echo "2" > /data/zookeeper/myid'</span></span><br><span class="line">ssh nn3 <span class="string">'echo "3" > /data/zookeeper/myid'</span></span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>配置Zookeeper环境变量</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 在其他所有主机也执行</span></span><br><span class="line"><span class="built_in">sudo</span> <span class="built_in">chown</span> -R hadoop:hadoop /etc/profile.d/myEnv.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">echo</span> <span class="string">'export ZOOKEEPER_HOME=/usr/local/zookeeper'</span> >> /etc/profile.d/myEnv.sh</span><br><span class="line"><span class="built_in">echo</span> <span class="string">'export PATH=$PATH:$ZOOKEEPER_HOME/bin'</span> >> /etc/profile.d/myEnv.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 分发到nn2、nn3</span></span><br><span class="line">scp_all_zookeeper.sh /etc/profile.d/myEnv.sh /etc/profile.d/</span><br><span class="line"><span class="comment"># source 环境变量</span></span><br><span class="line">ssh_all_zookeeper.sh <span class="built_in">source</span> /etc/profile</span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
<h3 id="Zookeeper的命令"><a href="#Zookeeper的命令" class="headerlink" title="Zookeeper的命令"></a>Zookeeper的命令</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment">#启动zk服务</span></span><br><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start</span><br><span class="line"><span class="comment">#查看每个机器ZK运行的状态</span></span><br><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status</span><br><span class="line"><span class="comment">#整体停止服务</span></span><br><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop </span><br><span class="line"><span class="comment">#重启zk服务</span></span><br><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_">#</span><span class="language-bash">启动zookeeper客户端，并连接zookeeper集群</span></span><br><span class="line">/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">可以简化为:</span></span><br><span class="line">zkCli.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>组件使用</title>
|
||||
<url>/posts/33957.html</url>
|
||||
<content><![CDATA[<h3 id="组件自动导入"><a href="#组件自动导入" class="headerlink" title="组件自动导入"></a>组件自动导入</h3><figure class="highlight json"><table><tr><td class="code"><pre><span class="line"><span class="attr">"easycom"</span><span class="punctuation">:</span><span class="punctuation">{</span></span><br><span class="line"> <span class="attr">"autoscan"</span><span class="punctuation">:</span> <span class="literal"><span class="keyword">true</span></span><span class="punctuation">,</span></span><br><span class="line"> <span class="attr">"custom"</span><span class="punctuation">:</span> <span class="punctuation">{</span></span><br><span class="line"> <span class="attr">"^tui-(.*)"</span><span class="punctuation">:</span> <span class="string">"@/components/thorui/tui-$1/tui-$1.vue"</span> <span class="comment">// 匹配components目录内的vue文件</span></span><br><span class="line"> <span class="punctuation">}</span></span><br><span class="line"><span class="punctuation">}</span></span><br></pre></td></tr></table></figure>
|
||||
|
||||
<h3 id="tui-sticky-吸顶容器"><a href="#tui-sticky-吸顶容器" class="headerlink" title="tui-sticky 吸顶容器"></a><code>tui-sticky 吸顶容器</code></h3><blockquote>
|
||||
<p>包含 以下 <code>tui</code> 组件 :</p>
|
||||
<ul>
|
||||
<li>tui-sticky</li>
|
||||
<li>tui-list-view</li>
|
||||
<li>tui-list-cell</li>
|
||||
</ul>
|
||||
</blockquote>
|
||||
<figure class="highlight html"><table><tr><td class="code"><pre><span class="line"><span class="tag"><<span class="name">tui-sticky</span> <span class="attr">:scrollTop</span>=<span class="string">"scrollTop"</span> <span class="attr">stickyHeight</span>=<span class="string">"104rpx"</span> <span class="attr">container</span>></span></span><br><span class="line"> <span class="comment"><!-- header start --></span></span><br><span class="line"> <span class="tag"><<span class="name">template</span> <span class="attr">v-slot:header</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">view</span> <span class="attr">class</span>=<span class="string">"sticky-item"</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">view</span> <span class="attr">class</span>=<span class="string">"setting"</span>></span>设置<span class="tag"></<span class="name">view</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">view</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">template</span>></span></span><br><span class="line"> <span class="comment"><!-- header end --></span></span><br><span class="line"> <span class="comment"><!--内容 start--></span></span><br><span class="line"> <span class="tag"><<span class="name">template</span> <span class="attr">v-slot:content</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">tui-list-view</span> <span class="attr">class</span>=<span class="string">"content"</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">tui-list-cell</span> <span class="attr">:arrow</span>=<span class="string">"false"</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">switch</span> <span class="attr">class</span>=<span class="string">'switch'</span> <span class="attr">checked</span> <span class="attr">color</span>=<span class="string">"#FFCC33"</span> /></span></span><br><span class="line"> <span class="tag"></<span class="name">tui-list-cell</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">tui-list-view</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">template</span>></span></span><br><span class="line"> <span class="comment"><!--内容 end--></span></span><br><span class="line"><span class="tag"></<span class="name">tui-sticky</span>></span></span><br><span class="line"></span><br><span class="line"><span class="tag"><<span class="name">script</span> <span class="attr">setup</span>></span><span class="language-javascript"></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">import</span> { ref } <span class="keyword">from</span> <span class="string">'vue'</span></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">import</span> { onPageScroll } <span class="keyword">from</span> <span class="string">'@dcloudio/uni-app'</span></span></span><br><span class="line"><span class="language-javascript"></span></span><br><span class="line"><span class="language-javascript"> <span class="comment">// 定义 scrollTop 响应式变量</span></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">const</span> scrollTop = <span class="title function_">ref</span>(<span class="number">0</span>)</span></span><br><span class="line"><span class="language-javascript"> <span class="comment">// 监听页面滚动事件</span></span></span><br><span class="line"><span class="language-javascript"> <span class="title function_">onPageScroll</span>(<span class="function">(<span class="params">e</span>) =></span> {</span></span><br><span class="line"><span class="language-javascript"> scrollTop.<span class="property">value</span> = e.<span class="property">scrollTop</span></span></span><br><span class="line"><span class="language-javascript"> })</span></span><br><span class="line"><span class="language-javascript"></span><span class="tag"></<span class="name">script</span>></span></span><br></pre></td></tr></table></figure>
|
||||
]]></content>
|
||||
<tags>
|
||||
<tag>uniapp</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Docker被墙，如何继续使用？</title>
|
||||
<url>/posts/47807.html</url>
|
||||
<content><![CDATA[<h2 id="Docker-Download"><a href="#Docker-Download" class="headerlink" title="Docker Download"></a>Docker Download</h2><blockquote>
|
||||
<p>自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。<br>解决docker安装问题：<a href="https://github.com/shenjianZ/docker_installer">https://github.com/shenjianZ/docker_installer</a></p>
|
||||
</blockquote>
|
||||
<ol>
|
||||
<li><p>安装docker </p>
|
||||
<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>启动docker</p>
|
||||
<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">sudo systemctl start docker</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>设置开机自启</p>
|
||||
<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">sudo systemctl enable docker</span><br></pre></td></tr></table></figure>
|
||||
</li>
|
||||
<li><p>Docker pull images</p>
|
||||
<blockquote>
|
||||
<p>将image下载到阿里云镜像仓库中<br>解决docker pull 镜像问题:<a href="https://github.com/shenjianZ/docker_image_pusher">https://github.com/shenjianZ/docker_image_pusher</a></p>
|
||||
</blockquote>
|
||||
<ol>
|
||||
<li><p><strong>登录阿里云镜像服务</strong> <a href="https://cr.console.aliyun.com/">https://cr.console.aliyun.com/</a>,</p>
|
||||
<p>启用个人实例，创建一个命名空间（<code>ALIYUN_NAME_SPACE</code>）</p>
|
||||
</li>
|
||||
<li><p>在<strong>访问凭证</strong> 可以看到账号 用户名（<code>ALIYUN_REGISTRY_USER</code>)</p>
|
||||
<p>密码（<code>ALIYUN_REGISTRY_PASSWORD</code>)</p>
|
||||
<p>仓库地址（<code>ALIYUN_REGISTRY</code>）</p>
|
||||
</li>
|
||||
<li><p><strong>启动Action</strong><br>进入您自己的项目，点击<code>Action</code>，启用<code>Github Action</code>功能</p>
|
||||
</li>
|
||||
<li><p><strong>配置环境变量</strong><br>进入Settings->Secret and variables->Actions->New Repository secret<br> 将上一步的四个值<br> <code>ALIYUN_NAME_SPACE</code>,</p>
|
||||
<p> <code> ALIYUN_REGISTRY_USER</code>，</p>
|
||||
<p> <code>ALIYUN_REGISTRY_PASSWORD</code>，</p>
|
||||
<p> <code>ALIYUN_REGISTRY</code><br> 配置成环境变量</p>
|
||||
</li>
|
||||
<li><p><strong>添加镜像</strong><br> 打开<code>images.txt</code>文件，添加你想要的镜像 可以加tag</p>
|
||||
</li>
|
||||
<li><p>使用镜像<br>回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)</p>
|
||||
</li>
|
||||
</ol>
|
||||
</li>
|
||||
</ol>
|
||||
]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>uniapp 开发</title>
|
||||
<url>/posts/58817.html</url>
|
||||
<content><![CDATA[<ul>
|
||||
<li><a href="../component1">uniapp component</a></li>
|
||||
</ul>
|
||||
]]></content>
|
||||
<tags>
|
||||
<tag>uniapp</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>无法访问外网？需要订阅代理服务？</title>
|
||||
<url>/posts/14011.html</url>
|
||||
@@ -561,4 +474,91 @@
|
||||
<tag>网络代理</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Hadoop集群Zookeeper配置</title>
|
||||
<url>/posts/61251.html</url>
|
||||
<content><![CDATA[<h3 id="Zookeeper脚本配置"><a href="#Zookeeper脚本配置" class="headerlink" title="Zookeeper脚本配置"></a>Zookeeper脚本配置</h3><ul>
|
||||
<li>拷贝ips<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">cp</span> ips ips_zookeeper</span><br></pre></td></tr></table></figure>
|
||||
修改为<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">nn1</span><br><span class="line">nn2</span><br><span class="line">nn3</span><br></pre></td></tr></table></figure></li>
|
||||
<li>拷贝三个脚本<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">cp</span> scp_all.sh scp_all_zookeeper.sh </span><br><span class="line"><span class="built_in">cp</span> ssh_all.sh ssh_all_zookeeper.sh </span><br><span class="line"><span class="built_in">cp</span> ssh_root.sh ssh_root_zookeeper.sh</span><br></pre></td></tr></table></figure></li>
|
||||
<li>修改脚本<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line">vim scp_all_zookeeper.sh </span><br><span class="line">vim ssh_all_zookeeper.sh </span><br><span class="line">vim ssh_root_zookeeper.sh </span><br></pre></td></tr></table></figure>
|
||||
|
||||
将三个脚本中的ips改为ips_zookeeper</li>
|
||||
</ul>
|
||||
<h3 id="Zookeeper安装"><a href="#Zookeeper安装" class="headerlink" title="Zookeeper安装"></a>Zookeeper安装</h3><ul>
|
||||
<li>上传到<code>/tmp</code>目录下，解压 <figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">sudo</span> tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/</span><br><span class="line">scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/</span><br><span class="line">scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/</span><br><span class="line">ssh_root_zookeeper.sh <span class="built_in">chown</span> -R hadoop:hadoop /usr/local/zookeeper-3.4.8</span><br><span class="line">ssh_root_zookeeper.s <span class="built_in">ln</span> -s /usr/local/zookeeper-3.4.8/ /usr/local/zookeeper</span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
<h3 id="Zookeeper配置"><a href="#Zookeeper配置" class="headerlink" title="Zookeeper配置"></a>Zookeeper配置</h3><ul>
|
||||
<li><p>zoo.cfg配置</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">cd</span> /usr/local/zookeeper/conf/</span><br><span class="line"><span class="built_in">cp</span> zoo_sample.cfg zoo.cfg</span><br></pre></td></tr></table></figure>
|
||||
<p>然后<code>vim zoo.cfg</code>,修改如下：</p>
|
||||
<figure class="highlight properties"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 修改dataDir</span></span><br><span class="line"><span class="attr">dataDir</span>=<span class="string">/data/zookeeper</span></span><br><span class="line"><span class="comment"># 添加一下内容</span></span><br><span class="line"><span class="attr">server.1</span>=<span class="string">nn1:2888:3888 </span></span><br><span class="line"><span class="attr">server.2</span>=<span class="string">nn2:2888:3888 </span></span><br><span class="line"><span class="attr">server.3</span>=<span class="string">nn3:2888:3888</span></span><br></pre></td></tr></table></figure>
|
||||
<p>分发给nn2、nn3<br> <code>scp_all_zookeeper.sh /usr/local/zookeeper/conf/zoo.cfg /usr/local/zookeeper/conf/</code></p>
|
||||
</li>
|
||||
<li><p><code>zkEnv.sh</code>配置<br><code>vim /usr/local/zookeeper/bin/zkEnv.sh</code><br><img src="/../../../img/000001.png" alt="000001.png"><br>分发到nn2、nn3</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">scp_all_zookeeper.sh /usr/local/zookeeper/bin/zkEnv.sh /usr/local/zookeeper/bin/</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>创建zookeeper数据目录</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">ssh_root_zookeeper.sh <span class="built_in">mkdir</span> -p /data/zookeeper</span><br><span class="line">ssh_root_zookeeper.sh <span class="built_in">chown</span> -R hadoop:hadoop /data</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>创建myid文件</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">ssh nn1 <span class="string">'echo "1" > /data/zookeeper/myid'</span></span><br><span class="line">ssh nn2 <span class="string">'echo "2" > /data/zookeeper/myid'</span></span><br><span class="line">ssh nn3 <span class="string">'echo "3" > /data/zookeeper/myid'</span></span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>配置Zookeeper环境变量</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 在其他所有主机也执行</span></span><br><span class="line"><span class="built_in">sudo</span> <span class="built_in">chown</span> -R hadoop:hadoop /etc/profile.d/myEnv.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">echo</span> <span class="string">'export ZOOKEEPER_HOME=/usr/local/zookeeper'</span> >> /etc/profile.d/myEnv.sh</span><br><span class="line"><span class="built_in">echo</span> <span class="string">'export PATH=$PATH:$ZOOKEEPER_HOME/bin'</span> >> /etc/profile.d/myEnv.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 分发到nn2、nn3</span></span><br><span class="line">scp_all_zookeeper.sh /etc/profile.d/myEnv.sh /etc/profile.d/</span><br><span class="line"><span class="comment"># source 环境变量</span></span><br><span class="line">ssh_all_zookeeper.sh <span class="built_in">source</span> /etc/profile</span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
<h3 id="Zookeeper的命令"><a href="#Zookeeper的命令" class="headerlink" title="Zookeeper的命令"></a>Zookeeper的命令</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment">#启动zk服务</span></span><br><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start</span><br><span class="line"><span class="comment">#查看每个机器ZK运行的状态</span></span><br><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status</span><br><span class="line"><span class="comment">#整体停止服务</span></span><br><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop </span><br><span class="line"><span class="comment">#重启zk服务</span></span><br><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight shell"><table><tr><td class="code"><pre><span class="line"><span class="meta prompt_">#</span><span class="language-bash">启动zookeeper客户端，并连接zookeeper集群</span></span><br><span class="line">/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181</span><br><span class="line"><span class="meta prompt_"># </span><span class="language-bash">可以简化为:</span></span><br><span class="line">zkCli.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Hadoop集群HDFS配置</title>
|
||||
<url>/posts/61252.html</url>
|
||||
<content><![CDATA[<h3 id="上传hadoop-3-1-4-tar-gz到-tmp，解压"><a href="#上传hadoop-3-1-4-tar-gz到-tmp，解压" class="headerlink" title="上传hadoop-3.1.4.tar.gz到/tmp，解压"></a>上传<code>hadoop-3.1.4.tar.gz</code>到<code>/tmp</code>，解压</h3><blockquote>
|
||||
<p>注意在六台机器均上传到<code>/tmp</code></p>
|
||||
</blockquote>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 在6台机器执行</span></span><br><span class="line"><span class="built_in">sudo</span> tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/</span><br><span class="line"><span class="comment"># 分发到其他主机</span></span><br><span class="line">ssh_root.sh <span class="built_in">chown</span> -R hadoop:hadoop /usr/local/hadoop-3.1.4</span><br><span class="line">ssh_root.sh <span class="built_in">ln</span> -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop</span><br></pre></td></tr></table></figure>
|
||||
<h3 id="配置环境变量"><a href="#配置环境变量" class="headerlink" title="配置环境变量"></a>配置环境变量</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">echo</span> <span class="string">'export HADOOP_HOME=/usr/local/hadoop'</span> >> /etc/profile.d/myEnv.sh</span><br><span class="line"><span class="built_in">echo</span> <span class="string">'export PATH=$PATH:$HADOOP_HOME/bin'</span> >> /etc/profile.d/myEnv.sh</span><br><span class="line"><span class="built_in">echo</span> <span class="string">'export PATH=$PATH:$HADOOP_HOME/sbin'</span> >> /etc/profile.d/myEnv.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 分发到nn2、nn3、s1、s2、s3</span></span><br><span class="line">scp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/</span><br><span class="line"><span class="comment"># source 环境变量</span></span><br><span class="line">ssh_root.sh <span class="built_in">source</span> /etc/profile</span><br></pre></td></tr></table></figure>
|
||||
<blockquote>
|
||||
<p>还需要创建 <code>/data</code>这个目录，由于nn1、nn2、nn3已经创建<code>/data</code>，其他三台需要创建一下</p>
|
||||
</blockquote>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment">### 在s1、s2、s3执行</span></span><br><span class="line"><span class="built_in">sudo</span> <span class="built_in">mkdir</span> /data</span><br><span class="line"><span class="built_in">sudo</span> <span class="built_in">chown</span> -R hadoop:hadoop /data</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<h3 id="修改core-site-xml"><a href="#修改core-site-xml" class="headerlink" title="修改core-site.xml"></a>修改core-site.xml</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">vim /usr/local/hadoop/etc/hadoop/core-site.xml </span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight xml"><table><tr><td class="code"><pre><span class="line"><span class="tag"><<span class="name">configuration</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>fs.defaultFS<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>hdfs://ns1<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>hadoop.tmp.dir<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>/data/tmp<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>数据存储目录<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>hadoop.proxyuser.root.groups<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>hadoop<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span></span><br><span class="line"> hdfs dfsadmin –refreshSuperUserGroupsConfiguration,</span><br><span class="line"> yarn rmadmin –refreshSuperUserGroupsConfiguration</span><br><span class="line"> 使用这两个命令不用重启就能刷新</span><br><span class="line"> <span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>hadoop.proxyuser.root.hosts<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>localhost<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>本地代理<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="comment"><!-- zkfc的配置 --></span></span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span> </span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>ha.zookeeper.quorum<span class="tag"></<span class="name">name</span>></span> </span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn1:2181,nn2:2181,nn3:2181<span class="tag"></<span class="name">value</span>></span> </span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>HA使用的zookeeper地址<span class="tag"></<span class="name">description</span>></span> </span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"><span class="tag"></<span class="name">configuration</span>></span></span><br></pre></td></tr></table></figure>
|
||||
<h3 id="修改hdfs-site-xml"><a href="#修改hdfs-site-xml" class="headerlink" title="修改hdfs-site.xml"></a>修改<code>hdfs-site.xml</code></h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">vim /usr/local/hadoop/etc/hadoop/hdfs-site.xml </span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight xml"><table><tr><td class="code"><pre><span class="line"><span class="tag"><<span class="name">configuration</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.namenode.name.dir<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>/data/namenode<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>namenode本地文件存放地址<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.nameservices<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>ns1<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>提供服务的NS逻辑名称，与core-site.xml里的对应<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="comment"><!-- namenode的配置 --></span></span><br><span class="line"> <span class="comment"><!-- 主要的 --></span></span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.ha.namenodes.ns1<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn1,nn2,nn3<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>列出该逻辑名称下的NameNode逻辑名称<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.namenode.rpc-address.ns1.nn1<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn1:9000<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>指定NameNode的RPC位置<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.namenode.http-address.ns1.nn1<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn1:50070<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">description</span>></span>指定NameNode的Web Server位置<span class="tag"></<span class="name">description</span>></span></span><br><span class="line"> <span class="tag"></<span class="name">property</span>></span></span><br><span class="line"> </span><br><span class="line"> <span class="tag"><<span class="name">property</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">name</span>></span>dfs.namenode.rpc-address.ns1.nn2<span class="tag"></<span class="name">name</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">value</span>></span>nn2:9000<span class="tag"></<span class="name">value</span>></span></span><br><span class="line"> <span class="tag"><<span class="name">descriLine truncated
|
||||
<h3 id="修改hadoop-env-sh"><a href="#修改hadoop-env-sh" class="headerlink" title="修改hadoop-env.sh"></a>修改<code>hadoop-env.sh</code></h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">vim /usr/local/hadoop/etc/hadoop/hadoop-env.sh</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 添加这两行</span></span><br><span class="line"><span class="built_in">source</span> /etc/profile </span><br><span class="line"><span class="built_in">export</span> HADOOP_HEAPSIZE_MAX=512</span><br></pre></td></tr></table></figure>
|
||||
<h3 id="分发这些配置文件"><a href="#分发这些配置文件" class="headerlink" title="分发这些配置文件"></a>分发这些配置文件</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">scp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/</span><br><span class="line">scp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/</span><br><span class="line">scp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/</span><br></pre></td></tr></table></figure>
|
||||
<h3 id="集群初始化"><a href="#集群初始化" class="headerlink" title="集群初始化"></a>集群初始化</h3><ul>
|
||||
<li><p>需要先启动zookeeper集群</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start</span><br></pre></td></tr></table></figure>
|
||||
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 第一次启动先启动journalnode，便于3个namenode的元数据同步</span></span><br><span class="line">ssh_all_zookeeper.sh hadoop-daemon.sh start journalnode</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p><code>zkfc</code>搭建</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment">#在nn1节点执行 </span></span><br><span class="line">hdfs zkfc -formatZK</span><br><span class="line"><span class="comment">#nn1 nn2 nn3启动zkfc </span></span><br><span class="line">hadoop-daemon.sh start zkfc</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>初始化nn1的namenode，在nn1执行</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">hdfs namenode -format </span><br><span class="line">hadoop-daemon.sh start namenode</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>格式化第二台和第三台namenode，并且启动namenode,在nn2、nn3执行</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">hdfs namenode -bootstrapStandby </span><br><span class="line">hadoop-daemon.sh start namenode</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>修改<strong>workers</strong></p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">vim /usr/local/hadoop/etc/hadoop/workers</span><br></pre></td></tr></table></figure>
|
||||
<p> 修改为</p>
|
||||
<figure class="highlight text"><table><tr><td class="code"><pre><span class="line">s1</span><br><span class="line">s2</span><br><span class="line">s3</span><br></pre></td></tr></table></figure>
|
||||
<p> 分发给其他机器</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">scp_all.sh /usr/local/hadoop/etc/hadoop/workers /usr/local/hadoop/etc/hadoop</span><br></pre></td></tr></table></figure></li>
|
||||
<li><p>启动datanode节点，在s1、s2、s3执行</p>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment">#启动各个节点的datanode</span></span><br><span class="line">hadoop-daemons.sh start datanode</span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
<h3 id="集群启动"><a href="#集群启动" class="headerlink" title="集群启动"></a>集群启动</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line">start-dfs.sh</span><br></pre></td></tr></table></figure>]]></content>
|
||||
</entry>
|
||||
</search>
|
||||
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})(window)</script><meta name="generator" content="Hexo 7.3.0"></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="/img/avatar.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">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</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> Home</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> Archives</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> Tags</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> Categories</span></a></div><div class="menus_item"><a class="site-page group" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> List</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> Music</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> Movie</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> Link</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> About</span></a></div></div></div></div><div class="page" id="body-wrap"><header class="not-top-img fixed" id="page-header"><nav id="nav"><span id="blog-info"><a href="/" title="QuickReference"><span class="site-name">QuickReference</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> Home</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> Archives</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> Tags</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> Categories</span></a></div><div class="menus_item"><a class="site-page group" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> List</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> Music</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> Movie</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> Link</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> About</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></header><main class="layout" id="content-inner"><div id="page"><h1 class="page-title">tags</h1><div class="tag-cloud-list is-center"><a href="/tags/C-C/" style="font-size: 1.2em; color: rgb(184, 139, 78)">C C++</a><a href="/tags/decisiontree/" style="font-size: 1.2em; color: rgb(18, 200, 144)">decisiontree</a><a href="/tags/ensemble-learning/" style="font-size: 1.2em; color: rgb(129, 43, 38)">ensemble-learning</a><a href="/tags/linear-regression/" style="font-size: 1.2em; color: rgb(5, 11, 122)">linear-regression</a><a href="/tags/KNN/" style="font-size: 1.2em; color: rgb(157, 12, 55)">KNN</a><a href="/tags/logistic-regression/" style="font-size: 1.2em; color: rgb(187, 17, 125)">logistic-regression</a><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.2em; color: rgb(199, 185, 132)">古文观止</a><a href="/tags/uniapp/" style="font-size: 2.1em; color: rgb(22, 5, 13)">uniapp</a><a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.2em; color: rgb(67, 192, 163)">网络代理</a></div></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细Line truncated
|
||||
})(window)</script><meta name="generator" content="Hexo 7.3.0"></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="/img/avatar.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">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</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> Home</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> Archives</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> Tags</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> Categories</span></a></div><div class="menus_item"><a class="site-page group" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> List</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> Music</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> Movie</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> Link</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> About</span></a></div></div></div></div><div class="page" id="body-wrap"><header class="not-top-img fixed" id="page-header"><nav id="nav"><span id="blog-info"><a href="/" title="QuickReference"><span class="site-name">QuickReference</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> Home</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> Archives</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> Tags</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> Categories</span></a></div><div class="menus_item"><a class="site-page group" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> List</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> Music</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> Movie</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> Link</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> About</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></header><main class="layout" id="content-inner"><div id="page"><h1 class="page-title">tags</h1><div class="tag-cloud-list is-center"><a href="/tags/C-C/" style="font-size: 1.2em; color: rgb(24, 30, 97)">C C++</a><a href="/tags/decisiontree/" style="font-size: 1.2em; color: rgb(171, 199, 56)">decisiontree</a><a href="/tags/KNN/" style="font-size: 1.2em; color: rgb(59, 29, 47)">KNN</a><a href="/tags/ensemble-learning/" style="font-size: 1.2em; color: rgb(23, 165, 149)">ensemble-learning</a><a href="/tags/linear-regression/" style="font-size: 1.2em; color: rgb(187, 112, 72)">linear-regression</a><a href="/tags/logistic-regression/" style="font-size: 1.2em; color: rgb(0, 164, 141)">logistic-regression</a><a href="/tags/uniapp/" style="font-size: 2.1em; color: rgb(124, 197, 170)">uniapp</a><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.2em; color: rgb(158, 150, 175)">古文观止</a><a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.2em; color: rgb(156, 104, 109)">网络代理</a></div></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的Line truncated
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<i class="fas fa-folder-open"></i>
|
||||
<span>分类</span>
|
||||
|
||||
</div>
|
||||
<ul class="card-category-list" id="aside-cat-list">
|
||||
<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:19:31.591Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-10-16T03:26:08.219Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
|
||||
POWERMODE.shake = true;
|
||||
POWERMODE.mobile = false;
|
||||
document.body.addEventListener('input', POWERMODE);
|
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|
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@@ -54,7 +54,7 @@
|
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isHome: false,
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isHighlightShrink: undefined,
|
||||
isToc: false,
|
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postUpdate: '2025-10-16 11:19:31'
|
||||
postUpdate: '2025-10-16 11:26:08'
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}</script><script>(win=>{
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win.saveToLocal = {
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set: (key, value, ttl) => {
|
||||
|
||||
@@ -54,7 +54,7 @@
|
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isHome: false,
|
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isHighlightShrink: undefined,
|
||||
isToc: false,
|
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postUpdate: '2025-10-16 11:19:31'
|
||||
postUpdate: '2025-10-16 11:26:08'
|
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}</script><script>(win=>{
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win.saveToLocal = {
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set: (key, value, ttl) => {
|
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|
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@@ -54,7 +54,7 @@
|
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isHome: false,
|
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isHighlightShrink: undefined,
|
||||
isToc: false,
|
||||
postUpdate: '2025-10-16 11:19:31'
|
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postUpdate: '2025-10-16 11:26:08'
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}</script><script>(win=>{
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win.saveToLocal = {
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set: (key, value, ttl) => {
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@@ -54,7 +54,7 @@
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isHome: false,
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isHighlightShrink: undefined,
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isToc: false,
|
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postUpdate: '2025-10-16 11:19:31'
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postUpdate: '2025-10-16 11:26:08'
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}</script><script>(win=>{
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win.saveToLocal = {
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set: (key, value, ttl) => {
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|
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@@ -54,7 +54,7 @@
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isHome: false,
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isHighlightShrink: undefined,
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isToc: false,
|
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postUpdate: '2025-10-16 11:19:31'
|
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postUpdate: '2025-10-16 11:26:08'
|
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}</script><script>(win=>{
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win.saveToLocal = {
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set: (key, value, ttl) => {
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||||
|
||||
Reference in new issue
Block a user