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detectApple()
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||||
Random-Forest 就是Bagging + Decisiontree
|
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|
||||
1234567891011121314151617181920212223242526272829303132333435import seaborn as snsimport pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_split,GridSearchCVfrom sklearn.feature_extraction import DictVectorizerfrom sklearn.ensemble import RandomForestClassifier# 1.获取数据集 - 加载 Titanic 数据集titanic = sns.load_dataset('titanic')missing_age_count = titanic['age'].isna().sum()# print(f"缺失的 age 数量:  ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/95.html" title="决策树算法"><img class="post-bg" src="https://th.bing.com/th/id/OIP.XaPUn6eccfS_z_wTLQNFzgHaEK?w=240&h=180&c=7&r=0&o=5&dpr=1.9&pid=1.7" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="决策树算法"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/95.html" title="决策树算法">决策树算法</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="2025-01-24T04:39:59.000Z" title="发表于 2025-01-24 12:39:59">2025-01-24</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/machinelearning/">machinelearning</a></span></div><div class="content">C4.5C4.5 是一种用于生成决策树的算法，不再使用信息增益，而是使用信息增益比，来避免偏向于选择取值较多的特征。信息增益比是信息增益与特征的熵的比值。
|
||||
1234567891011121314151617181920212223242526272829303132333435import seaborn as snsimport pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_split,GridSearchCVfrom sklearn.feature_extraction import DictVectorizerfrom sklearn.ensemble import RandomForestClassifier# 1.获取数据集 - 加载 Titanic 数据集titanic = sns.load_dataset('titanic')missing_age_count = titanic['age'].isna().sum()# print(f"缺失的 age 数量:  ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/95.html" title="决策树算法"><img class="post-bg" src="/img/machinelearning/decision-tree.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="决策树算法"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/95.html" title="决策树算法">决策树算法</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="2025-01-24T04:39:59.000Z" title="发表于 2025-01-24 12:39:59">2025-01-24</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/machinelearning/">machinelearning</a></span></div><div class="content">C4.5C4.5 是一种用于生成决策树的算法，不再使用信息增益，而是使用信息增益比，来避免偏向于选择取值较多的特征。信息增益比是信息增益与特征的熵的比值。
|
||||
ID3D3 是一种基于信息增益（Information Gain）的决策树算法
|
||||
CartCART（分类与回归树）一种决策树算法，，CART 使用 二叉树结构，即每个节点只能有两个子节点。
|
||||
cart剪枝CART 决策树的剪枝方法分为 预剪枝（Pre-pruning）和 后剪枝（Post-pruning）两种：
|
||||
@@ -175,15 +175,15 @@ cart剪枝CART 决策树的剪枝方法分为 预剪枝（Pre-pruning）和 后
|
||||
$y$ 是目标变量（因变量），即我们希望预测的值。
|
||||
$x1,x2,…,xn$ 是特征变量（自变量），即输入的值。
|
||||
|
||||
损失函数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 均方误差（MSE）：$$MSE = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{y}_i)^2$$
|
||||
损失函数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 均方误差（MSE）：$$J(\theta) = \frac{1}{2N} \sum_{i=1}^{N} (y_i - f_\theta(x_i))^2$$
|
||||
|
||||
m 是样本的数量。
|
||||
N 是样本的数量。
|
||||
$y_i$ 是第 i 个样本的真实值。
|
||||
$\hat{y}_i$ 是模型预测的第 i 个样本的值。
|
||||
$f_\theta(x_i)$ 是模型预测的第 i 个样本的值。
|
||||
|
||||
线性回归优化
|
||||
梯度下降法
|
||||
123456789101112131415161718192021222324252627282930from sklearn.datasets import fetch_california_housingfrom sklearn.mode ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/12462.html" title="C lang">C lang</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="2025-01-15T12:41:26.000Z" title="发表于 2025-01-15 20:41:26">2025-01-15</time></span></div><div class="content">c lang在windows下的开发（VS code）WinLibs - GCC+MinGW-w64 compiler for Windows下载你需要的版本解压到D:\ProgramModule，并将 bin\加入环境变量PATH打开新的Terminal输入gcc -v，查看gcc是否安装成功在VS code 的插件管理下载Code Runner、C\C++这两个插件在*.c源文件的内容区，右键点击Run Code ，即可运行成功
|
||||
123456789101112131415161718192021222324252627282930from sklearn.datasets import fetch_california_housingfro ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/12462.html" title="C lang">C lang</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="2025-01-15T12:41:26.000Z" title="发表于 2025-01-15 20:41:26">2025-01-15</time></span></div><div class="content">c lang在windows下的开发（VS code）WinLibs - GCC+MinGW-w64 compiler for Windows下载你需要的版本解压到D:\ProgramModule，并将 bin\加入环境变量PATH打开新的Terminal输入gcc -v，查看gcc是否安装成功在VS code 的插件管理下载Code Runner、C\C++这两个插件在*.c源文件的内容区，右键点击Run Code ，即可运行成功
|
||||
数据类型
|
||||
整数类型1234567891011 short a = 12; int b = 100; long c = 1000L; long long d = 1000000LL; unsigned int e = 10; printf("a: %hd\n",a); printf("b: %d\n",b); printf("c: %ld\n",c); printf("d: %lld\n",d); printf(&quo ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/29139.html" title="k近邻算法（K-Nearest Neighbors）KNN">k近邻算法（K-Nearest Neighbors）KNN</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="2025-01-13T09:20:59.000Z" title="发表于 2025-01-13 17:20:59">2025-01-13</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/machinelearning/">machinelearning</a></span></div><div class="content">k近邻算法（K-Nearest Neighbors）KNN将当前样本的类别归类于距离最近的k个样本的类别
|
||||
距离公式(2维)
|
||||
@@ -211,15 +211,15 @@ k值
|
||||
通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程
|
||||
|
||||
|
||||
归一化将数据变换到指定区间（默认是[0,1]）$$ x’ = \frac{x- x_{\text {min}}}{x_{\text{max}} - x_{\text{min}}} $$若需要缩放到任意区间 ([a, b])，公式为： $$ x’ = a + \frac{(x - x_{\text{min}}) \cdot (b - a ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/61252.html" title="Hadoop集群HDFS配置">Hadoop集群HDFS配置</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-09-11T14:45:40.000Z" title="发表于 2024-09-11 22:45:40">2024-09-11</time></span></div><div class="content">上传hadoop-3.1.4.tar.gz到/tmp，解压
|
||||
注意在六台机器均上传到/tmp
|
||||
|
||||
12345# 在6台机器执行sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/# 分发到其他主机ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop
|
||||
配置环境变量123echo 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/m ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/61253.html" title="Hadoop集群搭建基础环境">Hadoop集群搭建基础环境</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-09-11T14:45:40.000Z" title="发表于 2024-09-11 22:45:40">2024-09-11</time></span></div><div class="content">防火墙关闭123# 在 6 台主机执行systemctl stop firewalldsystemctl disable firewalld
|
||||
归一化将数据变换到指定区间（默认是[0,1]）$$ x’ = \frac{x- x_{\text {min}}}{x_{\text{max}} - x_{\text{min}}} $$若需要缩放到任意区间 ([a, b])，公式为： $$ x’ = a + \frac{(x - x_{\text{min}}) \cdot (b - a ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/61253.html" title="Hadoop集群搭建基础环境">Hadoop集群搭建基础环境</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">防火墙关闭123# 在 6 台主机执行systemctl stop firewalldsystemctl disable firewalld
|
||||
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/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/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
|
||||
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
|
||||
@@ -241,14 +241,14 @@ More info: Generating
|
||||
Deploy to remote sites1$ hexo deploy
|
||||
|
||||
More info: Deployment
|
||||
</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/#content-inner">2</a><a class="extend next" rel="next" href="/page/2/#content-inner"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/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 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="https://th.bing.com/th/id/OIP.SZA5W6cF-tYiiZ08KZ7l7wHaEm?w=250&h=180&c=7&r=0&o=5&dpr=1.3&pid=1.7" 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 datetime="2025-01-25T07:12:08.000Z" title="发表于 2025-01-25 15:12:08">2025-01-25</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/posts/95.html" title="决策树算法"><img src="https://th.bing.com/th/id/OIP.XaPUn6eccfS_z_wTLQNFzgHaEK?w=240&h=180&c=7&r=0&o=5&dpr=1.9&pid=1.7" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="决策树算法"/></a><div class="content"><a class="title" href="/posts/95.html" title="决策树算法">决策树算法</a><time datetime="2025-01-24T04:39:59.000Z" title="发表于 2025-01-24 12:39:59">2025-01-24</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/60504.html" title="逻辑回归">逻辑回归</a><time datetime="2025-01-20T07:30:08.000Z" title="发表于 2025-01-20 15:30:08">2025-01-20</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/52662.html" title="线性回归">线性回归</a><time datetime="2025-01-19T08:46:51.000Z" title="发表于 2025-01-19 16:46:51">2025-01-19</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/12462.html" title="C lang">C lang</a><time datetime="2025-01-15T12:41:26.000Z" title="发表于 2025-01-15 20:41:26">2025-01-15</time></div></div></div></div><div class="card-widget card-categories"><div class="item-headline">
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<i class="fas fa-folder-open"></i>
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<span>分类</span>
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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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@@ -54,7 +54,7 @@
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win.saveToLocal = {
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set: (key, value, ttl) => {
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@@ -212,14 +212,14 @@ Docker pull images
|
||||
deploy
|
||||
|
||||
|
||||
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||||
<i class="fas fa-folder-open"></i>
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||||
<span>分类</span>
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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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@@ -165,7 +165,7 @@
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</ul>
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<h3 id="数组"><a href="#数组" class="headerlink" title="数组"></a>数组</h3><figure class="highlight c"><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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string"><stdio.h></span></span></span><br><span class="line"></span><br><span class="line"><span class="type">int</span> <span class="title function_">main</span><span class="params">()</span>{</span><br><span class="line"> <span class="type">int</span> arr [<span class="number">10</span>] = {<span class="number">2</span>,<span class="number">3</span>,<span class="number">4</span>,<span class="number">5</span>,<span class="number">6</span>,<span class="number">7</span>,<span class="number">8</span>,<span class="number">9</span>,<span class="number">10</span>,<span class="number">11</span>};</span><br><span class="line"> arr[<span class="number">0</span>] = <span class="number">1525</span>;</span><br><span class="line"> *(arr+<span class="number">1</span>) = <span class="number">25</span>;</span><br><span class="line"> <span class="type">int</span> len = <span class="keyword">sizeof</span>(arr)/<span class="keyword">sizeof</span>(arr[<span class="number">0</span>]);</span><br><span class="line"> <span class="type">void</span> <span class="title function_">printArr</span><span class="params">(<span class="type">int</span> arr[], <span class="type">int</span> len)</span>{</span><br><span class="line"> <span class="keyword">for</span> (<span class="type">int</span> i = <span class="number">0</span>; i < len;i++){</span><br><span class="line"> <span class="built_in">printf</span>(<span class="string">"%d\t"</span>,arr[i]);</span><br><span class="line"> }</span><br><span class="line"> }</span><br><span class="line"> printArr(arr,len);</span><br><span class="line"> <span class="keyword">return</span> <span class="number">0</span>;</span><br><span class="line">}</span><br></pre></td></tr></table></figure>
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<h3 id="指针"><a href="#指针" class="headerlink" title="指针"></a>指针</h3><figure class="highlight c"><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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">// swap the value of a and b</span></span><br><span class="line"> <span class="type">void</span> <span class="title function_">swap</span><span class="params">(<span class="type">int</span>* x, <span class="type">int</span>* y)</span>{</span><br><span class="line"> <span class="type">int</span> temp = *x;</span><br><span class="line"> *x = *y;</span><br><span class="line"> *y = temp;</span><br><span class="line"></span><br><span class="line"> }</span><br><span class="line"> <span class="type">int</span> a = <span class="number">5</span>;</span><br><span class="line"> <span class="type">int</span> b = <span class="number">10</span>;</span><br><span class="line"> swap(&a, &b);</span><br><span class="line"> <span class="built_in">printf</span>(<span class="string">"a = %d b = %d\n"</span>, a, b);</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/12462.html">https://rq.shenjianl.cn/posts/12462.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"><a class="post-meta__tags" href="/tags/C-C/">C C++</a></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/52662.html" title="线性回归"><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">线性回归</div></div></a></div><div class="next-post pull-right"><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">下一篇</div><div class="next_info">k近邻算法（K-Nearest Neighbors）KNN</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="#c-lang%E5%9C%A8windows%E4%B8%8B%E7%9A%84%E5%BC%80%E5%8F%91%EF%BC%88VS-code%EF%BC%89"><span class="toc-number">1.</span> <span class="toc-text">c lang在windows下的开发（VS code）</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E6%95%B0%E6%8D%AE%E7%B1%BB%E5%9E%8B"><span class="toc-number">2.</span> <span class="toc-text">数据类型</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%B1%BB%E5%9E%8B%E8%BD%AC%E6%8D%A2"><span class="toc-number">3.</span> <span class="toc-text">类型转换</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E6%95%B0%E7%BB%84"><span class="toc-number">4.</span> <span class="toc-text">数组</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E6%8C%87%E9%92%88"><span class="toc-number">5.</span> <span class="toc-text">指针</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 classLine 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/12462.html">https://rq.shenjianl.cn/posts/12462.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"><a class="post-meta__tags" href="/tags/C-C/">C C++</a></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/52662.html" title="线性回归"><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">线性回归</div></div></a></div><div class="next-post pull-right"><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">下一篇</div><div class="next_info">k近邻算法（K-Nearest Neighbors）KNN</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="#c-lang%E5%9C%A8windows%E4%B8%8B%E7%9A%84%E5%BC%80%E5%8F%91%EF%BC%88VS-code%EF%BC%89"><span class="toc-number">1.</span> <span class="toc-text">c lang在windows下的开发（VS code）</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E6%95%B0%E6%8D%AE%E7%B1%BB%E5%9E%8B"><span class="toc-number">2.</span> <span class="toc-text">数据类型</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%B1%BB%E5%9E%8B%E8%BD%AC%E6%8D%A2"><span class="toc-number">3.</span> <span class="toc-text">类型转换</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E6%95%B0%E7%BB%84"><span class="toc-number">4.</span> <span class="toc-text">数组</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E6%8C%87%E9%92%88"><span class="toc-number">5.</span> <span class="toc-text">指针</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 classLine truncated
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<h3 id="完成订阅后如何使用？"><a href="#完成订阅后如何使用？" class="headerlink" title="完成订阅后如何使用？"></a>完成订阅后如何使用？</h3><blockquote>
|
||||
<p>你可以在 <code>Windows</code>、<code>Mac</code>、<code>Android</code>等平台使用此服务<br>使用订阅的对应链接： <a target="_blank" rel="noopener" href="https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60">https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60</a></p>
|
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</blockquote>
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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/14011.html">https://rq.shenjianl.cn/posts/14011.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"><a class="post-meta__tags" href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/">网络代理</a></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/41168.html" title="网络相关"><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">网络相关</div></div></a></div><div class="next-post pull-right"><a href="/posts/33957.html" title="组件使用"><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">组件使用</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%BB%8B%E7%BB%8D"><span class="toc-number">1.</span> <span class="toc-text">介绍</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E8%A7%A3%E5%86%B3%E6%96%B9%E6%A1%88"><span class="toc-number">2.</span> <span class="toc-text">解决方案</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E6%9C%BA%E5%9C%BA%E9%80%89%E6%8B%A9%E7%9A%84%E5%BB%BA%E8%AE%AE%EF%BC%9A"><span class="toc-number">3.</span> <span class="toc-text">机场选择的建议：</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%A6%82%E4%BD%95%E8%AE%A2%E9%98%85%EF%BC%9F"><span class="toc-number">4.</span> <span class="toc-text">如何订阅？</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%AE%8C%E6%88%90%E8%AE%A2%E9%98%85%E5%90%8E%E5%A6%82%E4%BD%95%E4%BD%BF%E7%94%A8%EF%BC%9F"><span class="toc-number">5.</span> <span class="toc-text">完成订阅后如何使用？</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></divLine 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/14011.html">https://rq.shenjianl.cn/posts/14011.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"><a class="post-meta__tags" href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/">网络代理</a></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/41168.html" title="网络相关"><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">网络相关</div></div></a></div><div class="next-post pull-right"><a href="/posts/33957.html" title="组件使用"><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">组件使用</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%BB%8B%E7%BB%8D"><span class="toc-number">1.</span> <span class="toc-text">介绍</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E8%A7%A3%E5%86%B3%E6%96%B9%E6%A1%88"><span class="toc-number">2.</span> <span class="toc-text">解决方案</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E6%9C%BA%E5%9C%BA%E9%80%89%E6%8B%A9%E7%9A%84%E5%BB%BA%E8%AE%AE%EF%BC%9A"><span class="toc-number">3.</span> <span class="toc-text">机场选择的建议：</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%A6%82%E4%BD%95%E8%AE%A2%E9%98%85%EF%BC%9F"><span class="toc-number">4.</span> <span class="toc-text">如何订阅？</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%AE%8C%E6%88%90%E8%AE%A2%E9%98%85%E5%90%8E%E5%A6%82%E4%BD%95%E4%BD%BF%E7%94%A8%EF%BC%9F"><span class="toc-number">5.</span> <span class="toc-text">完成订阅后如何使用？</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></divLine truncated
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<li><a href="./deploy">deploy</a></li>
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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/1441.html">https://rq.shenjianl.cn/posts/1441.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-full"><a href="/posts/47807.html" title="Docker被墙，如何继续使用？"><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">Docker被墙，如何继续使用？</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 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="https://th.bing.com/th/id/OIP.SZA5W6cF-tYiiZ08KZ7l7wHaEm?w=250&h=180&c=7&r=0&o=5&dpr=1.3&pid=1.7" 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 datetime="2025-01-25T07:12:08.000Z" title="发表于 2025-01-25 15:12:08">2025-01-25</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/posts/95.html" title="决策树算法"><img src="https://th.bing.com/th/id/OIP.XaPUn6eccfS_z_wTLQNFzgHaEK?w=240&h=180&c=7&r=0&o=5&dpr=1.9&pid=1.7" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="决策树算法"/></a><div class="content"><a class="title" href="/posts/95.html" title="决策树算法">决策树算法</a><time datetime="2025-01-24T04:39:59.000Z" title="发表于 2025-01-24 12:39:59">2025-01-24</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/60504.html" title="逻辑回归">逻辑回归</a><time datetime="2025-01-20T07:30:08.000Z" title="发表于 2025-01-20 15:30:08">2025-01-20</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/52662.html" title="线性回归">线性回归</a><time datetime="2025-01-19T08:46:51.000Z" title="发表于 2025-01-19 16:46:51">2025-01-19</time></div></div><div class="aside-list-Line 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/1441.html">https://rq.shenjianl.cn/posts/1441.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-full"><a href="/posts/47807.html" title="Docker被墙，如何继续使用？"><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">Docker被墙，如何继续使用？</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 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 datetime="2025-01-25T07:12:08.000Z" title="发表于 2025-01-25 15:12:08">2025-01-25</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/posts/95.html" title="决策树算法"><img src="/img/machinelearning/decision-tree.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="决策树算法"/></a><div class="content"><a class="title" href="/posts/95.html" title="决策树算法">决策树算法</a><time datetime="2025-01-24T04:39:59.000Z" title="发表于 2025-01-24 12:39:59">2025-01-24</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/60504.html" title="逻辑回归">逻辑回归</a><time datetime="2025-01-20T07:30:08.000Z" title="发表于 2025-01-20 15:30:08">2025-01-20</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/52662.html" title="线性回归">线性回归</a><time datetime="2025-01-19T08:46:51.000Z" title="发表于 2025-01-19 16:46:51">2025-01-19</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/12462.html" title="C lang">C lang</a><time datetime="2025-01-15T12:41:26.000Z" title="发表Line truncated
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<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>
|
||||
</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/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/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="https://th.bing.com/th/id/OIP.SZA5W6cF-tYiiZ08KZ7l7wHaEm?w=250&h=180&c=7&r=0&o=5&Line 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/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/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/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'" aLine truncated
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<li>自助法</li>
|
||||
<li>交叉验证法</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/29139.html">https://rq.shenjianl.cn/posts/29139.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"><a class="post-meta__tags" href="/tags/KNN/">KNN</a></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/12462.html" title="C lang"><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">C lang</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-2"><a class="toc-link" href="#k%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95%EF%BC%88K-Nearest-Neighbors%EF%BC%89KNN"><span class="toc-number">1.</span> <span class="toc-text">k近邻算法（K-Nearest Neighbors）KNN</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E8%B7%9D%E7%A6%BB%E5%85%AC%E5%BC%8F-2%E7%BB%B4"><span class="toc-number">1.0.1.</span> <span class="toc-text">距离公式(2维)</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#k%E5%80%BC%E9%80%89%E6%8B%A9%E9%97%AE%E9%A2%98"><span class="toc-number">1.0.2.</span> <span class="toc-text">k值选择问题</span></a></li></ol></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%89%B9%E5%BE%81%E9%A2%84%E5%A4%84%E7%90%86"><span class="toc-number">1.1.</span> <span class="toc-text">特征预处理</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#KNN%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0"><span class="toc-number">1.2.</span> <span class="toc-text">KNN代码实现</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E4%B8%8E%E7%BD%91%E6%A0%BC%E6%90%Line 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/29139.html">https://rq.shenjianl.cn/posts/29139.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"><a class="post-meta__tags" href="/tags/KNN/">KNN</a></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/12462.html" title="C lang"><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">C lang</div></div></a></div><div class="next-post pull-right"><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">下一篇</div><div class="next_info">Hadoop集群搭建基础环境</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="#k%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95%EF%BC%88K-Nearest-Neighbors%EF%BC%89KNN"><span class="toc-number">1.</span> <span class="toc-text">k近邻算法（K-Nearest Neighbors）KNN</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E8%B7%9D%E7%A6%BB%E5%85%AC%E5%BC%8F-2%E7%BB%B4"><span class="toc-number">1.0.1.</span> <span class="toc-text">距离公式(2维)</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#k%E5%80%BC%E9%80%89%E6%8B%A9%E9%97%AE%E9%A2%98"><span class="toc-number">1.0.2.</span> <span class="toc-text">k值选择问题</span></a></li></ol></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%89%B9%E5%BE%81%E9%A2%84%E5%A4%84%E7%90%86"><span class="toc-number">1.1.</span> <span class="toc-text">特征预处理</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#KNN%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0"><span class="toc-number">1.2.</span> <span class="toc-text">KNN代码实现</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E4%B8%8E%E7%BD%91Line truncated
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</ul>
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<figure class="highlight html"><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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br></pre></td><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 fLine 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/33957.html">https://rq.shenjianl.cn/posts/33957.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"><a class="post-meta__tags" href="/tags/uniapp/">uniapp</a></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/14011.html" title="无法访问外网？需要订阅代理服务？"><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">无法访问外网？需要订阅代理服务？</div></div></a></div><div class="next-post pull-right"><a href="/posts/58817.html" title="uniapp 开发"><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">uniapp 开发</div></div></a></div></nav><div class="relatedPosts"><div class="headline"><i class="fas fa-thumbs-up fa-fw"></i><span>相关推荐</span></div><div class="relatedPosts-list"><div><a href="/posts/58817.html" title="uniapp 开发"><div class="cover" style="background: var(--default-bg-color)"></div><div class="content is-center"><div class="date"><i class="far fa-calendar-alt fa-fw"></i> 2024-08-05</div><div class="title">uniapp 开发</div></div></a></div></div></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="#%E7%BB%84%E4%BB%B6%E8%87%AA%E5%8A%A8%E5%AF%BC%E5%85%A5"><span class="toc-number">1.</span> <span class="toc-text">组件自动导入</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#tui-sticky-%E5%90%B8%E9%A1%B6%E5%AE%B9%E5%99%A8"><span class="toc-number">2.</span> <span class="toc-text">tui-sticky 吸顶容器</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="hLine 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/33957.html">https://rq.shenjianl.cn/posts/33957.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"><a class="post-meta__tags" href="/tags/uniapp/">uniapp</a></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/14011.html" title="无法访问外网？需要订阅代理服务？"><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">无法访问外网？需要订阅代理服务？</div></div></a></div><div class="next-post pull-right"><a href="/posts/58817.html" title="uniapp 开发"><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">uniapp 开发</div></div></a></div></nav><div class="relatedPosts"><div class="headline"><i class="fas fa-thumbs-up fa-fw"></i><span>相关推荐</span></div><div class="relatedPosts-list"><div><a href="/posts/58817.html" title="uniapp 开发"><div class="cover" style="background: var(--default-bg-color)"></div><div class="content is-center"><div class="date"><i class="far fa-calendar-alt fa-fw"></i> 2024-08-05</div><div class="title">uniapp 开发</div></div></a></div></div></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="#%E7%BB%84%E4%BB%B6%E8%87%AA%E5%8A%A8%E5%AF%BC%E5%85%A5"><span class="toc-number">1.</span> <span class="toc-text">组件自动导入</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#tui-sticky-%E5%90%B8%E9%A1%B6%E5%AE%B9%E5%99%A8"><span class="toc-number">2.</span> <span class="toc-text">tui-sticky 吸顶容器</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="/Line truncated
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POWERMODE.shake = true;
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document.body.addEventListener('input', POWERMODE);
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@@ -157,7 +157,7 @@
|
||||
<p><strong>保存脚本到/usr/local/bin目录下</strong></p>
|
||||
<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"><span class="built_in">mv</span> usage.sh /usr/local/bin/usage</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="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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><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></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/34849.html">https://rq.shenjianl.cn/posts/34849.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/16107.html" title="Hello World"><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">Hello World</div></div></a></div><div class="next-post pull-right"><a href="/posts/58638.html" title="郑伯克段于鄢"><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">郑伯克段于鄢</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-ceLine truncated
|
||||
<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="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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><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></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/34849.html">https://rq.shenjianl.cn/posts/34849.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/16107.html" title="Hello World"><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">Hello World</div></div></a></div><div class="next-post pull-right"><a href="/posts/58638.html" title="郑伯克段于鄢"><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">郑伯克段于鄢</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-ceLine truncated
|
||||
POWERMODE.shake = true;
|
||||
POWERMODE.mobile = false;
|
||||
document.body.addEventListener('input', POWERMODE);
|
||||
|
||||
@@ -153,7 +153,7 @@
|
||||
}
|
||||
}
|
||||
detectApple()
|
||||
})(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="post" id="body-wrap"><header class="post-bg fixed" id="page-header" style="background-image: url('/img/site01.jpg')"><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><div id="post-info"><h1 class="post-title">网络相关</h1><div id="post-meta"><div class="meta-firstline"><span class="post-meta-date"><i class="far fa-calendar-alt fa-fw post-meta-icon"></i><span class="post-meta-label">发表于</span><time class="post-meta-date-created" datetime="2024-08-07T02:06:08.000Z" title="发表于 2024-08-07 10:06:08">2024-08-07</time><span class="post-meta-separator">|</span><i class="fas fa-history fa-fw post-meta-icon"></i><span class="post-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-08-09T12:21:50.028Z" title="更新于 2024-08-09 20:21:50">2024-08-09</time></span></div><div class="meta-secondline"><span class="post-meta-separator">|</span><span class="post-meta-pv-cv" id="" data-flag-title="网络相关"><i class="far fa-eye fa-fw post-meta-icon"></i><span class="post-meta-label">阅读量:</span><span id="busuanzi_value_page_pv"><i class="fa-solid fa-spinner fa-spin"></i></span></span></div></div></div></header><main class="layout" id="content-inner"><div id="post"><article class="post-content" id="article-container"></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 clasLine 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="post" id="body-wrap"><header class="post-bg fixed" id="page-header" style="background-image: url('/img/site01.jpg')"><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><div id="post-info"><h1 class="post-title">网络相关</h1><div id="post-meta"><div class="meta-firstline"><span class="post-meta-date"><i class="far fa-calendar-alt fa-fw post-meta-icon"></i><span class="post-meta-label">发表于</span><time class="post-meta-date-created" datetime="2024-08-07T02:06:08.000Z" title="发表于 2024-08-07 10:06:08">2024-08-07</time><span class="post-meta-separator">|</span><i class="fas fa-history fa-fw post-meta-icon"></i><span class="post-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-08-09T12:21:50.028Z" title="更新于 2024-08-09 20:21:50">2024-08-09</time></span></div><div class="meta-secondline"><span class="post-meta-separator">|</span><span class="post-meta-pv-cv" id="" data-flag-title="网络相关"><i class="far fa-eye fa-fw post-meta-icon"></i><span class="post-meta-label">阅读量:</span><span id="busuanzi_value_page_pv"><i class="fa-solid fa-spinner fa-spin"></i></span></span></div></div></div></header><main class="layout" id="content-inner"><div id="post"><article class="post-content" id="article-container"></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 clasLine truncated
|
||||
POWERMODE.shake = true;
|
||||
POWERMODE.mobile = false;
|
||||
document.body.addEventListener('input', POWERMODE);
|
||||
|
||||
@@ -192,7 +192,7 @@
|
||||
</ol>
|
||||
</li>
|
||||
</ol>
|
||||
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|
||||
<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>
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@@ -161,11 +161,11 @@
|
||||
<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>
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||||
<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>$$MSE = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{y}_i)^2$$</p>
|
||||
<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>m 是样本的数量。</li>
|
||||
<li>N 是样本的数量。</li>
|
||||
<li>$y_i$ 是第 i 个样本的真实值。</li>
|
||||
<li>$\hat{y}_i$ 是模型预测的第 i 个样本的值。</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>
|
||||
@@ -180,7 +180,7 @@
|
||||
<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="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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br></pre></td><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"> <spanLine truncated
|
||||
<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="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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br></pre></td><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"> <spanLine truncated
|
||||
window.MathJax = {
|
||||
tex: {
|
||||
inlineMath: [['$', '$'], ['\\(', '\\)']],
|
||||
|
||||
@@ -155,7 +155,7 @@
|
||||
detectApple()
|
||||
})(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="post" id="body-wrap"><header class="post-bg fixed" id="page-header" style="background-image: url('/img/site01.jpg')"><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><div id="post-info"><h1 class="post-title">郑伯克段于鄢</h1><div id="post-meta"><div class="meta-firstline"><span class="post-meta-date"><i class="far fa-calendar-alt fa-fw post-meta-icon"></i><span class="post-meta-label">发表于</span><time class="post-meta-date-created" datetime="2024-08-09T12:00:13.000Z" title="发表于 2024-08-09 20:00:13">2024-08-09</time><span class="post-meta-separator">|</span><i class="fas fa-history fa-fw post-meta-icon"></i><span class="post-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2024-08-10T02:31:03.678Z" title="更新于 2024-08-10 10:31:03">2024-08-10</time></span><span class="post-meta-categories"><span class="post-meta-separator">|</span><i class="fas fa-inbox fa-fw post-meta-icon"></i><a class="post-meta-categories" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/">古文观止</a></span></div><div class="meta-secondline"><span class="post-meta-separator">|</span><span class="post-meta-pv-cv" id="" data-flag-title="郑伯克段于鄢"><i class="far fa-eye fa-fw post-meta-icon"></i><span class="post-meta-label">阅读量:</span><span id="busuanzi_value_page_pv"><i class="fa-solid fa-spinner fa-spin"></i></span></span></div></div></div></header><main class="layout" id="content-inner"><div id="post"><article class="post-content" id="article-container"><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>
|
||||
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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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@@ -156,7 +156,7 @@
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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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@@ -186,7 +186,7 @@
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</ul>
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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/60504.html">https://rq.shenjianl.cn/posts/60504.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"><a class="post-meta__tags" href="/tags/logistic-regression/">logistic-regression</a></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/95.html" title="决策树算法"><img class="cover" src="https://th.bing.com/th/id/OIP.XaPUn6eccfS_z_wTLQNFzgHaEK?w=240&h=180&c=7&r=0&o=5&dpr=1.9&pid=1.7" onerror="onerror=null;src='/img/404.jpg'" alt="cover of previous post"><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">决策树算法</div></div></a></div><div class="next-post pull-right"><a href="/posts/52662.html" title="线性回归"><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">线性回归</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="#logistic-regression-code"><span class="toc-number">1.</span> <span class="toc-text">logistic regression code</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%88%86%E7%B1%BB%E8%AF%84%E4%BC%B0%E7%9A%84%E5%8F%82%E6%95%B0"><span class="toc-number">2.</span> <span class="toc-text">分类评估的参数</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%B1%BB%E5%88%AB%E4%B8%8D%E5%B9%B3%E8%A1%A1%E7%9A%84%E5%A4%84%E7%90%86"><span class="toc-number">3.</span> <span class="toc-text">类别不平衡的处理</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="https://th.bing.com/th/id/OIP.SZA5W6cF-tYiiZ08KZ7l7wHaEm?w=250&h=180&c=7&r=0&o=5&dpr=1.3&pid=1.7"Line 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/60504.html">https://rq.shenjianl.cn/posts/60504.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"><a class="post-meta__tags" href="/tags/logistic-regression/">logistic-regression</a></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/95.html" title="决策树算法"><img class="cover" src="/img/machinelearning/decision-tree.png" onerror="onerror=null;src='/img/404.jpg'" alt="cover of previous post"><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">决策树算法</div></div></a></div><div class="next-post pull-right"><a href="/posts/52662.html" title="线性回归"><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">线性回归</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="#logistic-regression-code"><span class="toc-number">1.</span> <span class="toc-text">logistic regression code</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%88%86%E7%B1%BB%E8%AF%84%E4%BC%B0%E7%9A%84%E5%8F%82%E6%95%B0"><span class="toc-number">2.</span> <span class="toc-text">分类评估的参数</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%B1%BB%E5%88%AB%E4%B8%8D%E5%B9%B3%E8%A1%A1%E7%9A%84%E5%A4%84%E7%90%86"><span class="toc-number">3.</span> <span class="toc-text">类别不平衡的处理</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">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/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/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="https://th.bing.com/th/id/OIP.SZA5W6cF-tYiiZ08KZ7l7wHaEm?w=250&h=180&c=7&r=0&o=5&dpr=1.3&pid=1.7" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="集成学习"/></a><div class="Line 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/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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<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/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/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">下一篇</div><div class="next_info">Hadoop集群搭建基础环境</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 tocLine 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/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
|
||||
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/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/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%BD%AE%E5%85%8D%E5%AF%86%E5%88%87%E6%8D%ALine 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/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
|
||||
POWERMODE.shake = true;
|
||||
POWERMODE.mobile = false;
|
||||
document.body.addEventListener('input', POWERMODE);
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<p><code>Random-Forest</code> 就是<code>Bagging + Decisiontree</code></p>
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</blockquote>
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<figure class="highlight python"><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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br></pre></td><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 clasLine truncated
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<p><img src="/img/machinelearning/random-forest.png"></p>
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<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="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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br></pre></td><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">Line truncated
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<p><img src="/img/machinelearning/ott.png"></p>
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<h3 id="ID3"><a href="#ID3" class="headerlink" title="ID3"></a>ID3</h3><p>D3 是一种基于信息增益（Information Gain）的决策树算法</p>
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<h3 id="Cart"><a href="#Cart" class="headerlink" title="Cart"></a>Cart</h3><p>CART（分类与回归树）一种决策树算法，，CART 使用 <strong>二叉树结构</strong>，即每个节点只能有两个子节点。</p>
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<h3 id="cart剪枝"><a href="#cart剪枝" class="headerlink" title="cart剪枝"></a>cart剪枝</h3><p>CART 决策树的剪枝方法分为 <strong>预剪枝</strong>（Pre-pruning）和 <strong>后剪枝</strong>（Post-pruning）两种：</p>
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@@ -186,7 +186,7 @@
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<p><a target="_blank" rel="noopener" href="http://webgraphviz.com/">Webgraphviz</a>，这个网站可以将<code>tree.dot</code>文件的内容生成对应的可视化树</p>
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<h4 id="回归决策树与线性回归的对比"><a href="#回归决策树与线性回归的对比" class="headerlink" title="回归决策树与线性回归的对比"></a>回归决策树与线性回归的对比</h4><figure class="highlight python"><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><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br></pre></td><td class="code"><pre><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> sklearn.linear_model <span class="keyword">import</span> LinearRegression</span><br><span class="line"><span class="keyword">from</span> sklearn.tree <span class="keyword">import</span> DecisionTreeRegressor</span><br><span class="line"><span class="keyword">from</span> matplotlib <span class="keyword">import</span> rcParams</span><br><span class="line"></span><br><span class="line"><span class="comment"># 设置matplotlib使用的字体为SimHei（黑体）</span></span><br><span class="line">rcParams[<span class="string">'font.sans-serif'</span>] = [<span class="string">'SimHei'</span>] <span class="comment"># 也可以使用 'Microsoft YaHei'</span></span><br><span class="line">rcParams[<span class="string">'axes.unicode_minus'</span>] = <span class="literal">False</span> <span class="comment"># 正常显示负号</span></span><br><span class="line">x = np.array(<span class="built_in">list</span>(<span class="built_in">range</span>(<span class="number">1</span>,<span class="number">11</span>))).reshape(-<span class="number">1</span>,<span class="number">1</span>)</span><br><span class="line">y = ([<span class="number">5.56</span>,<span class="number">5.70</span>,<span class="number">5.91</span>,<span class="number">6.40</span>,<span class="number">6.80</span>,<span class="number">7.05</span>,<span class="number">8.90</span>,<span class="number">8.70</span>,<span class="number">9.00</span>,<span class="number">9.05</span>])</span><br><span class="line"></span><br><span class="line">m1 = DecisionTreeRegressor(max_depth=<span class="number">1</span>)</span><br><span class="line">m2 = DecisionTreeRegressor(max_depth=<span class="number">3</span>)</span><br><span class="line">m3 = DecisionTreeRegressor()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 模型训练</span></span><br><span class="line">m1.fit(x,y)</span><br><span class="line">m2.fit(x,y)</span><br><span class="line">m3.fit(x,y)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 模型预测</span></span><br><span class="line">x_test = np.arange(<span class="number">0</span>,<span class="number">10</span>,<span class="number">0.01</span>).reshape(-<span class="number">1</span>,<span class="number">1</span>)</span><br><span class="line">y_1 = m1.predict(x_test)</span><br><span class="line">y_2 = m2.predict(x_test)</span><br><span class="line">y_3 = m3.predict(x_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 结果展示</span></span><br><span class="line">plt.figure(figsize=(<span class="number">10</span>,<span class="number">6</span>),dpi=<span class="number">100</span>)</span><br><span class="line">plt.scatter(x,y ,label = <span class="string">"data"</span>)</span><br><span class="line">plt.plot(x_test,y_1,label = <span class="string">"max_depth=1"</span>)</span><br><span class="line">plt.plot(x_test,y_2,label = <span class="string">"max_depth=3"</span>)</span><br><span class="line">plt.plot(x_test,y_3,label = <span class="string">"linearregression"</span>)</span><br><span class="line">plt.xlabel(<span class="string">"Line 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/95.html">https://rq.shenjianl.cn/posts/95.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"><a class="post-meta__tags" href="/tags/decisiontree/">decisiontree</a></div><div class="post_share"><div class="social-share" data-image="/img/machinelearning/decision-tree.png" 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/8816.html" title="集成学习"><img class="cover" src="/img/machinelearning/ensemble-learning.png" onerror="onerror=null;src='/img/404.jpg'" alt="cover of previous post"><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">集成学习</div></div></a></div><div class="next-post pull-right"><a href="/posts/60504.html" title="逻辑回归"><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">逻辑回归</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="#C4-5"><span class="toc-number">1.</span> <span class="toc-text">C4.5</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#ID3"><span class="toc-number">2.</span> <span class="toc-text">ID3</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Cart"><span class="toc-number">3.</span> <span class="toc-text">Cart</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#cart%E5%89%AA%E6%9E%9D"><span class="toc-number">4.</span> <span class="toc-text">cart剪枝</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E9%A2%84%E5%89%AA%E6%9E%9D%EF%BC%9A"><span class="toc-number">4.1.</span> <span class="toc-text">预剪枝：</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E5%90%8E%E5%89%AA%E6%9E%9D%EF%BC%9A"><span class="toc-number">4.2.</span> <span class="toc-text">后剪枝：</span></a></li></ol></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B-%E7%89%B9%E5%BE%81Line truncated
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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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+114
-114
@@ -16,15 +16,6 @@
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<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>
|
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</entry>
|
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<entry>
|
||||
<title>page</title>
|
||||
<url>/posts/1441.html</url>
|
||||
<content><![CDATA[<ul>
|
||||
<li><a href="./deploy">deploy</a></li>
|
||||
<li></li>
|
||||
</ul>
|
||||
]]></content>
|
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</entry>
|
||||
<entry>
|
||||
@@ -47,6 +38,15 @@
|
||||
<tag>C C++</tag>
|
||||
</tags>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>page</title>
|
||||
<url>/posts/1441.html</url>
|
||||
<content><![CDATA[<ul>
|
||||
<li><a href="./deploy">deploy</a></li>
|
||||
<li></li>
|
||||
</ul>
|
||||
]]></content>
|
||||
</entry>
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<entry>
|
||||
<title>script</title>
|
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<url>/posts/34849.html</url>
|
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@@ -183,49 +183,6 @@
|
||||
<tag>decisiontree</tag>
|
||||
</tags>
|
||||
</entry>
|
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<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>$$MSE = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{y}_i)^2$$</p>
|
||||
<ul>
|
||||
<li>m 是样本的数量。</li>
|
||||
<li>$y_i$ 是第 i 个样本的真实值。</li>
|
||||
<li>$\hat{y}_i$ 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>逻辑回归</title>
|
||||
<url>/posts/60504.html</url>
|
||||
@@ -271,51 +228,47 @@
|
||||
</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>
|
||||
<title>网络相关</title>
|
||||
<url>/posts/41168.html</url>
|
||||
<content><![CDATA[]]></content>
|
||||
</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>
|
||||
<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>
|
||||
<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="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>
|
||||
<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>郑伯克段于鄢</title>
|
||||
@@ -389,6 +342,53 @@
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">echo</span> <span class="string">'export PATH=$PATH:/home/hadoop/bin'</span> >> ~/.bashrc && <span class="built_in">source</span> ~/.bashrc</span><br><span class="line">scp_all.sh /home/hadoop/.bashrc /home/hadoop/</span><br><span class="line">ssh_all.sh <span class="built_in">source</span> ~/.bashrc </span><br></pre></td></tr></table></figure></li>
|
||||
</ul>
|
||||
]]></content>
|
||||
</entry>
|
||||
<entry>
|
||||
<title>Hadoop集群HDFS配置</title>
|
||||
<url>/posts/61252.html</url>
|
||||
<content><![CDATA[<h3 id="上传hadoop-3-1-4-tar-gz到-tmp，解压"><a href="#上传hadoop-3-1-4-tar-gz到-tmp，解压" class="headerlink" title="上传hadoop-3.1.4.tar.gz到/tmp，解压"></a>上传<code>hadoop-3.1.4.tar.gz</code>到<code>/tmp</code>，解压</h3><blockquote>
|
||||
<p>注意在六台机器均上传到<code>/tmp</code></p>
|
||||
</blockquote>
|
||||
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="comment"># 在6台机器执行</span></span><br><span class="line"><span class="built_in">sudo</span> tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/</span><br><span class="line"><span class="comment"># 分发到其他主机</span></span><br><span class="line">ssh_root.sh <span class="built_in">chown</span> -R hadoop:hadoop /usr/local/hadoop-3.1.4</span><br><span class="line">ssh_root.sh <span class="built_in">ln</span> -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop</span><br></pre></td></tr></table></figure>
|
||||
<h3 id="配置环境变量"><a href="#配置环境变量" class="headerlink" title="配置环境变量"></a>配置环境变量</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">echo</span> <span class="string">'export HADOOP_HOME=/usr/local/hadoop'</span> >> /etc/profile.d/myEnv.sh</span><br><span class="line"><span class="built_in">echo</span> <span class="string">'export PATH=$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>
|
||||
<entry>
|
||||
<title>Docker被墙，如何继续使用？</title>
|
||||
@@ -434,6 +434,25 @@
|
||||
</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>Hadoop集群Zookeeper配置</title>
|
||||
<url>/posts/61251.html</url>
|
||||
@@ -474,25 +493,6 @@
|
||||
|
||||
]]></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>
|
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|
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@@ -54,7 +54,7 @@
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set: (key, value, ttl) => {
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@@ -151,7 +151,7 @@
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}
|
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}
|
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detectApple()
|
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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-home-page fixed" id="page-header" style="background-image: url('/img/site01.jpg')"><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><div id="page-site-info"><h1 id="site-title">decisiontree</h1></div></header><main class="layout hide-aside" id="content-inner"><div id="tag"><div class="article-sort-title">标签 - decisiontree</div><div class="article-sort"><div class="article-sort-item year">2025</div><div class="article-sort-item"><a class="article-sort-item-img" href="/posts/95.html" title="决策树算法"><img src="https://th.bing.com/th/id/OIP.XaPUn6eccfS_z_wTLQNFzgHaEK?w=240&h=180&c=7&r=0&o=5&dpr=1.9&pid=1.7" alt="决策树算法" onerror="this.onerror=null;this.src='/img/404.jpg'"></a><div class="article-sort-item-info"><div class="article-sort-item-time"><i class="far fa-calendar-alt"></i><time class="post-meta-date-created" datetime="2025-01-24T04:39:59.000Z" title="发表于 2025-01-24 12:39:59">2025-01-24</time></div><a class="article-sort-item-title" href="/posts/95.html" title="决策树算法">决策树算法</a></div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span></div></nav></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><aLine truncated
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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-home-page fixed" id="page-header" style="background-image: url('/img/site01.jpg')"><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><div id="page-site-info"><h1 id="site-title">decisiontree</h1></div></header><main class="layout hide-aside" id="content-inner"><div id="tag"><div class="article-sort-title">标签 - decisiontree</div><div class="article-sort"><div class="article-sort-item year">2025</div><div class="article-sort-item"><a class="article-sort-item-img" href="/posts/95.html" title="决策树算法"><img src="/img/machinelearning/decision-tree.png" alt="决策树算法" onerror="this.onerror=null;this.src='/img/404.jpg'"></a><div class="article-sort-item-info"><div class="article-sort-item-time"><i class="far fa-calendar-alt"></i><time class="post-meta-date-created" datetime="2025-01-24T04:39:59.000Z" title="发表于 2025-01-24 12:39:59">2025-01-24</time></div><a class="article-sort-item-title" href="/posts/95.html" title="决策树算法">决策树算法</a></div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span></div></nav></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-buttLine truncated
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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-home-page fixed" id="page-header" style="background-image: url('/img/site01.jpg')"><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><div id="page-site-info"><h1 id="site-title">ensemble-learning</h1></div></header><main class="layout hide-aside" id="content-inner"><div id="tag"><div class="article-sort-title">标签 - ensemble-learning</div><div class="article-sort"><div class="article-sort-item year">2025</div><div class="article-sort-item"><a class="article-sort-item-img" href="/posts/8816.html" title="集成学习"><img src="https://th.bing.com/th/id/OIP.SZA5W6cF-tYiiZ08KZ7l7wHaEm?w=250&h=180&c=7&r=0&o=5&dpr=1.3&pid=1.7" alt="集成学习" onerror="this.onerror=null;this.src='/img/404.jpg'"></a><div class="article-sort-item-info"><div class="article-sort-item-time"><i class="far fa-calendar-alt"></i><time class="post-meta-date-created" datetime="2025-01-25T07:12:08.000Z" title="发表于 2025-01-25 15:12:08">2025-01-25</time></div><a class="article-sort-item-title" href="/posts/8816.html" title="集成学习">集成学习</a></div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span></div></nav></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>Line truncated
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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(104, 179, 120)">C C++</a><a href="/tags/ensemble-learning/" style="font-size: 1.2em; color: rgb(186, 54, 135)">ensemble-learning</a><a href="/tags/KNN/" style="font-size: 1.2em; color: rgb(171, 120, 112)">KNN</a><a href="/tags/decisiontree/" style="font-size: 1.2em; color: rgb(120, 160, 72)">decisiontree</a><a href="/tags/linear-regression/" style="font-size: 1.2em; color: rgb(146, 38, 172)">linear-regression</a><a href="/tags/logistic-regression/" style="font-size: 1.2em; color: rgb(73, 16, 136)">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(141, 196, 84)">古文观止</a><a href="/tags/uniapp/" style="font-size: 2.1em; color: rgb(62, 200, 149)">uniapp</a><a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.2em; color: rgb(132, 68, 28)">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Line truncated
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<span>分类</span>
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@@ -4,7 +4,8 @@ tags: decisiontree
|
||||
categories: machinelearning
|
||||
abbrlink: 95
|
||||
date: 2025-01-24 12:39:59
|
||||
cover: https://th.bing.com/th/id/OIP.XaPUn6eccfS_z_wTLQNFzgHaEK?w=240&h=180&c=7&r=0&o=5&dpr=1.9&pid=1.7
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cover: /img/machinelearning/decision-tree.png
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---
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||||
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### C4.5
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||||
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@@ -1,4 +0,0 @@
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[ViewState]
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Mode=
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Vid=
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FolderType=Generic
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@@ -4,7 +4,7 @@ tags: ensemble-learning
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categories: machinelearning
|
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abbrlink: 8816
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date: 2025-01-25 15:12:08
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cover: https://th.bing.com/th/id/OIP.SZA5W6cF-tYiiZ08KZ7l7wHaEm?w=250&h=180&c=7&r=0&o=5&dpr=1.3&pid=1.7
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cover: /img/machinelearning/ensemble-learning.png
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---
|
||||
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### Bagging
|
||||
|
||||
@@ -17,10 +17,10 @@ $$y = w_1x_1 + w_2x_2 + \cdot\cdot\cdot+w_nx_n$$
|
||||
### 损失函数
|
||||
|
||||
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 **均方误差（MSE）**：
|
||||
$$MSE = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{y}_i)^2$$
|
||||
- m 是样本的数量。
|
||||
$$J(\theta) = \frac{1}{2N} \sum_{i=1}^{N} (y_i - f_\theta(x_i))^2$$
|
||||
- N 是样本的数量。
|
||||
- $y_i$ 是第 i 个样本的真实值。
|
||||
- $\hat{y}_i$ 是模型预测的第 i 个样本的值。
|
||||
- $f_\theta(x_i)$ 是模型预测的第 i 个样本的值。
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### 线性回归优化
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Reference in new issue
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