Useful link: November 2012

[Latex] 如何在Latex上寫algorithm

[转]

作者： Wei Chung Cheng 發佈於上午1:57 2011年1月21日星期五

做個紀錄吧，我下關鍵字都找不到清晰的教學XD

希望以後有人要用到的時候比較好查！

首先你需要灌一些package，灌package的方式，我以MiKTeX為例子。

簡單的來說，開始 -> MiKTeX -> Maintenance (Admin) -> Package Manager (Admin)

點進去之後，他會搜尋所有的Package。

然後你點選你需要的Package 再按那個"+"的按鈕就會更新package了！

通常Algorithm 會用到哪些package呢？

你可以灌以下這些Package：
algorithms, algorithm2e, program, alg, algorithmicx, pseudocode

安裝完成之後，回到你的tex檔。

在\begin{document}之前，加入以下這兩行：
\usepackage{algorithmic} - 對應algorithmicx

\usepackage{algorithm} - 對應algorithms

因為我目前只用到這兩個，相對應其他package的usepackage 稍微搜尋一下就會有了！

有時候加完之後complie 會出現algorithm.sty找不到的訊息。

這時候就手動將algorithm.sty放入你目前的資料夾中就可以了！

至於去哪找到algorithm.sty呢？

資料夾 "MiKTeX 2.8\tex\latex\algorithms" 底下就找得到。

另外附上簡單的Sample

\algsetup{indent=2em}

\renewcommand{\algorithmicrequire}{\textbf{Input:}}

\renewcommand{\algorithmicensure}{\textbf{Output:}}

\newcommand{\factorial}{\ensuremath{\mbox{\sc Factorial}}}

\begin{algorithm}[h!]

\caption{$\factorial(n)$}\label{alg:factorial}

\begin{algorithmic}[1]

\REQUIRE An integer $n \geq 0$.

\ENSURE The value of $n!$.

\medskip

\IF {$n = 0$}

\RETURN $1$

\ELSE

\RETURN $n \cdot \factorial(n-1)$

\ENDIF

\end{algorithmic}

\end{algorithm

前兩行renewcommand 是：
將require以Input: 形式表示及ensure以Output:形式表示。

網路上範例一：
\begin{algorithm}[htb]
\caption{ Framework of ensemble learning for our system.}
\label{alg:Framwork}
\begin{algorithmic}[1]
\Require
The set of positive samples for current batch, $P_n$;
The set of unlabelled samples for current batch, $U_n$;
Ensemble of classifiers on former batches, $E_{n-1}$;
\Ensure
Ensemble of classifiers on the current batch, $E_n$;
\State Extracting the set of reliable negative and/or positive samples $T_n$ from $U_n$ with help of $P_n$;
\label{code:fram:extract}
\State Training ensemble of classifiers $E$ on $T_n \cup P_n$, with help of data in former batches;
\label{code:fram:trainbase}
\State $E_n=E_{n-1}cup E$;
\label{code:fram:add}
\State Classifying samples in $U_n-T_n$ by $E_n$;
\label{code:fram:classify}
\State Deleting some weak classifiers in $E_n$ so as to keep the capacity of $E_n$;
\label{code:fram:select} \\
\Return $E_n$;
\end{algorithmic}
\end{algorithm}

排版效果圖：

網路上範例二：

\begin{algorithm}[h]
\caption{An example for format For \& While Loop in Algorithm}
\begin{algorithmic}[1]
\For{each $i\in [1,9]$}
\State initialize a tree $T_{i}$ with only a leaf (the root);
\State $T=T\cup T_{i};$
\EndFor
\ForAll {$c$ such that $c\in RecentMBatch(E_{n-1})$}
\label{code:TrainBase:getc}
\State $T=T\cup PosSample(c)$;
\label{code:TrainBase:pos}
\EndFor;
\For{$i=1$; $i<n$; $i++$ }
\State $//$ Your source here;
\EndFor
\For{$i=1$ to $n$}
\State $//$ Your source here;
\EndFor
\State $//$ Reusing recent base classifiers.
\label{code:recentStart}
\While {$(|E_n| \leq L_1 )and( D \neq \phi)$}
\State Selecting the most recent classifier $c_i$ from $D$;
\State $D=D-c_i$;
\State $E_n=E_n+c_i$;
\EndWhile
\label{code:recentEnd}
\end{algorithmic}
\end{algorithm}

排版效果圖：

個人範例：

\begin{algorithm}[h]
\caption{Conjugate Gradient Algorithm with Dynamic Step-Size Control}
\label{alg::conjugateGradient}
\begin{algorithmic}[1]
\Require
$f(x)$: objective funtion;
$x_0$: initial solution;
$s$: step size;
\Ensure
optimal $x^{*}$
\State initial $g_0=0$ and $d_0=0$;
\Repeat
\State compute gradient directions $g_k=\bigtriangledown f(x_k)$;
\State compute Polak-Ribiere parameter $\beta_k=\frac{g_k^{T}(g_k-g_{k-1})}{\parallel g_{k-1} \parallel^{2}}$;
\State compute the conjugate directions $d_k=-g_k+\beta_k d_{k-1}$;
\State compute the step size $\alpha_k=s/\parallel d_k \parallel_{2}$;
\Until{($f(x_k)>f(x_{k-1})$)}
\end{algorithmic}
\end{algorithm}

排版效果圖：

先前所使用的套件為algorithm或algorithmic

接下來介紹另一個寫algorithm的套件alogrithm2e

首先使用\usepackage指令
\usepackage[linesnumbered,boxed]{algorithm2e}

接下來是網路範例：
\begin{algorithm}
\caption{identifyRowContext}
\KwIn{$r_i$, $Backgrd(T_i)$=${T_1,T_2,\ldots ,T_n}$ and similarity threshold $\theta_r$}
\KwOut{$con(r_i)$}
$con(r_i)= \Phi$\;
\For{$j=1;j \le n;j \ne i$}
{
float $maxSim=0$\;
$r^{maxSim}=null$\;
\While{not end of $T_j$}
{
compute Jaro($r_i,r_m$)($r_m\in T_j$)\;
\If{$(Jaro(r_i,r_m) \ge \theta_r)\wedge (Jaro(r_i,r_m)\ge r^{maxSim})$}
{
replace $r^{maxSim}$ with $r_m$\;
}
}
$con(r_i)=con(r_i)\cup {r^{maxSim}}$\;
}
return $con(r_i)$\;
\end{algorithm}

排版效果圖：
用LaTex寫algorithm_4