Latex 中进行算法的排版
在论文写作和排版过程中,常常会用到算法描述,在LaTex中,算法描述块的排版会用到两个宏包 \usepackage{algorithm}和\usepackage{algorithmic}。算法的排版,主要在于控制缩进、粗体、横线等格式,这些都会在这篇博客中进行介绍。
在论文写作和排版过程中,常常会用到算法描述,在LaTex中,算法描述块的排版会用到两个宏包 \usepackage{algorithm} 和 \usepackage{algorithmic}。算法的排版,主要在于控制缩进、粗体、横线等格式,这些都会在这篇博客中进行介绍。
在开始算法排版之前,首先在文档开头加入下面两句,以导入宏包:
\usepackage{algorithm} \usepackage{algorithmic}
例1
那么,我们首先看一个例子:
\begin{algorithm} \caption{A} \label{alg:A} \begin{algorithmic} \STATE {set $r(t)=x(t)$} \REPEAT \STATE set $h(t)=r(t)$ \REPEAT \STATE set $h(t)=r(t)$ \UNTIL{B} \UNTIL{B} \end{algorithmic} \end{algorithm}
在编译之后,显示为:
使用algorithmic包时,关键字全部大写,如果使用的是algorithmicx包,那么关键字首字母大写,后面小写。
例2
第二个例子更加详细的展示了缩进的控制,可以自己编译一下:
\begin{algorithm} \caption{Calculate $y = x^n$} \label{alg1} \begin{algorithmic} \REQUIRE $n \geq 0 \vee x \neq 0$ \ENSURE $y = x^n$ \STATE $y \Leftarrow 1$ \IF{$n < 0$} \STATE $X \Leftarrow 1 / x$ \STATE $N \Leftarrow -n$ \ELSE \STATE $X \Leftarrow x$ \STATE $N \Leftarrow n$ \ENDIF \WHILE{$N \neq 0$} \IF{$N$ is even} \STATE $X \Leftarrow X \times X$ \STATE $N \Leftarrow N / 2$ \ELSE[$N$ is odd] \STATE $y \Leftarrow y \times X$ \STATE $N \Leftarrow N - 1$ \ENDIF \ENDWHILE \end{algorithmic} \end{algorithm}
例3
如果需要显示Input和Output:
\begin{algorithm} \caption{Fourier-Mellin Based KCF} \label{alg:A} \hspace*{0.02in}{\bf Input:} Image $I$\\preprocessed kernelized template $T_\kappa$\\ \hspace*{0.02in}{\bf Output:} scale $\sigma$, angle $\theta$ relation between $I$ and $T$ \begin{algorithmic}[1] \STATE {fourier transform: $F=\mathcal{F}(I)$} \STATE {high pass filter: $F_h=\mathcal{H}(F)$\\$\mathcal{H}(x,y)=(1.0-cos(\pi x)cos(\pi y))(2.0-cos(\pi x)cos(\pi y))$} \STATE {log-polar transform: $F_{lp}=\mathcal{L}(F_h)$} \STATE {apply kernel function: $F_\kappa=\mathcal{K}(F_{lp})$} \STATE {phase correlation: $(\Delta x, \Delta y)=\mathcal{C}(F_\kappa, T_\kappa)$} \STATE {resolove scale and rotation:\\ $\theta=\alpha \Delta x$, $\sigma=log(\Delta y)$\\ where $\alpha$ is translation factor of pixel translation on fourier domain and polar angle on origin image } \end{algorithmic} \end{algorithm}
这样,就在开头显示了输入和输出。{algorithmic}[1]表示显示行号,当然,还可以显示竖线,不过要使用额外的宏包,请参考文后链接。
例4
还可以使用\renewcommand 改变现有命令,在导言区加入下列语句
\renewcommand{\algorithmicrequire}{ \textbf{Input:}} %Use Input in the format of Algorithm \renewcommand{\algorithmicensure}{ \textbf{Output:}} %UseOutput in the format of Algorithm
使得原来软件包中定义的命令\REQUIRE和\ENSURE显示为Input:和Output:
\begin{algorithm}[htb] \caption{ Framework of ensemble learning for our system.} \label{alg:Framwork} \begin{algorithmic}[1] %这个1 表示每一行都显示数字 \REQUIRE ~~\\ %算法的输入参数:Input 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 ~~\\ %算法的输出:Output 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 }%对此行的标记,方便在文中引用算法的某个步骤 \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 $E_n$; %算法的返回值 \end{algorithmic} \end{algorithm}
排版结果如下:
例5
最后一个例子:
\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}
排版结果为:
内容参考博主
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