LaTeX算法排版例子
2013-03-26 20:31
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1)首先在导言区加入语句:
2)例1
排版结果如下:
3)例2
排版结果如下:
4)\renewcommand 改变现有命令的定义。在导言区加入如下语句:
使得原来软件包中定义的命令\REQUIRE和\ENSURE显示为Input:和Output:
一个例子如下:
排版结果如下:
5)最后一个例子
排版结果如下:
\usepackage{algorithm} \usepackage{algorithmic}
2)例1
\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}
排版结果如下:
3)例2
\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}
排版结果如下:
4)\renewcommand 改变现有命令的定义。在导言区加入如下语句:
\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:
一个例子如下:
\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:
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}
排版结果如下:
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