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【答案】应助回帖
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感谢参与，应助指数 +1
自私的猫1988: 金币+2, 鼓励交流 2017-08-08 18:42:58
排版可能需要的包：
/usepackage{algorithm} //format of the algorithm
/usepackage{algorithmic} //format of the algorithm
/usepackage{multirow} //multirow for format of table
/usepackage{amsmath}
/usepackage{xcolor}
/DeclareMathOperator*{/argmin}{argmin} //argmin或argmax公式的排版
/renewcommand{/algorithmicrequire}{/textbf{Input:}} //Use Input in the format of Algorithm
/renewcommand{/algorithmicensure}{/textbf{Output:}} //UseOutput in the format of Algorithm
排版图片可能需要的包：
/usepackage{graphics}
/usepackage{graphicx}
/usepackage{epsfig}
算法的排版举例：
/begin{algorithm}[htb] %算法的开始
/caption{ Framework of ensemble learning for our system.} %算法的标题
/label{alg:Framwork} %给算法一个标签，这样方便在文中对算法的引用
/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} %算法的一个陈述，对应算法的一个步骤或公式之类的； /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}
排版效果图：
![Latex 算法排版求助]()
在文中对算法和算法的某个步骤的引用：Therefore, in step /ref{code:fram:extract} of algorithm /ref{alg:Framwork}, we extract $T_n$, a set of reliable negative samples
1、 For和While循环语句的排版举例
（1） 排版效果图
![Latex 算法排版求助-1]()
（2） 排版代码
/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 $T=T/bigcup T_{i};$//
/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} |
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