LaTeX 算法代碼排版 --latex2e範例總結

LaTeX 寫做： 算法代碼排版 --latex2e範例總結

 

latex2e 宏包的使用範例：

 

• \usepackage[ruled]{algorithm2e}                                     %算法排版樣式1
• \usepackage[ruled,vlined]{algorithm2e}                          %算法排版樣式2
• \usepackage[linesnumbered,boxed]{algorithm2e}        %算法排版樣式3

 

使用上述代碼中三種不一樣的宏包編譯後的代碼排版樣式預覽：

樣式一：

樣式二：

樣式三：

 

latex代碼排版.tex文件： 

 

\documentclass{article}

 \usepackage[ruled]{algorithm2e}                 %算法排版樣式1 %\usepackage[ruled,vlined]{algorithm2e}          %算法排版樣式2 %\usepackage[linesnumbered,boxed]{algorithm2e}   %算法排版樣式3
    
 \begin{document}
   
    How to use the algorithm2e in \LaTeX ~ file.
        
    Examples: 

%    ------------------------------Example - 1---------------------------------------------
        \begin{algorithm}[H]
            \caption{How to write algorithms}
            \KwIn{this text}
            \KwOut{how to write algorithm with \LaTeX2e }
            
            initialization\;
            \While{not at end of this document}{
                read current\;
                \eIf{understand}
                {
                        go to next section\;
                        current section becomes this one\;
                }
                {
                    go back to the beginning of current section\;
                }
        }
    \end{algorithm}
    
%    ---------------------------Example - 2------------------------------------------------
    \begin{algorithm} 
%    \SetAlgoNoLine  %去掉以前的豎線
     \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}
    %--------------------------------------------------------------------------------------

 some special information The algorithm2e LaTeX package conflicts with several others over the use of the algorithm identifier. A common indicator is something like this message: To resolve the issues, simply put the following just before the inclusion of the algorithm2e package: %\makeatletter %\newif\if@restonecol %\makeatother %\let\algorithm\relax %\let\endalgorithm\relax

\end{document}

 中文latex模式下，更改Input爲輸入，更改Output爲輸出的方法： 在算法內部插入、

 

\SetKwInOut{KIN}{輸入}

\SetKwInOut{KOUT}{輸出}

 

用咱們定義的新的宏名「\KIN」 在算法開頭相應位置替換掉自帶的\KwInput，用"\KOUT" 替換掉\KwOutput 便可，實現中文的輸入和輸出.

具體事例以下：

 

 1 \renewcommand{\algorithmcfname}{算法}  2 \begin{algorithm}[H]
 3 %\SetAlgoNoLine
 4 \SetKwInOut{KIN}{輸入} 5 \SetKwInOut{KOUT}{輸出}  6 %\BlankLine  %空一行
 7     \caption{標準DE算法 }
 8     \label{DE_algo} %
 9  \KIN{Population: $ M$; Dimension: $ D $; Genetation: $ T $ }
10  \KOUT{The best vector (solution)  $ \varDelta $ }
11     $ t \leftarrow 1 (initialization) $\;
12     \For{$i=1$ to $ M $    }
13     {\For{$j=1$ to $ D $}
14         {
15             $  {x}_{i,t}^j=x_{min}^j + rand(0,1)\cdotp (x_{max}^j-x_{min}^j) $\;
16         }
17     }        
18     %--------------------------------------------------    
19 \While{$(|f(\varDelta)| \geq\varepsilon )$      or     $(t \leq T )$}
20     {
21         \For{$ i=1$  to $M$}
22         {
23 \emph{$ \blacktriangleright $ (Mutation and Crossover)}\\            
24 %\textit{ $ \blacktriangleright $ (Mutation and Crossover) }\\
25             \For{$j=1$ to $ D $}
26             {    
27                 $ v_{i,t}^j =Mutation(x_{i,t}^j)$\;    
28                 $ u_{i,t}^j =Crossover(x_{i,t}^j,v_{i,t}^j)$\;
29             }
30 \emph{$ \blacktriangleright $ (Greedy Selection)}\\
31             %\textit{ $ \blacktriangleright $ (Greedy Selection) }\\
32             \eIf{$ f(\textbf{u}_{i,t}) <  f(\textbf{x}_{i,t}) $}
33             {
34                 $  \textbf{x}_{i,t} \leftarrow\textbf{u}_{i,t}$\;    
35                 \If{$  f(\textbf{x}_{i,t}) < f(\varDelta)$}
36                 {
37                     $ \varDelta \leftarrow \textbf{x}_{i,t}$ \;
38                 }
39             }
40             {
41                 $  \textbf{x}_{i,t} \leftarrow \textbf{x}_{i,t} $\;
42             }
43         }
44         $ t \leftarrow t+1 $\;    
45     }    %While        
46     \Return the best vector  $\varDelta$\;
47 \end{algorithm}