algorithm2e 中如何显示算法步骤？

Question

我正在尝试弄清楚如何显示算法步骤algorithm2e。

我的代码如下：

\title{AlgorithmTemplate}
\documentclass[17pt]{article}
\usepackage{fullpage}
\usepackage{times}
\usepackage{fancyhdr,graphicx,amsmath,amssymb}
\usepackage[ruled,vlined]{algorithm2e}
\include{pythonlisting}
\begin{document} 
\begin{algorithm}[H]
\SetAlgoLined
\KwIn{Multi-label dataset : $\left(x^{(n)}, \mathbf{y}^{(n)}\right), n=1,2, \dots, N$ ; \newline A zero matrix $A \in \mathbb{R}{^{n\times n}}$ ; \newline A numpy matrix $H^l \in \mathbb{R}{^{n\times d}}$}

\KwOut{Predicted label set $\mathbf{\widehat y}$}
\emph{Feature vector from bi-directional LSTM}\;

\For{each epoch}{
\emph{Feature vector from bi-directional LSTM}\;
\For{each batch}{\label{forins}
$1)$ Compute \textit{x} using equation (1)\newline
$\mathbf{\textit{x} = f_{rnn}(f_{word2vec}(I;\theta_{word2vec});\theta_{rnn})\in\mathbb{R}^{D}}$\newline 


Compute forward pass for lstm \newline
$\overrightarrow{\boldsymbol{h}}_{i}=\overrightarrow{\mathrm{LSTM}}\left(\overrightarrow{\boldsymbol{h}}_{i-1}, \boldsymbol{x}_{i}\right)$\newline
Compute forward pass for lstm \newline
$\overleftarrow{\boldsymbol{h}}_{i}=\overleftarrow{\mathrm{LSTM}}\left(\overleftarrow{\boldsymbol{h}}_{i+1}, \boldsymbol{x}_{i}\right)$ \newline
concatenating the hidden states from both directions \newline
$\boldsymbol{h}_{i}=\left[\overrightarrow{\boldsymbol{h}_{i}} ; \overleftarrow{\boldsymbol{h}}_{i}\right]$\newline
$2)$ Compute \textit{W} using equation (10)\newline
$\mathbf{W = f_{network} (I;J;\theta_{network})}$ \newline
compute mulit-head attention using equation (9)
\mathbf{H_{i}^{(l+1)}=\sigma\left(\frac{1}{K} \sum_{k=1}^{K} \sum_{j \in N(i)} \alpha_{i j, k}^{(l)} H_{j}^{(l)} W^{(l)}\right)}\newline

Matrix multiplication 


$\mathbf{\widehat y}$ = \textit{x} \mathbf{\odot} \textit{W} \newline

Compute cross entropy \newline

$\mathbf{\mathcal{L}=\sum_{c=1}^{C} y^{c} \log \left(\sigma\left(\hat{y}^{c}\right)\right)+\left(1-y^{c}\right) \log \left(1-\sigma\left(\hat{y}^{c}\right)\right)}$

loss = reduce ( cross entropy )


update the parameters basis on loss using back propagation \newline


$\mathbf{\theta_{t+1}=\theta_{t}-\frac{\eta}{\sqrt{\hat{v}_{t}}+\epsilon} \hat{m}_{t}}$

}
}

\caption{Algorithm}

\end{algorithm}
\end{document}

如何获得这样的编号：

编辑算法：

\title{AlgorithmTemplate}
\documentclass[17pt]{article}
\usepackage{fullpage}
\usepackage{times}
\usepackage{fancyhdr,graphicx,amsmath,amssymb}
\usepackage[ruled,vlined,linesnumbered]{algorithm2e}
\include{pythonlisting}
\begin{document} 
\SetAlgoLined

\begin{algorithm}[H]
\KwIn{Multi-label dataset : $\left(x^{(n)}, \mathbf{y}^{(n)}\right), n=1,2, \dots, N$ ; \newline A zero matrix $A \in \mathbb{R}{^{n\times n}}$ ; \newline A numpy matrix $H^l \in \mathbb{R}{^{n\times d}}$}

\KwOut{Predicted label set $\mathbf{\widehat y}$}
\emph{Feature vector from bi-directional LSTM}\;

\For{each epoch}{
\emph{Feature vector from bi-directional LSTM}\;
\For{each batch}{\label{forins}
$1)$ Compute \textit{x} using equation (1)

$\mathbf{\textit{x} = f_{rnn}(f_{word2vec}(I;\theta_{word2vec});\theta_{rnn})\in\mathbb{R}^{D}}$



Compute forward pass for lstm 

$\overrightarrow{\boldsymbol{h}}_{i}=\overrightarrow{\mathrm{LSTM}}\left(\overrightarrow{\boldsymbol{h}}_{i-1}, \boldsymbol{x}_{i}\right)$

Compute forward pass for lstm 

$\overleftarrow{\boldsymbol{h}}_{i}=\overleftarrow{\mathrm{LSTM}}\left(\overleftarrow{\boldsymbol{h}}_{i+1}, \boldsymbol{x}_{i}\right)$ 

concatenating the hidden states from both directions 

$\boldsymbol{h}_{i}=\left[\overrightarrow{\boldsymbol{h}_{i}} ; \overleftarrow{\boldsymbol{h}}_{i}\right]$

$2)$ Compute \textit{W} using equation (10)

$\mathbf{W = f_{network} (I;J;\theta_{network})}$

compute mulit-head attention using equation (9)

\mathbf{H_{i}^{(l+1)}=\sigma\left(\frac{1}{K} \sum_{k=1}^{K} \sum_{j \in N(i)} \alpha_{i j, k}^{(l)} H_{j}^{(l)} W^{(l)}\right)}


Matrix multiplication 


$\mathbf{\widehat y}$ = \textit{x} \mathbf{\odot} \textit{W}

Compute cross entropy

\begin{aligned}

\mathbf{\mathcal{L}=\sum_{c=1}^{C} y^{c} \log \left(\sigma\left(\hat{y}^{c}\right)\right)+ \\
\left(1-y^{c}\right) \log \left(1-\sigma\left(\hat{y}^{c}\right)\right)}
\end{aligned}




loss = reduce ( cross entropy )


update the parameters basis on loss using back propagation



$\mathbf{\theta_{t+1}=\theta_{t}-\frac{\eta}{\sqrt{\hat{v}_{t}}+\epsilon} \hat{m}_{t}}$

}}

\caption{Algorithm}
\end{algorithm}
\end{document}

Answer 1

您需要添加选项linesnumbered您需要在加载时algorithm2e包裹或者\LinesNumbered加载后调用：

\documentclass{article}

\usepackage[ruled,vlined,linesnumbered]{algorithm2e}

\begin{document} 

\begin{algorithm}[H]
  \SetAlgoLined
  \KwData{this text}
  \KwResult{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\;
    }
  }
  \caption{How to write algorithms}
\end{algorithm}

\end{document}

如果您希望更改样式以包含冒号:后缀，那么您还可以添加

\SetNlSty{textbf}{}{:}% Add colon after line number
\IncMargin{.2em}% Push algorithm to the right (allowing for larger line numbering)

到你的序言（加载后algorithm2e）。