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1.需要说明输入、输出; 2.方法 (函数) 名可写可不写, 如果被别的方法调用就必须写; 3.需要写出主要步骤的注释; 4.长度控制在 15-30 行; 5.可使用数学式子或对已有数学式子的引用; 6.不重要的步骤可以省略; 7.一般需要进行时间、空间复杂度分析, 并写出配套的 property 以及相应的表格, 以使其更标准. 8.使用Latex编写算法伪代码
例子：
\begin{algorithm}[!htb]
\renewcommand{\algorithmicrequire}{\textbf{Input:}}
\renewcommand{\algorithmicensure}{\textbf{Output:}}
\caption{Multi-label active learning through serial-parallel neural networks}
\label{algorithm: masp}
\begin{algorithmic}[1]
\REQUIRE
data matrix $\mathbf{X}$,
label matrix $\mathbf{Y}$ for query,
query budget $Q$,
cold-start query budget $P$,
number of representative instances $R$,
instance batch size $B_i$,
label batch size $B_l$
\ENSURE
queried instance-label pairs $\mathbf{Q}$, prediction network $\Theta$.
\STATE Initialize the serial-parallel prediction network;
\STATE $\mathbf{Q} = \emptyset$;\\
// Stage 1. Cold start.
\STATE Compute instance representativeness according to Eq. \eqref{equation: dp-representativeness};
\STATE Select the top-$R$ representative instances to reorganize the training set $\mathbf{X}$;
\STATE Update $\mathbf{Q}$ and $\mathbf{Y}'$ by querying $B_l$ labels for each of the top $\lfloor Q / B_l \rfloor$ representative instances;
\STATE Train the prediction network using $\mathbf{X}$ and $\mathbf{Y}'$;\\
// Stage 2. Main learning process.
\REPEAT
\STATE Compute $\hat{\mathbf{Y}}$ using the prediction network and Eq. \eqref{equation: label-prediction};
\STATE Compute label uncertainty according to Eq. \eqref{equation: label-uncertainty};
\STATE Query top-$B_i$ uncertain instance-label pairs to update $\mathbf{Q}$ and $\mathbf{Y}'$;
\STATE Update the prediction network using $\mathbf{X}$ and $\mathbf{Y}'$;\\
\UNTIL{($|\mathbf{Q}| \geq Q$)}
\end{algorithmic}
\end{algorithm}
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