论文写作 12: 算法伪代码 (含实例)

算法伪代码是论文的核心之一.

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.

例子:

以下是该算法的 tex 源码:

\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}