作者:赖军菁佳瑄柏昌 | 来源:互联网 | 2023-07-25 18:25
我想实现下述伪代码算法,注意不同于三线表哦
1 三线表要记得加入这个“宏包”
粗细可调: https://blog.csdn.net/lishoubox/article/details/7331653
\usepackage{booktabs}
表格
\begin{table}[htbp]
\caption{\label{tab:test}示例表格} %标题
\begin{tabular}{lcl} %三列,居左,中,左
\toprule %第一条线
a11 & a12 & a13 \\
\midrule %第二条线
a21 & a22 & a23 \\
a31 & a32 & a33 \\
\bottomrule %第三条线
\end{tabular}
\end{table}
\begin{table}[htbp]
\caption{\label{tab:test}示例表格} %标题
\begin{tabular}{lcl} %三列,居左,中,左
\toprule %第一条线
a11 & a12 & a13 \\
\midrule %第二条线
a21 & a22 & a23 \\
a31 & a32 & a33 \\
\bottomrule %第三条线
\end{tabular}
\end{table}
2 伪代码:
宏包+算法: https://www.cnblogs.com/52ml/p/3823802.html
多种伪代码格式以及宏包: https://blog.csdn.net/lwb102063/article/details/53046265
https://blog.csdn.net/lwb102063/article/details/53046265
for循环
与if条件句
\For{$i=1;i\leq N;i\leftarrow i+1$}
\If {$i=N$}
\State $middle \gets (left + right) / 2$
\State $result \gets result +$ \Call{MergerSort}{$Array, left, middle$}
\State $result \gets result +$ \Call{MergerSort}{$Array, middle, right$}
\State $result \gets result +$ \Call{Merger}{$Array,left,middle,right$}
\EndIf
\State $d^{3}, d^{4}, d^{5} = D_{d_{3}}(f_{s}^{2}), D_{d_{4}}(f_{s}^{2}), D_{d_{5}}(f_{s}^{2})$ ;
\State $f_{s}^{2}, f_{s}^{3}, f_{s}^{4}, f_{s}^{5} = D_{f_{i}}(f_{s}^{2}, f_{s}^{3}+d^{3}, f_{s}^{4}+d^{4}, f_{s}^{5}+d^{5});$
\State $m^{2}, m^{3}, m^{4}, m^{5} = Conv_{2}(f_{s}^{2}), Conv_{3}(f_{s}^{3}), Conv_{4}(f_{s}^{4}), Conv_{5}(f_{s}^{5});$
\EndFor
宏包:前期准备
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{amsmath}
\renewcommand{\algorithmicrequire}{\textbf{Input:}} % Use Input in the format of Algorithm
\renewcommand{\algorithmicensure}{\textbf{Output:}} % Use Output in the format of Algorithm
\begin{algorithm}[htb]
\caption{ Framework of ensemble learning for our system.}
\label{alg:Framwork}
\begin{algorithmic}[1]
\Require
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
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}
\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 $E_n$;
\end{algorithmic}
\end{algorithm}