论文实验伪代码

该篇文章讲述了论文实验部分的伪代码，该实验采用python语言编写，框架采用深度学习框架keras，整体实验分为一下几个部分：

0 Latex伪代码框架

\documentclass[11pt]{ctexart}  
\usepackage[top=2cm, bottom=2cm, left=2cm, right=2cm]{geometry}  
\usepackage{algorithm}  
\usepackage{algorithmicx}  
\usepackage{algpseudocode}  
\usepackage{amsmath}
\usepackage{amsfonts,amssymb}

\floatname{algorithm}{Algorithm}  
\renewcommand{\algorithmicrequire}{\textbf{Input:}}  
\renewcommand{\algorithmicensure}{\textbf{Output:}}  

\begin{document}
	...伪代码内容
\end{document}

1 第一次训练（first.py）

功能实现：
根据输入的数据文件，处理数据后，切割为训练集和测试集，并在本地生成对应的文件。对整体数据，使用CountVectorizer对邮件文本进行向量化，并且生成了一个字典。用词袋模型将训练集的邮件文本数据转化为词袋特征，并用这些特征训练模型，将该模型生成本地文件。最后，加载训练集文件对模型进行评估，自此该文件运行完毕。

Latex effect map：

Latex Pseudocode：

\begin{algorithm}  
		\caption{first training}  
		\begin{algorithmic}[1] %每行显示行号  
			\Require $d$ is the data of first training; $K$ is cross-validation times; Algorithm SGD is named $sgd$			  
			\Ensure the first train model $m$; $trainSet$ and $testSet$; training time $T$; evaluation result $res$			
			\State $(trainSet, testSet) \gets $ split($d$)
			\State $S \gets $ (split $trainSet$ in equal parts of $K$)	
			\For {each round $t=1,2,...,K$} 
				\State $\left\{verify, train\right\} \gets \left\{S_{t}, S-S_{t}\right\} $
				\State $(tf, vf) \gets $ (generate spam feature of $train$ and $verify$)	
				\State $m_{t} \gets $ modelFit($sgd, tf$)
				\State $r_{t} \gets $ modelEvaluate($m_{t}, vf$)
				\State $T_{t} \gets $ (statistics training time)
			\EndFor
			\State $m \gets $ bestModel($\left\{ (m_{t}, r_{t}) | t=1,2,...,K \right\}$)
			\State $T \gets   \mathbb{E}({\left\{ T_{t} | t=1,2,...,K \right\}})$
			\State $test \gets $ (generate spam feature of $testSet$)
			\State $res \gets $ modelEvaluate($m, test$)
			
		\end{algorithmic}  
	\end{algorithm} 

2 第二次训练（second.py）

Latex effect map：

Latex Pseudocode：

\begin{algorithm}  
		\caption{second training}  
		\begin{algorithmic}[1] %每行显示行号  
			\Require $D$ is the training data set for all clients; the $K$ clients are indexed by $k$; $m$ is the first training model			
			\Ensure the second train model set $\left\{m_{k}|k=1,2,...,K \right\}$; training time $t_{k}$ for each $m_{k}$			
			\For {each client data $d \in D$ }
			\State $f \gets $ (generate spam feature of $d$ )
			\State $m_{k} \gets $ modelTrainedAgain($m,f$)	  
			\State $t_{k} \gets $ (statistics training time)
			\EndFor 			
		\end{algorithmic}  
	\end{algorithm}

3 平均模型与加权平均模型（avgmodel.py）

Latex effect map：

Pseudocode：

	\begin{algorithm}  
		\caption{avgmodel}  
		\begin{algorithmic}[1] %每行显示行号  
			\Require $C$ is the model set for all clients; $w$ is the weight of each model;  the $K$ clients are indexed by $k$ and $i$; $m$ is the first training model ; $d$ is the test data of second training			
			\Ensure average model $am$ and weighted average model $wm$; $r$ is the evaluation result of $am$ and $wm$; $t$ is the respective training and evaluation times for $am$ and $wm$ 			
			\For {each client model $c \in C$ }
				\State $d_{k} \gets $ (read the data in model $c$)
			\EndFor 
			\State $ad \gets \sum\limits_{k=1}^{K}(\frac{d_{k}}{K})$
			\State $wd \gets \sum\limits_{k=1}^{K}(\frac{w_{k}}{\sum\limits_{i=1}^{K}w_{i} } \times d_{k})$
			\State $(am, wm) \gets $ (set model $ad$ and $wd$ into $m$ respectively)
			\State $f \gets $ (generate spam feature of $d$)
			\State $r \gets $ (evaluate model $am$ and $wm$ with $f$ )
			\State $t \gets $ (count the training time of $a$ and $w$ respectively)
		\end{algorithmic}  
	\end{algorithm}