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放大 tikzpicture 中的区块

tag-icon • tag-icon tikz-pgf
放大 tikzpicture 中的区块

我想放大 tikzpicture 的块。此外，我想尊重页面的边距。

\documentclass[preprint,12pt]{elsarticle}
\usepackage{amssymb}
\usepackage{amsmath}

\usepackage{algorithmic}
\usepackage{algorithm}
\usepackage{subfigure}
\usepackage{tikz}

\usetikzlibrary{shapes,arrows}
\tikzstyle{decision} = [diamond, draw, fill=blue!20, text width=4.5em, text badly centered, node distance=3cm, inner sep=0pt]
\tikzstyle{block} = [rectangle, draw, fill=blue!20, text width=5em, text centered, rounded corners, minimum height=4em]
\tikzstyle{line} = [draw, -latex]

\newcommand{\bs}{\boldsymbol}
\begin{document}

\begin{tikzpicture}[auto,
block_center/.style ={rectangle, draw=black, thick, fill=white,
    text width=30em, text centered,
    minimum height=3em, inner sep=2pt},
block_left/.style ={rectangle, draw=black, thick, fill=white,
    text width=16em, text ragged, minimum height=4em, inner sep=6pt},
block_noborder/.style ={rectangle, draw=none, thick, fill=none,
    text width=18em, text centered, minimum height=1em},
block_assign/.style ={rectangle, draw=black, thick, fill=white,
    text width=18em, text ragged, minimum height=3em, inner sep=6pt},
block_lost/.style ={rectangle, draw=black, thick, fill=white,
    text width=16em, text ragged, minimum height=3em, inner sep=6pt},
line/.style ={draw, thick, -latex', shorten >=0pt}]
% outlining the flowchart using the PGF/TikZ matrix funtion
\matrix [column sep=5mm,row sep=3mm] {
    % enrollment - row 1
    \node [block_center] (referred) {(\textbf{1. Input Data} )\\
        %\large
        \scriptsize
        %\tiny
        \begin{itemize}
        \item Fixed a data configuration with three regressors ($v=3$),  generate a sample of size 100 for each $X^c_v$, $X^r_v$, $\varepsilon^c$, and  $\varepsilon^r$ $v=1,2,3$;
        %
        \item Compute the response  as:  $y^c=\beta_0+\beta_1^c x^c_1+\beta_2^c x^c_2+\beta_3^c x^c_3$+ $\varepsilon^c$ and  $y^r=\beta_0+\beta_1^r x^r_1+\beta_2^r x^c_2+\beta_3^r x^c_3+ \varepsilon^r$ with $(\beta^c_0, \beta^c_1, \beta^c_2, \beta^c_3)^T = (0, 2, -2, 5)^T$ and $(\beta^r_0, \beta^r_1, \beta^r_2, \beta^r_3)^T = (0, 1, 2, 3)^T$;        
        \end{itemize}
        %
        % CENTER AND RANGE MODEL
        %
        \begin{itemize}
        \item Center and Range Model
        \begin{itemize}
        \item Fixed $L$, compute the vertices of each regular polygon $P_{jl}=(a_{jl},b_{jl}) \in \mathbb{R}^2$ with $j=1,\ldots,m$ $l=1,\ldots,L$ using  the Equation (\ref{eq:aggregation}) being $c_j=x^c$ and $r_j=x^r$ for regressors and $c_j=y^c$ and $r_j=y^r$ for response.
        \item Obtain the intervals for regressors $[a_j^v,b^v_j]$ with  $a_{j}^v = min \{a_{j1}^v,\ldots,a_{jL}^v\}$ and $b_{j}^v = max \{b_{j1}^v,\ldots,b_{jL}^v\}$  for $j=1\ldots,m$, $v=1,2,3$. Obtain the intervals for response $[\lambda,\gamma]$ with  $\lambda_{j} = min \{a_{j1},\ldots,a_{jL}\}$ and $\gamma_{j} = max \{b_{j1},\ldots,b_{jL}\}$  for $j=1\ldots,m$
        %
        \item By interval $(a_j, b_j)$ construct the center $c_j = (b_j + a_j)/2$ and range $r_j = (b_j - a_j)/2$.
        \end{itemize}
        %
        % POLYGONAL REGRESSION MODEL
        %
        \item Polygonal Regression Model
        \begin{itemize}
        \item Fixed $L$, compute the vertices of each regular polygon $P_{jl}=(a_{jl},b_{jl}) \in \mathbb{R}^2$ with $j=1,\ldots,m$ $l=1,\ldots,L$ using  the Equation~\ref{eq:aggregation} being $c_j=x^c$ and $r_j=x^r$ for regressors and $c_j=y^c$ and $r_j=y^r$ for response.
        \end{itemize}
        \item To split the data set to do 10-fod cross validation.
        \end{itemize}
    };&\\
    \node [block_center] (assessment0) {(\textbf{2. Estimating Regression Models})
        %\large
        \scriptsize
        %\tiny
        \begin{itemize}
        \item Center and Range Model
        \begin{itemize}
        \item Construct the CRLR model;
        \item Compute the $\bs{\hat{y^c}}$ and $\bs{\hat{y^r}}$;
        \item Construct the predicted squared based on the $\bs{\hat{y^c}}$ and $\bs{\hat{y^r}}$ estimates.
        \end{itemize}
        \item Polygonal Regression Model
        \begin{itemize}
        \item Construct the PLR model based on the Equation~\ref{eq:regression_model};
        \item Compute the $\bs{\hat{y^c}}$ and $\bs{\hat{y^r}}$ given by Algorith~\ref{alg:algorith};
        \item Construct the $\hat{P}$ by PLR model based on the Equation~\ref{eq:aggregation};
        \item Calculate the area for real polygon $P_j$ and predicted polygon $\hat{P}_j$.
        \end{itemize}
        \end{itemize}
    };&\\
    % enrollment - row 4
    \node [block_center] (assessment1) {(\textbf{3. Evaluation Models})
        %\large
        \scriptsize
        %\tiny
        \begin{itemize}
        \item Center and Range Model
        \begin{itemize}
        \item Calculate the area for real polygon $P_j$ and predicted polygon $\hat{P}_j$;
        \item Compute the RMSE for area of $\hat{P}$ and predicted squared.
        \end{itemize}
        \item Polygonal Regression Model
        \begin{itemize}
        \item Calculate the area for real polygon $P_j$ and predicted polygon $\hat{P}_j$;
        \item Compute the RMSE for area of $\hat{P}$ and $P$.
        \end{itemize}
        \end{itemize}
    }; \\
};% end matrix

\begin{scope}[every path/.style=line]
\path (referred)   -- (assessment0);
\path (assessment0) -- (assessment1);
\end{scope}
\end{tikzpicture}

\end{document}

答案1

基本上你尝试过的方法行不通，因为tikzpicture不能跨页。自定义tcolorbox可能更好。不过我找不到在框之间制作箭头的好方法。

在此处输入图片描述

\documentclass[preprint,12pt]{elsarticle}
\usepackage{amssymb}
\usepackage{amsmath}

\usepackage{tcolorbox}
\tcbuselibrary{breakable,skins}
\newtcolorbox[auto counter]{plainbox}[2][]{%
  enhanced,
  breakable,
  size=small,
  colback=white,
  colframe=black,
  fonttitle=\bfseries\centering\large,
  titlerule=0pt,
  colbacktitle=white, 
  coltitle=black,
  title=(\thetcbcounter. #2),
  #1
}

\newcommand{\bs}{\boldsymbol}

\begin{document}

\begin{plainbox}{Input data}
        \begin{itemize}
        \item Fixed a data configuration with three regressors ($v=3$),  generate a sample of size 100 for each $X^c_v$, $X^r_v$, $\varepsilon^c$, and  $\varepsilon^r$ $v=1,2,3$;
        %
        \item Compute the response  as:  $y^c=\beta_0+\beta_1^c x^c_1+\beta_2^c x^c_2+\beta_3^c x^c_3$+ $\varepsilon^c$ and  $y^r=\beta_0+\beta_1^r x^r_1+\beta_2^r x^c_2+\beta_3^r x^c_3+ \varepsilon^r$ with $(\beta^c_0, \beta^c_1, \beta^c_2, \beta^c_3)^T = (0, 2, -2, 5)^T$ and $(\beta^r_0, \beta^r_1, \beta^r_2, \beta^r_3)^T = (0, 1, 2, 3)^T$;        
        \end{itemize}
        %
        % CENTER AND RANGE MODEL
        %
        \begin{itemize}
        \item Center and Range Model
        \begin{itemize}
        \item Fixed $L$, compute the vertices of each regular polygon $P_{jl}=(a_{jl},b_{jl}) \in \mathbb{R}^2$ with $j=1,\ldots,m$ $l=1,\ldots,L$ using  the Equation (\ref{eq:aggregation}) being $c_j=x^c$ and $r_j=x^r$ for regressors and $c_j=y^c$ and $r_j=y^r$ for response.
        \item Obtain the intervals for regressors $[a_j^v,b^v_j]$ with  $a_{j}^v = min \{a_{j1}^v,\ldots,a_{jL}^v\}$ and $b_{j}^v = max \{b_{j1}^v,\ldots,b_{jL}^v\}$  for $j=1\ldots,m$, $v=1,2,3$. Obtain the intervals for response $[\lambda,\gamma]$ with  $\lambda_{j} = min \{a_{j1},\ldots,a_{jL}\}$ and $\gamma_{j} = max \{b_{j1},\ldots,b_{jL}\}$  for $j=1\ldots,m$
        %
        \item By interval $(a_j, b_j)$ construct the center $c_j = (b_j + a_j)/2$ and range $r_j = (b_j - a_j)/2$.
        \end{itemize}
        %
        % POLYGONAL REGRESSION MODEL
        %
        \item Polygonal Regression Model
        \begin{itemize}
        \item Fixed $L$, compute the vertices of each regular polygon $P_{jl}=(a_{jl},b_{jl}) \in \mathbb{R}^2$ with $j=1,\ldots,m$ $l=1,\ldots,L$ using  the Equation~\ref{eq:aggregation} being $c_j=x^c$ and $r_j=x^r$ for regressors and $c_j=y^c$ and $r_j=y^r$ for response.
        \end{itemize}
        \item To split the data set to do 10-fod cross validation.
        \end{itemize}
\end{plainbox}
\hfill$\downarrow$\hfill{}
\begin{plainbox}{Estimating regression models}
        \begin{itemize}
        \item Center and Range Model
        \begin{itemize}
        \item Construct the CRLR model;
        \item Compute the $\bs{\hat{y^c}}$ and $\bs{\hat{y^r}}$;
        \item Construct the predicted squared based on the $\bs{\hat{y^c}}$ and $\bs{\hat{y^r}}$ estimates.
        \end{itemize}
        \item Polygonal Regression Model
        \begin{itemize}
        \item Construct the PLR model based on the Equation~\ref{eq:regression_model};
        \item Compute the $\bs{\hat{y^c}}$ and $\bs{\hat{y^r}}$ given by Algorith~\ref{alg:algorith};
        \item Construct the $\hat{P}$ by PLR model based on the Equation~\ref{eq:aggregation};
        \item Calculate the area for real polygon $P_j$ and predicted polygon $\hat{P}_j$.
        \end{itemize}
        \end{itemize}
\end{plainbox}
\hfill$\downarrow$\hfill{}
\begin{plainbox}{Evaluation Models}
        \begin{itemize}
        \item Center and Range Model
        \begin{itemize}
        \item Calculate the area for real polygon $P_j$ and predicted polygon $\hat{P}_j$;
        \item Compute the RMSE for area of $\hat{P}$ and predicted squared.
        \end{itemize}
        \item Polygonal Regression Model
        \begin{itemize}
        \item Calculate the area for real polygon $P_j$ and predicted polygon $\hat{P}_j$;
        \item Compute the RMSE for area of $\hat{P}$ and $P$.
        \end{itemize}
        \end{itemize}
\end{plainbox}
\end{document}

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