创建与 algorithm2e 有关联 **end** 的关键字

Question

作为 Latex 课程家庭作业的一部分，我被要求用 Latex 编写该算法。

我已经用这段代码完成了大部分工作：-

\documentclass[12pt]{article}
\usepackage[margin=1in]{geometry}
\usepackage{graphicx, blindtext}
\usepackage{algorithm2e}
\usepackage{amsmath}

\begin{document}
    \RestyleAlgo{boxed}
    \begin{algorithm}[h!]
        \DontPrintSemicolon
        \SetKwInput{kwInput}{Input}
        \SetKwInput{kwInit}{Initialization}
        \SetKwInput{kwMain}{Forward Inclusion}
        \SetKwInput{kwOutput}{Output}
        \kwInput{
        \par
        \vspace{0.25cm}
            Training examples $ Z = \{(x_1 , y_1 ), . . . , (x_N , y_N )\} $, where $N = a + b$, of which $a$ examples have $y_i = 1$ and $b$ examples have $ y_i = −1 $\;
            \vspace{0.25cm}
            The number $M$ of weak classifiers to be combined.

        }
        \kwInit{
        \par
        \vspace{0.25cm}
        \Indp{
            $
                w_i^{(0)}= \begin{cases}
                    \frac{1}{2a} & \text{$for\ those\ examples\ with\ y_i=1$} \\
                    \frac{1}{2b} & \text{$otherwise$}
                \end{cases}
            $
        }
        \vspace{0.25cm}
        }
        \kwMain{
        \par
        \vspace{0.25cm}
            \For{m = 1, . . . , M}{
                Choose optimal $h_m$ to minimize the weighted error\;
                Choose $a_m$ according to (2.17)\;
                Update $w_i^{(m)} \gets w_i^{(m-1)} e^{-y_ia_mh_m(x_i)}$ and normalize to $\sum_i{w_i^{(m)}} = 1$\;
            }
        \vspace{0.25cm}
        }
        \kwOutput{
        \par
            Classification Function: $H_M(x)$ as in (2.20)\;
            Class Label Prediction: $\hat{y}(x) = sgn(H_M(x))$\;
        }
        \caption{AdaBoost Learning Algorithm [35]}
    \end{algorithm}

\end{document}

但是，我无法创建新的关键字（例如\kwInit具有自己关联的新关键字（如上面的代码所示）结尾，以及自动缩进的代码段。

作为 Latex 排版系统的新手，非常欢迎任何帮助。

作为参考，我的代码产生如下输出：-

Answer 1

只需替换\SetKwInput{word}为\SetKwBlock{beginword}{endword}。请参阅algorithm2e手动的第 11.2 节第 35 页。此修改提供

\documentclass[12pt]{article}
\usepackage[margin=1in]{geometry}
\usepackage{graphicx, blindtext}
\usepackage{algorithm2e}
\usepackage{amsmath}

\begin{document}
    \RestyleAlgo{boxed}
    \begin{algorithm}[h!]
        \DontPrintSemicolon
        \SetKwInput{kwInput}{Input}
        \SetKwBlock{kwInit}{Initialization}{end}
        \SetKwBlock{kwMain}{Forward Inclusion}{end}
        \SetKwInput{kwOutput}{Output}
        \kwInput{
        \par
        \vspace{0.25cm}
            Training examples $ Z = \{(x_1 , y_1 ), . . . , (x_N , y_N )\} $, where $N = a + b$, of which $a$ examples have $y_i = 1$ and $b$ examples have $ y_i = −1 $\;
            \vspace{0.25cm}
            The number $M$ of weak classifiers to be combined.

        }
        \kwInit{
        \par
        \vspace{0.25cm}
        \Indp{
            $
                w_i^{(0)}= \begin{cases}
                    \frac{1}{2a} & \text{$for\ those\ examples\ with\ y_i=1$} \\
                    \frac{1}{2b} & \text{$otherwise$}
                \end{cases}
            $
        }
        \vspace{0.25cm}
        }
        \kwMain{
        \par
        \vspace{0.25cm}
            \For{m = 1, . . . , M}{
                Choose optimal $h_m$ to minimize the weighted error\;
                Choose $a_m$ according to (2.17)\;
                Update $w_i^{(m)} \gets w_i^{(m-1)} e^{-y_ia_mh_m(x_i)}$ and normalize to $\sum_i{w_i^{(m)}} = 1$\;
            }
        \vspace{0.25cm}
        }
        \kwOutput{
        \par
            Classification Function: $H_M(x)$ as in (2.20)\;
            Class Label Prediction: $\hat{y}(x) = sgn(H_M(x))$\;
        }
        \caption{AdaBoost Learning Algorithm [35]}
    \end{algorithm}

\end{document}