test pseudocode.js

This is a demo file for a working prototype of using pseudocode.js in a R Markdown document. Code could be downloaded using the upper right download button.

How to use pseudocode.js

This is what we need to include in the header of the doc:

<script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [['$','$'], ['\\(','\\)']],
            displayMath: [['$$','$$'], ['\\[','\\]']],
            processEscapes: true,
            processEnvironments: true,
        }
    });
</script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/pseudocode@latest/build/pseudocode.min.css">
<script src="https://cdn.jsdelivr.net/npm/pseudocode@latest/build/pseudocode.min.js"></script>

This is in a file head.html and we insert it using `yaml html_document: includes: in_header: head.html

Mathajx is already configured in a R Markdown doc but Mathjax config seems to be required for Math to work inside the algorithm environment

Elements containing the code are rendered based on a selector using a JS function. We’ll use the fenced code attributes features to render all pre with an expected algorigthm attributes. We’ll also set some options - this can be change.

This is will we’ll add this JS code using a js knitr block:

window.addEventListener('load', (event) => {
  elem = document.querySelectorAll(".algorithm")
  elem.forEach(e => {
    pseudocode.renderElement(e, {lineNumber: true, lineNumberPunc: ' '});
  })
});

We could also add it in the previous head.html. This will run pseudocode.renderElement() on each element of class algorithm and replace the whole element

With this, everything should work.

Mathjax is still working

A block \[ 1+1 \]

or inline math like \(\alpha = x/y\)

Inserting an algorithm environment

You can use an algorithm code block using syntax from pseudocode.js by setting the fenced div attribute algorithm as below

```{.algorithm}
% This quicksort algorithm is extracted from Chapter 7, Introduction to Algorithms (3rd edition)
\begin{algorithm}
\caption{Quicksort}
\begin{algorithmic}
\PROCEDURE{Quicksort}{$A, p, r$}
    \IF{$p < r$} 
        \STATE $q = $ \CALL{Partition}{$A, p, r$}
        \STATE \CALL{Quicksort}{$A, p, q - 1$}
        \STATE \CALL{Quicksort}{$A, q + 1, r$}
    \ENDIF
\ENDPROCEDURE
\PROCEDURE{Partition}{$A, p, r$}
    \STATE $x = A[r]$
    \STATE $i = p - 1$
    \FOR{$j = p$ \TO $r - 1$}
        \IF{$A[j] < x$}
            \STATE $i = i + 1$
            \STATE exchange
            $A[i]$ with $A[j]$
        \ENDIF
        \STATE exchange $A[i]$ with $A[r]$
    \ENDFOR
\ENDPROCEDURE
\end{algorithmic}
\end{algorithm}
```

This would render to

% This quicksort algorithm is extracted from Chapter 7, Introduction to Algorithms (3rd edition)
\begin{algorithm}
\caption{Quicksort}
\begin{algorithmic}
\PROCEDURE{Quicksort}{$A, p, r$}
    \IF{$p < r$} 
        \STATE $q = $ \CALL{Partition}{$A, p, r$}
        \STATE \CALL{Quicksort}{$A, p, q - 1$}
        \STATE \CALL{Quicksort}{$A, q + 1, r$}
    \ENDIF
\ENDPROCEDURE
\PROCEDURE{Partition}{$A, p, r$}
    \STATE $x = A[r]$
    \STATE $i = p - 1$
    \FOR{$j = p$ \TO $r - 1$}
        \IF{$A[j] < x$}
            \STATE $i = i + 1$
            \STATE exchange
            $A[i]$ with $A[j]$
        \ENDIF
        \STATE exchange $A[i]$ with $A[r]$
    \ENDFOR
\ENDPROCEDURE
\end{algorithmic}
\end{algorithm}

Another one

```{.algorithm}
\begin{algorithm}
\caption{The original \textsf{Relief} algorithm for ranking predictor variables in classification models with two classes.}
\begin{algorithmic}
\STATE Initialize the predictor scores $S_j$ to zero;
\FOR{$i = 1\ldots m$ randomly selected training set samples ($R_i$)}
\STATE Find the nearest miss and hit in the training set;
\FOR{$j = 1\ldots p$ predictor variables}
   \STATE Adjust the score for each predictor  based on the proximity of $R_j$ to the nearest miss and hit:\\
   $S_j = S_j - diff_j(R_j, Hit)^2/m + diff_j(R_j, Miss)^2/m$;
\ENDFOR
\ENDFOR
\end{algorithmic}
\end{algorithm}
```

\begin{algorithm}
\caption{The original \textsf{Relief} algorithm for ranking predictor variables in classification models with two classes.}
\begin{algorithmic}
\STATE Initialize the predictor scores $S_j$ to zero;
\FOR{$i = 1\ldots m$ randomly selected training set samples ($R_i$)}
\STATE Find the nearest miss and hit in the training set;
\FOR{$j = 1\ldots p$ predictor variables}
   \STATE Adjust the score for each predictor  based on the proximity of $R_j$ to the nearest miss and hit:\\
   $S_j = S_j - diff_j(R_j, Hit)^2/m + diff_j(R_j, Miss)^2/m$;
\ENDFOR
\ENDFOR
\end{algorithmic}
\end{algorithm}