Highlight Matlab code in knitr

knitr-matlab.Rnw

\documentclass{article}
<<adjust-preamble, echo=FALSE, results='asis'>>=
if (opts_knit$get('use.highlight')) cat('\\def\\usehighlightdefs{1}\n')
@
\ifx\highlightexists\undefined
\newcommand{\hlstd}[1]{\textcolor[rgb]{0,0,0}{#1}}
\newcommand{\hlnum}[1]{\textcolor[rgb]{0.69,0.49,0}{#1}}
\newcommand{\hlesc}[1]{\textcolor[rgb]{1,0,1}{#1}}
\newcommand{\hlstr}[1]{\textcolor[rgb]{0.75,0.01,0.01}{#1}}
\newcommand{\hlpps}[1]{\textcolor[rgb]{0.51,0.51,0}{#1}}
\newcommand{\hlslc}[1]{\textcolor[rgb]{0.51,0.51,0.51}{\it{#1}}}
\newcommand{\hlcom}[1]{\textcolor[rgb]{0.51,0.51,0.51}{\it{#1}}}
\newcommand{\hlppc}[1]{\textcolor[rgb]{0,0.51,0}{#1}}
\newcommand{\hlopt}[1]{\textcolor[rgb]{0,0,0}{#1}}
\newcommand{\hllin}[1]{\textcolor[rgb]{0.33,0.33,0.33}{#1}}
\newcommand{\hlkwa}[1]{\textcolor[rgb]{0,0,0}{\bf{#1}}}
\newcommand{\hlkwb}[1]{\textcolor[rgb]{0,0.34,0.68}{#1}}
\newcommand{\hlkwc}[1]{\textcolor[rgb]{0,0,0}{\bf{#1}}}
\newcommand{\hlkwd}[1]{\textcolor[rgb]{0,0,0.51}{#1}}
\definecolor{bgcolor}{rgb}{0.88,0.92,0.93}
\else
\newcommand{\hlopt}[1]{\textcolor[rgb]{0,0,0}{#1}}
\fi

\begin{document}

Normal R chunks.

<<test-R>>=
1+1
rnorm(5)
@

Highlight matlab chunks.

<<test-matlab, engine='highlight', highlight.opts='-S matlab -O latex'>>=
function Y = kalmanM(pos)
  dt=1;
  %% Initialize state transition matrix
  A=[ 1 0 dt 0 0 0;...     % [x  ]
     0 1 0 dt 0 0;...     % [y  ]
     0 0 1 0 dt 0;...     % [Vx]
     0 0 0 1 0 dt;...     % [Vy]
     0 0 0 0 1 0 ;...     % [Ax]
     0 0 0 0 0 1 ];       % [Ay]
  % Initialize measurement matrix
  H = [ 1 0 0 0 0 0; 0 1 0 0 0 0 ];
  Q = eye(6);
  R = 1000 * eye(2);
  x_est = zeros(6, 1);
  p_est = zeros(6, 6);

  numPts = size(pos,1);
  Y = zeros(numPts, 2);

  for idx = 1:numPts
    z = pos(idx, :)';

    %% Predicted state and covariance
    x_prd = A * x_est;
    p_prd = A * p_est * A' + Q;
    %% Estimation
    S = H * p_prd' * H' + R;
    B = H * p_prd';
    klm_gain = (S \ B)';
    %% Estimated state and covariance
    x_est = x_prd + klm_gain * (z - H * x_prd);
    p_est = p_prd - klm_gain * H * p_prd;
    %% Compute the estimated measurements
    Y(idx, :) = H * x_est;
  end                % of the function
end   % of the function
@

\end{document}