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vendor/scrivo/highlight.php/test/detect/matlab/default.txt

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+n = 20; % number of points
+points = [random('unid', 100, n, 1), random('unid', 100, n, 1)];
+len = zeros(1, n - 1);
+points = sortrows(points);
+%% Initial set of points
+plot(points(:,1),points(:,2));
+for i = 1: n-1
+    len(i) = points(i + 1, 1) - points(i, 1);
+end
+while(max(len) > 2 * min(len))
+    [d, i] = max(len);
+    k = on_margin(points, i, d, -1);
+    m = on_margin(points, i + 1, d, 1);
+    xm = 0; ym = 0;
+%% New point
+    if(i == 1 || i + 1 == n)
+        xm = mean(points([i,i+1],1))
+        ym = mean(points([i,i+1],2))
+    else
+        [xm, ym] = dlg1(points([k, i, i + 1, m], 1), ...
+            points([k, i, i + 1, m], 2))
+    end
+
+    points = [ points(1:i, :); [xm, ym]; points(i + 1:end, :)];
+end
+
+%{
+    This is a block comment. Please ignore me.
+%}
+
+function [net] = get_fit_network(inputs, targets)
+    % Create Network
+    numHiddenNeurons = 20;  % Adjust as desired
+    net = newfit(inputs,targets,numHiddenNeurons);
+    net.trainParam.goal = 0.01;
+    net.trainParam.epochs = 1000;
+    % Train and Apply Network
+    [net,tr] = train(net,inputs,targets);
+end
+
+foo_matrix = [1, 2, 3; 4, 5, 6]''';
+foo_cell = {1, 2, 3; 4, 5, 6}''.'.';
+
+cell2flatten = {1,2,3,4,5};
+flattenedcell = cat(1, cell2flatten{:});