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{:});