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