45 lines
1.3 KiB
Mathematica
45 lines
1.3 KiB
Mathematica
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clear variables;
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close all;
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warning OFF;
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addpath ../datasets
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addpath ../datasets/Meshes
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[Pts_spirals,Pts_clusterin,Pts_corn,Pts_halfk,Pts_fullmoon,Pts_out] = getPoints();
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close all;
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TITLES = ["Two Spirals", "Cluster in cluster", "Corners", "Half crescent", "Full crescent", "Outlier"];
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RUNS = {Pts_spirals, Pts_clusterin, Pts_corn, Pts_halfk, Pts_fullmoon, Pts_out};
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KS = {2, 2, 4, 2, 2, 4};
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B = zeros(100, 6);
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for j = 20:70
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for i = 1:6
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p = (j - 20) / 10;
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% Specify the number of clusters
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Pts = RUNS{i};
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K = KS{i};
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n = size(Pts, 1);
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% Create Gaussian similarity function
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[S] = similarityfunc(Pts(:,1:2), 10^p * log(n));
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% Find the minimal spanning tree of the full graph. Use the information
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% to determine a valid value for epsilon
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H = minSpanTree(S);
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epsilon = max(H(H > 0), [], 'all');
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% Create the epsilon similarity graph
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[G] = epsilonSimGraph(epsilon,Pts);
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% Create the adjacency matrix for the epsilon case
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W = S .* G;
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% Create the Laplacian matrix and implement spectral clustering
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[L,Diag] = CreateLapl(W);
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B(j, i) = rcond(L);
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fprintf("%s - %g: %g\n", TITLES(i), 10^p, B(j,i));
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end
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end
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