clear variables;
close all;
warning OFF;

addpath ../datasets
addpath ../datasets/Meshes

[Pts_spirals,Pts_clusterin,Pts_corn,Pts_halfk,Pts_fullmoon,Pts_out] = getPoints();
close all;

TITLES = ["Two Spirals", "Cluster in cluster", "Corners", "Half crescent", "Full crescent", "Outlier"];
RUNS = {Pts_spirals, Pts_clusterin, Pts_corn, Pts_halfk, Pts_fullmoon, Pts_out};
KS = {2, 2, 4, 2, 2, 4};

B = zeros(100, 6);

for j = 20:70
    for i = 1:6
        p = (j - 20) / 10;
        % Specify the number of clusters
        Pts = RUNS{i};
        K = KS{i};
        n = size(Pts, 1);

        % Create Gaussian similarity function
        [S] = similarityfunc(Pts(:,1:2), 10^p * log(n));

        % Find the minimal spanning tree of the full graph. Use the information
        % to determine a valid value for epsilon        
        H = minSpanTree(S);
        epsilon = max(H(H > 0), [], 'all');

        % Create the epsilon similarity graph
        [G] = epsilonSimGraph(epsilon,Pts);

        % Create the adjacency matrix for the epsilon case
        W = S .* G;
        
        % Create the Laplacian matrix and implement spectral clustering
        [L,Diag] = CreateLapl(W);
        B(j, i) = rcond(L);
        fprintf("%s - %g: %g\n", TITLES(i), 10^p, B(j,i));
    end
end