data_95 = importdata('data/itu_phonedata-95.csv',',');

G           = data_95.data(1:193, 1:193);
G(isnan(G)) = 0;
[nrow ncol] = size(G);
names       = data_95.textdata(1, 2:194);


G_sym   = G + G';
G1_sym  = G_sym / sum(sum(G_sym));

weights = repmat(sum(G1_sym')', 1, ncol);
G1_norm = G1_sym ./ weights;

% calculate G2_sym
N      = nrow;
G2_sym = G1_norm > (1/N);
G2_sym = G2_sym .* G2_sym';


% (B) Thresholding
G1_norm_t = G1_norm > (1/N);
G1_sym_t  = G1_sym > (1/N);

% GraphToGml
% graphtogml('graphs/yed/G1_sym.gml' , triu(G1_sym_t),  names);
% graphtogml('graphs/yed/G2_sym.gml' , triu(G2_sym),    names);
% graphtogml('graphs/yed/G1_norm.gml', G1_norm_t,       names);


% (C) compute the laplacian for G1_sym and G2_sym

% laplacian for g1_sym
a = G1_sym;
degree   = sum(a)';
d_matrix = diag(degree);
Lapcian  = d_matrix - a;

[vlap dlap]    = eig(Lapcian);
eigenvalues    = diag(dlap);
index          = find(eigenvalues > 1e-12, 3);
G1_eigs        = vlap(:, index);

s = scatter3(G1_eigs(:,1), G1_eigs(:,2), G1_eigs(:,3));
saveas(s, 'graphs/scatter/G1s' ,'jpg')

% laplacian for g2_sym
a = G2_sym;
degree   = sum(a)';
d_matrix = diag(degree);
Lapcian  = d_matrix - a;

[vlap dlap]    = eig(Lapcian);
eigenvalues    = diag(dlap);
index          = find(eigenvalues > 1e-12, 3);
G2_eigs        = vlap(:, index);

s = scatter3(G2_eigs(:,1), G2_eigs(:,2), G2_eigs(:,3));
saveas(s, 'graphs/scatter/G2s' ,'jpg')

% Fieldler Vectors

G1_f   = G1_eigs(:,1) ;
G1_f   = G1_f / max(abs(G1_f));
s = scatter(G1_f(:,1), zeros(length(G1_f),1));
saveas(s, 'graphs/scatter/G1_f' ,'eps')

G2_f = G2_eigs(:,1);
G2_f = G2_f / max(abs(G2_f));
s = scatter(G2_f(:,1), zeros(length(G2_f),1));
saveas(s, 'graphs/scatter/G2_f' ,'eps')

% K-MEANS 1-D embeddings

a = G2_eigs;
matrix = a(:,1);
close

for i = 2:5

	f = figure;
	[IDX, C] = kmeans(matrix, i);
	red    = matrix(IDX == 1);
	scatter(red, zeros(length(red), 1), 'red')
	hold on

	blue   = matrix(IDX == 2);
	scatter(blue, zeros(length(blue), 1), 'blue')
	hold on

	if i > 2
		yellow = matrix(IDX == 3);
		scatter(yellow, zeros(length(yellow), 1), 'yellow')
		hold on
	end

	if i > 3
		cyan   = matrix(IDX == 4);
		scatter(cyan, zeros(length(cyan), 1), 'cyan')
		hold on
	end

	if i > 4
		green  = matrix(IDX == 5);
		scatter(green, zeros(length(green), 1), 'green')
	end
	title(['kmeans ' name ' - ' i ' clusters'])
	disp(i)
	saveas(f, ['graphs/kmeans/' 'G2_eigs 1D ' int2str(i) ' clusters'], 'epsc' )
	close

end


% K-MEANS 2-D embeddings


matrix = a(:,[1 2]);
close

for i = 2:5

	f = figure;
	[IDX, C] = kmeans(matrix, i);
	red    = matrix(IDX == 1, :);
	scatter(red(:,1), red(:,2) , 'red')
	hold on

	blue   = matrix(IDX == 2, :);
	scatter(blue(:, 1), blue(: ,2), 'blue')
	hold on

	if i > 2
		yellow = matrix(IDX == 3, :);
		scatter(yellow(:, 1), yellow(:, 2), 'yellow')
		hold on
	end

	if i > 3
		cyan   = matrix(IDX == 4, :);
		scatter(cyan(:,1), cyan(:,2), 'cyan')
		hold on
	end

	if i > 4
		green  = matrix(IDX == 5, :);
		scatter(green(:,1), green(:,2), 'green')
	end
	title(['kmeans ' name ' - ' i ' clusters'])
	disp(i)
	saveas(f, ['graphs/kmeans/' 'G2_eigs 2D ' int2str(i) ' clusters'], 'epsc' )
	close
end

% K-MEANS 3-D embeddings


matrix = a(:,[1 2 3]);
close

for i = 2:5

	f = figure;
	[IDX, C] = kmeans(matrix, i);
	red    = matrix(IDX == 1, :);
	scatter3(red(:,1), red(:,2), red(:,3), 'red')
	hold on

	blue   = matrix(IDX == 2, :);
	scatter3(blue(:, 1), blue(: ,2), blue(: ,3), 'blue')
	hold on

	if i > 2
		yellow = matrix(IDX == 3, :);
		scatter3(yellow(:, 1), yellow(:, 2), yellow(:, 3), 'yellow')
		hold on
	end

	if i > 3
		cyan   = matrix(IDX == 4, :);
		scatter3(cyan(:,1), cyan(:,2), cyan(:,3), 'cyan')
		hold on
	end

	if i > 4
		green  = matrix(IDX == 5, :);
		scatter3(green(:,1), green(:,2), green(:,3), 'green')
	end
	title(['kmeans ' name ' - ' i ' clusters'])
	disp(i)
	saveas(f, ['graphs/kmeans/' 'G2_eigs 3D ' int2str(i) ' clusters'], 'epsc' )
	close
end