% 
% Written by:
% -- 
% John L. Weatherwax                2005-08-14
% 
% email: wax@alum.mit.edu
% 
% Please send comments and especially bug reports to the
% above email address.
% 
%-----

close all; drawnow; 
clc; 
clear; addpath( '../../Code/eda_data' ); addpath( '../../Code/eda_toolbox' ); addpath( '../Chapter1' );

% Example 8.4
[X,clabs] = load_iris(1,0,1); 

% Get the hierarchical clustering.
Y = pdist(X);
Z = linkage(Y,'complete');
dendrogram(Z);

% Example 8.7
% Applies Ling's method to interpoint distance matrix.
n = size(X,1); 
data = X;
% Randomly reorder the data and view the 
% interpoint distance matrix as an image.
data = data(randperm(n),:);
Y = pdist(data);
Ys = squareform(Y);
imagesc(Ys);
colormap(gray(256))
title('Interpoint Distance Matrix - Random Order')
axis off

% Now apply Ling's method. First need to
% get a partition or clustering.
Z = linkage(Y,'complete');
% Now get the ordering based on the 
% cluster scheme.
T = cluster(Z,'maxclust',3);
% Sort these, so points in the same cluster
% are adjacent.
[Ts,inds] = sort(T);
% Sort the distance matrix using this and replot.
figure
imagesc(Ys(inds,inds))
title('Interpoint Distance Matrix - Cluster Order')
colormap(gray(256))
axis off
saveas( gcf, '../../WriteUp/Graphics/Chapter8/prob_8_7_co', 'epsc' );