%
% Examples 9.15 and 9.17
% 
% epage 392 (cluster/
% 
% Written by:
% -- 
% John L. Weatherwax                2008-02-20
% 
% email: wax@alum.mit.edu
% 
% Please send comments and especially bug reports to the
% above email address.
% 
%-----

clear all; 
close all; 
clc; 

addpath('../../Code/CSTool');

%--
% First Example 9.15:
%--

% Let's make up a data set - 2-D.
x = [1 1; 1 2; 2 1; -1 -1; -1 -2];
plot(x(:,1),x(:,2),'kx') % plots the points.
axis([-3 3 -3 3])
text(x(:,1)+.1,x(:,2)+.1,'1|2|3|4|5');

% Find the Euclidean distance using pdist.
% Convert to matrix form for easier reading.
ye = pdist(x,'euclid');
%ye_mat = squareform(ye);
ze_link = linkage(ye); 

fprintf('the found clustering array T is\n'); 
%T = cluster(ze_link,'maxclust',3);
T = cluster(ze_link,'maxclust',2);
T

% color the points according to what cluster they are in ...
uT = unique(T); 
nu = length(unique(T));
colors = 'rbkmc'; 
for ii=1:nu,
  inds=find(T==uT(ii));
  plot( x(inds,1),x(inds,2),['o',colors(ii)] ); hold on;
end
axis([-3 3 -3 3])

fprintf('print the inconsistent values of a cluster tree ... \n'); 
inconsistent(ze_link)