%
% Example 6.7
% This shows how to use the function for the whole MBC procedure.
% 
% 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' ); 

%[X,midden,beachdune] = load_oronsay(0,0,0,0);
[X,midden,beachdune] = load_oronsay(1,0,1,0);
[X,midden,beachdune] = load_oronsay(1,0,1,1); 
[n,p] = size(X); data = X; 

truth_labels = midden; truth_type = 'midden'; 

% Call the model-based clustering procedure with a maximum of 6 clusters.
% ... this reqires 
% 
%      9 * max_num_of_clusters 
% 
% estimates of finite mixture models 
%
[bics,bestmodel,allmodel,Z,clabs] = mbclust(data,6);

% display the dendrogram: 
%figure; dendrogram(Z); title('Results for Oronsay Data - MBC')

% Display the BIC curves.
figure; plotbic(bics);
%saveas( gcf, '../../WriteUp/Graphics/Chapter6/prob_6_2_bic_curves', 'epsc' ); 

% We can apply the silhouette procedure for this result after we find a partition. Use 5 clusters
cind = cluster(Z,'maxclust',5);
figure; 
[S,H] = silhouette(X,cind);
title('Silhouette Plot - Model-Based Agglomerative MBC')
%saveas( gcf, '../../WriteUp/Graphics/Chapter6/prob_6_2_silhouette', 'epsc' ); 

fprintf('mean silhoutte value = %10.6f\n', mean( S ) ); 

fprintf('computing the NEC value\n');
out = NEC(X,allmodel); 
if( 0 ) 
  figure; hold on; 
  for mi=1:size(out,1)
    plot( out(mi,:) ); 
  end
else
  figure; plotbic(out); axis tight; title('NEC Clustering Criterion'); 
  saveas( gcf, '../../WriteUp/Graphics/Chapter6/prob_6_11_nec', 'epsc' );
end

return;