%
% 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; 
rehash; 
clc; 
clear; addpath( '../../Code/eda_data' ); addpath( '../../Code/eda_toolbox' ); addpath( '../Chapter1' );

[X,lab] = load_lungB(0,0,0,0);
%X = load_lungB(1,1,0,0); % <- order by columns with the largest variance first ... then apply PCA 
%X = load_lungB(1,1,1,0); % <- order by columns with the largest variance first, compute the zscore, then apply PCA 
X = load_lungB(1,1,0,1); % <- order by columns with the largest variance first ... then apply PCA ... SEEMS THE BEST 
%X = load_lungB(1,0,1,1); % <- zscore ... then apply PCA 
%X = load_lungB(1,1,1,1); % <- order by columns with the largest variance, compute the zscore, ... then apply PCA 
%X = load_lungB(1,1,1,2); % <- order by columns with the largest variance, compute the zscore, ... then apply SVD  
[n,p] = size(X); data = X; 

% Call the model-based clustering procedure with a maximum of 6 clusters.
[bics,bestmodel,allmodel,Z,clabs] = mbclust(data,6);

% Display the BIC curves.
figure; plotbic(bics); 
title( 'Model 5, 2 clusters are optimal' ); 
%saveas( gcf, '../../WriteUp/Graphics/Chapter6/prob_6_17_lungB_bic_curves', 'epsc' ); 

return;