%% ct_clustprocs Review of cluster procedures.
%
% <http://37steps.com/prtools/ PRTools> and <http://37steps.com/clustertools/ 
% ClusterTools> should be in the path
%
% Goto <http://37steps.com/clustertools/clustertools-examples/ 
% ClusterTools examples> for a review of all examples.
%
% <http://37steps.com/exam_ct/ct_clustprocs/ct_clustprocs.m Download 
% the m-file from here>.
%
% It shows the groundtruth of a 2D example with 10 clusters and compares it 
% with the result of 11 cluster procedures. Evaluation is done by assigning
% all objects in a cluster to the true class of its prototype.
%
% In CLUSTEX1 the same experiment is coded in a more elaborate way.

%% Prepare data and set parameters
randreset   % initialise random generator
k = 10;     % Search for k clusters
n = 1000;   % Speed up hierarchical and exemplar clustering by reducing
            % the dataset to at most n points by modeseeking
m = 3000;   % total number of objects in the dataset
x = gendatclust1(m); % generate a 10 cluster problem (3000 objects)
a = +x;     % create unlabeled data
prtime(5)   % stop iterations after some seconds

%% Show the original data with target classes
figure;
lab = getnlab(x); 
scatn(lab,a,'Ground truth');


%% define cluster routines 
% The cluster routines are defined as PRTools mappings for creating k
% clusters. For every cluster procedure its scatterplot for 10 clusters is
% shown. In addition the classification error is printed based on the
% assignment of all objects in a cluster to the true class of its prototype.
proc = cell(1,11);
proc{1}  = clustk(k,'KMeans');             % define KMeans
proc{2}  = clustk(k,'KCenters');           % define KCenters
proc{3}  = clustk(k,'KMedoids');           % define KMedoids
proc{4}  = clusth(k,'Single Linkage',n);   % define Single Linkage
proc{5}  = clusth(k,'Average Linkage',n);  % define Average Linkage
proc{6}  = clusth(k,'Central Linkage',n);  % define Central Linkage
proc{7}  = clusth(k,'Complete Linkage',n); % define Complete Linkage
proc{8}  = cluste(k,n);                    % define Exemplar
proc{9}  = clustm(k);                      % define KNN ModeSeek
proc{10} = clusts(k);                      % define Mean Shift
proc{11} = clustf(k);                      % define FFT

figure;
set(gcf,'Position',[100 100 1200 800])
subplot(3,4,1)
lab = getnlab(x); 
scatn(lab,a,'Ground truth');
for j=1:11
  subplot(3,4,j+1);
  scatn(ones(size(x,1),1),x,getname(proc{j}));
  text(8,-2,'... in progress ...','FontSize',15);
end

% compute results
for j=1:11
  subplot(3,4,j+1);
  lab = a*proc{j};
  scatn(lab,a,getname(proc{j}));
  % compute active learning error based on prototypes
  e = clusteval(lab,x,'actl');  
  text(10,-2,num2str(e,'%5.3f'),'FontSize',15);
  drawnow;
end

%% comments
% The main purpose of this example is to show how the software can be used.
% Note that using 100000 (m) instead 3000 objects is also feasible due to 
% the preclustering used in the routines and the default settings of the
% parameters.