%% (Internal) Cluster data with expectation-maximization algorithm
%
% [clust_labels w_Trained_Classifier] = cluster_data_with_EM_clust(dsTrain, w_mapp, CantClusters, iter_times)
%
% Arguments:
%
% + dsTrain: PRdataset with data
%
% + w_mapp: mapping to perform clustering.
%
% + CantClusters: clusters to discover.
%
% + iter_times: iterations to perform clustering.
%
% Output:
%
% + clust_labels: Cluster pertenence labels
%
% + w_Trained_Classifier: Mapping that perform the clustering found.
%
% Example:
%
% warning off all; prwarning(0);
% Clust_Labels = cluster_data_with_EM_clust(featMat_clust(pending_hb_idx,:), qdc_new([],1e-6,1e-6, []), CantClusters, iter_times);
% warning on all; prwarning(1);
%
% See also a2hbc_main
%
% Author: Mariano Llamedo Soria llamedom@electron.frba.utn.edu.ar
% Version: 0.1 beta
% Last update: 14/5/2014
% Birthdate : 21/4/2015
% Copyright 2008-2015
%
function [clust_labels w_Trained_Classifier] = cluster_data_with_EM_clust(dsTrain, w_mapp, CantClusters, iter_times)
if nargin < 4 || isempty(iter_times)
iter_times = 1;
end
if nargin < 3 || isempty(CantClusters)
CantClusters = 5;
end
m = getsize(dsTrain,1);
std_train = std(+dsTrain);
dsTrain = setdata(dsTrain, bsxfun(@rdivide, +dsTrain, std_train ));
clustered_labels = repmat('0', m, iter_times);
jj = 1;
% for CantClusters = 2:5
for jj = 1:iter_times
%EMclust
ii = 0;
bContinuar = true;
while(bContinuar && ii < CantClusters)
try
% dbclear if caught error
% [clust_labels w_Trained_Classifier ] = emclust_new(dsTrain, w_mapp, CantClusters-ii );
clust_labels = [];
[clust_labels w_Trained_Classifier ] = emclust(dsTrain, w_mapp, CantClusters-ii );
% dbstop if caught error
bContinuar = false;
catch ME
if(strcmpi(ME.message, 'Not possible to find desired number of components'))
ii = ii + 1;
else
rethrow(ME)
end
end
end
if( isempty(clust_labels) )
error('Clustering failed, check data.')
else
clustered_labels(:,jj) = char(97+clust_labels);
end
% jj = jj + 1;
end
bClustered = true;
while(bClustered)
%analizo las coincidencias en las distintas iteraciones respecto a
%los etiquetados.
[~, sort_idx] = sort( cellstr(clustered_labels) );
[all_clusters, aux_location] = unique(clustered_labels(sort_idx,:), 'rows', 'first');
aux_location = [colvec(aux_location); m+1];
cluster_sizes = diff(aux_location);
[~, clust_sort_idx] = sort(cluster_sizes, 'descend');
%agrupo todos los subclusters a una dist maxima max_distance
max_distance = round(0.2*iter_times); %distancia maxima para considerarse un cluster
cant_clusters = size(all_clusters,1);
cant_iter = size(all_clusters,2);
aux_1 = repmat( all_clusters, cant_clusters, 1);
aux_idx = colvec(repmat(1:cant_clusters,cant_clusters,1));
aux_2 = all_clusters(aux_idx,:);
distances = reshape( sum(aux_1 ~= aux_2,2), cant_clusters, cant_clusters );
remaining_clusters = 1:cant_clusters;
%clusterizo igual todo los clusters iguales hasta max_distance
for ii = rowvec(clust_sort_idx)
bClustered = false;
cluster2fusion_idx = find(distances(:,ii) <= max_distance);
cluster2fusion_idx = cluster2fusion_idx(cluster2fusion_idx < ii | cluster2fusion_idx > ii );
for jj = 1:length(cluster2fusion_idx)
aux_idx = find(strcmpi(cellstr(clustered_labels), cellstr(all_clusters(cluster2fusion_idx(jj),:))));
if( ~isempty(aux_idx) )
bClustered = true;
remaining_clusters( remaining_clusters == cluster2fusion_idx(jj)) = [];
clustered_labels( aux_idx ,:) = repmat(all_clusters(ii,:), length(aux_idx), 1 );
all_clusters(cluster2fusion_idx(jj),:) = all_clusters(ii,:);
end
end
if(bClustered)
remaining_clusters( remaining_clusters == ii ) = [];
%fuerzo el recalclo de distancias, para que no se junte todo y solo
%los clusters grandes se coman a los mas chicos.
break
end
end
end
% %luego clusterizo todo lo que fue quedando a distancias mayores, al estilo
% %clustering jerarquico.
% kk = max_distance+1;
% while( kk <= iter_times)
% for ii = remaining_clusters
% bClustered = false;
% cluster2fusion_idx = find(distances(:,ii) == kk );
% cluster2fusion_idx = cluster2fusion_idx(cluster2fusion_idx > ii );
% cluster2fusion_idx = intersect(cluster2fusion_idx, remaining_clusters);
%
% for jj = 1:length(cluster2fusion_idx)
% aux_idx = find(strcmpi(cellstr(clustered_labels), cellstr(all_clusters(cluster2fusion_idx(jj),:))));
% if( ~isempty(aux_idx) )
% bClustered = true;
% remaining_clusters( remaining_clusters == cluster2fusion_idx(jj)) = [];
% clustered_labels( aux_idx ,:) = repmat(all_clusters(ii,:), length(aux_idx), 1 );
% end
% end
% if(bClustered)
% remaining_clusters( remaining_clusters == ii ) = [];
% end
% end
% kk = kk + 1;
% end
%REanalizo las coincidencias en las distintas iteraciones respecto a
%los etiquetados.
[~, sort_idx] = sort( cellstr(clustered_labels) );
[all_clusters, aux_location] = unique(clustered_labels(sort_idx,:), 'rows', 'first');
aux_location = [colvec(aux_location); m+1];
cluster_sizes = diff(aux_location);
% esto es por si interesaria filtrar clusters chicos, no parece buena idea
% porque agrupa juntas las clases que aparecen ocacionalmente, haciendolas
% indetectables.
%
% big_clusters_start_idx = find(cluster_sizes > 50);
% big_clusters_range_idx = [ colvec(aux_location( big_clusters_start_idx )) colvec(aux_location( big_clusters_start_idx+1 )-1)];
%
% big_clusters_idx = [];
% group_labels = [];
% cant_big_clusters = length(big_clusters_start_idx);
% for ii = 1:cant_big_clusters
% big_clusters_idx = [ big_clusters_idx; colvec(big_clusters_range_idx(ii,1):big_clusters_range_idx(ii,2))];
% group_labels = [group_labels ; repmat(ii, cluster_sizes(big_clusters_start_idx(ii)) ,1)];
% end
%
% clust_labels = repmat(cant_big_clusters+1,m,1);
% cl ust_labels(sort_idx(big_clusters_idx)) = group_labels;
cant_clusters = size(all_clusters,1);
clust_labels = nan(m,1);
for ii = 1:cant_clusters
clust_labels(sort_idx(aux_location(ii):(aux_location(ii+1)-1))) = ii;
end
if(any(isnan(clust_labels)))
error()
end