fs_unsup_llcfs_single_func.m

function [FeaNumCandi,res_gs,res_aio, res_gs_ps] = fs_unsup_llcfs_single_func(dataset, exp_settings, algo_settings)
%Unsupervised feature selection using all features

%======================setup===========================
FeaNumCandi = exp_settings.FeaNumCandi;
nKmeans = exp_settings.nKmeans;
prefix_mdcs = [];
if isfield(exp_settings, 'prefix_mdcs')
    prefix_mdcs = exp_settings.prefix_mdcs;
end
%================================

disp(['dataset:',dataset]);
[X, Y] = extractXY(dataset);
[nSmp,nDim] = size(X);
nClass = length(unique(Y));

%===================setup=======================
knnCandi = 5;
graphTypeCandi = [2];
betaCandidates = 10.^[-5:5];
paramCell = fs_unsup_llcfs_build_param(nClass, knnCandi, betaCandidates, graphTypeCandi );
%===============================================

disp('LLCFS ...');
t_start = clock;
feaSubsets = cell(length(paramCell), 1);
parfor i1 = 1:length(paramCell)
    fprintf(['LLCFS parameter search %d out of %d...\n'], i1, length(paramCell));
    param = paramCell{i1};
    [~, tao] = fs_unsup_llcfs(X,param);
	[~, idx] = sort(tao, 'descend');
    feaSubsets{i1,1} = idx;
end
t_end = clock;
t1 = etime(t_end,t_start);
disp(['exe time: ',num2str(t1)]);

t_start = clock;
disp('evaluation ...');
res_aio = cell(length(paramCell), length(FeaNumCandi));
for i2 = 1:length(FeaNumCandi)
    m = FeaNumCandi(i2);
    parfor i1 = 1:length(paramCell)
        fprintf('LLCFS parameter evaluation %d outof %d  ... %d out of %d...\n', i2, length(FeaNumCandi), i1, length(paramCell));
        idx = feaSubsets{i1,1};
        res_aio{i1, i2} = evalUnSupFS(X, Y, idx(1:m), struct('nKm', nKmeans));
    end
end
[res_gs, res_gs_ps] = grid_search_fs(res_aio);
res_gs.feaset = FeaNumCandi;
t_end = clock;
t2 = etime(t_end,t_start);
disp(['exe time: ',num2str(t2)]);
res_gs.time = t1;
res_gs.time2 = t2;

save(fullfile(prefix_mdcs, [dataset, '_best_result_LLCFS.mat']),'FeaNumCandi','res_gs','res_aio', 'res_gs_ps');
end