wolffd@0: function [r, valididx, clip_ids] = ranking_from_comparison_ClipSimGraphMulti(comparison, comparison_ids)
wolffd@0: % returns rankings for compared songs.
wolffd@0: % rankings are genrerated using a Multigraph analysis of the comparison
wolffd@0: % data 
wolffd@0: % 
wolffd@0: % r(idx,1) = clip being similar to clip idx
wolffd@0: % r(idx,2) = clip being less similar to clip idx
wolffd@0: % r(idx,3) = weight of this ranking
wolffd@0: %
wolffd@0: % the indices in clipids correspond to the indices in comparison_ids
wolffd@0: 
wolffd@0: % create similarity graph
wolffd@0: % G = ClipSimGraphMulti(comparison, comparison_ids);
wolffd@0: % G.remove_cycles_length2;
wolffd@0: % save('comp_SimGraphMulti.mat', 'G');
wolffd@0: cprint(2, 'loading Multigraph for Similarity Constraints')
wolffd@0: load('comp_SimGraphMulti.mat', 'G');
wolffd@0: 
wolffd@0: 
wolffd@0: 
wolffd@0: % inverse comparison index
wolffd@0: inv_comparison = sparse(comparison_ids,1, 1:numel(comparison_ids));
wolffd@0: 
wolffd@0: % ---
wolffd@0: % derive similarity information: this
wolffd@0: % returns the weights of edges meaning sim(a,b) > sim(a,c)
wolffd@0: % ---   
wolffd@0: [weights, a, b, c] = G.similarities();
wolffd@0: % --- 
wolffd@0: % NOTE: it is yet unclear which index 
wolffd@0: % (the comparison or the mtt clip index) 
wolffd@0: % is best to use
wolffd@0: % 
wolffd@0: % The lines below transform the MTT ids from the graph into comparison ids
wolffd@0: % ---
wolffd@0: a = inv_comparison(a);
wolffd@0: b = inv_comparison(b);
wolffd@0: c = inv_comparison(c);
wolffd@0: 
wolffd@0: % ---
wolffd@0: % reformat into ranking: the first 1019 clips are ordered
wolffd@0: % as in comparison_ids. then, clips mentioned more than once 
wolffd@0: % are attached. 
wolffd@0: % ---
wolffd@0: [a, idx] = sort(a);
wolffd@0: b = b(idx);
wolffd@0: c = c(idx);
wolffd@0: 
wolffd@0: 
wolffd@0: clip_ids = zeros(numel(a), 1);
wolffd@0: 
wolffd@0: % ---
wolffd@0: % NOTE: r may grow over its allocated size
wolffd@0: % ---
wolffd@0: r = cell(numel(a),1 );
wolffd@0: 
wolffd@0: % keep track of constraints which have been attached
wolffd@0: visited = zeros(1, numel(a));
wolffd@0: for i = 1:numel(comparison_ids)
wolffd@0:     
wolffd@0:     % ---
wolffd@0:     % get first occurence of this index,
wolffd@0:     % and save the data into ranking
wolffd@0:     % ---
wolffd@0:     tmp_idx = find(a == i, 1, 'first');
wolffd@0:     
wolffd@0:     clip_ids(i) = i; % == a(tmp_idx)
wolffd@0:         
wolffd@0:     if ~isempty(tmp_idx)
wolffd@0:         visited(tmp_idx) = true;
wolffd@0:         
wolffd@0:         r{i,1} = b(tmp_idx);
wolffd@0:         r{i,2} = c(tmp_idx);
wolffd@0:         r{i,3} = weights(tmp_idx);
wolffd@0:         
wolffd@0:         valididx(i) = true;
wolffd@0:     else
wolffd@0:         % invalid ranking
wolffd@0:         valididx(i) = false;
wolffd@0:         r{i,3} = 0;
wolffd@0:     end
wolffd@0: end
wolffd@0: 
wolffd@0: % ---
wolffd@0: % Now we attach the remaining constraints
wolffd@0: % ---
wolffd@0: remaining = find(~visited);
wolffd@0: clip_ids( numel(comparison_ids)+1 : numel(comparison_ids)+numel(remaining)) = a(remaining);
wolffd@0: 
wolffd@0: for i = 1:numel(remaining)
wolffd@0:     r{ numel(comparison_ids)+i, 1} = b(remaining(i));
wolffd@0:     r{ numel(comparison_ids)+i , 2} = c(remaining(i));
wolffd@0:     r{ numel(comparison_ids)+i , 3} = weights(remaining(i));
wolffd@0: end
wolffd@0: 
wolffd@0: valididx(numel(comparison_ids)+1 : ...
wolffd@0:     numel(comparison_ids)+numel(remaining)) = true;
wolffd@0: