wolffd@0: function [out, equal, singles, valid] = metric_fulfills_ranking(met, Y, clips)
wolffd@0: %  out = sim_fulfills_ranking(met, Y, ids)
wolffd@0: %
wolffd@0: % outputs (0-1) percentage of ranking constraints 
wolffd@0: % completely fulfilled by the given distance measure
wolffd@0: 
wolffd@0: % ---
wolffd@0: % for each valid query, check if the ranking is fulfilled
wolffd@0: % we also count eeventswhere the distance is equal
wolffd@0: % ---
wolffd@0: valid = ~(cellfun(@isempty, Y(:,1)) | cellfun(@isempty, Y(:,2)));
wolffd@0: 
wolffd@0: equal = 0; 
wolffd@0: singles = false(size(Y,1),1);
wolffd@0: for i = 1:size(Y,1)
wolffd@0:     
wolffd@0:     if valid(i)
wolffd@0:         
wolffd@0:         if numel(Y{i,1}) == 1 && numel(Y{i,2}) == 1 
wolffd@0:         % ---
wolffd@0:         % Efficient singular ranking comparison
wolffd@0:         % using met.distance(clipa,clipb)
wolffd@0:         % ---
wolffd@0:         % NOTE: by using the < here, we loose half of the 
wolffd@0:         % examples which have been same distance and 
wolffd@0:         % randomly positioned in earlier runs :(
wolffd@0:         % ---
wolffd@0:             distgood = met.distance(clips(i), clips(Y{i,1}));
wolffd@0:             distbad = met.distance(clips(i), clips(Y{i,2}));
wolffd@0:             
wolffd@0:             singles(i) = distgood < distbad;
wolffd@0:             if distgood == distbad
wolffd@0:                 equal = equal + 1;
wolffd@0:             end
wolffd@0:         else
wolffd@0:             % ---
wolffd@0:             % NOTE: Only reactivated for testing of 
wolffd@0:             % outdated similarity data
wolffd@0:             % ---
wolffd@0:             
wolffd@0:             warning 'code only for outdated similarity data\n';
wolffd@0: 
wolffd@0:             % ---
wolffd@0:             % NOTE: this code is analogous to the above
wolffd@0:             % ---
wolffd@0:              singles(i) = 1;
wolffd@0:              for j=1:numel(Y{i,1})
wolffd@0:                  for k = 1:numel(Y{i,2})
wolffd@0:                      
wolffd@0:                     % ---
wolffd@0:                     % All the entries in Y(i,1) have to come before
wolffd@0:                     % the ones in Y(i,2)
wolffd@0:                     % ---
wolffd@0:                     distgood = met.distance(clips(i), clips(Y{i,1}(j)));
wolffd@0:                     distbad = met.distance(clips(i), clips(Y{i,2}(k)));
wolffd@0:                     singles(i) = singles(i) && (distgood < distbad);
wolffd@0:                     
wolffd@0:                     % count equalities
wolffd@0:                     if distgood == distbad
wolffd@0:                         equal = equal + 1;
wolffd@0:                     end
wolffd@0:                     if ~singles(i)
wolffd@0:                         break;
wolffd@0:                     end
wolffd@0:                  end
wolffd@0:              end
wolffd@0:              
wolffd@0:              
wolffd@0:         end
wolffd@0:     else
wolffd@0:     end
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: out(1) = mean(singles(valid));
wolffd@0: if size(Y,2) == 3
wolffd@0:     % ---
wolffd@0:     % weighted sum of singles: count satisfied votes
wolffd@0:     % ---
wolffd@0:     weights = cell2mat( Y( valid, 3));
wolffd@0:     out(2) = sum(weights(singles(valid))) / sum(weights);
wolffd@0: else
wolffd@0:     out(2) = -1;
wolffd@0: end
wolffd@0: out = out';
wolffd@0: 
wolffd@0: % markup invalid rankings
wolffd@0: % singles(~valid) = -1;
wolffd@0: 
wolffd@0: