wolffd@0: function mlr_unittest(X, Yrel)
wolffd@0: 
wolffd@0: % Loss values to test
wolffd@0:     LOSS    = {'AUC', 'Prec@k', 'MAP', 'MRR', 'NDCG'};
wolffd@0: 
wolffd@0: 
wolffd@0:     % Regularization values to test
wolffd@0:     REG = [0,1,2,3];
wolffd@0: 
wolffd@0:     % Batch sizes to test
wolffd@0:     BATCH = [0 1 5];
wolffd@0: 
wolffd@0:     % Diagonal settings
wolffd@0:     DIAG = [0 1];
wolffd@0: 
wolffd@0:     figure(1);
wolffd@0:     for l = 1:length(LOSS)
wolffd@0:         display(['Testing ', LOSS{l}]);
wolffd@0:         for r = 1:length(REG)
wolffd@0:             display(sprintf('\tREG=%d', REG(r)));
wolffd@0:             for b = 1:length(BATCH)
wolffd@0:                 display(sprintf('\tB=%d', BATCH(b)));
wolffd@0:                 for d = 1:length(DIAG)
wolffd@0:                     display(sprintf('\tDiagonal=%d', DIAG(d)));
wolffd@0:                     [W, Xi, D] = mlr_train(X, Yrel, 10e5, LOSS{l}, REG(r), DIAG(d), BATCH(b));
wolffd@0:                     imagesc(W); drawnow;
wolffd@0: %                     [W, Xi, D] = mlr_train(X, Yclass, 10e5, LOSS{l}, REG(r), DIAG(d), BATCH(b));
wolffd@0: %                     imagesc(W); drawnow;
wolffd@0:                 end
wolffd@0:             end
wolffd@0:         end
wolffd@0:     end
wolffd@0: 
wolffd@0: end