wolffd@0: function [partBinTrn, partBinTst, partBinNoTrn] = sim_from_comparison_UNfair_components(comparison, comparison_ids, k, trainpart, filename)
wolffd@0: %
wolffd@0: % FOR ISMIR 2012 
wolffd@0: %
wolffd@0: % creates a cross-validation partitioning of the 
wolffd@0: % similarity data in "multiG", NOT PRESERVING the 
wolffd@0: % connected components in it during partitioning
wolffd@0: 
wolffd@0: % ---
wolffd@0: % get the similarity multigraph and remove cycles
wolffd@0: % ---
wolffd@0: cprint(2, 'creating graph')
wolffd@0: % Gm = ClipSimGraphMulti(comparison, comparison_ids);
wolffd@0: % Gm.remove_cycles_length2; 
wolffd@0: cprint(2, 'loading Multigraph for Similarity Constraints')
wolffd@0: load('comp_SimGraphMulti.mat', 'G');
wolffd@0: 
wolffd@0: 
wolffd@0: % get similarity data
wolffd@0: [weights, a, b, c] = G.similarities();
wolffd@0: 
wolffd@0: % ---
wolffd@0: % We randomise the constraint succession 
wolffd@0: % ---
wolffd@0: datPermu = randperm(numel(a));
wolffd@0: a = a(datPermu);
wolffd@0: b = b(datPermu);
wolffd@0: c = c(datPermu);
wolffd@0: weights = weights(datPermu);
wolffd@0: 
wolffd@0: % ---
wolffd@0: % NOTE: we try the easy route: partition the graphs
wolffd@0: % and look at which constraints  balance we end up with
wolffd@0: % ---
wolffd@0: P = cvpartition(numel(a), 'k', k);
wolffd@0: 
wolffd@0: % ---
wolffd@0: % here we export  similarity test sets
wolffd@0: % ---
wolffd@0: cprint(2, 'export test similarity')
wolffd@0: partBinTst = {};
wolffd@0: for i = 1:P.NumTestSets  % test runs
wolffd@0: 
wolffd@0:     partBinTst{i} = [a(P.test(i))' b(P.test(i))' c(P.test(i))' weights(P.test(i))];
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: % ---
wolffd@0: % Note: This uses a "truly" increasing training set
wolffd@0: % to do the partial training partition
wolffd@0: % ---
wolffd@0: cprint(2, 'export train similarity')
wolffd@0: for m = 1:numel(trainpart)
wolffd@0: 
wolffd@0:     % save test indices
wolffd@0:     Ptrain(m) = cvpartition_trunctrain_incsubsets(P, trainpart(m));
wolffd@0: end
wolffd@0: 
wolffd@0: % ---
wolffd@0: % here we export similarity training sets
wolffd@0: % ---
wolffd@0: partBinTrn = {};
wolffd@0: for i = 1:P.NumTestSets % train runs
wolffd@0: 
wolffd@0:     for m = 1:numel(trainpart) % increasing training sets
wolffd@0: 
wolffd@0:         % get training indices
wolffd@0:         idxB = Ptrain(m).training(i);
wolffd@0:         
wolffd@0:         % save into cell
wolffd@0:         partBinTrn{i,m} = [a(idxB)' b(idxB)' c(idxB)' weights(idxB)];
wolffd@0:     end   
wolffd@0: end
wolffd@0: 
wolffd@0: partBinNoTrn = {};
wolffd@0: for i = 1:P.NumTestSets % train runs
wolffd@0: 
wolffd@0:     for m = 1:numel(trainpart) % increasing training sets
wolffd@0: 
wolffd@0:         % get training indices
wolffd@0:         idxB = ~Ptrain(m).training(i) & ~Ptrain(m).test(i);
wolffd@0:         
wolffd@0:         % save into cell
wolffd@0:         partBinNoTrn{i,m} = [a(idxB)' b(idxB)' c(idxB)' weights(idxB)];
wolffd@0:     end   
wolffd@0: end
wolffd@0: 
wolffd@0: if nargin == 5
wolffd@0:     save(filename, 'partBinTrn', 'partBinTst', 'partBinNoTrn')
wolffd@0: end
wolffd@0: end