Mercurial > hg > camir-aes2014
view core/magnatagatune/sim_from_comparison_UNfair_components.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function [partBinTrn, partBinTst, partBinNoTrn] = sim_from_comparison_UNfair_components(comparison, comparison_ids, k, trainpart, filename) % % FOR ISMIR 2012 % % creates a cross-validation partitioning of the % similarity data in "multiG", NOT PRESERVING the % connected components in it during partitioning % --- % get the similarity multigraph and remove cycles % --- cprint(2, 'creating graph') % Gm = ClipSimGraphMulti(comparison, comparison_ids); % Gm.remove_cycles_length2; cprint(2, 'loading Multigraph for Similarity Constraints') load('comp_SimGraphMulti.mat', 'G'); % get similarity data [weights, a, b, c] = G.similarities(); % --- % We randomise the constraint succession % --- datPermu = randperm(numel(a)); a = a(datPermu); b = b(datPermu); c = c(datPermu); weights = weights(datPermu); % --- % NOTE: we try the easy route: partition the graphs % and look at which constraints balance we end up with % --- P = cvpartition(numel(a), 'k', k); % --- % here we export similarity test sets % --- cprint(2, 'export test similarity') partBinTst = {}; for i = 1:P.NumTestSets % test runs partBinTst{i} = [a(P.test(i))' b(P.test(i))' c(P.test(i))' weights(P.test(i))]; end % --- % Note: This uses a "truly" increasing training set % to do the partial training partition % --- cprint(2, 'export train similarity') for m = 1:numel(trainpart) % save test indices Ptrain(m) = cvpartition_trunctrain_incsubsets(P, trainpart(m)); end % --- % here we export similarity training sets % --- partBinTrn = {}; for i = 1:P.NumTestSets % train runs for m = 1:numel(trainpart) % increasing training sets % get training indices idxB = Ptrain(m).training(i); % save into cell partBinTrn{i,m} = [a(idxB)' b(idxB)' c(idxB)' weights(idxB)]; end end partBinNoTrn = {}; for i = 1:P.NumTestSets % train runs for m = 1:numel(trainpart) % increasing training sets % get training indices idxB = ~Ptrain(m).training(i) & ~Ptrain(m).test(i); % save into cell partBinNoTrn{i,m} = [a(idxB)' b(idxB)' c(idxB)' weights(idxB)]; end end if nargin == 5 save(filename, 'partBinTrn', 'partBinTst', 'partBinNoTrn') end end