Mercurial > hg > camir-aes2014
view reproduce_AES53rd/rerun_figure2.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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% --- % This script plots 50-dimensional RBM features as in figure 2 of % % Feature Preprocessing with RBMs for Music Similarity Learning % Son N. Tran, Daniel Wolff, Tillman Weyde, Artur Garcez, AES53rd % conference % --- % cr_: training correct % cr : testing correct feature_file = 'rel_music_raw_features+simdata_ISMIR12'; vars = whos('-file', feature_file); A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); raw_features = A.(vars(1).name); indices = A.(vars(2).name); tst_inx = A.(vars(3).name); trn_inx = A.(vars(4).name); % figure(1); imagesc(raw_features);colorbar; title 'Original Features'; % load pregenerated RBM features mod = load('rbm_50'); % --- % uncomment the following line to use newly calculated RBM features % mod = new_rbm(50,'grad'); % --- features = logistic(raw_features*mod.W_max{1} + repmat(mod.hB_max{1},size(raw_features,1),1)); figure(2); imagesc(features);colorbar; title 'RBM Features'; num_case = size(trn_inx,1); [trnd_12 trnd_13] = subspace_distances(trn_inx,features,indices,1,1); [tstd_12 tstd_13] = subspace_distances(tst_inx,features,indices,1,1); cr_ = 0; % correct rate for training cr = 0; % correct rate for testing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%