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1 function DL = SMALL_learn(Problem,DL)
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2 %% SMALL Dictionary Learning
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3 %
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4 % Function gets as input Problem and Dictionary Learning (DL) structures
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5 % In Problem structure field b with the training set needs to be defined
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6 % In DL fields with name of the toolbox and solver, and parameters file
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7 % for particular dictionary learning technique needs to be present.
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8 %
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9 % Outputs are Learned dictionary and time spent as a part of DL structure
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10
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11 %
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12 % Centre for Digital Music, Queen Mary, University of London.
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13 % This file copyright 2009 Ivan Damnjanovic.
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14 %
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15 % This program is free software; you can redistribute it and/or
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16 % modify it under the terms of the GNU General Public License as
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17 % published by the Free Software Foundation; either version 2 of the
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18 % License, or (at your option) any later version. See the file
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19 % COPYING included with this distribution for more information.
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20 %%
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21
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22 SMALLboxInit
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23
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24 if (DL.profile)
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25 fprintf('\nStarting Dictionary Learning %s... \n', DL.name);
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26 end
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27
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28 start=cputime;
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29 tStart=tic;
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30
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31 % toolboxes configuration file
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32 run(fullfile(SMALL_path, 'config/SMALL_learn_config.m'));
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33
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34 %%
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35 % Dictionary Learning time
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36 tElapsed=toc(tStart);
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37 DL.time = cputime - start;
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38 if (DL.profile)
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39 fprintf('\n%s finished task in %2f seconds (cpu time). \n', DL.name, DL.time);
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40 fprintf('\n%s finished task in %2f seconds (tic-toc time). \n', DL.name, tElapsed);
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41 end
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42 DL.time=tElapsed;
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43 % If dictionary is given as a sparse matrix change it to full
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44
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45 DL.D = full(D);
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46
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47 end
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48
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