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1 % ---
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2 % This script trains similarity measures and evaluates the
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3 % impact of the number of hidden states as displayed in figure 3 of
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4 % % Feature Preprocessing with RBMs for Music Similarity Learning
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5 % Son N. Tran, Daniel Wolff, Tillman Weyde, Artur Garcez, AES53rd
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6 % conference
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7 %
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8 % The output is printed in the console and plotted afterwards
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9 %
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10 % please note that the RBM training is a probabilistic process, and
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11 % thus the papers' results can only be reproduced approximately with
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12 % large numbers iterations of this script, and selection of RBMs according to
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13 % their training set performance.
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14 % ---
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15
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16 % this version reproduces the figure approximately using precomputed RBM
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17 [test(1), train(1)] = rbm_fig3('rbm_h30');
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18 [test(2), train(2)] = rbm_fig3('rbm_h50');
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19 [test(3), train(3)] = rbm_fig3('rbm_h100');
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20 [test(4), train(4)] = rbm_fig3('rbm_h500');
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21 [test(5), train(5)] = rbm_fig3('rbm_h1000');
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22
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23 % optionally, in order to test new RBMs, use the code below
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24 % [test(1), train(1)] = rbm_fig3(30);
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25 % [test(2), train(2)] = rbm_fig3(50);
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26 % [test(3), train(3)] = rbm_fig3(100);
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27 % [test(4), train(4)] = rbm_fig3(500);
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28 % [test(5), train(5)] = rbm_fig3(1000);
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29
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30 hidNum = [30 50 100 500 1000];
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31 hFig = figure;
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32 set(hFig,'Units','centimeters');
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33 set(hFig, 'Position', [10 10 10 6]);
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34 plot(hidNum,train*100,'--rx');
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35 hold on
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36 plot(hidNum,test*100,'-bo');
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37 lg = legend('Training','Test');
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38 set(lg,'Location','SouthEast');
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39 title ('Figure 4: GRADIENT results for different hidNum');
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