comparison reproduce_AES53rd/rerun_figure3.m @ 0:e9a9cd732c1e tip

first hg version after svn

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