diff reproduce_AES53rd/rerun_figure4.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/reproduce_AES53rd/rerun_figure4.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,103 @@
+% ---
+% This script trains similarity measures and evaluates the 
+% impact of the number of hidden states as displayed in figure 4 of
+%
+% Feature Preprocessing with RBMs for Music Similarity Learning
+% Son N. Tran, Daniel Wolff, Tillman Weyde, Artur Garcez, AES53rd
+% conference 
+% 
+% please note that the RBM training is a probabilistic process, and 
+% thus the papers' results can only be reproduced approximately with 
+% some iterations of this script, and selection of RBMs according to
+% their training set performance.
+% This takes considerable time, and for the sake of usability of this script
+% is left our. % It is exemplarily done for hidnum = 1000  in the script "table 1" in the
+% correpsonding folder.
+%
+% % The train and test performances are plotted in a figure
+%
+% For convenicence, The precomputed RBM features are stored in the files
+% accompaining this script.
+% In order to compute new SVM features, delete these files.
+% ---
+
+global globalvars;
+globalvars.debug = 3;
+
+% ---
+% vary feature parameters of mixed features
+% ---
+        
+ftype = 'MTTMixedFeatureSonRBM'; %'MTTMixedFeatureStober11Genre';
+
+fparams_all = struct(...
+            ... % ---
+            ... % these are SONfeatRaw parameters
+            ... % ---
+            'son_filename',{{'rel_music_raw_features+simdata_ISMIR12.mat'}}, ...
+            'son_conf', 2, ...
+             ... % --- 
+            ... % Following: RBM params
+            ... % ---
+            'norm_pre_rbm', 0, ... % norm before RBM?
+            'norm_post_rbm',0, ... % norm before RBM?
+            'rbm_hidNum',[30 50 100 500 1000], ...   % number of hidden units % 500
+            'rbm_eNum', 100, ...
+            'rbm_bNum', 1, ...
+            'rbm_gNum', 1, ...
+            'rbm_lrate1' , [0.05], ... % initial learning rate % 0.01
+            'rbm_lrate2', [0.17], ... %  learning rate, %0.05  
+            'rbm_momentum', [0.1], ... % 0.5
+            'rbm_cost', [0.00002], ... % cost function
+            'rbm_N', 50, ...
+            'rbm_MAX_INC', 10 ...
+        );
+
+% ---
+% vary parameters for svmlight
+% ---    
+
+trainparams_all = struct(...
+            'C', [1], ...%    
+            'weighted', [0], ...
+            'dataset', {{'comp_partBinData_ISMIR12_01.mat'}}, ...
+            'inctrain', 0 ...
+            ... % this optional
+            ... %'deltafun', {{'conv_subspace_delta'}}, ...
+            ... %'deltafun_params', {{{[1],[0]},{[5],[1]},{[10],[1]},{[20],[1]},{[30],[1]},{[50],[1]},{[70],[1]}}} ... % normalisation improves results
+            );
+
+% set training function
+trainfun = @svmlight_wrapper;
+
+
+% create test directory
+akt_dir = migrate_to_test_dir();
+
+
+% call eval
+out = test_generic_features_parameters_crossval...
+    (fparams_all, trainparams_all, trainfun, ftype);
+
+% get number of Hidden Units
+fparams = [out(:).fparams];
+hidNum = [fparams(:).rbm_hidNum];
+[hidNum,idx] =sort(hidNum);
+
+% get sorted test data results
+svm_test_results = [out(idx).mean_ok_test];
+svm_test_results = svm_test_results(1,:);
+
+% get sorted train data results
+svm_train_results = [out(idx).mean_ok_train];
+svm_train_results = svm_train_results(1,:);
+
+% ---
+% plot results
+% ---
+figure;
+plot(hidNum,svm_train_results,'r-');
+hold on
+plot(hidNum,svm_test_results,'b');
+legend('Training','Test');
+title ('Figure 4: SVM results for different hidNum');