wolffd@0: % ---
wolffd@0: % this script trains similarity measures using RBM and SVM as in Table 3
wolffd@0: % please note that the RBM training is a probabilistic process.
wolffd@0: % Here, training is done on 20 random initialisations of RBM features ,
wolffd@0: % the test results corresponding to the RBM with the best training result are then
wolffd@0: % returned.
wolffd@0: % ---
wolffd@0: 
wolffd@0: % ---
wolffd@0: % vary feature parameters of mixed features
wolffd@0: % ---
wolffd@0: 
wolffd@0: global globalvars;
wolffd@0: globalvars.debug = 3;
wolffd@0:         
wolffd@0: ftype = 'MTTMixedFeatureSonRBM'; %'MTTMixedFeatureStober11Genre';
wolffd@0: 
wolffd@0: fparams_all = struct(...
wolffd@0:             ... % ---
wolffd@0:             ... % these are SONfeatRaw parameters
wolffd@0:             ... % ---
wolffd@0:             'son_filename',{{'rel_music_raw_features+simdata_ISMIR12.mat'}}, ...
wolffd@0:             'son_conf', 1:5, ...
wolffd@0:              ... % --- 
wolffd@0:             ... % Following: RBM params
wolffd@0:             ... % ---
wolffd@0:             'norm_pre_rbm', 0, ... % norm before RBM?
wolffd@0:             'norm_post_rbm',0, ... % norm before RBM?
wolffd@0:             'rbm_hidNum',[1000], ...   % number of hidden units % 500
wolffd@0:             'rbm_eNum', 100, ...
wolffd@0:             'rbm_bNum', 1, ...
wolffd@0:             'rbm_gNum', 1, ...
wolffd@0:             'rbm_lrate1' , [0.05], ... % initial learning rate % 0.01
wolffd@0:             'rbm_lrate2', [0.10], ... %  learning rate, %0.05  
wolffd@0:             'rbm_momentum', [0.1], ... % 0.5
wolffd@0:             'rbm_cost', [0.00002], ... % cost function
wolffd@0:             'rbm_N', 50, ...
wolffd@0:             'rbm_MAX_INC', 10 ...
wolffd@0:         );
wolffd@0: 
wolffd@0: % ---
wolffd@0: % vary parameters for svmlight
wolffd@0: % ---    
wolffd@0: 
wolffd@0: trainparams_all = struct(...
wolffd@0:             'C', [1], ...%    
wolffd@0:             'weighted', [0], ...
wolffd@0:             'dataset', {{'comp_partBinData_ISMIR12_01.mat'}}, ...
wolffd@0:             'inctrain', 0 ...
wolffd@0:             ... % this optional
wolffd@0:             ... %'deltafun', {{'conv_subspace_delta'}}, ...
wolffd@0:             ... %'deltafun_params', {{{[1],[0]},{[5],[1]},{[10],[1]},{[20],[1]},{[30],[1]},{[50],[1]},{[70],[1]}}} ... % normalisation improves results
wolffd@0:             );
wolffd@0: 
wolffd@0: % set training function
wolffd@0: trainfun = @svmlight_wrapper;
wolffd@0: 
wolffd@0: 
wolffd@0: % create test directory
wolffd@0: akt_dir = migrate_to_test_dir();
wolffd@0: 
wolffd@0: 
wolffd@0: % call eval
wolffd@0: out = test_generic_features_parameters_crossval...
wolffd@0:     (fparams_all, trainparams_all, trainfun, ftype);
wolffd@0: 
wolffd@0: % ---
wolffd@0: % check training results and select best RBM according to trainign data
wolffd@0: % ---
wolffd@0: svm_train_performances = [out(:).mean_ok_train];
wolffd@0: [bestTrain, idx] = max(svm_train_performances(1,:));
wolffd@0: result = out(idx);
wolffd@0: 
wolffd@0: % get corresponding test performance
wolffd@0: svm_test_performance = result.mean_ok_test(1,:);
wolffd@0: fprintf('SVM RBM Test/Train Result=%f / %f\n',svm_test_performance*100,bestTrain*100);