wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: % Experiment code templat                                                 %
wolffd@0: % Project: sub-euclidean distance for music similarity                    %
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: %% Load features
wolffd@0: feature_file = 'rel_music_raw_features.mat';
wolffd@0: vars = whos('-file', feature_file);
wolffd@0: A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name);
wolffd@0: raw_features = A.(vars(1).name);
wolffd@0: indices      = A.(vars(2).name);
wolffd@0: tst_inx      = A.(vars(3).name);
wolffd@0: trn_inx      = A.(vars(4).name);
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: % Define directory to save parameters & results
wolffd@0: % dir    = '/home/funzi/Documents/';
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: dmr    = [0 5 10 20 30 50];    % dimension reduction by PCA
wolffd@0: ws     = [0 5 10 20 30 50 70]; % window size
wolffd@0: % parameters of rbm (if it is used for extraction)
wolffd@0: hidNum = [30 50 100 500];
wolffd@0: lr_1   = [0.01 0.05 0.1 0.5];
wolffd@0: lr_2   = [0.05 0.1 0.5 0.7];
wolffd@0: mmt    = [0.02 0.05 0.1 0.5];
wolffd@0: cost   = [0.00002 0.01 0.1];
wolffd@0: 
wolffd@0: %% Select parameters (if grid-search is not applied)
wolffd@0: di  = 1;
wolffd@0: wi  = 1;
wolffd@0: hi  = 4;
wolffd@0: l1i = 1;
wolffd@0: l2i = 1;
wolffd@0: mi  = 4;
wolffd@0: ci  = 1;
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: % If grid search is define
wolffd@0: % log_file = strcat(dir,'exp_.mat');
wolffd@0: % inx = resume_from_grid(log_file,8);
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: %% Feature extraction
wolffd@0: EXT_TYPE = 2;
wolffd@0: switch (EXT_TYPE)
wolffd@0:     case 1  % Using PCA
wolffd@0:         assert(~exist('OCTAVE_VERSION'),'This script cannot run in octave');
wolffd@0:         coeff = princomp(raw_features);
wolffd@0:         coeff = coeff(:,1:end-dmr(di));  % Change value of dmr(di) to reduce the dimensionality
wolffd@0:         features = raw_features*coeff;
wolffd@0:           % normalizing
wolffd@0:         mm = minmax(features')';
wolffd@0:         inn= (find(mm(1,:)~=mm(2,:)));
wolffd@0:         mm = mm(:,inn);
wolffd@0:         features = features(:,inn);
wolffd@0:         features = (features-repmat(mm(1,:),size(features,1),1))./(repmat(mm(2,:),size(features,1),1)-repmat(mm(1,:),size(features,1),1));
wolffd@0:         
wolffd@0:         % describe config
wolffd@0:         s_conf = xml_format(dmr(di));
wolffd@0:     case 2  % Using rbm
wolffd@0:         conf.hidNum = hidNum(hi);
wolffd@0:         conf.eNum   = 100;
wolffd@0:         conf.sNum   = size(raw_features,1);
wolffd@0:         conf.bNum   = 1;
wolffd@0:         conf.gNum   = 1;
wolffd@0:         conf.params = [lr_1(l1i) lr_2(l2i) mmt(mi) cost(ci)];
wolffd@0:         conf.N    = 50;
wolffd@0:         conf.MAX_INC = 10;
wolffd@0:         W1 = zeros(0,0);
wolffd@0:         [W1 vB1 hB1] = training_rbm_(conf,W1,raw_features);
wolffd@0:         features = logistic(raw_features*W1 + repmat(hB1,conf.sNum,1));
wolffd@0:         
wolffd@0:         % describe config
wolffd@0:         s_conf = xml_format(conf);
wolffd@0: end
wolffd@0: 
wolffd@0: correct = 0;   % correct rate
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: %% CODE HERE                               %%
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: nfvec = features';
wolffd@0: feature_file = 'son_out_files.mat';
wolffd@0: save(feature_file,'nfvec');
wolffd@0: 
wolffd@0: 
wolffd@0: % the calling of daniels script
wolffd@0: ftype = 'MTTMixedFeatureSon';
wolffd@0: global sonfeatbase;
wolffd@0: sonfeatbase = [];
wolffd@0: 
wolffd@0: global db_MTTMixedFeatureSon
wolffd@0: db_MTTMixedFeatureSon.reset
wolffd@0: 
wolffd@0: global globalvars;
wolffd@0: globalvars.debug = 2
wolffd@0: 
wolffd@0: fparams_all.son_conf = {s_conf};
wolffd@0: fparams_all.son_filename = {feature_file};
wolffd@0: 
wolffd@0: % ---
wolffd@0: % vary parameters for svmlight
wolffd@0: % ---    
wolffd@0: 
wolffd@0: trainparams_all = struct(...
wolffd@0:             'C', [0.05 0.1 3 10 100], ...   %you just want to try this parameter approx (0.01..........1000) (3-100 is nice for daniel)
wolffd@0:             ...               
wolffd@0:             'weighted', [0], ...
wolffd@0:             'dataset',{{'comp_partBinData_cupaper_01'}}, ...
wolffd@0:             'inctrain', 0, ...
wolffd@0:             'deltafun', {{'conv_subspace_delta'}}, ...
wolffd@0:             'deltafun_params', {{{[0],[1]},{[5],[1]},{[20],[1]},{[40],[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 dirxectory
wolffd@0: akt_dir = migrate_to_test_dir();
wolffd@0: 
wolffd@0: % call eval
wolffd@0: result = test_generic_features_parameters_crossval...
wolffd@0:     (fparams_all, trainparams_all, trainfun, ftype);
wolffd@0: 
wolffd@0: % correct = result.mean_ok_test(1,1);
wolffd@0: % fprintf('Correct = %f\n',correct);
wolffd@0: 
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: % Using the logging function to save paramters
wolffd@0: % and the result for plotting or in grid search
wolffd@0: % logging(log_file,[i1 i2 i3 i4 i5 correct]);
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: 
wolffd@0: 
wolffd@0: