Mercurial > hg > camir-aes2014
view core/magnatagatune/tests_evals/rbm_subspace/Exp_grad.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Experiment with gradient ascent % % Project: sub-euclidean distance for music similarity % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Load features %feature_file = 'rel_music_raw_features.mat'; feature_file = 'rel_music_raw_features+simdata_ISMIR12.mat'; vars = whos('-file', feature_file); A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); raw_features = A.(vars(1).name); indices = A.(vars(2).name); tst_inx = A.(vars(3).name); trn_inx = A.(vars(4).name); %% Params setting dmr = [0 5 10 20 30 50]; % dimension reduction by PCA ws = [0 5 10 20 30 50 70]; % window size % parameters of rbm (if it is used for extraction) hidNum = 0; lr_1 = 0; lr_2 = 0; mmt = 0; cost = 0; %% Select parameters (if grid-search is not applied) di = 1; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % If grid search is define % Define directory to save parameters & results if ~isempty(findstr('WIN',computer())) dir = 'C:\Pros\Experiments\ISMIR_2013\grad\'; % In windows platform dlm = '\'; elseif ~isempty(findstr('linux',computer())) || ~isempty(findstr('LNX',computer())) dir = '/home/funzi/Documents/Experiments/ISMIR_2013/grad/'; % In lunix platform dlm = '/'; end EXT_TYPE = 2; switch (EXT_TYPE) case 1 dir = strcat(dir,'pca',dlm); case 2 dir = strcat(dir,'rbm',dlm); hidNum = [100 500 1000 1200]; lr_1 = [0.5 0.7]; lr_2 = [0.7]; mmt = [0.1]; cost = [0.00002]; otherwise dir = strcat(dir,'none',dlm); end w_num = size(ws,2); for iiii = 1:200 % set the higher range to search for better features in case of ext using rbm log_file = strcat(dir,'exp',num2str(iiii),'.mat') inx = resume_from_grid(log_file,8 + w_num); if inx(end-w_num+1:end)==ones(1,w_num) max_= zeros(1,w_num); else max_ = inx(end-w_num+1:end); end results = zeros(1,w_num); W_max = cell(1,w_num); vB_max = cell(1,w_num); hB_max = cell(1,w_num); Ws_max = cell(1,w_num); for hi = inx(1):size(hidNum,2) for l1i = inx(2):size(lr_1,2) % for l1i = inx(3):size(lr_2,2) for mi = inx(4):size(mmt,2) for ci = inx(5):size(cost,2) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Feature extraction features = raw_features; switch (EXT_TYPE) case 1 % Using PCA assert(~exist('OCTAVE_VERSION'),'This script cannot run in octave'); coeff = princomp(raw_features); coeff = coeff(:,1:6); % best = 6 features = raw_features*coeff; % normalizing mm = minmax(features')'; inn= (find(mm(1,:)~=mm(2,:))); mm = mm(:,inn); features = features(:,inn); features = (features-repmat(mm(1,:),size(features,1),1))./(repmat(mm(2,:),size(features,1),1)-repmat(mm(1,:),size(features,1),1)); case 2 % Using rbm conf.hidNum = hidNum(hi); conf.eNum = 100; conf.sNum = size(raw_features,1); conf.bNum = 1; conf.gNum = 1; conf.params = [lr_1(l1i) lr_1(l1i) mmt(mi) cost(ci)]; conf.N = 50; conf.MAX_INC = 10; W1 = zeros(0,0); [W1 vB1 hB1] = training_rbm_(conf,W1,raw_features); features = logistic(raw_features*W1 + repmat(hB1,conf.sNum,1)); otherwise % normalizing % mm = minmax(features')'; % inn= (find(mm(1,:)~=mm(2,:))); % mm = mm(:,inn); % features = features(:,inn); % features = (features-repmat(mm(1,:),size(features,1),1))./(repmat(mm(2,:),size(features,1),1)-repmat(mm(1,:),size(features,1),1)); end for wi = inx(6):w_num %% Sub-euclidean computation w = ws(wi); % w = subspace window size num_case = size(trn_inx,1); [trnd_12 trnd_13] = subspace_distances(trn_inx,features,indices,w,0); [tstd_12 tstd_13] = subspace_distances(tst_inx,features,indices,w,0); cr_ = 0; % correct rate for training cr = 0; % correct rate for testing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% CODE HERE %% [Ws cr_] = gradient_ascent(trnd_12,trnd_13,0.1,0.1,0.00002); for i = 1:num_case cr = cr + sum((tstd_13{i}-tstd_12{i})*Ws{i}' > 0, 1)/size(tstd_12{i},1); end cr = cr/num_case; if cr_>max_(wi) max_(wi) = cr_; results(wi) = cr; if EXT_TYPE==2 W_max{wi} = W1; vB_max{wi} = vB1; hB_max{wi} = hB1; Ws_max{wi} = Ws; end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% fprintf('[window|train|test]= %2d |%f |%f\n',w,cr_,cr); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Using the logging function to save paramters % and the result for plotting or in grid search switch EXT_TYPE case 1 % logging(log_file,[100 100 100 100 100 wi cr_ cr max_]); case 2 logging(log_file,[hi l1i l1i mi ci wi cr_ cr max_ conf.hidNum conf.eNum conf.params]); otherwise logging(log_file,[100 100 100 100 100 wi cr_ cr max_]); end end inx(6)=1; end inx(5) = 1; end inx(4) = 1; end inx(2) = 1; end inx(1) = 1; %% Test on best features save(strcat(dir,'res_',num2str(iiii),'.mat'),'max_','results','W_max','vB_max','hB_max','Ws_max','ws'); [dummy pos] = max(max_); fprintf('Accuracy (RBM best fts): w = %d train = %f test = %f\n',ws(pos),max_(pos),results(pos)); clc; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear;