diff core/magnatagatune/tests_evals/rbm_subspace/Exp_grad.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/core/magnatagatune/tests_evals/rbm_subspace/Exp_grad.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,170 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% 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;