diff core/magnatagatune/tests_evals/rbm_subspace/Exp_normalise_deltas.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_normalise_deltas.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,125 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Experiment code templat                                                 %
+% Project: sub-euclidean distance for music similarity,
+%          in the last part all the 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% Load features
+feature_file = 'rel_music_raw_features.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);
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Define directory to save parameters & results
+% dir    = '/home/funzi/Documents/';
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+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 = [30 50 100 500];
+lr_1   = [0.05 0.1 0.5];
+lr_2   = [0.1 0.5 0.7];
+mmt    = [0.02 0.05 0.1];
+cost   = [0.00002 0.01 0.1];
+
+%% Select parameters (if grid-search is not applied)
+di  = 1;
+wi  = 1;
+hi  = 1;
+l1i = 1;
+l2i = 1;
+mi  = 1;
+ci  = 1;
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% If grid search is define
+% log_file = strcat(dir,'exp_.mat');
+% inx = resume_from_grid(log_file,8);
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% Feature extraction
+EXT_TYPE = 2;
+switch (EXT_TYPE)
+    case 1  % Using PCA
+        assert(~exist('OCTAVE_VERSION'),'This script cannot run in octave');
+        coeff = princomp(raw_features);
+        coeff = coeff(:,1:end-dmr(di));  % Change value of dmr(di) to reduce the dimensionality
+        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_2(l2i) 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 = raw_features*W1 + repmat(hB1,conf.sNum,1);
+end
+
+%% Sub-euclidean computation
+num_case = size(trn_inx,1);
+trnd_12 = cell(1,num_case);
+trnd_13 = cell(1,num_case);
+tstd_12 = cell(1,num_case);
+tstd_13 = cell(1,num_case);
+
+w = ws(wi);
+
+% w = subspace window size
+if w == 0 % trnd_12 = d(a,b) , trnd_13= d(a,c) 
+  for i = 1:num_case % over all cross-validation folds (num_case)
+    [trnd_12{i} trnd_13{i}] = simple_dist(trn_inx{i},features,indices);
+    [tstd_12{i} tstd_13{i}] = simple_dist(tst_inx{i},features,indices);
+  end
+else
+  for i = 1:num_case % for w > 1
+    [trnd_12{i} trnd_13{i}] = conv_euclidean_dist(trn_inx{i},features,indices,w,1);    %% normalize is better than no normalize  
+    [tstd_12{i} tstd_13{i}] = conv_euclidean_dist(tst_inx{i},features,indices,w,1);
+  end
+end
+%% Data preparation
+trn_dat1 = cell(1,num_case);
+trn_dat2 = cell(1,num_case);
+tst_dat1 = cell(1,num_case);
+tst_dat2 = cell(1,num_case);
+
+for i=1:num_case 
+ %=> Compute hypothesis
+ trn_dat1{i}  = trnd_13{i} - trnd_12{i};
+ trn_dat2{i}  = trnd_12{i} - trnd_13{i};
+ tst_dat1{i}   = tstd_13{i} - tstd_12{i};
+ tst_dat2{i}   = tstd_12{i} - tstd_13{i};
+ 
+
+ % ---
+ % Cheat: Normalize over all training and test delta values using min-max
+ % Son reports this can give about 95% accuracy
+ % ---
+ 
+ mm = minmax([trn_dat1{i};tst_dat1{i}]')';
+ inn= find(mm(1,:)~=mm(2,:));
+ mm = mm(:,inn);    
+ trn_dat1{i} =
+ (trn_dat1{i}(:,inn)-repmat(mm(1,:),size(trn_dat1{i},1),1))./repmat(mm(2,:)-mm(1,:),size(trn_dat1{i},1),1);
+ tst_dat1{i} = (tst_dat1{i}(:,inn)-repmat(mm(1,:),size(tst_dat1{i},1),1))./repmat(mm(2,:)-mm(1,:),size(tst_dat1{i},1),1);
+ 
+ mm = minmax([trn_dat2{i};tst_dat2{i}]');
+ inn= find(mm(1,:)~=mm(2,:));
+ mm = mm(:,inn);    
+ trn_dat2{i} =
+ (trn_dat2{i}(:,inn)-repmat(mm(1,:),size(trn_dat2{i},1),1))./repmat(mm(2,:)-mm(1,:),size(trn_dat2{i},1),1);
+ tst_dat2{i} = (tst_dat2{i}(:,inn)-repmat(mm(1,:),size(tst_dat2{i},1),1))./repmat(mm(2,:)-mm(1,:),size(tst_dat2{i},1),1);
+
+
+
+
+end