comparison core/magnatagatune/sim_from_comparison_naive.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [sim, dissim, confidence] = sim_from_comparison_naive(comparison, comparison_ids, symmetrical)
+%
+%  [sim, dissim, confidence] = sim_from_comparison_naive(comparison)
+%
+% derives symmetric, absolute similarity measurements
+% from relative magnatagatune comparisons
+% naive implementation for first tests of the ITML algorithm
+%
+
+% reindex comparison for more simple evaluation
+% makro_prepare_comparison
+
+% ---
+% analyse the number of comparisons for each pair of songs
+% ---
+[num_compares] = get_comparison_stats(comparison, comparison_ids);
+
+% ---
+% in comparison, the outlying piece is highlighted. 
+% thus, we naively consider that
+% a. both of the remaining pieces are more similar to each other.
+% b. the outlier is dissimilar to both of the other pieces
+% ---
+[outsort, outidx] = sort(comparison(:,4:6),2,'ascend');
+
+% ---
+% similarity of the two non-outliers a, b 
+% they are similar if both of them have scores way smaller 
+% than the outlier c:
+% score (a,b) = 1 - (max(a,b)/c)
+%
+% dissimilarity: clip b is considered more different to clip c than
+% a, as clip a seems to share some properties with both songs
+% dissim(b,c) = 0.5 + b/(2c)
+% ---
+
+sim = sparse(numel(comparison_ids),numel(comparison_ids));
+dissim = sparse(numel(comparison_ids),numel(comparison_ids));
+for i = 1:size(comparison,1)
+    
+    % get the outlier votes
+    simpair = comparison(i,outidx(i,1:2));
+    c = comparison(i,outidx(i,3));
+    
+    % we want a triangular similarity matrix
+    [simpair, simidx] = sort(simpair);
+    outsort(i,1:2) = outsort(i,simidx);
+    
+    % ---
+    % save the distance between the second biggest vote and the max vote.
+    % NOTE: we bias the vote by dividing through the number of total
+    % comparisons for the particular pair of clips
+    % ---
+    sim(simpair(1), simpair(2)) = sim(simpair(1), simpair(2)) +  ...
+        (1 - outsort(i,2) / outsort(i,3)) * (1 / num_compares(simpair(1),simpair(2)));
+    
+    dissim(simpair(1:2), c) = 0.5 + (outsort(i,1:2) ./ (2 * outsort(i,3)));
+end
+
+% ---
+% mirror to make matrix symmetrical 
+% ---
+if nargin == 3 && symmetrical
+    sim = sim + sim';
+    dissim = dissim + dissim';
+end
+
+% ---
+% TODO: use number of votes and std or similar to 
+% rate the confidence for each similarity mesurement
+% ---
+confidence = [];
+