wolffd@0: function [X, C, idx] = get_itml_deltas(r, in)
wolffd@0: % [X, C, idx] = get_itml_deltas(r, in)
wolffd@0: 
wolffd@0: %ITML Specs
wolffd@0: % C: 4 column matrix
wolffd@0: %      column 1, 2: index of constrained points.  Indexes between 1 and n
wolffd@0: %      column 3: 1 if points are similar, -1 if dissimilar
wolffd@0: %      column 4: right-hand side (lower or upper bound, depending on 
wolffd@0: %                   whether points are similar or dissimilar)
wolffd@0: %
wolffd@0: % X: (n x m) data matrix - each row corresponds to a single instance
wolffd@0: % ---
wolffd@0: % NOTE: X is thus input in transposed shape for the ITML algorithm
wolffd@0: % ---
wolffd@0: 
wolffd@0: % ---
wolffd@0: % NOTE: this preallocation is not complete
wolffd@0: % ---
wolffd@0: X = zeros(size(in,1), 0);
wolffd@0: C = zeros(0,4);
wolffd@0: idx = zeros(0,2);
wolffd@0: 
wolffd@0: for i = 1:size(r,1)
wolffd@0:     
wolffd@0:     % feature indexing
wolffd@0:     a = i;
wolffd@0:     
wolffd@0:     % check if ranking is valid
wolffd@0:     if ~isempty(r{i,1}) && ~isempty(r{i,2})&& ...
wolffd@0:         isempty(intersect(r{i,1}, r{i,2}));
wolffd@0:     
wolffd@0:         % ---
wolffd@0:         % NOTE / TODO: the follwing is intended for compability
wolffd@0:         %  both sides of the ranking may have more than one entry.
wolffd@0:         %  for the MTT database, the ranking may be correct, but the 
wolffd@0:         %  inequalities build from non-singular rankings are not
wolffd@0:         %  based on the actual data
wolffd@0:         % ---
wolffd@0:         for j = 1:numel(r{i,1})
wolffd@0:             b = r{i,1}(j);
wolffd@0:             
wolffd@0:             for k = 1:numel(r{i,2})
wolffd@0:                 c = r{i,2}(k);
wolffd@0: 
wolffd@0:                 % ---
wolffd@0:                 % get vector deltas
wolffd@0:                 % ---
wolffd@0:                 [dab] = in(:,a) - in(:,b);
wolffd@0:                 [dac] = in(:,a) - in(:,c);
wolffd@0:                 
wolffd@0:                 % ---
wolffd@0:                 % save deltas in new feature matrix
wolffd@0:                 % TODO: this method has duplicate entries
wolffd@0:                 %  if the pairs appear more than once
wolffd@0:                 %  index the data set and use more efficiently!!!
wolffd@0:                 % ---
wolffd@0:                 X = [X dab];
wolffd@0:                 idx(end+1,:) = [a b];
wolffd@0:                 iab = size(idx, 1);
wolffd@0:                 
wolffd@0:                 X = [X dac];
wolffd@0:                 idx(end+1,:) = [a c];
wolffd@0:                 iac = size(idx, 1);
wolffd@0:                 
wolffd@0:                 % ---
wolffd@0:                 % NOTE:
wolffd@0:                 % in terms of the constraint,
wolffd@0:                 %  this should mean: dac - dab >= 1
wolffd@0:                 %
wolffd@0:                 % 4th position cannot be 0, converges to Inf if > 1
wolffd@0:                 % -1,-1 learns the opposite of what constraitns say
wolffd@0:                 % ---
wolffd@0:                 C(end+1, :) = [iab iac -1 -1];
wolffd@0:             end
wolffd@0:         end
wolffd@0:     end
wolffd@0: end
wolffd@0: 
wolffd@0: % % ---
wolffd@0: % % NOTE: here, we transpose the X for usage i nthe training
wolffd@0: % % ---
wolffd@0: % X = X';
wolffd@0: 
wolffd@0: end
wolffd@0: