wolffd@0: function D = setDistanceDiagMKL(X, W, Ifrom, Ito)
wolffd@0: %
wolffd@0: % D = setDistanceDiagMKL(X, W, Ifrom, Ito)
wolffd@0: %
wolffd@0: %   X       = d-by-n data matrix
wolffd@0: %   W       = d-by-1 PSD matrix
wolffd@0: %   Ifrom   = k-by-1 vector of source points
wolffd@0: %   Ito     = j-by-1 vector of destination points
wolffd@0: %
wolffd@0: %   D = n-by-n matrix of squared euclidean distances from Ifrom to Ito
wolffd@0: %       D is sparse, and only the rows corresponding to Ifrom and
wolffd@0: %       columns corresponding to Ito are populated.
wolffd@0: 
wolffd@0:     [d,n,m]       = size(X);
wolffd@0:     L           = W.^0.5;
wolffd@0:    
wolffd@0:     D = 0;
wolffd@0:     for i = 1:m
wolffd@0:         Vfrom       = bsxfun(@times, L(:,i), X(:,Ifrom,i));
wolffd@0: 
wolffd@0:         if nargin == 4
wolffd@0:             Vto     = bsxfun(@times, L(:,i), X(:,Ito,i));
wolffd@0:         else
wolffd@0:             Vto     = bsxfun(@times, L(:,i), X(:,:,i));
wolffd@0:             Ito     = 1:n;
wolffd@0:         end
wolffd@0: 
wolffd@0:         D = D + distToFrom(n, Vto, Vfrom, Ito, Ifrom);
wolffd@0:     end
wolffd@0: end