camir-aes2014: toolboxes/distance_learning/mlr/distance/setDistanceDiagMKL.m annotate

first hg version after svn

rev	line source
wolffd@0	1 function D = setDistanceDiagMKL(X, W, Ifrom, Ito)
wolffd@0	2 %
wolffd@0	3 % D = setDistanceDiagMKL(X, W, Ifrom, Ito)
wolffd@0	4 %
wolffd@0	5 % X = d-by-n data matrix
wolffd@0	6 % W = d-by-1 PSD matrix
wolffd@0	7 % Ifrom = k-by-1 vector of source points
wolffd@0	8 % Ito = j-by-1 vector of destination points
wolffd@0	9 %
wolffd@0	10 % D = n-by-n matrix of squared euclidean distances from Ifrom to Ito
wolffd@0	11 % D is sparse, and only the rows corresponding to Ifrom and
wolffd@0	12 % columns corresponding to Ito are populated.
wolffd@0	13
wolffd@0	14 [d,n,m] = size(X);
wolffd@0	15 L = W.^0.5;
wolffd@0	16
wolffd@0	17 D = 0;
wolffd@0	18 for i = 1:m
wolffd@0	19 Vfrom = bsxfun(@times, L(:,i), X(:,Ifrom,i));
wolffd@0	20
wolffd@0	21 if nargin == 4
wolffd@0	22 Vto = bsxfun(@times, L(:,i), X(:,Ito,i));
wolffd@0	23 else
wolffd@0	24 Vto = bsxfun(@times, L(:,i), X(:,:,i));
wolffd@0	25 Ito = 1:n;
wolffd@0	26 end
wolffd@0	27
wolffd@0	28 D = D + distToFrom(n, Vto, Vfrom, Ito, Ifrom);
wolffd@0	29 end
wolffd@0	30 end

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