comparison toolboxes/distance_learning/mlr/distance/setDistanceDiagMKL.m @ 0:e9a9cd732c1e tip

first hg version after svn

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