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

first hg version after svn

+function D = setDistanceDODMKL(X, W, Ifrom, Ito)
+%
+% D = setDistanceDODMKL(X, W, Ifrom, Ito)
+%
+%   X       = d-by-n data matrix
+%   W       = m-by-m-by-n 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
+        for j = i:m
+            Vfrom       = bsxfun(@times, squeeze(L(i,j)), X(:,Ifrom,i) + X(:,Ifrom,j));
+
+            if nargin == 4
+                Vto     = bsxfun(@times, squeeze(L(i,j)), X(:,Ito,i) + X(:,Ito,j));
+            else
+                Vto     = bsxfun(@times, squeeze(L(i,j)), X(:,:,i) + X(:,:,j));
+                Ito     = 1:n;
+            end
+
+            if i == j
+                s = 0.5;
+            else
+                s = 1;
+            end
+
+            D = D + s * distToFrom(n, Vto, Vfrom, Ito, Ifrom);
+        end
+    end
+
+end