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

first hg version after svn

+function D = setDistanceFull(X, W, Ifrom, Ito)
+%
+% D = setDistanceFull(X, W, Ifrom, Ito)
+%
+%   X       = d-by-n data matrix
+%   W       = d-by-d 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]       = size(X);
+    [vecs,vals] = eig(0.5 * (W + W'));
+    L           = real(abs(vals)).^0.5 * vecs';
+
+    Vfrom   = L * X(:,Ifrom);
+
+    if nargin == 4
+        Vto     = L * X(:,Ito);
+    else
+        Vto     = L * X;
+        Ito     = 1:n;
+    end
+
+    D = distToFrom(n, Vto, Vfrom, Ito, Ifrom);
+end