Mercurial > hg > camir-aes2014
annotate toolboxes/distance_learning/mlr/distance/setDistanceDiag.m @ 0:e9a9cd732c1e tip
first hg version after svn
| author | wolffd |
|---|---|
| date | Tue, 10 Feb 2015 15:05:51 +0000 |
| parents | |
| children |
| rev | line source |
|---|---|
| wolffd@0 | 1 function D = setDistanceDiag(X, W, Ifrom, Ito) |
| wolffd@0 | 2 % |
| wolffd@0 | 3 % D = setDistanceDiag(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] = size(X); |
| wolffd@0 | 15 L = W.^0.5; |
| wolffd@0 | 16 |
| wolffd@0 | 17 Vfrom = bsxfun(@times, L, X(:,Ifrom)); |
| wolffd@0 | 18 |
| wolffd@0 | 19 if nargin == 4 |
| wolffd@0 | 20 Vto = bsxfun(@times, L, X(:,Ito)); |
| wolffd@0 | 21 else |
| wolffd@0 | 22 Vto = bsxfun(@times, L, X); |
| wolffd@0 | 23 Ito = 1:n; |
| wolffd@0 | 24 end |
| wolffd@0 | 25 |
| wolffd@0 | 26 D = distToFrom(n, Vto, Vfrom, Ito, Ifrom); |
| wolffd@0 | 27 end |