annotate toolboxes/distance_learning/mlr/loss/lossHinge.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function Xi = lossHinge(W, Psi, M, gradient)
wolffd@0 2 %
wolffd@0 3 % Xi = lossHinge(W, Psi, M, gradient)
wolffd@0 4 %
wolffd@0 5 % W: d*d metric
wolffd@0 6 % Psi: d*d feature matrix
wolffd@0 7 % M: the desired margin
wolffd@0 8 % gradient: if 0, returns the loss value
wolffd@0 9 % if 1, returns the gradient of the loss WRT W
wolffd@0 10
wolffd@0 11 Xi = max(0, M - sum(sum(W .* Psi)));
wolffd@0 12
wolffd@0 13 if gradient & Xi > 0
wolffd@0 14 Xi = -Psi;
wolffd@0 15 end
wolffd@0 16 end