diff toolboxes/distance_learning/mlr/separationOracle/separationOracleAUC.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/distance_learning/mlr/separationOracle/separationOracleAUC.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,47 @@
+function [Y, Loss] = separationOracleAUC(q, D, pos, neg, k)
+%
+%   [Y,Loss]  = separationOracleAUC(q, D, pos, neg, k)
+%
+%   q   = index of the query point
+%   D   = the current distance matrix
+%   pos = indices of relevant results for q
+%   neg = indices of irrelevant results for q
+%   k   = length of the list to consider (unused in AUC)
+%
+%   Y is a permutation 1:n corresponding to the maximally
+%   violated constraint
+%
+%   Loss is the loss for Y, in this case, 1-AUC(Y)
+
+
+    % First, sort the documents in descending order of W'Phi(q,x)
+    % Phi = - (X(q) - X(x)) * (X(q) - X(x))'
+    
+    % Sort the positive documents
+    ScorePos        = - D(pos,q);
+    [Vpos, Ipos]    = sort(full(ScorePos'), 'descend');
+    Ipos            = pos(Ipos);
+    
+    % Sort the negative documents
+    ScoreNeg        = - D(neg,q);
+    [Vneg, Ineg]    = sort(full(ScoreNeg'), 'descend');
+    Ineg            = neg(Ineg);
+
+
+    % How many pos and neg documents are we using here?
+    numPos  = length(pos);
+    numNeg  = length(neg);
+    n       = numPos + numNeg;
+
+
+    NegsBefore = sum(bsxfun(@lt, Vpos, Vneg' + 0.5),1);
+
+    % Construct Y from NegsBefore
+    Y                           = nan * ones(n,1);
+    Y((1:numPos) + NegsBefore)  = Ipos;
+    Y(isnan(Y))                 = Ineg;
+
+    % Compute AUC loss for this ranking
+    Loss = 1 - sum(NegsBefore) / (numPos * numNeg * 2);
+end
+