public double sumLoss(
List<Pair<Matrix>> pairs, Matrix u, Matrix w, Matrix bias, BilinearLearnerParameters params) {
LossFunction loss = params.getTyped(BilinearLearnerParameters.LOSS);
loss = new MatLossFunction(loss);
double total = 0;
int i = 0;
int ntasks = 0;
for (Pair<Matrix> pair : pairs) {
Matrix X = pair.firstObject();
Matrix Y = pair.secondObject();
SparseMatrix Yexp = BilinearSparseOnlineLearner.expandY(Y);
Matrix expectedAll = u.transpose().times(X.transpose()).times(w);
loss.setY(Yexp);
loss.setX(expectedAll);
if (bias != null) loss.setBias(bias);
logger.debug("Testing pair: " + i);
total += loss.eval(null); // Assums an identity w.
i++;
ntasks += Y.getNumColumns();
}
total /= ntasks;
return Math.sqrt(total);
}
