@Override
public Ensemble fit(final VecDataSet learn, final GlobalLoss globalLoss) {
final Vec cursor = new ArrayVec(globalLoss.xdim());
final List<Trans> weakModels = new ArrayList<>(iterationsCount);
final Trans gradient = globalLoss.gradient();
for (int t = 0; t < iterationsCount; t++) {
final Vec gradientValueAtCursor = gradient.trans(cursor);
final L2 localLoss = DataTools.newTarget(factory, gradientValueAtCursor, learn);
final Trans weakModel = weak.fit(learn, localLoss);
weakModels.add(weakModel);
invoke(new Ensemble(weakModels, -step));
VecTools.append(cursor, VecTools.scale(weakModel.transAll(learn.data()), -step));
}
return new Ensemble(weakModels, -step);
}
public CMLMetricOptimization(
final VecDataSet ds,
final BlockwiseMLLLogit target,
final Mx S,
final double c,
final double step) {
this.ds = ds;
this.target = target;
this.step = step;
this.classesIdxs = MCTools.splitClassesIdxs(target.labels());
this.laplacian = VecTools.copy(S);
VecTools.scale(laplacian, -1.0);
for (int i = 0; i < laplacian.rows(); i++) {
final double diagElem = VecTools.sum(S.row(i));
laplacian.adjust(i, i, diagElem);
}
this.c = c;
}
@Override
public Vec gradient(final Vec mu) {
final Vec grad = new ArrayVec(mu.dim());
for (int k = 0; k < grad.dim(); k++) {
final TIntList idxs = classesIdxs.get(k);
double val = 0.0;
for (final TIntIterator listIter = idxs.iterator(); listIter.hasNext(); ) {
final Vec x = ds.data().row(listIter.next());
final double trans = binClassifier.value(x);
final double sigmoid = MathTools.sigmoid(trans);
val -= (2 * sigmoid - 1) / (mu.get(k) * sigmoid + (1 - mu.get(k)) * (1 - sigmoid));
grad.set(k, val);
}
}
final double norm = VecTools.norm(grad);
VecTools.scale(grad, 1 / norm);
for (int k = 0; k < grad.dim(); k++) {
final double val = VecTools.multiply(laplacian.row(k), mu);
grad.adjust(k, val);
}
return grad;
}
