public Vec optimizeColumn(final FuncC1 func, final Vec codingColumn) {
final Vec mu = new ArrayVec(codingColumn.dim());
for (int i = 0; i < mu.dim(); i++) {
final double code = codingColumn.get(i);
if (code == 1.0) mu.set(i, 1.0);
else if (code == -1.0) mu.set(i, 0.0);
else mu.set(i, 0.5);
}
double error = 100500;
while (error > 1e-3) {
final Vec muPrev = VecTools.copy(mu);
final Vec gradient = func.gradient(mu);
VecTools.incscale(mu, gradient, -step);
for (int i = 0; i < mu.dim(); i++) {
final double code = codingColumn.get(i);
final double val = mu.get(i);
if (code == 1.0 || val > 1.0) {
mu.set(i, 1.0);
} else if (code == -1.0 || val < 0) {
mu.set(i, 0);
}
}
System.out.println(mu);
error = VecTools.norm(VecTools.subtract(muPrev, mu));
}
return new ArrayVec(codingColumn.dim());
}
@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;
}