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());
}
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;
}
public void backward() {
Mx cnc = null;
if (bias_b != 0) {
cnc = leftContract(output);
} else {
cnc = VecTools.copy(output);
}
difference = MxTools.multiply(MxTools.transpose(cnc), activations);
for (int i = 0; i < difference.dim(); i++) {
difference.set(i, difference.get(i) / activations.rows());
}
input = MxTools.multiply(cnc, weights);
rectifier.grad(activations, activations);
for (int i = 0; i < input.dim(); i++) {
input.set(i, input.get(i) * activations.get(i));
if (dropoutFraction > 0) {
input.set(i, input.get(i) * dropoutMask.get(i));
}
}
}
public void forward() {
if (bias != 0) {
activations = leftExtend(input);
} else {
activations = VecTools.copy(input);
}
output = MxTools.multiply(activations, MxTools.transpose(weights));
rectifier.value(output, output);
if (dropoutFraction > 0) {
if (isTrain) {
dropoutMask = getDropoutMask();
for (int i = 0; i < output.dim(); i++) {
output.set(i, output.get(i) * dropoutMask.get(i));
}
} else {
for (int i = 0; i < output.dim(); i++) {
output.set(i, output.get(i) * (1 - dropoutFraction));
}
}
}
}
