/**
* Compute loglikelihood, derivatives for all data, and derivatives at each data point Store in
* eachDerivatives
*
* @param w value of parameters
*/
public void computeAllValues(double[] w) {
// init eachDerivative
logli = new Loglikelihood(w.length);
eachDerivatives = new double[data.size()][w.length];
for (int i = 0; i < w.length; i++) {
for (int j = 0; j < data.size(); j++) {
eachDerivatives[j][i] = -(w[i] * featureGen.getParams().getInvSigmaSquare());
}
logli.logli -= ((w[i] * w[i]) * featureGen.getParams().getInvSigmaSquare()) / 2;
logli.derivatives[i] = -(w[i] * featureGen.getParams().getInvSigmaSquare());
}
ExtLogliComputer logliComp = new ExtLogliComputer(w, featureGen, data, logli, eachDerivatives);
Scheduler sch =
new Scheduler(
logliComp, featureGen.getParams().getNumthreads(), Scheduler.DYNAMIC_NEXT_AVAILABLE);
try {
sch.run();
} catch (Exception e) {
System.out.println("Errors occur when training in parallel! " + e);
}
// Change sign to maximize
for (int i = 0; i < w.length; i++) {
logli.derivatives[i] = -logli.derivatives[i] / data.size();
for (int j = 0; j < data.size(); j++) {
eachDerivatives[j][i] = -eachDerivatives[j][i] / data.size();
}
}
logli.logli = -logli.logli / data.size();
}
@Override
public double[] takeDerivative(double[] w, int index) {
Loglikelihood llh =
new Loglikelihood(
w.length); // can set loglikelihood any value; we dont care about this value
LogliComputer llc = new LogliComputer(w, featureGen, data, llh);
double[] result = ((Loglikelihood) llc.compute(index)).getDerivatives();
for (int i = 0; i < w.length; i++) {
result[i] -= (w[i] * featureGen.getParams().getInvSigmaSquare());
result[i] = -result[i] / data.size(); // Change sign to maximize
}
return result;
}
