@Override
public double[] getLogLikelihoods(List<Beacon> beacons, State[] locations) {
List<Beacon> beaconsCleansed = Beacon.filterBeacons(beacons, minRssi, maxRssi);
beaconsCleansed = beaconFilter.setBLEBeacon(bleBeacons).filter(beaconsCleansed, locations);
BLEBeacon.setBLEBeaconIdsToMeasuredBeacons(bleBeacons, beaconsCleansed);
int[] activeBeaconList = ModelAdaptUtils.beaconsToActiveBeaconArray(beaconsCleansed);
final double[] ySub = ModelAdaptUtils.beaconsToVecSubset(beaconsCleansed);
final double[][] X = ModelAdaptUtils.locationsToMat(locations);
// Adjust bias by average bias
final double[] rssiBiases = ModelAdaptUtils.biasesToVec(locations);
double logLLs[] = null;
try {
Class<?> cls = gpLDPL.getClass();
Constructor<?> cst = cls.getConstructor(gpLDPL.getClass());
final GaussianProcessLDPLMean gpLDPLtmp = (GaussianProcessLDPLMean) cst.newInstance(gpLDPL);
gpLDPLtmp.updateByActiveBeaconList(activeBeaconList);
int n = X.length;
final double logpro[] = new double[n];
Future<?>[] futures = new Future<?>[n];
for (int i = 0; i < n; i++) {
final int idx = i;
futures[idx] =
ExecutorServiceHolder.getExecutorService()
.submit(
new Runnable() {
public void run() {
// Subtract bias from an observation vector.
double[] ySubAdjusted = ArrayUtils.addScalar(ySub, -rssiBiases[idx]);
logpro[idx] = gpLDPLtmp.logProbaygivenx(X[idx], ySubAdjusted);
}
});
}
for (int i = 0; i < n; i++) {
futures[i].get();
}
logLLs = logpro;
} catch (InstantiationException
| IllegalAccessException
| IllegalArgumentException
| InvocationTargetException
| NoSuchMethodException
| SecurityException e) {
e.printStackTrace();
} catch (InterruptedException | ExecutionException e) {
e.printStackTrace();
}
return logLLs;
}
