public void train(List<Sample> samples) {
BLEBeacon.setBLEBeaconIdsToSamples(bleBeacons, samples);
List<List<Sample>> samplesList = Sample.samplesToConsecutiveSamplesList(samples);
if (doHyperParameterOptimize) {
doHyperParameterOptimize = false;
trainWithHyperParameters(samplesList);
}
List<Sample> samplesAvaragedTrain = Sample.meanTrimList(samplesList, minRssi);
this.trainByAveragedSamples(samplesAvaragedTrain);
}
public static List<BLEBeacon> readMapBLEBeacons(InputStream is) {
int z = 0;
List<BLEBeacon> bleBeacons = new ArrayList<>();
try {
JSONObject json = (JSONObject) JSON.parse(is);
String type = json.getString("type");
if (type.equals("FeatureCollection")) {
JSONArray features = json.getJSONArray("features");
Iterator<JSONObject> iter = features.iterator();
while (iter.hasNext()) {
JSONObject feature = iter.next();
JSONObject properties = feature.getJSONObject("properties");
JSONObject geometry = feature.getJSONObject("geometry");
type = properties.getString("type");
if (type.equals("beacon")) {
int major = Integer.parseInt(properties.getString("major"));
int minor = Integer.parseInt(properties.getString("minor"));
String uuid = properties.getString("uuid");
int outPower = Integer.parseInt(properties.getString("power"));
int interval = Integer.parseInt(properties.getString("interval"));
double x = geometry.getJSONArray("coordinates").getInt(0);
double y = geometry.getJSONArray("coordinates").getInt(1);
BLEBeacon bleBeacon = new BLEBeacon(minor, major, minor, x, y, z);
bleBeacon.setUuid(uuid);
bleBeacon.setOutputPower(outPower);
bleBeacons.add(bleBeacon);
}
}
}
} catch (NullPointerException | JSONException e) {
e.printStackTrace();
}
return bleBeacons;
}
@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;
}
protected void trainByAveragedSamples(List<Sample> samples) {
int nBeacons = bleBeacons.size();
BLEBeacon.setBLEBeaconIdsToSamples(bleBeacons, samples);
setupGPLDPL();
this.samples = samples;
double[][] X = ModelAdaptUtils.samplesToLocationsMat(samples);
double[][] Y = ModelAdaptUtils.samplesToBeaconsListMat(samples, minRssi, nBeacons);
double[][] bLocs = ModelAdaptUtils.BLEBeaconsToMat(bleBeacons, nBeacons);
gpLDPL.setSourceLocs(bLocs);
int[] actBecList = new int[nBeacons];
for (int i = 0; i < nBeacons; i++) actBecList[i] = i;
gpLDPL.setActiveBeaconList(actBecList);
gpLDPL.fit(X, Y);
}
