new HiddenMarkovModel<Double>(initialClassProbs,
classTransProbs, Lists.newArrayList(
s1Likelihood, s2Likelihood)));
final HmmPlFilter<StandardHMM<Double>, GaussianArTransitionState, Double> wfFilter =
new GaussianArHmmPlFilter(trueHmm1, prior, a, sigma2, sigma_y2, random, true);
final GaussianArHmmMkfFilter rsFilter =
new GaussianArHmmMkfFilter(trueHmm1, prior, a, sigma2, sigma_y2, random);
String outputFilename = args[0] + "/hmm-nar-wf-rs-10000-class-errors-m1.csv";
final int K = 10;
final int T = 300;
List<SimHmmObservedValue<Vector, Vector>> simulation = dlmHmm.sample(random, T);
wfFilter.setNumParticles(N);
wfFilter.setResampleOnly(false);
rsFilter.setNumParticles(N);
CSVWriter writer = new CSVWriter(new FileWriter(outputFilename), ',');
String[] header = "rep,t,filter.type,measurement.type,resample.type,measurement".split(",");
writer.writeNext(header);
GaussianArHmmClassEvaluator wfClassEvaluator = new GaussianArHmmClassEvaluator("wf-pl",
writer);
GaussianArHmmClassEvaluator mkfClassEvaluator = new GaussianArHmmClassEvaluator("mkf",
writer);
GaussianArHmmRmseEvaluator wfRmseEvaluator = new GaussianArHmmRmseEvaluator("wf-pl",
writer);
GaussianArHmmRmseEvaluator mkfRmseEvaluator = new GaussianArHmmRmseEvaluator("mkf",
writer);
RingAccumulator<MutableDouble> mkfLatency =
new RingAccumulator<MutableDouble>();
RingAccumulator<MutableDouble> wfLatency =
new RingAccumulator<MutableDouble>();
Stopwatch mkfWatch = new Stopwatch();
Stopwatch wfWatch = new Stopwatch();
for (int k = 0; k < K; k++) {
log.info("Processing replication " + k);
CountedDataDistribution<GaussianArTransitionState> wfDistribution =
(CountedDataDistribution<GaussianArTransitionState>) wfFilter.getUpdater().createInitialParticles(N);
// ((HmmPlFilter.HmmPlUpdater<Double>) wfFilter.getUpdater()).baumWelchInitialization(sample.getFirst(), N);
DataDistribution<GaussianArTransitionState> rsDistribution =
rsFilter.getUpdater().createInitialParticles(N);
final long numPreRuns = -1l;//wfDistribution.getMaxValueKey().getTime();
/*
* Recurse through the particle filter
*/
for (int i = 0; i < T; i++) {
final double x = simulation.get(i).getClassId();
final Double y = simulation.get(i).getObservedValue().getElement(0);
// lame hack need until I refactor to use DlmHiddenMarkovModel in the filters
final ObservedValue<Double, Void> obsState = new SimHmmObservedValue<Double, Double>(i,
(int)x , simulation.get(i).getState().getElement(0), y);
if (i > 0) {
mkfWatch.reset();
mkfWatch.start();
rsFilter.update(rsDistribution, obsState);
mkfWatch.stop();
final long latency = mkfWatch.elapsed(TimeUnit.MILLISECONDS);
mkfLatency.accumulate(new MutableDouble(latency));
writer.writeNext(new String[] {
Integer.toString(k), Integer.toString(i),
"mkf", "latency", "NA",
Long.toString(latency)
});
}
if (i > numPreRuns) {
if (i > 0) {
wfWatch.reset();
wfWatch.start();
wfFilter.update(wfDistribution, obsState);
wfWatch.stop();
final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
wfLatency.accumulate(new MutableDouble(latency));
writer.writeNext(new String[] {
Integer.toString(k), Integer.toString(i),