Package gov.sandia.cognition.statistics.bayesian

Examples of gov.sandia.cognition.statistics.bayesian.KalmanFilter

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  private FruehwirthMultiParticle sufficientStatUpdate(
      FruehwirthMultiParticle priorParticle, ObservedValue<Vector, Matrix> data) {
    final FruehwirthMultiParticle updatedParticle = priorParticle.clone();
   
    final Vector sampledAugResponse = priorParticle.getAugResponseSample();
    final KalmanFilter filter = updatedParticle.getRegressionFilter(
        priorParticle.getCategoryId());
    final UnivariateGaussian evComponent = updatedParticle.EVcomponent;
    // TODO we should've already set this, so it might be redundant.
    filter.setMeasurementCovariance(
        MatrixFactory.getDefault().copyArray(new double[][] {{
          evComponent.getVariance()}}));

    final MultivariateGaussian posteriorState = updatedParticle.getLinearState();
    filter.update(posteriorState, sampledAugResponse);
   
    return updatedParticle;
  }
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    LogitPGParticle clone = (LogitPGParticle) super.clone();
    clone.previousParticle = this.previousParticle;
    // when do we ever need a deep copy?  we don't alter
    // the components of a kalman filter in place...
    clone.regressionFilter =
        new KalmanFilter(
            new LinearDynamicalSystem(
                this.regressionFilter.getModel().getA(),
                this.regressionFilter.getModel().getB(),
                this.regressionFilter.getModel().getC()),
            this.regressionFilter.getModelCovariance(),
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       * TODO when using FS aug sampling, we should probably go all the way
       * and replicate their beta sampling.
       * That would require a change here...
       */
      final MultivariateGaussian predictivePrior = particle.getLinearState().clone();
      KalmanFilter kf = particle.getRegressionFilter(particle.getCategoryId());
      final Matrix G = kf.getModel().getA();
      predictivePrior.setMean(G.times(predictivePrior.getMean()));
      predictivePrior.setCovariance(
          G.times(predictivePrior.getCovariance()).times(G.transpose())
            .plus(kf.getModelCovariance()));
      final Matrix F = kf.getModel().getC();

      final UnivariateGaussian evComponent = particle.getEVcomponent();
      final double predPriorObsMean = F.times(predictivePrior.getMean()).getElement(0)
          + evComponent.getMean();
      final double predPriorObsCov = F.times(predictivePrior.getCovariance()).times(F.transpose())
          .plus(kf.getMeasurementCovariance()).getElement(0, 0);
      particle.setPriorPredMean(predPriorObsMean);
      particle.setPriorPredCov(predPriorObsCov);

      double logLikelihood = UnivariateGaussian.PDF.logEvaluate(
            particle.getAugResponseSample().getElement(0),
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      final DataDistribution<FruehwirthMultiParticle> initialParticles =
          CountedDataDistribution.create(true);
      for (int i = 0; i < numParticles; i++) {
       
        final MultivariateGaussian initialPriorState = initialPrior.clone();
        final KalmanFilter kf = this.initialFilter.clone();
       
        /*
         * Without an observation, start with any category...
         */
        final int categoryId = this.rng.nextInt(this.numCategories);
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      Matrix F, Matrix G, Matrix  modelCovariance,
      int K, Random rng) {
    Preconditions.checkArgument(F.getNumRows() == 1);
    Preconditions.checkArgument(F.getNumColumns() == G.getNumRows());
    Preconditions.checkArgument(G.getNumColumns() == modelCovariance.getNumRows());
    this.initialFilter = new KalmanFilter(
            new LinearDynamicalSystem(
                G,
                MatrixFactory.getDefault().createMatrix(G.getNumRows(), G.getNumColumns()),
                F),
            modelCovariance,
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        });
    for (Entry<LogitPGParticle, ? extends Number> particleEntry : target.asMap().entrySet()) {
      final LogitPGParticle particle = particleEntry.getKey();

      final MultivariateGaussian predictivePrior = particle.getLinearState().clone();
      KalmanFilter kf = particle.getRegressionFilter();
      final Matrix G = kf.getModel().getA();
      final Matrix F = data.getObservedData();
      predictivePrior.setMean(G.times(predictivePrior.getMean()));
      predictivePrior.setCovariance(
          G.times(predictivePrior.getCovariance()).times(G.transpose())
            .plus(kf.getModelCovariance()));
      final Vector betaMean = predictivePrior.getMean();
     
      final double k_t = data.getObservedValue().getElement(0) - 1d/2d;

      final int particleCount;
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  private LogitPGParticle sufficientStatUpdate(
      LogitPGParticle priorParticle, ObservedValue<Vector, Matrix> data) {

    final LogitPGParticle updatedParticle = priorParticle.clone();
    final KalmanFilter filter = updatedParticle.getRegressionFilter();

    final MultivariateGaussian posteriorState = updatedParticle.getLinearState().clone();
    filter.update(posteriorState, updatedParticle.getAugResponseSample());

    updatedParticle.setLinearState(posteriorState);
   
    return updatedParticle;
  }
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      final DataDistribution<LogitPGParticle> initialParticles =
          CountedDataDistribution.create(true);
      for (int i = 0; i < numParticles; i++) {
       
        final MultivariateGaussian initialPriorState = initialPrior.clone();
        final KalmanFilter kf = this.initialFilter.clone();
       
        final LogitPGParticle particleMvgDPDist =
            new LogitPGParticle(null, kf, initialPriorState);
        initialParticles.increment(particleMvgDPDist);
      }
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    LinearDynamicalSystem dlm = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{truePsi.getElement(1)}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}})
      );
    KalmanFilter trueKf = new KalmanFilter(dlm, modelCovariance, measurementCovariance);
    trueKf.setCurrentInput(VectorFactory.getDefault().copyValues(truePsi.getElement(0)));
   
    final double sigmaPriorMean = Math.pow(0.4, 2);
    final double sigmaPriorShape = 2d;
    final double sigmaPriorScale = sigmaPriorMean*(sigmaPriorShape - 1d);
    final InverseGammaDistribution sigmaPrior = new InverseGammaDistribution(sigmaPriorShape,
        sigmaPriorScale);
   
    final Vector phiMean = VectorFactory.getDefault().copyArray(new double[] {
        0d, 0.8d
    });
    final Matrix phiCov = MatrixFactory.getDefault().copyArray(new double[][] {
        {2d + 4d * sigmaPriorMean, 0d},
        { 0d, 4d * sigmaPriorMean}
    });
    final MultivariateGaussian phiPrior = new MultivariateGaussian(phiMean, phiCov);

    final int K = 3;
    final int T = 200;
    final int N = 1000;

    final GaussianArHpWfPlFilter wfFilter =
        new GaussianArHpWfPlFilter(trueKf, sigmaPrior, phiPrior, random, K, true);

    /*
     * Note: replications are over the same set of simulated observations.
     */
    List<SimObservedValue<Vector, Matrix, Vector>> simulations = DlmUtils.sampleDlm(
        random, T, trueKf.createInitialLearnedObject(), trueKf);

    wfFilter.setNumParticles(N);

//    log.info("rep\tt\tfilter.type\tmeasurement.type\tresample.type\tmeasurement");

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              kf.getModel().getStateDimensionality(),
              kf.getModel().getStateDimensionality());
          kf.setModelCovariance(modelIdent.scale(invScaleSample));
        }

        final KalmanFilter kf = Iterables.get(particlePriorHmm.getStateFilters(),
            sampledClass);
        final MultivariateGaussian priorState = kf.createInitialLearnedObject();
        final Vector priorStateSample = priorState.sample(this.rng);

        final GaussianArHpTransitionState particle =
            new GaussianArHpTransitionState(particlePriorHmm, sampledClass,
                ObservedValue.<Vector>create(0, null), priorState,
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Related Classes of gov.sandia.cognition.statistics.bayesian.KalmanFilter

  • plm.ensemble.GaussianEnsFilter$GaussianEnsUpdater
  • plm.ensemble.GaussianEnsParticle
  • plm.gaussian.GaussianArHpWfPlFilter
  • plm.gaussian.GaussianArHpWfPlFilter$GaussianArHpWfPlUpdater
  • plm.gaussian.GaussianArHpWfPlFilterTest
  • plm.gaussian.GaussianArHpWfPlFilterV2
  • plm.gaussian.GaussianArHpWfPlFilterV2$GaussianArHpWfPlUpdater
  • plm.hmm.DlmHiddenMarkovModel
  • plm.hmm.gaussian.GaussianArHmmMkfFilterTest
  • plm.hmm.gaussian.GaussianArHpHmmFilterTest
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