Package plm.logit.fruehwirth

Examples of plm.logit.fruehwirth.LogitFSWFFilter$LogitFSWFUpdater

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    final Matrix G = MatrixFactory.getDefault().copyArray(new double[][] {
        {1d}});
    final Matrix modelCovariance = MatrixFactory.getDefault().copyArray(new double[][] {
        {1d}});

    final LogitFSWFFilter plFilter =
        new LogitFSWFFilter(initialPrior,
            F, G, modelCovariance, rng);
    plFilter.setNumParticles(2000);

    final DataDistribution<LogitMixParticle> currentMixtureDistribution =
        plFilter.createInitialLearnedObject();
    double lastRMSE = Double.POSITIVE_INFINITY;
    for (int i = 0; i < N; i++) {
      final ObservedValue<Vector, Matrix> observation = observations.get(i);
      log.info("obs:" + observation);
      plFilter.update(currentMixtureDistribution, observation);

      List<WeightedValue<Vector>> wMeanValues = Lists.newArrayList();
      List<WeightedValue<Matrix>> wCovValues = Lists.newArrayList();
      final Vector trueState = dlmSamples.get(i).getTrueState();
      double sum = 0d;
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        {0d, 1d}});
    final Matrix modelCovariance = MatrixFactory.getDefault().copyArray(new double[][] {
        {0d, 0d},
        {0d, 0d}});

    final LogitFSWFFilter plFilter =
        new LogitFSWFFilter(initialPrior,
            F, G, modelCovariance, rng);
    plFilter.setNumParticles(50);

    final DataDistribution<LogitMixParticle> currentMixtureDistribution =
        plFilter.createInitialLearnedObject();
    double lastRMSE = Double.POSITIVE_INFINITY;
    for (int i = 0; i < N; i++) {
      final ObservedValue<Vector, Matrix> observation = observations.get(i);
      log.info("obs:" + observation);
      plFilter.update(currentMixtureDistribution, observation);

      List<WeightedValue<Vector>> wMeanValues = Lists.newArrayList();
      List<WeightedValue<Matrix>> wCovValues = Lists.newArrayList();
      final Vector trueState = dlmSamples.get(i).getTrueState();
      double sum = 0d;
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    final Matrix G = MatrixFactory.getDefault().copyArray(new double[][] {
        {1d}});
    final Matrix modelCovariance = MatrixFactory.getDefault().copyArray(new double[][] {
        {0d}});

    final LogitFSWFFilter plFilter =
        new LogitFSWFFilter(initialPrior, F, G,
            modelCovariance, rng);
    plFilter.setNumParticles(1000);

    double lastRMSE = Double.POSITIVE_INFINITY;
    final DataDistribution<LogitMixParticle> currentMixtureDistribution =
        plFilter.createInitialLearnedObject();
    for (int i = 0; i < N; i++) {
      final ObservedValue<Vector, Matrix> observation = observations.get(i);
      log.info("obs:" + observation);
      plFilter.update(currentMixtureDistribution, observation);

      List<WeightedValue<Vector>> wMeanValues = Lists.newArrayList();
      List<WeightedValue<Matrix>> wCovValues = Lists.newArrayList();
      final Vector trueState = dlmSamples.get(i).getTrueState();
      double sum = 0d;
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        {1d, 0d},
        {0d, 1d}});
    final Matrix modelCovariance = MatrixFactory.getDefault().copyArray(new double[][] {
        {0d, 0d},
        {0d, 0d}});
    final LogitFSWFFilter plFilter =
        new LogitFSWFFilter(initialPrior, F, G, modelCovariance, rng);
    plFilter.setNumParticles(10000);

    final DataDistribution<LogitMixParticle> currentMixtureDistribution =
        plFilter.createInitialLearnedObject();
    for (int i = 0; i < N; i++) {
      final ObservedValue<Vector, Matrix> observation = observations.get(i);
      System.out.println("obs:" + observation);
      plFilter.update(currentMixtureDistribution, observation);

      /*
       * Compute some summary stats. TODO We need to compute something informative for this
       * situation.
       */
 
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Related Classes of plm.logit.fruehwirth.LogitFSWFFilter$LogitFSWFUpdater

  • gov.sandia.cognition.math.matrix.Matrix
  • gov.sandia.cognition.math.matrix.Vector
  • gov.sandia.cognition.math.signals.LinearDynamicalSystem
  • gov.sandia.cognition.statistics.bayesian.KalmanFilter
  • gov.sandia.cognition.statistics.distribution.MultivariateGaussian
  • gov.sandia.cognition.statistics.distribution.UnivariateGaussian
  • plm.logit.fruehwirth.LogitFSWFFilterTest
  • plm.util.logit.fruehwirth.FruehwirthLogitRunner
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