Package plm.logit.fruehwirth

Examples of plm.logit.fruehwirth.LogitFSWFFilter.update()

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        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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        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 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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    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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