Examples of gov.sandia.cognition.statistics.bayesian.KalmanFilter.update()

Package gov.sandia.cognition.statistics.bayesian

Class gov.sandia.cognition.statistics.bayesian.KalmanFilter

Examples of gov.sandia.cognition.statistics.bayesian.KalmanFilter.update()

gov.sandia.cognition.statistics.bayesian.KalmanFilter.update()

    // TODO FIXME gross hack!
//    posteriorState.getCovariance().scaleEquals(sigma2Sample);
//    kf.setMeasurementCovariance(Iy.scale(sigma2Sample));
//    kf.setModelCovariance(Ix.scale(sigma2Sample));
    kf.predict(posteriorState);
    kf.update(posteriorState, data.getObservedValue());
//    kf.setMeasurementCovariance(Iy);
//    kf.setModelCovariance(Ix);
    


    final GaussianArHpWfParticle newTransState =

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    filter.setMeasurementCovariance(
        MatrixFactory.getDefault().copyArray(new double[][] {{
          evComponent.getVariance()}}));


    final MultivariateGaussian posteriorState = updatedParticle.getLinearState();
    filter.update(posteriorState, sampledAugResponse);
    
    return updatedParticle;
  }

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    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 MultivariateGaussian posteriorState = predState.getState().clone();
      final DlmHiddenMarkovModel newHmm = predState.getHmm().clone();
      KalmanFilter kf = Iterables.get(newHmm.getStateFilters(), 
          predState.getClassId());
      kf.update(posteriorState, predState.getObservation().getObservedValue());




      /*
       * The following are the parameter learning updates;
       * they can be done off-line, but we'll do them now.

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            - partComponent.getMean().doubleValue());


    updatedParticle.setAugResponseSample(sampledAugResponse); 


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


    updatedParticle.setLinearState(posteriorState);
    
    return updatedParticle;
  }

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    public GaussianArHpWfParticle update(
      GaussianArHpWfParticle predState) {


      final MultivariateGaussian posteriorState = predState.getState().clone();
      final KalmanFilter kf = predState.getFilter().clone();
      kf.update(posteriorState, predState.getObservation().getObservedValue());


      /*
       * The following are the parameter learning updates;
       * they can be done off-line, but we'll do them now.
       * TODO FIXME check that the input/offset thing is working!

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        sampledAugResponse.minus(VectorFactory.getDefault().copyArray(
        new double[] {
            evComponent.getMean().doubleValue()
            }));
    final MultivariateGaussian posteriorState = updatedParticle.getLinearState().clone();
    filter.update(posteriorState, 
        diffAugResponse);
    updatedParticle.setLinearState(posteriorState);
    
    return updatedParticle;
  }

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            - partComponent.getMean().doubleValue());


    updatedParticle.setAugResponseSample(sampledAugResponse); 


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


    updatedParticle.setLinearState(posteriorState);
    
    return updatedParticle;
  }

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