Package gov.sandia.cognition.math.signals

Examples of gov.sandia.cognition.math.signals.LinearDynamicalSystem

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        new double[][] {{Math.pow(0.2d, 2)}});
    Matrix modelCovariance2 = MatrixFactory.getDefault().copyArray(
        new double[][] {{Math.pow(1.2d, 2)}});
    Matrix measurementCovariance = MatrixFactory.getDefault().copyArray(
        new double[][] {{Math.pow(0.3d, 2)}});
    LinearDynamicalSystem model1 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{0.9d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1}})
      );
    LinearDynamicalSystem model2 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{0.9d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1}})
      );
    KalmanFilter trueKf1 = new KalmanFilter(model1, modelCovariance1, measurementCovariance);
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    List<Vector> truePsis = Lists.newArrayList(
        VectorFactory.getDefault().copyValues(3d, 0.2d),
        VectorFactory.getDefault().copyValues(1d, 0.9d));

    LinearDynamicalSystem model1 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{truePsis.get(0).getElement(1)}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}})
      );
    LinearDynamicalSystem model2 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{truePsis.get(1).getElement(1)}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}})
      );
    KalmanFilter trueKf1 = new KalmanFilter(model1, modelCovariance1, measurementCovariance);
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    Matrix measurementCovariance = MatrixFactory.getDefault().copyArray(
        new double[][] {{trueSigma2}});

    Vector truePsi = VectorFactory.getDefault().copyValues(3d, 0.2d);

    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);
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        new double[][] {{Math.pow(0.2d, 2)}});
    Matrix modelCovariance2 = MatrixFactory.getDefault().copyArray(
        new double[][] {{Math.pow(1.2d, 2)}});
    Matrix measurementCovariance = MatrixFactory.getDefault().copyArray(
        new double[][] {{Math.pow(0.3d, 2)}});
    LinearDynamicalSystem model1 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{0.9d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1}})
      );
    LinearDynamicalSystem model2 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{0.9d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1}})
      );
    KalmanFilter trueKf1 = new KalmanFilter(model1, modelCovariance1, measurementCovariance);
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    List<Vector> truePsis = Lists.newArrayList(
        VectorFactory.getDefault().copyValues(3d, 0.2d),
        VectorFactory.getDefault().copyValues(-1d, 0.9d));

    LinearDynamicalSystem model1 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{truePsis.get(0).getElement(1)}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}})
      );
    LinearDynamicalSystem model2 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{truePsis.get(1).getElement(1)}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}})
      );
    KalmanFilter trueKf1 = new KalmanFilter(model1, modelCovariance1, measurementCovariance);
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    List<Vector> truePsis = Lists.newArrayList(
        VectorFactory.getDefault().copyValues(3d, 0.2d),
        VectorFactory.getDefault().copyValues(-1d, 0.9d));

    LinearDynamicalSystem model1 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{truePsis.get(0).getElement(1)}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}})
      );
    LinearDynamicalSystem model2 = new LinearDynamicalSystem(
        MatrixFactory.getDefault().copyArray(new double[][] {{truePsis.get(1).getElement(1)}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}}),
        MatrixFactory.getDefault().copyArray(new double[][] {{1d}})
      );
    KalmanFilter trueKf1 = new KalmanFilter(model1, modelCovariance1, measurementCovariance);
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    /*
     * Sample from a logit model.
     */
    final KalmanFilter initialFilter = new KalmanFilter(
          new LinearDynamicalSystem(
              MatrixFactory.getDefault().copyArray(new double[][] {
                  {1d}}),
              MatrixFactory.getDefault().copyArray(new double[][] {
                  {0d}}),
              MatrixFactory.getDefault().copyArray(new double[][] {
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    /*
     * Sample from a logit model.
     */
    final KalmanFilter initialFilter = new KalmanFilter(
          new LinearDynamicalSystem(
              MatrixFactory.getDefault().copyArray(new double[][] {
                  {1d}}),
              MatrixFactory.getDefault().copyArray(new double[][] {
                  {0d}}),
              MatrixFactory.getDefault().copyArray(new double[][] {
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    /*
     * Sample from a logit model.
     */
    final KalmanFilter initialFilter = new KalmanFilter(
          new LinearDynamicalSystem(
              MatrixFactory.getDefault().copyArray(new double[][] {
                  {1d, 0d},
                  {0d, 1d}}),
              MatrixFactory.getDefault().copyArray(new double[][] {
                  {0d, 0d},
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    /*
     * Sample from a logit model.
     */
    final KalmanFilter initialFilter = new KalmanFilter(
          new LinearDynamicalSystem(
              MatrixFactory.getDefault().copyArray(new double[][] {
                  {1d}}),
              MatrixFactory.getDefault().copyArray(new double[][] {
                  {0d}}),
              MatrixFactory.getDefault().copyArray(new double[][] {
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Related Classes of gov.sandia.cognition.math.signals.LinearDynamicalSystem

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  • plm.gaussian.GaussianArHpWfPlFilterTest
  • plm.hmm.gaussian.GaussianArHmmMkfFilterTest
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  • plm.logit.fruehwirth.LogitFSWFFilter
  • plm.logit.fruehwirth.LogitFSWFFilterTest
  • plm.logit.fruehwirth.LogitMixParticle
  • plm.logit.fruehwirth.LogitParRBCWFFilter
  • plm.logit.fruehwirth.LogitParRBCWFFilterTest
  • plm.logit.fruehwirth.LogitRBCWFFilter
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