Package gov.sandia.cognition.math.signals

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

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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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      int numCategories, 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,
            MatrixFactory.getDefault().copyArray(new double[][] {{0}})   
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    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(),
            this.regressionFilter.getMeasurementCovariance());
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      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,
            MatrixFactory.getDefault().copyArray(new double[][] {{0}})   
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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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  @Override
  public GaussianEnsParticle clone() {
    GaussianEnsParticle clone = (GaussianEnsParticle) super.clone();
    clone.kf =
        new KalmanFilter(
            new LinearDynamicalSystem(
                this.kf.getModel().getA(),
                this.kf.getModel().getB(),
                this.kf.getModel().getC()),
            this.kf.getModelCovariance(),
            this.kf.getMeasurementCovariance());
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    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(),
            this.regressionFilter.getMeasurementCovariance());
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      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,
            MatrixFactory.getDefault().copyArray(new double[][] {{0}})   
View Full Code Here
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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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      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,
            MatrixFactory.getDefault().copyArray(new double[][] {{0}})   
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Related Classes of gov.sandia.cognition.math.signals.LinearDynamicalSystem

  • plm.ensemble.GaussianEnsParticle
  • plm.gaussian.GaussianArHpWfPlFilterTest
  • plm.hmm.gaussian.GaussianArHmmMkfFilterTest
  • plm.hmm.gaussian.GaussianArHpHmmFilterTest
  • 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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