Package gov.sandia.cognition.statistics.bayesian

Examples of gov.sandia.cognition.statistics.bayesian.KalmanFilter

36
37
38
39
40
41
42
43
44
45
46
    final Random rng = new Random(829351983l);
   
    /*
     * 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[][] {
View Full Code Here
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
      Vector classProbs = (currentState == null) ? this.marginalClassProbs :
        this.classTransProbs.getColumn(currentClass);

      currentClass = DiscreteSamplingUtil.sampleIndexFromProbabilities(random, classProbs);

      KalmanFilter currentFilter = this.stateFilters.get(currentClass);
      if (currentState == null) {
        currentState = currentFilter.createInitialLearnedObject().sample(random);
      } else {
        final Matrix G = currentFilter.getModel().getA();
        Matrix modelCovSqrt = CholeskyDecompositionMTJ.create(
            (DenseMatrix) currentFilter.getModelCovariance()).getR();
        currentState = MultivariateGaussian.sample(G.times(currentState), modelCovSqrt, random);
      }
      currentState.plusEquals(currentFilter.getModel().getB().times(
          currentFilter.getCurrentInput()));

      final Matrix F = currentFilter.getModel().getC();
      Vector observationMean = F.times(currentState);
      Matrix measurementCovSqrt = CholeskyDecompositionMTJ.create(
          (DenseMatrix) currentFilter.getMeasurementCovariance()).getR();
      Vector observation = MultivariateGaussian.sample(observationMean,
          measurementCovSqrt, random);
 
      results.add(new SimHmmObservedValue<Vector, Vector>(i, currentClass,
          currentState.clone(), observation));
View Full Code Here
99
100
101
102
103
104
105
106
107
108
109
110
111
112
    return this.marginalClassProbs.getDimensionality();
  }

  @Override
  public MultivariateGaussian getEmissionFunction(MultivariateGaussian state, int classId) {
    KalmanFilter kf = this.stateFilters.get(classId);
    final Vector mean = kf.getModel().getC().times(state.getMean());
    final Matrix cov = kf.getModel().getC().times(state.getCovariance())
        .times(kf.getModel().getC().transpose())
        .plus(kf.getMeasurementCovariance());
    final MultivariateGaussian likelihood = new MultivariateGaussian(
        mean, cov);
    return likelihood;
  }
View Full Code Here
152
153
154
155
156
157
158
159
160
161
162
      Matrix F, Matrix G, Matrix  modelCovariance,
      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,
View Full Code Here
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
      /*
       * Fruewirth-Schnatter's method for upper utility sampling, where
       * instead of sampling the predictors, we use the mean.
       */
      final MultivariateGaussian predictivePrior = particle.getLinearState().clone();
      KalmanFilter kf = particle.getRegressionFilter();
      final Matrix G = kf.getModel().getA();
      final Matrix F = data.getObservedData();
      predictivePrior.setMean(G.times(predictivePrior.getMean()));
      predictivePrior.setCovariance(
          G.times(predictivePrior.getCovariance()).times(G.transpose())
            .plus(kf.getModelCovariance()));
      final Vector betaSample =
          predictivePrior.sample(getRandom());
//          predictivePrior.getMean();
      final double predPriorObsMean =
            F.times(betaSample).getElement(0);
View Full Code Here
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
  private LogitMixParticle sufficientStatUpdate(
      LogitMixParticle priorParticle, ObservedValue<Vector, Matrix> data) {
    final LogitMixParticle updatedParticle = priorParticle.clone();
   
    final KalmanFilter filter = updatedParticle.getRegressionFilter();
    final UnivariateGaussian evComponent = updatedParticle.EVcomponent;
    // TODO we should've already set this, so it might be redundant.
    filter.setMeasurementCovariance(
        MatrixFactory.getDefault().copyArray(new double[][] {{
          evComponent.getVariance()}}));

    final Vector sampledAugResponse = priorParticle.getAugResponseSample();
    final Vector diffAugResponse =
        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;
  }
View Full Code Here
108
109
110
111
112
113
114
115
116
117
118
      final DataDistribution<LogitMixParticle> initialParticles =
          CountedDataDistribution.create(true);
      for (int i = 0; i < numParticles; i++) {
       
        final MultivariateGaussian initialPriorState = initialPrior.clone();
        final KalmanFilter kf = this.initialFilter.clone();
        final int componentId = this.rng.nextInt(10);
        final UnivariateGaussian evDist = this.evDistribution.
            getDistributions().get(componentId);
       
        final LogitMixParticle particleMvgDPDist =
View Full Code Here
277
278
279
280
281
282
283
284
285
286
287
288
      GaussianArHpTransitionState prevState, int predClass, ObservedValue<Vector,Void> data) {
    /*
     * Perform the filtering step
     */
    MultivariateGaussian priorPredictedState = prevState.getState().clone();
    KalmanFilter kf = Iterables.get(prevState.getHmm().getStateFilters(), predClass);
    kf.predict(priorPredictedState);
   
    final DlmHiddenMarkovModel newHmm = prevState.getHmm().clone();
    final InverseGammaDistribution invScaleSS = prevState.getInvScaleSS().clone();
    final List<MultivariateGaussian> psiSS =
        ObjectUtil.cloneSmartElementsAsArrayList(prevState.getPsiSS());
View Full Code Here
158
159
160
161
162
163
164
165
166
167
168
      Matrix F, Matrix G, Matrix  modelCovariance,
      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,
View Full Code Here
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
        });
    for (Entry<LogitMixParticle, ? extends Number> particleEntry : target.asMap().entrySet()) {
      final LogitMixParticle particle = particleEntry.getKey();

      final MultivariateGaussian predictivePrior = particle.getLinearState().clone();
      KalmanFilter kf = particle.getRegressionFilter();
      final Matrix G = kf.getModel().getA();
      final Matrix F = data.getObservedData();
      predictivePrior.setMean(G.times(predictivePrior.getMean()));
      predictivePrior.setCovariance(
          G.times(predictivePrior.getCovariance()).times(G.transpose())
            .plus(kf.getModelCovariance()));
      final Vector betaMean = predictivePrior.getMean();

      final int particleCount;
      if (particleEntry.getValue() instanceof MutableDoubleCount) {
        particleCount = ((MutableDoubleCount)particleEntry.getValue()).count;
View Full Code Here
TOP

Related Classes of gov.sandia.cognition.statistics.bayesian.KalmanFilter

  • plm.ensemble.GaussianEnsFilter$GaussianEnsUpdater
  • plm.ensemble.GaussianEnsParticle
  • plm.gaussian.GaussianArHpWfPlFilter
  • plm.gaussian.GaussianArHpWfPlFilter$GaussianArHpWfPlUpdater
  • plm.gaussian.GaussianArHpWfPlFilterTest
  • plm.gaussian.GaussianArHpWfPlFilterV2
  • plm.gaussian.GaussianArHpWfPlFilterV2$GaussianArHpWfPlUpdater
  • plm.hmm.DlmHiddenMarkovModel
  • plm.hmm.gaussian.GaussianArHmmMkfFilterTest
  • plm.hmm.gaussian.GaussianArHpHmmFilterTest
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.