Package gov.sandia.cognition.math.matrix

Examples of gov.sandia.cognition.math.matrix.Matrix.transpose()

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        final MultivariateGaussian predictivePrior = particle.getLinearState().clone();
        KalmanFilter kf = particle.getRegressionFilter(i);
        final Matrix G = kf.getModel().getA();
        predictivePrior.setMean(G.times(predictivePrior.getMean()));
        predictivePrior.setCovariance(
            G.times(predictivePrior.getCovariance()).times(G.transpose())
              .plus(kf.getModelCovariance()));
 
        // X * beta
        final double lambda = Math.exp(data.getObservedData().times(
            predictivePrior.getMean()).getElement(0));
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      final MultivariateGaussian predictivePrior = particle.getLinearState().clone();
      KalmanFilter kf = particle.getRegressionFilter(particle.getCategoryId());
      final Matrix G = kf.getModel().getA();
      predictivePrior.setMean(G.times(predictivePrior.getMean()));
      predictivePrior.setCovariance(
          G.times(predictivePrior.getCovariance()).times(G.transpose())
            .plus(kf.getModelCovariance()));
      final Matrix F = kf.getModel().getC();

      final UnivariateGaussian evComponent = particle.getEVcomponent();
      final double predPriorObsMean = F.times(predictivePrior.getMean()).getElement(0)
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      final Matrix F = kf.getModel().getC();

      final UnivariateGaussian evComponent = particle.getEVcomponent();
      final double predPriorObsMean = F.times(predictivePrior.getMean()).getElement(0)
          + evComponent.getMean();
      final double predPriorObsCov = F.times(predictivePrior.getCovariance()).times(F.transpose())
          .plus(kf.getMeasurementCovariance()).getElement(0, 0);
      particle.setPriorPredMean(predPriorObsMean);
      particle.setPriorPredCov(predPriorObsCov);

      double logLikelihood = UnivariateGaussian.PDF.logEvaluate(
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