Package gov.sandia.cognition.math.matrix

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

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        thisKf.getModel().setB(offsetIdent);

        final Matrix measIdent = MatrixFactory.getDefault().createIdentity(
            thisKf.getModel().getOutputDimensionality(),
            thisKf.getModel().getOutputDimensionality());
        thisKf.setMeasurementCovariance(measIdent.scale(scaleSample));

        final Matrix modelIdent = MatrixFactory.getDefault().createIdentity(
            thisKf.getModel().getStateDimensionality(),
            thisKf.getModel().getStateDimensionality());
        thisKf.setModelCovariance(modelIdent.scale(scaleSample));
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        thisKf.setMeasurementCovariance(measIdent.scale(scaleSample));

        final Matrix modelIdent = MatrixFactory.getDefault().createIdentity(
            thisKf.getModel().getStateDimensionality(),
            thisKf.getModel().getStateDimensionality());
        thisKf.setModelCovariance(modelIdent.scale(scaleSample));

        final MultivariateGaussian priorState = thisKf.createInitialLearnedObject();
        final Vector priorStateSample = priorState.sample(this.rng);

        final GaussianArHpWfParticle particle =
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      // TODO FIXME: ewww.  inverse.
      final Vector postPhiMean = postAInv.times(priorAInv.times(phiPriorSmpl).plus(
          H.transpose().times(postStateSample)));
      final MultivariateGaussian postPhi = systemOffsetsSS;
      postPhi.setMean(postPhiMean);
      postPhi.setCovariance(postAInv.scale(newScaleSmpl));
     
      final Vector postPhiSmpl = postPhi.sample(this.rng);
      final Matrix smplArTerms = MatrixFactory.getDefault().createDiagonal(
          postPhiSmpl.subVector(
              postPhiSmpl.getDimensionality()/2,
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          kf.getModel().setB(offsetIdent);

          final Matrix measIdent = MatrixFactory.getDefault().createIdentity(
              kf.getModel().getOutputDimensionality(),
              kf.getModel().getOutputDimensionality());
          kf.setMeasurementCovariance(measIdent.scale(invScaleSample));

          final Matrix modelIdent = MatrixFactory.getDefault().createIdentity(
              kf.getModel().getStateDimensionality(),
              kf.getModel().getStateDimensionality());
          kf.setModelCovariance(modelIdent.scale(invScaleSample));
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          kf.setMeasurementCovariance(measIdent.scale(invScaleSample));

          final Matrix modelIdent = MatrixFactory.getDefault().createIdentity(
              kf.getModel().getStateDimensionality(),
              kf.getModel().getStateDimensionality());
          kf.setModelCovariance(modelIdent.scale(invScaleSample));
        }

        final KalmanFilter kf = Iterables.get(particlePriorHmm.getStateFilters(),
            sampledClass);
        final MultivariateGaussian priorState = kf.createInitialLearnedObject();
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      // TODO FIXME: ewww.  inverse.
      final Vector postPhiMean = postAInv.times(priorAInv.times(phiPriorSmpl).plus(
          H.transpose().times(postStateSample)));
      final MultivariateGaussian postPhi = systemOffsetsSS.get(predState.getClassId());
      postPhi.setMean(postPhiMean);
      postPhi.setCovariance(postAInv.scale(newInvScaleSmpl));
     
      final Vector postPhiSmpl = postPhi.sample(this.rng);
      final Matrix smplArTerms = MatrixFactory.getDefault().createDiagonal(
          postPhiSmpl.subVector(
              postPhiSmpl.getDimensionality()/2,
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     */
    final int centeringCovDof = 2 + 2;
    final Matrix centeringCovPriorMean =
        MatrixFactory.getDenseDefault().copyArray(new double[][] { {1000d, 0d}, {0d, 1000d}});
    final InverseWishartDistribution centeringCovariancePrior =
        new InverseWishartDistribution(centeringCovPriorMean.scale(centeringCovDof
            - centeringCovPriorMean.getNumColumns() - 1d), centeringCovDof);
    final MultivariateGaussian centeringMeanPrior =
        new MultivariateGaussian(VectorFactory.getDenseDefault().copyArray(new double[] {0d, 0d}),
            centeringCovariancePrior.getMean());
    final double centeringCovDivisor = 0.25d;
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