Package gov.sandia.cognition.math.matrix

Examples of gov.sandia.cognition.math.matrix.Vector.outerProduct()

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       */
      final Vector Hv = H.convertToVector();
      /*
       * TODO FIXME inverse!  ewww.
       */
      final Matrix postAInv = priorAInv.plus(Hv.outerProduct(Hv)).inverse();
      final Vector postPsiMean = postAInv.times(priorAInv.times(psiPriorSmpl).plus(
          H.transpose().times(postStateSample)));
      final MultivariateGaussian postPsi = predState.getPsiSS().clone();
      postPsi.setMean(postPsiMean);
      postPsi.setCovariance(postAInv);
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      /*
       * TODO FIXME: we don't have a generalized outer product, so we're only
       * supporting the 1d case for now.
       */
      final Vector Hv = H.convertToVector();
      final Matrix postAInv = priorAInv.plus(Hv.outerProduct(Hv)).inverse();
      // TODO FIXME: ewww.  inverse.
      final Vector postPhiMean = postAInv.times(priorAInv.times(phiPriorSmpl).plus(
          H.transpose().times(postStateSample)));
      final MultivariateGaussian postPhi = systemOffsetsSS;
      postPhi.setMean(postPhiMean);
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            sampledComponentDist
                .getCovariance()
                .plus(data.outerProduct(data))
                .plus(oldComponentMean.outerProduct(oldComponentMean).scale(oldComponentCount))
                .minus(
                    updatedComponentMean.outerProduct(updatedComponentMean).scale(
                        updatedComponentCount));

        sampledComponentDist.setCovariance(updatedComponentSS);
        sampledComponentDist.setMean(updatedComponentMean);
      }
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        final Vector componentCenteringMeanDiff =
            centeringDist.getGaussian().getMean().minus(component.getMean());
        final Matrix componentD =
            component.getCovariance().plus(
                componentCenteringMeanDiff.outerProduct(componentCenteringMeanDiff).scale(
                    kappa * componentN / (kappa + componentN)));

        final Matrix componentPriorPredCovariance =
            centeringDist
                .getInverseWishart()
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      /*
       * TODO FIXME: we don't have a generalized outer product, so we're only
       * supporting the 1d case for now.
       */
      final Vector Hv = H.convertToVector();
      final Matrix postAInv = priorAInv.plus(Hv.outerProduct(Hv)).inverse();
      // 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);
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