Examples of org.apache.commons.math3.linear.RealVector$SparseEntryIterator

org.apache.commons.math3.linear.RealVector
This class should rarely be used, but is here to provide a default implementation of sparseIterator(), which is implemented by walking over the entries, skipping those that are zero. Concrete subclasses which are SparseVector implementations should make their own sparse iterator, rather than using this one. This implementation might be useful for ArrayRealVector, when expensive operations which preserve the default value are to be done on the entries, and the fraction of non-default values is small (i.e. someone took a SparseVector, and passed it into the copy-constructor of ArrayRealVector)

  public double[] solveFToD(float[] b) {
    ArrayRealVector bVec = new ArrayRealVector(b.length);
    for (int i = 0; i < b.length; i++) {
      bVec.setEntry(i, b[i]);
    }
    RealVector vec = solver.solve(bVec);
    double[] result = new double[b.length];
    for (int i = 0; i < result.length; i++) {
      result[i] = vec.getEntry(i);
    }
    return result;
  }

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        // semi-definite matrix, we can use the cholesky decomposition
        DecompositionSolver solver = new CholeskyDecomposition(s).getSolver();
        RealMatrix invertedS = solver.getInverse();


        // Inn = z(k) - H * xHat(k)-
        RealVector innovation = z.subtract(measurementMatrix.operate(stateEstimation));


        // calculate gain matrix
        // K(k) = P(k)- * H' * (H * P(k)- * H' + R)^-1
        // K(k) = P(k)- * H' * S^-1
        RealMatrix kalmanGain = errorCovariance.multiply(measurementMatrixT).multiply(invertedS);

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                return Double.compare(weightedResidual(o1),
                                      weightedResidual(o2));
            }


            private double weightedResidual(final PointVectorValuePair pv) {
                final RealVector v = new ArrayRealVector(pv.getValueRef(), false);
                final RealVector r = target.subtract(v);
                return r.dotProduct(weight.operate(r));
            }
        };
    }

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    /**
     * {@inheritDoc}
     */
    public double[] estimateRegressionParameters() {
        RealVector b = calculateBeta();
        return b.toArray();
    }

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    /**
     * {@inheritDoc}
     */
    public double[] estimateResiduals() {
        RealVector b = calculateBeta();
        RealVector e = yVector.subtract(xMatrix.operate(b));
        return e.toArray();
    }

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     *
     * @return error variance estimate
     * @since 2.2
     */
    protected double calculateErrorVariance() {
        RealVector residuals = calculateResiduals();
        return residuals.dotProduct(residuals) /
               (xMatrix.getRowDimension() - xMatrix.getColumnDimension());
    }

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     * </pre>
     *
     * @return The residuals [n,1] matrix
     */
    protected RealVector calculateResiduals() {
        RealVector b = calculateBeta();
        return yVector.subtract(xMatrix.operate(b));
    }

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                    }
                    hs.setEntry(i, hs.getEntry(i) + modelSecondDerivativesValues.getEntry(ih) * s.getEntry(j));
                    ih++;
                }
            }
            final RealVector tmp = interpolationPoints.operate(s).ebeMultiply(modelSecondDerivativesParameters);
            for (int k = 0; k < npt; k++) {
                if (modelSecondDerivativesParameters.getEntry(k) != ZERO) {
                    for (int i = 0; i < n; i++) {
                        hs.setEntry(i, hs.getEntry(i) + tmp.getEntry(k) * interpolationPoints.getEntry(k, i));
                    }
                }
            }
            if (crvmin != ZERO) {
                state = 50; break;

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     *
     * @return residual sum of squares
     * @since 2.2
     */
    public double calculateResidualSumOfSquares() {
        final RealVector residuals = calculateResiduals();
        // No advertised DME, args are valid
        return residuals.dotProduct(residuals);
    }

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     * @return error variance
     * @since 2.2
     */
    @Override
    protected double calculateErrorVariance() {
        RealVector residuals = calculateResiduals();
        double t = residuals.dotProduct(getOmegaInverse().operate(residuals));
        return t / (getX().getRowDimension() - getX().getColumnDimension());


    }

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