Examples of org.apache.commons.math3.util.ResizableDoubleArray

org.apache.commons.math3.linear.RealVector

A variable length {@link DoubleArray} implementation that automaticallyhandles expanding and contracting its internal storage array as elements are added and removed.

Important note: Usage should not assume that this class is thread-safe even though some of the methods are {@code synchronized}. This qualifier will be dropped in the next major release (4.0).

The internal storage array starts with capacity determined by the {@code initialCapacity} property, which can be set by the constructor.The default initial capacity is 16. Adding elements using {@link #addElement(double)} appends elements to the end of the array.When there are no open entries at the end of the internal storage array, the array is expanded. The size of the expanded array depends on the {@code expansionMode} and {@code expansionFactor} properties.The {@code expansionMode} determines whether the size of the array ismultiplied by the {@code expansionFactor}( {@link ExpansionMode#MULTIPLICATIVE}) or if the expansion is additive ( {@link ExpansionMode#ADDITIVE} -- {@code expansionFactor} storagelocations added). The default {@code expansionMode} is {@code MULTIPLICATIVE} and the default{@code expansionFactor} is 2.

The {@link #addElementRolling(double)} method adds a new element to the endof the internal storage array and adjusts the "usable window" of the internal array forward by one position (effectively making what was the second element the first, and so on). Repeated activations of this method (or activation of {@link #discardFrontElements(int)}) will effectively orphan the storage locations at the beginning of the internal storage array. To reclaim this storage, each time one of these methods is activated, the size of the internal storage array is compared to the number of addressable elements (the {@code numElements} property) and if the differenceis too large, the internal array is contracted to size {@code numElements + 1}. The determination of when the internal storage array is "too large" depends on the {@code expansionMode} and{@code contractionFactor} properties. If the {@code expansionMode}is {@code MULTIPLICATIVE}, contraction is triggered when the ratio between storage array length and {@code numElements} exceeds{@code contractionFactor.} If the {@code expansionMode}is {@code ADDITIVE}, the number of excess storage locations is compared to {@code contractionFactor}.

To avoid cycles of expansions and contractions, the {@code expansionFactor} must not exceed the {@code contractionFactor}. Constructors and mutators for both of these properties enforce this requirement, throwing a {@code MathIllegalArgumentException} if it isviolated.


        }


        // solve the rectangular system in the least square sense
        // to get the best estimate of the Nordsieck vector [s2 ... sk]
        QRDecomposition decomposition;
        decomposition = new QRDecomposition(new Array2DRowRealMatrix(a, false));
        RealMatrix x = decomposition.getSolver().solve(new Array2DRowRealMatrix(b, false));
        return new Array2DRowRealMatrix(x.getData(), false);
    }

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     * if the covariance matrix cannot be computed (singular problem).
     */
    public double[][] computeCovariances(double[] params,
                                         double threshold) {
        // Set up the Jacobian.
        final RealMatrix j = computeWeightedJacobian(params);


        // Compute transpose(J)J.
        final RealMatrix jTj = j.transpose().multiply(j);


        // Compute the covariances matrix.
        final DecompositionSolver solver
            = new QRDecomposition(jTj, threshold).getSolver();
        return solver.getInverse().getData();

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     * @return the square-root of the weight matrix.
     */
    private RealMatrix squareRoot(RealMatrix m) {
        if (m instanceof DiagonalMatrix) {
            final int dim = m.getRowDimension();
            final RealMatrix sqrtM = new DiagonalMatrix(dim);
            for (int i = 0; i < dim; i++) {
                sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
            }
            return sqrtM;
        } else {
            final EigenDecomposition dec = new EigenDecomposition(m);
            return dec.getSquareRoot();

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      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
      
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
      
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);

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     * @param matrix matrix with columns representing variables to correlate
     * @return correlation matrix
     */
    public RealMatrix computeCorrelationMatrix(final RealMatrix matrix) {
        int nVars = matrix.getColumnDimension();
        RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
        for (int i = 0; i < nVars; i++) {
            for (int j = 0; j < i; j++) {
                double corr = correlation(matrix.getColumn(i), matrix.getColumn(j));
                outMatrix.setEntry(i, j, corr);
                outMatrix.setEntry(j, i, corr);
            }
            outMatrix.setEntry(i, i, 1d);
        }
        return outMatrix;
    }

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        // solve the rectangular system in the least square sense
        // to get the best estimate of the Nordsieck vector [s2 ... sk]
        QRDecomposition decomposition;
        decomposition = new QRDecomposition(new Array2DRowRealMatrix(a, false));
        RealMatrix x = decomposition.getSolver().solve(new Array2DRowRealMatrix(b, false));
        return new Array2DRowRealMatrix(x.getData(), false);
    }

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        double[] qtf     = new double[nR];
        double[] work1   = new double[nC];
        double[] work2   = new double[nC];
        double[] work3   = new double[nC];


        final RealMatrix weightMatrixSqrt = getWeightSquareRoot();


        // Evaluate the function at the starting point and calculate its norm.
        double[] currentObjective = computeObjectiveValue(currentPoint);
        double[] currentResiduals = computeResiduals(currentObjective);
        PointVectorValuePair current = new PointVectorValuePair(currentPoint, currentObjective);
        double currentCost = computeCost(currentResiduals);


        // Outer loop.
        lmPar = 0;
        boolean firstIteration = true;
        final ConvergenceChecker<PointVectorValuePair> checker = getConvergenceChecker();
        while (true) {
            incrementIterationCount();


            final PointVectorValuePair previous = current;


            // QR decomposition of the jacobian matrix
            qrDecomposition(computeWeightedJacobian(currentPoint));


            weightedResidual = weightMatrixSqrt.operate(currentResiduals);
            for (int i = 0; i < nR; i++) {
                qtf[i] = weightedResidual[i];
            }


            // compute Qt.res

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        double[] qtf     = new double[nR];
        double[] work1   = new double[nC];
        double[] work2   = new double[nC];
        double[] work3   = new double[nC];


        final RealMatrix weightMatrixSqrt = getWeightSquareRoot();


        // Evaluate the function at the starting point and calculate its norm.
        double[] currentObjective = computeObjectiveValue(currentPoint);
        double[] currentResiduals = computeResiduals(currentObjective);
        PointVectorValuePair current = new PointVectorValuePair(currentPoint, currentObjective);
        double currentCost = computeCost(currentResiduals);


        // Outer loop.
        lmPar = 0;
        boolean firstIteration = true;
        int iter = 0;
        final ConvergenceChecker<PointVectorValuePair> checker = getConvergenceChecker();
        while (true) {
            ++iter;
            final PointVectorValuePair previous = current;


            // QR decomposition of the jacobian matrix
            qrDecomposition(computeWeightedJacobian(currentPoint));


            weightedResidual = weightMatrixSqrt.operate(currentResiduals);
            for (int i = 0; i < nR; i++) {
                qtf[i] = weightedResidual[i];
            }


            // compute Qt.res

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    for (SiteWithPolynomial site : sites) {
      
      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
      
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
      
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);
        vector.setEntry(row, nearSite.pos.y);
      }
      
      QRDecomposition qr = new QRDecomposition(matrix);
      RealVector solution = qr.getSolver().solve(vector);
        
      double[] coeffs = solution.toArray();
      
      for (double coeff : coeffs) {
        if (coeff > 10e3) {
          continue calculatePolynomials;
        }

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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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Examples of org.apache.commons.math3.util.ResizableDoubleArray

Important note: Usage should not assume that this class is thread-safe even though some of the methods are {@code synchronized}. This qualifier will be dropped in the next major release (4.0).

Related Classes of org.apache.commons.math3.util.ResizableDoubleArray