List of walkInOptimizedOrder() Examples

Examples of walkInOptimizedOrder()

org.apache.commons.math.linear.Array2DRowRealMatrix.walkInOptimizedOrder()
org.apache.commons.math.linear.RealMatrix.walkInOptimizedOrder()
Visit (and possibly change) all matrix entries using the fastest possible order.
The fastest walking order depends on the exact matrix class. It may be different from traditional row or column orders.
@param visitor visitor used to process all matrix entries @exception MatrixVisitorException if the visitor cannot process an entry @see #walkInRowOrder(RealMatrixChangingVisitor) @see #walkInRowOrder(RealMatrixPreservingVisitor) @see #walkInRowOrder(RealMatrixChangingVisitor,int,int,int,int) @see #walkInRowOrder(RealMatrixPreservingVisitor,int,int,int,int) @see #walkInColumnOrder(RealMatrixChangingVisitor) @see #walkInColumnOrder(RealMatrixPreservingVisitor) @see #walkInColumnOrder(RealMatrixChangingVisitor,int,int,int,int) @see #walkInColumnOrder(RealMatrixPreservingVisitor,int,int,int,int) @see #walkInOptimizedOrder(RealMatrixPreservingVisitor) @see #walkInOptimizedOrder(RealMatrixChangingVisitor,int,int,int,int) @see #walkInOptimizedOrder(RealMatrixPreservingVisitor,int,int,int,int) @return the value returned by {@link RealMatrixChangingVisitor#end()} at the endof the walk
org.apache.commons.math3.linear.Array2DRowRealMatrix.walkInOptimizedOrder()
Visits (but does not alter) all entries of this vector in optimized order. The order in which the entries are visited is selected so as to lead to the most efficient implementation; it might depend on the concrete implementation of this abstract class. @param visitor the visitor to be used to process the entries of thisvector @return the value returned by {@link FieldVectorPreservingVisitor#end()}at the end of the walk @since 3.3
org.apache.commons.math3.linear.RealMatrix.walkInOptimizedOrder()
Visit (and possibly change) all matrix entries using the fastest possible order.
The fastest walking order depends on the exact matrix class. It may be different from traditional row or column orders.
@param visitor visitor used to process all matrix entries @see #walkInRowOrder(RealMatrixChangingVisitor) @see #walkInRowOrder(RealMatrixPreservingVisitor) @see #walkInRowOrder(RealMatrixChangingVisitor,int,int,int,int) @see #walkInRowOrder(RealMatrixPreservingVisitor,int,int,int,int) @see #walkInColumnOrder(RealMatrixChangingVisitor) @see #walkInColumnOrder(RealMatrixPreservingVisitor) @see #walkInColumnOrder(RealMatrixChangingVisitor,int,int,int,int) @see #walkInColumnOrder(RealMatrixPreservingVisitor,int,int,int,int) @see #walkInOptimizedOrder(RealMatrixPreservingVisitor) @see #walkInOptimizedOrder(RealMatrixChangingVisitor,int,int,int,int) @see #walkInOptimizedOrder(RealMatrixPreservingVisitor,int,int,int,int) @return the value returned by {@link RealMatrixChangingVisitor#end()} at the endof the walk

Examples of org.apache.commons.math.linear.Array2DRowRealMatrix.walkInOptimizedOrder()

                               1e-14);
        RealMatrix errors =
            new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
        final double[] s = { 1.0, -1.0 /  2.0, -1.0 /  3.0, -1.0 /  4.0, -1.0 /  5.0, -1.0 /  6.0 };
        RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
        referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
            @Override
            public double visit(int row, int column, double value)
                throws MatrixVisitorException {
                if (row == 0) {
                    return s[column];

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Examples of org.apache.commons.math.linear.Array2DRowRealMatrix.walkInOptimizedOrder()

                }
                nordsieckTmp = updateHighOrderDerivativesPhase1(nordsieck);
                updateHighOrderDerivativesPhase2(scaled, predictedScaled, nordsieckTmp);


                // apply correction (C in the PECE sequence)
                error = nordsieckTmp.walkInOptimizedOrder(new Corrector(y, predictedScaled, yTmp));


                if (error >= 1.0) {
                    // reject the step and attempt to reduce error by stepsize control
                    final double factor = computeStepGrowShrinkFactor(error);
                    hNew = filterStep(stepSize * factor, forward, false);

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Examples of org.apache.commons.math.linear.Array2DRowRealMatrix.walkInOptimizedOrder()

                               1e-14);
        RealMatrix errors =
            new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
        final double[] s = { 1.0, -1.0 /  2.0, -1.0 /  3.0, -1.0 /  4.0, -1.0 /  5.0, -1.0 /  6.0 };
        RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
        referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
            @Override
            public double visit(int row, int column, double value) {
                if (row == 0) {
                    return s[column];
                }

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Examples of org.apache.commons.math.linear.Array2DRowRealMatrix.walkInOptimizedOrder()

                }
                final Array2DRowRealMatrix nordsieckTmp = updateHighOrderDerivativesPhase1(nordsieck);
                updateHighOrderDerivativesPhase2(scaled, predictedScaled, nordsieckTmp);


                // apply correction (C in the PECE sequence)
                error = nordsieckTmp.walkInOptimizedOrder(new Corrector(y, predictedScaled, yTmp));


                if (error <= 1.0) {


                    // evaluate a final estimate of the derivative (second E in the PECE sequence)
                    computeDerivatives(stepEnd, yTmp, yDot);

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Examples of org.apache.commons.math.linear.Array2DRowRealMatrix.walkInOptimizedOrder()

                               1e-14);
        RealMatrix errors =
            new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
        final double[] s = { 1.0, -1.0 /  2.0, -1.0 /  3.0, -1.0 /  4.0, -1.0 /  5.0, -1.0 /  6.0 };
        RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
        referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
            @Override
            public double visit(int row, int column, double value)
                throws MatrixVisitorException {
                if (row == 0) {
                    return s[column];

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Examples of org.apache.commons.math.linear.Array2DRowRealMatrix.walkInOptimizedOrder()

                }
                final Array2DRowRealMatrix nordsieckTmp = updateHighOrderDerivativesPhase1(nordsieck);
                updateHighOrderDerivativesPhase2(scaled, predictedScaled, nordsieckTmp);


                // apply correction (C in the PECE sequence)
                error = nordsieckTmp.walkInOptimizedOrder(new Corrector(y, predictedScaled, yTmp));


                if (error <= 1.0) {


                    // evaluate a final estimate of the derivative (second E in the PECE sequence)
                    computeDerivatives(stepEnd, yTmp, yDot);

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Examples of org.apache.commons.math.linear.RealMatrix.walkInOptimizedOrder()

                               1e-14);
        RealMatrix errors =
            new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
        final double[] s = { 1.0, -1.0 /  2.0, -1.0 /  3.0, -1.0 /  4.0, -1.0 /  5.0, -1.0 /  6.0 };
        RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
        referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
            @Override
            public double visit(int row, int column, double value)
                throws MatrixVisitorException {
                if (row == 0) {
                    return s[column];

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Examples of org.apache.commons.math.linear.RealMatrix.walkInOptimizedOrder()

                               1e-14);
        RealMatrix errors =
            new Array2DRowRealMatrix(regression.estimateRegressionParametersVariance(), false);
        final double[] s = { 1.0, -1.0 /  2.0, -1.0 /  3.0, -1.0 /  4.0, -1.0 /  5.0, -1.0 /  6.0 };
        RealMatrix referenceVariance = new Array2DRowRealMatrix(s.length, s.length);
        referenceVariance.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
            @Override
            public double visit(int row, int column, double value) {
                if (row == 0) {
                    return s[column];
                }

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Examples of org.apache.commons.math.linear.RealMatrix.walkInOptimizedOrder()


    }


    private RealMatrix createTestMatrix(final Random r, final int rows, final int columns) {
        RealMatrix m = MatrixUtils.createRealMatrix(rows, columns);
        m.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor(){
            @Override
            public double visit(int row, int column, double value)
                throws MatrixVisitorException {
                return 2.0 * r.nextDouble() - 1.0;
            }

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Examples of org.apache.commons.math.linear.RealMatrix.walkInOptimizedOrder()

        RealMatrix   xRef = createTestMatrix(r, q, BlockRealMatrix.BLOCK_SIZE + 3);


        // build a perturbed system: A.X + noise = B
        RealMatrix b = a.multiply(xRef);
        final double noise = 0.001;
        b.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {
            @Override
            public double visit(int row, int column, double value) {
                return value * (1.0 + noise * (2 * r.nextDouble() - 1));
            }
        });

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