Package org.apache.commons.math.linear

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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                               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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    }

    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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        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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                               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];
View Full Code Here

       
    }

    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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