Examples of RealVector

org.apache.commons.math.linear.RealVector
Interface defining a real-valued vector with basic algebraic operations.
vector element indexing is 0-based -- e.g., getEntry(0) returns the first element of the vector.

The various mapXxx and mapXxxToSelf methods operate on vectors element-wise, i.e. they perform the same operation (adding a scalar, applying a function ...) on each element in turn. The mapXxx versions create a new vector to hold the result and do not change the instance. The mapXxxToSelf versions use the instance itself to store the results, so the instance is changed by these methods. In both cases, the result vector is returned by the methods, this allows to use the fluent API style, like this:
```
 RealVector result = v.mapAddToSelf(3.0).mapTanToSelf().mapSquareToSelf(); 
```
Remark on the deprecated {@code mapXxx} and {@code mapXxxToSelf} methods: InCommons-Math v3.0, the same functionality will be achieved by directly using the {@link #map(UnivariateRealFunction)} and {@link #mapToSelf(UnivariateRealFunction)}together with new function objects (not available in v2.2).
@version $Revision: 1070725 $ $Date: 2011-02-15 02:31:12 +0100 (mar. 15 févr. 2011) $ @since 2.0
org.apache.commons.math3.linear.RealVector
Class defining a real-valued vector with basic algebraic operations.
vector element indexing is 0-based -- e.g., {@code getEntry(0)}returns the first element of the vector.

The {@code code map} and {@code mapToSelf} methods operateon vectors element-wise, i.e. they perform the same operation (adding a scalar, applying a function ...) on each element in turn. The {@code map}versions create a new vector to hold the result and do not change the instance. The {@code mapToSelf} version uses the instance itself to store theresults, so the instance is changed by this method. In all cases, the result vector is returned by the methods, allowing the fluent API style, like this:
```
 RealVector result = v.mapAddToSelf(3.4).mapToSelf(new Tan()).mapToSelf(new Power(2.3)); 
```
@since 2.1

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

    public void testEvaluateCopiesPoint() throws IOException {
        //setup
        StatisticalReferenceDataset dataset
                = StatisticalReferenceDatasetFactory.createKirby2();
        LeastSquaresProblem lsp = builder(dataset).build();
        RealVector point = new ArrayRealVector(lsp.getParameterSize());


        //action
        Evaluation evaluation = lsp.evaluate(point);


        //verify
        Assert.assertNotSame(point, evaluation.getPoint());
        point.setEntry(0, 1);
        Assert.assertEquals(evaluation.getPoint().getEntry(0), 0, 0);
    }

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

        Assert.assertEquals(evaluation.getPoint().getEntry(0), 0, 0);
    }


    @Test
    public void testLazyEvaluation() {
        final RealVector dummy = new ArrayRealVector(new double[] { 0 });


        final LeastSquaresProblem p
            = LeastSquaresFactory.create(LeastSquaresFactory.model(dummyModel(), dummyJacobian()),
                                         dummy, dummy, null, null, 0, 0, true, null);

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

    }


    // MATH-1151
    @Test
    public void testLazyEvaluationPrecondition() {
        final RealVector dummy = new ArrayRealVector(new double[] { 0 });


        // "ValueAndJacobianFunction" is required but we implement only
        // "MultivariateJacobianFunction".
        final MultivariateJacobianFunction m1 = new MultivariateJacobianFunction() {
                public Pair<RealVector, RealMatrix> value(RealVector notUsed) {

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

        LeastSquaresFactory.create(m2, dummy, dummy, null, null, 0, 0, true, null);
    }


    @Test
    public void testDirectEvaluation() {
        final RealVector dummy = new ArrayRealVector(new double[] { 0 });


        final LeastSquaresProblem p
            = LeastSquaresFactory.create(LeastSquaresFactory.model(dummyModel(), dummyJacobian()),
                                         dummy, dummy, null, null, 0, 0, false, null);

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

                        .start(start)
                        .maxIterations(20)
                        .build()
        );


        final RealVector solution = optimum.getPoint();
        final double[] expectedSolution = { 10.4, 958.3, 131.4, 33.9, 205.0 };


        final RealMatrix covarMatrix = optimum.getCovariances(1e-14);
        final double[][] expectedCovarMatrix = {
            { 3.38, -3.69, 27.98, -2.34, -49.24 },
            { -3.69, 2492.26, 81.89, -69.21, -8.9 },
            { 27.98, 81.89, 468.99, -44.22, -615.44 },
            { -2.34, -69.21, -44.22, 6.39, 53.80 },
            { -49.24, -8.9, -615.44, 53.8, 929.45 }
        };


        final int numParams = expectedSolution.length;


        // Check that the computed solution is within the reference error range.
        for (int i = 0; i < numParams; i++) {
            final double error = FastMath.sqrt(expectedCovarMatrix[i][i]);
            Assert.assertEquals("Parameter " + i, expectedSolution[i], solution.getEntry(i), error);
        }


        // Check that each entry of the computed covariance matrix is within 10%
        // of the reference matrix entry.
        for (int i = 0; i < numParams; i++) {

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

                    }
                    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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Examples of org.apache.commons.math3.linear.RealVector

    /**
     * @param point Interpolation point.
     * @return the interpolated value.
     */
    public double value(double[] point) {
        final RealVector p = new ArrayRealVector(point);


        // Reset.
        for (MicrosphereSurfaceElement md : microsphere) {
            md.reset();
        }


        // Compute contribution of each sample points to the microsphere elements illumination
        for (Map.Entry<RealVector, Double> sd : samples.entrySet()) {


            // Vector between interpolation point and current sample point.
            final RealVector diff = sd.getKey().subtract(p);
            final double diffNorm = diff.getNorm();


            if (FastMath.abs(diffNorm) < FastMath.ulp(1d)) {
                // No need to interpolate, as the interpolation point is
                // actually (very close to) one of the sampled points.
                return sd.getValue();

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


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

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


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

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

     *
     * @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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