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

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

        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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    }


    // 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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        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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                        .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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                    }
                    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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    /**
     * @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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    /**
     * {@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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