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


        Optimum optimum = optimizer.optimize(
                problem.getBuilder().start(new double[]{2, 2, 2, 2, 2, 2}).build());


        Assert.assertEquals(0, optimum.getRMS(), TOl);
        RealVector point = optimum.getPoint();
        //the first two elements are under constrained
        //check first two elements obey the constraint: sum to 3
        Assert.assertEquals(3, point.getEntry(0) + point.getEntry(1), TOl);
        //#constrains = #states fro the last 4 elements
        assertEquals(TOl, point.getSubVector(2, 4), 3, 4, 5, 6);
    }

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                           final double errParams,
                           final double errParamsSd) {


        final Optimum optimum = optimizer.optimize(builder(dataset).build());


        final RealVector actual = optimum.getPoint();
        for (int i = 0; i < actual.getDimension(); i++) {
            double expected = dataset.getParameter(i);
            double delta = FastMath.abs(errParams * expected);
            Assert.assertEquals(dataset.getName() + ", param #" + i,
                    expected, actual.getEntry(i), delta);
        }
    }

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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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          {27, 37, 47}   
        };
        OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
        regression.newSampleData(y, x);
        RealMatrix combinedX = regression.getX().copy();
        RealVector combinedY = regression.getY().copy();
        regression.newXSampleData(x);
        regression.newYSampleData(y);
        Assert.assertEquals(combinedX, regression.getX());
        Assert.assertEquals(combinedY, regression.getY());

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    }


    @Test
    public void testComputeResiduals() {
        //setup
        RealVector point = new ArrayRealVector(2);
        Evaluation evaluation = new LeastSquaresBuilder()
                .target(new ArrayRealVector(new double[]{3,-1}))
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector point) {
                        return new Pair<RealVector, RealMatrix>(

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    }


    @Test
    public void testComputeCovariance() throws IOException {
        //setup
        RealVector point = new ArrayRealVector(2);
        Evaluation evaluation = new LeastSquaresBuilder()
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector point) {
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(2),

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    }


    @Test
    public void testComputeValueAndJacobian() {
        //setup
        final RealVector point = new ArrayRealVector(new double[]{1, 2});
        Evaluation evaluation = new LeastSquaresBuilder()
                .weight(new DiagonalMatrix(new double[]{16, 4}))
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector actualPoint) {
                        //verify correct values passed in
                        Assert.assertArrayEquals(
                                point.toArray(), actualPoint.toArray(), Precision.EPSILON);
                        //return values
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(new double[]{3, 4}),
                                MatrixUtils.createRealMatrix(new double[][]{{5, 6}, {7, 8}})
                        );
                    }
                })
                .target(new double[2])
                .build()
                .evaluate(point);


        //action
        RealVector residuals = evaluation.getResiduals();
        RealMatrix jacobian = evaluation.getJacobian();


        //verify
        Assert.assertArrayEquals(evaluation.getPoint().toArray(), point.toArray(), 0);
        Assert.assertArrayEquals(new double[]{-12, -8}, residuals.toArray(), Precision.EPSILON);
        TestUtils.assertEquals(
                "jacobian",
                jacobian,
                MatrixUtils.createRealMatrix(new double[][]{{20, 24},{14, 16}}),
                Precision.EPSILON);

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        final double[] expected = dataset.getParametersStandardDeviations();


        final Evaluation evaluation = lsp.evaluate(lsp.getStart());
        final double cost = evaluation.getCost();
        final RealVector sig = evaluation.getSigma(1e-14);
        final int dof = lsp.getObservationSize() - lsp.getParameterSize();
        for (int i = 0; i < sig.getDimension(); i++) {
            final double actual = FastMath.sqrt(cost * cost / dof) * sig.getEntry(i);
            Assert.assertEquals(dataset.getName() + ", parameter #" + i,
                                expected[i], actual, 1e-6 * expected[i]);
        }
    }

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    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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        for (int i = 0; i < numParams; i++) {
            paramsFoundByDirectSolution[i] = new SummaryStatistics();
            sigmaEstimate[i] = new SummaryStatistics();
        }


        final RealVector init = new ArrayRealVector(new double[]{ slope, offset }, false);


        // Monte-Carlo (generates many sets of observations).
        final int mcRepeat = MONTE_CARLO_RUNS;
        int mcCount = 0;
        while (mcCount < mcRepeat) {
            // Observations.
            final Point2D.Double[] obs = lineGenerator.generate(numObs);


            final StraightLineProblem problem = new StraightLineProblem(yError);
            for (int i = 0; i < numObs; i++) {
                final Point2D.Double p = obs[i];
                problem.addPoint(p.x, p.y);
            }


            // Direct solution (using simple regression).
            final double[] regress = problem.solve();


            // Estimation of the standard deviation (diagonal elements of the
            // covariance matrix).
            final LeastSquaresProblem lsp = builder(problem).build();


            final RealVector sigma = lsp.evaluate(init).getSigma(1e-14);


            // Accumulate statistics.
            for (int i = 0; i < numParams; i++) {
                paramsFoundByDirectSolution[i].addValue(regress[i]);
                sigmaEstimate[i].addValue(sigma.getEntry(i));
            }


            // Next Monte-Carlo.
            ++mcCount;
        }

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