Examples of Optimum

org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum
Weight matrix of the residuals between model and observations.
Immutable class. @version $Id: Weight.java 1416643 2012-12-03 19:37:14Z tn $ @since 3.1

Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum

        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }
        final double[] start = {0, 0};


        Optimum optimum = optimizer.optimize(
                builder(circle).weight(new DiagonalMatrix(weights)).start(start).build());


        assertEquals(1e-6, optimum.getPoint(), -0.1517383071957963, 0.2074999736353867);
        Assert.assertEquals(0.04268731682389561, optimum.getRMS(), 1e-8);
    }

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Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum

    public void doTestStRD(final StatisticalReferenceDataset dataset,
                           final LeastSquaresOptimizer optimizer,
                           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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Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum

                        Assert.assertArrayEquals(new double[] {1, 2, 3}, current.getPoint().toArray(), TOl);
                        checked[0] = true;
                        return true;
                    }
                });
        Optimum optimum = optimizer.optimize(builder.build());


        Assert.assertThat(checked[0], is(true));
    }

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Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum

                    {  1, 2, -3 },
                    {  2, 1,  3 },
                    { -3, 0, -9 }
            }, new double[] { 1, 1, 1 });


            final Optimum optimum = optimizer.optimize(
                    problem.getBuilder().maxIterations(20).build());


            //TODO check that it is a bad fit? Why the extra conditions?
            Assert.assertTrue(FastMath.sqrt(problem.getTarget().length) * optimum.getRMS() > 0.6);


            optimum.getCovariances(1.5e-14);


            fail(optimizer);
        }catch (SingularMatrixException e){
            //expected
        }

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Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum

                             dataPoints[1][i]);


            weights[i] = 1 / dataPoints[1][i];
        }


        final Optimum optimum = optimizer.optimize(
                builder(problem)
                        .target(dataPoints[1])
                        .weight(new DiagonalMatrix(weights))
                        .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 },

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Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum

        }


        // First guess for the center's coordinates and radius.
        final double[] init = { 90, 659, 115 };


        final Optimum optimum = optimizer.optimize(
                builder(circle).maxIterations(50).start(init).build());


        final double[] paramFound = optimum.getPoint().toArray();


        // Retrieve errors estimation.
        final double[] asymptoticStandardErrorFound = optimum.getSigma(1e-14).toArray();


        // Check that the parameters are found within the assumed error bars.
        Assert.assertEquals(xCenter, paramFound[0], asymptoticStandardErrorFound[0]);
        Assert.assertEquals(yCenter, paramFound[1], asymptoticStandardErrorFound[1]);
        Assert.assertEquals(radius, paramFound[2], asymptoticStandardErrorFound[2]);

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Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum

                    }
                })
                .build();


        //action
        Optimum optimum = optimizer.optimize(lsp);


        //verify
        //check iterations and evaluations are not switched.
        Assert.assertThat(optimum.getIterations(), is(1));
        Assert.assertThat(optimum.getEvaluations(), is(2));
    }

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Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum

                .weight(new DiagonalMatrix(function.getWeight()))
                .start(function.getStartPoint())
                .build();


        try {
            final Optimum optimum = optimizer.optimize(problem);
            Assert.assertFalse(exceptionExpected);
            function.checkTheoreticalMinCost(optimum.getRMS());
            function.checkTheoreticalMinParams(optimum.getPoint().toArray());
        } catch (TooManyEvaluationsException e) {
            Assert.assertTrue(exceptionExpected);
        }
    }

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