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* <tr> * </table> * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests) * @author Burton S. Garbow (original fortran minpack tests) * @author Kenneth E. Hillstrom (original fortran minpack tests) * @author Jorge J. More (original fortran minpack tests) * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation) */ @Deprecated public class LevenbergMarquardtEstimatorTest extends TestCase { public LevenbergMarquardtEstimatorTest(String name) { super(name); } public void testTrivial() throws EstimationException { LinearProblem problem = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { new EstimatedParameter("p0", 0) }, 3.0) }); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(problem); assertEquals(0, estimator.getRMS(problem), 1.0e-10); try { estimator.guessParametersErrors(problem); fail("an exception should have been thrown"); } catch (EstimationException ee) { // expected behavior } catch (Exception e) { fail("wrong exception caught"); } assertEquals(1.5, problem.getUnboundParameters()[0].getEstimate(), 1.0e-10); } public void testQRColumnsPermutation() throws EstimationException { EstimatedParameter[] x = { new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 0) }; LinearProblem problem = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] { 1.0, -1.0 }, new EstimatedParameter[] { x[0], x[1] }, 4.0), new LinearMeasurement(new double[] { 2.0 }, new EstimatedParameter[] { x[1] }, 6.0), new LinearMeasurement(new double[] { 1.0, -2.0 }, new EstimatedParameter[] { x[0], x[1] }, 1.0) }); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(problem); assertEquals(0, estimator.getRMS(problem), 1.0e-10); assertEquals(7.0, x[0].getEstimate(), 1.0e-10); assertEquals(3.0, x[1].getEstimate(), 1.0e-10); } public void testNoDependency() throws EstimationException { EstimatedParameter[] p = new EstimatedParameter[] { new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 0), new EstimatedParameter("p2", 0), new EstimatedParameter("p3", 0), new EstimatedParameter("p4", 0), new EstimatedParameter("p5", 0) }; LinearProblem problem = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[0] }, 0.0), new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[1] }, 1.1), new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[2] }, 2.2), new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[3] }, 3.3), new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[4] }, 4.4), new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[5] }, 5.5) }); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(problem); assertEquals(0, estimator.getRMS(problem), 1.0e-10); for (int i = 0; i < p.length; ++i) { assertEquals(0.55 * i, p[i].getEstimate(), 1.0e-10); } } public void testOneSet() throws EstimationException { EstimatedParameter[] p = { new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 0), new EstimatedParameter("p2", 0) }; LinearProblem problem = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] { 1.0 }, new EstimatedParameter[] { p[0] }, 1.0), new LinearMeasurement(new double[] { -1.0, 1.0 }, new EstimatedParameter[] { p[0], p[1] }, 1.0), new LinearMeasurement(new double[] { -1.0, 1.0 }, new EstimatedParameter[] { p[1], p[2] }, 1.0) }); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(problem); assertEquals(0, estimator.getRMS(problem), 1.0e-10); assertEquals(1.0, p[0].getEstimate(), 1.0e-10); assertEquals(2.0, p[1].getEstimate(), 1.0e-10); assertEquals(3.0, p[2].getEstimate(), 1.0e-10); } public void testTwoSets() throws EstimationException { EstimatedParameter[] p = { new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 1), new EstimatedParameter("p2", 2), new EstimatedParameter("p3", 3), new EstimatedParameter("p4", 4), new EstimatedParameter("p5", 5) }; double epsilon = 1.0e-7; LinearProblem problem = new LinearProblem(new LinearMeasurement[] { // 4 elements sub-problem new LinearMeasurement(new double[] { 2.0, 1.0, 4.0 }, new EstimatedParameter[] { p[0], p[1], p[3] }, 2.0), new LinearMeasurement(new double[] { -4.0, -2.0, 3.0, -7.0 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, -9.0), new LinearMeasurement(new double[] { 4.0, 1.0, -2.0, 8.0 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, 2.0), new LinearMeasurement(new double[] { -3.0, -12.0, -1.0 }, new EstimatedParameter[] { p[1], p[2], p[3] }, 2.0), // 2 elements sub-problem new LinearMeasurement(new double[] { epsilon, 1.0 }, new EstimatedParameter[] { p[4], p[5] }, 1.0 + epsilon * epsilon), new LinearMeasurement(new double[] { 1.0, 1.0 }, new EstimatedParameter[] { p[4], p[5] }, 2.0) }); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(problem); assertEquals(0, estimator.getRMS(problem), 1.0e-10); assertEquals( 3.0, p[0].getEstimate(), 1.0e-10); assertEquals( 4.0, p[1].getEstimate(), 1.0e-10); assertEquals(-1.0, p[2].getEstimate(), 1.0e-10); assertEquals(-2.0, p[3].getEstimate(), 1.0e-10); assertEquals( 1.0 + epsilon, p[4].getEstimate(), 1.0e-10); assertEquals( 1.0 - epsilon, p[5].getEstimate(), 1.0e-10); } public void testNonInversible() throws EstimationException { EstimatedParameter[] p = { new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 0), new EstimatedParameter("p2", 0) }; LinearMeasurement[] m = new LinearMeasurement[] { new LinearMeasurement(new double[] { 1.0, 2.0, -3.0 }, new EstimatedParameter[] { p[0], p[1], p[2] }, 1.0), new LinearMeasurement(new double[] { 2.0, 1.0, 3.0 }, new EstimatedParameter[] { p[0], p[1], p[2] }, 1.0), new LinearMeasurement(new double[] { -3.0, -9.0 }, new EstimatedParameter[] { p[0], p[2] }, 1.0) }; LinearProblem problem = new LinearProblem(m); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); double initialCost = estimator.getRMS(problem); estimator.estimate(problem); assertTrue(estimator.getRMS(problem) < initialCost); assertTrue(Math.sqrt(m.length) * estimator.getRMS(problem) > 0.6); try { estimator.getCovariances(problem); fail("an exception should have been thrown"); } catch (EstimationException ee) { // expected behavior } catch (Exception e) { fail("wrong exception caught"); } double dJ0 = 2 * (m[0].getResidual() * m[0].getPartial(p[0]) + m[1].getResidual() * m[1].getPartial(p[0]) + m[2].getResidual() * m[2].getPartial(p[0])); double dJ1 = 2 * (m[0].getResidual() * m[0].getPartial(p[1]) + m[1].getResidual() * m[1].getPartial(p[1])); double dJ2 = 2 * (m[0].getResidual() * m[0].getPartial(p[2]) + m[1].getResidual() * m[1].getPartial(p[2]) + m[2].getResidual() * m[2].getPartial(p[2])); assertEquals(0, dJ0, 1.0e-10); assertEquals(0, dJ1, 1.0e-10); assertEquals(0, dJ2, 1.0e-10); } public void testIllConditioned() throws EstimationException { EstimatedParameter[] p = { new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 1), new EstimatedParameter("p2", 2), new EstimatedParameter("p3", 3) }; LinearProblem problem1 = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] { 10.0, 7.0, 8.0, 7.0 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, 32.0), new LinearMeasurement(new double[] { 7.0, 5.0, 6.0, 5.0 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, 23.0), new LinearMeasurement(new double[] { 8.0, 6.0, 10.0, 9.0 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, 33.0), new LinearMeasurement(new double[] { 7.0, 5.0, 9.0, 10.0 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, 31.0) }); LevenbergMarquardtEstimator estimator1 = new LevenbergMarquardtEstimator(); estimator1.estimate(problem1); assertEquals(0, estimator1.getRMS(problem1), 1.0e-10); assertEquals(1.0, p[0].getEstimate(), 1.0e-10); assertEquals(1.0, p[1].getEstimate(), 1.0e-10); assertEquals(1.0, p[2].getEstimate(), 1.0e-10); assertEquals(1.0, p[3].getEstimate(), 1.0e-10); LinearProblem problem2 = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] { 10.0, 7.0, 8.1, 7.2 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, 32.0), new LinearMeasurement(new double[] { 7.08, 5.04, 6.0, 5.0 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, 23.0), new LinearMeasurement(new double[] { 8.0, 5.98, 9.89, 9.0 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, 33.0), new LinearMeasurement(new double[] { 6.99, 4.99, 9.0, 9.98 }, new EstimatedParameter[] { p[0], p[1], p[2], p[3] }, 31.0) }); LevenbergMarquardtEstimator estimator2 = new LevenbergMarquardtEstimator(); estimator2.estimate(problem2); assertEquals(0, estimator2.getRMS(problem2), 1.0e-10); assertEquals(-81.0, p[0].getEstimate(), 1.0e-8); assertEquals(137.0, p[1].getEstimate(), 1.0e-8); assertEquals(-34.0, p[2].getEstimate(), 1.0e-8); assertEquals( 22.0, p[3].getEstimate(), 1.0e-8); } public void testMoreEstimatedParametersSimple() throws EstimationException { EstimatedParameter[] p = { new EstimatedParameter("p0", 7), new EstimatedParameter("p1", 6), new EstimatedParameter("p2", 5), new EstimatedParameter("p3", 4) }; LinearProblem problem = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] { 3.0, 2.0 }, new EstimatedParameter[] { p[0], p[1] }, 7.0), new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 }, new EstimatedParameter[] { p[1], p[2], p[3] }, 3.0), new LinearMeasurement(new double[] { 2.0, 1.0 }, new EstimatedParameter[] { p[0], p[2] }, 5.0) }); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(problem); assertEquals(0, estimator.getRMS(problem), 1.0e-10); } public void testMoreEstimatedParametersUnsorted() throws EstimationException { EstimatedParameter[] p = { new EstimatedParameter("p0", 2), new EstimatedParameter("p1", 2), new EstimatedParameter("p2", 2), new EstimatedParameter("p3", 2), new EstimatedParameter("p4", 2), new EstimatedParameter("p5", 2) }; LinearProblem problem = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] { 1.0, 1.0 }, new EstimatedParameter[] { p[0], p[1] }, 3.0), new LinearMeasurement(new double[] { 1.0, 1.0, 1.0 }, new EstimatedParameter[] { p[2], p[3], p[4] }, 12.0), new LinearMeasurement(new double[] { 1.0, -1.0 }, new EstimatedParameter[] { p[4], p[5] }, -1.0), new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 }, new EstimatedParameter[] { p[3], p[2], p[5] }, 7.0), new LinearMeasurement(new double[] { 1.0, -1.0 }, new EstimatedParameter[] { p[4], p[3] }, 1.0) }); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(problem); assertEquals(0, estimator.getRMS(problem), 1.0e-10); assertEquals(3.0, p[2].getEstimate(), 1.0e-10); assertEquals(4.0, p[3].getEstimate(), 1.0e-10); assertEquals(5.0, p[4].getEstimate(), 1.0e-10); assertEquals(6.0, p[5].getEstimate(), 1.0e-10); } public void testRedundantEquations() throws EstimationException { EstimatedParameter[] p = { new EstimatedParameter("p0", 1), new EstimatedParameter("p1", 1) }; LinearProblem problem = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] { 1.0, 1.0 }, new EstimatedParameter[] { p[0], p[1] }, 3.0), new LinearMeasurement(new double[] { 1.0, -1.0 }, new EstimatedParameter[] { p[0], p[1] }, 1.0), new LinearMeasurement(new double[] { 1.0, 3.0 }, new EstimatedParameter[] { p[0], p[1] }, 5.0) }); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(problem); assertEquals(0, estimator.getRMS(problem), 1.0e-10); assertEquals(2.0, p[0].getEstimate(), 1.0e-10); assertEquals(1.0, p[1].getEstimate(), 1.0e-10); } public void testInconsistentEquations() throws EstimationException { EstimatedParameter[] p = { new EstimatedParameter("p0", 1), new EstimatedParameter("p1", 1) }; LinearProblem problem = new LinearProblem(new LinearMeasurement[] { new LinearMeasurement(new double[] { 1.0, 1.0 }, new EstimatedParameter[] { p[0], p[1] }, 3.0), new LinearMeasurement(new double[] { 1.0, -1.0 }, new EstimatedParameter[] { p[0], p[1] }, 1.0), new LinearMeasurement(new double[] { 1.0, 3.0 }, new EstimatedParameter[] { p[0], p[1] }, 4.0) }); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(problem); assertTrue(estimator.getRMS(problem) > 0.1); } public void testControlParameters() { Circle circle = new Circle(98.680, 47.345); circle.addPoint( 30.0, 68.0); circle.addPoint( 50.0, -6.0); circle.addPoint(110.0, -20.0); circle.addPoint( 35.0, 15.0); circle.addPoint( 45.0, 97.0); checkEstimate(circle, 0.1, 10, 1.0e-14, 1.0e-16, 1.0e-10, false); checkEstimate(circle, 0.1, 10, 1.0e-15, 1.0e-17, 1.0e-10, true); checkEstimate(circle, 0.1, 5, 1.0e-15, 1.0e-16, 1.0e-10, true); circle.addPoint(300, -300); checkEstimate(circle, 0.1, 20, 1.0e-18, 1.0e-16, 1.0e-10, true); } private void checkEstimate(EstimationProblem problem, double initialStepBoundFactor, int maxCostEval, double costRelativeTolerance, double parRelativeTolerance, double orthoTolerance, boolean shouldFail) { try { LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.setInitialStepBoundFactor(initialStepBoundFactor); estimator.setMaxCostEval(maxCostEval); estimator.setCostRelativeTolerance(costRelativeTolerance); estimator.setParRelativeTolerance(parRelativeTolerance); estimator.setOrthoTolerance(orthoTolerance); estimator.estimate(problem); assertTrue(! shouldFail); } catch (EstimationException ee) { assertTrue(shouldFail); } catch (Exception e) { fail("wrong exception type caught"); } } public void testCircleFitting() throws EstimationException { Circle circle = new Circle(98.680, 47.345); circle.addPoint( 30.0, 68.0); circle.addPoint( 50.0, -6.0); circle.addPoint(110.0, -20.0); circle.addPoint( 35.0, 15.0); circle.addPoint( 45.0, 97.0); LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(circle); assertTrue(estimator.getCostEvaluations() < 10); assertTrue(estimator.getJacobianEvaluations() < 10); double rms = estimator.getRMS(circle); assertEquals(1.768262623567235, Math.sqrt(circle.getM()) * rms, 1.0e-10); assertEquals(69.96016176931406, circle.getRadius(), 1.0e-10); assertEquals(96.07590211815305, circle.getX(), 1.0e-10); assertEquals(48.13516790438953, circle.getY(), 1.0e-10); double[][] cov = estimator.getCovariances(circle); assertEquals(1.839, cov[0][0], 0.001); assertEquals(0.731, cov[0][1], 0.001); assertEquals(cov[0][1], cov[1][0], 1.0e-14); assertEquals(0.786, cov[1][1], 0.001); double[] errors = estimator.guessParametersErrors(circle); assertEquals(1.384, errors[0], 0.001); assertEquals(0.905, errors[1], 0.001); // add perfect measurements and check errors are reduced double cx = circle.getX(); double cy = circle.getY(); double r = circle.getRadius(); for (double d= 0; d < 2 * Math.PI; d += 0.01) { circle.addPoint(cx + r * Math.cos(d), cy + r * Math.sin(d)); } estimator = new LevenbergMarquardtEstimator(); estimator.estimate(circle); cov = estimator.getCovariances(circle); assertEquals(0.004, cov[0][0], 0.001); assertEquals(6.40e-7, cov[0][1], 1.0e-9); assertEquals(cov[0][1], cov[1][0], 1.0e-14); assertEquals(0.003, cov[1][1], 0.001); errors = estimator.guessParametersErrors(circle); assertEquals(0.004, errors[0], 0.001); assertEquals(0.004, errors[1], 0.001); } public void testCircleFittingBadInit() throws EstimationException { Circle circle = new Circle(-12, -12); double[][] points = new double[][] { {-0.312967, 0.072366}, {-0.339248, 0.132965}, {-0.379780, 0.202724}, {-0.390426, 0.260487}, {-0.361212, 0.328325}, {-0.346039, 0.392619}, {-0.280579, 0.444306}, {-0.216035, 0.470009}, {-0.149127, 0.493832}, {-0.075133, 0.483271}, {-0.007759, 0.452680}, { 0.060071, 0.410235}, { 0.103037, 0.341076}, { 0.118438, 0.273884}, { 0.131293, 0.192201}, { 0.115869, 0.129797}, { 0.072223, 0.058396}, { 0.022884, 0.000718}, {-0.053355, -0.020405}, {-0.123584, -0.032451}, {-0.216248, -0.032862}, {-0.278592, -0.005008}, {-0.337655, 0.056658}, {-0.385899, 0.112526}, {-0.405517, 0.186957}, {-0.415374, 0.262071}, {-0.387482, 0.343398}, {-0.347322, 0.397943}, {-0.287623, 0.458425}, {-0.223502, 0.475513}, {-0.135352, 0.478186}, {-0.061221, 0.483371}, { 0.003711, 0.422737}, { 0.065054, 0.375830}, { 0.108108, 0.297099}, { 0.123882, 0.222850}, { 0.117729, 0.134382}, { 0.085195, 0.056820}, { 0.029800, -0.019138}, {-0.027520, -0.072374}, {-0.102268, -0.091555}, {-0.200299, -0.106578}, {-0.292731, -0.091473}, {-0.356288, -0.051108}, {-0.420561, 0.014926}, {-0.471036, 0.074716}, {-0.488638, 0.182508}, {-0.485990, 0.254068}, {-0.463943, 0.338438}, {-0.406453, 0.404704}, {-0.334287, 0.466119}, {-0.254244, 0.503188}, {-0.161548, 0.495769}, {-0.075733, 0.495560}, { 0.001375, 0.434937}, { 0.082787, 0.385806}, { 0.115490, 0.323807}, { 0.141089, 0.223450}, { 0.138693, 0.131703}, { 0.126415, 0.049174}, { 0.066518, -0.010217}, {-0.005184, -0.070647}, {-0.080985, -0.103635}, {-0.177377, -0.116887}, {-0.260628, -0.100258}, {-0.335756, -0.056251}, {-0.405195, -0.000895}, {-0.444937, 0.085456}, {-0.484357, 0.175597}, {-0.472453, 0.248681}, {-0.438580, 0.347463}, {-0.402304, 0.422428}, {-0.326777, 0.479438}, {-0.247797, 0.505581}, {-0.152676, 0.519380}, {-0.071754, 0.516264}, { 0.015942, 0.472802}, { 0.076608, 0.419077}, { 0.127673, 0.330264}, { 0.159951, 0.262150}, { 0.153530, 0.172681}, { 0.140653, 0.089229}, { 0.078666, 0.024981}, { 0.023807, -0.037022}, {-0.048837, -0.077056}, {-0.127729, -0.075338}, {-0.221271, -0.067526} }; for (int i = 0; i < points.length; ++i) { circle.addPoint(points[i][0], points[i][1]); } LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator(); estimator.estimate(circle); assertTrue(estimator.getCostEvaluations() < 15); assertTrue(estimator.getJacobianEvaluations() < 10); assertEquals( 0.030184491196225207, estimator.getRMS(circle), 1.0e-9); assertEquals( 0.2922350065939634, circle.getRadius(), 1.0e-9); assertEquals(-0.15173845023862165, circle.getX(), 1.0e-8); assertEquals( 0.20750021499570379, circle.getY(), 1.0e-8); } public void testMath199() { try { QuadraticProblem problem = new QuadraticProblem(); problem.addPoint (0, -3.182591015485607, 0.0); problem.addPoint (1, -2.5581184967730577, 4.4E-323); problem.addPoint (2, -2.1488478161387325, 1.0); problem.addPoint (3, -1.9122489313410047, 4.4E-323); problem.addPoint (4, 1.7785661310051026, 0.0); new LevenbergMarquardtEstimator().estimate(problem); fail("an exception should have been thrown"); } catch (EstimationException ee) { // expected behavior } } private static class LinearProblem implements EstimationProblem { public LinearProblem(LinearMeasurement[] measurements) { this.measurements = measurements; } public WeightedMeasurement[] getMeasurements() { return measurements; } public EstimatedParameter[] getUnboundParameters() { return getAllParameters(); } public EstimatedParameter[] getAllParameters() { HashSet<EstimatedParameter> set = new HashSet(); for (int i = 0; i < measurements.length; ++i) { EstimatedParameter[] parameters = measurements[i].getParameters(); for (int j = 0; j < parameters.length; ++j) { set.add(parameters[j]); } } return set.toArray(new EstimatedParameter[set.size()]); } private LinearMeasurement[] measurements; } private static class LinearMeasurement extends WeightedMeasurement { public LinearMeasurement(double[] factors, EstimatedParameter[] parameters, double setPoint) { super(1.0, setPoint); this.factors = factors; this.parameters = parameters; } @Override public double getTheoreticalValue() { double v = 0; for (int i = 0; i < factors.length; ++i) { v += factors[i] * parameters[i].getEstimate(); } return v; } @Override public double getPartial(EstimatedParameter parameter) { for (int i = 0; i < parameters.length; ++i) { if (parameters[i] == parameter) { return factors[i]; } } return 0; } public EstimatedParameter[] getParameters() { return parameters; } private double[] factors; private EstimatedParameter[] parameters; private static final long serialVersionUID = -3922448707008868580L; } private static class Circle implements EstimationProblem { public Circle(double cx, double cy) { this.cx = new EstimatedParameter("cx", cx); this.cy = new EstimatedParameter("cy", cy); points = new ArrayList<PointModel>(); } public void addPoint(double px, double py) { points.add(new PointModel(this, px, py)); } public int getM() { return points.size(); } public WeightedMeasurement[] getMeasurements() { return points.toArray(new PointModel[points.size()]); } public EstimatedParameter[] getAllParameters() { return new EstimatedParameter[] { cx, cy }; } public EstimatedParameter[] getUnboundParameters() { return new EstimatedParameter[] { cx, cy }; } public double getPartialRadiusX() { double dRdX = 0; for (PointModel point : points) { dRdX += point.getPartialDiX(); } return dRdX / points.size(); } public double getPartialRadiusY() { double dRdY = 0; for (PointModel point : points) { dRdY += point.getPartialDiY(); } return dRdY / points.size(); } public double getRadius() { double r = 0; for (PointModel point : points) { r += point.getCenterDistance(); } return r / points.size(); } public double getX() { return cx.getEstimate(); } public double getY() { return cy.getEstimate(); } private static class PointModel extends WeightedMeasurement { public PointModel(Circle circle, double px, double py) { super(1.0, 0.0); this.px = px; this.py = py; this.circle = circle; } @Override public double getPartial(EstimatedParameter parameter) { if (parameter == circle.cx) { return getPartialDiX() - circle.getPartialRadiusX(); } else if (parameter == circle.cy) { return getPartialDiY() - circle.getPartialRadiusY(); } return 0; } public double getCenterDistance() { double dx = px - circle.cx.getEstimate(); double dy = py - circle.cy.getEstimate(); return Math.sqrt(dx * dx + dy * dy); } public double getPartialDiX() { return (circle.cx.getEstimate() - px) / getCenterDistance(); } public double getPartialDiY() { return (circle.cy.getEstimate() - py) / getCenterDistance(); } @Override public double getTheoreticalValue() { return getCenterDistance() - circle.getRadius(); } private double px; private double py; private transient final Circle circle; private static final long serialVersionUID = 1L; } private EstimatedParameter cx; private EstimatedParameter cy; private ArrayList<PointModel> points; } private static class QuadraticProblem extends SimpleEstimationProblem { private EstimatedParameter a; private EstimatedParameter b; private EstimatedParameter c; public QuadraticProblem() { a = new EstimatedParameter("a", 0.0); b = new EstimatedParameter("b", 0.0); c = new EstimatedParameter("c", 0.0); addParameter(a); addParameter(b); addParameter(c); } public void addPoint(double x, double y, double w) { addMeasurement(new LocalMeasurement(this, x, y, w)); } public double theoreticalValue(double x) { return ( (a.getEstimate() * x + b.getEstimate() ) * x + c.getEstimate()); } private double partial(double x, EstimatedParameter parameter) { if (parameter == a) { return x * x; } else if (parameter == b) { return x; } else { return 1.0; } } private static class LocalMeasurement extends WeightedMeasurement { private static final long serialVersionUID = 1555043155023729130L; private final double x; private transient final QuadraticProblem pb; // constructor public LocalMeasurement(QuadraticProblem pb, double x, double y, double w) { super(w, y); this.x = x; this.pb = pb; } @Override public double getTheoreticalValue() { return pb.theoreticalValue(x); } @Override public double getPartial(EstimatedParameter parameter) { return pb.partial(x, parameter); } } } }

Other Commons Math examples (source code examples)

Here is a short list of links related to this Commons Math LevenbergMarquardtEstimatorTest.java source code file:

Commons Math example source code file (LevenbergMarquardtEstimatorTest.java)

This example Commons Math source code file (LevenbergMarquardtEstimatorTest.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Java - Commons Math tags/keywords

circle, estimatedparameter, estimatedparameter, estimationexception, levenbergmarquardtestimator, levenbergmarquardtestimator, linearmeasurement, linearmeasurement, linearproblem, linearproblem, override, pointmodel, quadraticproblem, util, weightedmeasurement

The Commons Math LevenbergMarquardtEstimatorTest.java source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.commons.math.estimation;

import java.util.ArrayList;
import java.util.HashSet;

import junit.framework.TestCase;

/**
 * <p>Some of the unit tests are re-implementations of the MINPACK  and  test files.
 * The redistribution policy for MINPACK is available <a
 * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
 * convenience, it is reproduced below.</p>

 * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
 * <tr>
 * Minpack Copyright Notice (1999) University of Chicago. * All rights reserved * </td>
* Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * <ol> * <li>Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer.</li> * <li>Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution.</li> * <li>The end-user documentation included with the redistribution, if any, * must include the following acknowledgment: * <code>This product includes software developed by the University of * Chicago, as Operator of Argonne National Laboratory.</code> * Alternately, this acknowledgment may appear in the software itself, * if and wherever such third-party acknowledgments normally appear.</li> * <li>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS" * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4) * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL * BE CORRECTED.</strong> * <li>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT, * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE, * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong> * <ol>
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