Examples of org.apache.commons.math.estimation.LevenbergMarquardtEstimator

Package org.apache.commons.math.estimation

Examples of org.apache.commons.math.estimation.LevenbergMarquardtEstimator

org.apache.commons.math.estimation.LevenbergMarquardtEstimator

etlib.org/minpack/lmder.f">lmder routine with minor changes. The changes include the over-determined resolution and the Q.R. decomposition which has been rewritten following the algorithm described in the P. Lascaux and R. Theodor book Analyse numérique matricielle appliquée à l'art de l'ingénieur, Masson 1986.

The authors of the original fortran version are:

Argonne National Laboratory. MINPACK project. March 1980
Burton S. Garbow
Kenneth E. Hillstrom
Jorge J. More

The redistribution policy for MINPACK is available here, for convenience, it is reproduced below.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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.
The end-user documentation included with the redistribution, if any, must include the following acknowledgment: This product includes software developed by the University of Chicago, as Operator of Argonne National Laboratory. Alternately, this acknowledgment may appear in the software itself, if and wherever such third-party acknowledgments normally appear.
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.
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@version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $ @since 1.2 @deprecated as of 2.0, everything in package org.apache.commons.math.estimation hasbeen deprecated and replaced by package org.apache.commons.math.optimization.general

                                       5.67534206273052
                                     }), false);
  }


  private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
    LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
    estimator.setMaxCostEval(100 * (function.getN() + 1));
    estimator.setCostRelativeTolerance(Math.sqrt(2.22044604926e-16));
    estimator.setParRelativeTolerance(Math.sqrt(2.22044604926e-16));
    estimator.setOrthoTolerance(2.22044604926e-16);
    assertTrue(function.checkTheoreticalStartCost(estimator.getRMS(function)));
    try {
      estimator.estimate(function);
      assertFalse(exceptionExpected);
    } catch (EstimationException lsse) {
      assertTrue(exceptionExpected);
    }
    assertTrue(function.checkTheoreticalMinCost(estimator.getRMS(function)));
    assertTrue(function.checkTheoreticalMinParams());
  }

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                    {410.0, 0.410522 }, {430.0, 0.382701 }, {450.0, 0.356957 }, {470.0, 0.332400 } };
            for (int i = 0; i < angles.length; ++i) {
                problem.addAngularMeasurement(3.0e7, angles[i][0], angles[i][1]);
            };


            LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
            estimator.estimate(problem);
            System.out.println("initial position: " + problem.getX0() + " " + problem.getY0());
            System.out.println("velocity: " + problem.getVx0() + " " + problem.getVy0());


        } catch (EstimationException ee) {
          System.err.println(ee.getMessage());

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                                       5.67534206273052
                                     }), false);
  }


  private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
    LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
    estimator.setMaxCostEval(100 * (function.getN() + 1));
    estimator.setCostRelativeTolerance(FastMath.sqrt(2.22044604926e-16));
    estimator.setParRelativeTolerance(FastMath.sqrt(2.22044604926e-16));
    estimator.setOrthoTolerance(2.22044604926e-16);
    assertTrue(function.checkTheoreticalStartCost(estimator.getRMS(function)));
    try {
      estimator.estimate(function);
      assertFalse(exceptionExpected);
    } catch (EstimationException lsse) {
      assertTrue(exceptionExpected);
    }
    assertTrue(function.checkTheoreticalMinCost(estimator.getRMS(function)));
    assertTrue(function.checkTheoreticalMinParams());
  }

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                                       5.67534206273052
                                     }), false);
  }


  private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
    LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
    estimator.setMaxCostEval(100 * (function.getN() + 1));
    estimator.setCostRelativeTolerance(Math.sqrt(2.22044604926e-16));
    estimator.setParRelativeTolerance(Math.sqrt(2.22044604926e-16));
    estimator.setOrthoTolerance(2.22044604926e-16);
    assertTrue(function.checkTheoreticalStartCost(estimator.getRMS(function)));
    try {
      estimator.estimate(function);
      assertFalse(exceptionExpected);
    } catch (EstimationException lsse) {
      assertTrue(exceptionExpected);
    }
    assertTrue(function.checkTheoreticalMinCost(estimator.getRMS(function)));
    assertTrue(function.checkTheoreticalMinParams());
  }

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      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);


  }

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      {-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);
  }

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                                       5.67534206273052
                                     }), false);
  }


  private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
    LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
    estimator.setMaxCostEval(100 * (function.getN() + 1));
    estimator.setCostRelativeTolerance(Math.sqrt(2.22044604926e-16));
    estimator.setParRelativeTolerance(Math.sqrt(2.22044604926e-16));
    estimator.setOrthoTolerance(2.22044604926e-16);
    assertTrue(function.checkTheoreticalStartCost(estimator.getRMS(function)));
    try {
      estimator.estimate(function);
      assertFalse(exceptionExpected);
    } catch (EstimationException lsse) {
      assertTrue(exceptionExpected);
    }
    assertTrue(function.checkTheoreticalMinCost(estimator.getRMS(function)));
    assertTrue(function.checkTheoreticalMinParams());
  }

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        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");

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      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);


  }

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      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);
  }
}

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