Examples of org.apache.commons.math.estimation.LevenbergMarquardtEstimator.estimate()

Package org.apache.commons.math.estimation

Class org.apache.commons.math.estimation.LevenbergMarquardtEstimator

Examples of org.apache.commons.math.estimation.LevenbergMarquardtEstimator.estimate()

org.apache.commons.math.estimation.LevenbergMarquardtEstimator.estimate()
etlib.org/minpack/lmder.f">lmder routine. The algorithm settings must have been set up before this method is called with the {@link #setInitialStepBoundFactor}, {@link #setMaxCostEval}, {@link #setCostRelativeTolerance}, {@link #setParRelativeTolerance} and {@link #setOrthoTolerance} methods.If these methods have not been called, the default values set up by the {@link #LevenbergMarquardtEstimator() constructor} will be used.
The authors of the original fortran function are:
- Argonne National Laboratory. MINPACK project. March 1980
- Burton S. Garbow
- Kenneth E. Hillstrom
- Jorge J. More
Luc Maisonobe did the Java translation.
@param problem estimation problem to solve @exception EstimationException if convergence cannot bereached with the specified algorithm settings or if there are more variables than equations @see #setInitialStepBoundFactor @see #setCostRelativeTolerance @see #setParRelativeTolerance @see #setOrthoTolerance

      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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                            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);

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                            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);

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    };
    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");

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      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);

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      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);

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                             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 {

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                           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);

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                             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);


  }

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                            new EstimatedParameter[] { p[0], p[1] },
                            4.0)
    });


    LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
    estimator.estimate(problem);
    assertTrue(estimator.getRMS(problem) > 0.1);


  }


  public void testControlParameters() throws EstimationException {

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