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MATH-798 

Test case provided by the reporter, adapted to become a unit test, shows
that the same convergence criterion generates a very similar solution by
both "LevenbergMarquardtOptimizer" and "GaussNewtonOptimizer".



git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1345538 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Gilles Sadowski 2012-06-02 17:53:05 +00:00
parent bbf926511f
commit 290002cb88
1 changed files with 68 additions and 0 deletions
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68
src/test/java/org/apache/commons/math3/optimization/fitting/CurveFitterTest.java
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@ -18,8 +18,13 @@
package org.apache.commons.math3.optimization.fitting;
import org.apache.commons.math3.optimization.general.LevenbergMarquardtOptimizer;
import org.apache.commons.math3.optimization.general.GaussNewtonOptimizer;
import org.apache.commons.math3.optimization.DifferentiableMultivariateVectorOptimizer;
import org.apache.commons.math3.optimization.SimpleVectorValueChecker;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.analysis.polynomials.PolynomialFunction;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import org.junit.Assert;
import org.junit.Test;
@ -133,6 +138,69 @@ public class CurveFitterTest {
}
@Test
public void testMath798() {
final double tol = 1e-14;
final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
final double[] init = new double[] { 0, 0 };
final int maxEval = 3;
final double[] lm = doMath798(new LevenbergMarquardtOptimizer(checker), maxEval, init);
final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);
for (int i = 0; i <= 1; i++) {
Assert.assertEquals(lm[i], gn[i], tol);
}
}
/**
* @param optimizer Optimizer.
* @param maxEval Maximum number of function evaluations.
* @param init First guess.
* @return the solution found by the given optimizer.
*/
private double[] doMath798(DifferentiableMultivariateVectorOptimizer optimizer,
int maxEval,
double[] init) {
final CurveFitter fitter = new CurveFitter(optimizer);
fitter.addObservedPoint(-0.2, -7.12442E-13);
fitter.addObservedPoint(-0.199, -4.33397E-13);
fitter.addObservedPoint(-0.198, -2.823E-13);
fitter.addObservedPoint(-0.197, -1.40405E-13);
fitter.addObservedPoint(-0.196, -7.80821E-15);
fitter.addObservedPoint(-0.195, 6.20484E-14);
fitter.addObservedPoint(-0.194, 7.24673E-14);
fitter.addObservedPoint(-0.193, 1.47152E-13);
fitter.addObservedPoint(-0.192, 1.9629E-13);
fitter.addObservedPoint(-0.191, 2.12038E-13);
fitter.addObservedPoint(-0.19, 2.46906E-13);
fitter.addObservedPoint(-0.189, 2.77495E-13);
fitter.addObservedPoint(-0.188, 2.51281E-13);
fitter.addObservedPoint(-0.187, 2.64001E-13);
fitter.addObservedPoint(-0.186, 2.8882E-13);
fitter.addObservedPoint(-0.185, 3.13604E-13);
fitter.addObservedPoint(-0.184, 3.14248E-13);
fitter.addObservedPoint(-0.183, 3.1172E-13);
fitter.addObservedPoint(-0.182, 3.12912E-13);
fitter.addObservedPoint(-0.181, 3.06761E-13);
fitter.addObservedPoint(-0.18, 2.8559E-13);
fitter.addObservedPoint(-0.179, 2.86806E-13);
fitter.addObservedPoint(-0.178, 2.985E-13);
fitter.addObservedPoint(-0.177, 2.67148E-13);
fitter.addObservedPoint(-0.176, 2.94173E-13);
fitter.addObservedPoint(-0.175, 3.27528E-13);
fitter.addObservedPoint(-0.174, 3.33858E-13);
fitter.addObservedPoint(-0.173, 2.97511E-13);
fitter.addObservedPoint(-0.172, 2.8615E-13);
fitter.addObservedPoint(-0.171, 2.84624E-13);
final double[] coeff = fitter.fit(maxEval,
new PolynomialFunction.Parametric(),
init);
return coeff;
}
private static class SimpleInverseFunction implements ParametricUnivariateFunction {
public double value(double x, double ... parameters) {