Author: erans
Date: Sat Jun 2 17:53:05 2012
New Revision: 1345538
URL: http://svn.apache.org/viewvc?rev=1345538&view=rev
Log:
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".
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/fitting/CurveFitterTest.java
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/fitting/CurveFitterTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/fitting/CurveFitterTest.java?rev=1345538&r1=1345537&r2=1345538&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/fitting/CurveFitterTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/fitting/CurveFitterTest.java
Sat Jun 2 17:53:05 2012
@@ -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) {
