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// to you under the Apache License, Version 2.0 (the
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//
// http://www.apache.org/licenses/LICENSE-2.0
//
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package org.apache.commons.math.optimization.fitting;
import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer;
import org.junit.Assert;
import org.junit.Test;
public class CurveFitterTest {
@Test
public void testMath303()
throws OptimizationException, FunctionEvaluationException {
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
CurveFitter fitter = new CurveFitter(optimizer);
fitter.addObservedPoint(2.805d, 0.6934785852953367d);
fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
fitter.addObservedPoint(1.655d, 0.9474675497289684);
fitter.addObservedPoint(1.725d, 0.9013594835804194d);
ParametricRealFunction sif = new SimpleInverseFunction();
double[] initialguess1 = new double[1];
initialguess1[0] = 1.0d;
Assert.assertEquals(1, fitter.fit(sif, initialguess1).length);
double[] initialguess2 = new double[2];
initialguess2[0] = 1.0d;
initialguess2[1] = .5d;
Assert.assertEquals(2, fitter.fit(sif, initialguess2).length);
}
@Test
public void testMath304()
throws OptimizationException, FunctionEvaluationException {
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
CurveFitter fitter = new CurveFitter(optimizer);
fitter.addObservedPoint(2.805d, 0.6934785852953367d);
fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
fitter.addObservedPoint(1.655d, 0.9474675497289684);
fitter.addObservedPoint(1.725d, 0.9013594835804194d);
ParametricRealFunction sif = new SimpleInverseFunction();
double[] initialguess1 = new double[1];
initialguess1[0] = 1.0d;
Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);
double[] initialguess2 = new double[1];
initialguess2[0] = 10.0d;
Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);
}
private static class SimpleInverseFunction implements ParametricRealFunction {
public double value(double x, double[] parameters) {
return parameters[0] / x + (parameters.length < 2 ? 0 : parameters[1]);
}
public double[] gradient(double x, double[] doubles) {
double[] gradientVector = new double[doubles.length];
gradientVector[0] = 1 / x;
if (doubles.length >= 2) {
gradientVector[1] = 1;
}
return gradientVector;
}
}
}