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//
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//
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package org.apache.commons.math3.optimization.fitting;
import org.apache.commons.math3.optimization.general.LevenbergMarquardtOptimizer;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
@Deprecated
public class CurveFitterTest {
@Test
public void testMath303() {
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(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);
ParametricUnivariateFunction 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() {
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(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);
ParametricUnivariateFunction 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);
}
@Test
public void testMath372() {
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
CurveFitter<ParametricUnivariateFunction> curveFitter = new CurveFitter<ParametricUnivariateFunction>(optimizer);
curveFitter.addObservedPoint( 15, 4443);
curveFitter.addObservedPoint( 31, 8493);
curveFitter.addObservedPoint( 62, 17586);
curveFitter.addObservedPoint(125, 30582);
curveFitter.addObservedPoint(250, 45087);
curveFitter.addObservedPoint(500, 50683);
ParametricUnivariateFunction f = new ParametricUnivariateFunction() {
public double value(double x, double ... parameters) {
double a = parameters[0];
double b = parameters[1];
double c = parameters[2];
double d = parameters[3];
return d + ((a - d) / (1 + FastMath.pow(x / c, b)));
}
public double[] gradient(double x, double ... parameters) {
double a = parameters[0];
double b = parameters[1];
double c = parameters[2];
double d = parameters[3];
double[] gradients = new double[4];
double den = 1 + FastMath.pow(x / c, b);
// derivative with respect to a
gradients[0] = 1 / den;
// derivative with respect to b
// in the reported (invalid) issue, there was a sign error here
gradients[1] = -((a - d) * FastMath.pow(x / c, b) * FastMath.log(x / c)) / (den * den);
// derivative with respect to c
gradients[2] = (b * FastMath.pow(x / c, b - 1) * (x / (c * c)) * (a - d)) / (den * den);
// derivative with respect to d
gradients[3] = 1 - (1 / den);
return gradients;
}
};
double[] initialGuess = new double[] { 1500, 0.95, 65, 35000 };
double[] estimatedParameters = curveFitter.fit(f, initialGuess);
Assert.assertEquals( 2411.00, estimatedParameters[0], 500.00);
Assert.assertEquals( 1.62, estimatedParameters[1], 0.04);
Assert.assertEquals( 111.22, estimatedParameters[2], 0.30);
Assert.assertEquals(55347.47, estimatedParameters[3], 300.00);
Assert.assertTrue(optimizer.getRMS() < 600.0);
}
private static class SimpleInverseFunction implements ParametricUnivariateFunction {
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;
}
}
}