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* (the "License"); you may not use this file except in compliance with
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* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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package org.apache.commons.math3.optimization;
import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction;
import org.apache.commons.math3.geometry.euclidean.twod.Vector2D;
import org.apache.commons.math3.optimization.general.CircleScalar;
import org.apache.commons.math3.optimization.general.ConjugateGradientFormula;
import org.apache.commons.math3.optimization.general.NonLinearConjugateGradientOptimizer;
import org.apache.commons.math3.random.GaussianRandomGenerator;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomVectorGenerator;
import org.apache.commons.math3.random.UncorrelatedRandomVectorGenerator;
import org.junit.Assert;
import org.junit.Test;
@Deprecated
public class MultivariateDifferentiableMultiStartOptimizerTest {
@Test
public void testCircleFitting() {
CircleScalar circle = new CircleScalar();
circle.addPoint( 30.0, 68.0);
circle.addPoint( 50.0, -6.0);
circle.addPoint(110.0, -20.0);
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
// TODO: the wrapper around NonLinearConjugateGradientOptimizer is a temporary hack for
// version 3.1 of the library. It should be removed when NonLinearConjugateGradientOptimizer
// will officially be declared as implementing MultivariateDifferentiableOptimizer
MultivariateDifferentiableOptimizer underlying =
new MultivariateDifferentiableOptimizer() {
private final NonLinearConjugateGradientOptimizer cg =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleValueChecker(1.0e-10, 1.0e-10));
public PointValuePair optimize(int maxEval,
MultivariateDifferentiableFunction f,
GoalType goalType,
double[] startPoint) {
return cg.optimize(maxEval, f, goalType, startPoint);
}
public int getMaxEvaluations() {
return cg.getMaxEvaluations();
}
public int getEvaluations() {
return cg.getEvaluations();
}
public ConvergenceChecker<PointValuePair> getConvergenceChecker() {
return cg.getConvergenceChecker();
}
};
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(753289573253l);
RandomVectorGenerator generator =
new UncorrelatedRandomVectorGenerator(new double[] { 50.0, 50.0 }, new double[] { 10.0, 10.0 },
new GaussianRandomGenerator(g));
MultivariateDifferentiableMultiStartOptimizer optimizer =
new MultivariateDifferentiableMultiStartOptimizer(underlying, 10, generator);
PointValuePair optimum =
optimizer.optimize(200, circle, GoalType.MINIMIZE, new double[] { 98.680, 47.345 });
Assert.assertEquals(200, optimizer.getMaxEvaluations());
PointValuePair[] optima = optimizer.getOptima();
for (PointValuePair o : optima) {
Vector2D center = new Vector2D(o.getPointRef()[0], o.getPointRef()[1]);
Assert.assertEquals(69.960161753, circle.getRadius(center), 1.0e-8);
Assert.assertEquals(96.075902096, center.getX(), 1.0e-8);
Assert.assertEquals(48.135167894, center.getY(), 1.0e-8);
}
Assert.assertTrue(optimizer.getEvaluations() > 70);
Assert.assertTrue(optimizer.getEvaluations() < 90);
Assert.assertEquals(3.1267527, optimum.getValue(), 1.0e-8);
}
}