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* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.commons.math.optimization;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
import java.awt.geom.Point2D;
import java.util.ArrayList;
import org.apache.commons.math.analysis.DifferentiableMultivariateRealFunction;
import org.apache.commons.math.analysis.MultivariateRealFunction;
import org.apache.commons.math.analysis.MultivariateVectorialFunction;
import org.apache.commons.math.analysis.solvers.BrentSolver;
import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.optimization.general.ConjugateGradientFormula;
import org.apache.commons.math.optimization.general.NonLinearConjugateGradientOptimizer;
import org.apache.commons.math.random.GaussianRandomGenerator;
import org.apache.commons.math.random.JDKRandomGenerator;
import org.apache.commons.math.random.RandomVectorGenerator;
import org.apache.commons.math.random.UncorrelatedRandomVectorGenerator;
import org.junit.Test;
public class MultiStartDifferentiableMultivariateRealOptimizerTest {
@Test
public void testCircleFitting() throws Exception {
Circle circle = new Circle();
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);
NonLinearConjugateGradientOptimizer underlying =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
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));
MultiStartDifferentiableMultivariateRealOptimizer optimizer =
new MultiStartDifferentiableMultivariateRealOptimizer(underlying, 10, generator);
optimizer.setMaxIterations(100);
assertEquals(100, optimizer.getMaxIterations());
optimizer.setMaxEvaluations(100);
assertEquals(100, optimizer.getMaxEvaluations());
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-10, 1.0e-10));
BrentSolver solver = new BrentSolver();
solver.setAbsoluteAccuracy(1.0e-13);
solver.setRelativeAccuracy(1.0e-15);
RealPointValuePair optimum =
optimizer.optimize(circle, GoalType.MINIMIZE, new double[] { 98.680, 47.345 });
RealPointValuePair[] optima = optimizer.getOptima();
for (RealPointValuePair o : optima) {
Point2D.Double center = new Point2D.Double(o.getPointRef()[0], o.getPointRef()[1]);
assertEquals(69.960161753, circle.getRadius(center), 1.0e-8);
assertEquals(96.075902096, center.x, 1.0e-8);
assertEquals(48.135167894, center.y, 1.0e-8);
}
assertTrue(optimizer.getGradientEvaluations() > 650);
assertTrue(optimizer.getGradientEvaluations() < 700);
assertTrue(optimizer.getEvaluations() > 70);
assertTrue(optimizer.getEvaluations() < 90);
assertTrue(optimizer.getIterations() > 70);
assertTrue(optimizer.getIterations() < 90);
assertEquals(3.1267527, optimum.getValue(), 1.0e-8);
}
private static class Circle implements DifferentiableMultivariateRealFunction {
private ArrayList<Point2D.Double> points;
public Circle() {
points = new ArrayList<Point2D.Double>();
}
public void addPoint(double px, double py) {
points.add(new Point2D.Double(px, py));
}
public double getRadius(Point2D.Double center) {
double r = 0;
for (Point2D.Double point : points) {
r += point.distance(center);
}
return r / points.size();
}
private double[] gradient(double[] point) {
// optimal radius
Point2D.Double center = new Point2D.Double(point[0], point[1]);
double radius = getRadius(center);
// gradient of the sum of squared residuals
double dJdX = 0;
double dJdY = 0;
for (Point2D.Double pk : points) {
double dk = pk.distance(center);
dJdX += (center.x - pk.x) * (dk - radius) / dk;
dJdY += (center.y - pk.y) * (dk - radius) / dk;
}
dJdX *= 2;
dJdY *= 2;
return new double[] { dJdX, dJdY };
}
public double value(double[] variables)
throws IllegalArgumentException, FunctionEvaluationException {
Point2D.Double center = new Point2D.Double(variables[0], variables[1]);
double radius = getRadius(center);
double sum = 0;
for (Point2D.Double point : points) {
double di = point.distance(center) - radius;
sum += di * di;
}
return sum;
}
public MultivariateVectorialFunction gradient() {
return new MultivariateVectorialFunction() {
public double[] value(double[] point) {
return gradient(point);
}
};
}
public MultivariateRealFunction partialDerivative(final int k) {
return new MultivariateRealFunction() {
public double value(double[] point) {
return gradient(point)[k];
}
};
}
}
}