added new tests for multistart optimizers

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@795608 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe 2009-07-19 20:05:34 +00:00
parent 28bb294968
commit 56b70eb138
2 changed files with 204 additions and 0 deletions

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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
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.FunctionEvaluationException;
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.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 FunctionEvaluationException, OptimizationException {
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);
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.getEvaluations() > 70);
assertTrue(optimizer.getEvaluations() < 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];
}
};
}
}
}

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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math.optimization;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
import org.apache.commons.math.MathException;
import org.apache.commons.math.analysis.SinFunction;
import org.apache.commons.math.analysis.UnivariateRealFunction;
import org.apache.commons.math.optimization.univariate.BrentOptimizer;
import org.apache.commons.math.random.JDKRandomGenerator;
import org.junit.Test;
public class MultiStartUnivariateRealOptimizerTest {
@Test
public void testSinMin() throws MathException {
UnivariateRealFunction f = new SinFunction();
UnivariateRealOptimizer underlying = new BrentOptimizer();
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(44428400075l);
MultiStartUnivariateRealOptimizer minimizer =
new MultiStartUnivariateRealOptimizer(underlying, 10, g);
minimizer.optimize(f, GoalType.MINIMIZE, -100.0, 100.0);
double[] optima = minimizer.getOptima();
for (int i = 1; i < optima.length; ++i) {
double d = (optima[i] - optima[i-1]) / (2 * Math.PI);
assertTrue (Math.abs(d - Math.rint(d)) < 1.0e-8);
assertEquals(-1.0, f.value(optima[i]), 1.0e-10);
}
assertTrue(minimizer.getEvaluations() > 2900);
assertTrue(minimizer.getEvaluations() < 3100);
}
}