| /* |
| * 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.math4.legacy.optim.nonlinear.scalar.noderiv; |
| |
| import java.util.Arrays; |
| import org.apache.commons.math4.legacy.analysis.MultivariateFunction; |
| import org.apache.commons.math4.legacy.exception.MathUnsupportedOperationException; |
| import org.apache.commons.math4.legacy.optim.InitialGuess; |
| import org.apache.commons.math4.legacy.optim.MaxEval; |
| import org.apache.commons.math4.legacy.optim.PointValuePair; |
| import org.apache.commons.math4.legacy.optim.SimpleBounds; |
| import org.apache.commons.math4.legacy.optim.nonlinear.scalar.GoalType; |
| import org.apache.commons.math4.legacy.optim.nonlinear.scalar.ObjectiveFunction; |
| import org.apache.commons.math4.legacy.optim.nonlinear.scalar.TestFunction; |
| import org.apache.commons.math4.legacy.core.MathArrays; |
| import org.apache.commons.math4.legacy.core.jdkmath.AccurateMath; |
| import org.junit.Assert; |
| import org.junit.Test; |
| import org.junit.Ignore; |
| |
| /** |
| * Tests for {@link MultiDirectionalTransform}. |
| */ |
| public class SimplexOptimizerMultiDirectionalTest { |
| private static final int DIM = 13; |
| |
| @Test(expected=MathUnsupportedOperationException.class) |
| public void testBoundsUnsupported() { |
| SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30); |
| final OptimTestUtils.FourExtrema fourExtrema = new OptimTestUtils.FourExtrema(); |
| |
| optimizer.optimize(new MaxEval(100), |
| new ObjectiveFunction(fourExtrema), |
| GoalType.MINIMIZE, |
| new InitialGuess(new double[] { -3, 0 }), |
| Simplex.alongAxes(new double[] { 0.2, 0.2 }), |
| new MultiDirectionalTransform(), |
| new SimpleBounds(new double[] { -5, -1 }, |
| new double[] { 5, 1 })); |
| } |
| |
| @Test |
| public void testMath283() { |
| SimplexOptimizer optimizer = new SimplexOptimizer(1e-14, 1e-14); |
| final Gaussian2D function = new Gaussian2D(0, 0, 1); |
| PointValuePair estimate = optimizer.optimize(new MaxEval(1000), |
| new ObjectiveFunction(function), |
| GoalType.MAXIMIZE, |
| new InitialGuess(function.getMaximumPosition()), |
| Simplex.equalSidesAlongAxes(2, 1d), |
| new MultiDirectionalTransform()); |
| final double EPSILON = 1e-5; |
| final double expectedMaximum = function.getMaximum(); |
| final double actualMaximum = estimate.getValue(); |
| Assert.assertEquals(expectedMaximum, actualMaximum, EPSILON); |
| |
| final double[] expectedPosition = function.getMaximumPosition(); |
| final double[] actualPosition = estimate.getPoint(); |
| Assert.assertEquals(expectedPosition[0], actualPosition[0], EPSILON ); |
| Assert.assertEquals(expectedPosition[1], actualPosition[1], EPSILON ); |
| } |
| |
| @Test |
| public void testFourExtremaMinimize1() { |
| final OptimTestUtils.FourExtrema f = new OptimTestUtils.FourExtrema(); |
| doTest(f, |
| OptimTestUtils.point(new double[] {-3, 0}, 1e-1), |
| GoalType.MINIMIZE, |
| 105, |
| Simplex.alongAxes(OptimTestUtils.point(2, 0.2, 1e-2)), |
| new PointValuePair(new double[] {f.xM, f.yP}, f.valueXmYp), |
| 1e-6); |
| } |
| @Test |
| public void testFourExtremaMaximize1() { |
| final OptimTestUtils.FourExtrema f = new OptimTestUtils.FourExtrema(); |
| doTest(f, |
| OptimTestUtils.point(new double[] {-3, 0}, 1e-1), |
| GoalType.MAXIMIZE, |
| 100, |
| Simplex.alongAxes(OptimTestUtils.point(2, 0.2, 1e-2)), |
| new PointValuePair(new double[] {f.xM, f.yM}, f.valueXmYm), |
| 1e-6); |
| } |
| @Test |
| public void testFourExtremaMinimize2() { |
| final OptimTestUtils.FourExtrema f = new OptimTestUtils.FourExtrema(); |
| doTest(f, |
| OptimTestUtils.point(new double[] {1, 0}, 1e-1), |
| GoalType.MINIMIZE, |
| 100, |
| Simplex.alongAxes(OptimTestUtils.point(2, 0.2, 1e-2)), |
| new PointValuePair(new double[] {f.xP, f.yM}, f.valueXpYm), |
| 1e-6); |
| } |
| @Test |
| public void testFourExtremaMaximize2() { |
| final OptimTestUtils.FourExtrema f = new OptimTestUtils.FourExtrema(); |
| doTest(f, |
| OptimTestUtils.point(new double[] {1, 0}, 1e-1), |
| GoalType.MAXIMIZE, |
| 110, |
| Simplex.alongAxes(OptimTestUtils.point(2, 0.2, 1e-2)), |
| new PointValuePair(new double[] {f.xP, f.yP}, f.valueXpYp), |
| 1e-6); |
| } |
| |
| @Ignore("See MATH-1552")@Test |
| public void testElliRotated() { |
| doTest(new OptimTestUtils.ElliRotated(), |
| OptimTestUtils.point(DIM, 1.0, 1e-1), |
| GoalType.MINIMIZE, |
| 911, |
| new PointValuePair(OptimTestUtils.point(DIM, 0.0), 0.0), |
| 1e-9); |
| } |
| |
| /** |
| * @param func Function to optimize. |
| * @param startPoint Starting point. |
| * @param goal Minimization or maximization. |
| * @param maxEvaluations Maximum number of evaluations. |
| * @param expected Expected optimum. |
| * @param tol Tolerance for checking that the optimum is correct. |
| */ |
| private void doTest(MultivariateFunction func, |
| double[] startPoint, |
| GoalType goal, |
| int maxEvaluations, |
| PointValuePair expected, |
| double tol) { |
| doTest(func, |
| startPoint, |
| goal, |
| maxEvaluations, |
| Simplex.equalSidesAlongAxes(startPoint.length, 1), |
| expected, |
| tol); |
| } |
| |
| /** |
| * @param func Function to optimize. |
| * @param startPoint Starting point. |
| * @param goal Minimization or maximization. |
| * @param maxEvaluations Maximum number of evaluations. |
| * @param simplexSteps Initial simplex. |
| * @param expected Expected optimum. |
| * @param tol Tolerance for checking that the optimum is correct. |
| */ |
| private void doTest(MultivariateFunction func, |
| double[] startPoint, |
| GoalType goal, |
| int maxEvaluations, |
| Simplex simplex, |
| PointValuePair expected, |
| double tol) { |
| final int maxEval = Math.max(maxEvaluations, 12000); |
| final SimplexOptimizer optim = new SimplexOptimizer(1e-13, 1e-14); |
| final PointValuePair result = optim.optimize(new MaxEval(maxEval), |
| new ObjectiveFunction(func), |
| goal, |
| new InitialGuess(startPoint), |
| simplex, |
| new NelderMeadTransform()); |
| final String name = func.toString(); |
| |
| final double[] endPoint = result.getPoint(); |
| final double funcValue = result.getValue(); |
| Assert.assertEquals(name + ": value at " + Arrays.toString(endPoint), |
| expected.getValue(), |
| funcValue, 1e-2); |
| |
| final double dist = MathArrays.distance(expected.getPoint(), |
| endPoint); |
| Assert.assertEquals(name + ": distance to optimum", 0d, dist, tol); |
| |
| final int nEval = optim.getEvaluations(); |
| Assert.assertTrue(name + ": nEval=" + nEval, |
| nEval < maxEvaluations); |
| } |
| |
| private static class Gaussian2D implements MultivariateFunction { |
| private final double[] maximumPosition; |
| private final double std; |
| |
| Gaussian2D(double xOpt, double yOpt, double std) { |
| maximumPosition = new double[] { xOpt, yOpt }; |
| this.std = std; |
| } |
| |
| public double getMaximum() { |
| return value(maximumPosition); |
| } |
| |
| public double[] getMaximumPosition() { |
| return maximumPosition.clone(); |
| } |
| |
| @Override |
| public double value(double[] point) { |
| final double x = point[0]; |
| final double y = point[1]; |
| final double twoS2 = 2.0 * std * std; |
| return 1.0 / (twoS2 * AccurateMath.PI) * AccurateMath.exp(-(x * x + y * y) / twoS2); |
| } |
| } |
| } |