| /* |
| * 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.math3.optimization.direct; |
| |
| |
| import org.apache.commons.math3.analysis.MultivariateFunction; |
| import org.apache.commons.math3.optimization.GoalType; |
| import org.apache.commons.math3.optimization.PointValuePair; |
| import org.apache.commons.math3.optimization.SimplePointChecker; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| @Deprecated |
| public class MultivariateFunctionPenaltyAdapterTest { |
| |
| @Test |
| public void testStartSimplexInsideRange() { |
| |
| final BiQuadratic biQuadratic = new BiQuadratic(2.0, 2.5, 1.0, 3.0, 2.0, 3.0); |
| final MultivariateFunctionPenaltyAdapter wrapped = |
| new MultivariateFunctionPenaltyAdapter(biQuadratic, |
| biQuadratic.getLower(), |
| biQuadratic.getUpper(), |
| 1000.0, new double[] { 100.0, 100.0 }); |
| |
| SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30); |
| optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 })); |
| |
| final PointValuePair optimum |
| = optimizer.optimize(300, wrapped, GoalType.MINIMIZE, new double[] { 1.5, 2.25 }); |
| |
| Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7); |
| Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7); |
| |
| } |
| |
| @Test |
| public void testStartSimplexOutsideRange() { |
| |
| final BiQuadratic biQuadratic = new BiQuadratic(2.0, 2.5, 1.0, 3.0, 2.0, 3.0); |
| final MultivariateFunctionPenaltyAdapter wrapped = |
| new MultivariateFunctionPenaltyAdapter(biQuadratic, |
| biQuadratic.getLower(), |
| biQuadratic.getUpper(), |
| 1000.0, new double[] { 100.0, 100.0 }); |
| |
| SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30); |
| optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 })); |
| |
| final PointValuePair optimum |
| = optimizer.optimize(300, wrapped, GoalType.MINIMIZE, new double[] { -1.5, 4.0 }); |
| |
| Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7); |
| Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7); |
| |
| } |
| |
| @Test |
| public void testOptimumOutsideRange() { |
| |
| final BiQuadratic biQuadratic = new BiQuadratic(4.0, 0.0, 1.0, 3.0, 2.0, 3.0); |
| final MultivariateFunctionPenaltyAdapter wrapped = |
| new MultivariateFunctionPenaltyAdapter(biQuadratic, |
| biQuadratic.getLower(), |
| biQuadratic.getUpper(), |
| 1000.0, new double[] { 100.0, 100.0 }); |
| |
| SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<PointValuePair>(1.0e-11, 1.0e-20)); |
| optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 })); |
| |
| final PointValuePair optimum |
| = optimizer.optimize(600, wrapped, GoalType.MINIMIZE, new double[] { -1.5, 4.0 }); |
| |
| Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7); |
| Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7); |
| |
| } |
| |
| @Test |
| public void testUnbounded() { |
| |
| final BiQuadratic biQuadratic = new BiQuadratic(4.0, 0.0, |
| Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, |
| Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY); |
| final MultivariateFunctionPenaltyAdapter wrapped = |
| new MultivariateFunctionPenaltyAdapter(biQuadratic, |
| biQuadratic.getLower(), |
| biQuadratic.getUpper(), |
| 1000.0, new double[] { 100.0, 100.0 }); |
| |
| SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30); |
| optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 })); |
| |
| final PointValuePair optimum |
| = optimizer.optimize(300, wrapped, GoalType.MINIMIZE, new double[] { -1.5, 4.0 }); |
| |
| Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7); |
| Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7); |
| |
| } |
| |
| @Test |
| public void testHalfBounded() { |
| |
| final BiQuadratic biQuadratic = new BiQuadratic(4.0, 4.0, |
| 1.0, Double.POSITIVE_INFINITY, |
| Double.NEGATIVE_INFINITY, 3.0); |
| final MultivariateFunctionPenaltyAdapter wrapped = |
| new MultivariateFunctionPenaltyAdapter(biQuadratic, |
| biQuadratic.getLower(), |
| biQuadratic.getUpper(), |
| 1000.0, new double[] { 100.0, 100.0 }); |
| |
| SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<PointValuePair>(1.0e-10, 1.0e-20)); |
| optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 })); |
| |
| final PointValuePair optimum |
| = optimizer.optimize(400, wrapped, GoalType.MINIMIZE, new double[] { -1.5, 4.0 }); |
| |
| Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7); |
| Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7); |
| |
| } |
| |
| private static class BiQuadratic implements MultivariateFunction { |
| |
| private final double xOptimum; |
| private final double yOptimum; |
| |
| private final double xMin; |
| private final double xMax; |
| private final double yMin; |
| private final double yMax; |
| |
| public BiQuadratic(final double xOptimum, final double yOptimum, |
| final double xMin, final double xMax, |
| final double yMin, final double yMax) { |
| this.xOptimum = xOptimum; |
| this.yOptimum = yOptimum; |
| this.xMin = xMin; |
| this.xMax = xMax; |
| this.yMin = yMin; |
| this.yMax = yMax; |
| } |
| |
| public double value(double[] point) { |
| |
| // the function should never be called with out of range points |
| Assert.assertTrue(point[0] >= xMin); |
| Assert.assertTrue(point[0] <= xMax); |
| Assert.assertTrue(point[1] >= yMin); |
| Assert.assertTrue(point[1] <= yMax); |
| |
| final double dx = point[0] - xOptimum; |
| final double dy = point[1] - yOptimum; |
| return dx * dx + dy * dy; |
| |
| } |
| |
| public double[] getLower() { |
| return new double[] { xMin, yMin }; |
| } |
| |
| public double[] getUpper() { |
| return new double[] { xMax, yMax }; |
| } |
| |
| public double getBoundedXOptimum() { |
| return (xOptimum < xMin) ? xMin : ((xOptimum > xMax) ? xMax : xOptimum); |
| } |
| |
| public double getBoundedYOptimum() { |
| return (yOptimum < yMin) ? yMin : ((yOptimum > yMax) ? yMax : yOptimum); |
| } |
| |
| } |
| |
| } |