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Java example source code file (MultivariateFunctionPenaltyAdapterTest.java)

This example Java source code file (MultivariateFunctionPenaltyAdapterTest.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

biquadratic, deprecated, multivariatefunctionpenaltyadapter, multivariatefunctionpenaltyadaptertest, neldermeadsimplex, pointvaluepair, simplexoptimizer, test

The MultivariateFunctionPenaltyAdapterTest.java Java example source code

/*
 * 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);
        }

    }

}

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