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Java example source code file (SimplexOptimizerMultiDirectionalTest.java)
The SimplexOptimizerMultiDirectionalTest.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.SimpleValueChecker; import org.apache.commons.math3.util.FastMath; import org.junit.Assert; import org.junit.Test; @Deprecated public class SimplexOptimizerMultiDirectionalTest { @Test public void testMinimize1() { SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30); optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 })); final FourExtrema fourExtrema = new FourExtrema(); final PointValuePair optimum = optimizer.optimize(200, fourExtrema, GoalType.MINIMIZE, new double[] { -3, 0 }); Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 4e-6); Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6); Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 8e-13); Assert.assertTrue(optimizer.getEvaluations() > 120); Assert.assertTrue(optimizer.getEvaluations() < 150); } @Test public void testMinimize2() { SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30); optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 })); final FourExtrema fourExtrema = new FourExtrema(); final PointValuePair optimum = optimizer.optimize(200, fourExtrema, GoalType.MINIMIZE, new double[] { 1, 0 }); Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8); Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6); Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 2e-12); Assert.assertTrue(optimizer.getEvaluations() > 120); Assert.assertTrue(optimizer.getEvaluations() < 150); } @Test public void testMaximize1() { SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30); optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 })); final FourExtrema fourExtrema = new FourExtrema(); final PointValuePair optimum = optimizer.optimize(200, fourExtrema, GoalType.MAXIMIZE, new double[] { -3.0, 0.0 }); Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 7e-7); Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-7); Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 2e-14); Assert.assertTrue(optimizer.getEvaluations() > 120); Assert.assertTrue(optimizer.getEvaluations() < 150); } @Test public void testMaximize2() { SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleValueChecker(1e-15, 1e-30)); optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 })); final FourExtrema fourExtrema = new FourExtrema(); final PointValuePair optimum = optimizer.optimize(200, fourExtrema, GoalType.MAXIMIZE, new double[] { 1, 0 }); Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8); Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6); Assert.assertEquals(fourExtrema.valueXpYp, optimum.getValue(), 2e-12); Assert.assertTrue(optimizer.getEvaluations() > 180); Assert.assertTrue(optimizer.getEvaluations() < 220); } @Test public void testRosenbrock() { MultivariateFunction rosenbrock = new MultivariateFunction() { public double value(double[] x) { ++count; double a = x[1] - x[0] * x[0]; double b = 1.0 - x[0]; return 100 * a * a + b * b; } }; count = 0; SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3); optimizer.setSimplex(new MultiDirectionalSimplex(new double[][] { { -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 } })); PointValuePair optimum = optimizer.optimize(100, rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1 }); Assert.assertEquals(count, optimizer.getEvaluations()); Assert.assertTrue(optimizer.getEvaluations() > 50); Assert.assertTrue(optimizer.getEvaluations() < 100); Assert.assertTrue(optimum.getValue() > 1e-2); } @Test public void testPowell() { MultivariateFunction powell = new MultivariateFunction() { public double value(double[] x) { ++count; double a = x[0] + 10 * x[1]; double b = x[2] - x[3]; double c = x[1] - 2 * x[2]; double d = x[0] - x[3]; return a * a + 5 * b * b + c * c * c * c + 10 * d * d * d * d; } }; count = 0; SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3); optimizer.setSimplex(new MultiDirectionalSimplex(4)); PointValuePair optimum = optimizer.optimize(1000, powell, GoalType.MINIMIZE, new double[] { 3, -1, 0, 1 }); Assert.assertEquals(count, optimizer.getEvaluations()); Assert.assertTrue(optimizer.getEvaluations() > 800); Assert.assertTrue(optimizer.getEvaluations() < 900); Assert.assertTrue(optimum.getValue() > 1e-2); } @Test public void testMath283() { // fails because MultiDirectional.iterateSimplex is looping forever // the while(true) should be replaced with a convergence check SimplexOptimizer optimizer = new SimplexOptimizer(1e-14, 1e-14); optimizer.setSimplex(new MultiDirectionalSimplex(2)); final Gaussian2D function = new Gaussian2D(0, 0, 1); PointValuePair estimate = optimizer.optimize(1000, function, GoalType.MAXIMIZE, function.getMaximumPosition()); 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 ); } private static class FourExtrema implements MultivariateFunction { // The following function has 4 local extrema. final double xM = -3.841947088256863675365; final double yM = -1.391745200270734924416; final double xP = 0.2286682237349059125691; final double yP = -yM; final double valueXmYm = 0.2373295333134216789769; // Local maximum. final double valueXmYp = -valueXmYm; // Local minimum. final double valueXpYm = -0.7290400707055187115322; // Global minimum. final double valueXpYp = -valueXpYm; // Global maximum. public double value(double[] variables) { final double x = variables[0]; final double y = variables[1]; return (x == 0 || y == 0) ? 0 : FastMath.atan(x) * FastMath.atan(x + 2) * FastMath.atan(y) * FastMath.atan(y) / (x * y); } } private static class Gaussian2D implements MultivariateFunction { private final double[] maximumPosition; private final double std; public 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(); } public double value(double[] point) { final double x = point[0], y = point[1]; final double twoS2 = 2.0 * std * std; return 1.0 / (twoS2 * FastMath.PI) * FastMath.exp(-(x * x + y * y) / twoS2); } } private int count; } Other Java examples (source code examples)Here is a short list of links related to this Java SimplexOptimizerMultiDirectionalTest.java source code file: |
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