| /* |
| * 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; |
| |
| |
| import org.apache.commons.math3.analysis.differentiation.DerivativeStructure; |
| import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction; |
| import org.apache.commons.math3.exception.MathIllegalStateException; |
| import org.apache.commons.math3.linear.BlockRealMatrix; |
| import org.apache.commons.math3.linear.RealMatrix; |
| import org.apache.commons.math3.optimization.general.GaussNewtonOptimizer; |
| import org.apache.commons.math3.random.GaussianRandomGenerator; |
| import org.apache.commons.math3.random.JDKRandomGenerator; |
| import org.apache.commons.math3.random.RandomVectorGenerator; |
| import org.apache.commons.math3.random.UncorrelatedRandomVectorGenerator; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| /** |
| * <p>Some of the unit tests are re-implementations of the MINPACK <a |
| * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a |
| * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files. |
| * The redistribution policy for MINPACK is available <a |
| * href="http://www.netlib.org/minpack/disclaimer">here</a>, for |
| * convenience, it is reproduced below.</p> |
| |
| * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0"> |
| * <tr><td> |
| * Minpack Copyright Notice (1999) University of Chicago. |
| * All rights reserved |
| * </td></tr> |
| * <tr><td> |
| * Redistribution and use in source and binary forms, with or without |
| * modification, are permitted provided that the following conditions |
| * are met: |
| * <ol> |
| * <li>Redistributions of source code must retain the above copyright |
| * notice, this list of conditions and the following disclaimer.</li> |
| * <li>Redistributions in binary form must reproduce the above |
| * copyright notice, this list of conditions and the following |
| * disclaimer in the documentation and/or other materials provided |
| * with the distribution.</li> |
| * <li>The end-user documentation included with the redistribution, if any, |
| * must include the following acknowledgment: |
| * <code>This product includes software developed by the University of |
| * Chicago, as Operator of Argonne National Laboratory.</code> |
| * Alternately, this acknowledgment may appear in the software itself, |
| * if and wherever such third-party acknowledgments normally appear.</li> |
| * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS" |
| * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE |
| * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND |
| * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES |
| * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE |
| * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY |
| * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR |
| * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF |
| * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4) |
| * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION |
| * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL |
| * BE CORRECTED.</strong></li> |
| * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT |
| * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF |
| * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT, |
| * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF |
| * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF |
| * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER |
| * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT |
| * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE, |
| * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE |
| * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li> |
| * <ol></td></tr> |
| * </table> |
| |
| * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests) |
| * @author Burton S. Garbow (original fortran minpack tests) |
| * @author Kenneth E. Hillstrom (original fortran minpack tests) |
| * @author Jorge J. More (original fortran minpack tests) |
| * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation) |
| */ |
| @Deprecated |
| public class MultivariateDifferentiableVectorMultiStartOptimizerTest { |
| |
| @Test |
| public void testTrivial() { |
| LinearProblem problem = |
| new LinearProblem(new double[][] { { 2 } }, new double[] { 3 }); |
| // TODO: the wrapper around GaussNewtonOptimizer is a temporary hack for |
| // version 3.1 of the library. It should be removed when GaussNewtonOptimizer |
| // will officialy be declared as implementing MultivariateDifferentiableVectorOptimizer |
| MultivariateDifferentiableVectorOptimizer underlyingOptimizer = |
| new MultivariateDifferentiableVectorOptimizer() { |
| private GaussNewtonOptimizer gn = |
| new GaussNewtonOptimizer(true, |
| new SimpleVectorValueChecker(1.0e-6, 1.0e-6)); |
| |
| public PointVectorValuePair optimize(int maxEval, |
| MultivariateDifferentiableVectorFunction f, |
| double[] target, |
| double[] weight, |
| double[] startPoint) { |
| return gn.optimize(maxEval, f, target, weight, startPoint); |
| } |
| |
| public int getMaxEvaluations() { |
| return gn.getMaxEvaluations(); |
| } |
| |
| public int getEvaluations() { |
| return gn.getEvaluations(); |
| } |
| |
| public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() { |
| return gn.getConvergenceChecker(); |
| } |
| }; |
| JDKRandomGenerator g = new JDKRandomGenerator(); |
| g.setSeed(16069223052l); |
| RandomVectorGenerator generator = |
| new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g)); |
| MultivariateDifferentiableVectorMultiStartOptimizer optimizer = |
| new MultivariateDifferentiableVectorMultiStartOptimizer(underlyingOptimizer, |
| 10, generator); |
| |
| // no optima before first optimization attempt |
| try { |
| optimizer.getOptima(); |
| Assert.fail("an exception should have been thrown"); |
| } catch (MathIllegalStateException ise) { |
| // expected |
| } |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 }); |
| Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(3.0, optimum.getValue()[0], 1.0e-10); |
| PointVectorValuePair[] optima = optimizer.getOptima(); |
| Assert.assertEquals(10, optima.length); |
| for (int i = 0; i < optima.length; ++i) { |
| Assert.assertEquals(1.5, optima[i].getPoint()[0], 1.0e-10); |
| Assert.assertEquals(3.0, optima[i].getValue()[0], 1.0e-10); |
| } |
| Assert.assertTrue(optimizer.getEvaluations() > 20); |
| Assert.assertTrue(optimizer.getEvaluations() < 50); |
| Assert.assertEquals(100, optimizer.getMaxEvaluations()); |
| } |
| |
| @Test(expected=TestException.class) |
| public void testNoOptimum() { |
| |
| // TODO: the wrapper around GaussNewtonOptimizer is a temporary hack for |
| // version 3.1 of the library. It should be removed when GaussNewtonOptimizer |
| // will officialy be declared as implementing MultivariateDifferentiableVectorOptimizer |
| MultivariateDifferentiableVectorOptimizer underlyingOptimizer = |
| new MultivariateDifferentiableVectorOptimizer() { |
| private GaussNewtonOptimizer gn = |
| new GaussNewtonOptimizer(true, |
| new SimpleVectorValueChecker(1.0e-6, 1.0e-6)); |
| |
| public PointVectorValuePair optimize(int maxEval, |
| MultivariateDifferentiableVectorFunction f, |
| double[] target, |
| double[] weight, |
| double[] startPoint) { |
| return gn.optimize(maxEval, f, target, weight, startPoint); |
| } |
| |
| public int getMaxEvaluations() { |
| return gn.getMaxEvaluations(); |
| } |
| |
| public int getEvaluations() { |
| return gn.getEvaluations(); |
| } |
| |
| public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() { |
| return gn.getConvergenceChecker(); |
| } |
| }; |
| JDKRandomGenerator g = new JDKRandomGenerator(); |
| g.setSeed(12373523445l); |
| RandomVectorGenerator generator = |
| new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g)); |
| MultivariateDifferentiableVectorMultiStartOptimizer optimizer = |
| new MultivariateDifferentiableVectorMultiStartOptimizer(underlyingOptimizer, |
| 10, generator); |
| optimizer.optimize(100, new MultivariateDifferentiableVectorFunction() { |
| public double[] value(double[] point) { |
| throw new TestException(); |
| } |
| public DerivativeStructure[] value(DerivativeStructure[] point) { |
| return point; |
| } |
| }, new double[] { 2 }, new double[] { 1 }, new double[] { 0 }); |
| } |
| |
| private static class TestException extends RuntimeException { |
| private static final long serialVersionUID = -7809988995389067683L; |
| } |
| |
| private static class LinearProblem implements MultivariateDifferentiableVectorFunction { |
| |
| final RealMatrix factors; |
| final double[] target; |
| public LinearProblem(double[][] factors, double[] target) { |
| this.factors = new BlockRealMatrix(factors); |
| this.target = target; |
| } |
| |
| public double[] value(double[] variables) { |
| return factors.operate(variables); |
| } |
| |
| public DerivativeStructure[] value(DerivativeStructure[] variables) { |
| DerivativeStructure[] y = new DerivativeStructure[factors.getRowDimension()]; |
| for (int i = 0; i < y.length; ++i) { |
| y[i] = variables[0].getField().getZero(); |
| for (int j = 0; j < factors.getColumnDimension(); ++j) { |
| y[i] = y[i].add(variables[j].multiply(factors.getEntry(i, j))); |
| } |
| } |
| return y; |
| } |
| |
| } |
| |
| } |
