http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/test/java/org/apache/commons/math4/optimization/general/CircleVectorial.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/optimization/general/CircleVectorial.java b/src/test/java/org/apache/commons/math4/optimization/general/CircleVectorial.java deleted file mode 100644 index b0f4da5..0000000 --- a/src/test/java/org/apache/commons/math4/optimization/general/CircleVectorial.java +++ /dev/null @@ -1,91 +0,0 @@ -/* - * 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.optimization.general; - -import java.util.ArrayList; - -import org.apache.commons.math4.analysis.differentiation.DerivativeStructure; -import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableVectorFunction; -import org.apache.commons.math4.geometry.euclidean.twod.Vector2D; - -/** - * Class used in the tests. - */ -@Deprecated -class CircleVectorial implements MultivariateDifferentiableVectorFunction { - private ArrayList<Vector2D> points; - - public CircleVectorial() { - points = new ArrayList<Vector2D>(); - } - - public void addPoint(double px, double py) { - points.add(new Vector2D(px, py)); - } - - public int getN() { - return points.size(); - } - - public double getRadius(Vector2D center) { - double r = 0; - for (Vector2D point : points) { - r += point.distance(center); - } - return r / points.size(); - } - - private DerivativeStructure distance(Vector2D point, - DerivativeStructure cx, DerivativeStructure cy) { - DerivativeStructure dx = cx.subtract(point.getX()); - DerivativeStructure dy = cy.subtract(point.getY()); - return dx.multiply(dx).add(dy.multiply(dy)).sqrt(); - } - - public DerivativeStructure getRadius(DerivativeStructure cx, DerivativeStructure cy) { - DerivativeStructure r = cx.getField().getZero(); - for (Vector2D point : points) { - r = r.add(distance(point, cx, cy)); - } - return r.divide(points.size()); - } - - public double[] value(double[] variables) { - Vector2D center = new Vector2D(variables[0], variables[1]); - double radius = getRadius(center); - - double[] residuals = new double[points.size()]; - for (int i = 0; i < residuals.length; ++i) { - residuals[i] = points.get(i).distance(center) - radius; - } - - return residuals; - } - - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure radius = getRadius(variables[0], variables[1]); - - DerivativeStructure[] residuals = new DerivativeStructure[points.size()]; - for (int i = 0; i < residuals.length; ++i) { - residuals[i] = distance(points.get(i), variables[0], variables[1]).subtract(radius); - } - - return residuals; - } - -}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/test/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizerTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizerTest.java b/src/test/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizerTest.java deleted file mode 100644 index 88e2f3a..0000000 --- a/src/test/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizerTest.java +++ /dev/null @@ -1,154 +0,0 @@ -/* - * 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.optimization.general; - -import java.io.IOException; - -import org.apache.commons.math4.exception.ConvergenceException; -import org.apache.commons.math4.exception.TooManyEvaluationsException; -import org.apache.commons.math4.optimization.SimpleVectorValueChecker; -import org.apache.commons.math4.optimization.general.AbstractLeastSquaresOptimizer; -import org.apache.commons.math4.optimization.general.GaussNewtonOptimizer; -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 GaussNewtonOptimizerTest - extends AbstractLeastSquaresOptimizerAbstractTest { - - @Override - public AbstractLeastSquaresOptimizer createOptimizer() { - return new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6)); - } - - @Override - @Test(expected = ConvergenceException.class) - public void testMoreEstimatedParametersSimple() { - /* - * Exception is expected with this optimizer - */ - super.testMoreEstimatedParametersSimple(); - } - - @Override - @Test(expected=ConvergenceException.class) - public void testMoreEstimatedParametersUnsorted() { - /* - * Exception is expected with this optimizer - */ - super.testMoreEstimatedParametersUnsorted(); - } - - @Test(expected=TooManyEvaluationsException.class) - public void testMaxEvaluations() throws Exception { - CircleVectorial circle = new CircleVectorial(); - circle.addPoint( 30.0, 68.0); - circle.addPoint( 50.0, -6.0); - circle.addPoint(110.0, -20.0); - circle.addPoint( 35.0, 15.0); - circle.addPoint( 45.0, 97.0); - - GaussNewtonOptimizer optimizer - = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-30, 1.0e-30)); - - optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 }, - new double[] { 1, 1, 1, 1, 1 }, - new double[] { 98.680, 47.345 }); - } - - @Override - @Test(expected=ConvergenceException.class) - public void testCircleFittingBadInit() { - /* - * This test does not converge with this optimizer. - */ - super.testCircleFittingBadInit(); - } - - @Override - @Test(expected = ConvergenceException.class) - public void testHahn1() - throws IOException { - /* - * TODO This test leads to a singular problem with the Gauss-Newton - * optimizer. This should be inquired. - */ - super.testHahn1(); - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/test/java/org/apache/commons/math4/optimization/general/LevenbergMarquardtOptimizerTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/optimization/general/LevenbergMarquardtOptimizerTest.java b/src/test/java/org/apache/commons/math4/optimization/general/LevenbergMarquardtOptimizerTest.java deleted file mode 100644 index da77546..0000000 --- a/src/test/java/org/apache/commons/math4/optimization/general/LevenbergMarquardtOptimizerTest.java +++ /dev/null @@ -1,388 +0,0 @@ -/* - * 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.optimization.general; - -import java.io.Serializable; -import java.util.ArrayList; -import java.util.List; - -import org.apache.commons.math4.analysis.differentiation.DerivativeStructure; -import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableVectorFunction; -import org.apache.commons.math4.exception.ConvergenceException; -import org.apache.commons.math4.exception.DimensionMismatchException; -import org.apache.commons.math4.exception.TooManyEvaluationsException; -import org.apache.commons.math4.geometry.euclidean.twod.Vector2D; -import org.apache.commons.math4.linear.SingularMatrixException; -import org.apache.commons.math4.optimization.PointVectorValuePair; -import org.apache.commons.math4.optimization.general.AbstractLeastSquaresOptimizer; -import org.apache.commons.math4.optimization.general.LevenbergMarquardtOptimizer; -import org.apache.commons.math4.util.FastMath; -import org.apache.commons.math4.util.Precision; -import org.junit.Assert; -import org.junit.Test; -import org.junit.Ignore; - -/** - * <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 LevenbergMarquardtOptimizerTest extends AbstractLeastSquaresOptimizerAbstractTest { - - @Override - public AbstractLeastSquaresOptimizer createOptimizer() { - return new LevenbergMarquardtOptimizer(); - } - - @Override - @Test(expected=SingularMatrixException.class) - public void testNonInvertible() { - /* - * Overrides the method from parent class, since the default singularity - * threshold (1e-14) does not trigger the expected exception. - */ - LinearProblem problem = new LinearProblem(new double[][] { - { 1, 2, -3 }, - { 2, 1, 3 }, - { -3, 0, -9 } - }, new double[] { 1, 1, 1 }); - - AbstractLeastSquaresOptimizer optimizer = createOptimizer(); - PointVectorValuePair optimum = optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 }); - Assert.assertTrue(FastMath.sqrt(problem.target.length) * optimizer.getRMS() > 0.6); - - optimizer.computeCovariances(optimum.getPoint(), 1.5e-14); - } - - @Test - public void testControlParameters() { - CircleVectorial circle = new CircleVectorial(); - circle.addPoint( 30.0, 68.0); - circle.addPoint( 50.0, -6.0); - circle.addPoint(110.0, -20.0); - circle.addPoint( 35.0, 15.0); - circle.addPoint( 45.0, 97.0); - checkEstimate(circle, 0.1, 10, 1.0e-14, 1.0e-16, 1.0e-10, false); - checkEstimate(circle, 0.1, 10, 1.0e-15, 1.0e-17, 1.0e-10, true); - checkEstimate(circle, 0.1, 5, 1.0e-15, 1.0e-16, 1.0e-10, true); - circle.addPoint(300, -300); - checkEstimate(circle, 0.1, 20, 1.0e-18, 1.0e-16, 1.0e-10, true); - } - - private void checkEstimate(MultivariateDifferentiableVectorFunction problem, - double initialStepBoundFactor, int maxCostEval, - double costRelativeTolerance, double parRelativeTolerance, - double orthoTolerance, boolean shouldFail) { - try { - LevenbergMarquardtOptimizer optimizer - = new LevenbergMarquardtOptimizer(initialStepBoundFactor, - costRelativeTolerance, - parRelativeTolerance, - orthoTolerance, - Precision.SAFE_MIN); - optimizer.optimize(maxCostEval, problem, new double[] { 0, 0, 0, 0, 0 }, - new double[] { 1, 1, 1, 1, 1 }, - new double[] { 98.680, 47.345 }); - Assert.assertTrue(!shouldFail); - } catch (DimensionMismatchException ee) { - Assert.assertTrue(shouldFail); - } catch (TooManyEvaluationsException ee) { - Assert.assertTrue(shouldFail); - } - } - - // Test is skipped because it fails with the latest code update. - @Ignore@Test - public void testMath199() { - try { - QuadraticProblem problem = new QuadraticProblem(); - problem.addPoint (0, -3.182591015485607); - problem.addPoint (1, -2.5581184967730577); - problem.addPoint (2, -2.1488478161387325); - problem.addPoint (3, -1.9122489313410047); - problem.addPoint (4, 1.7785661310051026); - LevenbergMarquardtOptimizer optimizer - = new LevenbergMarquardtOptimizer(100, 1e-10, 1e-10, 1e-10, 0); - optimizer.optimize(100, problem, - new double[] { 0, 0, 0, 0, 0 }, - new double[] { 0.0, 4.4e-323, 1.0, 4.4e-323, 0.0 }, - new double[] { 0, 0, 0 }); - Assert.fail("an exception should have been thrown"); - } catch (ConvergenceException ee) { - // expected behavior - } - } - - /** - * Non-linear test case: fitting of decay curve (from Chapter 8 of - * Bevington's textbook, "Data reduction and analysis for the physical sciences"). - * XXX The expected ("reference") values may not be accurate and the tolerance too - * relaxed for this test to be currently really useful (the issue is under - * investigation). - */ - @Test - public void testBevington() { - final double[][] dataPoints = { - // column 1 = times - { 15, 30, 45, 60, 75, 90, 105, 120, 135, 150, - 165, 180, 195, 210, 225, 240, 255, 270, 285, 300, - 315, 330, 345, 360, 375, 390, 405, 420, 435, 450, - 465, 480, 495, 510, 525, 540, 555, 570, 585, 600, - 615, 630, 645, 660, 675, 690, 705, 720, 735, 750, - 765, 780, 795, 810, 825, 840, 855, 870, 885, }, - // column 2 = measured counts - { 775, 479, 380, 302, 185, 157, 137, 119, 110, 89, - 74, 61, 66, 68, 48, 54, 51, 46, 55, 29, - 28, 37, 49, 26, 35, 29, 31, 24, 25, 35, - 24, 30, 26, 28, 21, 18, 20, 27, 17, 17, - 14, 17, 24, 11, 22, 17, 12, 10, 13, 16, - 9, 9, 14, 21, 17, 13, 12, 18, 10, }, - }; - - final BevingtonProblem problem = new BevingtonProblem(); - - final int len = dataPoints[0].length; - final double[] weights = new double[len]; - for (int i = 0; i < len; i++) { - problem.addPoint(dataPoints[0][i], - dataPoints[1][i]); - - weights[i] = 1 / dataPoints[1][i]; - } - - final LevenbergMarquardtOptimizer optimizer - = new LevenbergMarquardtOptimizer(); - - final PointVectorValuePair optimum - = optimizer.optimize(100, problem, dataPoints[1], weights, - new double[] { 10, 900, 80, 27, 225 }); - - final double[] solution = optimum.getPoint(); - final double[] expectedSolution = { 10.4, 958.3, 131.4, 33.9, 205.0 }; - - final double[][] covarMatrix = optimizer.computeCovariances(solution, 1e-14); - final double[][] expectedCovarMatrix = { - { 3.38, -3.69, 27.98, -2.34, -49.24 }, - { -3.69, 2492.26, 81.89, -69.21, -8.9 }, - { 27.98, 81.89, 468.99, -44.22, -615.44 }, - { -2.34, -69.21, -44.22, 6.39, 53.80 }, - { -49.24, -8.9, -615.44, 53.8, 929.45 } - }; - - final int numParams = expectedSolution.length; - - // Check that the computed solution is within the reference error range. - for (int i = 0; i < numParams; i++) { - final double error = FastMath.sqrt(expectedCovarMatrix[i][i]); - Assert.assertEquals("Parameter " + i, expectedSolution[i], solution[i], error); - } - - // Check that each entry of the computed covariance matrix is within 10% - // of the reference matrix entry. - for (int i = 0; i < numParams; i++) { - for (int j = 0; j < numParams; j++) { - Assert.assertEquals("Covariance matrix [" + i + "][" + j + "]", - expectedCovarMatrix[i][j], - covarMatrix[i][j], - FastMath.abs(0.1 * expectedCovarMatrix[i][j])); - } - } - } - - @Test - public void testCircleFitting2() { - final double xCenter = 123.456; - final double yCenter = 654.321; - final double xSigma = 10; - final double ySigma = 15; - final double radius = 111.111; - // The test is extremely sensitive to the seed. - final long seed = 59421061L; - final RandomCirclePointGenerator factory - = new RandomCirclePointGenerator(xCenter, yCenter, radius, - xSigma, ySigma, - seed); - final CircleProblem circle = new CircleProblem(xSigma, ySigma); - - final int numPoints = 10; - for (Vector2D p : factory.generate(numPoints)) { - circle.addPoint(p); - // System.out.println(p.x + " " + p.y); - } - - // First guess for the center's coordinates and radius. - final double[] init = { 90, 659, 115 }; - - final LevenbergMarquardtOptimizer optimizer - = new LevenbergMarquardtOptimizer(); - final PointVectorValuePair optimum = optimizer.optimize(100, circle, - circle.target(), circle.weight(), - init); - - final double[] paramFound = optimum.getPoint(); - - // Retrieve errors estimation. - final double[][] covMatrix = optimizer.computeCovariances(paramFound, 1e-14); - final double[] asymptoticStandardErrorFound = optimizer.guessParametersErrors(); - final double[] sigmaFound = new double[covMatrix.length]; - for (int i = 0; i < covMatrix.length; i++) { - sigmaFound[i] = FastMath.sqrt(covMatrix[i][i]); -// System.out.println("i=" + i + " value=" + paramFound[i] -// + " sigma=" + sigmaFound[i] -// + " ase=" + asymptoticStandardErrorFound[i]); - } - - // System.out.println("chi2=" + optimizer.getChiSquare()); - - // Check that the parameters are found within the assumed error bars. - Assert.assertEquals(xCenter, paramFound[0], asymptoticStandardErrorFound[0]); - Assert.assertEquals(yCenter, paramFound[1], asymptoticStandardErrorFound[1]); - Assert.assertEquals(radius, paramFound[2], asymptoticStandardErrorFound[2]); - } - - private static class QuadraticProblem implements MultivariateDifferentiableVectorFunction, Serializable { - - private static final long serialVersionUID = 7072187082052755854L; - private List<Double> x; - private List<Double> y; - - public QuadraticProblem() { - x = new ArrayList<Double>(); - y = new ArrayList<Double>(); - } - - public void addPoint(double x, double y) { - this.x.add(x); - this.y.add(y); - } - - public double[] value(double[] variables) { - double[] values = new double[x.size()]; - for (int i = 0; i < values.length; ++i) { - values[i] = (variables[0] * x.get(i) + variables[1]) * x.get(i) + variables[2]; - } - return values; - } - - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure[] values = new DerivativeStructure[x.size()]; - for (int i = 0; i < values.length; ++i) { - values[i] = (variables[0].multiply(x.get(i)).add(variables[1])).multiply(x.get(i)).add(variables[2]); - } - return values; - } - - } - - private static class BevingtonProblem - implements MultivariateDifferentiableVectorFunction { - private List<Double> time; - private List<Double> count; - - public BevingtonProblem() { - time = new ArrayList<Double>(); - count = new ArrayList<Double>(); - } - - public void addPoint(double t, double c) { - time.add(t); - count.add(c); - } - - public double[] value(double[] params) { - double[] values = new double[time.size()]; - for (int i = 0; i < values.length; ++i) { - final double t = time.get(i); - values[i] = params[0] - + params[1] * FastMath.exp(-t / params[3]) - + params[2] * FastMath.exp(-t / params[4]); - } - return values; - } - - public DerivativeStructure[] value(DerivativeStructure[] params) { - DerivativeStructure[] values = new DerivativeStructure[time.size()]; - for (int i = 0; i < values.length; ++i) { - final double t = time.get(i); - values[i] = params[0].add( - params[1].multiply(params[3].reciprocal().multiply(-t).exp())).add( - params[2].multiply(params[4].reciprocal().multiply(-t).exp())); - } - return values; - } - - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/test/java/org/apache/commons/math4/optimization/general/MinpackTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/optimization/general/MinpackTest.java b/src/test/java/org/apache/commons/math4/optimization/general/MinpackTest.java deleted file mode 100644 index 50440c3..0000000 --- a/src/test/java/org/apache/commons/math4/optimization/general/MinpackTest.java +++ /dev/null @@ -1,1212 +0,0 @@ -/* - * 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.optimization.general; - -import java.io.Serializable; -import java.util.Arrays; - -import org.apache.commons.math4.analysis.differentiation.DerivativeStructure; -import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableVectorFunction; -import org.apache.commons.math4.exception.TooManyEvaluationsException; -import org.apache.commons.math4.optimization.PointVectorValuePair; -import org.apache.commons.math4.optimization.general.LevenbergMarquardtOptimizer; -import org.apache.commons.math4.util.FastMath; -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 MinpackTest { - - @Test - public void testMinpackLinearFullRank() { - minpackTest(new LinearFullRankFunction(10, 5, 1.0, - 5.0, 2.23606797749979), false); - minpackTest(new LinearFullRankFunction(50, 5, 1.0, - 8.06225774829855, 6.70820393249937), false); - } - - @Test - public void testMinpackLinearRank1() { - minpackTest(new LinearRank1Function(10, 5, 1.0, - 291.521868819476, 1.4638501094228), false); - minpackTest(new LinearRank1Function(50, 5, 1.0, - 3101.60039334535, 3.48263016573496), false); - } - - @Test - public void testMinpackLinearRank1ZeroColsAndRows() { - minpackTest(new LinearRank1ZeroColsAndRowsFunction(10, 5, 1.0), false); - minpackTest(new LinearRank1ZeroColsAndRowsFunction(50, 5, 1.0), false); - } - - @Test - public void testMinpackRosenbrok() { - minpackTest(new RosenbrockFunction(new double[] { -1.2, 1.0 }, - FastMath.sqrt(24.2)), false); - minpackTest(new RosenbrockFunction(new double[] { -12.0, 10.0 }, - FastMath.sqrt(1795769.0)), false); - minpackTest(new RosenbrockFunction(new double[] { -120.0, 100.0 }, - 11.0 * FastMath.sqrt(169000121.0)), false); - } - - @Test - public void testMinpackHelicalValley() { - minpackTest(new HelicalValleyFunction(new double[] { -1.0, 0.0, 0.0 }, - 50.0), false); - minpackTest(new HelicalValleyFunction(new double[] { -10.0, 0.0, 0.0 }, - 102.95630140987), false); - minpackTest(new HelicalValleyFunction(new double[] { -100.0, 0.0, 0.0}, - 991.261822123701), false); - } - - @Test - public void testMinpackPowellSingular() { - minpackTest(new PowellSingularFunction(new double[] { 3.0, -1.0, 0.0, 1.0 }, - 14.6628782986152), false); - minpackTest(new PowellSingularFunction(new double[] { 30.0, -10.0, 0.0, 10.0 }, - 1270.9838708654), false); - minpackTest(new PowellSingularFunction(new double[] { 300.0, -100.0, 0.0, 100.0 }, - 126887.903284750), false); - } - - @Test - public void testMinpackFreudensteinRoth() { - minpackTest(new FreudensteinRothFunction(new double[] { 0.5, -2.0 }, - 20.0124960961895, 6.99887517584575, - new double[] { - 11.4124844654993, - -0.896827913731509 - }), false); - minpackTest(new FreudensteinRothFunction(new double[] { 5.0, -20.0 }, - 12432.833948863, 6.9988751744895, - new double[] { - 11.41300466147456, - -0.896796038685959 - }), false); - minpackTest(new FreudensteinRothFunction(new double[] { 50.0, -200.0 }, - 11426454.595762, 6.99887517242903, - new double[] { - 11.412781785788564, - -0.8968051074920405 - }), false); - } - - @Test - public void testMinpackBard() { - minpackTest(new BardFunction(1.0, 6.45613629515967, 0.0906359603390466, - new double[] { - 0.0824105765758334, - 1.1330366534715, - 2.34369463894115 - }), false); - minpackTest(new BardFunction(10.0, 36.1418531596785, 4.17476870138539, - new double[] { - 0.840666673818329, - -158848033.259565, - -164378671.653535 - }), false); - minpackTest(new BardFunction(100.0, 384.114678637399, 4.17476870135969, - new double[] { - 0.840666673867645, - -158946167.205518, - -164464906.857771 - }), false); - } - - @Test - public void testMinpackKowalikOsborne() { - minpackTest(new KowalikOsborneFunction(new double[] { 0.25, 0.39, 0.415, 0.39 }, - 0.0728915102882945, - 0.017535837721129, - new double[] { - 0.192807810476249, - 0.191262653354071, - 0.123052801046931, - 0.136053221150517 - }), false); - minpackTest(new KowalikOsborneFunction(new double[] { 2.5, 3.9, 4.15, 3.9 }, - 2.97937007555202, - 0.032052192917937, - new double[] { - 728675.473768287, - -14.0758803129393, - -32977797.7841797, - -20571594.1977912 - }), false); - minpackTest(new KowalikOsborneFunction(new double[] { 25.0, 39.0, 41.5, 39.0 }, - 29.9590617016037, - 0.0175364017658228, - new double[] { - 0.192948328597594, - 0.188053165007911, - 0.122430604321144, - 0.134575665392506 - }), false); - } - - @Test - public void testMinpackMeyer() { - minpackTest(new MeyerFunction(new double[] { 0.02, 4000.0, 250.0 }, - 41153.4665543031, 9.37794514651874, - new double[] { - 0.00560963647102661, - 6181.34634628659, - 345.223634624144 - }), false); - minpackTest(new MeyerFunction(new double[] { 0.2, 40000.0, 2500.0 }, - 4168216.89130846, 792.917871779501, - new double[] { - 1.42367074157994e-11, - 33695.7133432541, - 901.268527953801 - }), true); - } - - @Test - public void testMinpackWatson() { - - minpackTest(new WatsonFunction(6, 0.0, - 5.47722557505166, 0.0478295939097601, - new double[] { - -0.0157249615083782, 1.01243488232965, - -0.232991722387673, 1.26043101102818, - -1.51373031394421, 0.99299727291842 - }), false); - minpackTest(new WatsonFunction(6, 10.0, - 6433.12578950026, 0.0478295939096951, - new double[] { - -0.0157251901386677, 1.01243485860105, - -0.232991545843829, 1.26042932089163, - -1.51372776706575, 0.99299573426328 - }), false); - minpackTest(new WatsonFunction(6, 100.0, - 674256.040605213, 0.047829593911544, - new double[] { - -0.0157247019712586, 1.01243490925658, - -0.232991922761641, 1.26043292929555, - -1.51373320452707, 0.99299901922322 - }), false); - - minpackTest(new WatsonFunction(9, 0.0, - 5.47722557505166, 0.00118311459212420, - new double[] { - -0.153070644166722e-4, 0.999789703934597, - 0.0147639634910978, 0.146342330145992, - 1.00082109454817, -2.61773112070507, - 4.10440313943354, -3.14361226236241, - 1.05262640378759 - }), false); - minpackTest(new WatsonFunction(9, 10.0, - 12088.127069307, 0.00118311459212513, - new double[] { - -0.153071334849279e-4, 0.999789703941234, - 0.0147639629786217, 0.146342334818836, - 1.00082107321386, -2.61773107084722, - 4.10440307655564, -3.14361222178686, - 1.05262639322589 - }), false); - minpackTest(new WatsonFunction(9, 100.0, - 1269109.29043834, 0.00118311459212384, - new double[] { - -0.153069523352176e-4, 0.999789703958371, - 0.0147639625185392, 0.146342341096326, - 1.00082104729164, -2.61773101573645, - 4.10440301427286, -3.14361218602503, - 1.05262638516774 - }), false); - - minpackTest(new WatsonFunction(12, 0.0, - 5.47722557505166, 0.217310402535861e-4, - new double[] { - -0.660266001396382e-8, 1.00000164411833, - -0.000563932146980154, 0.347820540050756, - -0.156731500244233, 1.05281515825593, - -3.24727109519451, 7.2884347837505, - -10.271848098614, 9.07411353715783, - -4.54137541918194, 1.01201187975044 - }), false); - minpackTest(new WatsonFunction(12, 10.0, - 19220.7589790951, 0.217310402518509e-4, - new double[] { - -0.663710223017410e-8, 1.00000164411787, - -0.000563932208347327, 0.347820540486998, - -0.156731503955652, 1.05281517654573, - -3.2472711515214, 7.28843489430665, - -10.2718482369638, 9.07411364383733, - -4.54137546533666, 1.01201188830857 - }), false); - minpackTest(new WatsonFunction(12, 100.0, - 2018918.04462367, 0.217310402539845e-4, - new double[] { - -0.663806046485249e-8, 1.00000164411786, - -0.000563932210324959, 0.347820540503588, - -0.156731504091375, 1.05281517718031, - -3.24727115337025, 7.28843489775302, - -10.2718482410813, 9.07411364688464, - -4.54137546660822, 1.0120118885369 - }), false); - - } - - @Test - public void testMinpackBox3Dimensional() { - minpackTest(new Box3DimensionalFunction(10, new double[] { 0.0, 10.0, 20.0 }, - 32.1115837449572), false); - } - - @Test - public void testMinpackJennrichSampson() { - minpackTest(new JennrichSampsonFunction(10, new double[] { 0.3, 0.4 }, - 64.5856498144943, 11.1517793413499, - new double[] { - // 0.2578330049, 0.257829976764542 - 0.2578199266368004, 0.25782997676455244 - }), false); - } - - @Test - public void testMinpackBrownDennis() { - minpackTest(new BrownDennisFunction(20, - new double[] { 25.0, 5.0, -5.0, -1.0 }, - 2815.43839161816, 292.954288244866, - new double[] { - -11.59125141003, 13.2024883984741, - -0.403574643314272, 0.236736269844604 - }), false); - minpackTest(new BrownDennisFunction(20, - new double[] { 250.0, 50.0, -50.0, -10.0 }, - 555073.354173069, 292.954270581415, - new double[] { - -11.5959274272203, 13.2041866926242, - -0.403417362841545, 0.236771143410386 - }), false); - minpackTest(new BrownDennisFunction(20, - new double[] { 2500.0, 500.0, -500.0, -100.0 }, - 61211252.2338581, 292.954306151134, - new double[] { - -11.5902596937374, 13.2020628854665, - -0.403688070279258, 0.236665033746463 - }), false); - } - - @Test - public void testMinpackChebyquad() { - minpackTest(new ChebyquadFunction(1, 8, 1.0, - 1.88623796907732, 1.88623796907732, - new double[] { 0.5 }), false); - minpackTest(new ChebyquadFunction(1, 8, 10.0, - 5383344372.34005, 1.88424820499951, - new double[] { 0.9817314924684 }), false); - minpackTest(new ChebyquadFunction(1, 8, 100.0, - 0.118088726698392e19, 1.88424820499347, - new double[] { 0.9817314852934 }), false); - minpackTest(new ChebyquadFunction(8, 8, 1.0, - 0.196513862833975, 0.0593032355046727, - new double[] { - 0.0431536648587336, 0.193091637843267, - 0.266328593812698, 0.499999334628884, - 0.500000665371116, 0.733671406187302, - 0.806908362156733, 0.956846335141266 - }), false); - minpackTest(new ChebyquadFunction(9, 9, 1.0, - 0.16994993465202, 0.0, - new double[] { - 0.0442053461357828, 0.199490672309881, - 0.23561910847106, 0.416046907892598, - 0.5, 0.583953092107402, - 0.764380891528940, 0.800509327690119, - 0.955794653864217 - }), false); - minpackTest(new ChebyquadFunction(10, 10, 1.0, - 0.183747831178711, 0.0806471004038253, - new double[] { - 0.0596202671753563, 0.166708783805937, - 0.239171018813509, 0.398885290346268, - 0.398883667870681, 0.601116332129320, - 0.60111470965373, 0.760828981186491, - 0.833291216194063, 0.940379732824644 - }), false); - } - - @Test - public void testMinpackBrownAlmostLinear() { - minpackTest(new BrownAlmostLinearFunction(10, 0.5, - 16.5302162063499, 0.0, - new double[] { - 0.979430303349862, 0.979430303349862, - 0.979430303349862, 0.979430303349862, - 0.979430303349862, 0.979430303349862, - 0.979430303349862, 0.979430303349862, - 0.979430303349862, 1.20569696650138 - }), false); - minpackTest(new BrownAlmostLinearFunction(10, 5.0, - 9765624.00089211, 0.0, - new double[] { - 0.979430303349865, 0.979430303349865, - 0.979430303349865, 0.979430303349865, - 0.979430303349865, 0.979430303349865, - 0.979430303349865, 0.979430303349865, - 0.979430303349865, 1.20569696650135 - }), false); - minpackTest(new BrownAlmostLinearFunction(10, 50.0, - 0.9765625e17, 0.0, - new double[] { - 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0 - }), false); - minpackTest(new BrownAlmostLinearFunction(30, 0.5, - 83.476044467848, 0.0, - new double[] { - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 0.997754216442807, - 0.997754216442807, 1.06737350671578 - }), false); - minpackTest(new BrownAlmostLinearFunction(40, 0.5, - 128.026364472323, 0.0, - new double[] { - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 1.00000000000002, 1.00000000000002, - 0.999999999999121 - }), false); - } - - @Test - public void testMinpackOsborne1() { - minpackTest(new Osborne1Function(new double[] { 0.5, 1.5, -1.0, 0.01, 0.02, }, - 0.937564021037838, 0.00739249260904843, - new double[] { - 0.375410049244025, 1.93584654543108, - -1.46468676748716, 0.0128675339110439, - 0.0221227011813076 - }), false); - } - - @Test - public void testMinpackOsborne2() { - - minpackTest(new Osborne2Function(new double[] { - 1.3, 0.65, 0.65, 0.7, 0.6, - 3.0, 5.0, 7.0, 2.0, 4.5, 5.5 - }, - 1.44686540984712, 0.20034404483314, - new double[] { - 1.30997663810096, 0.43155248076, - 0.633661261602859, 0.599428560991695, - 0.754179768272449, 0.904300082378518, - 1.36579949521007, 4.82373199748107, - 2.39868475104871, 4.56887554791452, - 5.67534206273052 - }), false); - } - - private void minpackTest(MinpackFunction function, boolean exceptionExpected) { - LevenbergMarquardtOptimizer optimizer - = new LevenbergMarquardtOptimizer(FastMath.sqrt(2.22044604926e-16), - FastMath.sqrt(2.22044604926e-16), - 2.22044604926e-16); -// Assert.assertTrue(function.checkTheoreticalStartCost(optimizer.getRMS())); - try { - PointVectorValuePair optimum = - optimizer.optimize(400 * (function.getN() + 1), function, - function.getTarget(), function.getWeight(), - function.getStartPoint()); - Assert.assertFalse(exceptionExpected); - function.checkTheoreticalMinCost(optimizer.getRMS()); - function.checkTheoreticalMinParams(optimum); - } catch (TooManyEvaluationsException e) { - Assert.assertTrue(exceptionExpected); - } - } - - private static abstract class MinpackFunction - implements MultivariateDifferentiableVectorFunction, Serializable { - - private static final long serialVersionUID = -6209760235478794233L; - protected int n; - protected int m; - protected double[] startParams; - protected double theoreticalMinCost; - protected double[] theoreticalMinParams; - protected double costAccuracy; - protected double paramsAccuracy; - - protected MinpackFunction(int m, double[] startParams, - double theoreticalMinCost, double[] theoreticalMinParams) { - this.m = m; - this.n = startParams.length; - this.startParams = startParams.clone(); - this.theoreticalMinCost = theoreticalMinCost; - this.theoreticalMinParams = theoreticalMinParams; - this.costAccuracy = 1.0e-8; - this.paramsAccuracy = 1.0e-5; - } - - protected static double[] buildArray(int n, double x) { - double[] array = new double[n]; - Arrays.fill(array, x); - return array; - } - - public double[] getTarget() { - return buildArray(m, 0.0); - } - - public double[] getWeight() { - return buildArray(m, 1.0); - } - - public double[] getStartPoint() { - return startParams.clone(); - } - - protected void setCostAccuracy(double costAccuracy) { - this.costAccuracy = costAccuracy; - } - - protected void setParamsAccuracy(double paramsAccuracy) { - this.paramsAccuracy = paramsAccuracy; - } - - public int getN() { - return startParams.length; - } - - public void checkTheoreticalMinCost(double rms) { - double threshold = costAccuracy * (1.0 + theoreticalMinCost); - Assert.assertEquals(theoreticalMinCost, FastMath.sqrt(m) * rms, threshold); - } - - public void checkTheoreticalMinParams(PointVectorValuePair optimum) { - double[] params = optimum.getPointRef(); - if (theoreticalMinParams != null) { - for (int i = 0; i < theoreticalMinParams.length; ++i) { - double mi = theoreticalMinParams[i]; - double vi = params[i]; - Assert.assertEquals(mi, vi, paramsAccuracy * (1.0 + FastMath.abs(mi))); - } - } - } - - public double[] value(double[] variables) { - DerivativeStructure[] dsV = new DerivativeStructure[variables.length]; - for (int i = 0; i < variables.length; ++i) { - dsV[i] = new DerivativeStructure(0, 0, variables[i]); - } - DerivativeStructure[] dsY = value(dsV); - double[] y = new double[dsY.length]; - for (int i = 0; i < dsY.length; ++i) { - y[i] = dsY[i].getValue(); - } - return y; - } - - public abstract DerivativeStructure[] value(DerivativeStructure[] variables); - - } - - private static class LinearFullRankFunction extends MinpackFunction { - - private static final long serialVersionUID = -9030323226268039536L; - - public LinearFullRankFunction(int m, int n, double x0, - double theoreticalStartCost, - double theoreticalMinCost) { - super(m, buildArray(n, x0), theoreticalMinCost, - buildArray(n, -1.0)); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure sum = variables[0].getField().getZero(); - for (int i = 0; i < n; ++i) { - sum = sum.add(variables[i]); - } - DerivativeStructure t = sum.multiply(2.0 / m).add(1); - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < n; ++i) { - f[i] = variables[i].subtract(t); - } - Arrays.fill(f, n, m, t.negate()); - return f; - } - - } - - private static class LinearRank1Function extends MinpackFunction { - - private static final long serialVersionUID = 8494863245104608300L; - - public LinearRank1Function(int m, int n, double x0, - double theoreticalStartCost, - double theoreticalMinCost) { - super(m, buildArray(n, x0), theoreticalMinCost, null); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure[] f = new DerivativeStructure[m]; - DerivativeStructure sum = variables[0].getField().getZero(); - for (int i = 0; i < n; ++i) { - sum = sum.add(variables[i].multiply(i + 1)); - } - for (int i = 0; i < m; ++i) { - f[i] = sum.multiply(i + 1).subtract(1); - } - return f; - } - - } - - private static class LinearRank1ZeroColsAndRowsFunction extends MinpackFunction { - - private static final long serialVersionUID = -3316653043091995018L; - - public LinearRank1ZeroColsAndRowsFunction(int m, int n, double x0) { - super(m, buildArray(n, x0), - FastMath.sqrt((m * (m + 3) - 6) / (2.0 * (2 * m - 3))), - null); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure[] f = new DerivativeStructure[m]; - DerivativeStructure sum = variables[0].getField().getZero(); - for (int i = 1; i < (n - 1); ++i) { - sum = sum.add(variables[i].multiply(i + 1)); - } - for (int i = 0; i < (m - 1); ++i) { - f[i] = sum.multiply(i).subtract(1); - } - f[m - 1] = variables[0].getField().getOne().negate(); - return f; - } - - } - - private static class RosenbrockFunction extends MinpackFunction { - - private static final long serialVersionUID = 2893438180956569134L; - - public RosenbrockFunction(double[] startParams, double theoreticalStartCost) { - super(2, startParams, 0.0, buildArray(2, 1.0)); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - return new DerivativeStructure[] { - x2.subtract(x1.multiply(x1)).multiply(10), - x1.negate().add(1) - }; - } - - } - - private static class HelicalValleyFunction extends MinpackFunction { - - private static final long serialVersionUID = 220613787843200102L; - - public HelicalValleyFunction(double[] startParams, - double theoreticalStartCost) { - super(3, startParams, 0.0, new double[] { 1.0, 0.0, 0.0 }); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - DerivativeStructure x3 = variables[2]; - DerivativeStructure tmp1 = variables[0].getField().getZero(); - if (x1.getValue() == 0) { - tmp1 = tmp1.add((x2.getValue() >= 0) ? 0.25 : -0.25); - } else { - tmp1 = x2.divide(x1).atan().divide(twoPi); - if (x1.getValue() < 0) { - tmp1 = tmp1.add(0.5); - } - } - DerivativeStructure tmp2 = x1.multiply(x1).add(x2.multiply(x2)).sqrt(); - return new DerivativeStructure[] { - x3.subtract(tmp1.multiply(10)).multiply(10), - tmp2.subtract(1).multiply(10), - x3 - }; - } - - private static final double twoPi = 2.0 * FastMath.PI; - - } - - private static class PowellSingularFunction extends MinpackFunction { - - private static final long serialVersionUID = 7298364171208142405L; - - public PowellSingularFunction(double[] startParams, - double theoreticalStartCost) { - super(4, startParams, 0.0, buildArray(4, 0.0)); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - DerivativeStructure x3 = variables[2]; - DerivativeStructure x4 = variables[3]; - return new DerivativeStructure[] { - x1.add(x2.multiply(10)), - x3.subtract(x4).multiply(sqrt5), - x2.subtract(x3.multiply(2)).multiply(x2.subtract(x3.multiply(2))), - x1.subtract(x4).multiply(x1.subtract(x4)).multiply(sqrt10) - }; - } - - private static final double sqrt5 = FastMath.sqrt( 5.0); - private static final double sqrt10 = FastMath.sqrt(10.0); - - } - - private static class FreudensteinRothFunction extends MinpackFunction { - - private static final long serialVersionUID = 2892404999344244214L; - - public FreudensteinRothFunction(double[] startParams, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(2, startParams, theoreticalMinCost, - theoreticalMinParams); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - return new DerivativeStructure[] { - x1.subtract(13.0).add(x2.negate().add(5.0).multiply(x2).subtract(2).multiply(x2)), - x1.subtract(29.0).add(x2.add(1).multiply(x2).subtract(14).multiply(x2)) - }; - } - - } - - private static class BardFunction extends MinpackFunction { - - private static final long serialVersionUID = 5990442612572087668L; - - public BardFunction(double x0, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(15, buildArray(3, x0), theoreticalMinCost, - theoreticalMinParams); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - DerivativeStructure x3 = variables[2]; - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < m; ++i) { - double tmp1 = i + 1; - double tmp2 = 15 - i; - double tmp3 = (i <= 7) ? tmp1 : tmp2; - f[i] = x1.add(x2.multiply(tmp2).add(x3.multiply(tmp3)).reciprocal().multiply(tmp1)).negate().add(y[i]); - } - return f; - } - - private static final double[] y = { - 0.14, 0.18, 0.22, 0.25, 0.29, - 0.32, 0.35, 0.39, 0.37, 0.58, - 0.73, 0.96, 1.34, 2.10, 4.39 - }; - - } - - private static class KowalikOsborneFunction extends MinpackFunction { - - private static final long serialVersionUID = -4867445739880495801L; - - public KowalikOsborneFunction(double[] startParams, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(11, startParams, theoreticalMinCost, - theoreticalMinParams); - if (theoreticalStartCost > 20.0) { - setCostAccuracy(2.0e-4); - setParamsAccuracy(5.0e-3); - } - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - DerivativeStructure x3 = variables[2]; - DerivativeStructure x4 = variables[3]; - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < m; ++i) { - f[i] = x1.multiply(x2.add(v[i]).multiply(v[i])).divide(x4.add(x3.add(v[i]).multiply(v[i]))).negate().add(y[i]); - } - return f; - } - - private static final double[] v = { - 4.0, 2.0, 1.0, 0.5, 0.25, 0.167, 0.125, 0.1, 0.0833, 0.0714, 0.0625 - }; - - private static final double[] y = { - 0.1957, 0.1947, 0.1735, 0.1600, 0.0844, 0.0627, - 0.0456, 0.0342, 0.0323, 0.0235, 0.0246 - }; - - } - - private static class MeyerFunction extends MinpackFunction { - - private static final long serialVersionUID = -838060619150131027L; - - public MeyerFunction(double[] startParams, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(16, startParams, theoreticalMinCost, - theoreticalMinParams); - if (theoreticalStartCost > 1.0e6) { - setCostAccuracy(7.0e-3); - setParamsAccuracy(2.0e-2); - } - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - DerivativeStructure x3 = variables[2]; - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < m; ++i) { - f[i] = x1.multiply(x2.divide(x3.add(5.0 * (i + 1) + 45.0)).exp()).subtract(y[i]); - } - return f; - } - - private static final double[] y = { - 34780.0, 28610.0, 23650.0, 19630.0, - 16370.0, 13720.0, 11540.0, 9744.0, - 8261.0, 7030.0, 6005.0, 5147.0, - 4427.0, 3820.0, 3307.0, 2872.0 - }; - - } - - private static class WatsonFunction extends MinpackFunction { - - private static final long serialVersionUID = -9034759294980218927L; - - public WatsonFunction(int n, double x0, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(31, buildArray(n, x0), theoreticalMinCost, - theoreticalMinParams); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < (m - 2); ++i) { - double div = (i + 1) / 29.0; - DerivativeStructure s1 = variables[0].getField().getZero(); - DerivativeStructure dx = variables[0].getField().getOne(); - for (int j = 1; j < n; ++j) { - s1 = s1.add(dx.multiply(j).multiply(variables[j])); - dx = dx.multiply(div); - } - DerivativeStructure s2 = variables[0].getField().getZero(); - dx = variables[0].getField().getOne(); - for (int j = 0; j < n; ++j) { - s2 = s2.add(dx.multiply(variables[j])); - dx = dx.multiply(div); - } - f[i] = s1.subtract(s2.multiply(s2)).subtract(1); - } - - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - f[m - 2] = x1; - f[m - 1] = x2.subtract(x1.multiply(x1)).subtract(1); - - return f; - - } - - } - - private static class Box3DimensionalFunction extends MinpackFunction { - - private static final long serialVersionUID = 5511403858142574493L; - - public Box3DimensionalFunction(int m, double[] startParams, - double theoreticalStartCost) { - super(m, startParams, 0.0, - new double[] { 1.0, 10.0, 1.0 }); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - DerivativeStructure x3 = variables[2]; - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < m; ++i) { - double tmp = (i + 1) / 10.0; - f[i] = x1.multiply(-tmp).exp().subtract(x2.multiply(-tmp).exp()).add( - x3.multiply(FastMath.exp(-i - 1) - FastMath.exp(-tmp))); - } - return f; - } - - } - - private static class JennrichSampsonFunction extends MinpackFunction { - - private static final long serialVersionUID = -2489165190443352947L; - - public JennrichSampsonFunction(int m, double[] startParams, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(m, startParams, theoreticalMinCost, - theoreticalMinParams); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < m; ++i) { - double temp = i + 1; - f[i] = x1.multiply(temp).exp().add(x2.multiply(temp).exp()).subtract(2 + 2 * temp).negate(); - } - return f; - } - - } - - private static class BrownDennisFunction extends MinpackFunction { - - private static final long serialVersionUID = 8340018645694243910L; - - public BrownDennisFunction(int m, double[] startParams, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(m, startParams, theoreticalMinCost, - theoreticalMinParams); - setCostAccuracy(2.5e-8); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - DerivativeStructure x3 = variables[2]; - DerivativeStructure x4 = variables[3]; - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < m; ++i) { - double temp = (i + 1) / 5.0; - DerivativeStructure tmp1 = x1.add(x2.multiply(temp)).subtract(FastMath.exp(temp)); - DerivativeStructure tmp2 = x3.add(x4.multiply(FastMath.sin(temp))).subtract(FastMath.cos(temp)); - f[i] = tmp1.multiply(tmp1).add(tmp2.multiply(tmp2)); - } - return f; - } - - } - - private static class ChebyquadFunction extends MinpackFunction { - - private static final long serialVersionUID = -2394877275028008594L; - - private static double[] buildChebyquadArray(int n, double factor) { - double[] array = new double[n]; - double inv = factor / (n + 1); - for (int i = 0; i < n; ++i) { - array[i] = (i + 1) * inv; - } - return array; - } - - public ChebyquadFunction(int n, int m, double factor, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(m, buildChebyquadArray(n, factor), theoreticalMinCost, - theoreticalMinParams); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - - DerivativeStructure[] f = new DerivativeStructure[m]; - Arrays.fill(f, variables[0].getField().getZero()); - - for (int j = 0; j < n; ++j) { - DerivativeStructure tmp1 = variables[0].getField().getOne(); - DerivativeStructure tmp2 = variables[j].multiply(2).subtract(1); - DerivativeStructure temp = tmp2.multiply(2); - for (int i = 0; i < m; ++i) { - f[i] = f[i].add(tmp2); - DerivativeStructure ti = temp.multiply(tmp2).subtract(tmp1); - tmp1 = tmp2; - tmp2 = ti; - } - } - - double dx = 1.0 / n; - boolean iev = false; - for (int i = 0; i < m; ++i) { - f[i] = f[i].multiply(dx); - if (iev) { - f[i] = f[i].add(1.0 / (i * (i + 2))); - } - iev = ! iev; - } - - return f; - - } - - } - - private static class BrownAlmostLinearFunction extends MinpackFunction { - - private static final long serialVersionUID = 8239594490466964725L; - - public BrownAlmostLinearFunction(int m, double factor, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(m, buildArray(m, factor), theoreticalMinCost, - theoreticalMinParams); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure[] f = new DerivativeStructure[m]; - DerivativeStructure sum = variables[0].getField().getZero().subtract(n + 1); - DerivativeStructure prod = variables[0].getField().getOne(); - for (int j = 0; j < n; ++j) { - sum = sum.add(variables[j]); - prod = prod.multiply(variables[j]); - } - for (int i = 0; i < n; ++i) { - f[i] = variables[i].add(sum); - } - f[n - 1] = prod.subtract(1); - return f; - } - - } - - private static class Osborne1Function extends MinpackFunction { - - private static final long serialVersionUID = 4006743521149849494L; - - public Osborne1Function(double[] startParams, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(33, startParams, theoreticalMinCost, - theoreticalMinParams); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x1 = variables[0]; - DerivativeStructure x2 = variables[1]; - DerivativeStructure x3 = variables[2]; - DerivativeStructure x4 = variables[3]; - DerivativeStructure x5 = variables[4]; - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < m; ++i) { - double temp = 10.0 * i; - DerivativeStructure tmp1 = x4.multiply(-temp).exp(); - DerivativeStructure tmp2 = x5.multiply(-temp).exp(); - f[i] = x1.add(x2.multiply(tmp1)).add(x3.multiply(tmp2)).negate().add(y[i]); - } - return f; - } - - private static final double[] y = { - 0.844, 0.908, 0.932, 0.936, 0.925, 0.908, 0.881, 0.850, 0.818, 0.784, 0.751, - 0.718, 0.685, 0.658, 0.628, 0.603, 0.580, 0.558, 0.538, 0.522, 0.506, 0.490, - 0.478, 0.467, 0.457, 0.448, 0.438, 0.431, 0.424, 0.420, 0.414, 0.411, 0.406 - }; - - } - - private static class Osborne2Function extends MinpackFunction { - - private static final long serialVersionUID = -8418268780389858746L; - - public Osborne2Function(double[] startParams, - double theoreticalStartCost, - double theoreticalMinCost, - double[] theoreticalMinParams) { - super(65, startParams, theoreticalMinCost, - theoreticalMinParams); - } - - @Override - public DerivativeStructure[] value(DerivativeStructure[] variables) { - DerivativeStructure x01 = variables[0]; - DerivativeStructure x02 = variables[1]; - DerivativeStructure x03 = variables[2]; - DerivativeStructure x04 = variables[3]; - DerivativeStructure x05 = variables[4]; - DerivativeStructure x06 = variables[5]; - DerivativeStructure x07 = variables[6]; - DerivativeStructure x08 = variables[7]; - DerivativeStructure x09 = variables[8]; - DerivativeStructure x10 = variables[9]; - DerivativeStructure x11 = variables[10]; - DerivativeStructure[] f = new DerivativeStructure[m]; - for (int i = 0; i < m; ++i) { - double temp = i / 10.0; - DerivativeStructure tmp1 = x05.multiply(-temp).exp(); - DerivativeStructure tmp2 = x06.negate().multiply(x09.subtract(temp).multiply(x09.subtract(temp))).exp(); - DerivativeStructure tmp3 = x07.negate().multiply(x10.subtract(temp).multiply(x10.subtract(temp))).exp(); - DerivativeStructure tmp4 = x08.negate().multiply(x11.subtract(temp).multiply(x11.subtract(temp))).exp(); - f[i] = x01.multiply(tmp1).add(x02.multiply(tmp2)).add(x03.multiply(tmp3)).add(x04.multiply(tmp4)).negate().add(y[i]); - } - return f; - } - - private static final double[] y = { - 1.366, 1.191, 1.112, 1.013, 0.991, - 0.885, 0.831, 0.847, 0.786, 0.725, - 0.746, 0.679, 0.608, 0.655, 0.616, - 0.606, 0.602, 0.626, 0.651, 0.724, - 0.649, 0.649, 0.694, 0.644, 0.624, - 0.661, 0.612, 0.558, 0.533, 0.495, - 0.500, 0.423, 0.395, 0.375, 0.372, - 0.391, 0.396, 0.405, 0.428, 0.429, - 0.523, 0.562, 0.607, 0.653, 0.672, - 0.708, 0.633, 0.668, 0.645, 0.632, - 0.591, 0.559, 0.597, 0.625, 0.739, - 0.710, 0.729, 0.720, 0.636, 0.581, - 0.428, 0.292, 0.162, 0.098, 0.054 - }; - - } - -}
