| /* |
| * 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.general; |
| |
| import java.io.IOException; |
| import java.io.Serializable; |
| import java.util.Arrays; |
| |
| import org.apache.commons.math3.analysis.differentiation.DerivativeStructure; |
| import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction; |
| import org.apache.commons.math3.exception.ConvergenceException; |
| import org.apache.commons.math3.exception.DimensionMismatchException; |
| import org.apache.commons.math3.exception.NumberIsTooSmallException; |
| import org.apache.commons.math3.geometry.euclidean.twod.Vector2D; |
| import org.apache.commons.math3.linear.BlockRealMatrix; |
| import org.apache.commons.math3.linear.RealMatrix; |
| import org.apache.commons.math3.optimization.PointVectorValuePair; |
| import org.apache.commons.math3.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 abstract class AbstractLeastSquaresOptimizerAbstractTest { |
| |
| public abstract AbstractLeastSquaresOptimizer createOptimizer(); |
| |
| @Test |
| public void testTrivial() { |
| LinearProblem problem = |
| new LinearProblem(new double[][] { { 2 } }, new double[] { 3 }); |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(3.0, optimum.getValue()[0], 1.0e-10); |
| try { |
| optimizer.guessParametersErrors(); |
| Assert.fail("an exception should have been thrown"); |
| } catch (NumberIsTooSmallException ee) { |
| // expected behavior |
| } |
| } |
| |
| @Test |
| public void testQRColumnsPermutation() { |
| |
| LinearProblem problem = |
| new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } }, |
| new double[] { 4.0, 6.0, 1.0 }); |
| |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(3.0, optimum.getPoint()[1], 1.0e-10); |
| Assert.assertEquals(4.0, optimum.getValue()[0], 1.0e-10); |
| Assert.assertEquals(6.0, optimum.getValue()[1], 1.0e-10); |
| Assert.assertEquals(1.0, optimum.getValue()[2], 1.0e-10); |
| } |
| |
| @Test |
| public void testNoDependency() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 2, 0, 0, 0, 0, 0 }, |
| { 0, 2, 0, 0, 0, 0 }, |
| { 0, 0, 2, 0, 0, 0 }, |
| { 0, 0, 0, 2, 0, 0 }, |
| { 0, 0, 0, 0, 2, 0 }, |
| { 0, 0, 0, 0, 0, 2 } |
| }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 }); |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 }, |
| new double[] { 0, 0, 0, 0, 0, 0 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| for (int i = 0; i < problem.target.length; ++i) { |
| Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10); |
| } |
| } |
| |
| @Test |
| public void testOneSet() { |
| |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1, 0, 0 }, |
| { -1, 1, 0 }, |
| { 0, -1, 1 } |
| }, 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.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(2.0, optimum.getPoint()[1], 1.0e-10); |
| Assert.assertEquals(3.0, optimum.getPoint()[2], 1.0e-10); |
| } |
| |
| @Test |
| public void testTwoSets() { |
| double epsilon = 1.0e-7; |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 2, 1, 0, 4, 0, 0 }, |
| { -4, -2, 3, -7, 0, 0 }, |
| { 4, 1, -2, 8, 0, 0 }, |
| { 0, -3, -12, -1, 0, 0 }, |
| { 0, 0, 0, 0, epsilon, 1 }, |
| { 0, 0, 0, 0, 1, 1 } |
| }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2}); |
| |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 }, |
| new double[] { 0, 0, 0, 0, 0, 0 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10); |
| Assert.assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10); |
| Assert.assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10); |
| Assert.assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10); |
| Assert.assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10); |
| } |
| |
| @Test(expected=ConvergenceException.class) |
| public void testNonInvertible() throws Exception { |
| |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1, 2, -3 }, |
| { 2, 1, 3 }, |
| { -3, 0, -9 } |
| }, new double[] { 1, 1, 1 }); |
| |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 }); |
| } |
| |
| @Test |
| public void testIllConditioned() { |
| LinearProblem problem1 = new LinearProblem(new double[][] { |
| { 10.0, 7.0, 8.0, 7.0 }, |
| { 7.0, 5.0, 6.0, 5.0 }, |
| { 8.0, 6.0, 10.0, 9.0 }, |
| { 7.0, 5.0, 9.0, 10.0 } |
| }, new double[] { 32, 23, 33, 31 }); |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum1 = |
| optimizer.optimize(100, problem1, problem1.target, new double[] { 1, 1, 1, 1 }, |
| new double[] { 0, 1, 2, 3 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-10); |
| Assert.assertEquals(1.0, optimum1.getPoint()[2], 1.0e-10); |
| Assert.assertEquals(1.0, optimum1.getPoint()[3], 1.0e-10); |
| |
| LinearProblem problem2 = new LinearProblem(new double[][] { |
| { 10.00, 7.00, 8.10, 7.20 }, |
| { 7.08, 5.04, 6.00, 5.00 }, |
| { 8.00, 5.98, 9.89, 9.00 }, |
| { 6.99, 4.99, 9.00, 9.98 } |
| }, new double[] { 32, 23, 33, 31 }); |
| PointVectorValuePair optimum2 = |
| optimizer.optimize(100, problem2, problem2.target, new double[] { 1, 1, 1, 1 }, |
| new double[] { 0, 1, 2, 3 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-8); |
| Assert.assertEquals(137.0, optimum2.getPoint()[1], 1.0e-8); |
| Assert.assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-8); |
| Assert.assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-8); |
| } |
| |
| @Test |
| public void testMoreEstimatedParametersSimple() { |
| |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 3.0, 2.0, 0.0, 0.0 }, |
| { 0.0, 1.0, -1.0, 1.0 }, |
| { 2.0, 0.0, 1.0, 0.0 } |
| }, new double[] { 7.0, 3.0, 5.0 }); |
| |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, |
| new double[] { 7, 6, 5, 4 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| } |
| |
| @Test |
| public void testMoreEstimatedParametersUnsorted() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1.0, 1.0, 0.0, 0.0, 0.0, 0.0 }, |
| { 0.0, 0.0, 1.0, 1.0, 1.0, 0.0 }, |
| { 0.0, 0.0, 0.0, 0.0, 1.0, -1.0 }, |
| { 0.0, 0.0, -1.0, 1.0, 0.0, 1.0 }, |
| { 0.0, 0.0, 0.0, -1.0, 1.0, 0.0 } |
| }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 }); |
| |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1 }, |
| new double[] { 2, 2, 2, 2, 2, 2 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals(3.0, optimum.getPointRef()[2], 1.0e-10); |
| Assert.assertEquals(4.0, optimum.getPointRef()[3], 1.0e-10); |
| Assert.assertEquals(5.0, optimum.getPointRef()[4], 1.0e-10); |
| Assert.assertEquals(6.0, optimum.getPointRef()[5], 1.0e-10); |
| } |
| |
| @Test |
| public void testRedundantEquations() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1.0, 1.0 }, |
| { 1.0, -1.0 }, |
| { 1.0, 3.0 } |
| }, new double[] { 3.0, 1.0, 5.0 }); |
| |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, |
| new double[] { 1, 1 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals(2.0, optimum.getPointRef()[0], 1.0e-10); |
| Assert.assertEquals(1.0, optimum.getPointRef()[1], 1.0e-10); |
| } |
| |
| @Test |
| public void testInconsistentEquations() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1.0, 1.0 }, |
| { 1.0, -1.0 }, |
| { 1.0, 3.0 } |
| }, new double[] { 3.0, 1.0, 4.0 }); |
| |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 1, 1 }); |
| Assert.assertTrue(optimizer.getRMS() > 0.1); |
| } |
| |
| @Test(expected=DimensionMismatchException.class) |
| public void testInconsistentSizes1() { |
| LinearProblem problem = |
| new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 }); |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10); |
| |
| optimizer.optimize(100, problem, problem.target, |
| new double[] { 1 }, |
| new double[] { 0, 0 }); |
| } |
| |
| @Test(expected=DimensionMismatchException.class) |
| public void testInconsistentSizes2() { |
| LinearProblem problem = |
| new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 }); |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 }); |
| Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10); |
| Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10); |
| |
| optimizer.optimize(100, problem, new double[] { 1 }, |
| new double[] { 1 }, |
| new double[] { 0, 0 }); |
| } |
| |
| @Test |
| public void testCircleFitting() { |
| 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); |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum |
| = optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 }, new double[] { 1, 1, 1, 1, 1 }, |
| new double[] { 98.680, 47.345 }); |
| Assert.assertTrue(optimizer.getEvaluations() < 10); |
| Assert.assertTrue(optimizer.getJacobianEvaluations() < 10); |
| double rms = optimizer.getRMS(); |
| Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, 1.0e-10); |
| Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]); |
| Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1.0e-6); |
| Assert.assertEquals(96.07590211815305, center.getX(), 1.0e-6); |
| Assert.assertEquals(48.13516790438953, center.getY(), 1.0e-6); |
| double[][] cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14); |
| Assert.assertEquals(1.839, cov[0][0], 0.001); |
| Assert.assertEquals(0.731, cov[0][1], 0.001); |
| Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14); |
| Assert.assertEquals(0.786, cov[1][1], 0.001); |
| |
| // add perfect measurements and check errors are reduced |
| double r = circle.getRadius(center); |
| for (double d= 0; d < 2 * FastMath.PI; d += 0.01) { |
| circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d)); |
| } |
| double[] target = new double[circle.getN()]; |
| Arrays.fill(target, 0.0); |
| double[] weights = new double[circle.getN()]; |
| Arrays.fill(weights, 2.0); |
| optimum = optimizer.optimize(100, circle, target, weights, new double[] { 98.680, 47.345 }); |
| cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14); |
| Assert.assertEquals(0.0016, cov[0][0], 0.001); |
| Assert.assertEquals(3.2e-7, cov[0][1], 1.0e-9); |
| Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14); |
| Assert.assertEquals(0.0016, cov[1][1], 0.001); |
| } |
| |
| @Test |
| public void testCircleFittingBadInit() { |
| CircleVectorial circle = new CircleVectorial(); |
| double[][] points = circlePoints; |
| double[] target = new double[points.length]; |
| Arrays.fill(target, 0.0); |
| double[] weights = new double[points.length]; |
| Arrays.fill(weights, 2.0); |
| for (int i = 0; i < points.length; ++i) { |
| circle.addPoint(points[i][0], points[i][1]); |
| } |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum |
| = optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 }); |
| Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]); |
| Assert.assertTrue(optimizer.getEvaluations() < 25); |
| Assert.assertTrue(optimizer.getJacobianEvaluations() < 20); |
| Assert.assertEquals( 0.043, optimizer.getRMS(), 1.0e-3); |
| Assert.assertEquals( 0.292235, circle.getRadius(center), 1.0e-6); |
| Assert.assertEquals(-0.151738, center.getX(), 1.0e-6); |
| Assert.assertEquals( 0.2075001, center.getY(), 1.0e-6); |
| } |
| |
| @Test |
| public void testCircleFittingGoodInit() { |
| CircleVectorial circle = new CircleVectorial(); |
| double[][] points = circlePoints; |
| double[] target = new double[points.length]; |
| Arrays.fill(target, 0.0); |
| double[] weights = new double[points.length]; |
| Arrays.fill(weights, 2.0); |
| for (int i = 0; i < points.length; ++i) { |
| circle.addPoint(points[i][0], points[i][1]); |
| } |
| AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| PointVectorValuePair optimum = |
| optimizer.optimize(100, circle, target, weights, new double[] { 0, 0 }); |
| Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1.0e-6); |
| Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1.0e-6); |
| Assert.assertEquals(0.04268731682389561, optimizer.getRMS(), 1.0e-8); |
| } |
| |
| private final double[][] circlePoints = new double[][] { |
| {-0.312967, 0.072366}, {-0.339248, 0.132965}, {-0.379780, 0.202724}, |
| {-0.390426, 0.260487}, {-0.361212, 0.328325}, {-0.346039, 0.392619}, |
| {-0.280579, 0.444306}, {-0.216035, 0.470009}, {-0.149127, 0.493832}, |
| {-0.075133, 0.483271}, {-0.007759, 0.452680}, { 0.060071, 0.410235}, |
| { 0.103037, 0.341076}, { 0.118438, 0.273884}, { 0.131293, 0.192201}, |
| { 0.115869, 0.129797}, { 0.072223, 0.058396}, { 0.022884, 0.000718}, |
| {-0.053355, -0.020405}, {-0.123584, -0.032451}, {-0.216248, -0.032862}, |
| {-0.278592, -0.005008}, {-0.337655, 0.056658}, {-0.385899, 0.112526}, |
| {-0.405517, 0.186957}, {-0.415374, 0.262071}, {-0.387482, 0.343398}, |
| {-0.347322, 0.397943}, {-0.287623, 0.458425}, {-0.223502, 0.475513}, |
| {-0.135352, 0.478186}, {-0.061221, 0.483371}, { 0.003711, 0.422737}, |
| { 0.065054, 0.375830}, { 0.108108, 0.297099}, { 0.123882, 0.222850}, |
| { 0.117729, 0.134382}, { 0.085195, 0.056820}, { 0.029800, -0.019138}, |
| {-0.027520, -0.072374}, {-0.102268, -0.091555}, {-0.200299, -0.106578}, |
| {-0.292731, -0.091473}, {-0.356288, -0.051108}, {-0.420561, 0.014926}, |
| {-0.471036, 0.074716}, {-0.488638, 0.182508}, {-0.485990, 0.254068}, |
| {-0.463943, 0.338438}, {-0.406453, 0.404704}, {-0.334287, 0.466119}, |
| {-0.254244, 0.503188}, {-0.161548, 0.495769}, {-0.075733, 0.495560}, |
| { 0.001375, 0.434937}, { 0.082787, 0.385806}, { 0.115490, 0.323807}, |
| { 0.141089, 0.223450}, { 0.138693, 0.131703}, { 0.126415, 0.049174}, |
| { 0.066518, -0.010217}, {-0.005184, -0.070647}, {-0.080985, -0.103635}, |
| {-0.177377, -0.116887}, {-0.260628, -0.100258}, {-0.335756, -0.056251}, |
| {-0.405195, -0.000895}, {-0.444937, 0.085456}, {-0.484357, 0.175597}, |
| {-0.472453, 0.248681}, {-0.438580, 0.347463}, {-0.402304, 0.422428}, |
| {-0.326777, 0.479438}, {-0.247797, 0.505581}, {-0.152676, 0.519380}, |
| {-0.071754, 0.516264}, { 0.015942, 0.472802}, { 0.076608, 0.419077}, |
| { 0.127673, 0.330264}, { 0.159951, 0.262150}, { 0.153530, 0.172681}, |
| { 0.140653, 0.089229}, { 0.078666, 0.024981}, { 0.023807, -0.037022}, |
| {-0.048837, -0.077056}, {-0.127729, -0.075338}, {-0.221271, -0.067526} |
| }; |
| |
| public void doTestStRD(final StatisticalReferenceDataset dataset, |
| final double errParams, final double errParamsSd) { |
| final AbstractLeastSquaresOptimizer optimizer = createOptimizer(); |
| final double[] w = new double[dataset.getNumObservations()]; |
| Arrays.fill(w, 1.0); |
| |
| final double[][] data = dataset.getData(); |
| final double[] initial = dataset.getStartingPoint(0); |
| final MultivariateDifferentiableVectorFunction problem; |
| problem = dataset.getLeastSquaresProblem(); |
| final PointVectorValuePair optimum; |
| optimum = optimizer.optimize(100, problem, data[1], w, initial); |
| |
| final double[] actual = optimum.getPoint(); |
| for (int i = 0; i < actual.length; i++) { |
| double expected = dataset.getParameter(i); |
| double delta = FastMath.abs(errParams * expected); |
| Assert.assertEquals(dataset.getName() + ", param #" + i, |
| expected, actual[i], delta); |
| } |
| } |
| |
| @Test |
| public void testKirby2() throws IOException { |
| doTestStRD(StatisticalReferenceDatasetFactory.createKirby2(), 1E-7, 1E-7); |
| } |
| |
| @Test |
| public void testHahn1() throws IOException { |
| doTestStRD(StatisticalReferenceDatasetFactory.createHahn1(), 1E-7, 1E-4); |
| } |
| |
| static class LinearProblem implements MultivariateDifferentiableVectorFunction, Serializable { |
| |
| private static final long serialVersionUID = 703247177355019415L; |
| 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[] value = new DerivativeStructure[factors.getRowDimension()]; |
| for (int i = 0; i < value.length; ++i) { |
| value[i] = variables[0].getField().getZero(); |
| for (int j = 0; j < factors.getColumnDimension(); ++j) { |
| value[i] = value[i].add(variables[j].multiply(factors.getEntry(i, j))); |
| } |
| |
| } |
| return value; |
| } |
| |
| } |
| } |
