add tests for Gauss-Newton estimator
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@591692 13f79535-47bb-0310-9956-ffa450edef68
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Luc Maisonobe
parent
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commit
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math.estimation;
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import java.util.ArrayList;
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import java.util.HashSet;
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import java.util.Iterator;
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import org.apache.commons.math.estimation.EstimatedParameter;
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import org.apache.commons.math.estimation.EstimationException;
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import org.apache.commons.math.estimation.EstimationProblem;
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import org.apache.commons.math.estimation.GaussNewtonEstimator;
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import org.apache.commons.math.estimation.WeightedMeasurement;
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import junit.framework.*;
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/**
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* <p>Some of the unit tests are re-implementations of the MINPACK <a
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* href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
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* href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
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* The redistribution policy for MINPACK is available <a
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* href="http://www.netlib.org/minpack/disclaimer">here</a>, for
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* convenience, it is reproduced below.</p>
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* <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
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* <tr><td>
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* Minpack Copyright Notice (1999) University of Chicago.
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* All rights reserved
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* </td></tr>
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* <tr><td>
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* <ol>
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* <li>Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.</li>
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* <li>Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.</li>
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* <li>The end-user documentation included with the redistribution, if any,
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* must include the following acknowledgment:
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* <code>This product includes software developed by the University of
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* Chicago, as Operator of Argonne National Laboratory.</code>
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* Alternately, this acknowledgment may appear in the software itself,
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* if and wherever such third-party acknowledgments normally appear.</li>
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* <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
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* WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
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* UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
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* THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
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* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
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* OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
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* OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
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* USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
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* THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
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* DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
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* UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
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* BE CORRECTED.</strong></li>
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* <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
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* HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
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* ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
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* INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
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* ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
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* PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
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* SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
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* (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
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* EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
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* POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
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* <ol></td></tr>
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* </table>
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* @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
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* @author Burton S. Garbow (original fortran minpack tests)
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* @author Kenneth E. Hillstrom (original fortran minpack tests)
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* @author Jorge J. More (original fortran minpack tests)
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* @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
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*/
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public class GaussNewtonEstimatorTest
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extends TestCase {
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public GaussNewtonEstimatorTest(String name) {
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super(name);
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}
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public void testTrivial() throws EstimationException {
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LinearProblem problem =
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new LinearProblem(new LinearMeasurement[] {
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new LinearMeasurement(new double[] {2},
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new EstimatedParameter[] {
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new EstimatedParameter("p0", 0)
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}, 3.0)
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});
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GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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estimator.estimate(problem);
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assertEquals(0, estimator.getRMS(problem), 1.0e-10);
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assertEquals(1.5,
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problem.getUnboundParameters()[0].getEstimate(),
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1.0e-10);
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}
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public void testQRColumnsPermutation() throws EstimationException {
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EstimatedParameter[] x = {
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new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 0)
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};
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LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
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new LinearMeasurement(new double[] { 1.0, -1.0 },
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new EstimatedParameter[] { x[0], x[1] },
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4.0),
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new LinearMeasurement(new double[] { 2.0 },
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new EstimatedParameter[] { x[1] },
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6.0),
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new LinearMeasurement(new double[] { 1.0, -2.0 },
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new EstimatedParameter[] { x[0], x[1] },
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1.0)
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});
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GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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estimator.estimate(problem);
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assertEquals(0, estimator.getRMS(problem), 1.0e-10);
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assertEquals(7.0, x[0].getEstimate(), 1.0e-10);
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assertEquals(3.0, x[1].getEstimate(), 1.0e-10);
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}
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public void testNoDependency() throws EstimationException {
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EstimatedParameter[] p = new EstimatedParameter[] {
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new EstimatedParameter("p0", 0),
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new EstimatedParameter("p1", 0),
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new EstimatedParameter("p2", 0),
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new EstimatedParameter("p3", 0),
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new EstimatedParameter("p4", 0),
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new EstimatedParameter("p5", 0)
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};
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LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
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new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[0] }, 0.0),
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new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[1] }, 1.1),
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new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[2] }, 2.2),
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new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[3] }, 3.3),
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new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[4] }, 4.4),
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new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[5] }, 5.5)
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});
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GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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estimator.estimate(problem);
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assertEquals(0, estimator.getRMS(problem), 1.0e-10);
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for (int i = 0; i < p.length; ++i) {
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assertEquals(0.55 * i, p[i].getEstimate(), 1.0e-10);
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}
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}
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public void testOneSet() throws EstimationException {
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EstimatedParameter[] p = {
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new EstimatedParameter("p0", 0),
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new EstimatedParameter("p1", 0),
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new EstimatedParameter("p2", 0)
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};
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LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
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new LinearMeasurement(new double[] { 1.0 },
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new EstimatedParameter[] { p[0] },
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1.0),
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new LinearMeasurement(new double[] { -1.0, 1.0 },
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new EstimatedParameter[] { p[0], p[1] },
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1.0),
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new LinearMeasurement(new double[] { -1.0, 1.0 },
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new EstimatedParameter[] { p[1], p[2] },
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1.0)
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});
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GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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estimator.estimate(problem);
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assertEquals(0, estimator.getRMS(problem), 1.0e-10);
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assertEquals(1.0, p[0].getEstimate(), 1.0e-10);
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assertEquals(2.0, p[1].getEstimate(), 1.0e-10);
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assertEquals(3.0, p[2].getEstimate(), 1.0e-10);
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}
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public void testTwoSets() throws EstimationException {
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EstimatedParameter[] p = {
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new EstimatedParameter("p0", 0),
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new EstimatedParameter("p1", 1),
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new EstimatedParameter("p2", 2),
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new EstimatedParameter("p3", 3),
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new EstimatedParameter("p4", 4),
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new EstimatedParameter("p5", 5)
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};
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double epsilon = 1.0e-7;
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LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
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// 4 elements sub-problem
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new LinearMeasurement(new double[] { 2.0, 1.0, 4.0 },
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new EstimatedParameter[] { p[0], p[1], p[3] },
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2.0),
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new LinearMeasurement(new double[] { -4.0, -2.0, 3.0, -7.0 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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-9.0),
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new LinearMeasurement(new double[] { 4.0, 1.0, -2.0, 8.0 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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2.0),
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new LinearMeasurement(new double[] { -3.0, -12.0, -1.0 },
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new EstimatedParameter[] { p[1], p[2], p[3] },
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2.0),
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// 2 elements sub-problem
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new LinearMeasurement(new double[] { epsilon, 1.0 },
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new EstimatedParameter[] { p[4], p[5] },
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1.0 + epsilon * epsilon),
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new LinearMeasurement(new double[] { 1.0, 1.0 },
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new EstimatedParameter[] { p[4], p[5] },
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2.0)
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});
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GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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estimator.estimate(problem);
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assertEquals(0, estimator.getRMS(problem), 1.0e-10);
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assertEquals( 3.0, p[0].getEstimate(), 1.0e-10);
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assertEquals( 4.0, p[1].getEstimate(), 1.0e-10);
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assertEquals(-1.0, p[2].getEstimate(), 1.0e-10);
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assertEquals(-2.0, p[3].getEstimate(), 1.0e-10);
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assertEquals( 1.0 + epsilon, p[4].getEstimate(), 1.0e-10);
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assertEquals( 1.0 - epsilon, p[5].getEstimate(), 1.0e-10);
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}
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public void testNonInversible() throws EstimationException {
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EstimatedParameter[] p = {
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new EstimatedParameter("p0", 0),
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new EstimatedParameter("p1", 0),
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new EstimatedParameter("p2", 0)
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};
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LinearMeasurement[] m = new LinearMeasurement[] {
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new LinearMeasurement(new double[] { 1.0, 2.0, -3.0 },
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new EstimatedParameter[] { p[0], p[1], p[2] },
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1.0),
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new LinearMeasurement(new double[] { 2.0, 1.0, 3.0 },
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new EstimatedParameter[] { p[0], p[1], p[2] },
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1.0),
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new LinearMeasurement(new double[] { -3.0, -9.0 },
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new EstimatedParameter[] { p[0], p[2] },
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1.0)
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};
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LinearProblem problem = new LinearProblem(m);
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GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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try {
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estimator.estimate(problem);
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fail("an exception should have been caught");
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} catch (EstimationException ee) {
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// expected behavior
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} catch (Exception e) {
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fail("wrong exception type caught");
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}
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}
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public void testIllConditioned() throws EstimationException {
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EstimatedParameter[] p = {
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new EstimatedParameter("p0", 0),
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new EstimatedParameter("p1", 1),
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new EstimatedParameter("p2", 2),
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new EstimatedParameter("p3", 3)
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};
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LinearProblem problem1 = new LinearProblem(new LinearMeasurement[] {
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new LinearMeasurement(new double[] { 10.0, 7.0, 8.0, 7.0 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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32.0),
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new LinearMeasurement(new double[] { 7.0, 5.0, 6.0, 5.0 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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23.0),
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new LinearMeasurement(new double[] { 8.0, 6.0, 10.0, 9.0 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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33.0),
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new LinearMeasurement(new double[] { 7.0, 5.0, 9.0, 10.0 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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31.0)
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});
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GaussNewtonEstimator estimator1 = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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estimator1.estimate(problem1);
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assertEquals(0, estimator1.getRMS(problem1), 1.0e-10);
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assertEquals(1.0, p[0].getEstimate(), 1.0e-10);
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assertEquals(1.0, p[1].getEstimate(), 1.0e-10);
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assertEquals(1.0, p[2].getEstimate(), 1.0e-10);
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assertEquals(1.0, p[3].getEstimate(), 1.0e-10);
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LinearProblem problem2 = new LinearProblem(new LinearMeasurement[] {
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new LinearMeasurement(new double[] { 10.0, 7.0, 8.1, 7.2 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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32.0),
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new LinearMeasurement(new double[] { 7.08, 5.04, 6.0, 5.0 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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23.0),
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new LinearMeasurement(new double[] { 8.0, 5.98, 9.89, 9.0 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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33.0),
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new LinearMeasurement(new double[] { 6.99, 4.99, 9.0, 9.98 },
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new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
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31.0)
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});
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GaussNewtonEstimator estimator2 = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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estimator2.estimate(problem2);
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assertEquals(0, estimator2.getRMS(problem2), 1.0e-10);
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assertEquals(-81.0, p[0].getEstimate(), 1.0e-8);
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assertEquals(137.0, p[1].getEstimate(), 1.0e-8);
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assertEquals(-34.0, p[2].getEstimate(), 1.0e-8);
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assertEquals( 22.0, p[3].getEstimate(), 1.0e-8);
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}
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public void testMoreEstimatedParametersSimple() throws EstimationException {
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EstimatedParameter[] p = {
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new EstimatedParameter("p0", 7),
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new EstimatedParameter("p1", 6),
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new EstimatedParameter("p2", 5),
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new EstimatedParameter("p3", 4)
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};
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LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
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new LinearMeasurement(new double[] { 3.0, 2.0 },
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new EstimatedParameter[] { p[0], p[1] },
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7.0),
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new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 },
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new EstimatedParameter[] { p[1], p[2], p[3] },
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3.0),
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new LinearMeasurement(new double[] { 2.0, 1.0 },
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new EstimatedParameter[] { p[0], p[2] },
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5.0)
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});
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GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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try {
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estimator.estimate(problem);
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fail("an exception should have been caught");
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} catch (EstimationException ee) {
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// expected behavior
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} catch (Exception e) {
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fail("wrong exception type caught");
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}
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}
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public void testMoreEstimatedParametersUnsorted() throws EstimationException {
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EstimatedParameter[] p = {
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new EstimatedParameter("p0", 2),
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new EstimatedParameter("p1", 2),
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new EstimatedParameter("p2", 2),
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new EstimatedParameter("p3", 2),
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new EstimatedParameter("p4", 2),
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new EstimatedParameter("p5", 2)
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};
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LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
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new LinearMeasurement(new double[] { 1.0, 1.0 },
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new EstimatedParameter[] { p[0], p[1] },
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3.0),
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new LinearMeasurement(new double[] { 1.0, 1.0, 1.0 },
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new EstimatedParameter[] { p[2], p[3], p[4] },
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12.0),
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new LinearMeasurement(new double[] { 1.0, -1.0 },
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new EstimatedParameter[] { p[4], p[5] },
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-1.0),
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new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 },
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new EstimatedParameter[] { p[3], p[2], p[5] },
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7.0),
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new LinearMeasurement(new double[] { 1.0, -1.0 },
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new EstimatedParameter[] { p[4], p[3] },
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1.0)
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});
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GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
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try {
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estimator.estimate(problem);
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fail("an exception should have been caught");
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} catch (EstimationException ee) {
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// expected behavior
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} catch (Exception e) {
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fail("wrong exception type caught");
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}
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}
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public void testRedundantEquations() throws EstimationException {
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EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 1),
|
||||
new EstimatedParameter("p1", 1)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { 1.0, 3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
5.0)
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals(2.0, p[0].getEstimate(), 1.0e-10);
|
||||
assertEquals(1.0, p[1].getEstimate(), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testInconsistentEquations() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 1),
|
||||
new EstimatedParameter("p1", 1)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { 1.0, 3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
4.0)
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertTrue(estimator.getRMS(problem) > 0.1);
|
||||
|
||||
}
|
||||
|
||||
public void testCircleFitting() throws EstimationException {
|
||||
Circle circle = new Circle(98.680, 47.345);
|
||||
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);
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-10, 1.0e-10);
|
||||
estimator.estimate(circle);
|
||||
double rms = estimator.getRMS(circle);
|
||||
assertEquals(1.768262623567235, Math.sqrt(circle.getM()) * rms, 1.0e-10);
|
||||
assertEquals(69.96016176931406, circle.getRadius(), 1.0e-10);
|
||||
assertEquals(96.07590211815305, circle.getX(), 1.0e-10);
|
||||
assertEquals(48.13516790438953, circle.getY(), 1.0e-10);
|
||||
}
|
||||
|
||||
public void testCircleFittingBadInit() throws EstimationException {
|
||||
Circle circle = new Circle(-12, -12);
|
||||
double[][] points = 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}
|
||||
};
|
||||
for (int i = 0; i < points.length; ++i) {
|
||||
circle.addPoint(points[i][0], points[i][1]);
|
||||
}
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
try {
|
||||
estimator.estimate(circle);
|
||||
fail("an exception should have been caught");
|
||||
} catch (EstimationException ee) {
|
||||
// expected behavior
|
||||
} catch (Exception e) {
|
||||
fail("wrong exception type caught");
|
||||
}
|
||||
}
|
||||
|
||||
private static class LinearProblem extends SimpleEstimationProblem {
|
||||
|
||||
public LinearProblem(LinearMeasurement[] measurements) {
|
||||
HashSet set = new HashSet();
|
||||
for (int i = 0; i < measurements.length; ++i) {
|
||||
addMeasurement(measurements[i]);
|
||||
EstimatedParameter[] parameters = measurements[i].getParameters();
|
||||
for (int j = 0; j < parameters.length; ++j) {
|
||||
set.add(parameters[j]);
|
||||
}
|
||||
}
|
||||
for (Iterator iterator = set.iterator(); iterator.hasNext();) {
|
||||
addParameter((EstimatedParameter) iterator.next());
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
private static class LinearMeasurement extends WeightedMeasurement {
|
||||
|
||||
public LinearMeasurement(double[] factors, EstimatedParameter[] parameters,
|
||||
double setPoint) {
|
||||
super(1.0, setPoint, true);
|
||||
this.factors = factors;
|
||||
this.parameters = parameters;
|
||||
setIgnored(false);
|
||||
}
|
||||
|
||||
public double getTheoreticalValue() {
|
||||
double v = 0;
|
||||
for (int i = 0; i < factors.length; ++i) {
|
||||
v += factors[i] * parameters[i].getEstimate();
|
||||
}
|
||||
return v;
|
||||
}
|
||||
|
||||
public double getPartial(EstimatedParameter parameter) {
|
||||
for (int i = 0; i < parameters.length; ++i) {
|
||||
if (parameters[i] == parameter) {
|
||||
return factors[i];
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getParameters() {
|
||||
return parameters;
|
||||
}
|
||||
|
||||
private double[] factors;
|
||||
private EstimatedParameter[] parameters;
|
||||
private static final long serialVersionUID = -3922448707008868580L;
|
||||
|
||||
}
|
||||
|
||||
private static class Circle implements EstimationProblem {
|
||||
|
||||
public Circle(double cx, double cy) {
|
||||
this.cx = new EstimatedParameter("cx", cx);
|
||||
this.cy = new EstimatedParameter(new EstimatedParameter("cy", cy));
|
||||
points = new ArrayList();
|
||||
}
|
||||
|
||||
public void addPoint(double px, double py) {
|
||||
points.add(new PointModel(px, py));
|
||||
}
|
||||
|
||||
public int getM() {
|
||||
return points.size();
|
||||
}
|
||||
|
||||
public WeightedMeasurement[] getMeasurements() {
|
||||
return (WeightedMeasurement[]) points.toArray(new PointModel[points.size()]);
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getAllParameters() {
|
||||
return new EstimatedParameter[] { cx, cy };
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getUnboundParameters() {
|
||||
return new EstimatedParameter[] { cx, cy };
|
||||
}
|
||||
|
||||
public double getPartialRadiusX() {
|
||||
double dRdX = 0;
|
||||
for (Iterator iterator = points.iterator(); iterator.hasNext();) {
|
||||
dRdX += ((PointModel) iterator.next()).getPartialDiX();
|
||||
}
|
||||
return dRdX / points.size();
|
||||
}
|
||||
|
||||
public double getPartialRadiusY() {
|
||||
double dRdY = 0;
|
||||
for (Iterator iterator = points.iterator(); iterator.hasNext();) {
|
||||
dRdY += ((PointModel) iterator.next()).getPartialDiY();
|
||||
}
|
||||
return dRdY / points.size();
|
||||
}
|
||||
|
||||
public double getRadius() {
|
||||
double r = 0;
|
||||
for (Iterator iterator = points.iterator(); iterator.hasNext();) {
|
||||
r += ((PointModel) iterator.next()).getCenterDistance();
|
||||
}
|
||||
return r / points.size();
|
||||
}
|
||||
|
||||
public double getX() {
|
||||
return cx.getEstimate();
|
||||
}
|
||||
|
||||
public double getY() {
|
||||
return cy.getEstimate();
|
||||
}
|
||||
|
||||
private class PointModel extends WeightedMeasurement {
|
||||
|
||||
public PointModel(double px, double py) {
|
||||
super(1.0, 0.0);
|
||||
this.px = px;
|
||||
this.py = py;
|
||||
}
|
||||
|
||||
public double getPartial(EstimatedParameter parameter) {
|
||||
if (parameter == cx) {
|
||||
return getPartialDiX() - getPartialRadiusX();
|
||||
} else if (parameter == cy) {
|
||||
return getPartialDiY() - getPartialRadiusY();
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
public double getCenterDistance() {
|
||||
double dx = px - cx.getEstimate();
|
||||
double dy = py - cy.getEstimate();
|
||||
return Math.sqrt(dx * dx + dy * dy);
|
||||
}
|
||||
|
||||
public double getPartialDiX() {
|
||||
return (cx.getEstimate() - px) / getCenterDistance();
|
||||
}
|
||||
|
||||
public double getPartialDiY() {
|
||||
return (cy.getEstimate() - py) / getCenterDistance();
|
||||
}
|
||||
|
||||
public double getTheoreticalValue() {
|
||||
return getCenterDistance() - getRadius();
|
||||
}
|
||||
|
||||
private double px;
|
||||
private double py;
|
||||
private static final long serialVersionUID = 1L;
|
||||
|
||||
}
|
||||
|
||||
private EstimatedParameter cx;
|
||||
private EstimatedParameter cy;
|
||||
private ArrayList points;
|
||||
|
||||
}
|
||||
|
||||
public static Test suite() {
|
||||
return new TestSuite(GaussNewtonEstimatorTest.class);
|
||||
}
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue