Author: luc
Date: Sat Nov 3 14:07:04 2007
New Revision: 591692
URL: http://svn.apache.org/viewvc?rev=591692&view=rev
Log:
add tests for Gauss-Newton estimator
Added:
commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/GaussNewtonEstimatorTest.java
(with props)
Added:
commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/GaussNewtonEstimatorTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/GaussNewtonEstimatorTest.java?rev=591692&view=auto
==============================================================================
---
commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/GaussNewtonEstimatorTest.java
(added)
+++
commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/GaussNewtonEstimatorTest.java
Sat Nov 3 14:07:04 2007
@@ -0,0 +1,677 @@
+/*
+ * 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.math.estimation;
+
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.Iterator;
+
+import org.apache.commons.math.estimation.EstimatedParameter;
+import org.apache.commons.math.estimation.EstimationException;
+import org.apache.commons.math.estimation.EstimationProblem;
+import org.apache.commons.math.estimation.GaussNewtonEstimator;
+import org.apache.commons.math.estimation.WeightedMeasurement;
+
+import junit.framework.*;
+
+/**
+ * <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)
+ */
+public class GaussNewtonEstimatorTest
+ extends TestCase {
+
+ public GaussNewtonEstimatorTest(String name) {
+ super(name);
+ }
+
+ public void testTrivial() throws EstimationException {
+ LinearProblem problem =
+ new LinearProblem(new LinearMeasurement[] {
+ new LinearMeasurement(new double[] {2},
+ new EstimatedParameter[] {
+ new EstimatedParameter("p0", 0)
+ }, 3.0)
+ });
+ GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ estimator.estimate(problem);
+ assertEquals(0, estimator.getRMS(problem), 1.0e-10);
+ assertEquals(1.5,
+ problem.getUnboundParameters()[0].getEstimate(),
+ 1.0e-10);
+ }
+
+ public void testQRColumnsPermutation() throws EstimationException {
+
+ EstimatedParameter[] x = {
+ new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 0)
+ };
+ LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
+ new LinearMeasurement(new double[] { 1.0, -1.0 },
+ new EstimatedParameter[] { x[0], x[1] },
+ 4.0),
+ new LinearMeasurement(new double[] { 2.0 },
+ new EstimatedParameter[] { x[1] },
+ 6.0),
+ new LinearMeasurement(new double[] { 1.0, -2.0 },
+ new EstimatedParameter[] { x[0], x[1] },
+ 1.0)
+ });
+
+ GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ estimator.estimate(problem);
+ assertEquals(0, estimator.getRMS(problem), 1.0e-10);
+ assertEquals(7.0, x[0].getEstimate(), 1.0e-10);
+ assertEquals(3.0, x[1].getEstimate(), 1.0e-10);
+
+ }
+
+ public void testNoDependency() throws EstimationException {
+ EstimatedParameter[] p = new EstimatedParameter[] {
+ new EstimatedParameter("p0", 0),
+ new EstimatedParameter("p1", 0),
+ new EstimatedParameter("p2", 0),
+ new EstimatedParameter("p3", 0),
+ new EstimatedParameter("p4", 0),
+ new EstimatedParameter("p5", 0)
+ };
+ LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
+ new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[0]
}, 0.0),
+ new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[1]
}, 1.1),
+ new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[2]
}, 2.2),
+ new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[3]
}, 3.3),
+ new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[4]
}, 4.4),
+ new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[5]
}, 5.5)
+ });
+ GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ estimator.estimate(problem);
+ assertEquals(0, estimator.getRMS(problem), 1.0e-10);
+ for (int i = 0; i < p.length; ++i) {
+ assertEquals(0.55 * i, p[i].getEstimate(), 1.0e-10);
+ }
+}
+
+ public void testOneSet() throws EstimationException {
+
+ EstimatedParameter[] p = {
+ new EstimatedParameter("p0", 0),
+ new EstimatedParameter("p1", 0),
+ new EstimatedParameter("p2", 0)
+ };
+ LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
+ new LinearMeasurement(new double[] { 1.0 },
+ new EstimatedParameter[] { p[0] },
+ 1.0),
+ new LinearMeasurement(new double[] { -1.0, 1.0 },
+ new EstimatedParameter[] { p[0], p[1] },
+ 1.0),
+ new LinearMeasurement(new double[] { -1.0, 1.0 },
+ new EstimatedParameter[] { p[1], p[2] },
+ 1.0)
+ });
+
+ GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ estimator.estimate(problem);
+ assertEquals(0, estimator.getRMS(problem), 1.0e-10);
+ assertEquals(1.0, p[0].getEstimate(), 1.0e-10);
+ assertEquals(2.0, p[1].getEstimate(), 1.0e-10);
+ assertEquals(3.0, p[2].getEstimate(), 1.0e-10);
+
+ }
+
+ public void testTwoSets() throws EstimationException {
+ EstimatedParameter[] p = {
+ new EstimatedParameter("p0", 0),
+ new EstimatedParameter("p1", 1),
+ new EstimatedParameter("p2", 2),
+ new EstimatedParameter("p3", 3),
+ new EstimatedParameter("p4", 4),
+ new EstimatedParameter("p5", 5)
+ };
+
+ double epsilon = 1.0e-7;
+ LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
+
+ // 4 elements sub-problem
+ new LinearMeasurement(new double[] { 2.0, 1.0, 4.0 },
+ new EstimatedParameter[] { p[0], p[1], p[3] },
+ 2.0),
+ new LinearMeasurement(new double[] { -4.0, -2.0, 3.0, -7.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
+ -9.0),
+ new LinearMeasurement(new double[] { 4.0, 1.0, -2.0, 8.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3]
},
+ 2.0),
+ new LinearMeasurement(new double[] { -3.0, -12.0, -1.0 },
+ new EstimatedParameter[] { p[1], p[2], p[3] },
+ 2.0),
+
+ // 2 elements sub-problem
+ new LinearMeasurement(new double[] { epsilon, 1.0 },
+ new EstimatedParameter[] { p[4], p[5] },
+ 1.0 + epsilon * epsilon),
+ new LinearMeasurement(new double[] { 1.0, 1.0 },
+ new EstimatedParameter[] { p[4], p[5] },
+ 2.0)
+
+ });
+
+ GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ estimator.estimate(problem);
+ assertEquals(0, estimator.getRMS(problem), 1.0e-10);
+ assertEquals( 3.0, p[0].getEstimate(), 1.0e-10);
+ assertEquals( 4.0, p[1].getEstimate(), 1.0e-10);
+ assertEquals(-1.0, p[2].getEstimate(), 1.0e-10);
+ assertEquals(-2.0, p[3].getEstimate(), 1.0e-10);
+ assertEquals( 1.0 + epsilon, p[4].getEstimate(), 1.0e-10);
+ assertEquals( 1.0 - epsilon, p[5].getEstimate(), 1.0e-10);
+
+ }
+
+ public void testNonInversible() throws EstimationException {
+
+ EstimatedParameter[] p = {
+ new EstimatedParameter("p0", 0),
+ new EstimatedParameter("p1", 0),
+ new EstimatedParameter("p2", 0)
+ };
+ LinearMeasurement[] m = new LinearMeasurement[] {
+ new LinearMeasurement(new double[] { 1.0, 2.0, -3.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2] },
+ 1.0),
+ new LinearMeasurement(new double[] { 2.0, 1.0, 3.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2] },
+ 1.0),
+ new LinearMeasurement(new double[] { -3.0, -9.0 },
+ new EstimatedParameter[] { p[0], p[2] },
+ 1.0)
+ };
+ LinearProblem problem = new LinearProblem(m);
+
+ GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ try {
+ estimator.estimate(problem);
+ fail("an exception should have been caught");
+ } catch (EstimationException ee) {
+ // expected behavior
+ } catch (Exception e) {
+ fail("wrong exception type caught");
+ }
+ }
+
+ public void testIllConditioned() throws EstimationException {
+ EstimatedParameter[] p = {
+ new EstimatedParameter("p0", 0),
+ new EstimatedParameter("p1", 1),
+ new EstimatedParameter("p2", 2),
+ new EstimatedParameter("p3", 3)
+ };
+
+ LinearProblem problem1 = new LinearProblem(new LinearMeasurement[] {
+ new LinearMeasurement(new double[] { 10.0, 7.0, 8.0, 7.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3]
},
+ 32.0),
+ new LinearMeasurement(new double[] { 7.0, 5.0, 6.0, 5.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3]
},
+ 23.0),
+ new LinearMeasurement(new double[] { 8.0, 6.0, 10.0, 9.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3]
},
+ 33.0),
+ new LinearMeasurement(new double[] { 7.0, 5.0, 9.0, 10.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3]
},
+ 31.0)
+ });
+ GaussNewtonEstimator estimator1 = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ estimator1.estimate(problem1);
+ assertEquals(0, estimator1.getRMS(problem1), 1.0e-10);
+ assertEquals(1.0, p[0].getEstimate(), 1.0e-10);
+ assertEquals(1.0, p[1].getEstimate(), 1.0e-10);
+ assertEquals(1.0, p[2].getEstimate(), 1.0e-10);
+ assertEquals(1.0, p[3].getEstimate(), 1.0e-10);
+
+ LinearProblem problem2 = new LinearProblem(new LinearMeasurement[] {
+ new LinearMeasurement(new double[] { 10.0, 7.0, 8.1, 7.2 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3]
},
+ 32.0),
+ new LinearMeasurement(new double[] { 7.08, 5.04, 6.0, 5.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3]
},
+ 23.0),
+ new LinearMeasurement(new double[] { 8.0, 5.98, 9.89, 9.0 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3]
},
+ 33.0),
+ new LinearMeasurement(new double[] { 6.99, 4.99, 9.0, 9.98 },
+ new EstimatedParameter[] { p[0], p[1], p[2], p[3]
},
+ 31.0)
+ });
+ GaussNewtonEstimator estimator2 = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ estimator2.estimate(problem2);
+ assertEquals(0, estimator2.getRMS(problem2), 1.0e-10);
+ assertEquals(-81.0, p[0].getEstimate(), 1.0e-8);
+ assertEquals(137.0, p[1].getEstimate(), 1.0e-8);
+ assertEquals(-34.0, p[2].getEstimate(), 1.0e-8);
+ assertEquals( 22.0, p[3].getEstimate(), 1.0e-8);
+
+ }
+
+ public void testMoreEstimatedParametersSimple() throws EstimationException {
+
+ EstimatedParameter[] p = {
+ new EstimatedParameter("p0", 7),
+ new EstimatedParameter("p1", 6),
+ new EstimatedParameter("p2", 5),
+ new EstimatedParameter("p3", 4)
+ };
+ LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
+ new LinearMeasurement(new double[] { 3.0, 2.0 },
+ new EstimatedParameter[] { p[0], p[1] },
+ 7.0),
+ new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 },
+ new EstimatedParameter[] { p[1], p[2], p[3] },
+ 3.0),
+ new LinearMeasurement(new double[] { 2.0, 1.0 },
+ new EstimatedParameter[] { p[0], p[2] },
+ 5.0)
+ });
+
+ GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ try {
+ estimator.estimate(problem);
+ fail("an exception should have been caught");
+ } catch (EstimationException ee) {
+ // expected behavior
+ } catch (Exception e) {
+ fail("wrong exception type caught");
+ }
+
+ }
+
+ public void testMoreEstimatedParametersUnsorted() throws EstimationException
{
+ EstimatedParameter[] p = {
+ new EstimatedParameter("p0", 2),
+ new EstimatedParameter("p1", 2),
+ new EstimatedParameter("p2", 2),
+ new EstimatedParameter("p3", 2),
+ new EstimatedParameter("p4", 2),
+ new EstimatedParameter("p5", 2)
+ };
+ 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, 1.0 },
+ new EstimatedParameter[] { p[2], p[3], p[4] },
+ 12.0),
+ new LinearMeasurement(new double[] { 1.0, -1.0 },
+ new EstimatedParameter[] { p[4], p[5] },
+ -1.0),
+ new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 },
+ new EstimatedParameter[] { p[3], p[2], p[5] },
+ 7.0),
+ new LinearMeasurement(new double[] { 1.0, -1.0 },
+ new EstimatedParameter[] { p[4], p[3] },
+ 1.0)
+ });
+
+ GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6,
1.0e-6);
+ try {
+ estimator.estimate(problem);
+ fail("an exception should have been caught");
+ } catch (EstimationException ee) {
+ // expected behavior
+ } catch (Exception e) {
+ fail("wrong exception type caught");
+ }
+
+ }
+
+ public void testRedundantEquations() 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] },
+ 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);
+ }
+
+}
Propchange:
commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/GaussNewtonEstimatorTest.java
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