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From l..@apache.org
Subject svn commit: r591692 - /commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/GaussNewtonEstimatorTest.java
Date Sat, 03 Nov 2007 21:07:04 GMT
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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    svn:eol-style = native



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