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From celes...@apache.org
Subject svn commit: r1332159 - in /commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general: AbstractLeastSquaresOptimizerAbstractTest.java GaussNewtonOptimizerTest.java LevenbergMarquardtOptimizerTest.java
Date Mon, 30 Apr 2012 11:07:43 GMT
Author: celestin
Date: Mon Apr 30 11:07:42 2012
New Revision: 1332159

URL: http://svn.apache.org/viewvc?rev=1332159&view=rev
Log:
Factored out some redundant code.

Added:
    commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
Modified:
    commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java
    commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/LevenbergMarquardtOptimizerTest.java

Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java?rev=1332159&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java (added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java Mon Apr 30 11:07:42 2012
@@ -0,0 +1,490 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.commons.math3.optimization.general;
+
+import java.awt.geom.Point2D;
+import java.io.Serializable;
+import java.util.Arrays;
+
+import org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction;
+import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
+import org.apache.commons.math3.exception.ConvergenceException;
+import org.apache.commons.math3.exception.DimensionMismatchException;
+import org.apache.commons.math3.exception.NumberIsTooSmallException;
+import org.apache.commons.math3.linear.BlockRealMatrix;
+import org.apache.commons.math3.linear.RealMatrix;
+import org.apache.commons.math3.optimization.PointVectorValuePair;
+import org.apache.commons.math3.util.FastMath;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * <p>Some of the unit tests are re-implementations of the MINPACK <a
+ * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
+ * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
+ * The redistribution policy for MINPACK is available <a
+ * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
+ * convenience, it is reproduced below.</p>
+
+ * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
+ * <tr><td>
+ *    Minpack Copyright Notice (1999) University of Chicago.
+ *    All rights reserved
+ * </td></tr>
+ * <tr><td>
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ * <ol>
+ *  <li>Redistributions of source code must retain the above copyright
+ *      notice, this list of conditions and the following disclaimer.</li>
+ * <li>Redistributions in binary form must reproduce the above
+ *     copyright notice, this list of conditions and the following
+ *     disclaimer in the documentation and/or other materials provided
+ *     with the distribution.</li>
+ * <li>The end-user documentation included with the redistribution, if any,
+ *     must include the following acknowledgment:
+ *     <code>This product includes software developed by the University of
+ *           Chicago, as Operator of Argonne National Laboratory.</code>
+ *     Alternately, this acknowledgment may appear in the software itself,
+ *     if and wherever such third-party acknowledgments normally appear.</li>
+ * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
+ *     WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
+ *     UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
+ *     THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
+ *     IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
+ *     OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
+ *     OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
+ *     OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
+ *     USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
+ *     THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
+ *     DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
+ *     UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
+ *     BE CORRECTED.</strong></li>
+ * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
+ *     HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
+ *     ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
+ *     INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
+ *     ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
+ *     PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
+ *     SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
+ *     (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
+ *     EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
+ *     POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
+ * <ol></td></tr>
+ * </table>
+
+ * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
+ * @author Burton S. Garbow (original fortran minpack tests)
+ * @author Kenneth E. Hillstrom (original fortran minpack tests)
+ * @author Jorge J. More (original fortran minpack tests)
+ * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
+ */
+public abstract class AbstractLeastSquaresOptimizerAbstractTest {
+
+    public abstract AbstractLeastSquaresOptimizer createOptimizer();
+
+    @Test
+    public void testTrivial() {
+        LinearProblem problem =
+            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
+        Assert.assertEquals(3.0, optimum.getValue()[0], 1.0e-10);
+        try {
+            optimizer.guessParametersErrors();
+            Assert.fail("an exception should have been thrown");
+        } catch (NumberIsTooSmallException ee) {
+            // expected behavior
+        }
+    }
+
+    @Test
+    public void testQRColumnsPermutation() {
+
+        LinearProblem problem =
+            new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
+                              new double[] { 4.0, 6.0, 1.0 });
+
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
+        Assert.assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
+        Assert.assertEquals(4.0, optimum.getValue()[0], 1.0e-10);
+        Assert.assertEquals(6.0, optimum.getValue()[1], 1.0e-10);
+        Assert.assertEquals(1.0, optimum.getValue()[2], 1.0e-10);
+    }
+
+    @Test
+    public void testNoDependency() {
+        LinearProblem problem = new LinearProblem(new double[][] {
+                { 2, 0, 0, 0, 0, 0 },
+                { 0, 2, 0, 0, 0, 0 },
+                { 0, 0, 2, 0, 0, 0 },
+                { 0, 0, 0, 2, 0, 0 },
+                { 0, 0, 0, 0, 2, 0 },
+                { 0, 0, 0, 0, 0, 2 }
+        }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
+                               new double[] { 0, 0, 0, 0, 0, 0 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        for (int i = 0; i < problem.target.length; ++i) {
+            Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
+        }
+    }
+
+    @Test
+    public void testOneSet() {
+
+        LinearProblem problem = new LinearProblem(new double[][] {
+                {  1,  0, 0 },
+                { -1,  1, 0 },
+                {  0, -1, 1 }
+        }, new double[] { 1, 1, 1});
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
+        Assert.assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
+        Assert.assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);
+    }
+
+    @Test
+    public void testTwoSets() {
+        double epsilon = 1.0e-7;
+        LinearProblem problem = new LinearProblem(new double[][] {
+                {  2,  1,   0,  4,       0, 0 },
+                { -4, -2,   3, -7,       0, 0 },
+                {  4,  1,  -2,  8,       0, 0 },
+                {  0, -3, -12, -1,       0, 0 },
+                {  0,  0,   0,  0, epsilon, 1 },
+                {  0,  0,   0,  0,       1, 1 }
+        }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
+
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
+                               new double[] { 0, 0, 0, 0, 0, 0 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10);
+        Assert.assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10);
+        Assert.assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10);
+        Assert.assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10);
+        Assert.assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10);
+        Assert.assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10);
+    }
+
+    @Test(expected=ConvergenceException.class)
+    public void testNonInvertible() throws Exception {
+
+        LinearProblem problem = new LinearProblem(new double[][] {
+                {  1, 2, -3 },
+                {  2, 1,  3 },
+                { -3, 0, -9 }
+        }, new double[] { 1, 1, 1 });
+
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+
+        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
+    }
+
+    @Test
+    public void testIllConditioned() {
+        LinearProblem problem1 = new LinearProblem(new double[][] {
+                { 10.0, 7.0,  8.0,  7.0 },
+                {  7.0, 5.0,  6.0,  5.0 },
+                {  8.0, 6.0, 10.0,  9.0 },
+                {  7.0, 5.0,  9.0, 10.0 }
+        }, new double[] { 32, 23, 33, 31 });
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum1 =
+            optimizer.optimize(100, problem1, problem1.target, new double[] { 1, 1, 1, 1 },
+                               new double[] { 0, 1, 2, 3 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-10);
+        Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-10);
+        Assert.assertEquals(1.0, optimum1.getPoint()[2], 1.0e-10);
+        Assert.assertEquals(1.0, optimum1.getPoint()[3], 1.0e-10);
+
+        LinearProblem problem2 = new LinearProblem(new double[][] {
+                { 10.00, 7.00, 8.10, 7.20 },
+                {  7.08, 5.04, 6.00, 5.00 },
+                {  8.00, 5.98, 9.89, 9.00 },
+                {  6.99, 4.99, 9.00, 9.98 }
+        }, new double[] { 32, 23, 33, 31 });
+        PointVectorValuePair optimum2 =
+            optimizer.optimize(100, problem2, problem2.target, new double[] { 1, 1, 1, 1 },
+                               new double[] { 0, 1, 2, 3 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-8);
+        Assert.assertEquals(137.0, optimum2.getPoint()[1], 1.0e-8);
+        Assert.assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-8);
+        Assert.assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-8);
+    }
+
+    @Test
+    public void testMoreEstimatedParametersSimple() {
+
+        LinearProblem problem = new LinearProblem(new double[][] {
+                { 3.0, 2.0,  0.0, 0.0 },
+                { 0.0, 1.0, -1.0, 1.0 },
+                { 2.0, 0.0,  1.0, 0.0 }
+        }, new double[] { 7.0, 3.0, 5.0 });
+
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
+                new double[] { 7, 6, 5, 4 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+    }
+
+    @Test
+    public void testMoreEstimatedParametersUnsorted() {
+        LinearProblem problem = new LinearProblem(new double[][] {
+                { 1.0, 1.0,  0.0,  0.0, 0.0,  0.0 },
+                { 0.0, 0.0,  1.0,  1.0, 1.0,  0.0 },
+                { 0.0, 0.0,  0.0,  0.0, 1.0, -1.0 },
+                { 0.0, 0.0, -1.0,  1.0, 0.0,  1.0 },
+                { 0.0, 0.0,  0.0, -1.0, 1.0,  0.0 }
+       }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
+
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1 },
+                               new double[] { 2, 2, 2, 2, 2, 2 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals(3.0, optimum.getPointRef()[2], 1.0e-10);
+        Assert.assertEquals(4.0, optimum.getPointRef()[3], 1.0e-10);
+        Assert.assertEquals(5.0, optimum.getPointRef()[4], 1.0e-10);
+        Assert.assertEquals(6.0, optimum.getPointRef()[5], 1.0e-10);
+    }
+
+    @Test
+    public void testRedundantEquations() {
+        LinearProblem problem = new LinearProblem(new double[][] {
+                { 1.0,  1.0 },
+                { 1.0, -1.0 },
+                { 1.0,  3.0 }
+        }, new double[] { 3.0, 1.0, 5.0 });
+
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
+                               new double[] { 1, 1 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals(2.0, optimum.getPointRef()[0], 1.0e-10);
+        Assert.assertEquals(1.0, optimum.getPointRef()[1], 1.0e-10);
+    }
+
+    @Test
+    public void testInconsistentEquations() {
+        LinearProblem problem = new LinearProblem(new double[][] {
+                { 1.0,  1.0 },
+                { 1.0, -1.0 },
+                { 1.0,  3.0 }
+        }, new double[] { 3.0, 1.0, 4.0 });
+
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 1, 1 });
+        Assert.assertTrue(optimizer.getRMS() > 0.1);
+    }
+
+    @Test(expected=DimensionMismatchException.class)
+    public void testInconsistentSizes1() {
+        LinearProblem problem =
+            new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
+        Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10);
+
+        optimizer.optimize(100, problem, problem.target,
+                           new double[] { 1 },
+                           new double[] { 0, 0 });
+    }
+
+    @Test(expected=DimensionMismatchException.class)
+    public void testInconsistentSizes2() {
+        LinearProblem problem =
+            new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
+        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+        Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
+        Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10);
+
+        optimizer.optimize(100, problem, new double[] { 1 },
+                           new double[] { 1 },
+                           new double[] { 0, 0 });
+    }
+
+    @Test
+    public void testCircleFitting() {
+        CircleVectorial circle = new CircleVectorial();
+        circle.addPoint( 30.0,  68.0);
+        circle.addPoint( 50.0,  -6.0);
+        circle.addPoint(110.0, -20.0);
+        circle.addPoint( 35.0,  15.0);
+        circle.addPoint( 45.0,  97.0);
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 }, new double[] { 1, 1, 1, 1, 1 },
+                               new double[] { 98.680, 47.345 });
+        Assert.assertTrue(optimizer.getEvaluations() < 10);
+        Assert.assertTrue(optimizer.getJacobianEvaluations() < 10);
+        double rms = optimizer.getRMS();
+        Assert.assertEquals(1.768262623567235,  FastMath.sqrt(circle.getN()) * rms,  1.0e-10);
+        Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
+        Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1.0e-6);
+        Assert.assertEquals(96.07590211815305, center.x,      1.0e-6);
+        Assert.assertEquals(48.13516790438953, center.y,      1.0e-6);
+        double[][] cov = optimizer.getCovariances();
+        Assert.assertEquals(1.839, cov[0][0], 0.001);
+        Assert.assertEquals(0.731, cov[0][1], 0.001);
+        Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14);
+        Assert.assertEquals(0.786, cov[1][1], 0.001);
+        double[] errors = optimizer.guessParametersErrors();
+        Assert.assertEquals(1.384, errors[0], 0.001);
+        Assert.assertEquals(0.905, errors[1], 0.001);
+
+        // add perfect measurements and check errors are reduced
+        double  r = circle.getRadius(center);
+        for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
+            circle.addPoint(center.x + r * FastMath.cos(d), center.y + r * FastMath.sin(d));
+        }
+        double[] target = new double[circle.getN()];
+        Arrays.fill(target, 0.0);
+        double[] weights = new double[circle.getN()];
+        Arrays.fill(weights, 2.0);
+        optimizer.optimize(100, circle, target, weights, new double[] { 98.680, 47.345 });
+        cov = optimizer.getCovariances();
+        Assert.assertEquals(0.0016, cov[0][0], 0.001);
+        Assert.assertEquals(3.2e-7, cov[0][1], 1.0e-9);
+        Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14);
+        Assert.assertEquals(0.0016, cov[1][1], 0.001);
+        errors = optimizer.guessParametersErrors();
+        Assert.assertEquals(0.004, errors[0], 0.001);
+        Assert.assertEquals(0.004, errors[1], 0.001);
+    }
+
+    @Test
+    public void testCircleFittingBadInit() {
+        CircleVectorial circle = new CircleVectorial();
+        double[][] points = circlePoints;
+        double[] target = new double[points.length];
+        Arrays.fill(target, 0.0);
+        double[] weights = new double[points.length];
+        Arrays.fill(weights, 2.0);
+        for (int i = 0; i < points.length; ++i) {
+            circle.addPoint(points[i][0], points[i][1]);
+        }
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 });
+        Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
+        Assert.assertTrue(optimizer.getEvaluations() < 25);
+        Assert.assertTrue(optimizer.getJacobianEvaluations() < 20);
+        Assert.assertEquals( 0.043, optimizer.getRMS(), 1.0e-3);
+        Assert.assertEquals( 0.292235,  circle.getRadius(center), 1.0e-6);
+        Assert.assertEquals(-0.151738,  center.x,      1.0e-6);
+        Assert.assertEquals( 0.2075001, center.y,      1.0e-6);
+    }
+
+    @Test
+    public void testCircleFittingGoodInit() {
+        CircleVectorial circle = new CircleVectorial();
+        double[][] points = circlePoints;
+        double[] target = new double[points.length];
+        Arrays.fill(target, 0.0);
+        double[] weights = new double[points.length];
+        Arrays.fill(weights, 2.0);
+        for (int i = 0; i < points.length; ++i) {
+            circle.addPoint(points[i][0], points[i][1]);
+        }
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        PointVectorValuePair optimum =
+            optimizer.optimize(100, circle, target, weights, new double[] { 0, 0 });
+        Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1.0e-6);
+        Assert.assertEquals(0.2074999736353867,  optimum.getPointRef()[1], 1.0e-6);
+        Assert.assertEquals(0.04268731682389561, optimizer.getRMS(),       1.0e-8);
+    }
+
+    private final double[][] circlePoints = new double[][] {
+        {-0.312967,  0.072366}, {-0.339248,  0.132965}, {-0.379780,  0.202724},
+        {-0.390426,  0.260487}, {-0.361212,  0.328325}, {-0.346039,  0.392619},
+        {-0.280579,  0.444306}, {-0.216035,  0.470009}, {-0.149127,  0.493832},
+        {-0.075133,  0.483271}, {-0.007759,  0.452680}, { 0.060071,  0.410235},
+        { 0.103037,  0.341076}, { 0.118438,  0.273884}, { 0.131293,  0.192201},
+        { 0.115869,  0.129797}, { 0.072223,  0.058396}, { 0.022884,  0.000718},
+        {-0.053355, -0.020405}, {-0.123584, -0.032451}, {-0.216248, -0.032862},
+        {-0.278592, -0.005008}, {-0.337655,  0.056658}, {-0.385899,  0.112526},
+        {-0.405517,  0.186957}, {-0.415374,  0.262071}, {-0.387482,  0.343398},
+        {-0.347322,  0.397943}, {-0.287623,  0.458425}, {-0.223502,  0.475513},
+        {-0.135352,  0.478186}, {-0.061221,  0.483371}, { 0.003711,  0.422737},
+        { 0.065054,  0.375830}, { 0.108108,  0.297099}, { 0.123882,  0.222850},
+        { 0.117729,  0.134382}, { 0.085195,  0.056820}, { 0.029800, -0.019138},
+        {-0.027520, -0.072374}, {-0.102268, -0.091555}, {-0.200299, -0.106578},
+        {-0.292731, -0.091473}, {-0.356288, -0.051108}, {-0.420561,  0.014926},
+        {-0.471036,  0.074716}, {-0.488638,  0.182508}, {-0.485990,  0.254068},
+        {-0.463943,  0.338438}, {-0.406453,  0.404704}, {-0.334287,  0.466119},
+        {-0.254244,  0.503188}, {-0.161548,  0.495769}, {-0.075733,  0.495560},
+        { 0.001375,  0.434937}, { 0.082787,  0.385806}, { 0.115490,  0.323807},
+        { 0.141089,  0.223450}, { 0.138693,  0.131703}, { 0.126415,  0.049174},
+        { 0.066518, -0.010217}, {-0.005184, -0.070647}, {-0.080985, -0.103635},
+        {-0.177377, -0.116887}, {-0.260628, -0.100258}, {-0.335756, -0.056251},
+        {-0.405195, -0.000895}, {-0.444937,  0.085456}, {-0.484357,  0.175597},
+        {-0.472453,  0.248681}, {-0.438580,  0.347463}, {-0.402304,  0.422428},
+        {-0.326777,  0.479438}, {-0.247797,  0.505581}, {-0.152676,  0.519380},
+        {-0.071754,  0.516264}, { 0.015942,  0.472802}, { 0.076608,  0.419077},
+        { 0.127673,  0.330264}, { 0.159951,  0.262150}, { 0.153530,  0.172681},
+        { 0.140653,  0.089229}, { 0.078666,  0.024981}, { 0.023807, -0.037022},
+        {-0.048837, -0.077056}, {-0.127729, -0.075338}, {-0.221271, -0.067526}
+    };
+
+    static class LinearProblem implements DifferentiableMultivariateVectorFunction, Serializable {
+
+        private static final long serialVersionUID = 703247177355019415L;
+        final RealMatrix factors;
+        final double[] target;
+        public LinearProblem(double[][] factors, double[] target) {
+            this.factors = new BlockRealMatrix(factors);
+            this.target  = target;
+        }
+
+        public double[] value(double[] variables) {
+            return factors.operate(variables);
+        }
+
+        public MultivariateMatrixFunction jacobian() {
+            return new MultivariateMatrixFunction() {
+                public double[][] value(double[] point) {
+                    return factors.getData();
+                }
+            };
+        }
+    }
+}

Modified: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java?rev=1332159&r1=1332158&r2=1332159&view=diff
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java (original)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java Mon Apr 30 11:07:42 2012
@@ -17,22 +17,9 @@
 
 package org.apache.commons.math3.optimization.general;
 
-import java.awt.geom.Point2D;
-import java.io.Serializable;
-import java.util.Arrays;
-
-
 import org.apache.commons.math3.exception.ConvergenceException;
 import org.apache.commons.math3.exception.TooManyEvaluationsException;
-import org.apache.commons.math3.exception.DimensionMismatchException;
-import org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction;
-import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math3.linear.BlockRealMatrix;
-import org.apache.commons.math3.linear.RealMatrix;
 import org.apache.commons.math3.optimization.SimpleVectorValueChecker;
-import org.apache.commons.math3.optimization.PointVectorValuePair;
-import org.apache.commons.math3.util.FastMath;
-import org.junit.Assert;
 import org.junit.Test;
 
 /**
@@ -97,273 +84,30 @@ import org.junit.Test;
  * @author Jorge J. More (original fortran minpack tests)
  * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
  */
-public class GaussNewtonOptimizerTest {
+public class GaussNewtonOptimizerTest
+    extends AbstractLeastSquaresOptimizerAbstractTest {
 
-    @Test
-    public void testTrivial() {
-        LinearProblem problem =
-            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(3.0, optimum.getValue()[0], 1.0e-10);
+    @Override
+    public AbstractLeastSquaresOptimizer createOptimizer() {
+        return new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
     }
 
-    @Test
-    public void testColumnsPermutation() {
-
-        LinearProblem problem =
-            new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
-                              new double[] { 4.0, 6.0, 1.0 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
-        Assert.assertEquals(4.0, optimum.getValue()[0], 1.0e-10);
-        Assert.assertEquals(6.0, optimum.getValue()[1], 1.0e-10);
-        Assert.assertEquals(1.0, optimum.getValue()[2], 1.0e-10);
-
-    }
-
-    @Test
-    public void testNoDependency() {
-        LinearProblem problem = new LinearProblem(new double[][] {
-                { 2, 0, 0, 0, 0, 0 },
-                { 0, 2, 0, 0, 0, 0 },
-                { 0, 0, 2, 0, 0, 0 },
-                { 0, 0, 0, 2, 0, 0 },
-                { 0, 0, 0, 0, 2, 0 },
-                { 0, 0, 0, 0, 0, 2 }
-        }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
-                               new double[] { 0, 0, 0, 0, 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        for (int i = 0; i < problem.target.length; ++i) {
-            Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
-        }
-    }
-
-    @Test
-    public void testOneSet() {
-
-        LinearProblem problem = new LinearProblem(new double[][] {
-                {  1,  0, 0 },
-                { -1,  1, 0 },
-                {  0, -1, 1 }
-        }, new double[] { 1, 1, 1});
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
-        Assert.assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);
-
-    }
-
-    @Test
-    public void testTwoSets() {
-        double epsilon = 1.0e-7;
-        LinearProblem problem = new LinearProblem(new double[][] {
-                {  2,  1,   0,  4,       0, 0 },
-                { -4, -2,   3, -7,       0, 0 },
-                {  4,  1,  -2,  8,       0, 0 },
-                {  0, -3, -12, -1,       0, 0 },
-                {  0,  0,   0,  0, epsilon, 1 },
-                {  0,  0,   0,  0,       1, 1 }
-        }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
-                               new double[] { 0, 0, 0, 0, 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10);
-        Assert.assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10);
-        Assert.assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10);
-        Assert.assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10);
-        Assert.assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10);
-
-    }
-
-    @Test(expected=ConvergenceException.class)
-    public void testNonInversible() throws Exception {
-
-        LinearProblem problem = new LinearProblem(new double[][] {
-                {  1, 2, -3 },
-                {  2, 1,  3 },
-                { -3, 0, -9 }
-        }, new double[] { 1, 1, 1 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
-    }
-
-    @Test
-    public void testIllConditioned() {
-        LinearProblem problem1 = new LinearProblem(new double[][] {
-                { 10.0, 7.0,  8.0,  7.0 },
-                {  7.0, 5.0,  6.0,  5.0 },
-                {  8.0, 6.0, 10.0,  9.0 },
-                {  7.0, 5.0,  9.0, 10.0 }
-        }, new double[] { 32, 23, 33, 31 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum1 =
-            optimizer.optimize(100, problem1, problem1.target, new double[] { 1, 1, 1, 1 },
-                               new double[] { 0, 1, 2, 3 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-10);
-        Assert.assertEquals(1.0, optimum1.getPoint()[2], 1.0e-10);
-        Assert.assertEquals(1.0, optimum1.getPoint()[3], 1.0e-10);
-
-        LinearProblem problem2 = new LinearProblem(new double[][] {
-                { 10.00, 7.00, 8.10, 7.20 },
-                {  7.08, 5.04, 6.00, 5.00 },
-                {  8.00, 5.98, 9.89, 9.00 },
-                {  6.99, 4.99, 9.00, 9.98 }
-        }, new double[] { 32, 23, 33, 31 });
-        PointVectorValuePair optimum2 =
-            optimizer.optimize(100, problem2, problem2.target, new double[] { 1, 1, 1, 1 },
-                               new double[] { 0, 1, 2, 3 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-8);
-        Assert.assertEquals(137.0, optimum2.getPoint()[1], 1.0e-8);
-        Assert.assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-8);
-        Assert.assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-8);
-
+    @Override
+    @Test(expected = ConvergenceException.class)
+    public void testMoreEstimatedParametersSimple() {
+        /*
+         * Exception is expected with this optimizer
+         */
+        super.testMoreEstimatedParametersSimple();
     }
 
+    @Override
     @Test(expected=ConvergenceException.class)
-    public void testMoreEstimatedParametersSimple() throws Exception {
-
-        LinearProblem problem = new LinearProblem(new double[][] {
-                { 3.0, 2.0,  0.0, 0.0 },
-                { 0.0, 1.0, -1.0, 1.0 },
-                { 2.0, 0.0,  1.0, 0.0 }
-        }, new double[] { 7.0, 3.0, 5.0 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
-                           new double[] { 7, 6, 5, 4 });
-    }
-
-    @Test(expected=ConvergenceException.class)
-    public void testMoreEstimatedParametersUnsorted() throws Exception {
-        LinearProblem problem = new LinearProblem(new double[][] {
-                 { 1.0, 1.0,  0.0,  0.0, 0.0,  0.0 },
-                 { 0.0, 0.0,  1.0,  1.0, 1.0,  0.0 },
-                 { 0.0, 0.0,  0.0,  0.0, 1.0, -1.0 },
-                 { 0.0, 0.0, -1.0,  1.0, 0.0,  1.0 },
-                 { 0.0, 0.0,  0.0, -1.0, 1.0,  0.0 }
-        }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1 },
-                           new double[] { 2, 2, 2, 2, 2, 2 });
-    }
-
-    @Test
-    public void testRedundantEquations() {
-        LinearProblem problem = new LinearProblem(new double[][] {
-                { 1.0,  1.0 },
-                { 1.0, -1.0 },
-                { 1.0,  3.0 }
-        }, new double[] { 3.0, 1.0, 5.0 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
-                               new double[] { 1, 1 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(2.0, optimum.getPoint()[0], 1.0e-8);
-        Assert.assertEquals(1.0, optimum.getPoint()[1], 1.0e-8);
-    }
-
-    @Test
-    public void testInconsistentEquations() {
-        LinearProblem problem = new LinearProblem(new double[][] {
-                { 1.0,  1.0 },
-                { 1.0, -1.0 },
-                { 1.0,  3.0 }
-        }, new double[] { 3.0, 1.0, 4.0 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 1, 1 });
-        Assert.assertTrue(optimizer.getRMS() > 0.1);
-
-    }
-
-    @Test(expected=DimensionMismatchException.class)
-    public void testInconsistentSizes1() {
-        LinearProblem problem =
-            new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10);
-
-        optimizer.optimize(100, problem, problem.target,
-                           new double[] { 1 },
-                           new double[] { 0, 0 });
-    }
-
-    @Test(expected=DimensionMismatchException.class)
-    public void testInconsistentSizes2() {
-        LinearProblem problem =
-            new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10);
-
-        optimizer.optimize(100, problem, new double[] { 1 },
-                           new double[] { 1 },
-                           new double[] { 0, 0 });
+    public void testMoreEstimatedParametersUnsorted() {
+        /*
+         * Exception is expected with this optimizer
+         */
+        super.testMoreEstimatedParametersUnsorted();
     }
 
     @Test(expected=TooManyEvaluationsException.class)
@@ -383,121 +127,12 @@ public class GaussNewtonOptimizerTest {
                            new double[] { 98.680, 47.345 });
     }
 
-    @Test
-    public void testCircleFitting() {
-        CircleVectorial circle = new CircleVectorial();
-        circle.addPoint( 30.0,  68.0);
-        circle.addPoint( 50.0,  -6.0);
-        circle.addPoint(110.0, -20.0);
-        circle.addPoint( 35.0,  15.0);
-        circle.addPoint( 45.0,  97.0);
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-13, 1.0e-13));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 },
-                               new double[] { 1, 1, 1, 1, 1 },
-                               new double[] { 98.680, 47.345 });
-        Assert.assertEquals(1.768262623567235,  FastMath.sqrt(circle.getN()) * optimizer.getRMS(),  1.0e-10);
-        Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
-        Assert.assertEquals(69.96016175359975, circle.getRadius(center), 1.0e-10);
-        Assert.assertEquals(96.07590209601095, center.x, 1.0e-10);
-        Assert.assertEquals(48.135167894714,   center.y, 1.0e-10);
-    }
-
+    @Override
     @Test(expected=ConvergenceException.class)
     public void testCircleFittingBadInit() {
-        CircleVectorial circle = new CircleVectorial();
-        double[][] points = circlePoints;
-        double[] target = new double[points.length];
-        Arrays.fill(target, 0.0);
-        double[] weights = new double[points.length];
-        Arrays.fill(weights, 2.0);
-        for (int i = 0; i < points.length; ++i) {
-            circle.addPoint(points[i][0], points[i][1]);
-        }
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 });
-    }
-
-    @Test
-    public void testCircleFittingGoodInit() {
-        CircleVectorial circle = new CircleVectorial();
-        double[][] points = circlePoints;
-        double[] target = new double[points.length];
-        Arrays.fill(target, 0.0);
-        double[] weights = new double[points.length];
-        Arrays.fill(weights, 2.0);
-        for (int i = 0; i < points.length; ++i) {
-            circle.addPoint(points[i][0], points[i][1]);
-        }
-
-        GaussNewtonOptimizer optimizer
-            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, circle, target, weights, new double[] { 0, 0 });
-        Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1.0e-6);
-        Assert.assertEquals(0.2074999736353867,  optimum.getPointRef()[1], 1.0e-6);
-        Assert.assertEquals(0.04268731682389561, optimizer.getRMS(),       1.0e-8);
-    }
-
-    private static class LinearProblem implements DifferentiableMultivariateVectorFunction, Serializable {
-
-        private static final long serialVersionUID = -8804268799379350190L;
-        final RealMatrix factors;
-        final double[] target;
-        public LinearProblem(double[][] factors, double[] target) {
-            this.factors = new BlockRealMatrix(factors);
-            this.target  = target;
-        }
-
-        public double[] value(double[] variables) {
-            return factors.operate(variables);
-        }
-
-        public MultivariateMatrixFunction jacobian() {
-            return new MultivariateMatrixFunction() {
-                public double[][] value(double[] point) {
-                    return factors.getData();
-                }
-            };
-        }
+        /*
+         * This test does not converge with this optimizer
+         */
+        super.testCircleFittingBadInit();
     }
-
-    private final double[][] circlePoints = new double[][] {
-        {-0.312967,  0.072366}, {-0.339248,  0.132965}, {-0.379780,  0.202724},
-        {-0.390426,  0.260487}, {-0.361212,  0.328325}, {-0.346039,  0.392619},
-        {-0.280579,  0.444306}, {-0.216035,  0.470009}, {-0.149127,  0.493832},
-        {-0.075133,  0.483271}, {-0.007759,  0.452680}, { 0.060071,  0.410235},
-        { 0.103037,  0.341076}, { 0.118438,  0.273884}, { 0.131293,  0.192201},
-        { 0.115869,  0.129797}, { 0.072223,  0.058396}, { 0.022884,  0.000718},
-        {-0.053355, -0.020405}, {-0.123584, -0.032451}, {-0.216248, -0.032862},
-        {-0.278592, -0.005008}, {-0.337655,  0.056658}, {-0.385899,  0.112526},
-        {-0.405517,  0.186957}, {-0.415374,  0.262071}, {-0.387482,  0.343398},
-        {-0.347322,  0.397943}, {-0.287623,  0.458425}, {-0.223502,  0.475513},
-        {-0.135352,  0.478186}, {-0.061221,  0.483371}, { 0.003711,  0.422737},
-        { 0.065054,  0.375830}, { 0.108108,  0.297099}, { 0.123882,  0.222850},
-        { 0.117729,  0.134382}, { 0.085195,  0.056820}, { 0.029800, -0.019138},
-        {-0.027520, -0.072374}, {-0.102268, -0.091555}, {-0.200299, -0.106578},
-        {-0.292731, -0.091473}, {-0.356288, -0.051108}, {-0.420561,  0.014926},
-        {-0.471036,  0.074716}, {-0.488638,  0.182508}, {-0.485990,  0.254068},
-        {-0.463943,  0.338438}, {-0.406453,  0.404704}, {-0.334287,  0.466119},
-        {-0.254244,  0.503188}, {-0.161548,  0.495769}, {-0.075733,  0.495560},
-        { 0.001375,  0.434937}, { 0.082787,  0.385806}, { 0.115490,  0.323807},
-        { 0.141089,  0.223450}, { 0.138693,  0.131703}, { 0.126415,  0.049174},
-        { 0.066518, -0.010217}, {-0.005184, -0.070647}, {-0.080985, -0.103635},
-        {-0.177377, -0.116887}, {-0.260628, -0.100258}, {-0.335756, -0.056251},
-        {-0.405195, -0.000895}, {-0.444937,  0.085456}, {-0.484357,  0.175597},
-        {-0.472453,  0.248681}, {-0.438580,  0.347463}, {-0.402304,  0.422428},
-        {-0.326777,  0.479438}, {-0.247797,  0.505581}, {-0.152676,  0.519380},
-        {-0.071754,  0.516264}, { 0.015942,  0.472802}, { 0.076608,  0.419077},
-        { 0.127673,  0.330264}, { 0.159951,  0.262150}, { 0.153530,  0.172681},
-        { 0.140653,  0.089229}, { 0.078666,  0.024981}, { 0.023807, -0.037022},
-        {-0.048837, -0.077056}, {-0.127729, -0.075338}, {-0.221271, -0.067526}
-    };
 }

Modified: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/LevenbergMarquardtOptimizerTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/LevenbergMarquardtOptimizerTest.java?rev=1332159&r1=1332158&r2=1332159&view=diff
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/LevenbergMarquardtOptimizerTest.java (original)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/LevenbergMarquardtOptimizerTest.java Mon Apr 30 11:07:42 2012
@@ -20,23 +20,17 @@ package org.apache.commons.math3.optimiz
 import java.awt.geom.Point2D;
 import java.io.Serializable;
 import java.util.ArrayList;
-import java.util.Arrays;
 import java.util.List;
 
-
+import org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction;
+import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
 import org.apache.commons.math3.exception.ConvergenceException;
 import org.apache.commons.math3.exception.DimensionMismatchException;
 import org.apache.commons.math3.exception.TooManyEvaluationsException;
-import org.apache.commons.math3.exception.NumberIsTooSmallException;
-import org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction;
-import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math3.linear.BlockRealMatrix;
-import org.apache.commons.math3.linear.RealMatrix;
 import org.apache.commons.math3.linear.SingularMatrixException;
-import org.apache.commons.math3.optimization.SimpleVectorValueChecker;
 import org.apache.commons.math3.optimization.PointVectorValuePair;
-import org.apache.commons.math3.util.Precision;
 import org.apache.commons.math3.util.FastMath;
+import org.apache.commons.math3.util.Precision;
 import org.junit.Assert;
 import org.junit.Test;
 
@@ -102,257 +96,34 @@ import org.junit.Test;
  * @author Jorge J. More (original fortran minpack tests)
  * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
  */
-public class LevenbergMarquardtOptimizerTest {
-
-    @Test
-    public void testTrivial() {
-        LinearProblem problem =
-            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        try {
-            optimizer.guessParametersErrors();
-            Assert.fail("an exception should have been thrown");
-        } catch (NumberIsTooSmallException ee) {
-            // expected behavior
-        }
-        Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(3.0, optimum.getValue()[0], 1.0e-10);
-    }
-
-    @Test
-    public void testQRColumnsPermutation() {
-
-        LinearProblem problem =
-            new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
-                              new double[] { 4.0, 6.0, 1.0 });
-
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
-        Assert.assertEquals(4.0, optimum.getValue()[0], 1.0e-10);
-        Assert.assertEquals(6.0, optimum.getValue()[1], 1.0e-10);
-        Assert.assertEquals(1.0, optimum.getValue()[2], 1.0e-10);
-    }
-
-    @Test
-    public void testNoDependency() {
-        LinearProblem problem = new LinearProblem(new double[][] {
-                { 2, 0, 0, 0, 0, 0 },
-                { 0, 2, 0, 0, 0, 0 },
-                { 0, 0, 2, 0, 0, 0 },
-                { 0, 0, 0, 2, 0, 0 },
-                { 0, 0, 0, 0, 2, 0 },
-                { 0, 0, 0, 0, 0, 2 }
-        }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
-                               new double[] { 0, 0, 0, 0, 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        for (int i = 0; i < problem.target.length; ++i) {
-            Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
-        }
-    }
-
-    @Test
-    public void testOneSet() {
-
-        LinearProblem problem = new LinearProblem(new double[][] {
-                {  1,  0, 0 },
-                { -1,  1, 0 },
-                {  0, -1, 1 }
-        }, new double[] { 1, 1, 1});
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
-        Assert.assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);
-    }
+public class LevenbergMarquardtOptimizerTest extends AbstractLeastSquaresOptimizerAbstractTest {
 
-    @Test
-    public void testTwoSets() {
-        double epsilon = 1.0e-7;
-        LinearProblem problem = new LinearProblem(new double[][] {
-                {  2,  1,   0,  4,       0, 0 },
-                { -4, -2,   3, -7,       0, 0 },
-                {  4,  1,  -2,  8,       0, 0 },
-                {  0, -3, -12, -1,       0, 0 },
-                {  0,  0,   0,  0, epsilon, 1 },
-                {  0,  0,   0,  0,       1, 1 }
-        }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
-
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
-                               new double[] { 0, 0, 0, 0, 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10);
-        Assert.assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10);
-        Assert.assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10);
-        Assert.assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10);
-        Assert.assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10);
+    @Override
+    public AbstractLeastSquaresOptimizer createOptimizer() {
+        return new LevenbergMarquardtOptimizer();
     }
 
+    @Override
     @Test(expected=SingularMatrixException.class)
     public void testNonInvertible() {
+        /*
+         * Overrides the method from parent class, since the default singularity
+         * threshold (1e-14) does not trigger the expected exception.
+         */
         LinearProblem problem = new LinearProblem(new double[][] {
                 {  1, 2, -3 },
                 {  2, 1,  3 },
                 { -3, 0, -9 }
         }, new double[] { 1, 1, 1 });
 
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        optimizer.optimize(100, problem, problem.target,
-                             new double[] { 1, 1, 1 },
-                             new double[] { 0, 0, 0 });
+        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
         Assert.assertTrue(FastMath.sqrt(problem.target.length) * optimizer.getRMS() > 0.6);
 
-        // The default singularity threshold (1e-14) does not trigger the
-        // expected exception.
         double[][] cov = optimizer.getCovariances(1.5e-14);
     }
 
     @Test
-    public void testIllConditioned() {
-        LinearProblem problem1 = new LinearProblem(new double[][] {
-                { 10.0, 7.0,  8.0,  7.0 },
-                {  7.0, 5.0,  6.0,  5.0 },
-                {  8.0, 6.0, 10.0,  9.0 },
-                {  7.0, 5.0,  9.0, 10.0 }
-        }, new double[] { 32, 23, 33, 31 });
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        PointVectorValuePair optimum1 =
-            optimizer.optimize(100, problem1, problem1.target, new double[] { 1, 1, 1, 1 },
-                               new double[] { 0, 1, 2, 3 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-10);
-        Assert.assertEquals(1.0, optimum1.getPoint()[2], 1.0e-10);
-        Assert.assertEquals(1.0, optimum1.getPoint()[3], 1.0e-10);
-
-        LinearProblem problem2 = new LinearProblem(new double[][] {
-                { 10.00, 7.00, 8.10, 7.20 },
-                {  7.08, 5.04, 6.00, 5.00 },
-                {  8.00, 5.98, 9.89, 9.00 },
-                {  6.99, 4.99, 9.00, 9.98 }
-        }, new double[] { 32, 23, 33, 31 });
-        PointVectorValuePair optimum2 =
-            optimizer.optimize(100, problem2, problem2.target, new double[] { 1, 1, 1, 1 },
-                               new double[] { 0, 1, 2, 3 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-8);
-        Assert.assertEquals(137.0, optimum2.getPoint()[1], 1.0e-8);
-        Assert.assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-8);
-        Assert.assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-8);
-    }
-
-    @Test
-    public void testMoreEstimatedParametersSimple() {
-
-        LinearProblem problem = new LinearProblem(new double[][] {
-                { 3.0, 2.0,  0.0, 0.0 },
-                { 0.0, 1.0, -1.0, 1.0 },
-                { 2.0, 0.0,  1.0, 0.0 }
-        }, new double[] { 7.0, 3.0, 5.0 });
-
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
-                new double[] { 7, 6, 5, 4 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-    }
-
-    @Test
-    public void testMoreEstimatedParametersUnsorted() {
-        LinearProblem problem = new LinearProblem(new double[][] {
-                { 1.0, 1.0,  0.0,  0.0, 0.0,  0.0 },
-                { 0.0, 0.0,  1.0,  1.0, 1.0,  0.0 },
-                { 0.0, 0.0,  0.0,  0.0, 1.0, -1.0 },
-                { 0.0, 0.0, -1.0,  1.0, 0.0,  1.0 },
-                { 0.0, 0.0,  0.0, -1.0, 1.0,  0.0 }
-       }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
-
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1 },
-                               new double[] { 2, 2, 2, 2, 2, 2 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(3.0, optimum.getPointRef()[2], 1.0e-10);
-        Assert.assertEquals(4.0, optimum.getPointRef()[3], 1.0e-10);
-        Assert.assertEquals(5.0, optimum.getPointRef()[4], 1.0e-10);
-        Assert.assertEquals(6.0, optimum.getPointRef()[5], 1.0e-10);
-    }
-
-    @Test
-    public void testRedundantEquations() {
-        LinearProblem problem = new LinearProblem(new double[][] {
-                { 1.0,  1.0 },
-                { 1.0, -1.0 },
-                { 1.0,  3.0 }
-        }, new double[] { 3.0, 1.0, 5.0 });
-
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
-                               new double[] { 1, 1 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(2.0, optimum.getPointRef()[0], 1.0e-10);
-        Assert.assertEquals(1.0, optimum.getPointRef()[1], 1.0e-10);
-    }
-
-    @Test
-    public void testInconsistentEquations() {
-        LinearProblem problem = new LinearProblem(new double[][] {
-                { 1.0,  1.0 },
-                { 1.0, -1.0 },
-                { 1.0,  3.0 }
-        }, new double[] { 3.0, 1.0, 4.0 });
-
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 1, 1 });
-        Assert.assertTrue(optimizer.getRMS() > 0.1);
-    }
-
-    @Test
-    public void testInconsistentSizes() {
-        LinearProblem problem =
-            new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
-        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
-        Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
-        Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10);
-
-        try {
-            optimizer.optimize(100, problem, problem.target,
-                               new double[] { 1 },
-                               new double[] { 0, 0 });
-            Assert.fail("an exception should have been thrown");
-        } catch (DimensionMismatchException oe) {
-            // expected behavior
-        }
-
-        try {
-            optimizer.optimize(100, problem, new double[] { 1 },
-                               new double[] { 1 },
-                               new double[] { 0, 0 });
-            Assert.fail("an exception should have been thrown");
-        } catch (DimensionMismatchException oe) {
-            // expected behavior
-        }
-    }
-
-    @Test
     public void testControlParameters() {
         CircleVectorial circle = new CircleVectorial();
         circle.addPoint( 30.0,  68.0);
@@ -390,109 +161,6 @@ public class LevenbergMarquardtOptimizer
     }
 
     @Test
-    public void testCircleFitting() {
-        CircleVectorial circle = new CircleVectorial();
-        circle.addPoint( 30.0,  68.0);
-        circle.addPoint( 50.0,  -6.0);
-        circle.addPoint(110.0, -20.0);
-        circle.addPoint( 35.0,  15.0);
-        circle.addPoint( 45.0,  97.0);
-        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 }, new double[] { 1, 1, 1, 1, 1 },
-                               new double[] { 98.680, 47.345 });
-        Assert.assertTrue(optimizer.getEvaluations() < 10);
-        Assert.assertTrue(optimizer.getJacobianEvaluations() < 10);
-        double rms = optimizer.getRMS();
-        Assert.assertEquals(1.768262623567235,  FastMath.sqrt(circle.getN()) * rms,  1.0e-10);
-        Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
-        Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1.0e-10);
-        Assert.assertEquals(96.07590211815305, center.x,      1.0e-10);
-        Assert.assertEquals(48.13516790438953, center.y,      1.0e-10);
-        double[][] cov = optimizer.getCovariances();
-        Assert.assertEquals(1.839, cov[0][0], 0.001);
-        Assert.assertEquals(0.731, cov[0][1], 0.001);
-        Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14);
-        Assert.assertEquals(0.786, cov[1][1], 0.001);
-        double[] errors = optimizer.guessParametersErrors();
-        Assert.assertEquals(1.384, errors[0], 0.001);
-        Assert.assertEquals(0.905, errors[1], 0.001);
-
-        // add perfect measurements and check errors are reduced
-        double  r = circle.getRadius(center);
-        for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
-            circle.addPoint(center.x + r * FastMath.cos(d), center.y + r * FastMath.sin(d));
-        }
-        double[] target = new double[circle.getN()];
-        Arrays.fill(target, 0.0);
-        double[] weights = new double[circle.getN()];
-        Arrays.fill(weights, 2.0);
-        optimizer.optimize(100, circle, target, weights, new double[] { 98.680, 47.345 });
-        cov = optimizer.getCovariances();
-        Assert.assertEquals(0.0016, cov[0][0], 0.001);
-        Assert.assertEquals(3.2e-7, cov[0][1], 1.0e-9);
-        Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14);
-        Assert.assertEquals(0.0016, cov[1][1], 0.001);
-        errors = optimizer.guessParametersErrors();
-        Assert.assertEquals(0.004, errors[0], 0.001);
-        Assert.assertEquals(0.004, errors[1], 0.001);
-    }
-
-    @Test
-    public void testCircleFittingBadInit() {
-        CircleVectorial circle = new CircleVectorial();
-        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}
-        };
-        double[] target = new double[points.length];
-        Arrays.fill(target, 0.0);
-        double[] weights = new double[points.length];
-        Arrays.fill(weights, 2.0);
-        for (int i = 0; i < points.length; ++i) {
-            circle.addPoint(points[i][0], points[i][1]);
-        }
-        LevenbergMarquardtOptimizer optimizer
-            = new LevenbergMarquardtOptimizer(new SimpleVectorValueChecker(1.0e-8, 1.0e-8));
-        PointVectorValuePair optimum =
-            optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 });
-        Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
-        Assert.assertTrue(optimizer.getEvaluations() < 25);
-        Assert.assertTrue(optimizer.getJacobianEvaluations() < 20);
-        Assert.assertEquals( 0.043, optimizer.getRMS(), 1.0e-3);
-        Assert.assertEquals( 0.292235,  circle.getRadius(center), 1.0e-6);
-        Assert.assertEquals(-0.151738,  center.x,      1.0e-6);
-        Assert.assertEquals( 0.2075001, center.y,      1.0e-6);
-    }
-
-    @Test
     public void testMath199() {
         try {
             QuadraticProblem problem = new QuadraticProblem();
@@ -552,11 +220,11 @@ public class LevenbergMarquardtOptimizer
 
         final LevenbergMarquardtOptimizer optimizer
             = new LevenbergMarquardtOptimizer();
-        
+
         final PointVectorValuePair optimum =
             optimizer.optimize(100, problem, dataPoints[1], weights,
                                new double[] { 10, 900, 80, 27, 225 });
-        
+
         final double chi2 = optimizer.getChiSquare();
         final double[] solution = optimum.getPoint();
         final double[] expectedSolution = { 10.4, 958.3, 131.4, 33.9, 205.0 };
@@ -639,29 +307,6 @@ public class LevenbergMarquardtOptimizer
         Assert.assertEquals(radius, paramFound[2], asymptoticStandardErrorFound[2]);
     }
 
-    private static class LinearProblem implements DifferentiableMultivariateVectorFunction, Serializable {
-
-        private static final long serialVersionUID = 703247177355019415L;
-        final RealMatrix factors;
-        final double[] target;
-        public LinearProblem(double[][] factors, double[] target) {
-            this.factors = new BlockRealMatrix(factors);
-            this.target  = target;
-        }
-
-        public double[] value(double[] variables) {
-            return factors.operate(variables);
-        }
-
-        public MultivariateMatrixFunction jacobian() {
-            return new MultivariateMatrixFunction() {
-                public double[][] value(double[] point) {
-                    return factors.getData();
-                }
-            };
-        }
-    }
-
     private static class QuadraticProblem implements DifferentiableMultivariateVectorFunction, Serializable {
 
         private static final long serialVersionUID = 7072187082052755854L;
@@ -726,7 +371,7 @@ public class LevenbergMarquardtOptimizer
             for (int i = 0; i < jacobian.length; ++i) {
                 final double t = time.get(i);
                 jacobian[i][0] = 1;
-                
+
                 final double p3 =  params[3];
                 final double p4 =  params[4];
                 final double tOp3 = t / p3;



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