/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math3.optimization.general;

import java.io.IOException;
import java.io.Serializable;
import java.util.Arrays;

import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction;
import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.geometry.euclidean.twod.Vector2D;
import org.apache.commons.math3.linear.BlockRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.optimization.PointVectorValuePair;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;

/**
 * <p>Some of the unit tests are re-implementations of the MINPACK <a
 * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
 * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
 * The redistribution policy for MINPACK is available <a
 * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
 * convenience, it is reproduced below.</p>

 * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
 * <tr><td>
 *    Minpack Copyright Notice (1999) University of Chicago.
 *    All rights reserved
 * </td></tr>
 * <tr><td>
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 * <ol>
 *  <li>Redistributions of source code must retain the above copyright
 *      notice, this list of conditions and the following disclaimer.</li>
 * <li>Redistributions in binary form must reproduce the above
 *     copyright notice, this list of conditions and the following
 *     disclaimer in the documentation and/or other materials provided
 *     with the distribution.</li>
 * <li>The end-user documentation included with the redistribution, if any,
 *     must include the following acknowledgment:
 *     <code>This product includes software developed by the University of
 *           Chicago, as Operator of Argonne National Laboratory.</code>
 *     Alternately, this acknowledgment may appear in the software itself,
 *     if and wherever such third-party acknowledgments normally appear.</li>
 * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
 *     WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
 *     UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
 *     THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
 *     IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
 *     OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
 *     OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
 *     OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
 *     USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
 *     THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
 *     DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
 *     UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
 *     BE CORRECTED.</strong></li>
 * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
 *     HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
 *     ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
 *     INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
 *     ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
 *     PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
 *     SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
 *     (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
 *     EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
 *     POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
 * <ol></td></tr>
 * </table>

 * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
 * @author Burton S. Garbow (original fortran minpack tests)
 * @author Kenneth E. Hillstrom (original fortran minpack tests)
 * @author Jorge J. More (original fortran minpack tests)
 * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
 * @version $Id: AbstractLeastSquaresOptimizerAbstractTest.java 1407467 2012-11-09 14:30:49Z erans $
 */
public abstract class AbstractLeastSquaresOptimizerAbstractTest {

    public abstract AbstractLeastSquaresOptimizer createOptimizer();

    @Test
    public void testTrivial() {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals(3.0, optimum.getValue()[0], 1.0e-10);
        try {
            optimizer.guessParametersErrors();
            Assert.fail("an exception should have been thrown");
        } catch (NumberIsTooSmallException ee) {
            // expected behavior
        }
    }

    @Test
    public void testQRColumnsPermutation() {

        LinearProblem problem =
            new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
                              new double[] { 4.0, 6.0, 1.0 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
        Assert.assertEquals(4.0, optimum.getValue()[0], 1.0e-10);
        Assert.assertEquals(6.0, optimum.getValue()[1], 1.0e-10);
        Assert.assertEquals(1.0, optimum.getValue()[2], 1.0e-10);
    }

    @Test
    public void testNoDependency() {
        LinearProblem problem = new LinearProblem(new double[][] {
                { 2, 0, 0, 0, 0, 0 },
                { 0, 2, 0, 0, 0, 0 },
                { 0, 0, 2, 0, 0, 0 },
                { 0, 0, 0, 2, 0, 0 },
                { 0, 0, 0, 0, 2, 0 },
                { 0, 0, 0, 0, 0, 2 }
        }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
                               new double[] { 0, 0, 0, 0, 0, 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        for (int i = 0; i < problem.target.length; ++i) {
            Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
        }
    }

    @Test
    public void testOneSet() {

        LinearProblem problem = new LinearProblem(new double[][] {
                {  1,  0, 0 },
                { -1,  1, 0 },
                {  0, -1, 1 }
        }, new double[] { 1, 1, 1});
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
        Assert.assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);
    }

    @Test
    public void testTwoSets() {
        double epsilon = 1.0e-7;
        LinearProblem problem = new LinearProblem(new double[][] {
                {  2,  1,   0,  4,       0, 0 },
                { -4, -2,   3, -7,       0, 0 },
                {  4,  1,  -2,  8,       0, 0 },
                {  0, -3, -12, -1,       0, 0 },
                {  0,  0,   0,  0, epsilon, 1 },
                {  0,  0,   0,  0,       1, 1 }
        }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
                               new double[] { 0, 0, 0, 0, 0, 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10);
        Assert.assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10);
        Assert.assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10);
        Assert.assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10);
        Assert.assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10);
    }

    @Test(expected=ConvergenceException.class)
    public void testNonInvertible() throws Exception {

        LinearProblem problem = new LinearProblem(new double[][] {
                {  1, 2, -3 },
                {  2, 1,  3 },
                { -3, 0, -9 }
        }, new double[] { 1, 1, 1 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();

        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
    }

    @Test
    public void testIllConditioned() {
        LinearProblem problem1 = new LinearProblem(new double[][] {
                { 10.0, 7.0,  8.0,  7.0 },
                {  7.0, 5.0,  6.0,  5.0 },
                {  8.0, 6.0, 10.0,  9.0 },
                {  7.0, 5.0,  9.0, 10.0 }
        }, new double[] { 32, 23, 33, 31 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum1 =
            optimizer.optimize(100, problem1, problem1.target, new double[] { 1, 1, 1, 1 },
                               new double[] { 0, 1, 2, 3 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-10);
        Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-10);
        Assert.assertEquals(1.0, optimum1.getPoint()[2], 1.0e-10);
        Assert.assertEquals(1.0, optimum1.getPoint()[3], 1.0e-10);

        LinearProblem problem2 = new LinearProblem(new double[][] {
                { 10.00, 7.00, 8.10, 7.20 },
                {  7.08, 5.04, 6.00, 5.00 },
                {  8.00, 5.98, 9.89, 9.00 },
                {  6.99, 4.99, 9.00, 9.98 }
        }, new double[] { 32, 23, 33, 31 });
        PointVectorValuePair optimum2 =
            optimizer.optimize(100, problem2, problem2.target, new double[] { 1, 1, 1, 1 },
                               new double[] { 0, 1, 2, 3 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-8);
        Assert.assertEquals(137.0, optimum2.getPoint()[1], 1.0e-8);
        Assert.assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-8);
        Assert.assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-8);
    }

    @Test
    public void testMoreEstimatedParametersSimple() {

        LinearProblem problem = new LinearProblem(new double[][] {
                { 3.0, 2.0,  0.0, 0.0 },
                { 0.0, 1.0, -1.0, 1.0 },
                { 2.0, 0.0,  1.0, 0.0 }
        }, new double[] { 7.0, 3.0, 5.0 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
                new double[] { 7, 6, 5, 4 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
    }

    @Test
    public void testMoreEstimatedParametersUnsorted() {
        LinearProblem problem = new LinearProblem(new double[][] {
                { 1.0, 1.0,  0.0,  0.0, 0.0,  0.0 },
                { 0.0, 0.0,  1.0,  1.0, 1.0,  0.0 },
                { 0.0, 0.0,  0.0,  0.0, 1.0, -1.0 },
                { 0.0, 0.0, -1.0,  1.0, 0.0,  1.0 },
                { 0.0, 0.0,  0.0, -1.0, 1.0,  0.0 }
       }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1 },
                               new double[] { 2, 2, 2, 2, 2, 2 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(3.0, optimum.getPointRef()[2], 1.0e-10);
        Assert.assertEquals(4.0, optimum.getPointRef()[3], 1.0e-10);
        Assert.assertEquals(5.0, optimum.getPointRef()[4], 1.0e-10);
        Assert.assertEquals(6.0, optimum.getPointRef()[5], 1.0e-10);
    }

    @Test
    public void testRedundantEquations() {
        LinearProblem problem = new LinearProblem(new double[][] {
                { 1.0,  1.0 },
                { 1.0, -1.0 },
                { 1.0,  3.0 }
        }, new double[] { 3.0, 1.0, 5.0 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
                               new double[] { 1, 1 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(2.0, optimum.getPointRef()[0], 1.0e-10);
        Assert.assertEquals(1.0, optimum.getPointRef()[1], 1.0e-10);
    }

    @Test
    public void testInconsistentEquations() {
        LinearProblem problem = new LinearProblem(new double[][] {
                { 1.0,  1.0 },
                { 1.0, -1.0 },
                { 1.0,  3.0 }
        }, new double[] { 3.0, 1.0, 4.0 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 1, 1 });
        Assert.assertTrue(optimizer.getRMS() > 0.1);
    }

    @Test(expected=DimensionMismatchException.class)
    public void testInconsistentSizes1() {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10);

        optimizer.optimize(100, problem, problem.target,
                           new double[] { 1 },
                           new double[] { 0, 0 });
    }

    @Test(expected=DimensionMismatchException.class)
    public void testInconsistentSizes2() {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10);

        optimizer.optimize(100, problem, new double[] { 1 },
                           new double[] { 1 },
                           new double[] { 0, 0 });
    }

    @Test
    public void testCircleFitting() {
        CircleVectorial circle = new CircleVectorial();
        circle.addPoint( 30.0,  68.0);
        circle.addPoint( 50.0,  -6.0);
        circle.addPoint(110.0, -20.0);
        circle.addPoint( 35.0,  15.0);
        circle.addPoint( 45.0,  97.0);
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum
            = optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 }, new double[] { 1, 1, 1, 1, 1 },
                                 new double[] { 98.680, 47.345 });
        Assert.assertTrue(optimizer.getEvaluations() < 10);
        Assert.assertTrue(optimizer.getJacobianEvaluations() < 10);
        double rms = optimizer.getRMS();
        Assert.assertEquals(1.768262623567235,  FastMath.sqrt(circle.getN()) * rms,  1.0e-10);
        Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
        Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1.0e-6);
        Assert.assertEquals(96.07590211815305, center.getX(),            1.0e-6);
        Assert.assertEquals(48.13516790438953, center.getY(),            1.0e-6);
        double[][] cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
        Assert.assertEquals(1.839, cov[0][0], 0.001);
        Assert.assertEquals(0.731, cov[0][1], 0.001);
        Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14);
        Assert.assertEquals(0.786, cov[1][1], 0.001);

        // add perfect measurements and check errors are reduced
        double  r = circle.getRadius(center);
        for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
            circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d));
        }
        double[] target = new double[circle.getN()];
        Arrays.fill(target, 0.0);
        double[] weights = new double[circle.getN()];
        Arrays.fill(weights, 2.0);
        optimum = optimizer.optimize(100, circle, target, weights, new double[] { 98.680, 47.345 });
        cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
        Assert.assertEquals(0.0016, cov[0][0], 0.001);
        Assert.assertEquals(3.2e-7, cov[0][1], 1.0e-9);
        Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14);
        Assert.assertEquals(0.0016, cov[1][1], 0.001);
    }

    @Test
    public void testCircleFittingBadInit() {
        CircleVectorial circle = new CircleVectorial();
        double[][] points = circlePoints;
        double[] target = new double[points.length];
        Arrays.fill(target, 0.0);
        double[] weights = new double[points.length];
        Arrays.fill(weights, 2.0);
        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum
            = optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 });
        Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
        Assert.assertTrue(optimizer.getEvaluations() < 25);
        Assert.assertTrue(optimizer.getJacobianEvaluations() < 20);
        Assert.assertEquals( 0.043, optimizer.getRMS(), 1.0e-3);
        Assert.assertEquals( 0.292235,  circle.getRadius(center), 1.0e-6);
        Assert.assertEquals(-0.151738,  center.getX(),            1.0e-6);
        Assert.assertEquals( 0.2075001, center.getY(),            1.0e-6);
    }

    @Test
    public void testCircleFittingGoodInit() {
        CircleVectorial circle = new CircleVectorial();
        double[][] points = circlePoints;
        double[] target = new double[points.length];
        Arrays.fill(target, 0.0);
        double[] weights = new double[points.length];
        Arrays.fill(weights, 2.0);
        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, circle, target, weights, new double[] { 0, 0 });
        Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1.0e-6);
        Assert.assertEquals(0.2074999736353867,  optimum.getPointRef()[1], 1.0e-6);
        Assert.assertEquals(0.04268731682389561, optimizer.getRMS(),       1.0e-8);
    }

    private final double[][] circlePoints = new double[][] {
        {-0.312967,  0.072366}, {-0.339248,  0.132965}, {-0.379780,  0.202724},
        {-0.390426,  0.260487}, {-0.361212,  0.328325}, {-0.346039,  0.392619},
        {-0.280579,  0.444306}, {-0.216035,  0.470009}, {-0.149127,  0.493832},
        {-0.075133,  0.483271}, {-0.007759,  0.452680}, { 0.060071,  0.410235},
        { 0.103037,  0.341076}, { 0.118438,  0.273884}, { 0.131293,  0.192201},
        { 0.115869,  0.129797}, { 0.072223,  0.058396}, { 0.022884,  0.000718},
        {-0.053355, -0.020405}, {-0.123584, -0.032451}, {-0.216248, -0.032862},
        {-0.278592, -0.005008}, {-0.337655,  0.056658}, {-0.385899,  0.112526},
        {-0.405517,  0.186957}, {-0.415374,  0.262071}, {-0.387482,  0.343398},
        {-0.347322,  0.397943}, {-0.287623,  0.458425}, {-0.223502,  0.475513},
        {-0.135352,  0.478186}, {-0.061221,  0.483371}, { 0.003711,  0.422737},
        { 0.065054,  0.375830}, { 0.108108,  0.297099}, { 0.123882,  0.222850},
        { 0.117729,  0.134382}, { 0.085195,  0.056820}, { 0.029800, -0.019138},
        {-0.027520, -0.072374}, {-0.102268, -0.091555}, {-0.200299, -0.106578},
        {-0.292731, -0.091473}, {-0.356288, -0.051108}, {-0.420561,  0.014926},
        {-0.471036,  0.074716}, {-0.488638,  0.182508}, {-0.485990,  0.254068},
        {-0.463943,  0.338438}, {-0.406453,  0.404704}, {-0.334287,  0.466119},
        {-0.254244,  0.503188}, {-0.161548,  0.495769}, {-0.075733,  0.495560},
        { 0.001375,  0.434937}, { 0.082787,  0.385806}, { 0.115490,  0.323807},
        { 0.141089,  0.223450}, { 0.138693,  0.131703}, { 0.126415,  0.049174},
        { 0.066518, -0.010217}, {-0.005184, -0.070647}, {-0.080985, -0.103635},
        {-0.177377, -0.116887}, {-0.260628, -0.100258}, {-0.335756, -0.056251},
        {-0.405195, -0.000895}, {-0.444937,  0.085456}, {-0.484357,  0.175597},
        {-0.472453,  0.248681}, {-0.438580,  0.347463}, {-0.402304,  0.422428},
        {-0.326777,  0.479438}, {-0.247797,  0.505581}, {-0.152676,  0.519380},
        {-0.071754,  0.516264}, { 0.015942,  0.472802}, { 0.076608,  0.419077},
        { 0.127673,  0.330264}, { 0.159951,  0.262150}, { 0.153530,  0.172681},
        { 0.140653,  0.089229}, { 0.078666,  0.024981}, { 0.023807, -0.037022},
        {-0.048837, -0.077056}, {-0.127729, -0.075338}, {-0.221271, -0.067526}
    };

    public void doTestStRD(final StatisticalReferenceDataset dataset,
        final double errParams, final double errParamsSd) {
        final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        final double[] w = new double[dataset.getNumObservations()];
        Arrays.fill(w, 1.0);

        final double[][] data = dataset.getData();
        final double[] initial = dataset.getStartingPoint(0);
        final MultivariateDifferentiableVectorFunction problem;
        problem = dataset.getLeastSquaresProblem();
        final PointVectorValuePair optimum;
        optimum = optimizer.optimize(100, problem, data[1], w, initial);

        final double[] actual = optimum.getPoint();
        for (int i = 0; i < actual.length; i++) {
            double expected = dataset.getParameter(i);
            double delta = FastMath.abs(errParams * expected);
            Assert.assertEquals(dataset.getName() + ", param #" + i,
                                expected, actual[i], delta);
        }
    }

    @Test
    public void testKirby2() throws IOException {
        doTestStRD(StatisticalReferenceDatasetFactory.createKirby2(), 1E-7, 1E-7);
    }

    @Test
    public void testHahn1() throws IOException {
        doTestStRD(StatisticalReferenceDatasetFactory.createHahn1(), 1E-7, 1E-4);
    }

    static class LinearProblem implements MultivariateDifferentiableVectorFunction, Serializable {

        private static final long serialVersionUID = 703247177355019415L;
        final RealMatrix factors;
        final double[] target;
        public LinearProblem(double[][] factors, double[] target) {
            this.factors = new BlockRealMatrix(factors);
            this.target  = target;
        }

        public double[] value(double[] variables) {
            return factors.operate(variables);
        }

        public DerivativeStructure[] value(DerivativeStructure[] variables) {
            DerivativeStructure[] value = new DerivativeStructure[factors.getRowDimension()];
            for (int i = 0; i < value.length; ++i) {
                value[i] = variables[0].getField().getZero();
                for (int j = 0; j < factors.getColumnDimension(); ++j) {
                    value[i] = value[i].add(variables[j].multiply(factors.getEntry(i, j)));
                }
                
            }
            return value;
        }

    }
}
