/*
* 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,
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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;
}
}
}