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Posted to commits@commons.apache.org by tn...@apache.org on 2015/04/11 16:06:10 UTC
[5/5] [math] Remove deprecated classes in optim package.
Remove deprecated classes in optim package.
Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo
Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/e31fde87
Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/e31fde87
Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/e31fde87
Branch: refs/heads/master
Commit: e31fde875c6075ae3da9572c6f910cc29ceaf6c3
Parents: 0737cf8
Author: Thomas Neidhart <th...@gmail.com>
Authored: Sat Apr 11 16:05:10 2015 +0200
Committer: Thomas Neidhart <th...@gmail.com>
Committed: Sat Apr 11 16:05:10 2015 +0200
----------------------------------------------------------------------
...ltiStartMultivariateVectorOptimizerTest.java | 253 ---
...stractLeastSquaresOptimizerAbstractTest.java | 641 --------
.../AbstractLeastSquaresOptimizerTest.java | 129 --
...ractLeastSquaresOptimizerTestValidation.java | 335 ----
.../vector/jacobian/CircleProblem.java | 179 ---
.../vector/jacobian/CircleVectorial.java | 99 --
.../jacobian/GaussNewtonOptimizerTest.java | 173 ---
.../LevenbergMarquardtOptimizerTest.java | 375 -----
.../nonlinear/vector/jacobian/MinpackTest.java | 1467 ------------------
.../jacobian/RandomCirclePointGenerator.java | 91 --
.../RandomStraightLinePointGenerator.java | 99 --
.../jacobian/StatisticalReferenceDataset.java | 385 -----
.../StatisticalReferenceDatasetFactory.java | 203 ---
.../vector/jacobian/StraightLineProblem.java | 169 --
14 files changed, 4598 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java
deleted file mode 100644
index 70b3f95..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.linear.BlockRealMatrix;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.OptimizationData;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.SimpleBounds;
-import org.apache.commons.math4.optim.SimpleVectorValueChecker;
-import org.apache.commons.math4.optim.nonlinear.vector.JacobianMultivariateVectorOptimizer;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.optim.nonlinear.vector.MultiStartMultivariateVectorOptimizer;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer;
-import org.apache.commons.math4.random.GaussianRandomGenerator;
-import org.apache.commons.math4.random.JDKRandomGenerator;
-import org.apache.commons.math4.random.RandomVectorGenerator;
-import org.apache.commons.math4.random.UncorrelatedRandomVectorGenerator;
-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)
- */
-@Deprecated
-public class MultiStartMultivariateVectorOptimizerTest {
-
- @Test(expected=NullPointerException.class)
- public void testGetOptimaBeforeOptimize() {
-
- JacobianMultivariateVectorOptimizer underlyingOptimizer
- = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
- JDKRandomGenerator g = new JDKRandomGenerator();
- g.setSeed(16069223052l);
- RandomVectorGenerator generator
- = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
- MultiStartMultivariateVectorOptimizer optimizer
- = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
-
- optimizer.getOptima();
- }
-
- @Test
- public void testTrivial() {
- LinearProblem problem
- = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
- JacobianMultivariateVectorOptimizer underlyingOptimizer
- = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
- JDKRandomGenerator g = new JDKRandomGenerator();
- g.setSeed(16069223052l);
- RandomVectorGenerator generator
- = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
- MultiStartMultivariateVectorOptimizer optimizer
- = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
-
- PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0 }));
- Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
- PointVectorValuePair[] optima = optimizer.getOptima();
- Assert.assertEquals(10, optima.length);
- for (int i = 0; i < optima.length; i++) {
- Assert.assertEquals(1.5, optima[i].getPoint()[0], 1e-10);
- Assert.assertEquals(3.0, optima[i].getValue()[0], 1e-10);
- }
- Assert.assertTrue(optimizer.getEvaluations() > 20);
- Assert.assertTrue(optimizer.getEvaluations() < 50);
- Assert.assertEquals(100, optimizer.getMaxEvaluations());
- }
-
- @Test
- public void testIssue914() {
- LinearProblem problem = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
- JacobianMultivariateVectorOptimizer underlyingOptimizer =
- new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6)) {
- @Override
- public PointVectorValuePair optimize(OptimizationData... optData) {
- // filter out simple bounds, as they are not supported
- // by the underlying optimizer, and we don't really care for this test
- OptimizationData[] filtered = optData.clone();
- for (int i = 0; i < filtered.length; ++i) {
- if (filtered[i] instanceof SimpleBounds) {
- filtered[i] = null;
- }
- }
- return super.optimize(filtered);
- }
- };
- JDKRandomGenerator g = new JDKRandomGenerator();
- g.setSeed(16069223052l);
- RandomVectorGenerator generator =
- new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
- MultiStartMultivariateVectorOptimizer optimizer =
- new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
-
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0 }),
- new SimpleBounds(new double[] { -1.0e-10 }, new double[] { 1.0e-10 }));
- PointVectorValuePair[] optima = optimizer.getOptima();
- // only the first start should have succeeded
- Assert.assertEquals(1, optima.length);
-
- }
-
- /**
- * Test demonstrating that the user exception is finally thrown if none
- * of the runs succeed.
- */
- @Test(expected=TestException.class)
- public void testNoOptimum() {
- JacobianMultivariateVectorOptimizer underlyingOptimizer
- = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
- JDKRandomGenerator g = new JDKRandomGenerator();
- g.setSeed(12373523445l);
- RandomVectorGenerator generator
- = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
- MultiStartMultivariateVectorOptimizer optimizer
- = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
- optimizer.optimize(new MaxEval(100),
- new Target(new double[] { 0 }),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0 }),
- new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] point) {
- throw new TestException();
- }
- }));
- }
-
- private static class TestException extends RuntimeException {
-
- private static final long serialVersionUID = 1L;}
-
- private static class LinearProblem {
- private final RealMatrix factors;
- private final double[] target;
-
- public LinearProblem(double[][] factors,
- double[] target) {
- this.factors = new BlockRealMatrix(factors);
- this.target = target;
- }
-
- public Target getTarget() {
- return new Target(target);
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] variables) {
- return factors.operate(variables);
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- return factors.getData();
- }
- });
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java
deleted file mode 100644
index e2814fc..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java
+++ /dev/null
@@ -1,641 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.io.IOException;
-import java.util.Arrays;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.exception.ConvergenceException;
-import org.apache.commons.math4.exception.DimensionMismatchException;
-import org.apache.commons.math4.geometry.euclidean.twod.Vector2D;
-import org.apache.commons.math4.linear.BlockRealMatrix;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer;
-import org.apache.commons.math4.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)
- */
-@Deprecated
-public abstract class AbstractLeastSquaresOptimizerAbstractTest {
-
- public abstract AbstractLeastSquaresOptimizer createOptimizer();
-
- @Test
- public void testGetIterations() {
- AbstractLeastSquaresOptimizer optim = createOptimizer();
- optim.optimize(new MaxEval(100), new Target(new double[] { 1 }),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 3 }),
- new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] point) {
- return new double[] {
- FastMath.pow(point[0], 4)
- };
- }
- }),
- new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- return new double[][] {
- { 0.25 * FastMath.pow(point[0], 3) }
- };
- }
- }));
-
- Assert.assertTrue(optim.getIterations() > 0);
- }
-
- @Test
- public void testTrivial() {
- LinearProblem problem
- = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
- }
-
- @Test
- public void testQRColumnsPermutation() {
-
- LinearProblem problem
- = new LinearProblem(new double[][] { { 1, -1 }, { 0, 2 }, { 1, -2 } },
- new double[] { 4, 6, 1 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(7, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(3, optimum.getPoint()[1], 1e-10);
- Assert.assertEquals(4, optimum.getValue()[0], 1e-10);
- Assert.assertEquals(6, optimum.getValue()[1], 1e-10);
- Assert.assertEquals(1, optimum.getValue()[2], 1e-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, 1.1, 2.2, 3.3, 4.4, 5.5 });
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- for (int i = 0; i < problem.target.length; ++i) {
- Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1e-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(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(1, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(2, optimum.getPoint()[1], 1e-10);
- Assert.assertEquals(3, optimum.getPoint()[2], 1e-10);
- }
-
- @Test
- public void testTwoSets() {
- double epsilon = 1e-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(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(3, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(4, optimum.getPoint()[1], 1e-10);
- Assert.assertEquals(-1, optimum.getPoint()[2], 1e-10);
- Assert.assertEquals(-2, optimum.getPoint()[3], 1e-10);
- Assert.assertEquals(1 + epsilon, optimum.getPoint()[4], 1e-10);
- Assert.assertEquals(1 - epsilon, optimum.getPoint()[5], 1e-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(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0, 0 }));
- }
-
- @Test
- public void testIllConditioned() {
- LinearProblem problem1 = new LinearProblem(new double[][] {
- { 10, 7, 8, 7 },
- { 7, 5, 6, 5 },
- { 8, 6, 10, 9 },
- { 7, 5, 9, 10 }
- }, new double[] { 32, 23, 33, 31 });
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum1 =
- optimizer.optimize(new MaxEval(100),
- problem1.getModelFunction(),
- problem1.getModelFunctionJacobian(),
- problem1.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 1, 2, 3 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(1, optimum1.getPoint()[0], 1e-10);
- Assert.assertEquals(1, optimum1.getPoint()[1], 1e-10);
- Assert.assertEquals(1, optimum1.getPoint()[2], 1e-10);
- Assert.assertEquals(1, optimum1.getPoint()[3], 1e-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(new MaxEval(100),
- problem2.getModelFunction(),
- problem2.getModelFunctionJacobian(),
- problem2.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 1, 2, 3 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(-81, optimum2.getPoint()[0], 1e-8);
- Assert.assertEquals(137, optimum2.getPoint()[1], 1e-8);
- Assert.assertEquals(-34, optimum2.getPoint()[2], 1e-8);
- Assert.assertEquals( 22, optimum2.getPoint()[3], 1e-8);
- }
-
- @Test
- public void testMoreEstimatedParametersSimple() {
-
- LinearProblem problem = new LinearProblem(new double[][] {
- { 3, 2, 0, 0 },
- { 0, 1, -1, 1 },
- { 2, 0, 1, 0 }
- }, new double[] { 7, 3, 5 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 7, 6, 5, 4 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- }
-
- @Test
- public void testMoreEstimatedParametersUnsorted() {
- LinearProblem problem = new LinearProblem(new double[][] {
- { 1, 1, 0, 0, 0, 0 },
- { 0, 0, 1, 1, 1, 0 },
- { 0, 0, 0, 0, 1, -1 },
- { 0, 0, -1, 1, 0, 1 },
- { 0, 0, 0, -1, 1, 0 }
- }, new double[] { 3, 12, -1, 7, 1 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 2, 2, 2, 2, 2, 2 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(3, optimum.getPointRef()[2], 1e-10);
- Assert.assertEquals(4, optimum.getPointRef()[3], 1e-10);
- Assert.assertEquals(5, optimum.getPointRef()[4], 1e-10);
- Assert.assertEquals(6, optimum.getPointRef()[5], 1e-10);
- }
-
- @Test
- public void testRedundantEquations() {
- LinearProblem problem = new LinearProblem(new double[][] {
- { 1, 1 },
- { 1, -1 },
- { 1, 3 }
- }, new double[] { 3, 1, 5 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 1, 1 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(2, optimum.getPointRef()[0], 1e-10);
- Assert.assertEquals(1, optimum.getPointRef()[1], 1e-10);
- }
-
- @Test
- public void testInconsistentEquations() {
- LinearProblem problem = new LinearProblem(new double[][] {
- { 1, 1 },
- { 1, -1 },
- { 1, 3 }
- }, new double[] { 3, 1, 4 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(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(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1 }),
- new InitialGuess(new double[] { 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
-
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1 }),
- new InitialGuess(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(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1 }),
- new InitialGuess(new double[] { 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
-
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(new double[] { 1 }),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0, 0 }));
- }
-
- @Test
- public void testCircleFitting() {
- CircleVectorial circle = new CircleVectorial();
- circle.addPoint( 30, 68);
- circle.addPoint( 50, -6);
- circle.addPoint(110, -20);
- circle.addPoint( 35, 15);
- circle.addPoint( 45, 97);
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(new double[] { 0, 0, 0, 0, 0 }),
- new Weight(new double[] { 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 98.680, 47.345 }));
- Assert.assertTrue(optimizer.getEvaluations() < 10);
- double rms = optimizer.getRMS();
- Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, 1e-10);
- Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
- Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1e-6);
- Assert.assertEquals(96.07590211815305, center.getX(), 1e-6);
- Assert.assertEquals(48.13516790438953, center.getY(), 1e-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], 1e-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);
- double[] weights = new double[circle.getN()];
- Arrays.fill(weights, 2);
- optimum = optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(target),
- new Weight(weights),
- new InitialGuess(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], 1e-9);
- Assert.assertEquals(cov[0][1], cov[1][0], 1e-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);
- double[] weights = new double[points.length];
- Arrays.fill(weights, 2);
- for (int i = 0; i < points.length; ++i) {
- circle.addPoint(points[i][0], points[i][1]);
- }
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(target),
- new Weight(weights),
- new InitialGuess(new double[] { -12, -12 }));
- Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
- Assert.assertTrue(optimizer.getEvaluations() < 25);
- Assert.assertEquals( 0.043, optimizer.getRMS(), 1e-3);
- Assert.assertEquals( 0.292235, circle.getRadius(center), 1e-6);
- Assert.assertEquals(-0.151738, center.getX(), 1e-6);
- Assert.assertEquals( 0.2075001, center.getY(), 1e-6);
- }
-
- @Test
- public void testCircleFittingGoodInit() {
- CircleVectorial circle = new CircleVectorial();
- double[][] points = circlePoints;
- double[] target = new double[points.length];
- Arrays.fill(target, 0);
- double[] weights = new double[points.length];
- Arrays.fill(weights, 2);
- for (int i = 0; i < points.length; ++i) {
- circle.addPoint(points[i][0], points[i][1]);
- }
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(target),
- new Weight(weights),
- new InitialGuess(new double[] { 0, 0 }));
- Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
- Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1e-6);
- Assert.assertEquals(0.04268731682389561, optimizer.getRMS(), 1e-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);
-
- final double[][] data = dataset.getData();
- final double[] initial = dataset.getStartingPoint(0);
- final StatisticalReferenceDataset.LeastSquaresProblem problem = dataset.getLeastSquaresProblem();
- final PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(data[1]),
- new Weight(w),
- new InitialGuess(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 {
- private final RealMatrix factors;
- private final double[] target;
-
- public LinearProblem(double[][] factors, double[] target) {
- this.factors = new BlockRealMatrix(factors);
- this.target = target;
- }
-
- public Target getTarget() {
- return new Target(target);
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] params) {
- return factors.operate(params);
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] params) {
- return factors.getData();
- }
- });
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java
deleted file mode 100644
index aad5e43..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java
+++ /dev/null
@@ -1,129 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.io.IOException;
-import java.util.Arrays;
-
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer;
-import org.apache.commons.math4.util.FastMath;
-import org.junit.Test;
-import org.junit.Assert;
-
-@Deprecated
-public class AbstractLeastSquaresOptimizerTest {
-
- public static AbstractLeastSquaresOptimizer createOptimizer() {
- return new AbstractLeastSquaresOptimizer(null) {
-
- @Override
- protected PointVectorValuePair doOptimize() {
- final double[] params = getStartPoint();
- final double[] res = computeResiduals(computeObjectiveValue(params));
- setCost(computeCost(res));
- return new PointVectorValuePair(params, null);
- }
- };
- }
-
- @Test
- public void testGetChiSquare() throws IOException {
- final StatisticalReferenceDataset dataset
- = StatisticalReferenceDatasetFactory.createKirby2();
- final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- final double[] a = dataset.getParameters();
- final double[] y = dataset.getData()[1];
- final double[] w = new double[y.length];
- Arrays.fill(w, 1.0);
-
- StatisticalReferenceDataset.LeastSquaresProblem problem
- = dataset.getLeastSquaresProblem();
-
- optimizer.optimize(new MaxEval(1),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(y),
- new Weight(w),
- new InitialGuess(a));
- final double expected = dataset.getResidualSumOfSquares();
- final double actual = optimizer.getChiSquare();
- Assert.assertEquals(dataset.getName(), expected, actual,
- 1E-11 * expected);
- }
-
- @Test
- public void testGetRMS() throws IOException {
- final StatisticalReferenceDataset dataset
- = StatisticalReferenceDatasetFactory.createKirby2();
- final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- final double[] a = dataset.getParameters();
- final double[] y = dataset.getData()[1];
- final double[] w = new double[y.length];
- Arrays.fill(w, 1);
-
- StatisticalReferenceDataset.LeastSquaresProblem problem
- = dataset.getLeastSquaresProblem();
-
- optimizer.optimize(new MaxEval(1),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(y),
- new Weight(w),
- new InitialGuess(a));
-
- final double expected = FastMath
- .sqrt(dataset.getResidualSumOfSquares() /
- dataset.getNumObservations());
- final double actual = optimizer.getRMS();
- Assert.assertEquals(dataset.getName(), expected, actual,
- 1E-11 * expected);
- }
-
- @Test
- public void testComputeSigma() throws IOException {
- final StatisticalReferenceDataset dataset
- = StatisticalReferenceDatasetFactory.createKirby2();
- final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- final double[] a = dataset.getParameters();
- final double[] y = dataset.getData()[1];
- final double[] w = new double[y.length];
- Arrays.fill(w, 1);
-
- StatisticalReferenceDataset.LeastSquaresProblem problem
- = dataset.getLeastSquaresProblem();
-
- final PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(1),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(y),
- new Weight(w),
- new InitialGuess(a));
-
- final double[] sig = optimizer.computeSigma(optimum.getPoint(), 1e-14);
-
- final int dof = y.length - a.length;
- final double[] expected = dataset.getParametersStandardDeviations();
- for (int i = 0; i < sig.length; i++) {
- final double actual = FastMath.sqrt(optimizer.getChiSquare() / dof) * sig[i];
- Assert.assertEquals(dataset.getName() + ", parameter #" + i,
- expected[i], actual, 1e-6 * expected[i]);
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTestValidation.java
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diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTestValidation.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTestValidation.java
deleted file mode 100644
index 9235e6b..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTestValidation.java
+++ /dev/null
@@ -1,335 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.Arrays;
-import java.util.List;
-import java.util.ArrayList;
-import java.awt.geom.Point2D;
-
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer;
-import org.apache.commons.math4.stat.descriptive.StatisticalSummary;
-import org.apache.commons.math4.stat.descriptive.SummaryStatistics;
-import org.apache.commons.math4.util.FastMath;
-import org.junit.Test;
-import org.junit.Assert;
-
-/**
- * This class demonstrates the main functionality of the
- * {@link AbstractLeastSquaresOptimizer}, common to the
- * optimizer implementations in package
- * {@link org.apache.commons.math4.optimization.general}.
- * <br/>
- * Not enabled by default, as the class name does not end with "Test".
- * <br/>
- * Invoke by running
- * <pre><code>
- * mvn test -Dtest=AbstractLeastSquaresOptimizerTestValidation
- * </code></pre>
- * or by running
- * <pre><code>
- * mvn test -Dtest=AbstractLeastSquaresOptimizerTestValidation -DargLine="-DmcRuns=1234 -server"
- * </code></pre>
- */
-@Deprecated
-public class AbstractLeastSquaresOptimizerTestValidation {
- private static final int MONTE_CARLO_RUNS = Integer.parseInt(System.getProperty("mcRuns",
- "100"));
-
- /**
- * Using a Monte-Carlo procedure, this test checks the error estimations
- * as provided by the square-root of the diagonal elements of the
- * covariance matrix.
- * <br/>
- * The test generates sets of observations, each sampled from
- * a Gaussian distribution.
- * <br/>
- * The optimization problem solved is defined in class
- * {@link StraightLineProblem}.
- * <br/>
- * The output (on stdout) will be a table summarizing the distribution
- * of parameters generated by the Monte-Carlo process and by the direct
- * estimation provided by the diagonal elements of the covariance matrix.
- */
- @Test
- public void testParametersErrorMonteCarloObservations() {
- // Error on the observations.
- final double yError = 15;
-
- // True values of the parameters.
- final double slope = 123.456;
- final double offset = -98.765;
-
- // Samples generator.
- final RandomStraightLinePointGenerator lineGenerator
- = new RandomStraightLinePointGenerator(slope, offset,
- yError,
- -1e3, 1e4,
- 138577L);
-
- // Number of observations.
- final int numObs = 100; // XXX Should be a command-line option.
- // number of parameters.
- final int numParams = 2;
-
- // Parameters found for each of Monte-Carlo run.
- final SummaryStatistics[] paramsFoundByDirectSolution = new SummaryStatistics[numParams];
- // Sigma estimations (square-root of the diagonal elements of the
- // covariance matrix), for each Monte-Carlo run.
- final SummaryStatistics[] sigmaEstimate = new SummaryStatistics[numParams];
-
- // Initialize statistics accumulators.
- for (int i = 0; i < numParams; i++) {
- paramsFoundByDirectSolution[i] = new SummaryStatistics();
- sigmaEstimate[i] = new SummaryStatistics();
- }
-
- // Dummy optimizer (to compute the covariance matrix).
- final AbstractLeastSquaresOptimizer optim = new DummyOptimizer();
- final double[] init = { slope, offset };
-
- // Monte-Carlo (generates many sets of observations).
- final int mcRepeat = MONTE_CARLO_RUNS;
- int mcCount = 0;
- while (mcCount < mcRepeat) {
- // Observations.
- final Point2D.Double[] obs = lineGenerator.generate(numObs);
-
- final StraightLineProblem problem = new StraightLineProblem(yError);
- for (int i = 0; i < numObs; i++) {
- final Point2D.Double p = obs[i];
- problem.addPoint(p.x, p.y);
- }
-
- // Direct solution (using simple regression).
- final double[] regress = problem.solve();
-
- // Estimation of the standard deviation (diagonal elements of the
- // covariance matrix).
- final PointVectorValuePair optimum
- = optim.optimize(new MaxEval(Integer.MAX_VALUE),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(problem.target()),
- new Weight(problem.weight()),
- new InitialGuess(init));
- final double[] sigma = optim.computeSigma(optimum.getPoint(), 1e-14);
-
- // Accumulate statistics.
- for (int i = 0; i < numParams; i++) {
- paramsFoundByDirectSolution[i].addValue(regress[i]);
- sigmaEstimate[i].addValue(sigma[i]);
- }
-
- // Next Monte-Carlo.
- ++mcCount;
- }
-
- // Print statistics.
- final String line = "--------------------------------------------------------------";
- System.out.println(" True value Mean Std deviation");
- for (int i = 0; i < numParams; i++) {
- System.out.println(line);
- System.out.println("Parameter #" + i);
-
- StatisticalSummary s = paramsFoundByDirectSolution[i].getSummary();
- System.out.printf(" %+.6e %+.6e %+.6e\n",
- init[i],
- s.getMean(),
- s.getStandardDeviation());
-
- s = sigmaEstimate[i].getSummary();
- System.out.printf("sigma: %+.6e (%+.6e)\n",
- s.getMean(),
- s.getStandardDeviation());
- }
- System.out.println(line);
-
- // Check the error estimation.
- for (int i = 0; i < numParams; i++) {
- Assert.assertEquals(paramsFoundByDirectSolution[i].getSummary().getStandardDeviation(),
- sigmaEstimate[i].getSummary().getMean(),
- 8e-2);
- }
- }
-
- /**
- * In this test, the set of observations is fixed.
- * Using a Monte-Carlo procedure, it generates sets of parameters,
- * and determine the parameter change that will result in the
- * normalized chi-square becoming larger by one than the value from
- * the best fit solution.
- * <br/>
- * The optimization problem solved is defined in class
- * {@link StraightLineProblem}.
- * <br/>
- * The output (on stdout) will be a list of lines containing:
- * <ul>
- * <li>slope of the straight line,</li>
- * <li>intercept of the straight line,</li>
- * <li>chi-square of the solution defined by the above two values.</li>
- * </ul>
- * The output is separated into two blocks (with a blank line between
- * them); the first block will contain all parameter sets for which
- * {@code chi2 < chi2_b + 1}
- * and the second block, all sets for which
- * {@code chi2 >= chi2_b + 1}
- * where {@code chi2_b} is the lowest chi-square (corresponding to the
- * best solution).
- */
- @Test
- public void testParametersErrorMonteCarloParameters() {
- // Error on the observations.
- final double yError = 15;
-
- // True values of the parameters.
- final double slope = 123.456;
- final double offset = -98.765;
-
- // Samples generator.
- final RandomStraightLinePointGenerator lineGenerator
- = new RandomStraightLinePointGenerator(slope, offset,
- yError,
- -1e3, 1e4,
- 13839013L);
-
- // Number of observations.
- final int numObs = 10;
-
- // Create a single set of observations.
- final Point2D.Double[] obs = lineGenerator.generate(numObs);
-
- final StraightLineProblem problem = new StraightLineProblem(yError);
- for (int i = 0; i < numObs; i++) {
- final Point2D.Double p = obs[i];
- problem.addPoint(p.x, p.y);
- }
-
- // Direct solution (using simple regression).
- final double[] regress = problem.solve();
-
- // Dummy optimizer (to compute the chi-square).
- final AbstractLeastSquaresOptimizer optim = new DummyOptimizer();
- // Get chi-square of the best parameters set for the given set of
- // observations.
- final double bestChi2N = getChi2N(optim, problem, regress);
- final double[] sigma = optim.computeSigma(regress, 1e-14);
-
- // Monte-Carlo (generates a grid of parameters).
- final int mcRepeat = MONTE_CARLO_RUNS;
- final int gridSize = (int) FastMath.sqrt(mcRepeat);
-
- // Parameters found for each of Monte-Carlo run.
- // Index 0 = slope
- // Index 1 = offset
- // Index 2 = normalized chi2
- final List<double[]> paramsAndChi2 = new ArrayList<double[]>(gridSize * gridSize);
-
- final double slopeRange = 10 * sigma[0];
- final double offsetRange = 10 * sigma[1];
- final double minSlope = slope - 0.5 * slopeRange;
- final double minOffset = offset - 0.5 * offsetRange;
- final double deltaSlope = slopeRange/ gridSize;
- final double deltaOffset = offsetRange / gridSize;
- for (int i = 0; i < gridSize; i++) {
- final double s = minSlope + i * deltaSlope;
- for (int j = 0; j < gridSize; j++) {
- final double o = minOffset + j * deltaOffset;
- final double chi2N = getChi2N(optim, problem, new double[] {s, o});
-
- paramsAndChi2.add(new double[] {s, o, chi2N});
- }
- }
-
- // Output (for use with "gnuplot").
-
- // Some info.
-
- // For plotting separately sets of parameters that have a large chi2.
- final double chi2NPlusOne = bestChi2N + 1;
- int numLarger = 0;
-
- final String lineFmt = "%+.10e %+.10e %.8e\n";
-
- // Point with smallest chi-square.
- System.out.printf(lineFmt, regress[0], regress[1], bestChi2N);
- System.out.println(); // Empty line.
-
- // Points within the confidence interval.
- for (double[] d : paramsAndChi2) {
- if (d[2] <= chi2NPlusOne) {
- System.out.printf(lineFmt, d[0], d[1], d[2]);
- }
- }
- System.out.println(); // Empty line.
-
- // Points outside the confidence interval.
- for (double[] d : paramsAndChi2) {
- if (d[2] > chi2NPlusOne) {
- ++numLarger;
- System.out.printf(lineFmt, d[0], d[1], d[2]);
- }
- }
- System.out.println(); // Empty line.
-
- System.out.println("# sigma=" + Arrays.toString(sigma));
- System.out.println("# " + numLarger + " sets filtered out");
- }
-
- /**
- * @return the normalized chi-square.
- */
- private double getChi2N(AbstractLeastSquaresOptimizer optim,
- StraightLineProblem problem,
- double[] params) {
- final double[] t = problem.target();
- final double[] w = problem.weight();
-
- optim.optimize(new MaxEval(Integer.MAX_VALUE),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(t),
- new Weight(w),
- new InitialGuess(params));
-
- return optim.getChiSquare() / (t.length - params.length);
- }
-}
-
-/**
- * A dummy optimizer.
- * Used for computing the covariance matrix.
- */
-@Deprecated
-class DummyOptimizer extends AbstractLeastSquaresOptimizer {
- public DummyOptimizer() {
- super(null);
- }
-
- /**
- * This method does nothing and returns a dummy value.
- */
- @Override
- public PointVectorValuePair doOptimize() {
- final double[] params = getStartPoint();
- final double[] res = computeResiduals(computeObjectiveValue(params));
- setCost(computeCost(res));
- return new PointVectorValuePair(params, null);
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleProblem.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleProblem.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleProblem.java
deleted file mode 100644
index 9458fe8..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleProblem.java
+++ /dev/null
@@ -1,179 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.ArrayList;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.util.FastMath;
-import org.apache.commons.math4.util.MathUtils;
-
-/**
- * Class that models a circle.
- * The parameters of problem are:
- * <ul>
- * <li>the x-coordinate of the circle center,</li>
- * <li>the y-coordinate of the circle center,</li>
- * <li>the radius of the circle.</li>
- * </ul>
- * The model functions are:
- * <ul>
- * <li>for each triplet (cx, cy, r), the (x, y) coordinates of a point on the
- * corresponding circle.</li>
- * </ul>
- */
-@Deprecated
-class CircleProblem {
- /** Cloud of points assumed to be fitted by a circle. */
- private final ArrayList<double[]> points;
- /** Error on the x-coordinate of the points. */
- private final double xSigma;
- /** Error on the y-coordinate of the points. */
- private final double ySigma;
- /** Number of points on the circumference (when searching which
- model point is closest to a given "observation". */
- private final int resolution;
-
- /**
- * @param xError Assumed error for the x-coordinate of the circle points.
- * @param yError Assumed error for the y-coordinate of the circle points.
- * @param searchResolution Number of points to try when searching the one
- * that is closest to a given "observed" point.
- */
- public CircleProblem(double xError,
- double yError,
- int searchResolution) {
- points = new ArrayList<double[]>();
- xSigma = xError;
- ySigma = yError;
- resolution = searchResolution;
- }
-
- /**
- * @param xError Assumed error for the x-coordinate of the circle points.
- * @param yError Assumed error for the y-coordinate of the circle points.
- */
- public CircleProblem(double xError,
- double yError) {
- this(xError, yError, 500);
- }
-
- public void addPoint(double px, double py) {
- points.add(new double[] { px, py });
- }
-
- public double[] target() {
- final double[] t = new double[points.size() * 2];
- for (int i = 0; i < points.size(); i++) {
- final double[] p = points.get(i);
- final int index = i * 2;
- t[index] = p[0];
- t[index + 1] = p[1];
- }
-
- return t;
- }
-
- public double[] weight() {
- final double wX = 1 / (xSigma * xSigma);
- final double wY = 1 / (ySigma * ySigma);
- final double[] w = new double[points.size() * 2];
- for (int i = 0; i < points.size(); i++) {
- final int index = i * 2;
- w[index] = wX;
- w[index + 1] = wY;
- }
-
- return w;
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] params) {
- final double cx = params[0];
- final double cy = params[1];
- final double r = params[2];
-
- final double[] model = new double[points.size() * 2];
-
- final double deltaTheta = MathUtils.TWO_PI / resolution;
- for (int i = 0; i < points.size(); i++) {
- final double[] p = points.get(i);
- final double px = p[0];
- final double py = p[1];
-
- double bestX = 0;
- double bestY = 0;
- double dMin = Double.POSITIVE_INFINITY;
-
- // Find the angle for which the circle passes closest to the
- // current point (using a resolution of 100 points along the
- // circumference).
- for (double theta = 0; theta <= MathUtils.TWO_PI; theta += deltaTheta) {
- final double currentX = cx + r * FastMath.cos(theta);
- final double currentY = cy + r * FastMath.sin(theta);
- final double dX = currentX - px;
- final double dY = currentY - py;
- final double d = dX * dX + dY * dY;
- if (d < dMin) {
- dMin = d;
- bestX = currentX;
- bestY = currentY;
- }
- }
-
- final int index = i * 2;
- model[index] = bestX;
- model[index + 1] = bestY;
- }
-
- return model;
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- return jacobian(point);
- }
- });
- }
-
- private double[][] jacobian(double[] params) {
- final double[][] jacobian = new double[points.size() * 2][3];
-
- for (int i = 0; i < points.size(); i++) {
- final int index = i * 2;
- // Partial derivative wrt x-coordinate of center.
- jacobian[index][0] = 1;
- jacobian[index + 1][0] = 0;
- // Partial derivative wrt y-coordinate of center.
- jacobian[index][1] = 0;
- jacobian[index + 1][1] = 1;
- // Partial derivative wrt radius.
- final double[] p = points.get(i);
- jacobian[index][2] = (p[0] - params[0]) / params[2];
- jacobian[index + 1][2] = (p[1] - params[1]) / params[2];
- }
-
- return jacobian;
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleVectorial.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleVectorial.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleVectorial.java
deleted file mode 100644
index 7b6a310..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleVectorial.java
+++ /dev/null
@@ -1,99 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.ArrayList;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.geometry.euclidean.twod.Vector2D;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-
-/**
- * Class used in the tests.
- */
-@Deprecated
-class CircleVectorial {
- private ArrayList<Vector2D> points;
-
- public CircleVectorial() {
- points = new ArrayList<Vector2D>();
- }
-
- public void addPoint(double px, double py) {
- points.add(new Vector2D(px, py));
- }
-
- public int getN() {
- return points.size();
- }
-
- public double getRadius(Vector2D center) {
- double r = 0;
- for (Vector2D point : points) {
- r += point.distance(center);
- }
- return r / points.size();
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] params) {
- Vector2D center = new Vector2D(params[0], params[1]);
- double radius = getRadius(center);
- double[] residuals = new double[points.size()];
- for (int i = 0; i < residuals.length; i++) {
- residuals[i] = points.get(i).distance(center) - radius;
- }
-
- return residuals;
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] params) {
- final int n = points.size();
- final Vector2D center = new Vector2D(params[0], params[1]);
-
- double dRdX = 0;
- double dRdY = 0;
- for (Vector2D pk : points) {
- double dk = pk.distance(center);
- dRdX += (center.getX() - pk.getX()) / dk;
- dRdY += (center.getY() - pk.getY()) / dk;
- }
- dRdX /= n;
- dRdY /= n;
-
- // Jacobian of the radius residuals.
- double[][] jacobian = new double[n][2];
- for (int i = 0; i < n; i++) {
- final Vector2D pi = points.get(i);
- final double di = pi.distance(center);
- jacobian[i][0] = (center.getX() - pi.getX()) / di - dRdX;
- jacobian[i][1] = (center.getY() - pi.getY()) / di - dRdY;
- }
-
- return jacobian;
- }
- });
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizerTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizerTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizerTest.java
deleted file mode 100644
index 7e73a9a..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizerTest.java
+++ /dev/null
@@ -1,173 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.io.IOException;
-
-import org.apache.commons.math4.exception.ConvergenceException;
-import org.apache.commons.math4.exception.MathUnsupportedOperationException;
-import org.apache.commons.math4.exception.TooManyEvaluationsException;
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.SimpleBounds;
-import org.apache.commons.math4.optim.SimpleVectorValueChecker;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer;
-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)
- */
-@Deprecated
-public class GaussNewtonOptimizerTest
- extends AbstractLeastSquaresOptimizerAbstractTest {
-
- @Override
- public AbstractLeastSquaresOptimizer createOptimizer() {
- return new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
- }
-
- @Test(expected=MathUnsupportedOperationException.class)
- public void testConstraintsUnsupported() {
- createOptimizer().optimize(new MaxEval(100),
- new Target(new double[] { 2 }),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 1, 2 }),
- new SimpleBounds(new double[] { -10, 0 },
- new double[] { 20, 30 }));
- }
-
- @Override
- @Test(expected = ConvergenceException.class)
- public void testMoreEstimatedParametersSimple() {
- /*
- * Exception is expected with this optimizer
- */
- super.testMoreEstimatedParametersSimple();
- }
-
- @Override
- @Test(expected=ConvergenceException.class)
- public void testMoreEstimatedParametersUnsorted() {
- /*
- * Exception is expected with this optimizer
- */
- super.testMoreEstimatedParametersUnsorted();
- }
-
- @Test(expected=TooManyEvaluationsException.class)
- public void testMaxEvaluations() throws Exception {
- 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(1e-30, 1e-30));
-
- optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(new double[] { 0, 0, 0, 0, 0 }),
- new Weight(new double[] { 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 98.680, 47.345 }));
- }
-
- @Override
- @Test(expected=ConvergenceException.class)
- public void testCircleFittingBadInit() {
- /*
- * This test does not converge with this optimizer.
- */
- super.testCircleFittingBadInit();
- }
-
- @Override
- @Test(expected = ConvergenceException.class)
- public void testHahn1()
- throws IOException {
- /*
- * TODO This test leads to a singular problem with the Gauss-Newton
- * optimizer. This should be inquired.
- */
- super.testHahn1();
- }
-}