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Java example source code file (EvaluationTest.java)

This example Java source code file (EvaluationTest.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

arrayrealvector, evaluation, ioexception, leastsquaresbuilder, leastsquaresproblem, mathillegalstateexception, multivariatejacobianfunction, multivariatematrixfunction, multivariatevectorfunction, pair, realmatrix, runtimeexception, statisticalreferencedataset, test, util

The EvaluationTest.java Java example source code

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

import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.DiagonalMatrix;
import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.linear.SingularMatrixException;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Pair;
import org.apache.commons.math3.util.Precision;
import org.junit.Assert;
import org.junit.Test;

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

/**
 * The only features tested here are utility methods defined
 * in {@link LeastSquaresProblem.Evaluation} that compute the
 * chi-square and parameters standard-deviations.
 */
public class EvaluationTest {

    /**
     * Create a {@link LeastSquaresBuilder} from a {@link StatisticalReferenceDataset}.
     *
     * @param dataset the source data
     * @return a builder for further customization.
     */
    public LeastSquaresBuilder builder(StatisticalReferenceDataset dataset) {
        StatisticalReferenceDataset.LeastSquaresProblem problem
                = dataset.getLeastSquaresProblem();
        final double[] start = dataset.getParameters();
        final double[] observed = dataset.getData()[1];
        final double[] weights = new double[observed.length];
        Arrays.fill(weights, 1d);

        return new LeastSquaresBuilder()
                .model(problem.getModelFunction(), problem.getModelFunctionJacobian())
                .target(observed)
                .weight(new DiagonalMatrix(weights))
                .start(start);
    }

    @Test
    public void testComputeResiduals() {
        //setup
        RealVector point = new ArrayRealVector(2);
        Evaluation evaluation = new LeastSquaresBuilder()
                .target(new ArrayRealVector(new double[]{3,-1}))
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector point) {
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(new double[]{1, 2}),
                                MatrixUtils.createRealIdentityMatrix(2)
                        );
                    }
                })
                .weight(MatrixUtils.createRealIdentityMatrix(2))
                .build()
                .evaluate(point);

        //action + verify
        Assert.assertArrayEquals(
                evaluation.getResiduals().toArray(),
                new double[]{2, -3},
                Precision.EPSILON);
    }

    @Test
    public void testComputeCovariance() throws IOException {
        //setup
        RealVector point = new ArrayRealVector(2);
        Evaluation evaluation = new LeastSquaresBuilder()
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector point) {
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(2),
                                MatrixUtils.createRealDiagonalMatrix(new double[]{1, 1e-2})
                        );
                    }
                })
                .weight(MatrixUtils.createRealDiagonalMatrix(new double[]{1, 1}))
                .target(new ArrayRealVector(2))
                .build()
                .evaluate(point);

        //action
        TestUtils.assertEquals(
                "covariance",
                evaluation.getCovariances(FastMath.nextAfter(1e-4, 0.0)),
                MatrixUtils.createRealMatrix(new double[][]{{1, 0}, {0, 1e4}}),
                Precision.EPSILON
        );

        //singularity fail
        try {
            evaluation.getCovariances(FastMath.nextAfter(1e-4, 1.0));
            Assert.fail("Expected Exception");
        } catch (SingularMatrixException e) {
            //expected
        }
    }

    @Test
    public void testComputeValueAndJacobian() {
        //setup
        final RealVector point = new ArrayRealVector(new double[]{1, 2});
        Evaluation evaluation = new LeastSquaresBuilder()
                .weight(new DiagonalMatrix(new double[]{16, 4}))
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector actualPoint) {
                        //verify correct values passed in
                        Assert.assertArrayEquals(
                                point.toArray(), actualPoint.toArray(), Precision.EPSILON);
                        //return values
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(new double[]{3, 4}),
                                MatrixUtils.createRealMatrix(new double[][]{{5, 6}, {7, 8}})
                        );
                    }
                })
                .target(new double[2])
                .build()
                .evaluate(point);

        //action
        RealVector residuals = evaluation.getResiduals();
        RealMatrix jacobian = evaluation.getJacobian();

        //verify
        Assert.assertArrayEquals(evaluation.getPoint().toArray(), point.toArray(), 0);
        Assert.assertArrayEquals(new double[]{-12, -8}, residuals.toArray(), Precision.EPSILON);
        TestUtils.assertEquals(
                "jacobian",
                jacobian,
                MatrixUtils.createRealMatrix(new double[][]{{20, 24},{14, 16}}),
                Precision.EPSILON);
    }

    @Test
    public void testComputeCost() throws IOException {
        final StatisticalReferenceDataset dataset
            = StatisticalReferenceDatasetFactory.createKirby2();

        final LeastSquaresProblem lsp = builder(dataset).build();

        final double expected = dataset.getResidualSumOfSquares();
        final double cost = lsp.evaluate(lsp.getStart()).getCost();
        final double actual = cost * cost;
        Assert.assertEquals(dataset.getName(), expected, actual, 1e-11 * expected);
    }

    @Test
    public void testComputeRMS() throws IOException {
        final StatisticalReferenceDataset dataset
            = StatisticalReferenceDatasetFactory.createKirby2();

        final LeastSquaresProblem lsp = builder(dataset).build();

        final double expected = FastMath.sqrt(dataset.getResidualSumOfSquares() /
                                              dataset.getNumObservations());
        final double actual = lsp.evaluate(lsp.getStart()).getRMS();
        Assert.assertEquals(dataset.getName(), expected, actual, 1e-11 * expected);
    }

    @Test
    public void testComputeSigma() throws IOException {
        final StatisticalReferenceDataset dataset
            = StatisticalReferenceDatasetFactory.createKirby2();

        final LeastSquaresProblem lsp = builder(dataset).build();

        final double[] expected = dataset.getParametersStandardDeviations();

        final Evaluation evaluation = lsp.evaluate(lsp.getStart());
        final double cost = evaluation.getCost();
        final RealVector sig = evaluation.getSigma(1e-14);
        final int dof = lsp.getObservationSize() - lsp.getParameterSize();
        for (int i = 0; i < sig.getDimension(); i++) {
            final double actual = FastMath.sqrt(cost * cost / dof) * sig.getEntry(i);
            Assert.assertEquals(dataset.getName() + ", parameter #" + i,
                                expected[i], actual, 1e-6 * expected[i]);
        }
    }

    @Test
    public void testEvaluateCopiesPoint() throws IOException {
        //setup
        StatisticalReferenceDataset dataset
                = StatisticalReferenceDatasetFactory.createKirby2();
        LeastSquaresProblem lsp = builder(dataset).build();
        RealVector point = new ArrayRealVector(lsp.getParameterSize());

        //action
        Evaluation evaluation = lsp.evaluate(point);

        //verify
        Assert.assertNotSame(point, evaluation.getPoint());
        point.setEntry(0, 1);
        Assert.assertEquals(evaluation.getPoint().getEntry(0), 0, 0);
    }

    @Test
    public void testLazyEvaluation() {
        final RealVector dummy = new ArrayRealVector(new double[] { 0 });

        final LeastSquaresProblem p
            = LeastSquaresFactory.create(LeastSquaresFactory.model(dummyModel(), dummyJacobian()),
                                         dummy, dummy, null, null, 0, 0, true, null);

        // Should not throw because actual evaluation is deferred.
        final Evaluation eval = p.evaluate(dummy);

        try {
            eval.getResiduals();
            Assert.fail("Exception expected");
        } catch (RuntimeException e) {
            // Expecting exception.
            Assert.assertEquals("dummyModel", e.getMessage());
        }

        try {
            eval.getJacobian();
            Assert.fail("Exception expected");
        } catch (RuntimeException e) {
            // Expecting exception.
            Assert.assertEquals("dummyJacobian", e.getMessage());
        }
    }

    // MATH-1151
    @Test
    public void testLazyEvaluationPrecondition() {
        final RealVector dummy = new ArrayRealVector(new double[] { 0 });

        // "ValueAndJacobianFunction" is required but we implement only
        // "MultivariateJacobianFunction".
        final MultivariateJacobianFunction m1 = new MultivariateJacobianFunction() {
                public Pair<RealVector, RealMatrix> value(RealVector notUsed) {
                    return new Pair<RealVector, RealMatrix>(null, null);
                }
            };

        try {
            // Should throw.
            LeastSquaresFactory.create(m1, dummy, dummy, null, null, 0, 0, true, null);
            Assert.fail("Expecting MathIllegalStateException");
        } catch (MathIllegalStateException e) {
            // Expected.
        }

        final MultivariateJacobianFunction m2 = new ValueAndJacobianFunction() {
                public Pair<RealVector, RealMatrix> value(RealVector notUsed) {
                    return new Pair<RealVector, RealMatrix>(null, null);
                }
                public RealVector computeValue(final double[] params) {
                    return null;
                }
                public RealMatrix computeJacobian(final double[] params) {
                    return null;
                }
            };

        // Should pass.
        LeastSquaresFactory.create(m2, dummy, dummy, null, null, 0, 0, true, null);
    }

    @Test
    public void testDirectEvaluation() {
        final RealVector dummy = new ArrayRealVector(new double[] { 0 });

        final LeastSquaresProblem p
            = LeastSquaresFactory.create(LeastSquaresFactory.model(dummyModel(), dummyJacobian()),
                                         dummy, dummy, null, null, 0, 0, false, null);

        try {
            // Should throw.
            p.evaluate(dummy);
            Assert.fail("Exception expected");
        } catch (RuntimeException e) {
            // Expecting exception.
            // Whether it is model or Jacobian that caused it is not significant.
            final String msg = e.getMessage();
            Assert.assertTrue(msg.equals("dummyModel") ||
                              msg.equals("dummyJacobian"));
        }
    }

    /** Used for testing direct vs lazy evaluation. */
    private MultivariateVectorFunction dummyModel() {
        return new MultivariateVectorFunction() {
            public double[] value(double[] p) {
                throw new RuntimeException("dummyModel");
            }
        };
    }

    /** Used for testing direct vs lazy evaluation. */
    private MultivariateMatrixFunction dummyJacobian() {
        return new MultivariateMatrixFunction() {
            public double[][] value(double[] p) {
                throw new RuntimeException("dummyJacobian");
            }
        };
    }
}

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