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Java example source code file (PolynomialCurveFitterTest.java)

This example Java source code file (PolynomialCurveFitterTest.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

polynomialcurvefitter, polynomialcurvefittertest, polynomialfunction, random, realdistribution, test, uniformrealdistribution, util, weightedobservedpoints

The PolynomialCurveFitterTest.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;

import java.util.Random;

import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.analysis.polynomials.PolynomialFunction;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.distribution.UniformRealDistribution;
import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;

/**
 * Test for class {@link PolynomialCurveFitter}.
 */
public class PolynomialCurveFitterTest {
    @Test
    public void testFit() {
        final RealDistribution rng = new UniformRealDistribution(-100, 100);
        rng.reseedRandomGenerator(64925784252L);

        final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
        final PolynomialFunction f = new PolynomialFunction(coeff);

        // Collect data from a known polynomial.
        final WeightedObservedPoints obs = new WeightedObservedPoints();
        for (int i = 0; i < 100; i++) {
            final double x = rng.sample();
            obs.add(x, f.value(x));
        }

        // Start fit from initial guesses that are far from the optimal values.
        final PolynomialCurveFitter fitter
            = PolynomialCurveFitter.create(0).withStartPoint(new double[] { -1e-20, 3e15, -5e25 });
        final double[] best = fitter.fit(obs.toList());

        TestUtils.assertEquals("best != coeff", coeff, best, 1e-12);
    }

    @Test
    public void testNoError() {
        final Random randomizer = new Random(64925784252l);
        for (int degree = 1; degree < 10; ++degree) {
            final PolynomialFunction p = buildRandomPolynomial(degree, randomizer);
            final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(degree);

            final WeightedObservedPoints obs = new WeightedObservedPoints();
            for (int i = 0; i <= degree; ++i) {
                obs.add(1.0, i, p.value(i));
            }

            final PolynomialFunction fitted = new PolynomialFunction(fitter.fit(obs.toList()));

            for (double x = -1.0; x < 1.0; x += 0.01) {
                final double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                    (1.0 + FastMath.abs(p.value(x)));
                Assert.assertEquals(0.0, error, 1.0e-6);
            }
        }
    }

    @Test
    public void testSmallError() {
        final Random randomizer = new Random(53882150042l);
        double maxError = 0;
        for (int degree = 0; degree < 10; ++degree) {
            final PolynomialFunction p = buildRandomPolynomial(degree, randomizer);
            final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(degree);

            final WeightedObservedPoints obs = new WeightedObservedPoints();
            for (double x = -1.0; x < 1.0; x += 0.01) {
                obs.add(1.0, x, p.value(x) + 0.1 * randomizer.nextGaussian());
            }

            final PolynomialFunction fitted = new PolynomialFunction(fitter.fit(obs.toList()));

            for (double x = -1.0; x < 1.0; x += 0.01) {
                final double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                    (1.0 + FastMath.abs(p.value(x)));
                maxError = FastMath.max(maxError, error);
                Assert.assertTrue(FastMath.abs(error) < 0.1);
            }
        }
        Assert.assertTrue(maxError > 0.01);
    }

    @Test
    public void testRedundantSolvable() {
        // Levenberg-Marquardt should handle redundant information gracefully
        checkUnsolvableProblem(true);
    }

    @Test
    public void testLargeSample() {
        final Random randomizer = new Random(0x5551480dca5b369bl);
        double maxError = 0;
        for (int degree = 0; degree < 10; ++degree) {
            final PolynomialFunction p = buildRandomPolynomial(degree, randomizer);
            final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(degree);

            final WeightedObservedPoints obs = new WeightedObservedPoints();
            for (int i = 0; i < 40000; ++i) {
                final double x = -1.0 + i / 20000.0;
                obs.add(1.0, x, p.value(x) + 0.1 * randomizer.nextGaussian());
            }

            final PolynomialFunction fitted = new PolynomialFunction(fitter.fit(obs.toList()));
            for (double x = -1.0; x < 1.0; x += 0.01) {
                final double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                    (1.0 + FastMath.abs(p.value(x)));
                maxError = FastMath.max(maxError, error);
                Assert.assertTrue(FastMath.abs(error) < 0.01);
            }
        }
        Assert.assertTrue(maxError > 0.001);
    }

    private void checkUnsolvableProblem(boolean solvable) {
        final Random randomizer = new Random(1248788532l);

        for (int degree = 0; degree < 10; ++degree) {
            final PolynomialFunction p = buildRandomPolynomial(degree, randomizer);
            final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(degree);
            final WeightedObservedPoints obs = new WeightedObservedPoints();
            // reusing the same point over and over again does not bring
            // information, the problem cannot be solved in this case for
            // degrees greater than 1 (but one point is sufficient for
            // degree 0)
            for (double x = -1.0; x < 1.0; x += 0.01) {
                obs.add(1.0, 0.0, p.value(0.0));
            }

            try {
                fitter.fit(obs.toList());
                Assert.assertTrue(solvable || (degree == 0));
            } catch(ConvergenceException e) {
                Assert.assertTrue((! solvable) && (degree > 0));
            }
        }
    }

    private PolynomialFunction buildRandomPolynomial(int degree, Random randomizer) {
        final double[] coefficients = new double[degree + 1];
        for (int i = 0; i <= degree; ++i) {
            coefficients[i] = randomizer.nextGaussian();
        }
        return new PolynomialFunction(coefficients);
    }
}

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