/*
* 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);
}
}