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// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
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//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
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// KIND, either express or implied. See the License for the
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package org.apache.commons.math3.optimization.fitting;
import java.util.Random;
import org.apache.commons.math3.analysis.function.HarmonicOscillator;
import org.apache.commons.math3.optimization.general.LevenbergMarquardtOptimizer;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.MathUtils;
import org.junit.Test;
import org.junit.Assert;
@Deprecated
public class HarmonicFitterTest {
@Test(expected=NumberIsTooSmallException.class)
public void testPreconditions1() {
HarmonicFitter fitter =
new HarmonicFitter(new LevenbergMarquardtOptimizer());
fitter.fit();
}
// This test fails (throwing "ConvergenceException" instead).
// @Test(expected=ZeroException.class)
// public void testPreconditions2() {
// HarmonicFitter fitter =
// new HarmonicFitter(new LevenbergMarquardtOptimizer());
// final double x = 1.2;
// fitter.addObservedPoint(1, x, 1);
// fitter.addObservedPoint(1, x, -1);
// fitter.addObservedPoint(1, x, 0.5);
// fitter.addObservedPoint(1, x, 0);
// final double[] fitted = fitter.fit();
// }
@Test
public void testNoError() {
final double a = 0.2;
final double w = 3.4;
final double p = 4.1;
HarmonicOscillator f = new HarmonicOscillator(a, w, p);
HarmonicFitter fitter =
new HarmonicFitter(new LevenbergMarquardtOptimizer());
for (double x = 0.0; x < 1.3; x += 0.01) {
fitter.addObservedPoint(1, x, f.value(x));
}
final double[] fitted = fitter.fit();
Assert.assertEquals(a, fitted[0], 1.0e-13);
Assert.assertEquals(w, fitted[1], 1.0e-13);
Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1e-13);
HarmonicOscillator ff = new HarmonicOscillator(fitted[0], fitted[1], fitted[2]);
for (double x = -1.0; x < 1.0; x += 0.01) {
Assert.assertTrue(FastMath.abs(f.value(x) - ff.value(x)) < 1e-13);
}
}
@Test
public void test1PercentError() {
Random randomizer = new Random(64925784252l);
final double a = 0.2;
final double w = 3.4;
final double p = 4.1;
HarmonicOscillator f = new HarmonicOscillator(a, w, p);
HarmonicFitter fitter =
new HarmonicFitter(new LevenbergMarquardtOptimizer());
for (double x = 0.0; x < 10.0; x += 0.1) {
fitter.addObservedPoint(1, x,
f.value(x) + 0.01 * randomizer.nextGaussian());
}
final double[] fitted = fitter.fit();
Assert.assertEquals(a, fitted[0], 7.6e-4);
Assert.assertEquals(w, fitted[1], 2.7e-3);
Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.3e-2);
}
@Test
public void testTinyVariationsData() {
Random randomizer = new Random(64925784252l);
HarmonicFitter fitter =
new HarmonicFitter(new LevenbergMarquardtOptimizer());
for (double x = 0.0; x < 10.0; x += 0.1) {
fitter.addObservedPoint(1, x, 1e-7 * randomizer.nextGaussian());
}
fitter.fit();
// This test serves to cover the part of the code of "guessAOmega"
// when the algorithm using integrals fails.
}
@Test
public void testInitialGuess() {
Random randomizer = new Random(45314242l);
final double a = 0.2;
final double w = 3.4;
final double p = 4.1;
HarmonicOscillator f = new HarmonicOscillator(a, w, p);
HarmonicFitter fitter =
new HarmonicFitter(new LevenbergMarquardtOptimizer());
for (double x = 0.0; x < 10.0; x += 0.1) {
fitter.addObservedPoint(1, x,
f.value(x) + 0.01 * randomizer.nextGaussian());
}
final double[] fitted = fitter.fit(new double[] { 0.15, 3.6, 4.5 });
Assert.assertEquals(a, fitted[0], 1.2e-3);
Assert.assertEquals(w, fitted[1], 3.3e-3);
Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.7e-2);
}
@Test
public void testUnsorted() {
Random randomizer = new Random(64925784252l);
final double a = 0.2;
final double w = 3.4;
final double p = 4.1;
HarmonicOscillator f = new HarmonicOscillator(a, w, p);
HarmonicFitter fitter =
new HarmonicFitter(new LevenbergMarquardtOptimizer());
// build a regularly spaced array of measurements
int size = 100;
double[] xTab = new double[size];
double[] yTab = new double[size];
for (int i = 0; i < size; ++i) {
xTab[i] = 0.1 * i;
yTab[i] = f.value(xTab[i]) + 0.01 * randomizer.nextGaussian();
}
// shake it
for (int i = 0; i < size; ++i) {
int i1 = randomizer.nextInt(size);
int i2 = randomizer.nextInt(size);
double xTmp = xTab[i1];
double yTmp = yTab[i1];
xTab[i1] = xTab[i2];
yTab[i1] = yTab[i2];
xTab[i2] = xTmp;
yTab[i2] = yTmp;
}
// pass it to the fitter
for (int i = 0; i < size; ++i) {
fitter.addObservedPoint(1, xTab[i], yTab[i]);
}
final double[] fitted = fitter.fit();
Assert.assertEquals(a, fitted[0], 7.6e-4);
Assert.assertEquals(w, fitted[1], 3.5e-3);
Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.5e-2);
}
@Test(expected=MathIllegalStateException.class)
public void testMath844() {
final double[] y = { 0, 1, 2, 3, 2, 1,
0, -1, -2, -3, -2, -1,
0, 1, 2, 3, 2, 1,
0, -1, -2, -3, -2, -1,
0, 1, 2, 3, 2, 1, 0 };
final int len = y.length;
final WeightedObservedPoint[] points = new WeightedObservedPoint[len];
for (int i = 0; i < len; i++) {
points[i] = new WeightedObservedPoint(1, i, y[i]);
}
// The guesser fails because the function is far from an harmonic
// function: It is a triangular periodic function with amplitude 3
// and period 12, and all sample points are taken at integer abscissae
// so function values all belong to the integer subset {-3, -2, -1, 0,
// 1, 2, 3}.
new HarmonicFitter.ParameterGuesser(points);
}
}