/*
* 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.fitting;
import org.apache.commons.math4.exception.MathIllegalArgumentException;
import org.apache.commons.math4.exception.TooManyIterationsException;
import org.apache.commons.math4.fitting.GaussianCurveFitter;
import org.apache.commons.math4.fitting.WeightedObservedPoints;
import org.junit.Assert;
import org.junit.Test;
/**
* Tests {@link GaussianCurveFitter}.
*
*/
public class GaussianCurveFitterTest {
/** Good data. */
protected static final double[][] DATASET1 = new double[][] {
{4.0254623, 531026.0},
{4.02804905, 664002.0},
{4.02934242, 787079.0},
{4.03128248, 984167.0},
{4.03386923, 1294546.0},
{4.03580929, 1560230.0},
{4.03839603, 1887233.0},
{4.0396894, 2113240.0},
{4.04162946, 2375211.0},
{4.04421621, 2687152.0},
{4.04550958, 2862644.0},
{4.04744964, 3078898.0},
{4.05003639, 3327238.0},
{4.05132976, 3461228.0},
{4.05326982, 3580526.0},
{4.05585657, 3576946.0},
{4.05779662, 3439750.0},
{4.06038337, 3220296.0},
{4.06167674, 3070073.0},
{4.0636168, 2877648.0},
{4.06620355, 2595848.0},
{4.06749692, 2390157.0},
{4.06943698, 2175960.0},
{4.07202373, 1895104.0},
{4.0733171, 1687576.0},
{4.07525716, 1447024.0},
{4.0778439, 1130879.0},
{4.07978396, 904900.0},
{4.08237071, 717104.0},
{4.08366408, 620014.0}
};
/** Poor data: right of peak not symmetric with left of peak. */
protected static final double[][] DATASET2 = new double[][] {
{-20.15, 1523.0},
{-19.65, 1566.0},
{-19.15, 1592.0},
{-18.65, 1927.0},
{-18.15, 3089.0},
{-17.65, 6068.0},
{-17.15, 14239.0},
{-16.65, 34124.0},
{-16.15, 64097.0},
{-15.65, 110352.0},
{-15.15, 164742.0},
{-14.65, 209499.0},
{-14.15, 267274.0},
{-13.65, 283290.0},
{-13.15, 275363.0},
{-12.65, 258014.0},
{-12.15, 225000.0},
{-11.65, 200000.0},
{-11.15, 190000.0},
{-10.65, 185000.0},
{-10.15, 180000.0},
{ -9.65, 179000.0},
{ -9.15, 178000.0},
{ -8.65, 177000.0},
{ -8.15, 176000.0},
{ -7.65, 175000.0},
{ -7.15, 174000.0},
{ -6.65, 173000.0},
{ -6.15, 172000.0},
{ -5.65, 171000.0},
{ -5.15, 170000.0}
};
/** Poor data: long tails. */
protected static final double[][] DATASET3 = new double[][] {
{-90.15, 1513.0},
{-80.15, 1514.0},
{-70.15, 1513.0},
{-60.15, 1514.0},
{-50.15, 1513.0},
{-40.15, 1514.0},
{-30.15, 1513.0},
{-20.15, 1523.0},
{-19.65, 1566.0},
{-19.15, 1592.0},
{-18.65, 1927.0},
{-18.15, 3089.0},
{-17.65, 6068.0},
{-17.15, 14239.0},
{-16.65, 34124.0},
{-16.15, 64097.0},
{-15.65, 110352.0},
{-15.15, 164742.0},
{-14.65, 209499.0},
{-14.15, 267274.0},
{-13.65, 283290.0},
{-13.15, 275363.0},
{-12.65, 258014.0},
{-12.15, 214073.0},
{-11.65, 182244.0},
{-11.15, 136419.0},
{-10.65, 97823.0},
{-10.15, 58930.0},
{ -9.65, 35404.0},
{ -9.15, 16120.0},
{ -8.65, 9823.0},
{ -8.15, 5064.0},
{ -7.65, 2575.0},
{ -7.15, 1642.0},
{ -6.65, 1101.0},
{ -6.15, 812.0},
{ -5.65, 690.0},
{ -5.15, 565.0},
{ 5.15, 564.0},
{ 15.15, 565.0},
{ 25.15, 564.0},
{ 35.15, 565.0},
{ 45.15, 564.0},
{ 55.15, 565.0},
{ 65.15, 564.0},
{ 75.15, 565.0}
};
/** Poor data: right of peak is missing. */
protected static final double[][] DATASET4 = new double[][] {
{-20.15, 1523.0},
{-19.65, 1566.0},
{-19.15, 1592.0},
{-18.65, 1927.0},
{-18.15, 3089.0},
{-17.65, 6068.0},
{-17.15, 14239.0},
{-16.65, 34124.0},
{-16.15, 64097.0},
{-15.65, 110352.0},
{-15.15, 164742.0},
{-14.65, 209499.0},
{-14.15, 267274.0},
{-13.65, 283290.0}
};
/** Good data, but few points. */
protected static final double[][] DATASET5 = new double[][] {
{4.0254623, 531026.0},
{4.03128248, 984167.0},
{4.03839603, 1887233.0},
{4.04421621, 2687152.0},
{4.05132976, 3461228.0},
{4.05326982, 3580526.0},
{4.05779662, 3439750.0},
{4.0636168, 2877648.0},
{4.06943698, 2175960.0},
{4.07525716, 1447024.0},
{4.08237071, 717104.0},
{4.08366408, 620014.0}
};
/**
* Basic.
*/
@Test
public void testFit01() {
GaussianCurveFitter fitter = GaussianCurveFitter.create();
double[] parameters = fitter.fit(createDataset(DATASET1).toList());
Assert.assertEquals(3496978.1837704973, parameters[0], 1e-4);
Assert.assertEquals(4.054933085999146, parameters[1], 1e-4);
Assert.assertEquals(0.015039355620304326, parameters[2], 1e-4);
}
@Test
public void testWithMaxIterations1() {
final int maxIter = 20;
final double[] init = { 3.5e6, 4.2, 0.1 };
GaussianCurveFitter fitter = GaussianCurveFitter.create();
double[] parameters = fitter
.withMaxIterations(maxIter)
.withStartPoint(init)
.fit(createDataset(DATASET1).toList());
Assert.assertEquals(3496978.1837704973, parameters[0], 1e-2);
Assert.assertEquals(4.054933085999146, parameters[1], 1e-4);
Assert.assertEquals(0.015039355620304326, parameters[2], 1e-4);
}
@Test(expected=TooManyIterationsException.class)
public void testWithMaxIterations2() {
final int maxIter = 1; // Too few iterations.
final double[] init = { 3.5e6, 4.2, 0.1 };
GaussianCurveFitter fitter = GaussianCurveFitter.create();
fitter.withMaxIterations(maxIter)
.withStartPoint(init)
.fit(createDataset(DATASET1).toList());
}
@Test
public void testWithStartPoint() {
final double[] init = { 3.5e6, 4.2, 0.1 };
GaussianCurveFitter fitter = GaussianCurveFitter.create();
double[] parameters = fitter
.withStartPoint(init)
.fit(createDataset(DATASET1).toList());
Assert.assertEquals(3496978.1837704973, parameters[0], 1e-2);
Assert.assertEquals(4.054933085999146, parameters[1], 1e-4);
Assert.assertEquals(0.015039355620304326, parameters[2], 1e-4);
}
/**
* Zero points is not enough observed points.
*/
@Test(expected=MathIllegalArgumentException.class)
public void testFit02() {
GaussianCurveFitter.create().fit(new WeightedObservedPoints().toList());
}
/**
* Two points is not enough observed points.
*/
@Test(expected=MathIllegalArgumentException.class)
public void testFit03() {
GaussianCurveFitter fitter = GaussianCurveFitter.create();
fitter.fit(createDataset(new double[][] {
{4.0254623, 531026.0},
{4.02804905, 664002.0}
}).toList());
}
/**
* Poor data: right of peak not symmetric with left of peak.
*/
@Test
public void testFit04() {
GaussianCurveFitter fitter = GaussianCurveFitter.create();
double[] parameters = fitter.fit(createDataset(DATASET2).toList());
Assert.assertEquals(233003.2967252038, parameters[0], 1e-4);
Assert.assertEquals(-10.654887521095983, parameters[1], 1e-4);
Assert.assertEquals(4.335937353196641, parameters[2], 1e-4);
}
/**
* Poor data: long tails.
*/
@Test
public void testFit05() {
GaussianCurveFitter fitter = GaussianCurveFitter.create();
double[] parameters = fitter.fit(createDataset(DATASET3).toList());
Assert.assertEquals(283863.81929180305, parameters[0], 1e-4);
Assert.assertEquals(-13.29641995105174, parameters[1], 1e-4);
Assert.assertEquals(1.7297330293549908, parameters[2], 1e-4);
}
/**
* Poor data: right of peak is missing.
*/
@Test
public void testFit06() {
GaussianCurveFitter fitter = GaussianCurveFitter.create();
double[] parameters = fitter.fit(createDataset(DATASET4).toList());
Assert.assertEquals(285250.66754309234, parameters[0], 1e-4);
Assert.assertEquals(-13.528375695228455, parameters[1], 1e-4);
Assert.assertEquals(1.5204344894331614, parameters[2], 1e-4);
}
/**
* Basic with smaller dataset.
*/
@Test
public void testFit07() {
GaussianCurveFitter fitter = GaussianCurveFitter.create();
double[] parameters = fitter.fit(createDataset(DATASET5).toList());
Assert.assertEquals(3514384.729342235, parameters[0], 1e-4);
Assert.assertEquals(4.054970307455625, parameters[1], 1e-4);
Assert.assertEquals(0.015029412832160017, parameters[2], 1e-4);
}
@Test
public void testMath519() {
// The optimizer will try negative sigma values but "GaussianCurveFitter"
// will catch the raised exceptions and return NaN values instead.
final double[] data = {
1.1143831578403364E-29,
4.95281403484594E-28,
1.1171347211930288E-26,
1.7044813962636277E-25,
1.9784716574832164E-24,
1.8630236407866774E-23,
1.4820532905097742E-22,
1.0241963854632831E-21,
6.275077366673128E-21,
3.461808994532493E-20,
1.7407124684715706E-19,
8.056687953553974E-19,
3.460193945992071E-18,
1.3883326374011525E-17,
5.233894983671116E-17,
1.8630791465263745E-16,
6.288759227922111E-16,
2.0204433920597856E-15,
6.198768938576155E-15,
1.821419346860626E-14,
5.139176445538471E-14,
1.3956427429045787E-13,
3.655705706448139E-13,
9.253753324779779E-13,
2.267636001476696E-12,
5.3880460095836855E-12,
1.2431632654852931E-11
};
final WeightedObservedPoints obs = new WeightedObservedPoints();
for (int i = 0; i < data.length; i++) {
obs.add(i, data[i]);
}
final double[] p = GaussianCurveFitter.create().fit(obs.toList());
Assert.assertEquals(53.1572792, p[1], 1e-7);
Assert.assertEquals(5.75214622, p[2], 1e-8);
}
@Test
public void testMath798() {
// When the data points are not commented out below, the fit stalls.
// This is expected however, since the whole dataset hardly looks like
// a Gaussian.
// When commented out, the fit proceeds fine.
final WeightedObservedPoints obs = new WeightedObservedPoints();
obs.add(0.23, 395.0);
//obs.add(0.68, 0.0);
obs.add(1.14, 376.0);
//obs.add(1.59, 0.0);
obs.add(2.05, 163.0);
//obs.add(2.50, 0.0);
obs.add(2.95, 49.0);
//obs.add(3.41, 0.0);
obs.add(3.86, 16.0);
//obs.add(4.32, 0.0);
obs.add(4.77, 1.0);
final double[] p = GaussianCurveFitter.create().fit(obs.toList());
// Values are copied from a previous run of this test.
Assert.assertEquals(420.8397296167364, p[0], 1e-12);
Assert.assertEquals(0.603770729862231, p[1], 1e-15);
Assert.assertEquals(1.0786447936766612, p[2], 1e-14);
}
/**
* Adds the specified points to specified <code>GaussianCurveFitter</code>
* instance.
*
* @param points Data points where first dimension is a point index and
* second dimension is an array of length two representing the point
* with the first value corresponding to X and the second value
* corresponding to Y.
* @return the collection of observed points.
*/
private static WeightedObservedPoints createDataset(double[][] points) {
final WeightedObservedPoints obs = new WeightedObservedPoints();
for (int i = 0; i < points.length; i++) {
obs.add(points[i][0], points[i][1]);
}
return obs;
}
}