1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * https://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17
18 /*
19 * This is not the original file distributed by the Apache Software Foundation
20 * It has been modified by the Hipparchus project
21 */
22 package org.hipparchus.fitting;
23
24 import java.util.Random;
25
26 import org.hipparchus.UnitTestUtils;
27 import org.hipparchus.analysis.ParametricUnivariateFunction;
28 import org.hipparchus.analysis.polynomials.PolynomialFunction;
29 import org.hipparchus.random.RandomDataGenerator;
30 import org.junit.Test;
31
32 /**
33 * Test for class {@link SimpleCurveFitter}.
34 */
35 public class SimpleCurveFitterTest {
36 @Test
37 public void testPolynomialFit() {
38 final Random randomizer = new Random(53882150042L);
39 final RandomDataGenerator randomDataGenerator = new RandomDataGenerator(64925784252L);
40
41 final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
42 final PolynomialFunction f = new PolynomialFunction(coeff);
43
44 // Collect data from a known polynomial.
45 final WeightedObservedPoints obs = new WeightedObservedPoints();
46 for (int i = 0; i < 100; i++) {
47 final double x = randomDataGenerator.nextUniform(-100, 100);
48 obs.add(x, f.value(x) + 0.1 * randomizer.nextGaussian());
49 }
50
51 final ParametricUnivariateFunction function = new PolynomialFunction.Parametric();
52 // Start fit from initial guesses that are far from the optimal values.
53 final SimpleCurveFitter fitter
54 = SimpleCurveFitter.create(function,
55 new double[] { -1e20, 3e15, -5e25 });
56 final double[] best = fitter.fit(obs.toList());
57
58 UnitTestUtils.assertEquals("best != coeff", coeff, best, 2e-2);
59 }
60 }