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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    *      http://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  }