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    * Licensed to the Apache Software Foundation (ASF) under one or more
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    * 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.legacy.fitting;
  
  import java.util.Random;
  
  import org.apache.commons.math4.legacy.TestUtils;
  import org.apache.commons.math4.legacy.analysis.ParametricUnivariateFunction;
  import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialFunction;
  import org.apache.commons.statistics.distribution.ContinuousDistribution;
  import org.apache.commons.statistics.distribution.UniformContinuousDistribution;
  import org.apache.commons.rng.simple.RandomSource;
  import org.junit.Test;
  
  /**
   * Test for class {@link SimpleCurveFitter}.
   */
  public class SimpleCurveFitterTest {
      @Test
      public void testPolynomialFit() {
          final Random randomizer = new Random(53882150042L);
          final ContinuousDistribution.Sampler rng
              = UniformContinuousDistribution.of(-100, 100).createSampler(RandomSource.WELL_512_A.create(64925784253L));
  
          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) + 0.1 * randomizer.nextGaussian());
          }
  
          final ParametricUnivariateFunction function = new PolynomialFunction.Parametric();
          // Start fit from initial guesses that are far from the optimal values.
          final SimpleCurveFitter fitter
              = SimpleCurveFitter.create(function,
                                         new double[] { -1e20, 3e15, -5e25 });
          final double[] best = fitter.fit(obs.toList());
  
          TestUtils.assertEquals("best != coeff", coeff, best, 2e-2);
      }
  }