/*
    * 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
    *
    *      https://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.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  
  package org.hipparchus.distribution.continuous;
  
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.special.Gamma;
  import org.hipparchus.util.FastMath;
  import org.junit.Assert;
  import org.junit.Test;
  
  /**
   * Test cases for WeibullDistribution.
   */
  public class WeibullDistributionTest extends RealDistributionAbstractTest {
  
      //-------------- Implementations for abstract methods -----------------------
  
      /** Creates the default continuous distribution instance to use in tests. */
      @Override
      public WeibullDistribution makeDistribution() {
          return new WeibullDistribution(1.2, 2.1);
      }
  
      /** Creates the default cumulative probability distribution test input values */
      @Override
      public double[] makeCumulativeTestPoints() {
          // quantiles computed using R version 2.9.2
          return new double[] {0.00664355180993, 0.0454328283309, 0.0981162737374, 0.176713524579, 0.321946865392,
                  10.5115496887, 7.4976304671, 6.23205600701, 5.23968436955, 4.2079028257};
      }
  
      /** Creates the default cumulative probability density test expected values */
      @Override
      public double[] makeCumulativeTestValues() {
          return new double[] {0.001, 0.01, 0.025, 0.05, 0.1, 0.999, 0.990, 0.975, 0.950, 0.900};
      }
  
      /** Creates the default probability density test expected values */
      @Override
      public double[] makeDensityTestValues() {
          return new double[] {0.180535929306, 0.262801138133, 0.301905425199, 0.330899152971,
            0.353441418887, 0.000788590320203, 0.00737060094841, 0.0177576041516, 0.0343043442574, 0.065664589369};
      }
  
      //---------------------------- Additional test cases -------------------------
  
      @Test
      public void testInverseCumulativeProbabilitySmallPAccuracy() {
          WeibullDistribution dist = new WeibullDistribution(2, 3);
          double t = dist.inverseCumulativeProbability(1e-17);
          // Analytically, answer is solution to 1e-17 = 1-exp(-(x/3)^2)
          // x = sqrt(-9*log(1-1e-17))
          // If we're not careful, answer will be 0. Answer below is computed with care in Octave:
          Assert.assertEquals(9.48683298050514e-9, t, 1e-17);
      }
  
      @Test
      public void testInverseCumulativeProbabilityExtremes() {
          setInverseCumulativeTestPoints(new double[] {0.0, 1.0});
          setInverseCumulativeTestValues(
                  new double[] {0.0, Double.POSITIVE_INFINITY});
          verifyInverseCumulativeProbabilities();
      }
  
      @Test
      public void testAlpha() {
          WeibullDistribution dist = new WeibullDistribution(1, 2);
          Assert.assertEquals(1, dist.getShape(), 0);
          try {
              new WeibullDistribution(0, 2);
              Assert.fail("MathIllegalArgumentException expected");
          } catch (MathIllegalArgumentException e) {
              // Expected.
          }
      }
  
      @Test
      public void testBeta() {
          WeibullDistribution dist = new WeibullDistribution(1, 2);
         Assert.assertEquals(2, dist.getScale(), 0);
         try {
             new WeibullDistribution(1, 0);
             Assert.fail("MathIllegalArgumentException expected");
         } catch (MathIllegalArgumentException e) {
             // Expected.
         }
     }
 
     @Test
     public void testMoments() {
         final double tol = 1e-9;
         WeibullDistribution dist;
 
         dist = new WeibullDistribution(2.5, 3.5);
         // In R: 3.5*gamma(1+(1/2.5)) (or emperically: mean(rweibull(10000, 2.5, 3.5)))
         Assert.assertEquals(dist.getNumericalMean(), 3.5 * FastMath.exp(Gamma.logGamma(1 + (1 / 2.5))), tol);
         Assert.assertEquals(dist.getNumericalVariance(), (3.5 * 3.5) *
                 FastMath.exp(Gamma.logGamma(1 + (2 / 2.5))) -
                 (dist.getNumericalMean() * dist.getNumericalMean()), tol);
 
         dist = new WeibullDistribution(10.4, 2.222);
         Assert.assertEquals(dist.getNumericalMean(), 2.222 * FastMath.exp(Gamma.logGamma(1 + (1 / 10.4))), tol);
         Assert.assertEquals(dist.getNumericalVariance(), (2.222 * 2.222) *
                 FastMath.exp(Gamma.logGamma(1 + (2 / 10.4))) -
                 (dist.getNumericalMean() * dist.getNumericalMean()), tol);
     }
 }