/*
    * 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.util.FastMath;
  import org.hipparchus.util.Precision;
  import org.junit.Assert;
  import org.junit.Test;
  
  /**
   * Test cases for ExponentialDistribution.
   */
  public class ExponentialDistributionTest extends RealDistributionAbstractTest {
  
      // --------------------- Override tolerance  --------------
      @Override
      public void setUp() {
          super.setUp();
          setTolerance(1E-9);
      }
  
      //-------------- Implementations for abstract methods -----------------------
  
      /** Creates the default continuous distribution instance to use in tests. */
      @Override
      public ExponentialDistribution makeDistribution() {
          return new ExponentialDistribution(5.0);
      }
  
      /** Creates the default cumulative probability distribution test input values */
      @Override
      public double[] makeCumulativeTestPoints() {
          // quantiles computed using R version 2.9.2
          return new double[] {0.00500250166792, 0.0502516792675, 0.126589039921, 0.256466471938,
                  0.526802578289, 34.5387763949, 23.0258509299, 18.4443972706, 14.9786613678, 11.5129254650};
      }
  
      /** 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[] {0.1998, 0.198, 0.195, 0.19, 0.18, 0.000200000000000,
                  0.00200000000002, 0.00499999999997, 0.00999999999994, 0.0199999999999};
      }
  
      //------------ Additional tests -------------------------------------------
  
      @Test
      public void testCumulativeProbabilityExtremes() {
          setCumulativeTestPoints(new double[] {-2, 0});
          setCumulativeTestValues(new double[] {0, 0});
          verifyCumulativeProbabilities();
      }
  
      @Test
      public void testInverseCumulativeProbabilityExtremes() {
           setInverseCumulativeTestPoints(new double[] {0, 1});
           setInverseCumulativeTestValues(new double[] {0, Double.POSITIVE_INFINITY});
           verifyInverseCumulativeProbabilities();
      }
  
      @Test
      public void testCumulativeProbability2() {
          double actual = getDistribution().probability(0.25, 0.75);
          Assert.assertEquals(0.0905214, actual, 10e-4);
      }
  
      @Test
      public void testDensity() {
          ExponentialDistribution d1 = new ExponentialDistribution(1);
          Assert.assertTrue(Precision.equals(0.0, d1.density(-1e-9), 1));
          Assert.assertTrue(Precision.equals(1.0, d1.density(0.0), 1));
          Assert.assertTrue(Precision.equals(0.0, d1.density(1000.0), 1));
         Assert.assertTrue(Precision.equals(FastMath.exp(-1), d1.density(1.0), 1));
         Assert.assertTrue(Precision.equals(FastMath.exp(-2), d1.density(2.0), 1));
 
         ExponentialDistribution d2 = new ExponentialDistribution(3);
         Assert.assertTrue(Precision.equals(1/3.0, d2.density(0.0), 1));
         // computed using  print(dexp(1, rate=1/3), digits=10) in R 2.5
         Assert.assertEquals(0.2388437702, d2.density(1.0), 1e-8);
 
         // computed using  print(dexp(2, rate=1/3), digits=10) in R 2.5
         Assert.assertEquals(0.1711390397, d2.density(2.0), 1e-8);
     }
 
     @Test
     public void testMeanAccessors() {
         ExponentialDistribution distribution = (ExponentialDistribution) getDistribution();
         Assert.assertEquals(5d, distribution.getMean(), Double.MIN_VALUE);
     }
 
     @Test(expected=MathIllegalArgumentException.class)
     public void testPreconditions() {
         new ExponentialDistribution(0);
     }
 
     @Test
     public void testMoments() {
         final double tol = 1e-9;
         ExponentialDistribution dist;
 
         dist = new ExponentialDistribution(11d);
         Assert.assertEquals(dist.getNumericalMean(), 11d, tol);
         Assert.assertEquals(dist.getNumericalVariance(), 11d * 11d, tol);
 
         dist = new ExponentialDistribution(10.5d);
         Assert.assertEquals(dist.getNumericalMean(), 10.5d, tol);
         Assert.assertEquals(dist.getNumericalVariance(), 10.5d * 10.5d, tol);
     }
 }