/*
    * 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.junit.Assert;
  import org.junit.Test;
  
  /**
   * Test cases for FDistribution.
   */
  public class FDistributionTest extends RealDistributionAbstractTest {
  
      //-------------- Implementations for abstract methods -----------------------
  
      /** Creates the default continuous distribution instance to use in tests. */
      @Override
      public FDistribution makeDistribution() {
          return new FDistribution(5.0, 6.0);
      }
  
      /** Creates the default cumulative probability distribution test input values */
      @Override
      public double[] makeCumulativeTestPoints() {
          // quantiles computed using R version 2.9.2
          return new double[] {0.0346808448626, 0.0937009113303, 0.143313661184, 0.202008445998, 0.293728320107,
                  20.8026639595, 8.74589525602, 5.98756512605, 4.38737418741, 3.10751166664};
      }
  
      /** 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[] {0.0689156576706, 0.236735653193, 0.364074131941, 0.481570789649, 0.595880479994,
                  0.000133443915657, 0.00286681303403, 0.00969192007502, 0.0242883861471, 0.0605491314658};
      }
  
      // --------------------- Override tolerance  --------------
      @Override
      public void setUp() {
          super.setUp();
          setTolerance(1e-9);
      }
  
      //---------------------------- Additional test cases -------------------------
  
      @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 testDfAccessors() {
          FDistribution dist = (FDistribution) getDistribution();
          Assert.assertEquals(5d, dist.getNumeratorDegreesOfFreedom(), Double.MIN_VALUE);
          Assert.assertEquals(6d, dist.getDenominatorDegreesOfFreedom(), Double.MIN_VALUE);
      }
  
      @Test
      public void testPreconditions() {
          try {
              new FDistribution(0, 1);
              Assert.fail("Expecting MathIllegalArgumentException for df = 0");
          } catch (MathIllegalArgumentException ex) {
              // Expected.
          }
         try {
             new FDistribution(1, 0);
             Assert.fail("Expecting MathIllegalArgumentException for df = 0");
         } catch (MathIllegalArgumentException ex) {
             // Expected.
         }
     }
 
     @Test
     public void testLargeDegreesOfFreedom() {
         FDistribution fd = new FDistribution(100000, 100000);
         double p = fd.cumulativeProbability(.999);
         double x = fd.inverseCumulativeProbability(p);
         Assert.assertEquals(.999, x, 1.0e-5);
     }
 
     @Test
     public void testSmallDegreesOfFreedom() {
         FDistribution fd = new FDistribution(1, 1);
         double p = fd.cumulativeProbability(0.975);
         double x = fd.inverseCumulativeProbability(p);
         Assert.assertEquals(0.975, x, 1.0e-5);
 
         fd = new FDistribution(1, 2);
         p = fd.cumulativeProbability(0.975);
         x = fd.inverseCumulativeProbability(p);
         Assert.assertEquals(0.975, x, 1.0e-5);
     }
 
     @Test
     public void testMoments() {
         final double tol = 1e-9;
         FDistribution dist;
 
         dist = new FDistribution(1, 2);
         Assert.assertTrue(Double.isNaN(dist.getNumericalMean()));
         Assert.assertTrue(Double.isNaN(dist.getNumericalVariance()));
 
         dist = new FDistribution(1, 3);
         Assert.assertEquals(dist.getNumericalMean(), 3d / (3d - 2d), tol);
         Assert.assertTrue(Double.isNaN(dist.getNumericalVariance()));
 
         dist = new FDistribution(1, 5);
         Assert.assertEquals(dist.getNumericalMean(), 5d / (5d - 2d), tol);
         Assert.assertEquals(dist.getNumericalVariance(), (2d * 5d * 5d * 4d) / 9d, tol);
     }
 
     @Test
     public void testMath785() {
         // this test was failing due to inaccurate results from ContinuedFraction.
 
         try {
             double prob = 0.01;
             FDistribution f = new FDistribution(200000, 200000);
             double result = f.inverseCumulativeProbability(prob);
             Assert.assertTrue(result < 1.0);
         } catch (Exception e) {
             Assert.fail("Failing to calculate inverse cumulative probability");
         }
     }
 }