/*
    * 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 UniformRealDistribution.
   */
  public class UniformRealDistributionTest extends RealDistributionAbstractTest {
  
      // --- Override tolerance -------------------------------------------------
  
      @Override
      public void setUp() {
          super.setUp();
          setTolerance(1e-4);
      }
  
      //--- Implementations for abstract methods --------------------------------
  
      /** Creates the default uniform real distribution instance to use in tests. */
      @Override
      public UniformRealDistribution makeDistribution() {
          return new UniformRealDistribution(-0.5, 1.25);
      }
  
      /** Creates the default cumulative probability distribution test input values */
      @Override
      public double[] makeCumulativeTestPoints() {
          return new double[] {-0.5001, -0.5, -0.4999, -0.25, -0.0001, 0.0,
                               0.0001, 0.25, 1.0, 1.2499, 1.25, 1.2501};
      }
  
      /** Creates the default cumulative probability density test expected values */
      @Override
      public double[] makeCumulativeTestValues() {
          return new double[] {0.0, 0.0, 0.0001, 0.25/1.75, 0.4999/1.75,
                               0.5/1.75, 0.5001/1.75, 0.75/1.75, 1.5/1.75,
                               1.7499/1.75, 1.0, 1.0};
      }
  
      /** Creates the default probability density test expected values */
      @Override
      public double[] makeDensityTestValues() {
          double d = 1 / 1.75;
          return new double[] {0, d, d, d, d, d, d, d, d, d, d, 0};
      }
  
      //--- Additional test cases -----------------------------------------------
  
      /** Test lower bound getter. */
      @Test
      public void testGetLowerBound() {
          UniformRealDistribution distribution = makeDistribution();
          Assert.assertEquals(-0.5, distribution.getSupportLowerBound(), 0);
      }
  
      /** Test upper bound getter. */
      @Test
      public void testGetUpperBound() {
          UniformRealDistribution distribution = makeDistribution();
          Assert.assertEquals(1.25, distribution.getSupportUpperBound(), 0);
      }
  
      /** Test pre-condition for equal lower/upper bound. */
      @Test(expected=MathIllegalArgumentException.class)
      public void testPreconditions1() {
          new UniformRealDistribution(0, 0);
      }
  
      /** Test pre-condition for lower bound larger than upper bound. */
      @Test(expected=MathIllegalArgumentException.class)
      public void testPreconditions2() {
          new UniformRealDistribution(1, 0);
      }
  
     /** Test mean/variance. */
     @Test
     public void testMeanVariance() {
         UniformRealDistribution dist;
 
         dist = new UniformRealDistribution(0, 1);
         Assert.assertEquals(dist.getNumericalMean(), 0.5, 0);
         Assert.assertEquals(dist.getNumericalVariance(), 1/12.0, 0);
 
         dist = new UniformRealDistribution(-1.5, 0.6);
         Assert.assertEquals(dist.getNumericalMean(), -0.45, 0);
         Assert.assertEquals(dist.getNumericalVariance(), 0.3675, 0);
 
         dist = new UniformRealDistribution(-0.5, 1.25);
         Assert.assertEquals(dist.getNumericalMean(), 0.375, 0);
         Assert.assertEquals(dist.getNumericalVariance(), 0.2552083333333333, 0);
     }
 
     /**
      * Check accuracy of analytical inverse CDF. Fails if a solver is used
      * with the default accuracy.
      */
     @Test
     public void testInverseCumulativeDistribution() {
         UniformRealDistribution dist = new UniformRealDistribution(0, 1e-9);
 
         Assert.assertEquals(2.5e-10, dist.inverseCumulativeProbability(0.25), 0);
     }
 }