/*
    * 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.
   */
  package org.hipparchus.distribution.discrete;
  
  import org.hipparchus.distribution.IntegerDistribution;
  import org.junit.jupiter.api.Test;
  
  import static org.junit.jupiter.api.Assertions.assertEquals;
  
  /**
   * Test cases for BinomialDistribution.
   */
  public class BinomialDistributionTest extends IntegerDistributionAbstractTest {
  
      /**
       * Constructor to override default tolerance.
       */
      public BinomialDistributionTest() {
          setTolerance(1e-12);
      }
  
      // -------------- Implementations for abstract methods
      // -----------------------
  
      /** Creates the default discrete distribution instance to use in tests. */
      @Override
      public IntegerDistribution makeDistribution() {
          return new BinomialDistribution(10, 0.70);
      }
  
      /** Creates the default probability density test input values. */
      @Override
      public int[] makeDensityTestPoints() {
          return new int[] { -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 };
      }
  
      /**
       * Creates the default probability density test expected values.
       * Reference values are from R, version 2.15.3.
       */
      @Override
      public double[] makeDensityTestValues() {
          return new double[] { 0d, 0.0000059049d, 0.000137781d, 0.0014467005,
              0.009001692, 0.036756909, 0.1029193452, 0.200120949, 0.266827932,
              0.2334744405, 0.121060821, 0.0282475249, 0d };
      }
  
      /** Creates the default cumulative probability density test input values */
      @Override
      public int[] makeCumulativeTestPoints() {
          return makeDensityTestPoints();
      }
  
      /**
       * Creates the default cumulative probability density test expected values.
       * Reference values are from R, version 2.15.3.
       */
      @Override
      public double[] makeCumulativeTestValues() {
          return new double[] { 0d, 5.9049e-06, 0.0001436859, 0.0015903864, 0.0105920784,  0.0473489874,
              0.1502683326, 0.3503892816, 0.6172172136, 0.8506916541, 0.9717524751, 1d, 1d };
      }
  
      /** Creates the default inverse cumulative probability test input values */
      @Override
      public double[] makeInverseCumulativeTestPoints() {
          return new double[] { 0, 0.001d, 0.010d, 0.025d, 0.050d, 0.100d,
                  0.999d, 0.990d, 0.975d, 0.950d, 0.900d, 1 };
      }
  
      /**
       * Creates the default inverse cumulative probability density test expected
       * values
       */
      @Override
      public int[] makeInverseCumulativeTestValues() {
          return new int[] { 0, 2, 3, 4, 5, 5, 10, 10, 10, 9, 9, 10 };
      }
  
      // ----------------- Additional test cases ---------------------------------
  
      /** Test degenerate case p = 0 */
      @Test
      void testDegenerate0() {
          BinomialDistribution dist = new BinomialDistribution(5, 0.0d);
          setDistribution(dist);
          setCumulativeTestPoints(new int[] { -1, 0, 1, 5, 10 });
          setCumulativeTestValues(new double[] { 0d, 1d, 1d, 1d, 1d });
         setDensityTestPoints(new int[] { -1, 0, 1, 10, 11 });
         setDensityTestValues(new double[] { 0d, 1d, 0d, 0d, 0d });
         setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d });
         setInverseCumulativeTestValues(new int[] { 0, 0 });
         verifyDensities();
         verifyCumulativeProbabilities();
         verifyInverseCumulativeProbabilities();
         assertEquals(0, dist.getSupportLowerBound());
         assertEquals(0, dist.getSupportUpperBound());
     }
 
     /** Test degenerate case p = 1 */
     @Test
     void testDegenerate1() {
         BinomialDistribution dist = new BinomialDistribution(5, 1.0d);
         setDistribution(dist);
         setCumulativeTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
         setCumulativeTestValues(new double[] { 0d, 0d, 0d, 0d, 1d, 1d });
         setDensityTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
         setDensityTestValues(new double[] { 0d, 0d, 0d, 0d, 1d, 0d });
         setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d });
         setInverseCumulativeTestValues(new int[] { 5, 5 });
         verifyDensities();
         verifyCumulativeProbabilities();
         verifyInverseCumulativeProbabilities();
         assertEquals(5, dist.getSupportLowerBound());
         assertEquals(5, dist.getSupportUpperBound());
     }
 
     /** Test degenerate case n = 0 */
     @Test
     void testDegenerate2() {
         BinomialDistribution dist = new BinomialDistribution(0, 0.01d);
         setDistribution(dist);
         setCumulativeTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
         setCumulativeTestValues(new double[] { 0d, 1d, 1d, 1d, 1d, 1d });
         setDensityTestPoints(new int[] { -1, 0, 1, 2, 5, 10 });
         setDensityTestValues(new double[] { 0d, 1d, 0d, 0d, 0d, 0d });
         setInverseCumulativeTestPoints(new double[] { 0.1d, 0.5d });
         setInverseCumulativeTestValues(new int[] { 0, 0 });
         verifyDensities();
         verifyCumulativeProbabilities();
         verifyInverseCumulativeProbabilities();
         assertEquals(0, dist.getSupportLowerBound());
         assertEquals(0, dist.getSupportUpperBound());
     }
 
     @Test
     void testMoments() {
         final double tol = 1e-9;
         BinomialDistribution dist;
 
         dist = new BinomialDistribution(10, 0.5);
         assertEquals(dist.getNumericalMean(), 10d * 0.5d, tol);
         assertEquals(dist.getNumericalVariance(), 10d * 0.5d * 0.5d, tol);
 
         dist = new BinomialDistribution(30, 0.3);
         assertEquals(dist.getNumericalMean(), 30d * 0.3d, tol);
         assertEquals(dist.getNumericalVariance(), 30d * 0.3d * (1d - 0.3d), tol);
     }
 
     @Test
     void testMath718() {
         // for large trials the evaluation of ContinuedFraction was inaccurate
         // do a sweep over several large trials to test if the current implementation is
         // numerically stable.
 
         for (int trials = 500000; trials < 20000000; trials += 100000) {
             BinomialDistribution dist = new BinomialDistribution(trials, 0.5);
             int p = dist.inverseCumulativeProbability(0.5);
             assertEquals(trials / 2, p);
         }
     }
 }