/*
    * 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
    *
    *      http://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.apache.commons.math4.legacy.distribution;
  
  import static org.junit.Assert.assertEquals;
  
  import java.util.ArrayList;
  import java.util.List;
  
  import org.apache.commons.statistics.distribution.ContinuousDistribution;
  import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
  import org.apache.commons.math4.legacy.exception.MathArithmeticException;
  import org.apache.commons.math4.legacy.exception.NotANumberException;
  import org.apache.commons.math4.legacy.exception.NotFiniteNumberException;
  import org.apache.commons.math4.legacy.exception.NotPositiveException;
  import org.apache.commons.math4.core.jdkmath.JdkMath;
  import org.apache.commons.math4.legacy.core.Pair;
  import org.apache.commons.rng.UniformRandomProvider;
  import org.apache.commons.rng.simple.RandomSource;
  import org.junit.Assert;
  import org.junit.Test;
  
  /**
   * Test class for {@link EnumeratedRealDistribution}.
   *
   */
  public class EnumeratedRealDistributionTest {
  
      /**
       * The distribution object used for testing.
       */
      private final EnumeratedRealDistribution testDistribution;
  
      /**
       * Creates the default distribution object used for testing.
       */
      public EnumeratedRealDistributionTest() {
          // Non-sorted singleton array with duplicates should be allowed.
          // Values with zero-probability do not extend the support.
          testDistribution = new EnumeratedRealDistribution(
                  new double[]{3.0, -1.0, 3.0, 7.0, -2.0, 8.0},
                  new double[]{0.2, 0.2, 0.3, 0.3, 0.0, 0.0});
      }
  
      /**
       * Tests if the {@link EnumeratedRealDistribution} constructor throws
       * exceptions for invalid data.
       */
      @Test
      public void testExceptions() {
          EnumeratedRealDistribution invalid = null;
          try {
              invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0});
              Assert.fail("Expected DimensionMismatchException");
          } catch (DimensionMismatchException e) {
          }
          try{
          invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, -1.0});
              Assert.fail("Expected NotPositiveException");
          } catch (NotPositiveException e) {
          }
          try {
              invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, 0.0});
              Assert.fail("Expected MathArithmeticException");
          } catch (MathArithmeticException e) {
          }
          try {
              invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, Double.NaN});
              Assert.fail("Expected NotANumberException");
          } catch (NotANumberException e) {
          }
          try {
              invalid = new EnumeratedRealDistribution(new double[]{1.0, 2.0}, new double[]{0.0, Double.POSITIVE_INFINITY});
              Assert.fail("Expected NotFiniteNumberException");
          } catch (NotFiniteNumberException e) {
          }
          Assert.assertNull("Expected non-initialized DiscreteRealDistribution", invalid);
      }
  
      /**
       * Tests if the distribution returns proper probability values.
       */
      @Test
      public void testProbability() {
          double[] points = new double[]{-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
         double[] results = new double[]{0, 0.2, 0, 0, 0, 0.5, 0, 0, 0, 0.3, 0};
         for (int p = 0; p < points.length; p++) {
             double density = testDistribution.density(points[p]);
             Assert.assertEquals(results[p], density, 0.0);
         }
     }
 
     /**
      * Tests if the distribution returns proper density values.
      */
     @Test
     public void testDensity() {
         double[] points = new double[]{-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
         double[] results = new double[]{0, 0.2, 0, 0, 0, 0.5, 0, 0, 0, 0.3, 0};
         for (int p = 0; p < points.length; p++) {
             double density = testDistribution.density(points[p]);
             Assert.assertEquals(results[p], density, 0.0);
         }
     }
 
     /**
      * Tests if the distribution returns proper cumulative probability values.
      */
     @Test
     public void testCumulativeProbability() {
         double[] points = new double[]{-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
         double[] results = new double[]{0, 0.2, 0.2, 0.2, 0.2, 0.7, 0.7, 0.7, 0.7, 1.0, 1.0};
         for (int p = 0; p < points.length; p++) {
             double probability = testDistribution.cumulativeProbability(points[p]);
             Assert.assertEquals(results[p], probability, 1e-10);
         }
     }
 
     /**
      * Tests if the distribution returns proper mean value.
      */
     @Test
     public void testGetNumericalMean() {
         Assert.assertEquals(3.4, testDistribution.getMean(), 1e-10);
     }
 
     /**
      * Tests if the distribution returns proper variance.
      */
     @Test
     public void testGetNumericalVariance() {
         Assert.assertEquals(7.84, testDistribution.getVariance(), 1e-10);
     }
 
     /**
      * Tests if the distribution returns proper lower bound.
      */
     @Test
     public void testGetSupportLowerBound() {
         Assert.assertEquals(-1, testDistribution.getSupportLowerBound(), 0);
     }
 
     /**
      * Tests if the distribution returns proper upper bound.
      */
     @Test
     public void testGetSupportUpperBound() {
         Assert.assertEquals(7, testDistribution.getSupportUpperBound(), 0);
     }
 
     /**
      * Tests sampling.
      */
     @Test
     public void testSample() {
         final int n = 1000000;
         final ContinuousDistribution.Sampler sampler =
             testDistribution.createSampler(RandomSource.XO_RO_SHI_RO_128_PP.create());
         final double[] samples = AbstractRealDistribution.sample(n, sampler);
         Assert.assertEquals(n, samples.length);
         double sum = 0;
         double sumOfSquares = 0;
         for (int i = 0; i < samples.length; i++) {
             sum += samples[i];
             sumOfSquares += samples[i] * samples[i];
         }
         final double mean = testDistribution.getMean();
         Assert.assertEquals("Mean", mean, sum / n, mean * 1e-2);
         final double var = testDistribution.getVariance();
         Assert.assertEquals("Variance", var, sumOfSquares / n - JdkMath.pow(sum / n, 2), var * 1e-2);
     }
 
     @Test
     public void testIssue942() {
         List<Pair<Object,Double>> list = new ArrayList<>();
         list.add(new Pair<Object, Double>(new Object() {}, Double.valueOf(0)));
         list.add(new Pair<Object, Double>(new Object() {}, Double.valueOf(1)));
         final UniformRandomProvider rng = RandomSource.WELL_512_A.create();
         Assert.assertEquals(1, new EnumeratedDistribution<>(list).createSampler(rng).sample(1).length);
     }
 
     @Test
     public void testIssue1065() {
         // Test Distribution for inverseCumulativeProbability
         //
         //         ^
         //         |
         // 1.000   +--------------------------------o===============
         //         |                               3|
         //         |                                |
         //         |                             1o=
         // 0.750   +-------------------------> o==  .
         //         |                          3|  . .
         //         |                   0       |  . .
         // 0.5625  +---------------> o==o======   . .
         //         |                 |  .      .  . .
         //         |                 |  .      .  . .
         //         |                5|  .      .  . .
         //         |                 |  .      .  . .
         //         |             o===   .      .  . .
         //         |             |   .  .      .  . .
         //         |            4|   .  .      .  . .
         //         |             |   .  .      .  . .
         // 0.000   +=============----+--+------+--+-+--------------->
         //                      14  18 21     28 31 33
         //
         // sum  = 4+5+0+3+1+3 = 16
 
         EnumeratedRealDistribution distribution = new EnumeratedRealDistribution(
                 new double[] { 14.0, 18.0, 21.0, 28.0, 31.0, 33.0 },
                 new double[] { 4.0 / 16.0, 5.0 / 16.0, 0.0 / 16.0, 3.0 / 16.0, 1.0 / 16.0, 3.0 / 16.0 });
 
         assertEquals(14.0, distribution.inverseCumulativeProbability(0.0000), 0.0);
         assertEquals(14.0, distribution.inverseCumulativeProbability(0.2500), 0.0);
         assertEquals(33.0, distribution.inverseCumulativeProbability(1.0000), 0.0);
 
         assertEquals(18.0, distribution.inverseCumulativeProbability(0.5000), 0.0);
         assertEquals(18.0, distribution.inverseCumulativeProbability(0.5624), 0.0);
         assertEquals(28.0, distribution.inverseCumulativeProbability(0.5626), 0.0);
         assertEquals(31.0, distribution.inverseCumulativeProbability(0.7600), 0.0);
         assertEquals(18.0, distribution.inverseCumulativeProbability(0.5625), 0.0);
         assertEquals(28.0, distribution.inverseCumulativeProbability(0.7500), 0.0);
     }
 
     @Test
     public void testCreateFromDoubles() {
         final double[] data = new double[] {0, 1, 1, 2, 2, 2};
         EnumeratedRealDistribution distribution = new EnumeratedRealDistribution(data);
         assertEquals(0.5, distribution.density(2), 0);
         assertEquals(0.5, distribution.cumulativeProbability(1), 0);
     }
 }