/*
    * 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.rng.sampling;
  
  import java.util.Arrays;
  import java.util.Collections;
  import java.util.List;
  import java.util.Map;
  import java.util.TreeMap;
  import org.apache.commons.rng.UniformRandomProvider;
  import org.junit.jupiter.api.Assertions;
  import org.junit.jupiter.api.Test;
  
  /**
   * Test class for {@link DiscreteProbabilityCollectionSampler}.
   */
  class DiscreteProbabilityCollectionSamplerTest {
      /** RNG. */
      private final UniformRandomProvider rng = RandomAssert.createRNG();
  
      @Test
      void testPrecondition1() {
          // Size mismatch
          final List<Double> collection = Arrays.asList(1d, 2d);
          final double[] probabilities = {0};
          Assertions.assertThrows(IllegalArgumentException.class,
              () -> new DiscreteProbabilityCollectionSampler<>(rng,
                   collection,
                   probabilities));
      }
  
      @Test
      void testPrecondition2() {
          // Negative probability
          final List<Double> collection = Arrays.asList(1d, 2d);
          final double[] probabilities = {0, -1};
          Assertions.assertThrows(IllegalArgumentException.class,
              () -> new DiscreteProbabilityCollectionSampler<>(rng,
                  collection,
                  probabilities));
      }
  
      @Test
      void testPrecondition3() {
          // Probabilities do not sum above 0
          final List<Double> collection = Arrays.asList(1d, 2d);
          final double[] probabilities = {0, 0};
          Assertions.assertThrows(IllegalArgumentException.class,
              () -> new DiscreteProbabilityCollectionSampler<>(rng,
                  collection,
                  probabilities));
      }
  
      @Test
      void testPrecondition4() {
          // NaN probability
          final List<Double> collection = Arrays.asList(1d, 2d);
          final double[] probabilities = {0, Double.NaN};
          Assertions.assertThrows(IllegalArgumentException.class,
              () -> new DiscreteProbabilityCollectionSampler<>(rng,
                  collection,
                  probabilities));
      }
  
      @Test
      void testPrecondition5() {
          // Infinite probability
          final List<Double> collection = Arrays.asList(1d, 2d);
          final double[] probabilities = {0, Double.POSITIVE_INFINITY};
          Assertions.assertThrows(IllegalArgumentException.class,
              () -> new DiscreteProbabilityCollectionSampler<>(rng,
                  collection,
                  probabilities));
      }
  
      @Test
      void testPrecondition6() {
          // Empty Map<T, Double> not allowed
          final Map<String, Double> collection = Collections.emptyMap();
          Assertions.assertThrows(IllegalArgumentException.class,
              () -> new DiscreteProbabilityCollectionSampler<>(rng,
                   collection));
      }
  
     @Test
     void testPrecondition7() {
         // Empty List<T> not allowed
         final List<Double> collection = Collections.emptyList();
         final double[] probabilities = {};
         Assertions.assertThrows(IllegalArgumentException.class,
             () -> new DiscreteProbabilityCollectionSampler<>(rng,
                 collection,
                 probabilities));
     }
 
     @Test
     void testSample() {
         final DiscreteProbabilityCollectionSampler<Double> sampler =
             new DiscreteProbabilityCollectionSampler<>(rng,
                                                        Arrays.asList(3d, -1d, 3d, 7d, -2d, 8d),
                                                        new double[] {0.2, 0.2, 0.3, 0.3, 0, 0});
         final double expectedMean = 3.4;
         final double expectedVariance = 7.84;
 
         final int n = 100000000;
         double sum = 0;
         double sumOfSquares = 0;
         for (int i = 0; i < n; i++) {
             final double rand = sampler.sample();
             sum += rand;
             sumOfSquares += rand * rand;
         }
 
         final double mean = sum / n;
         Assertions.assertEquals(expectedMean, mean, 1e-3);
         final double variance = sumOfSquares / n - mean * mean;
         Assertions.assertEquals(expectedVariance, variance, 2e-3);
     }
 
 
     @Test
     void testSampleUsingMap() {
         final UniformRandomProvider rng1 = RandomAssert.seededRNG();
         final UniformRandomProvider rng2 = RandomAssert.seededRNG();
         final List<Integer> items = Arrays.asList(1, 3, 4, 6, 9);
         final double[] probabilities = {0.1, 0.2, 0.3, 0.4, 0.5};
         final DiscreteProbabilityCollectionSampler<Integer> sampler1 =
             new DiscreteProbabilityCollectionSampler<>(rng1, items, probabilities);
 
         // Create a map version. The map iterator must be ordered so use a TreeMap.
         final Map<Integer, Double> map = new TreeMap<>();
         for (int i = 0; i < probabilities.length; i++) {
             map.put(items.get(i), probabilities[i]);
         }
         final DiscreteProbabilityCollectionSampler<Integer> sampler2 =
             new DiscreteProbabilityCollectionSampler<>(rng2, map);
 
         for (int i = 0; i < 50; i++) {
             Assertions.assertEquals(sampler1.sample(), sampler2.sample());
         }
     }
 
     /**
      * Edge-case test:
      * Create a sampler that will return 1 for nextDouble() forcing the search to
      * identify the end item of the cumulative probability array.
      */
     @Test
     void testSampleWithProbabilityAtLastItem() {
         // Ensure the samples pick probability 0 (the first item) and then
         // a probability (for the second item) that hits an edge case.
         final UniformRandomProvider dummyRng = new UniformRandomProvider() {
             private int count;
 
             @Override
             public long nextLong() {
                 return 0;
             }
 
             @Override
             public double nextDouble() {
                 // Return 0 then the 1.0 for the probability
                 return (count++ == 0) ? 0 : 1.0;
             }
         };
 
         final List<Double> items = Arrays.asList(1d, 2d);
         final DiscreteProbabilityCollectionSampler<Double> sampler =
             new DiscreteProbabilityCollectionSampler<>(dummyRng,
                                                        items,
                                                        new double[] {0.5, 0.5});
         final Double item1 = sampler.sample();
         final Double item2 = sampler.sample();
         // Check they are in the list
         Assertions.assertTrue(items.contains(item1), "Sample item1 is not from the list");
         Assertions.assertTrue(items.contains(item2), "Sample item2 is not from the list");
         // Test the two samples are different items
         Assertions.assertNotSame(item1, item2, "Item1 and 2 should be different");
     }
 
     /**
      * Test the SharedStateSampler implementation.
      */
     @Test
     void testSharedStateSampler() {
         final UniformRandomProvider rng1 = RandomAssert.seededRNG();
         final UniformRandomProvider rng2 = RandomAssert.seededRNG();
         final List<Double> items = Arrays.asList(1d, 2d, 3d, 4d);
         final DiscreteProbabilityCollectionSampler<Double> sampler1 =
             new DiscreteProbabilityCollectionSampler<>(rng1,
                                                        items,
                                                        new double[] {0.1, 0.2, 0.3, 0.4});
         final DiscreteProbabilityCollectionSampler<Double> sampler2 = sampler1.withUniformRandomProvider(rng2);
         RandomAssert.assertProduceSameSequence(sampler1, sampler2);
     }
 }