/*
    * 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.examples.jmh.sampling.distribution;
  
  import org.apache.commons.rng.UniformRandomProvider;
  import org.apache.commons.rng.examples.jmh.RandomSources;
  import org.apache.commons.rng.sampling.distribution.AliasMethodDiscreteSampler;
  import org.apache.commons.rng.sampling.distribution.DiscreteSampler;
  import org.apache.commons.rng.sampling.distribution.DiscreteUniformSampler;
  import org.apache.commons.rng.sampling.distribution.GeometricSampler;
  import org.apache.commons.rng.sampling.distribution.GuideTableDiscreteSampler;
  import org.apache.commons.rng.sampling.distribution.LargeMeanPoissonSampler;
  import org.apache.commons.rng.sampling.distribution.MarsagliaTsangWangDiscreteSampler;
  import org.apache.commons.rng.sampling.distribution.RejectionInversionZipfSampler;
  import org.apache.commons.rng.sampling.distribution.SmallMeanPoissonSampler;
  
  import org.openjdk.jmh.annotations.Benchmark;
  import org.openjdk.jmh.annotations.BenchmarkMode;
  import org.openjdk.jmh.annotations.Fork;
  import org.openjdk.jmh.annotations.Measurement;
  import org.openjdk.jmh.annotations.Mode;
  import org.openjdk.jmh.annotations.OutputTimeUnit;
  import org.openjdk.jmh.annotations.Param;
  import org.openjdk.jmh.annotations.Scope;
  import org.openjdk.jmh.annotations.Setup;
  import org.openjdk.jmh.annotations.State;
  import org.openjdk.jmh.annotations.Warmup;
  
  import java.util.concurrent.TimeUnit;
  
  /**
   * Executes benchmark to compare the speed of generation of random numbers
   * from the various source providers for different types of {@link DiscreteSampler}.
   */
  @BenchmarkMode(Mode.AverageTime)
  @OutputTimeUnit(TimeUnit.NANOSECONDS)
  @Warmup(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS)
  @Measurement(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS)
  @State(Scope.Benchmark)
  @Fork(value = 1, jvmArgs = {"-server", "-Xms128M", "-Xmx128M"})
  public class DiscreteSamplersPerformance {
      /**
       * The value.
       *
       * <p>This must NOT be final!</p>
       */
      private int value;
  
      /**
       * The {@link DiscreteSampler} samplers to use for testing. Creates the sampler for each
       * {@link org.apache.commons.rng.simple.RandomSource RandomSource} in the default
       * {@link RandomSources}.
       */
      @State(Scope.Benchmark)
      public static class Sources extends RandomSources {
          /** The probabilities for the discrete distribution. */
          private static final double[] DISCRETE_PROBABILITIES;
  
          static {
              // The size of this distribution will effect the relative performance
              // of the AliasMethodDiscreteSampler against the other samplers. The
              // Alias sampler is optimised for power of 2 tables and will zero pad
              // the distribution. Pick a midpoint value between a power of 2 size to
              // baseline half of a possible speed advantage.
              final int size = (32 + 64) / 2;
              DISCRETE_PROBABILITIES = new double[size];
              for (int i = 0; i < size; i++) {
                  DISCRETE_PROBABILITIES[i] = (i + 1.0) / size;
              }
          }
  
          /**
           * The sampler type.
           */
          @Param({"DiscreteUniformSampler",
                  "RejectionInversionZipfSampler",
                  "SmallMeanPoissonSampler",
                  "LargeMeanPoissonSampler",
                  "GeometricSampler",
                  "MarsagliaTsangWangDiscreteSampler",
                  "MarsagliaTsangWangPoissonSampler",
                  "MarsagliaTsangWangBinomialSampler",
                  "GuideTableDiscreteSampler",
                  "AliasMethodDiscreteSampler"})
         private String samplerType;
 
         /** The sampler. */
         private DiscreteSampler sampler;
 
         /**
          * @return the sampler.
          */
         public DiscreteSampler getSampler() {
             return sampler;
         }
 
         /** Instantiates sampler. */
         @Override
         @Setup
         public void setup() {
             super.setup();
             final UniformRandomProvider rng = getGenerator();
             if ("DiscreteUniformSampler".equals(samplerType)) {
                 sampler = DiscreteUniformSampler.of(rng, -98, 76);
             } else if ("RejectionInversionZipfSampler".equals(samplerType)) {
                 sampler = RejectionInversionZipfSampler.of(rng, 43, 2.1);
             } else if ("SmallMeanPoissonSampler".equals(samplerType)) {
                 sampler = SmallMeanPoissonSampler.of(rng, 8.9);
             } else if ("LargeMeanPoissonSampler".equals(samplerType)) {
                 // Note: Use with a fractional part to the mean includes a small mean sample
                 sampler = LargeMeanPoissonSampler.of(rng, 41.7);
             } else if ("GeometricSampler".equals(samplerType)) {
                 sampler = GeometricSampler.of(rng, 0.21);
             } else if ("MarsagliaTsangWangDiscreteSampler".equals(samplerType)) {
                 sampler = MarsagliaTsangWangDiscreteSampler.Enumerated.of(rng, DISCRETE_PROBABILITIES);
             } else if ("MarsagliaTsangWangPoissonSampler".equals(samplerType)) {
                 sampler = MarsagliaTsangWangDiscreteSampler.Poisson.of(rng, 8.9);
             } else if ("MarsagliaTsangWangBinomialSampler".equals(samplerType)) {
                 sampler = MarsagliaTsangWangDiscreteSampler.Binomial.of(rng, 20, 0.33);
             } else if ("GuideTableDiscreteSampler".equals(samplerType)) {
                 sampler = GuideTableDiscreteSampler.of(rng, DISCRETE_PROBABILITIES);
             } else if ("AliasMethodDiscreteSampler".equals(samplerType)) {
                 sampler = AliasMethodDiscreteSampler.of(rng, DISCRETE_PROBABILITIES);
             }
         }
     }
 
     // Benchmarks methods below.
 
     /**
      * Baseline for the JMH timing overhead for production of an {@code int} value.
      *
      * @return the {@code int} value
      */
     @Benchmark
     public int baseline() {
         return value;
     }
 
     /**
      * Run the sampler.
      *
      * @param sources Source of randomness.
      * @return the sample value
      */
     @Benchmark
     public int sample(Sources sources) {
         return sources.getSampler().sample();
     }
 }