/*
    * 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.math3.distribution.LevyDistribution;
  import org.apache.commons.rng.UniformRandomProvider;
  import org.apache.commons.rng.sampling.distribution.ContinuousInverseCumulativeProbabilityFunction;
  import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
  import org.apache.commons.rng.sampling.distribution.InverseTransformContinuousSampler;
  import org.apache.commons.rng.sampling.distribution.LevySampler;
  import org.apache.commons.rng.simple.RandomSource;
  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 a benchmark to compare the speed of generation of Levy distributed random numbers
   * using different methods.
   */
  @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 LevySamplersPerformance {
      /**
       * The value.
       *
       * <p>This must NOT be final!</p>
       */
      private double value;
  
      /**
       * The samplers's to use for testing. Defines the RandomSource  and the type of Levy sampler.
       */
      @State(Scope.Benchmark)
      public static class Sources {
          /**
           * RNG providers.
           *
           * <p>Use different speeds.</p>
           *
           * @see <a href="https://commons.apache.org/proper/commons-rng/userguide/rng.html">
           *      Commons RNG user guide</a>
           */
          @Param({"SPLIT_MIX_64",
                  "MWC_256",
                  "JDK"})
          private String randomSourceName;
  
          /**
           * The sampler type.
           */
          @Param({"LevySampler", "InverseTransformDiscreteSampler"})
          private String samplerType;
  
          /** The sampler. */
          private ContinuousSampler sampler;
  
          /**
           * @return the sampler.
           */
          public ContinuousSampler getSampler() {
              return sampler;
          }
  
          /** Instantiates sampler. */
          @Setup
          public void setup() {
              final RandomSource randomSource = RandomSource.valueOf(randomSourceName);
              final UniformRandomProvider rng = randomSource.create();
              final double location = 0.0;
              final double scale = 1.0;
             if ("LevySampler".equals(samplerType)) {
                 sampler = LevySampler.of(rng, location, scale);
             } else {
                 final ContinuousInverseCumulativeProbabilityFunction levyFunction =
                     new ContinuousInverseCumulativeProbabilityFunction() {
                         /** Use CM for the inverse CDF. null is for the unused RNG. */
                         private final LevyDistribution dist = new LevyDistribution(null, location, scale);
                         @Override
                         public double inverseCumulativeProbability(double p) {
                             return dist.inverseCumulativeProbability(p);
                         }
                     };
                 sampler = InverseTransformContinuousSampler.of(rng, levyFunction);
             }
         }
     }
 
     /**
      * Baseline for the JMH timing overhead for production of an {@code double} value.
      *
      * @return the {@code double} value
      */
     @Benchmark
     public double baseline() {
         return value;
     }
 
     /**
      * Run the sampler.
      *
      * @param sources Source of randomness.
      * @return the sample value
      */
     @Benchmark
     public double sample(Sources sources) {
         return sources.getSampler().sample();
     }
 }