/*
    * 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.AhrensDieterExponentialSampler;
  import org.apache.commons.rng.sampling.distribution.AhrensDieterMarsagliaTsangGammaSampler;
  import org.apache.commons.rng.sampling.distribution.BoxMullerNormalizedGaussianSampler;
  import org.apache.commons.rng.sampling.distribution.ChengBetaSampler;
  import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
  import org.apache.commons.rng.sampling.distribution.ContinuousUniformSampler;
  import org.apache.commons.rng.sampling.distribution.InverseTransformParetoSampler;
  import org.apache.commons.rng.sampling.distribution.LevySampler;
  import org.apache.commons.rng.sampling.distribution.LogNormalSampler;
  import org.apache.commons.rng.sampling.distribution.MarsagliaNormalizedGaussianSampler;
  import org.apache.commons.rng.sampling.distribution.StableSampler;
  import org.apache.commons.rng.sampling.distribution.TSampler;
  import org.apache.commons.rng.sampling.distribution.ZigguratSampler;
  import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler;
  
  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 ContinuousSampler}.
   */
  @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 ContinuousSamplersPerformance {
      /**
       * The value.
       *
       * <p>This must NOT be final!</p>
       */
      private double value;
  
      /**
       * The {@link ContinuousSampler} 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 sampler type.
           */
          @Param({"BoxMullerNormalizedGaussianSampler",
                  "MarsagliaNormalizedGaussianSampler",
                  "ZigguratNormalizedGaussianSampler",
                  "ZigguratSampler.NormalizedGaussian",
                  "AhrensDieterExponentialSampler",
                  "ZigguratSampler.Exponential",
                  "AhrensDieterGammaSampler",
                  "MarsagliaTsangGammaSampler",
                  "LevySampler",
                  "LogNormalBoxMullerNormalizedGaussianSampler",
                  "LogNormalMarsagliaNormalizedGaussianSampler",
                  "LogNormalZigguratNormalizedGaussianSampler",
                  "LogNormalSampler.ZigguratSampler.NormalizedGaussian",
                  "ChengBetaSampler",
                  "ContinuousUniformSampler",
                  "InverseTransformParetoSampler",
                  "StableSampler",
                  "TSampler"})
          private String samplerType;
  
          /** The sampler. */
         private ContinuousSampler sampler;
 
         /**
          * @return the sampler.
          */
         public ContinuousSampler getSampler() {
             return sampler;
         }
 
         /** Instantiates sampler. */
         @Override
         @Setup
         public void setup() {
             super.setup();
             final UniformRandomProvider rng = getGenerator();
             if ("BoxMullerNormalizedGaussianSampler".equals(samplerType)) {
                 sampler = BoxMullerNormalizedGaussianSampler.of(rng);
             } else if ("MarsagliaNormalizedGaussianSampler".equals(samplerType)) {
                 sampler = MarsagliaNormalizedGaussianSampler.of(rng);
             } else if ("ZigguratNormalizedGaussianSampler".equals(samplerType)) {
                 sampler = ZigguratNormalizedGaussianSampler.of(rng);
             } else if ("ZigguratSampler.NormalizedGaussian".equals(samplerType)) {
                 sampler = ZigguratSampler.NormalizedGaussian.of(rng);
             } else if ("AhrensDieterExponentialSampler".equals(samplerType)) {
                 sampler = AhrensDieterExponentialSampler.of(rng, 4.56);
             } else if ("ZigguratSampler.Exponential".equals(samplerType)) {
                 sampler = ZigguratSampler.Exponential.of(rng, 4.56);
             } else if ("AhrensDieterGammaSampler".equals(samplerType)) {
                 // This tests the Ahrens-Dieter algorithm since alpha < 1
                 sampler = AhrensDieterMarsagliaTsangGammaSampler.of(rng, 0.76, 9.8);
             } else if ("MarsagliaTsangGammaSampler".equals(samplerType)) {
                 // This tests the Marsaglia-Tsang algorithm since alpha > 1
                 sampler = AhrensDieterMarsagliaTsangGammaSampler.of(rng, 12.34, 9.8);
             } else if ("LevySampler".equals(samplerType)) {
                 sampler = LevySampler.of(rng, 1.23, 4.56);
             } else if ("LogNormalBoxMullerNormalizedGaussianSampler".equals(samplerType)) {
                 sampler = LogNormalSampler.of(BoxMullerNormalizedGaussianSampler.of(rng), 12.3, 4.6);
             } else if ("LogNormalMarsagliaNormalizedGaussianSampler".equals(samplerType)) {
                 sampler = LogNormalSampler.of(MarsagliaNormalizedGaussianSampler.of(rng), 12.3, 4.6);
             } else if ("LogNormalZigguratNormalizedGaussianSampler".equals(samplerType)) {
                 sampler = LogNormalSampler.of(ZigguratNormalizedGaussianSampler.of(rng), 12.3, 4.6);
             } else if ("LogNormalSampler.ZigguratSampler.NormalizedGaussian".equals(samplerType)) {
                 sampler = LogNormalSampler.of(ZigguratSampler.NormalizedGaussian.of(rng), 12.3, 4.6);
             } else if ("ChengBetaSampler".equals(samplerType)) {
                 sampler = ChengBetaSampler.of(rng, 0.45, 6.7);
             } else if ("ContinuousUniformSampler".equals(samplerType)) {
                 sampler = ContinuousUniformSampler.of(rng, 123.4, 5678.9);
             } else if ("InverseTransformParetoSampler".equals(samplerType)) {
                 sampler = InverseTransformParetoSampler.of(rng, 23.45, 0.1234);
             } else if ("StableSampler".equals(samplerType)) {
                 sampler = StableSampler.of(rng, 1.3, 0.2);
             } else if ("TSampler".equals(samplerType)) {
                 sampler = TSampler.of(rng, 1.23);
             }
         }
     }
 
     // Benchmarks methods below.
 
     /**
      * 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();
     }
 }