/*
    * 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.geometry.examples.jmh.euclidean;
  
  import java.util.ArrayList;
  import java.util.List;
  import java.util.concurrent.TimeUnit;
  import java.util.function.DoubleSupplier;
  import java.util.function.Function;
  import java.util.function.ToDoubleFunction;
  import java.util.function.UnaryOperator;
  
  import org.apache.commons.geometry.core.Vector;
  import org.apache.commons.geometry.euclidean.oned.Vector1D;
  import org.apache.commons.geometry.euclidean.threed.Vector3D;
  import org.apache.commons.geometry.euclidean.twod.Vector2D;
  import org.apache.commons.rng.UniformRandomProvider;
  import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler;
  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.Level;
  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 org.openjdk.jmh.infra.Blackhole;
  
  /**
   * Benchmarks for the Euclidean vector classes.
   */
  @BenchmarkMode(Mode.AverageTime)
  @OutputTimeUnit(TimeUnit.NANOSECONDS)
  @Warmup(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS)
  @Measurement(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS)
  @Fork(value = 1, jvmArgs = {"-server", "-Xms512M", "-Xmx512M"})
  public class VectorPerformance {
  
      /**
       * An array of edge numbers that will produce edge case results from functions:
       * {@code +/-inf, +/-max, +/-min, +/-0, nan}.
       */
      private static final double[] EDGE_NUMBERS = {
          Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, Double.MAX_VALUE,
          -Double.MAX_VALUE, Double.MIN_VALUE, -Double.MIN_VALUE, 0.0, -0.0, Double.NaN
      };
  
      /** String constant used to request random double values, excluding NaN and infinity. */
      private static final String RANDOM = "random";
  
      /** String constant used to request a set of double values capable of vector normalization. These
       * values are similar to those in the {@link #RANDOM} type but specifically exclude 0.
       */
      private static final String NORMALIZABLE = "normalizable";
  
      /** String constant used to request edge-case double values, which includes NaN, infinity, zero,
       * and the double min and max values.
       */
      private static final String EDGE = "edge";
  
      /** Base class for vector inputs.
       * @param <V> Vector implementation type
       */
      @State(Scope.Thread)
      public abstract static class VectorInputBase<V extends Vector<V>> {
  
          /** The dimension of the vector. */
          private final int dimension;
  
          /** Factory function used to create vectors from arrays of doubles. */
          private final Function<double[], V> vectorFactory;
  
          /** The number of vectors in the input list. */
          @Param({"100", "10000"})
          private int size;
  
          /** The vector for the instance. */
          private List<V> vectors;
  
          /** Create a new instance with the vector dimension.
          * @param dimension vector dimension
          * @param vectorFactory function for creating vectors from double arrays
          */
         VectorInputBase(final int dimension, final Function<double[], V> vectorFactory) {
             this.dimension = dimension;
             this.vectorFactory = vectorFactory;
         }
 
         /** Set up the instance for the benchmark.
          */
         @Setup(Level.Iteration)
         public void setup() {
             vectors = new ArrayList<>(size);
 
             final double[] values = new double[dimension];
             final DoubleSupplier doubleSupplier = createDoubleSupplier(getType(),
                     RandomSource.create(RandomSource.XO_RO_SHI_RO_128_PP));
 
             for (int i = 0; i < size; ++i) {
                 for (int j = 0; j < dimension; ++j) {
                     values[j] = doubleSupplier.getAsDouble();
                 }
 
                 vectors.add(vectorFactory.apply(values));
             }
         }
 
         /** Get the input vectors for the instance.
          * @return the input vectors for the instance
          */
         public List<V> getVectors() {
             return vectors;
         }
 
         /** Get the type of double values to use in the creation of input vectors.
          * @return the type of double values to use in the creation of input vectors
          */
         public abstract String getType();
 
         /** Create a supplier that produces doubles of the given type.
          * @param type type of doubles to produce
          * @param rng random provider
          * @return a supplier that produces doubles of the given type
          */
         private DoubleSupplier createDoubleSupplier(final String type, final UniformRandomProvider rng) {
             switch (type) {
             case RANDOM:
                 return () -> createRandomDouble(rng);
             case NORMALIZABLE:
                 final ZigguratNormalizedGaussianSampler sampler = ZigguratNormalizedGaussianSampler.of(rng);
                 return () -> {
                     double n = sampler.sample();
                     return n == 0 ? 0.1 : n; // do not return exactly zero
                 };
             case EDGE:
                 return () -> EDGE_NUMBERS[rng.nextInt(EDGE_NUMBERS.length)];
             default:
                 throw new IllegalStateException("Invalid input type: " + type);
             }
         }
     }
 
     /** Vector input class producing {@link Vector1D} instances with random
      * double values.
      */
     @State(Scope.Thread)
     public static class VectorInput1D extends VectorInputBase<Vector1D> {
 
         /** The type of values to use in the vector. */
         @Param({RANDOM, EDGE})
         private String type;
 
         /** Default constructor. */
         public VectorInput1D() {
             super(1, arr -> Vector1D.of(arr[0]));
         }
 
         /** {@inheritDoc} */
         @Override
         public String getType() {
             return type;
         }
     }
 
     /** Vector input class producing {@link Vector1D} instances capable of being normalized.
      */
     @State(Scope.Thread)
     public static class NormalizableVectorInput1D extends VectorInputBase<Vector1D> {
 
         /** Default constructor. */
         public NormalizableVectorInput1D() {
             super(1, arr -> Vector1D.of(arr[0]));
         }
 
         /** {@inheritDoc} */
         @Override
         public String getType() {
             return NORMALIZABLE;
         }
     }
 
     /** Vector input class producing {@link Vector2D} instances with random
      * double values.
      */
     @State(Scope.Thread)
     public static class VectorInput2D extends VectorInputBase<Vector2D> {
 
         /** The type of values to use in the vector. */
         @Param({RANDOM, EDGE})
         private String type;
 
         /** Default constructor. */
         public VectorInput2D() {
             super(2, Vector2D::of);
         }
 
         /** {@inheritDoc} */
         @Override
         public String getType() {
             return type;
         }
     }
 
     /** Vector input class producing {@link Vector2D} instances capable of being normalized.
      */
     @State(Scope.Thread)
     public static class NormalizableVectorInput2D extends VectorInputBase<Vector2D> {
 
         /** Default constructor. */
         public NormalizableVectorInput2D() {
             super(2, Vector2D::of);
         }
 
         /** {@inheritDoc} */
         @Override
         public String getType() {
             return NORMALIZABLE;
         }
     }
 
     /** Vector input class producing {@link Vector2D} instances with random
      * double values.
      */
     @State(Scope.Thread)
     public static class VectorInput3D extends VectorInputBase<Vector3D> {
 
         /** The type of values to use in the vector. */
         @Param({RANDOM, EDGE})
         private String type;
 
         /** Default constructor. */
         public VectorInput3D() {
             super(3, Vector3D::of);
         }
 
         /** {@inheritDoc} */
         @Override
         public String getType() {
             return type;
         }
     }
 
     /** Vector input class producing {@link Vector3D} instances capable of being normalized.
      */
     @State(Scope.Thread)
     public static class NormalizableVectorInput3D extends VectorInputBase<Vector3D> {
 
         /** Default constructor. */
         public NormalizableVectorInput3D() {
             super(3, Vector3D::of);
         }
 
         /** {@inheritDoc} */
         @Override
         public String getType() {
             return NORMALIZABLE;
         }
     }
 
     /** Creates a random double number with a random sign and mantissa and a large range for
      * the exponent. The numbers will not be uniform over the range.
      * @param rng random number generator
      * @return the random number
      */
     private static double createRandomDouble(final UniformRandomProvider rng) {
         // Create random doubles using random bits in the sign bit and the mantissa.
         // Then create an exponent in the range -64 to 64. Thus the sum product
         // of 4 max or min values will not over or underflow.
         final long mask = ((1L << 52) - 1) | 1L << 63;
         final long bits = rng.nextLong() & mask;
         // The exponent must be unsigned so + 1023 to the signed exponent
         final long exp = rng.nextInt(129) - 64 + 1023;
         return Double.longBitsToDouble(bits | (exp << 52));
     }
 
     /** Run a benchmark test on a function that produces a double.
      * @param <V> Vector implementation type
      * @param input vector input
      * @param bh jmh blackhole for consuming output
      * @param fn function to call
      */
     private static <V extends Vector<V>> void testToDouble(final VectorInputBase<V> input, final Blackhole bh,
             final ToDoubleFunction<V> fn) {
         for (final V vec : input.getVectors()) {
             bh.consume(fn.applyAsDouble(vec));
         }
     }
 
     /** Run a benchmark test on a function that produces a vector.
      * @param <V> Vector implementation type
      * @param input vector input
      * @param bh jmh blackhole for consuming output
      * @param fn function to call
      */
     private static <V extends Vector<V>> void testUnary(final VectorInputBase<V> input, final Blackhole bh,
             final UnaryOperator<V> fn) {
         for (final V vec : input.getVectors()) {
             bh.consume(fn.apply(vec));
         }
     }
 
     /** Benchmark testing just the overhead of the benchmark harness.
      * @param input benchmark state input
      * @param bh jmh blackhole for consuming output
      */
     @Benchmark
     public void baseline(final VectorInput1D input, final Blackhole bh) {
         testUnary(input, bh, UnaryOperator.identity());
     }
 
     /** Benchmark testing the performance of the {@link Vector1D#norm()} method.
      * @param input benchmark state input
      * @param bh jmh blackhole for consuming output
      */
     @Benchmark
     public void norm1D(final VectorInput1D input, final Blackhole bh) {
         testToDouble(input, bh, Vector1D::norm);
     }
 
     /** Benchmark testing the performance of the {@link Vector2D#norm()} method.
      * @param input benchmark state input
      * @param bh jmh blackhole for consuming output
      */
     @Benchmark
     public void norm2D(final VectorInput2D input, final Blackhole bh) {
         testToDouble(input, bh, Vector2D::norm);
     }
 
     /** Benchmark testing the performance of the {@link Vector3D#norm()} method.
      * @param input benchmark state input
      * @param bh jmh blackhole for consuming output
      */
     @Benchmark
     public void norm3D(final VectorInput3D input, final Blackhole bh) {
         testToDouble(input, bh, Vector3D::norm);
     }
 
     /** Benchmark testing the performance of the {@link Vector1D#normalize()} method.
      * @param input benchmark state input
      * @param bh jmh blackhole for consuming output
      */
     @Benchmark
     public void normalize1D(final NormalizableVectorInput1D input, final Blackhole bh) {
         testUnary(input, bh, Vector1D::normalize);
     }
 
     /** Benchmark testing the performance of the {@link Vector2D#normalize()}
      * method.
      * @param input benchmark state input
      * @param bh jmh blackhole for consuming output
      */
     @Benchmark
     public void normalize2D(final NormalizableVectorInput2D input, final Blackhole bh) {
         testUnary(input, bh, Vector2D::normalize);
     }
 
     /** Benchmark testing the performance of the {@link Vector3D#normalize()}
      * method.
      * @param input benchmark state input
      * @param bh jmh blackhole for consuming output
      */
     @Benchmark
     public void normalize3D(final NormalizableVectorInput3D input, final Blackhole bh) {
         testUnary(input, bh, Vector3D::normalize);
     }
 }