treeMultiset = TreeMultiset.create();

    Random random = new Random();

    int sizeRemaining = size;

    // TODO(kevinb): generate better test contents for multisets
    while (sizeRemaining > 0) {
      // The JVM will return interned values for small ints.
      Integer value = random.nextInt(1000) + 128;
      int count = min(random.nextInt(10) + 1, sizeRemaining);
      sizeRemaining -= count;

    treeMultiset = TreeMultiset.create();

    Random random = new Random();

    int sizeRemaining = size;

    // TODO(kevinb): generate better test contents for multisets
    while (sizeRemaining > 0) {
      // The JVM will return interned values for small ints.
      Integer value = random.nextInt(1000) + 128;
      int count = min(random.nextInt(10) + 1, sizeRemaining);
      sizeRemaining -= count;

  int datasetSize;

  @Param QuantilesAlgorithm algorithm;

  private double[][] datasets = new double[0x100][];

  @BeforeExperiment
  void setUp() {
    Random rng = new Random();
    for (int i = 0; i < 0x100; i++) {
      datasets[i] = new double[datasetSize];
      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  int datasetSize;

  @Param QuantilesAlgorithm algorithm;

  private double[][] datasets = new double[0x100][];

  @BeforeExperiment
  void setUp() {
    Random rng = new Random();
    for (int i = 0; i < 0x100; i++) {
      datasets[i] = new double[datasetSize];
      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private Queue<Integer> queue;

  private final Random random = new Random();

  @BeforeExperiment
  void setUp() {
    queue = heap.create(comparator.get());
    for (int i = 0; i < size; i++) {
      queue.add(random.nextInt());
    }
  }

  @Benchmark
  void pollAndAdd(int reps) {
    for (int i = 0; i < reps; i++) {
      // TODO(kevinb): precompute random #s?
      queue.add(queue.poll() ^ random.nextInt());

  }

  public void testCombineOrdered_randomHashCodes() {
    Random random = new Random(7);
    List<HashCode> hashCodes = Lists.newArrayList();
    for (int i = 0; i < 10; i++) {
      hashCodes.add(HashCode.fromLong(random.nextLong()));
    }
    HashCode hashCode1 = Hashing.combineOrdered(hashCodes);
    Collections.shuffle(hashCodes, random);
    HashCode hashCode2 = Hashing.combineOrdered(hashCodes);

  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);
  static AtomicLong misses = new AtomicLong(0);

  @BeforeExperiment
  void setUp() {
    // random integers will be generated in this range, then raised to the

  @BeforeExperiment
  void setUp() {
    Random random = new Random(randomSeed);
    xInts = new int[SAMPLE_SIZE];
    yInts = new int[SAMPLE_SIZE];
    xLongs = new long[SAMPLE_SIZE];
    yLongs = new long[SAMPLE_SIZE];
    constant = new int[SAMPLE_SIZE];
    for (int i = 0; i < SAMPLE_SIZE; i++) {
      xInts[i] = random.nextInt(Integer.MAX_VALUE);
      yInts[i] = random.nextInt(Integer.MAX_VALUE);

    return new String(result);
  }

  private static String newTestString(Random random, BitSet bitSet, int percentMatching) {
    final String allMatchingChars = allMatchingChars(bitSet);
    final char[] result = new char[STRING_LENGTH];
    // Fill with matching chars.
    for (int i = 0; i < result.length; i++) {
      result[i] = allMatchingChars.charAt(random.nextInt(allMatchingChars.length()));
    }

    return new String(result);
  }

  private static String newTestString(Random random, BitSet bitSet, int percentMatching) {
    final String allMatchingChars = allMatchingChars(bitSet);
    final char[] result = new char[STRING_LENGTH];
    // Fill with matching chars.
    for (int i = 0; i < result.length; i++) {
      result[i] = allMatchingChars.charAt(random.nextInt(allMatchingChars.length()));
    }