public void testCheckNonNegative_zeroDouble() {
    MathPreconditions.checkNonNegative("double", 0d);
  }

  public void testCheckNonNegative_maxDouble() {
    MathPreconditions.checkNonNegative("double", Double.MAX_VALUE);
  }

  public void testCheckNonNegative_minDouble() {
    MathPreconditions.checkNonNegative("double", Double.MIN_VALUE);
  }

  public void testCheckNonNegative_positiveDouble() {

  public void testCreateFromSortedSetMultimap() {
    SortedSetMultimap<Double, Double> tree = TreeMultimap.create(KEY_COMPARATOR, VALUE_COMPARATOR);
    tree.put(1.0, 2.0);
    tree.put(2.0, 3.0);
    tree.put(3.0, 4.0);
    tree.put(4.0, 5.0);

    SortedSetMultimap<Double, Double> sorted = Multimaps.unmodifiableSortedSetMultimap(tree);
    TreeMultimap<Double, Double> copyFromSorted = TreeMultimap.create(sorted);

    Random random = new Random();
    int warmupPermits = 10;
    double[] coldFactorsToTest = {2.0, 3.0, 10.0};
    double[] qpsToTest = {4.0, 2.0, 1.0, 0.5, 0.1};
    for (int trial = 0; trial < 100; trial++) {
      for (double coldFactor : coldFactorsToTest) {
        for (double qps : qpsToTest) {
          // If warmupPermits = maxPermits - thresholdPermits then

   * infinite {@code double} values are considered infinitely far away. For example, 2^2000 is not
   * representable as a double, but {@code roundToDouble(BigInteger.valueOf(2).pow(2000), HALF_UP)}
   * will return {@code Double.MAX_VALUE}, not {@code Double.POSITIVE_INFINITY}.
   *
   * <p>For the case of {@link RoundingMode#HALF_EVEN}, this implementation uses the IEEE 754

        assertEquals(expected5, DocumentUtil.getValue(doc, "key5", Long.class).longValue());
    }

    public void test_double() {
        Map<String, Object> doc = new HashMap<>();

        double expected6 = 999.999;
        doc.put("key6", expected6);
        assertEquals(expected6, DocumentUtil.getValue(doc, "key6", Double.class));
    }

    public void test_float() {
        Map<String, Object> doc = new HashMap<>();

    double expectedReportedFpp = (double) knownNumberOfFalsePositives / numInsertions;
    double actualReportedFpp = bf.expectedFpp();
    assertEquals(expectedReportedFpp, actualReportedFpp, 0.00033);
  }

  /** Sanity checking with many combinations of false positive rates and expected insertions */
  public void testBasic() {
    for (double fpr = 0.0000001; fpr < 0.1; fpr *= 10) {

public class MultipleSetContainsBenchmark {

  @Param({"0.0", "0.1", "0.7", "1.0"})
  double emptySetProportion;

  @Param({"0.0", "0.1", "0.7", "1.0"})
  double singletonSetProportion;

  @Param({"0.2", "0.8"})
  double hitRate;

  static final Object PRESENT = new Object();
  static final Object ABSENT = new Object();

    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();
      accumulator.addAll(values.asIterable());
      for (double value : values.asIterable()) {

  public void testCreateFromSortedSetMultimap() {
    SortedSetMultimap<Double, Double> tree = TreeMultimap.create(KEY_COMPARATOR, VALUE_COMPARATOR);
    tree.put(1.0, 2.0);
    tree.put(2.0, 3.0);
    tree.put(3.0, 4.0);
    tree.put(4.0, 5.0);

    SortedSetMultimap<Double, Double> sorted = Multimaps.unmodifiableSortedSetMultimap(tree);
    TreeMultimap<Double, Double> copyFromSorted = TreeMultimap.create(sorted);

          } else {
            diff[k] += 1;
          }
        }
      }
      // measure probability and assert it's within margin of error
      for (int j = 0; j < hashBits; j++) {
        double prob = (double) diff[j] / (double) (diff[j] + same[j]);
        Assert.assertEquals(0.50d, prob, epsilon);
      }
    }
  }

  /**