private final SpecialRandom random;
  private final double hitRate;
  private final int size;

  private final Set<Element> valuesInSet;
  private final Element[] queries;

  CollectionBenchmarkSampleData(int size) {
    this(true, new SpecialRandom(), 1.0, size);
  }

  CollectionBenchmarkSampleData(
      boolean isUserTypeFast, SpecialRandom random, double hitRate, int size) {
    this.isUserTypeFast = isUserTypeFast;

    // seem to be a worthwhile tradeoff in a browser.
    return matcher;
  }

  static String formatCompact4Digits(double value) {
    return toPrecision(value, 4);
  }

  @JsMethod(name = "Number.prototype.toPrecision.call", namespace = JsPackage.GLOBAL)
  private static native String toPrecision(double value, int precision);

  @JsMethod
  static native boolean stringIsNullOrEmpty(@Nullable String string) /*-{

            return ((Long) value).floatValue();
        }
        if (value instanceof Float) {
            return (Float) value;
        }
        if (value instanceof Double) {
            return ((Double) value).floatValue();
        }
        if (value != null) {
            return Float.parseFloat(value.toString());
        }

        return 0.0f;
    }

    public void setHitCount_PercentileRanks(double[] values) {
        setHitCount_PercentileRanks(values, null);
    }

    public void setHitCount_PercentileRanks(double[] values, ConditionOptionCall<PercentileRanksAggregationBuilder> opLambda) {
        setHitCount_PercentileRanks("hitCount", values, opLambda);
    }

   * #create(Funnel, int, double)}, or smaller. If it is significantly higher, it is usually the
   * case that too many elements (more than expected) have been put in the {@code BloomFilter},
   * degenerating it.
   *
   * @since 14.0 (since 11.0 as expectedFalsePositiveProbability())
   */
  public double expectedFpp() {
    return Math.pow((double) bits.bitCount() / bitSize(), numHashFunctions);
  }

    public void setGidNumber_PercentileRanks(double[] values) {
        setGidNumber_PercentileRanks(values, null);
    }

    public void setGidNumber_PercentileRanks(double[] values, ConditionOptionCall<PercentileRanksAggregationBuilder> opLambda) {
        setGidNumber_PercentileRanks("gidNumber", values, opLambda);
    }

    public void setGidNumber_PercentileRanks(String name, double[] values,

    Set<Class<?>> primitives = Primitives.allPrimitiveTypes();
    assertThat(primitives)
        .containsExactly(
            boolean.class,
            byte.class,
            char.class,
            double.class,
            float.class,
            int.class,
            long.class,
            short.class,
            void.class);

  private static final String S2 = "44 one million";
  private static final String S3 = "Lowly laundry lefties";
  private static final String S4 = "89273487U#*&#";
  private static final Double D0 = 9.234d;
  private static final Double D1 = -1.2e55;

  @Benchmark
  int hashString_2(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += Objects.hashCode(S0, S1);
    }
    return dummy;
  }

    return smear((o == null) ? 0 : o.hashCode());
  }

  private static final int MAX_TABLE_SIZE = Ints.MAX_POWER_OF_TWO;

  static int closedTableSize(int expectedEntries, double loadFactor) {
    // Get the recommended table size.
    // Round down to the nearest power of 2.
    expectedEntries = max(expectedEntries, 2);
    int tableSize = Integer.highestOneBit(expectedEntries);

          } 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]);
        assertThat(prob).isWithin(epsilon).of(0.50d);
      }
    }
  }

  /**