* MutableValueGraph<String, Double> graph =
 *     ValueGraphBuilder.undirected().allowsSelfLoops(true).build();
 * graph.putEdgeValue("San Francisco", "San Francisco", 0.0);
 * graph.putEdgeValue("San Jose", "San Jose", 0.0);
 * graph.putEdgeValue("San Francisco", "San Jose", 48.4);
 *
 * // Building an immutable value graph
 * ImmutableValueGraph<String, Double> immutableGraph =
 *     ValueGraphBuilder.undirected()

  @CanIgnoreReturnValue
  public final Hasher putBoolean(boolean b) {
    return putByte(b ? (byte) 1 : (byte) 0);
  }

  @Override
  @CanIgnoreReturnValue
  public final Hasher putDouble(double d) {
    return putLong(Double.doubleToRawLongBits(d));
  }

  @Override
  @CanIgnoreReturnValue
  public final Hasher putFloat(float f) {
    return putInt(Float.floatToRawIntBits(f));
  }

  @Override

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

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

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

    }

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

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

  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,