}

  /**
   * Returns statistics over a dataset containing the given values.
   *
   * @param values a series of values, which will be converted to {@code double} values (this may
   *     cause loss of precision)
   */
  public static Stats of(Iterable<? extends Number> values) {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(values);
    return accumulator.snapshot();
  }

    return (long) z;
  }

  private static final double MIN_LONG_AS_DOUBLE = -0x1p63;
  /*
   * We cannot store Long.MAX_VALUE as a double without losing precision. Instead, we store
   * Long.MAX_VALUE + 1 == -Long.MIN_VALUE, and then offset all comparisons by 1.
   */
  private static final double MAX_LONG_AS_DOUBLE_PLUS_ONE = 0x1p63;

  /**

				alterColumn = true
			}
		}
	} else {
		if precision, _, ok := columnType.DecimalSize(); ok && int64(field.Precision) != precision {
			if regexp.MustCompile(fmt.Sprintf("[^0-9]%d[^0-9]", field.Precision)).MatchString(m.DataTypeOf(field)) {
				alterColumn = true
			}
		}
	}

	// check nullable
	if nullable, ok := columnType.Nullable(); ok && nullable == field.NotNull {

    assertThat(transformation.slope()).isWithin(ALLOWED_ERROR).of(slope);
    // Note that we cannot test the actual mapping of points, as the results will be unreliable due
    // to loss of precision with this value of the slope.
  }

  public void testMappingWithSlope_maximalSlope() {
    double x1 = 1.2;
    double y1 = 3.4;
    double slope = Double.MAX_VALUE;

    assertThat(transformation.slope()).isWithin(ALLOWED_ERROR).of(slope);
    // Note that we cannot test the actual mapping of points, as the results will be unreliable due
    // to loss of precision with this value of the slope.
  }

  public void testMappingWithSlope_maximalSlope() {
    double x1 = 1.2;
    double y1 = 3.4;
    double slope = Double.MAX_VALUE;

   * with any fraction rounded down.
   *
   * <p><b>Note:</b> the overhead of measurement can be more than a microsecond, so it is generally
   * not useful to specify {@link TimeUnit#NANOSECONDS} precision here.
   *
   * <p>It is generally not a good idea to use an ambiguous, unitless {@code long} to represent
   * elapsed time. Therefore, we recommend using {@link #elapsed()} instead, which returns a

     * Computes the quantile value of the given dataset.
     *
     * @param dataset the dataset to do the calculation on, which must be non-empty, which will be
     *     cast to doubles (with any associated lost of precision), and which will not be mutated by
     *     this call (it is copied instead)
     * @return the quantile value
     */
    public double compute(Collection<? extends Number> dataset) {

   * with any fraction rounded down.
   *
   * <p><b>Note:</b> the overhead of measurement can be more than a microsecond, so it is generally
   * not useful to specify {@link TimeUnit#NANOSECONDS} precision here.
   *
   * <p>It is generally not a good idea to use an ambiguous, unitless {@code long} to represent
   * elapsed time. Therefore, we recommend using {@link #elapsed()} instead, which returns a

        byte[] buffer = new byte[8];

        for (long time : testTimes) {
            SMBUtil.writeTime(time, buffer, 0);
            long readTime = SMBUtil.readTime(buffer, 0);

            // Account for precision loss in conversion
            long expectedTime = ((time + SmbConstants.MILLISECONDS_BETWEEN_1970_AND_1601) * 10000L) / 10000L
                    - SmbConstants.MILLISECONDS_BETWEEN_1970_AND_1601;

	FNMADDS	F1, F2, F3, F4				// 4f822018

	// 20.8: Single-Precision Floating-Point Compare Instructions
	FEQS	F0, F1, X7				// d3a300a0
	FLTS	F0, F1, X7				// d39300a0
	FLES	F0, F1, X7				// d38300a0

	// 20.9: Single-Precision Floating-Point Classify Instruction
	FCLASSS	F0, X5					// d31200e0

	// 21.3: Double-Precision Load and Store Instructions
	FLD	(X5), F0				// 07b00200
	FLD	4(X5), F0				// 07b04200