Hi.
Le ven. 17 janv. 2020 à 13:15, Alex Herbert <[email protected]> a écrit :
>
> The UniformRandomProvider and ContinuousUniformSampler can create
> doubles uniformly in a range [0, 1) and [x, y).
>
> I would like to create a sampler that can create doubles with random
> mantissa bits and a specified range of exponents. These would not follow
> a standard distribution but would be distributed according to the IEEE
> 754 floating-point "double format" bit layout.
>
> The sampler would ensure all bits of the mantissa are uniformly sampled
> and the exponent range is uniformly sampled. It could be used for
> example to create random double data in a specified range for testing.
Are there use-cases other than providing inputs for unit tests?
>
> Following from UniformRandomProvider I propose to remove the sign bit to
> allow a maximum range sampler from [0, Double.MAX_VALUE].
Is there a trade-off to allowing generation of the whole range of "double"s?
> Thus we have
> the API:
>
> public final class FiniteDoubleSampler {
>
> /**
> * Creates a new finite double sampler.
> *
> * <p>This will return double values from the entire exponent range of
> a finite
> * {@code double} including sub-normal numbers. The value will be unsigned.
> *
> * @param rng Generator of uniformly distributed random numbers.
> * @return Sampler.
> * @since 1.4
> */
> public static SharedStateContinuousSampler of(UniformRandomProvider rng);
>
> /**
> * Creates a new finite double sampler.
> *
> * <p>This will return double values from the specified exponent range
> of a finite
> * {@code double}. This assumes all sub-normal numbers are identified
> with the exponent -1023.
> * The value will be unsigned.
> *
> * @param rng Generator of uniformly distributed random numbers.
> * @param minExponent Minimum exponent
> * @param maxExponent Maximum exponent
> * @see Double#MIN_EXPONENT
> * @see Double#MAX_EXPONENT
> * @throws IllegalArgumentException If the exponents are not in the
> range -1023 inclusive
> * to 1023 inclusive; or the min exponent is not {@code <=} max exponent.
> * @return Sampler.
> * @since 1.4
> */
> public static SharedStateContinuousSampler of(UniformRandomProvider rng,
> int minExponent,
> int maxExponent);
> }
>
> I have written many tests where I wanted full precision random mantissas
> in double values and wanted the doubles to represent all doubles that
> could occur.
How do you ensure it (short of listing them all)?
> Thus randomly sampling from the IEEE 754 representation
> seems to be more generic than for example rng.nextDouble() * constant.
What's the advantage of the former over the latter?
> For example:
>
> // Random numbers: [0, Double.MAX_VALUE]
> FiniteDoubleSampler.of(rng);
> FiniteDoubleSampler.of(rng, -1023, 1023);
> // Random sub-normal numbers: [0, Double.MIN_NORMAL)
> FiniteDoubleSampler.of(rng, -1023, -1023);
> // Random numbers that are close to overflow: (Double.MAX_VALUE/2,
> Double.MAX_VALUE]
> FiniteDoubleSampler.of(rng, 1023, 1023);
> // Random numbers in the range [1, 32)
> FiniteDoubleSampler.of(rng, 0, 4);
Will the distribution be the same as for "32 * rng.nextDouble()"?
Regards,
Gilles
> Thoughts on this?
>
> Alex
>
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