On 30/07/2019 10:56, Gilles Sadowski wrote:
Hello.
Le lun. 10 juin 2019 à 17:17, Alex Herbert <alex.d.herb...@gmail.com> a écrit :
On 10/06/2019 15:31, Gilles Sadowski wrote:
P.S. Thinking of releasing 1.3?
Not yet. I think there are a few outstanding items [...]
Anything missing?
- RNG-110: The PR for SharedSharedDiscrete/ContinuousSampler should have
a review [1]. I've left this while we finished GSoC phase 2 but it is ready.
I added factory methods for all samplers. For existing samplers this is
just for consistency. Some however use internal delegates and the
factory method can return the delegate directly which is an advantage.
One issue to look at is how I handled GaussianSampler and
LogNormalSampler. The samplers can only be shared state samplers if the
input NormalizedGaussianSampler is a shared state sampler. I handled
this with documentation. But this means a downstream user may be passed
a SharedStateContinuousSampler, use it as such and receive an exception
if it was created incorrectly.
The alternative is two factory methods which must have different names
due to type erasure:
public static ContinuousSampler of(NormalizedGaussianSampler gaussian,
double scale, double shape);
public static
<T extends NormalizedGaussianSampler &
SharedStateSampler<ContinuousSampler>>
SharedStateContinuousSampler
ofSharedState(T normalized,
double mean,
double standardDeviation) {
So the options are:
- As current but has the pitfall of throwing exceptions if you do create
a one with something that does not share state (i.e. not a sampler in
the library).
- Another factory method to explicitly create a SharedStateSampler using
a normalised Gaussian SharedStateSampler.
A few things that are 90% done:
- RNG-85: MiddleSquareWeylSequence generator
This is simple code and now the modifications have been made to the
ProviderBuilder it is possible to pass in a good quality increment for
the Weyl sequence. I have code to build the increment that can be added
to the SeedFactory. I did this months ago so will have to find it and
create the PR.
- RNG-95: DiscreteUniformSampler
I now have a reference for the alternative algorithm for choosing int
values from an interval. The code is done but should go after RNG-110 as
the code uses 5 internal delegates for different algorithms. This would
be optimised by the changes in RNG-110.
- RNG-109: DiscreteProbabilityCollectionSampler to use an internal
DiscreteSampler
I have to create a benchmark to compare the AliasMethodSampler against
the GuideTableSampler to see which is more suitable for a generic
probability distribution. This should not take long.
- RNG-94: RotateRotateMultiplyXorMultiplyXor
Simple code that is based on the same idea of using an output hash
function on a Weyl sequence like SplitMix. It is slightly slower but the
hash function is better and more robust to low complexity increments. So
we can add it using a seeded increment for the Weyl sequence. This would
take a day to add the two hash function variants.
Maybe for later:
- RNG-90: Improve nextInt(int)
This could use the same algorithm as RNG-95. I have not done the testing
yet. It also can be done for nextLong(long) which requires a 64-bit
product multiplication to be computed as a 128-bit result. I have code
for this but no performance tests.
Not done but...
The PCG family has extended generators: K-dimensionally equidistributed
or Cryptographic. These have a much larger period and the
equidistributed ones can be Jumpable.
[1] https://github.com/apache/commons-rng/pull/58
Regards,
Gilles
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