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From Otmar Ertl <>
Subject Re: [math] random boolean arrays
Date Mon, 13 Jul 2015 04:45:02 GMT
On Sun, Jul 12, 2015 at 8:16 PM, Gilles <> wrote:
> On Sun, 12 Jul 2015 19:38:45 +0200, Thomas Neidhart wrote:
>> On 07/12/2015 04:58 PM, Phil Steitz wrote:
>>> On 7/12/15 2:50 AM, Thomas Neidhart wrote:
>>>> On 07/11/2015 09:43 PM, Phil Steitz wrote:
>>>>> On 7/11/15 12:29 PM, Thomas Neidhart wrote:
>>>>>> On 07/11/2015 09:08 PM, Phil Steitz wrote:
>>>>>>> The code implemented in MATH-1242 to improve performance of KS
>>>>>>> monteCarloP in-lines efficient generation of random boolean arrays.
>>>>>>>   Unfortunately, I think the implementation is not quite random
>>>>>>> comments on the ticket).  To verify it, we need to be able to
>>>>>>> the random boolean array generation directly.  To do that, we
>>>>>>> to either expose the method (at least as protected) in the KS
>>>>>>> or add it somewhere else.  I propose the latter but am not sure
>>>>>>> where to put it.  For speed, we need to avoid array copies, so
>>>>>>> API will have to be something like randomize(boolean[], nTrue).
>>>>>>> could go in the swelling MathArrays class, or RandomDataGenerator.
>>>>>>> The latter probably makes more sense, but the API does not fit
>>>>>>> well.  Any ideas?
>>>>>> If it is just for testing purposes, you can also access the method
>>>>>> question via reflection, see an example here:
>>>>> Do you think it *should* be a private method of the K-S class?
>>>> Right now, I do not see much uses outside the class, but if we decide to
>>>> make it public then I would prefer a special util class in the random
>>>> package to avoid cluttering the MathArrays class.
>>> OK, for now we can make it private and use the reflection method
>>> above to test it.
>> ok, but I guess it is also fine to make it package private as sebb
>> suggested. We did something similar recently for some of the improved
>> sampling methods provided by Otmar.
>>>> Regarding the RandomDataGenerator: I think this class should be
>>>> deprecated and replaced by a Sampler interface as proposed by Gilles.
>>> Please consider keeping this class.  Consider this a user request.
>>> I have quite a few applications that use this class for two reasons:
>> ok, the reason why I thought the class should be deprecated is because
>> it was not kept up-to-date with all the new discrete and continuous
>> distributions that we added in the last 2-3 years. If you think it is
>> useful, then we can keep it of course.
>>> 1.  One object instance tied to one PRNG that generates data from
>>> multiple different distributions.   This is convenient.   Sure, I
>>> could refactor all of these apps to instantiate new objects for each
>>> type of generated data and hopefully still be able to peg them to
>>> one PRNG; but that is needless work that also complicates the code.
>>> 2.  There are quite a few methods in this class that have nothing to
>>> do with sampling (nextPermutation, nextHexString, nextSecureXxx,
>>> etc) but which conveniently share the RandomGenerator.  I guess the
>>> utility methods get moved out somewhere else.  Again, I end up
>>> having to refactor all of my code that uses them and when I want
>>> simulations to be based on a single PRNG, I have to find a way to
>>> pass the RandomGenerator around to them.
>>> I don't yet see the need to refactor the sampling support in the
>>> distributions package; but as my own apps are not impacted by this,
>>> if everyone else sees the user impact of the refactoring as
>>> outweighed by the benefit, I won't stand in the way.   Please lets
>>> just keep the RandomDataGenerator convenience class in the random
>>> package in any case.  I will take care of whatever adjustments are
>>> needed to adapt to whatever we settle on for sampling in the
>>> distributions package.
>> Well, it is not really necessary to do everything together and refactor
>> the distributions.
>> Probably it is better to start the other way round, and describe what I
>> want to add, and see how other things fit in:
>>  * I want a generic Sampler interface, i.e. something like this:
>>  ** nextSample()
>>  ** nextSamples(int size)
>>  ** nextSamples(double[] samples)
> +1
>> there could be a DiscreteSampler and ContinuousSampler interface to
>> handle the cases for int / double values.
> Perhaps the name should be IntegerSampler and DoubleSampler, to accomodate
> future needs (LongSampler, BooleanSampler (?)).

I would prefer consistent names for distributions and corresponding
samplers. The support of distributions must be of the same data type
as the return values of the corresponding sampler. Therefore, I would
call the samplers for RealDistribution and IntegerDistribution
RealSampler and IntegerSampler, respectively.

>> The distributions could either be changed to return such a sampler as
>> Gilles proposed (with the advantage that no random instance is tied to
>> the distribution itself), or implement the interface directly (with the
>> advantage that we would not need to refactor too much).
> Unless I'm missing something, the refactoring would be fairly the same:
> The latter case needs implementing 3 methods (2 new ones, one with a name
> change).
> The former needs implementing the factory method proposed in MATH-1158,
> plus the same methods as above (wrapped in the object returned by the
> factory method).
> Gilles
>>>> One can then create a sampler for any distribution or from other
>>>> sources, e.g. when needing a fast and efficient sampler without
>>>> replacement (see MATH-1239).
>>> +1 for sequential sampling.  I don't follow exactly why that
>>> requires refactoring the distributions; but if it helps in a way I
>>> don't yet understand, that will help convince me that refactoring
>>> sampling in the distributions package is worth the user pain.
>> as I said above, I wanted to combine two things in one step, maybe it is
>> better to go step by step.
>> Thomas
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