Sergey,
Much obliged. Very good suggestion. Math::Random and Math::Random::Discrete
have very rich sets of methods, and quite intuitive to use. Pure PDL tools
may be overkill for me since at this point my work only uses 1-dimensional
vectors. If or when I move up to multivariate sets then I think PDL is
boss... or R. I will experiment with pure PDL stuff as I learn more about
what it can do and may wind up building data sets with those Math::
packages and then assign the arrays to piddles for additional munging,
analysis and plotting. Just a note: finding the right PDL POD to read for
the task at hand can be challenging, at least for a PDL beginner. The PDL
Book blows one away but leaves beginners in the dust. A tutorial that
starts with the basics, something like Cotton's *Learning R*, and builds up
from there would be more useful. One probably exists but as yet I haven't
found it. I think the PDL Book attempts too hard to impress, and it does,
but it is not very illuminating.
Thanks again and regards,
Will
On Sat, Apr 7, 2018 at 10:33 PM, Sergey Kolychev <
[email protected]> wrote:
> Hi Will,
> I don't know solve the task via PDL, but in the past I used this
> http://search.cpan.org/~nwellnhof/Math-Random-
> Discrete-1.01/lib/Math/Random/Discrete.pm#rand to get a random weighed
> sample.
> Thanks.
>
>
> On Fri, Apr 6, 2018, 16:16 William Schmidt <[email protected]>
> wrote:
>
>> Hello Piddlers,
>>
>> I am moving from R to PDL, with tons of experience with Perl, lots with R
>> but zero with PDL,
>> so this is a pretty basic question. I can see from the PDL Book that PDL
>> is very
>> sophisticated, with much more functionality than I will ever use, but I
>> want
>> to master basic PDL to leverage my Perl. My focus is on probability in
>> two dimensions so
>> I will be working mostly with 1-dimensional vectors. Here is an example
>> from R that
>> I would like to learn how to do in PDL. It is a small example but once I
>> master
>> the construction of this data I will extend it to much larger vectors.
>>
>> Suppose I have random variable X whose values and probabilities are as
>> follows:
>>
>> *x* *p(x)*
>> 0 1/8
>> 1 3/8
>> 2 3/8
>> 3 1/8
>>
>> To get a sample of 50 random values drawn from this population with
>> replacement in R I
>> would say:
>>
>> x <- seq.int(0,3) # Concatenate a sequence of ints from 0 to 3.
>> x # print x.
>> [1] 0 1 2 3
>>
>> weights <- c(1/8, 3/8, 3/8, 1/8) # Another form of concatenation.
>> weights
>> [1] 0.125 0.375 0.375 0.125
>>
>> s <- sample(x, 50, replace=TRUE, prob=weights)
>> s
>> [1] 0 1 1 3 2 2 2 3 2 0 0 1 3 1 1 3 0 2 1 2 2 1 3 1 2 2 0 2 2 2 3 2
>> [33] 1 1 3 1 2 2 1 1 0 1 3 2 2 1 3 0 1 1
>>
>> I can now manipulate *s*, calculate its statistical properties and graph
>> its probability distribution. Fifty integer values is not very interesting
>> but the problems I am studying have thousands of values and very different
>> weights. How do I do this in PDL? I have PDL::Stats::Basic and
>> PDL::Stats::Distr installed along with PGPLOT but it's generating this
>> basic data that has me stumped.
>>
>> Thanks and regards,
>>
>> Will Schmidt
>> [email protected]
>>
>>
>>
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