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 <t.william.schm...@gmail.com>
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
> t.william.schm...@gmail.com
>
>
>
>
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