Craig,
Thank you, it works but first I had to pick those method calls apart to see
what each is doing and rtfm about *sever* in PDL::Core.
So, I am left with a set of questions best depicted with some examples. As
I understand the ::Core pod, that random call and floor are creating a
virtual piddle that functions to slice or index into $vec. And since the
virtual piddle is longer than $vec, it loops until exactly the number of
items in the virtual piddle are pushed from $vec into $rand. So, what is
the difference in the following invocations?
$rand = $vec->(floor(random(1e6) * 8))->sever; # yours
$rand = $vec->(floor(random(1e6) * 8));
$rand = $vec->index(floor(random(1e6) * 8))->sever;
$rand = $vec->index(floor(random(1e6) * 8));
When I inspect $rand, all are different, since random() probably generates
a fresh seed, but they all look to be examples of my weighted probability
distribution.
To be precise, is there an implied or default method call after *$rand =
$vec->...* in your example, and what is lost when *sever* is not invoked?
I think I can visualize how to generalize this algorithm but it is much
less intuitive than the way one can do it in R and building $vec can be
problematic, or am I missing something? A complex set of weights using
reals, for example: [0.18, 0.31, 0.05, 0.07,...] summing to 1 would require
a lot of fiddling for it to be used as a slice.
Regards,
Will
t.william.schm...@gmail.com
On Fri, Apr 6, 2018 at 7:58 PM, Craig DeForest <defor...@boulder.swri.edu>
wrote:
> Welcome, William!
>
> You are probably looking for “random()”, which has the same syntax as
> “zeroes()” but returns a vector of pseudorandom values on [0,1).
> To make a vector of a million of those, use “$a = random(1e6)”.
>
> To make random integers based on a histogram that you already have
> in-hand, a simple way is:
>
> $vec = pdl(0,1,1,1,2,2,2,3); # note 8 values
> $rand = $vec->(floor(random(1e6) * 8))->sever;
>
> The “random(1e6”) makes a million elements on [0,1). Multiply by 8 and
> take the floor to get integers on [0,7]. The outermost operation indexes
> $vec with the corresponding random value. Since there are three 1’s, 1 is
> three times as likely in the output.
>
> Does that work?
>
> Best,
> Craig
>
>
>
> On Apr 6, 2018, at 5:16 PM, 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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