Noting there is also the module PDL::GSL::RNG;
which has a lot of stuff from GSL
> On 7 Apr 2018, at 10:58 am, Craig DeForest <[email protected]> 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 <[email protected]
>> <mailto:[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 <http://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] <mailto:[email protected]>
>>
>>
>>
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