Every new feature, method, function or keyword argument adds cognitive
load, maintainer burden, makes the package larger, and can confuse users
so we reject much more than we accept.
The first step would be to present a convincing case _why_ Poisson
sampling should be added to NumPy, before discussing the _how_.
Matti
On 4/8/22 21:11, Robert Kern wrote:
As discussed on the corresponding Github issue[1], this is not going
to be functionality that we add onto `choice()` nor is it likely
something that we will add as a separate `Generator` method. The
desired computation is straightforward to do by composing existing
functionality.
[1] https://github.com/numpy/numpy/issues/22082
On Thu, Aug 4, 2022 at 2:07 PM Rodo-Singh <adityasinghd...@gmail.com>
wrote:
Proposed new feature or change:
Objective: Sample elements from the given iterator (a list, a
numpy array, etc.) based upon pre-defined probabilities associated
with each element which may not sums upto 1.
Overview
* In the numpy.random.choice function the cases where we're
explicitly providing the list of probabilistic values for an input
sample, the requirement expects the sum of the whole list to be 1.
This makes sense when all the elements are possible observation
for a single random variable whose pmf(probability mass function)
is nothing but the p list.
* But when every element in that list can be regarded as
observation of separate independent Bernoulli r.v. (random
variable), then the problem falls into the category of
multi-label. Intuitively speaking, for each element in the list or
array given to us, we'll toss a coin (may be biased or unbiased)
~B(p) (i.e., follows Bernoulli with p as success probability for
head).
* The output array or list would probably be a proper subset or
can be a full list or can be an empty one.
* Plus, here the argument size should automatically get shut down
as we just want which all elements got selected into the output
list after n coin tossess (where len(list) = n). Also it may
happen that each element is independent as argued above, but the
sum(p) = 1. Then we can probably put an extra argument
independence = True or independence = False.
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