Mailman 3 August 2022 - NumPy-Discussion

aarch64 wheels on github actions
by Andrew Nelson 07 Aug '22

07 Aug '22

At the moment Linux aarch64 wheels are being made in a TravisCI job. I've been experimenting recently about making scipy wheels for that target on Github Actions. cibuildwheel + github actions does quite well for that purpose. It takes around 2 hours for scipy wheels to be created (but not tested). I would expect numpy wheels to take less time to build. I think it would be a relatively minor modification of the .github/workflows/wheels script. cheers, Andrew. -- _____________________________________ Dr. Andrew Nelson _____________________________________

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generate a random sample based on independent priors in `random.choice` #22082
by Rodo-Singh 04 Aug '22

04 Aug '22

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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Speedup NumPy conjugate,square and CPU cache
by tim-berberich＠arcor.de 04 Aug '22

04 Aug '22

Hi all, I hope this is the right place to ask my questions. Recently I did some simple benchmarks on the numpy.conjugate routine for an 128 MB array, lets call it 'A'. It turned out that on my test machines there is a speed up of around 2.5 times if instead of simply calling A.conj() one loops over sub-matrices that fit into L1 cache of my CPU, like: for i in range(0,A.shape[0],size): A[i:i+size].conj() such that each A[i:i+size] fits into L1 cache. I posted example code and some graphs over at stackoverflow (https://stackoverflow.com/questions/73209565/strange-behaviour-during-multi…) I quickly checked and found a similar behavior for numpy.square. Now for my questions. 1. Is this a known/expected behavior of NumPy ? 2. Would it be possible/sensible to make simple numerical operations like numpy.conjugate & numpy.square cache aware ? Best, Tim

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