Hi Xuanbao,
On Thu, Nov 16, 2023 at 2:59 PM xuanbao via NumPy-Discussion <
numpy-discussion@python.org> wrote:
> Hello everyone! I am working on implementing a tool to assess the
> complexity of CPU architecture porting. It primarily focuses on RISC-V
> architecture porting. In fact, the tool may
The next NumPy Optimization Team meeting will be held on Monday, November
20th at 4pm UTC.
Join us via Zoom:
https://numfocus-org.zoom.us/j/88162122074?pwd=a3h0cTZubzlLcTZzMVhCL1AxUVFMdz09
.
Everyone is welcome and encouraged to attend.
To add to the meeting agenda the topics you’d like to discuss,
Hi all,
I am trying to sample k N-dimensional vectors from a uniform distribution
without replacement.
It seems like this should be straightforward, but I can't seem to pin it down.
Specifically, I am trying to get random indices in an d0 x d1 x d2.. x dN-1
array.
I thought about sneaking in a
On Fri, Nov 17, 2023 at 1:54 PM Stefan van der Walt via NumPy-Discussion <
numpy-discussion@python.org> wrote:
> Hi all,
>
> I am trying to sample k N-dimensional vectors from a uniform distribution
> without replacement.
> It seems like this should be straightforward, but I can't seem to pin it
>
On Fri, Nov 17, 2023 at 12:10 PM Robert Kern wrote:
>
> On Fri, Nov 17, 2023 at 1:54 PM Stefan van der Walt via NumPy-Discussion
> wrote:
>>
>> Hi all,
>>
>> I am trying to sample k N-dimensional vectors from a uniform distribution
>> without replacement.
>> It seems like this should be straigh
On Fri, Nov 17, 2023 at 4:15 PM Aaron Meurer wrote:
> On Fri, Nov 17, 2023 at 12:10 PM Robert Kern
> wrote:
> >
> > If the arrays you are drawing indices for are real in-memory arrays for
> present-day 64-bit computers, this should be adequate. If it's a notional
> array that is larger, then you
On Fri, Nov 17, 2023, at 11:07, Robert Kern wrote:
> If the arrays you are drawing indices for are real in-memory arrays for
> present-day 64-bit computers, this should be adequate. If it's a notional
> array that is larger, then you'll need actual arbitrary-sized integer
> sampling. The builtin
On Fri, Nov 17, 2023, at 14:28, Stefan van der Walt wrote:
> Attached is a script that implements this solution.
And the version with set duplicates checking.
Stéfan
import random
import functools
import itertools
import operator
import numpy as np
def cumulative_prod(arr):
return list(ite
On Fri, Nov 17, 2023 at 5:34 PM Stefan van der Walt
wrote:
> On Fri, Nov 17, 2023, at 14:28, Stefan van der Walt wrote:
>
> Attached is a script that implements this solution.
>
>
> And the version with set duplicates checking.
>
If you're going to do the set-checking yourself, then you don't ne
rng.integers() (or np.random.randint) lets you specify lists for low
and high. So you can just use rng.integers((0,)*len(dims), dims).
Although I'm not seeing how to use this to generate a bunch of vectors
at once. I would have thought something like size=(10, dims) would let
you generate 10 vecto
On Fri, Nov 17, 2023 at 7:11 PM Aaron Meurer wrote:
> rng.integers() (or np.random.randint) lets you specify lists for low
> and high. So you can just use rng.integers((0,)*len(dims), dims).
>
> Although I'm not seeing how to use this to generate a bunch of vectors
> at once. I would have thought
Is numba solution an option for you?
> On 17 Nov 2023, at 20:49, Stefan van der Walt via NumPy-Discussion
> wrote:
>
> Hi all,
>
> I am trying to sample k N-dimensional vectors from a uniform distribution
> without replacement.
> It seems like this should be straightforward, but I can't seem
On Fri, Nov 17, 2023, at 16:52, Robert Kern wrote:
> That optimistic optimization makes this the fastest solution.
That'd work great, thanks Robert, Aaron, and everyone who shared input.
Stéfan___
NumPy-Discussion mailing list -- numpy-discussion@pytho
13 matches
Mail list logo
