On Fri, Nov 17, 2023 at 7:11 PM Aaron Meurer <[email protected]> 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 something like size=(10, dims) would let
> you generate 10 vectors of length dims but it doesn't seem to work.
>
`size=(k, len(dims))`
def sample_indices(shape, size, rng=None):
rng = np.random.default_rng(rng)
ashape = np.array(shape)
seen = set()
while len(seen) < size:
dsize = size - len(seen)
seen.update(map(tuple, rng.integers(0, ashape, size=(dsize,
len(shape)))))
return list(seen)
That optimistic optimization makes this the fastest solution.
--
Robert Kern
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