Is numba solution an option for you? > On 17 Nov 2023, at 20:49, 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 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 structured dtype into `rng.integers`, but of > course that doesn't work. > > If we had a string sampler, I could sample k unique words (consisting of > digits), and convert them to indices. > > I could over-sample and filter out the non-unique indices. Or iteratively > draw blocks of samples until I've built up my k unique indices. > > The most straightforward solution would be to flatten indices, and to sample > from those. The integers get large quickly, though. The rng.integers > docstring suggests that it can handle object arrays for very large integers: > >> When using broadcasting with uint64 dtypes, the maximum value (2**64) >> cannot be represented as a standard integer type. >> The high array (or low if high is None) must have object dtype, e.g., >> array([2**64]). > > But, that doesn't work: > > In [35]: rng.integers(np.array([2**64], dtype=object)) > ValueError: high is out of bounds for int64 > > Is there an elegant way to handle this problem? > > Best regards, > Stéfan > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: dom.grigo...@gmail.com
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