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: arch...@mail-archive.com
