On Tue, May 12, 2015 at 1:17 AM, Stefan Otte <stefan.otte@gmail.com> wrote:Hello,indeed I was looking for the cartesian product.
I timed the two stackoverflow answers and the winner is not quite as clear:
n_elements: 10 cartesian 0.00427 cartesian2 0.00172
n_elements: 100 cartesian 0.02758 cartesian2 0.01044
n_elements: 1000 cartesian 0.97628 cartesian2 1.12145
n_elements: 5000 cartesian 17.14133 cartesian2 31.12241(This is for two arrays as parameters: np.linspace(0, 1, n_elements))
cartesian2 seems to be slower for bigger.On my system, the following variation on Pauli's answer is 2-4x faster than his for your test cases:def cartesian4(arrays, out=None):arrays = [np.asarray(x).ravel() for x in arrays]dtype = np.result_type(*arrays)n = np.prod([arr.size for arr in arrays])if out is None:out = np.empty((len(arrays), n), dtype=dtype)else:out = out.Tfor j, arr in enumerate(arrays):n /= arr.sizeout.shape = (len(arrays), -1, arr.size, n)out[j] = arr[np.newaxis, :, np.newaxis]out.shape = (len(arrays), -1)return out.TI'd really appreciate if this was be part of numpy. Should I create a pull request?There hasn't been any opposition, quite the contrary, so yes, I would go ahead an create that PR. I somehow feel this belongs with the set operations, rather than with the indexing ones. Other thoughts?Also for consideration: should it work on flattened arrays? or should we give it an axis argument, and then "broadcast on the rest", a la generalized ufunc?_______________________________________________
Jaime--(\__/)
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( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.
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