On Thu, May 21, 2015 at 6:06 PM, Alexander Belopolsky <[email protected]> wrote:
> 1. Is there a simple expression using existing numpy functions that
> implements PEP 465 semantics for @?
Not yet.
> 2. Suppose I have a function that takes two vectors x and y, and a matrix M
> and returns x.dot(M.dot(y)). I would like to "vectorize" this function so
> that it works with x and y of any ndim >= 1 and M of any ndim >= 2 treating
> multi-dimensional x and y as arrays of vectors and M as an array of matrices
> (broadcasting as necessary). The result should be an array of xMy products.
> How would I achieve that using PEP 465's @?
(x[..., np.newaxis, :] @ M @ y[..., :, np.newaxis])[..., 0, 0]
Alternatively, you might prefer something like this (though it won't
yet take advantage of BLAS):
np.einsum("...i,...ij,...j", x, M, y)
Alternatively, there's been some discussion of the possibility of
adding specialized gufuncs for broadcasted vector-vector,
vector-matrix, matrix-vector multiplication, which wouldn't do the
magic vector promotion that dot and @ do.
-n
--
Nathaniel J. Smith -- http://vorpus.org
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